Reproducibility and Prognosis of Quantitative Features Extracted from CT Images12

PubMed Central

Balagurunathan, Yoganand; Gu, Yuhua; Wang, Hua; Kumar, Virendra; Grove, Olya; Hawkins, Sam; Kim, Jongphil; Goldgof, Dmitry B; Hall, Lawrence O; Gatenby, Robert A; Gillies, Robert J

2014-01-01

We study the reproducibility of quantitative imaging features that are used to describe tumor shape, size, and texture from computed tomography (CT) scans of non-small cell lung cancer (NSCLC). CT images are dependent on various scanning factors. We focus on characterizing image features that are reproducible in the presence of variations due to patient factors and segmentation methods. Thirty-two NSCLC nonenhanced lung CT scans were obtained from the Reference Image Database to Evaluate Response data set. The tumors were segmented using both manual (radiologist expert) and ensemble (software-automated) methods. A set of features (219 three-dimensional and 110 two-dimensional) was computed, and quantitative image features were statistically filtered to identify a subset of reproducible and nonredundant features. The variability in the repeated experiment was measured by the test-retest concordance correlation coefficient (CCCTreT). The natural range in the features, normalized to variance, was measured by the dynamic range (DR). In this study, there were 29 features across segmentation methods found with CCCTreT and DR ≥ 0.9 and R2Bet ≥ 0.95. These reproducible features were tested for predicting radiologist prognostic score; some texture features (run-length and Laws kernels) had an area under the curve of 0.9. The representative features were tested for their prognostic capabilities using an independent NSCLC data set (59 lung adenocarcinomas), where one of the texture features, run-length gray-level nonuniformity, was statistically significant in separating the samples into survival groups (P ≤ .046). PMID:24772210

CT versus MR Techniques in the Detection of Cervical Artery Dissection.

PubMed

Hanning, Uta; Sporns, Peter B; Schmiedel, Meilin; Ringelstein, Erich B; Heindel, Walter; Wiendl, Heinz; Niederstadt, Thomas; Dittrich, Ralf

2017-11-01

Spontaneous cervical artery dissection (sCAD) is an important etiology of juvenile stroke. The gold standard for the diagnosis of sCAD is convential angiography. However, magnetic resonance imaging (MRI)/MR angiography (MRA) and computed tomography (CT)/CT angiography (CTA) are frequently used alternatives. New developments such as multislice CT/CTA have enabled routine acquisition of thinner sections with rapid imaging times. The goal of this study was to compare the capability of recent developed 128-slice CT/CTA to MRI/MRA to detect radiologic features of sCAD. Retrospective review of patients with suspected sCAD (n = 188) in a database of our Stroke center (2008-2014), who underwent CT/CTA and MRI/MRA on initial clinical work-up. A control group of 26 patients was added. All Images were evaluated concerning specific and sensitive radiological features for dissection by two experienced neuroradiologists. Imaging features were compared between the two modalities. Forty patients with 43 dissected arteries received both modalities (29 internal carotid arteries [ICAs] and 14 vertebral arteries [VAs]). All CADs were identified in CT/CTA and MRI/MRA. The features intimal flap, stenosis, and lumen irregularity appeared in both modalities. One high-grade stenosis was identified by CT/CTA that was expected occluded on MRI/MRA. Two MRI/MRA-confirmed pseudoaneurysms were missed by CT/CTA. None of the controls evidenced specific imaging signs for dissection. CT/CTA is a reliable and better available alternative to MRI/MRA for diagnosis of sCAD. CT/CTA should be used to complement MRI/MRA in cases where MRI/MRA suggests occlusion. Copyright © 2017 by the American Society of Neuroimaging.

TU-CD-BRB-01: Normal Lung CT Texture Features Improve Predictive Models for Radiation Pneumonitis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Krafft, S; The University of Texas Graduate School of Biomedical Sciences, Houston, TX; Briere, T

2015-06-15

Purpose: Existing normal tissue complication probability (NTCP) models for radiation pneumonitis (RP) traditionally rely on dosimetric and clinical data but are limited in terms of performance and generalizability. Extraction of pre-treatment image features provides a potential new category of data that can improve NTCP models for RP. We consider quantitative measures of total lung CT intensity and texture in a framework for prediction of RP. Methods: Available clinical and dosimetric data was collected for 198 NSCLC patients treated with definitive radiotherapy. Intensity- and texture-based image features were extracted from the T50 phase of the 4D-CT acquired for treatment planning. Amore » total of 3888 features (15 clinical, 175 dosimetric, and 3698 image features) were gathered and considered candidate predictors for modeling of RP grade≥3. A baseline logistic regression model with mean lung dose (MLD) was first considered. Additionally, a least absolute shrinkage and selection operator (LASSO) logistic regression was applied to the set of clinical and dosimetric features, and subsequently to the full set of clinical, dosimetric, and image features. Model performance was assessed by comparing area under the curve (AUC). Results: A simple logistic fit of MLD was an inadequate model of the data (AUC∼0.5). Including clinical and dosimetric parameters within the framework of the LASSO resulted in improved performance (AUC=0.648). Analysis of the full cohort of clinical, dosimetric, and image features provided further and significant improvement in model performance (AUC=0.727). Conclusions: To achieve significant gains in predictive modeling of RP, new categories of data should be considered in addition to clinical and dosimetric features. We have successfully incorporated CT image features into a framework for modeling RP and have demonstrated improved predictive performance. Validation and further investigation of CT image features in the context of RP NTCP modeling is warranted. This work was supported by the Rosalie B. Hite Fellowship in Cancer research awarded to SPK.« less

A Registration Method Based on Contour Point Cloud for 3D Whole-Body PET and CT Images

PubMed Central

Yang, Qiyao; Wang, Zhiguo; Zhang, Guoxu

2017-01-01

The PET and CT fusion image, combining the anatomical and functional information, has important clinical meaning. An effective registration of PET and CT images is the basis of image fusion. This paper presents a multithread registration method based on contour point cloud for 3D whole-body PET and CT images. Firstly, a geometric feature-based segmentation (GFS) method and a dynamic threshold denoising (DTD) method are creatively proposed to preprocess CT and PET images, respectively. Next, a new automated trunk slices extraction method is presented for extracting feature point clouds. Finally, the multithread Iterative Closet Point is adopted to drive an affine transform. We compare our method with a multiresolution registration method based on Mattes Mutual Information on 13 pairs (246~286 slices per pair) of 3D whole-body PET and CT data. Experimental results demonstrate the registration effectiveness of our method with lower negative normalization correlation (NC = −0.933) on feature images and less Euclidean distance error (ED = 2.826) on landmark points, outperforming the source data (NC = −0.496, ED = 25.847) and the compared method (NC = −0.614, ED = 16.085). Moreover, our method is about ten times faster than the compared one. PMID:28316979

Classification of CT examinations for COPD visual severity analysis

NASA Astrophysics Data System (ADS)

Tan, Jun; Zheng, Bin; Wang, Xingwei; Pu, Jiantao; Gur, David; Sciurba, Frank C.; Leader, J. Ken

2012-03-01

In this study we present a computational method of CT examination classification into visual assessed emphysema severity. The visual severity categories ranged from 0 to 5 and were rated by an experienced radiologist. The six categories were none, trace, mild, moderate, severe and very severe. Lung segmentation was performed for every input image and all image features are extracted from the segmented lung only. We adopted a two-level feature representation method for the classification. Five gray level distribution statistics, six gray level co-occurrence matrix (GLCM), and eleven gray level run-length (GLRL) features were computed for each CT image depicted segment lung. Then we used wavelets decomposition to obtain the low- and high-frequency components of the input image, and again extract from the lung region six GLCM features and eleven GLRL features. Therefore our feature vector length is 56. The CT examinations were classified using the support vector machine (SVM) and k-nearest neighbors (KNN) and the traditional threshold (density mask) approach. The SVM classifier had the highest classification performance of all the methods with an overall sensitivity of 54.4% and a 69.6% sensitivity to discriminate "no" and "trace visually assessed emphysema. We believe this work may lead to an automated, objective method to categorically classify emphysema severity on CT exam.

Prevalence of Imaging Biomarkers to Guide the Planning of Acute Stroke Reperfusion Trials.

PubMed

Jiang, Bin; Ball, Robyn L; Michel, Patrik; Jovin, Tudor; Desai, Manisha; Eskandari, Ashraf; Naqvi, Zack; Wintermark, Max

2017-06-01

Imaging biomarkers are increasingly used as selection criteria for stroke clinical trials. The goal of our study was to determine the prevalence of commonly studied imaging biomarkers in different time windows after acute ischemic stroke onset to better facilitate the design of stroke clinical trials using such biomarkers for patient selection. This retrospective study included 612 patients admitted with a clinical suspicion of acute ischemic stroke with symptom onset no more than 24 hours before completing baseline imaging. Patients with subacute/chronic/remote infarcts and hemorrhage were excluded from this study. Imaging biomarkers were extracted from baseline imaging, which included a noncontrast head computed tomography (CT), perfusion CT, and CT angiography. The prevalence of dichotomized versions of each of the imaging biomarkers in several time windows (time since symptom onset) was assessed and statistically modeled to assess time dependence (not lack thereof). We created tables showing the prevalence of the imaging biomarkers pertaining to the core, the penumbra and the arterial occlusion for different time windows. All continuous imaging features vary over time. The dichotomized imaging features that vary significantly over time include: noncontrast head computed tomography Alberta Stroke Program Early CT (ASPECT) score and dense artery sign, perfusion CT infarct volume, and CT angiography collateral score and visible clot. The dichotomized imaging features that did not vary significantly over time include the thresholded perfusion CT penumbra volumes. As part of the feasibility analysis in stroke clinical trials, this analysis and the resulting tables can help investigators determine sample size and the number needed to screen. © 2017 American Heart Association, Inc.

Periosteal ganglia: CT and MR imaging features.

PubMed

Abdelwahab, I F; Kenan, S; Hermann, G; Klein, M J; Lewis, M M

1993-07-01

The imaging features of four cases of periosteal ganglia were studied. Three lesions were located over the proximal shaft of the tibia, in proximity to the pes anserinus. The fourth lesion involved the distal shaft of the ulna. Three lesions had different degrees of external cortical erosion, scalloping, and thick spicules of periosteal bone on plain radiographs. The bone adjacent to the fourth lesion was not involved. Computed tomography (CT) showed these lesions to be sharply defined soft-tissue masses abutting the periosteum. All of the lesions had the same attenuation as fluid. Magnetic resonance (MR) imaging revealed the ganglia to be sharply defined masses that were isointense compared with neighboring muscles on T1-weighted images. There was markedly increased signal intensity compared with that of fat on T2-weighted images. The signal intensity on both types of images was homogeneous. The MR imaging features were consistent with the fluid nature of the lesions. Under the appropriate clinical circumstances, the MR imaging and CT features of periosteal ganglia are diagnostic.

Classifying features in CT imagery: accuracy for some single- and multiple-species classifiers

Treesearch

Daniel L. Schmoldt; Jing He; A. Lynn Abbott

1998-01-01

Our current approach to automatically label features in CT images of hardwood logs classifies each pixel of an image individually. These feature classifiers use a back-propagation artificial neural network (ANN) and feature vectors that include a small, local neighborhood of pixels and the distance of the target pixel to the center of the log. Initially, this type of...

TU-F-CAMPUS-J-05: Effect of Uncorrelated Noise Texture On Computed Tomography Quantitative Image Features

DOE Office of Scientific and Technical Information (OSTI.GOV)

Oliver, J; Budzevich, M; Moros, E

Purpose: To investigate the relationship between quantitative image features (i.e. radiomics) and statistical fluctuations (i.e. electronic noise) in clinical Computed Tomography (CT) using the standardized American College of Radiology (ACR) CT accreditation phantom and patient images. Methods: Three levels of uncorrelated Gaussian noise were added to CT images of phantom and patients (20) acquired in static mode and respiratory tracking mode. We calculated the noise-power spectrum (NPS) of the original CT images of the phantom, and of the phantom images with added Gaussian noise with means of 50, 80, and 120 HU. Concurrently, on patient images (original and noise-added images),more » image features were calculated: 14 shape, 19 intensity (1st order statistics from intensity volume histograms), 18 GLCM features (2nd order statistics from grey level co-occurrence matrices) and 11 RLM features (2nd order statistics from run-length matrices). These features provide the underlying structural information of the images. GLCM (size 128x128) was calculated with a step size of 1 voxel in 13 directions and averaged. RLM feature calculation was performed in 13 directions with grey levels binning into 128 levels. Results: Adding the electronic noise to the images modified the quality of the NPS, shifting the noise from mostly correlated to mostly uncorrelated voxels. The dramatic increase in noise texture did not affect image structure/contours significantly for patient images. However, it did affect the image features and textures significantly as demonstrated by GLCM differences. Conclusion: Image features are sensitive to acquisition factors (simulated by adding uncorrelated Gaussian noise). We speculate that image features will be more difficult to detect in the presence of electronic noise (an uncorrelated noise contributor) or, for that matter, any other highly correlated image noise. This work focuses on the effect of electronic, uncorrelated, noise and future work shall examine the influence of changes in quantum noise on the features. J. Oliver was supported by NSF FGLSAMP BD award HRD #1139850 and the McKnight Doctoral Fellowship.« less

Three-dimensional texture analysis of contrast enhanced CT images for treatment response assessment in Hodgkin lymphoma: Comparison with F-18-FDG PET

DOE Office of Scientific and Technical Information (OSTI.GOV)

Knogler, Thomas; El-Rabadi, Karem; Weber, Michael

2014-12-15

Purpose: To determine the diagnostic performance of three-dimensional (3D) texture analysis (TA) of contrast-enhanced computed tomography (CE-CT) images for treatment response assessment in patients with Hodgkin lymphoma (HL), compared with F-18-fludeoxyglucose (FDG) positron emission tomography/CT. Methods: 3D TA of 48 lymph nodes in 29 patients was performed on venous-phase CE-CT images before and after chemotherapy. All lymph nodes showed pathologically elevated FDG uptake at baseline. A stepwise logistic regression with forward selection was performed to identify classic CT parameters and texture features (TF) that enable the separation of complete response (CR) and persistent disease. Results: The TF fraction of imagemore » in runs, calculated for the 45° direction, was able to correctly identify CR with an accuracy of 75%, a sensitivity of 79.3%, and a specificity of 68.4%. Classical CT features achieved an accuracy of 75%, a sensitivity of 86.2%, and a specificity of 57.9%, whereas the combination of TF and CT imaging achieved an accuracy of 83.3%, a sensitivity of 86.2%, and a specificity of 78.9%. Conclusions: 3D TA of CE-CT images is potentially useful to identify nodal residual disease in HL, with a performance comparable to that of classical CT parameters. Best results are achieved when TA and classical CT features are combined.« less

Utility of Intermediate-Delay Washout CT Images for Differentiation of Malignant and Benign Adrenal Lesions: A Multivariate Analysis.

PubMed

Ng, Chaan S; Altinmakas, Emre; Wei, Wei; Ghosh, Payel; Li, Xiao; Grubbs, Elizabeth G; Perrier, Nancy D; Lee, Jeffrey E; Prieto, Victor G; Hobbs, Brian P

2018-06-27

The objective of this study was to identify features that impact the diagnostic performance of intermediate-delay washout CT for distinguishing malignant from benign adrenal lesions. This retrospective study evaluated 127 pathologically proven adrenal lesions (82 malignant, 45 benign) in 126 patients who had undergone portal venous phase and intermediate-delay washout CT (1-3 minutes after portal venous phase) with or without unenhanced images. Unenhanced images were available for 103 lesions. Quantitatively, lesion CT attenuation on unenhanced (UA) and delayed (DL) images, absolute and relative percentage of enhancement washout (APEW and RPEW, respectively), descriptive CT features (lesion size, margin characteristics, heterogeneity or homogeneity, fat, calcification), patient demographics, and medical history were evaluated for association with lesion status using multiple logistic regression with stepwise model selection. Area under the ROC curve (A z ) was calculated from both univariate and multivariate analyses. The predictive diagnostic performance of multivariate evaluations was ascertained through cross-validation. A z for DL, APEW, RPEW, and UA was 0.751, 0.795, 0.829, and 0.839, respectively. Multivariate analyses yielded the following significant CT quantitative features and associated A z when combined: RPEW and DL (A z = 0.861) when unenhanced images were not available and APEW and UA (A z = 0.889) when unenhanced images were available. Patient demographics and presence of a prior malignancy were additional significant factors, increasing A z to 0.903 and 0.927, respectively. The combined predictive classifier, without and with UA available, yielded 85.7% and 87.3% accuracies with cross-validation, respectively. When appropriately combined with other CT features, washout derived from intermediate-delay CT with or without additional clinical data has potential utility in differentiating malignant from benign adrenal lesions.

Inflammatory Pseudotumor-Like Follicular Dendritic Cell Sarcoma of the Spleen: Computed Tomography Imaging Characteristics in 5 Patients.

PubMed

Li, Xiumei; Shi, Zhenshan; You, Ruixiong; Li, Yueming; Cao, Dairong; Lin, Renjie; Huang, Xinming

The purpose of this study was to retrospectively review the computed tomography (CT) and clinicopathological characteristics of inflammatory pseudotumor (IPT)-like follicular dendritic cell sarcoma (FDCS) of the spleen in 5 patients. Clinical, pathologic, and CT imaging findings of 5 patients with IPT-like FDCS of the spleen were reviewed and analyzed. Computed tomography imaging and pathologic features were compared. Abdominal unenhanced CT revealed a well-defined hypodense mass in the spleen with complex internal architecture with focal necrosis and/or speckle-strip calcification. On postcontrast CT, slightly delayed enhancement was observed in 5 cases. Four patients had a normalized spleen. The fourth patient had lung metastasis. The fifth patient had 2 relatively small lesions as well as metastases to the spine. Computed tomography imaging features of IPT-like FDCS of the spleen are distinctly different from other hypovascular splenic neoplasm; however, the definitive diagnosis requires further confirmation with needle biopsy or surgery. Inflammatory pseudotumor-like FDCS of the spleen should be suggested by using the CT imaging features of the splenic mass with evidence of metastatic disease.

Hybrid registration of PET/CT in thoracic region with pre-filtering PET sinogram

NASA Astrophysics Data System (ADS)

Mokri, S. S.; Saripan, M. I.; Marhaban, M. H.; Nordin, A. J.; Hashim, S.

2015-11-01

The integration of physiological (PET) and anatomical (CT) images in cancer delineation requires an accurate spatial registration technique. Although hybrid PET/CT scanner is used to co-register these images, significant misregistrations exist due to patient and respiratory/cardiac motions. This paper proposes a hybrid feature-intensity based registration technique for hybrid PET/CT scanner. First, simulated PET sinogram was filtered with a 3D hybrid mean-median before reconstructing the image. The features were then derived from the segmented structures (lung, heart and tumor) from both images. The registration was performed based on modified multi-modality demon registration with multiresolution scheme. Apart from visual observations improvements, the proposed registration technique increased the normalized mutual information index (NMI) between the PET/CT images after registration. All nine tested datasets show marked improvements in mutual information (MI) index than free form deformation (FFD) registration technique with the highest MI increase is 25%.

Coregistered FDG PET/CT-based textural characterization of head and neck cancer for radiation treatment planning.

PubMed

Yu, Huan; Caldwell, Curtis; Mah, Katherine; Mozeg, Daniel

2009-03-01

Coregistered fluoro-deoxy-glucose (FDG) positron emission tomography/computed tomography (PET/CT) has shown potential to improve the accuracy of radiation targeting of head and neck cancer (HNC) when compared to the use of CT simulation alone. The objective of this study was to identify textural features useful in distinguishing tumor from normal tissue in head and neck via quantitative texture analysis of coregistered 18F-FDG PET and CT images. Abnormal and typical normal tissues were manually segmented from PET/CT images of 20 patients with HNC and 20 patients with lung cancer. Texture features including some derived from spatial grey-level dependence matrices (SGLDM) and neighborhood gray-tone-difference matrices (NGTDM) were selected for characterization of these segmented regions of interest (ROIs). Both K nearest neighbors (KNNs) and decision tree (DT)-based KNN classifiers were employed to discriminate images of abnormal and normal tissues. The area under the curve (AZ) of receiver operating characteristics (ROC) was used to evaluate the discrimination performance of features in comparison to an expert observer. The leave-one-out and bootstrap techniques were used to validate the results. The AZ of DT-based KNN classifier was 0.95. Sensitivity and specificity for normal and abnormal tissue classification were 89% and 99%, respectively. In summary, NGTDM features such as PET Coarseness, PET Contrast, and CT Coarseness extracted from FDG PET/CT images provided good discrimination performance. The clinical use of such features may lead to improvement in the accuracy of radiation targeting of HNC.

Dose assessment of digital tomosynthesis in pediatric imaging

NASA Astrophysics Data System (ADS)

Gislason, Amber; Elbakri, Idris A.; Reed, Martin

2009-02-01

We investigated the potential for digital tomosynthesis (DT) to reduce pediatric x-ray dose while maintaining image quality. We utilized the DT feature (VolumeRadTM) on the GE DefiniumTM 8000 flat panel system installed in the Winnipeg Children's Hospital. Facial bones, cervical spine, thoracic spine, and knee of children aged 5, 10, and 15 years were represented by acrylic phantoms for DT dose measurements. Effective dose was estimated for DT and for corresponding digital radiography (DR) and computed tomography (CT) patient image sets. Anthropomorphic phantoms of selected body parts were imaged by DR, DT, and CT. Pediatric radiologists rated visualization of selected anatomic features in these images. Dose and image quality comparisons between DR, DT, and CT determined the usefulness of tomosynthesis for pediatric imaging. CT effective dose was highest; total DR effective dose was not always lowest - depending how many projections were in the DR image set. For the cervical spine, DT dose was close to and occasionally lower than DR dose. Expert radiologists rated visibility of the central facial complex in a skull phantom as better than DR and comparable to CT. Digital tomosynthesis has a significantly lower dose than CT. This study has demonstrated DT shows promise to replace CT for some facial bones and spinal diagnoses. Other clinical applications will be evaluated in the future.

Imaging Characteristics of Pathologically Proven Thymic Hyperplasia: Identifying Features That Can Differentiate True From Lymphoid Hyperplasia

PubMed Central

Araki, Tetsuro; Sholl, Lynette M.; Gerbaudo, Victor H.; Hatabu, Hiroto; Nishino, Mizuki

2014-01-01

OBJECTIVE The purpose of this article is to investigate the imaging characteristics of pathologically proven thymic hyperplasia and to identify features that can differentiate true hyperplasia from lymphoid hyperplasia. MATERIALS AND METHODS Thirty-one patients (nine men and 22 women; age range, 20–68 years) with pathologically confirmed thymic hyperplasia (18 true and 13 lymphoid) who underwent preoperative CT (n = 27), PET/CT (n = 5), or MRI (n = 6) were studied. The length and thickness of each thymic lobe and the transverse and anterior-posterior diameters and attenuation of the thymus were measured on CT. Thymic morphologic features and heterogeneity on CT and chemical shift on MRI were evaluated. Maximum standardized uptake values were measured on PET. Imaging features between true and lymphoid hyperplasia were compared. RESULTS No significant differences were observed between true and lymphoid hyperplasia in terms of thymic length, thickness, diameters, morphologic features, and other qualitative features (p > 0.16). The length, thickness, and diameters of thymic hyperplasia were significantly larger than the mean values of normal glands in the corresponding age group (p < 0.001). CT attenuation of lymphoid hyperplasia was significantly higher than that of true hyperplasia among 15 patients with contrast-enhanced CT (median, 47.9 vs 31.4 HU; Wilcoxon p = 0.03). The receiver operating characteristic analysis yielded greater than 41.2 HU as the optimal threshold for differentiating lymphoid hyperplasia from true hyperplasia, with 83% sensitivity and 89% specificity. A decrease of signal intensity on opposed-phase images was present in all four cases with in- and opposed-phase imaging. The mean maximum standardized uptake value was 2.66. CONCLUSION CT attenuation of the thymus was significantly higher in lymphoid hyperplasia than in true hyperplasia, with an optimal threshold of greater than 41.2 HU in this cohort of patients with pathologically confirmed thymic hyperplasia. PMID:24555583

MRI for the detection of calcific features of vertebral haemangioma.

PubMed

Bender, Y Y; Böker, S M; Diederichs, G; Walter, T; Wagner, M; Fallenberg, E; Liebig, T; Rickert, M; Hamm, B; Makowski, M R

2017-08-01

To evaluate the diagnostic performance of susceptibility-weighted-magnetic-resonance imaging (SW-MRI) for the detection of vertebral haemangiomas (VHs) compared to T1/T2-weighted MRI sequences, radiographs, and computed tomography (CT). The study was approved by the local ethics review board. An SW-MRI sequence was added to the clinical spine imaging protocol. The image-based diagnosis of 56 VHs in 46 patients was established using T1/T2 MRI in combination with radiography/CT as the reference standard. VHs were assessed based on T1/T2-weighted MRI images alone and in combination with SW-MRI, while radiographs/CT images were excluded from the analysis. Fifty-one of 56 VHs could be identified on T1/T2 MRI images alone, if radiographs/CT images were excluded from analysis. In five cases (9.1%), additional radiographs/CT images were required for the imaging-based diagnosis. If T1/T2 and SW-MRI images were used in combination, all VHs could be diagnosed, without the need for radiography/CT. Size measurements revealed a close correlation between CT and SW-MRI (R 2 =0.94; p<0.05). This study demonstrates that SW-MRI enables reliable detection of the typical calcified features of VHs. This is of importance for routine MRI of the spine, as the use of additional CT/radiography can be minimized. Copyright © 2017 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Multiple supervised residual network for osteosarcoma segmentation in CT images.

PubMed

Zhang, Rui; Huang, Lin; Xia, Wei; Zhang, Bo; Qiu, Bensheng; Gao, Xin

2018-01-01

Automatic and accurate segmentation of osteosarcoma region in CT images can help doctor make a reasonable treatment plan, thus improving cure rate. In this paper, a multiple supervised residual network (MSRN) was proposed for osteosarcoma image segmentation. Three supervised side output modules were added to the residual network. The shallow side output module could extract image shape features, such as edge features and texture features. The deep side output module could extract semantic features. The side output module could compute the loss value between output probability map and ground truth and back-propagate the loss information. Then, the parameters of residual network could be modified by gradient descent method. This could guide the multi-scale feature learning of the network. The final segmentation results were obtained by fusing the results output by the three side output modules. A total of 1900 CT images from 15 osteosarcoma patients were used to train the network and a total of 405 CT images from another 8 osteosarcoma patients were used to test the network. Results indicated that MSRN enabled a dice similarity coefficient (DSC) of 89.22%, a sensitivity of 88.74% and a F1-measure of 0.9305, which were larger than those obtained by fully convolutional network (FCN) and U-net. Thus, MSRN for osteosarcoma segmentation could give more accurate results than FCN and U-Net. Copyright © 2018 Elsevier Ltd. All rights reserved.

Computed Tomography Image Origin Identification Based on Original Sensor Pattern Noise and 3-D Image Reconstruction Algorithm Footprints.

PubMed

Duan, Yuping; Bouslimi, Dalel; Yang, Guanyu; Shu, Huazhong; Coatrieux, Gouenou

2017-07-01

In this paper, we focus on the "blind" identification of the computed tomography (CT) scanner that has produced a CT image. To do so, we propose a set of noise features derived from the image chain acquisition and which can be used as CT-scanner footprint. Basically, we propose two approaches. The first one aims at identifying a CT scanner based on an original sensor pattern noise (OSPN) that is intrinsic to the X-ray detectors. The second one identifies an acquisition system based on the way this noise is modified by its three-dimensional (3-D) image reconstruction algorithm. As these reconstruction algorithms are manufacturer dependent and kept secret, our features are used as input to train a support vector machine (SVM) based classifier to discriminate acquisition systems. Experiments conducted on images issued from 15 different CT-scanner models of 4 distinct manufacturers demonstrate that our system identifies the origin of one CT image with a detection rate of at least 94% and that it achieves better performance than sensor pattern noise (SPN) based strategy proposed for general public camera devices.

Quantitative image feature variability amongst CT scanners with a controlled scan protocol

NASA Astrophysics Data System (ADS)

Ger, Rachel B.; Zhou, Shouhao; Chi, Pai-Chun Melinda; Goff, David L.; Zhang, Lifei; Lee, Hannah J.; Fuller, Clifton D.; Howell, Rebecca M.; Li, Heng; Stafford, R. Jason; Court, Laurence E.; Mackin, Dennis S.

2018-02-01

Radiomics studies often analyze patient computed tomography (CT) images acquired from different CT scanners. This may result in differences in imaging parameters, e.g. different manufacturers, different acquisition protocols, etc. However, quantifiable differences in radiomics features can occur based on acquisition parameters. A controlled protocol may allow for minimization of these effects, thus allowing for larger patient cohorts from many different CT scanners. In order to test radiomics feature variability across different CT scanners a radiomics phantom was developed with six different cartridges encased in high density polystyrene. A harmonized protocol was developed to control for tube voltage, tube current, scan type, pitch, CTDIvol, convolution kernel, display field of view, and slice thickness across different manufacturers. The radiomics phantom was imaged on 18 scanners using the control protocol. A linear mixed effects model was created to assess the impact of inter-scanner variability with decomposition of feature variation between scanners and cartridge materials. The inter-scanner variability was compared to the residual variability (the unexplained variability) and to the inter-patient variability using two different patient cohorts. The patient cohorts consisted of 20 non-small cell lung cancer (NSCLC) and 30 head and neck squamous cell carcinoma (HNSCC) patients. The inter-scanner standard deviation was at least half of the residual standard deviation for 36 of 49 quantitative image features. The ratio of inter-scanner to patient coefficient of variation was above 0.2 for 22 and 28 of the 49 features for NSCLC and HNSCC patients, respectively. Inter-scanner variability was a significant factor compared to patient variation in this small study for many of the features. Further analysis with a larger cohort will allow more thorough analysis with additional variables in the model to truly isolate the interscanner difference.

Relationship between solitary pulmonary nodule lung cancer and CT image features based on gradual clustering

NASA Astrophysics Data System (ADS)

Zhang, Weipeng

2017-06-01

The relationship between the medical characteristics of lung cancers and computer tomography (CT) images are explored so as to improve the early diagnosis rate of lung cancers. This research collected CT images of patients with solitary pulmonary nodule lung cancer, and used gradual clustering methodology to classify them. Preliminary classifications were made, followed by continuous modification and iteration to determine the optimal condensation point, until iteration stability was achieved. Reasonable classification results were obtained. the clustering results fell into 3 categories. The first type of patients was mostly female, with ages between 50 and 65 years. CT images of solitary pulmonary nodule lung cancer for this group contain complete lobulation and burr, with pleural indentation; The second type of patients was mostly male with ages between 50 and 80 years. CT images of solitary pulmonary nodule lung cancer for this group contain complete lobulation and burr, but with no pleural indentation; The third type of patients was also mostly male with ages between 50 and 80 years. CT images for this group showed no abnormalities. the application of gradual clustering methodology can scientifically classify CT image features of patients with lung cancer in the initial lesion stage. These findings provide the basis for early detection and treatment of malignant lesions in patients with lung cancer.

Feature-based Alignment of Volumetric Multi-modal Images

PubMed Central

Toews, Matthew; Zöllei, Lilla; Wells, William M.

2014-01-01

This paper proposes a method for aligning image volumes acquired from different imaging modalities (e.g. MR, CT) based on 3D scale-invariant image features. A novel method for encoding invariant feature geometry and appearance is developed, based on the assumption of locally linear intensity relationships, providing a solution to poor repeatability of feature detection in different image modalities. The encoding method is incorporated into a probabilistic feature-based model for multi-modal image alignment. The model parameters are estimated via a group-wise alignment algorithm, that iteratively alternates between estimating a feature-based model from feature data, then realigning feature data to the model, converging to a stable alignment solution with few pre-processing or pre-alignment requirements. The resulting model can be used to align multi-modal image data with the benefits of invariant feature correspondence: globally optimal solutions, high efficiency and low memory usage. The method is tested on the difficult RIRE data set of CT, T1, T2, PD and MP-RAGE brain images of subjects exhibiting significant inter-subject variability due to pathology. PMID:24683955

CT imaging of malignant metastatic hemangiopericytoma of the parotid gland with histopathological correlation

PubMed Central

Khoo, James B.; Sittampalam, Kesavan; Chee, Soo K.

2008-01-01

Abstract We report an extremely rare case of malignant hemangiopericytoma (HPC) of the parotid gland and its metastatic spread to lung, liver, and skeletal muscle. Computed tomography (CT) imaging, histopathological and immunohistochemical methods were employed to study the features of malignant HPC and its metastases. CT imaging was helpful to determine the exact location, involvement of adjacent structures and vascularity, as well as evaluating pulmonary, hepatic, peritoneal, and muscular metastases. Immunohistochemical and histopatholgical features of the primary tumor as well as the metastases were consistent with the diagnosis of malignant HPC. PMID:18940737

Predicting tumor hypoxia in non-small cell lung cancer by combining CT, FDG PET and dynamic contrast-enhanced CT.

PubMed

Even, Aniek J G; Reymen, Bart; La Fontaine, Matthew D; Das, Marco; Jochems, Arthur; Mottaghy, Felix M; Belderbos, José S A; De Ruysscher, Dirk; Lambin, Philippe; van Elmpt, Wouter

2017-11-01

Most solid tumors contain inadequately oxygenated (i.e., hypoxic) regions, which tend to be more aggressive and treatment resistant. Hypoxia PET allows visualization of hypoxia and may enable treatment adaptation. However, hypoxia PET imaging is expensive, time-consuming and not widely available. We aimed to predict hypoxia levels in non-small cell lung cancer (NSCLC) using more easily available imaging modalities: FDG-PET/CT and dynamic contrast-enhanced CT (DCE-CT). For 34 NSCLC patients, included in two clinical trials, hypoxia HX4-PET/CT, planning FDG-PET/CT and DCE-CT scans were acquired before radiotherapy. Scans were non-rigidly registered to the planning CT. Tumor blood flow (BF) and blood volume (BV) were calculated by kinetic analysis of DCE-CT images. Within the gross tumor volume, independent clusters, i.e., supervoxels, were created based on FDG-PET/CT. For each supervoxel, tumor-to-background ratios (TBR) were calculated (median SUV/aorta SUV mean ) for HX4-PET/CT and supervoxel features (median, SD, entropy) for the other modalities. Two random forest models (cross-validated: 10 folds, five repeats) were trained to predict the hypoxia TBR; one based on CT, FDG, BF and BV, and one with only CT and FDG features. Patients were split in a training (trial NCT01024829) and independent test set (trial NCT01210378). For each patient, predicted, and observed hypoxic volumes (HV) (TBR > 1.2) were compared. Fifteen patients (3291 supervoxels) were used for training and 19 patients (1502 supervoxels) for testing. The model with all features (RMSE training: 0.19 ± 0.01, test: 0.27) outperformed the model with only CT and FDG-PET features (RMSE training: 0.20 ± 0.01, test: 0.29). All tumors of the test set were correctly classified as normoxic or hypoxic (HV > 1 cm 3 ) by the best performing model. We created a data-driven methodology to predict hypoxia levels and hypoxia spatial patterns using CT, FDG-PET and DCE-CT features in NSCLC. The model correctly classifies all tumors, and could therefore, aid tumor hypoxia classification and patient stratification.

Respiratory trace feature analysis for the prediction of respiratory-gated PET quantification.

PubMed

Wang, Shouyi; Bowen, Stephen R; Chaovalitwongse, W Art; Sandison, George A; Grabowski, Thomas J; Kinahan, Paul E

2014-02-21

The benefits of respiratory gating in quantitative PET/CT vary tremendously between individual patients. Respiratory pattern is among many patient-specific characteristics that are thought to play an important role in gating-induced imaging improvements. However, the quantitative relationship between patient-specific characteristics of respiratory pattern and improvements in quantitative accuracy from respiratory-gated PET/CT has not been well established. If such a relationship could be estimated, then patient-specific respiratory patterns could be used to prospectively select appropriate motion compensation during image acquisition on a per-patient basis. This study was undertaken to develop a novel statistical model that predicts quantitative changes in PET/CT imaging due to respiratory gating. Free-breathing static FDG-PET images without gating and respiratory-gated FDG-PET images were collected from 22 lung and liver cancer patients on a PET/CT scanner. PET imaging quality was quantified with peak standardized uptake value (SUV(peak)) over lesions of interest. Relative differences in SUV(peak) between static and gated PET images were calculated to indicate quantitative imaging changes due to gating. A comprehensive multidimensional extraction of the morphological and statistical characteristics of respiratory patterns was conducted, resulting in 16 features that characterize representative patterns of a single respiratory trace. The six most informative features were subsequently extracted using a stepwise feature selection approach. The multiple-regression model was trained and tested based on a leave-one-subject-out cross-validation. The predicted quantitative improvements in PET imaging achieved an accuracy higher than 90% using a criterion with a dynamic error-tolerance range for SUV(peak) values. The results of this study suggest that our prediction framework could be applied to determine which patients would likely benefit from respiratory motion compensation when clinicians quantitatively assess PET/CT for therapy target definition and response assessment.

Respiratory trace feature analysis for the prediction of respiratory-gated PET quantification

NASA Astrophysics Data System (ADS)

Wang, Shouyi; Bowen, Stephen R.; Chaovalitwongse, W. Art; Sandison, George A.; Grabowski, Thomas J.; Kinahan, Paul E.

2014-02-01

The benefits of respiratory gating in quantitative PET/CT vary tremendously between individual patients. Respiratory pattern is among many patient-specific characteristics that are thought to play an important role in gating-induced imaging improvements. However, the quantitative relationship between patient-specific characteristics of respiratory pattern and improvements in quantitative accuracy from respiratory-gated PET/CT has not been well established. If such a relationship could be estimated, then patient-specific respiratory patterns could be used to prospectively select appropriate motion compensation during image acquisition on a per-patient basis. This study was undertaken to develop a novel statistical model that predicts quantitative changes in PET/CT imaging due to respiratory gating. Free-breathing static FDG-PET images without gating and respiratory-gated FDG-PET images were collected from 22 lung and liver cancer patients on a PET/CT scanner. PET imaging quality was quantified with peak standardized uptake value (SUVpeak) over lesions of interest. Relative differences in SUVpeak between static and gated PET images were calculated to indicate quantitative imaging changes due to gating. A comprehensive multidimensional extraction of the morphological and statistical characteristics of respiratory patterns was conducted, resulting in 16 features that characterize representative patterns of a single respiratory trace. The six most informative features were subsequently extracted using a stepwise feature selection approach. The multiple-regression model was trained and tested based on a leave-one-subject-out cross-validation. The predicted quantitative improvements in PET imaging achieved an accuracy higher than 90% using a criterion with a dynamic error-tolerance range for SUVpeak values. The results of this study suggest that our prediction framework could be applied to determine which patients would likely benefit from respiratory motion compensation when clinicians quantitatively assess PET/CT for therapy target definition and response assessment.

Radiomic analysis in prediction of Human Papilloma Virus status.

PubMed

Yu, Kaixian; Zhang, Youyi; Yu, Yang; Huang, Chao; Liu, Rongjie; Li, Tengfei; Yang, Liuqing; Morris, Jeffrey S; Baladandayuthapani, Veerabhadran; Zhu, Hongtu

2017-12-01

Human Papilloma Virus (HPV) has been associated with oropharyngeal cancer prognosis. Traditionally the HPV status is tested through invasive lab test. Recently, the rapid development of statistical image analysis techniques has enabled precise quantitative analysis of medical images. The quantitative analysis of Computed Tomography (CT) provides a non-invasive way to assess HPV status for oropharynx cancer patients. We designed a statistical radiomics approach analyzing CT images to predict HPV status. Various radiomics features were extracted from CT scans, and analyzed using statistical feature selection and prediction methods. Our approach ranked the highest in the 2016 Medical Image Computing and Computer Assisted Intervention (MICCAI) grand challenge: Oropharynx Cancer (OPC) Radiomics Challenge, Human Papilloma Virus (HPV) Status Prediction. Further analysis on the most relevant radiomic features distinguishing HPV positive and negative subjects suggested that HPV positive patients usually have smaller and simpler tumors.

SU-F-R-40: Robustness Test of Computed Tomography Textures of Lung Tissues to Varying Scanning Protocols Using a Realistic Phantom Environment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, S; Markel, D; Hegyi, G

2016-06-15

Purpose: The reliability of computed tomography (CT) textures is an important element of radiomics analysis. This study investigates the dependency of lung CT textures on different breathing phases and changes in CT image acquisition protocols in a realistic phantom setting. Methods: We investigated 11 CT texture features for radiation-induced lung disease from 3 categories (first-order, grey level co-ocurrence matrix (GLCM), and Law’s filter). A biomechanical swine lung phantom was scanned at two breathing phases (inhale/exhale) and two scanning protocols set for PET/CT and diagnostic CT scanning. Lung volumes acquired from the CT images were divided into 2-dimensional sub-regions with amore » grid spacing of 31 mm. The distribution of the evaluated texture features from these sub-regions were compared between the two scanning protocols and two breathing phases. The significance of each factor on feature values were tested at 95% significance level using analysis of covariance (ANCOVA) model with interaction terms included. Robustness of a feature to a scanning factor was defined as non-significant dependence on the factor. Results: Three GLCM textures (variance, sum entropy, difference entropy) were robust to breathing changes. Two GLCM (variance, sum entropy) and 3 Law’s filter textures (S5L5, E5L5, W5L5) were robust to scanner changes. Moreover, the two GLCM textures (variance, sum entropy) were consistent across all 4 scanning conditions. First-order features, especially Hounsfield unit intensity features, presented the most drastic variation up to 39%. Conclusion: Amongst the studied features, GLCM and Law’s filter texture features were more robust than first-order features. However, the majority of the features were modified by either breathing phase or scanner changes, suggesting a need for calibration when retrospectively comparing scans obtained at different conditions. Further investigation is necessary to identify the sensitivity of individual image acquisition parameters.« less

Feature-based US to CT registration of the aortic root

NASA Astrophysics Data System (ADS)

Lang, Pencilla; Chen, Elvis C. S.; Guiraudon, Gerard M.; Jones, Doug L.; Bainbridge, Daniel; Chu, Michael W.; Drangova, Maria; Hata, Noby; Jain, Ameet; Peters, Terry M.

2011-03-01

A feature-based registration was developed to align biplane and tracked ultrasound images of the aortic root with a preoperative CT volume. In transcatheter aortic valve replacement, a prosthetic valve is inserted into the aortic annulus via a catheter. Poor anatomical visualization of the aortic root region can result in incorrect positioning, leading to significant morbidity and mortality. Registration of pre-operative CT to transesophageal ultrasound and fluoroscopy images is a major step towards providing augmented image guidance for this procedure. The proposed registration approach uses an iterative closest point algorithm to register a surface mesh generated from CT to 3D US points reconstructed from a single biplane US acquisition, or multiple tracked US images. The use of a single simultaneous acquisition biplane image eliminates reconstruction error introduced by cardiac gating and TEE probe tracking, creating potential for real-time intra-operative registration. A simple initialization procedure is used to minimize changes to operating room workflow. The algorithm is tested on images acquired from excised porcine hearts. Results demonstrate a clinically acceptable accuracy of 2.6mm and 5mm for tracked US to CT and biplane US to CT registration respectively.

A reference dataset for deformable image registration spatial accuracy evaluation using the COPDgene study archive

NASA Astrophysics Data System (ADS)

Castillo, Richard; Castillo, Edward; Fuentes, David; Ahmad, Moiz; Wood, Abbie M.; Ludwig, Michelle S.; Guerrero, Thomas

2013-05-01

Landmark point-pairs provide a strategy to assess deformable image registration (DIR) accuracy in terms of the spatial registration of the underlying anatomy depicted in medical images. In this study, we propose to augment a publicly available database (www.dir-lab.com) of medical images with large sets of manually identified anatomic feature pairs between breath-hold computed tomography (BH-CT) images for DIR spatial accuracy evaluation. Ten BH-CT image pairs were randomly selected from the COPDgene study cases. Each patient had received CT imaging of the entire thorax in the supine position at one-fourth dose normal expiration and maximum effort full dose inspiration. Using dedicated in-house software, an imaging expert manually identified large sets of anatomic feature pairs between images. Estimates of inter- and intra-observer spatial variation in feature localization were determined by repeat measurements of multiple observers over subsets of randomly selected features. 7298 anatomic landmark features were manually paired between the 10 sets of images. Quantity of feature pairs per case ranged from 447 to 1172. Average 3D Euclidean landmark displacements varied substantially among cases, ranging from 12.29 (SD: 6.39) to 30.90 (SD: 14.05) mm. Repeat registration of uniformly sampled subsets of 150 landmarks for each case yielded estimates of observer localization error, which ranged in average from 0.58 (SD: 0.87) to 1.06 (SD: 2.38) mm for each case. The additions to the online web database (www.dir-lab.com) described in this work will broaden the applicability of the reference data, providing a freely available common dataset for targeted critical evaluation of DIR spatial accuracy performance in multiple clinical settings. Estimates of observer variance in feature localization suggest consistent spatial accuracy for all observers across both four-dimensional CT and COPDgene patient cohorts.

WE-G-207-05: Relationship Between CT Image Quality, Segmentation Performance, and Quantitative Image Feature Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, J; Nishikawa, R; Reiser, I

Purpose: Segmentation quality can affect quantitative image feature analysis. The objective of this study is to examine the relationship between computed tomography (CT) image quality, segmentation performance, and quantitative image feature analysis. Methods: A total of 90 pathology proven breast lesions in 87 dedicated breast CT images were considered. An iterative image reconstruction (IIR) algorithm was used to obtain CT images with different quality. With different combinations of 4 variables in the algorithm, this study obtained a total of 28 different qualities of CT images. Two imaging tasks/objectives were considered: 1) segmentation and 2) classification of the lesion as benignmore » or malignant. Twenty-three image features were extracted after segmentation using a semi-automated algorithm and 5 of them were selected via a feature selection technique. Logistic regression was trained and tested using leave-one-out-cross-validation and its area under the ROC curve (AUC) was recorded. The standard deviation of a homogeneous portion and the gradient of a parenchymal portion of an example breast were used as an estimate of image noise and sharpness. The DICE coefficient was computed using a radiologist’s drawing on the lesion. Mean DICE and AUC were used as performance metrics for each of the 28 reconstructions. The relationship between segmentation and classification performance under different reconstructions were compared. Distributions (median, 95% confidence interval) of DICE and AUC for each reconstruction were also compared. Results: Moderate correlation (Pearson’s rho = 0.43, p-value = 0.02) between DICE and AUC values was found. However, the variation between DICE and AUC values for each reconstruction increased as the image sharpness increased. There was a combination of IIR parameters that resulted in the best segmentation with the worst classification performance. Conclusion: There are certain images that yield better segmentation or classification performance. The best segmentation Result does not necessarily lead to the best classification Result. This work has been supported in part by grants from the NIH R21-EB015053. R Nishikawa is receives royalties form Hologic, Inc.« less

A region-based segmentation of tumour from brain CT images using nonlinear support vector machine classifier.

PubMed

Nanthagopal, A Padma; Rajamony, R Sukanesh

2012-07-01

The proposed system provides new textural information for segmenting tumours, efficiently and accurately and with less computational time, from benign and malignant tumour images, especially in smaller dimensions of tumour regions of computed tomography (CT) images. Region-based segmentation of tumour from brain CT image data is an important but time-consuming task performed manually by medical experts. The objective of this work is to segment brain tumour from CT images using combined grey and texture features with new edge features and nonlinear support vector machine (SVM) classifier. The selected optimal features are used to model and train the nonlinear SVM classifier to segment the tumour from computed tomography images and the segmentation accuracies are evaluated for each slice of the tumour image. The method is applied on real data of 80 benign, malignant tumour images. The results are compared with the radiologist labelled ground truth. Quantitative analysis between ground truth and the segmented tumour is presented in terms of segmentation accuracy and the overlap similarity measure dice metric. From the analysis and performance measures such as segmentation accuracy and dice metric, it is inferred that better segmentation accuracy and higher dice metric are achieved with the normalized cut segmentation method than with the fuzzy c-means clustering method.

Differential diagnosis of primary nasopharyngeal lymphoma and nasopharyngeal carcinoma focusing on CT, MRI, and PET/CT.

PubMed

Cho, Kyu-Sup; Kang, Dae-Woon; Kim, Hak-Jin; Lee, Jong-Kil; Roh, Hwan-Jung

2012-04-01

No study has done a comparative analysis of radiologic imaging findings between primary nasopharyngeal lymphoma (PNL) and nasopharyngeal carcinoma (NPC). The purpose of this study was to analyze computed tomography (CT) and magnetic resonance (MR) images and to evaluate the maximum standardized uptake value (SUV max) of positron emission tomography (PET)/CT between PNL and NPC, knowing the imaging features that distinguish PNL from NPC. Cross-sectional study. University tertiary care facility. The authors analyzed the features on CT, MR imaging, and PET/CT of 16 patients diagnosed with PNL and 32 patients diagnosed with NPC histopathologically. Patients with PNL had a larger tumor volume and showed symmetry of tumor shape than did patients with NPC. Patients with PNL also had higher tumor homogeneity than NPC patients on CT, T2-weighted, and postcontrast MR images. All PNL patients showed a high degree of enhancement without invasion to the adjacent deep structure. The involvement of the Waldeyer ring was significantly higher in PNL patients. Cervical and retropharyngeal lymphadenopathy and PET/CT SUV max showed no significant difference between PNL and NPC. If the images present a bulky, symmetric nasopharyngeal mass with marked homogeneity, a high degree of enhancement, and a higher Waldeyer ring involvement combined with no invasion into the deep structure, PNL should be considered over NPC.

Efficient and robust model-to-image alignment using 3D scale-invariant features.

PubMed

Toews, Matthew; Wells, William M

2013-04-01

This paper presents feature-based alignment (FBA), a general method for efficient and robust model-to-image alignment. Volumetric images, e.g. CT scans of the human body, are modeled probabilistically as a collage of 3D scale-invariant image features within a normalized reference space. Features are incorporated as a latent random variable and marginalized out in computing a maximum a posteriori alignment solution. The model is learned from features extracted in pre-aligned training images, then fit to features extracted from a new image to identify a globally optimal locally linear alignment solution. Novel techniques are presented for determining local feature orientation and efficiently encoding feature intensity in 3D. Experiments involving difficult magnetic resonance (MR) images of the human brain demonstrate FBA achieves alignment accuracy similar to widely-used registration methods, while requiring a fraction of the memory and computation resources and offering a more robust, globally optimal solution. Experiments on CT human body scans demonstrate FBA as an effective system for automatic human body alignment where other alignment methods break down. Copyright © 2012 Elsevier B.V. All rights reserved.

Efficient and Robust Model-to-Image Alignment using 3D Scale-Invariant Features

PubMed Central

Toews, Matthew; Wells, William M.

2013-01-01

This paper presents feature-based alignment (FBA), a general method for efficient and robust model-to-image alignment. Volumetric images, e.g. CT scans of the human body, are modeled probabilistically as a collage of 3D scale-invariant image features within a normalized reference space. Features are incorporated as a latent random variable and marginalized out in computing a maximum a-posteriori alignment solution. The model is learned from features extracted in pre-aligned training images, then fit to features extracted from a new image to identify a globally optimal locally linear alignment solution. Novel techniques are presented for determining local feature orientation and efficiently encoding feature intensity in 3D. Experiments involving difficult magnetic resonance (MR) images of the human brain demonstrate FBA achieves alignment accuracy similar to widely-used registration methods, while requiring a fraction of the memory and computation resources and offering a more robust, globally optimal solution. Experiments on CT human body scans demonstrate FBA as an effective system for automatic human body alignment where other alignment methods break down. PMID:23265799

Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma.

PubMed

Feng, Zhichao; Rong, Pengfei; Cao, Peng; Zhou, Qingyu; Zhu, Wenwei; Yan, Zhimin; Liu, Qianyun; Wang, Wei

2018-04-01

To evaluate the diagnostic performance of machine-learning based quantitative texture analysis of CT images to differentiate small (≤ 4 cm) angiomyolipoma without visible fat (AMLwvf) from renal cell carcinoma (RCC). This single-institutional retrospective study included 58 patients with pathologically proven small renal mass (17 in AMLwvf and 41 in RCC groups). Texture features were extracted from the largest possible tumorous regions of interest (ROIs) by manual segmentation in preoperative three-phase CT images. Interobserver reliability and the Mann-Whitney U test were applied to select features preliminarily. Then support vector machine with recursive feature elimination (SVM-RFE) and synthetic minority oversampling technique (SMOTE) were adopted to establish discriminative classifiers, and the performance of classifiers was assessed. Of the 42 extracted features, 16 candidate features showed significant intergroup differences (P < 0.05) and had good interobserver agreement. An optimal feature subset including 11 features was further selected by the SVM-RFE method. The SVM-RFE+SMOTE classifier achieved the best performance in discriminating between small AMLwvf and RCC, with the highest accuracy, sensitivity, specificity and AUC of 93.9 %, 87.8 %, 100 % and 0.955, respectively. Machine learning analysis of CT texture features can facilitate the accurate differentiation of small AMLwvf from RCC. • Although conventional CT is useful for diagnosis of SRMs, it has limitations. • Machine-learning based CT texture analysis facilitate differentiation of small AMLwvf from RCC. • The highest accuracy of SVM-RFE+SMOTE classifier reached 93.9 %. • Texture analysis combined with machine-learning methods might spare unnecessary surgery for AMLwvf.

Pulmonary nodule characterization, including computer analysis and quantitative features.

PubMed

Bartholmai, Brian J; Koo, Chi Wan; Johnson, Geoffrey B; White, Darin B; Raghunath, Sushravya M; Rajagopalan, Srinivasan; Moynagh, Michael R; Lindell, Rebecca M; Hartman, Thomas E

2015-03-01

Pulmonary nodules are commonly detected in computed tomography (CT) chest screening of a high-risk population. The specific visual or quantitative features on CT or other modalities can be used to characterize the likelihood that a nodule is benign or malignant. Visual features on CT such as size, attenuation, location, morphology, edge characteristics, and other distinctive "signs" can be highly suggestive of a specific diagnosis and, in general, be used to determine the probability that a specific nodule is benign or malignant. Change in size, attenuation, and morphology on serial follow-up CT, or features on other modalities such as nuclear medicine studies or MRI, can also contribute to the characterization of lung nodules. Imaging analytics can objectively and reproducibly quantify nodule features on CT, nuclear medicine, and magnetic resonance imaging. Some quantitative techniques show great promise in helping to differentiate benign from malignant lesions or to stratify the risk of aggressive versus indolent neoplasm. In this article, we (1) summarize the visual characteristics, descriptors, and signs that may be helpful in management of nodules identified on screening CT, (2) discuss current quantitative and multimodality techniques that aid in the differentiation of nodules, and (3) highlight the power, pitfalls, and limitations of these various techniques.

Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images.

PubMed

Wang, Hongkai; Zhou, Zongwei; Li, Yingci; Chen, Zhonghua; Lu, Peiou; Wang, Wenzhi; Liu, Wanyu; Yu, Lijuan

2017-12-01

This study aimed to compare one state-of-the-art deep learning method and four classical machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer (NSCLC) from 18 F-FDG PET/CT images. Another objective was to compare the discriminative power of the recently popular PET/CT texture features with the widely used diagnostic features such as tumor size, CT value, SUV, image contrast, and intensity standard deviation. The four classical machine learning methods included random forests, support vector machines, adaptive boosting, and artificial neural network. The deep learning method was the convolutional neural networks (CNN). The five methods were evaluated using 1397 lymph nodes collected from PET/CT images of 168 patients, with corresponding pathology analysis results as gold standard. The comparison was conducted using 10 times 10-fold cross-validation based on the criterion of sensitivity, specificity, accuracy (ACC), and area under the ROC curve (AUC). For each classical method, different input features were compared to select the optimal feature set. Based on the optimal feature set, the classical methods were compared with CNN, as well as with human doctors from our institute. For the classical methods, the diagnostic features resulted in 81~85% ACC and 0.87~0.92 AUC, which were significantly higher than the results of texture features. CNN's sensitivity, specificity, ACC, and AUC were 84, 88, 86, and 0.91, respectively. There was no significant difference between the results of CNN and the best classical method. The sensitivity, specificity, and ACC of human doctors were 73, 90, and 82, respectively. All the five machine learning methods had higher sensitivities but lower specificities than human doctors. The present study shows that the performance of CNN is not significantly different from the best classical methods and human doctors for classifying mediastinal lymph node metastasis of NSCLC from PET/CT images. Because CNN does not need tumor segmentation or feature calculation, it is more convenient and more objective than the classical methods. However, CNN does not make use of the import diagnostic features, which have been proved more discriminative than the texture features for classifying small-sized lymph nodes. Therefore, incorporating the diagnostic features into CNN is a promising direction for future research.

Automatic segmentation of solitary pulmonary nodules based on local intensity structure analysis and 3D neighborhood features in 3D chest CT images

NASA Astrophysics Data System (ADS)

Chen, Bin; Kitasaka, Takayuki; Honma, Hirotoshi; Takabatake, Hirotsugu; Mori, Masaki; Natori, Hiroshi; Mori, Kensaku

2012-03-01

This paper presents a solitary pulmonary nodule (SPN) segmentation method based on local intensity structure analysis and neighborhood feature analysis in chest CT images. Automated segmentation of SPNs is desirable for a chest computer-aided detection/diagnosis (CAS) system since a SPN may indicate early stage of lung cancer. Due to the similar intensities of SPNs and other chest structures such as blood vessels, many false positives (FPs) are generated by nodule detection methods. To reduce such FPs, we introduce two features that analyze the relation between each segmented nodule candidate and it neighborhood region. The proposed method utilizes a blob-like structure enhancement (BSE) filter based on Hessian analysis to augment the blob-like structures as initial nodule candidates. Then a fine segmentation is performed to segment much more accurate region of each nodule candidate. FP reduction is mainly addressed by investigating two neighborhood features based on volume ratio and eigenvector of Hessian that are calculates from the neighborhood region of each nodule candidate. We evaluated the proposed method by using 40 chest CT images, include 20 standard-dose CT images that we randomly chosen from a local database and 20 low-dose CT images that were randomly chosen from a public database: LIDC. The experimental results revealed that the average TP rate of proposed method was 93.6% with 12.3 FPs/case.

Radiotherapy volume delineation using dynamic [18F]-FDG PET/CT imaging in patients with oropharyngeal cancer: a pilot study.

PubMed

Silvoniemi, Antti; Din, Mueez U; Suilamo, Sami; Shepherd, Tony; Minn, Heikki

2016-11-01

Delineation of gross tumour volume in 3D is a critical step in the radiotherapy (RT) treatment planning for oropharyngeal cancer (OPC). Static [ 18 F]-FDG PET/CT imaging has been suggested as a method to improve the reproducibility of tumour delineation, but it suffers from low specificity. We undertook this pilot study in which dynamic features in time-activity curves (TACs) of [ 18 F]-FDG PET/CT images were applied to help the discrimination of tumour from inflammation and adjacent normal tissue. Five patients with OPC underwent dynamic [ 18 F]-FDG PET/CT imaging in treatment position. Voxel-by-voxel analysis was performed to evaluate seven dynamic features developed with the knowledge of differences in glucose metabolism in different tissue types and visual inspection of TACs. The Gaussian mixture model and K-means algorithms were used to evaluate the performance of the dynamic features in discriminating tumour voxels compared to the performance of standardized uptake values obtained from static imaging. Some dynamic features showed a trend towards discrimination of different metabolic areas but lack of consistency means that clinical application is not recommended based on these results alone. Impact of inflammatory tissue remains a problem for volume delineation in RT of OPC, but a simple dynamic imaging protocol proved practicable and enabled simple data analysis techniques that show promise for complementing the information in static uptake values.

CT scan

MedlinePlus

... this page: //medlineplus.gov/ency/article/003330.htm CT scan To use the sharing features on this page, please enable JavaScript. A computed tomography (CT) scan is an imaging method that uses x- ...

Quantitative Features of Liver Lesions, Lung Nodules, and Renal Stones at Multi-Detector Row CT Examinations: Dependency on Radiation Dose and Reconstruction Algorithm.

PubMed

Solomon, Justin; Mileto, Achille; Nelson, Rendon C; Roy Choudhury, Kingshuk; Samei, Ehsan

2016-04-01

To determine if radiation dose and reconstruction algorithm affect the computer-based extraction and analysis of quantitative imaging features in lung nodules, liver lesions, and renal stones at multi-detector row computed tomography (CT). Retrospective analysis of data from a prospective, multicenter, HIPAA-compliant, institutional review board-approved clinical trial was performed by extracting 23 quantitative imaging features (size, shape, attenuation, edge sharpness, pixel value distribution, and texture) of lesions on multi-detector row CT images of 20 adult patients (14 men, six women; mean age, 63 years; range, 38-72 years) referred for known or suspected focal liver lesions, lung nodules, or kidney stones. Data were acquired between September 2011 and April 2012. All multi-detector row CT scans were performed at two different radiation dose levels; images were reconstructed with filtered back projection, adaptive statistical iterative reconstruction, and model-based iterative reconstruction (MBIR) algorithms. A linear mixed-effects model was used to assess the effect of radiation dose and reconstruction algorithm on extracted features. Among the 23 imaging features assessed, radiation dose had a significant effect on five, three, and four of the features for liver lesions, lung nodules, and renal stones, respectively (P < .002 for all comparisons). Adaptive statistical iterative reconstruction had a significant effect on three, one, and one of the features for liver lesions, lung nodules, and renal stones, respectively (P < .002 for all comparisons). MBIR reconstruction had a significant effect on nine, 11, and 15 of the features for liver lesions, lung nodules, and renal stones, respectively (P < .002 for all comparisons). Of note, the measured size of lung nodules and renal stones with MBIR was significantly different than those for the other two algorithms (P < .002 for all comparisons). Although lesion texture was significantly affected by the reconstruction algorithm used (average of 3.33 features affected by MBIR throughout lesion types; P < .002, for all comparisons), no significant effect of the radiation dose setting was observed for all but one of the texture features (P = .002-.998). Radiation dose settings and reconstruction algorithms affect the extraction and analysis of quantitative imaging features in lesions at multi-detector row CT.

Augmenting atlas-based liver segmentation for radiotherapy treatment planning by incorporating image features proximal to the atlas contours

NASA Astrophysics Data System (ADS)

Li, Dengwang; Liu, Li; Chen, Jinhu; Li, Hongsheng; Yin, Yong; Ibragimov, Bulat; Xing, Lei

2017-01-01

Atlas-based segmentation utilizes a library of previously delineated contours of similar cases to facilitate automatic segmentation. The problem, however, remains challenging because of limited information carried by the contours in the library. In this studying, we developed a narrow-shell strategy to enhance the information of each contour in the library and to improve the accuracy of the exiting atlas-based approach. This study presented a new concept of atlas based segmentation method. Instead of using the complete volume of the target organs, only information along the organ contours from the atlas images was used for guiding segmentation of the new image. In setting up an atlas-based library, we included not only the coordinates of contour points, but also the image features adjacent to the contour. In this work, 139 CT images with normal appearing livers collected for radiotherapy treatment planning were used to construct the library. The CT images within the library were first registered to each other using affine registration. The nonlinear narrow shell was generated alongside the object contours of registered images. Matching voxels were selected inside common narrow shell image features of a library case and a new case using a speed-up robust features (SURF) strategy. A deformable registration was then performed using a thin plate splines (TPS) technique. The contour associated with the library case was propagated automatically onto the new image by exploiting the deformation field vectors. The liver contour was finally obtained by employing level set based energy optimization within the narrow shell. The performance of the proposed method was evaluated by comparing quantitatively the auto-segmentation results with that delineated by physicians. A novel atlas-based segmentation technique with inclusion of neighborhood image features through the introduction of a narrow-shell surrounding the target objects was established. Application of the technique to 30 liver cases suggested that the technique was capable to reliably segment liver cases from CT, 4D-CT, and CBCT images with little human interaction. The accuracy and speed of the proposed method are quantitatively validated by comparing automatic segmentation results with the manual delineation results. The Jaccard similarity metric between the automatically generated liver contours obtained by the proposed method and the physician delineated results are on an average 90%-96% for planning images. Incorporation of image features into the library contours improves the currently available atlas-based auto-contouring techniques and provides a clinically practical solution for auto-segmentation. The proposed mountainous narrow shell atlas based method can achieve efficient automatic liver propagation for CT, 4D-CT and CBCT images with following treatment planning and should find widespread application in future treatment planning systems.

Augmenting atlas-based liver segmentation for radiotherapy treatment planning by incorporating image features proximal to the atlas contours.

PubMed

Li, Dengwang; Liu, Li; Chen, Jinhu; Li, Hongsheng; Yin, Yong; Ibragimov, Bulat; Xing, Lei

2017-01-07

Atlas-based segmentation utilizes a library of previously delineated contours of similar cases to facilitate automatic segmentation. The problem, however, remains challenging because of limited information carried by the contours in the library. In this studying, we developed a narrow-shell strategy to enhance the information of each contour in the library and to improve the accuracy of the exiting atlas-based approach. This study presented a new concept of atlas based segmentation method. Instead of using the complete volume of the target organs, only information along the organ contours from the atlas images was used for guiding segmentation of the new image. In setting up an atlas-based library, we included not only the coordinates of contour points, but also the image features adjacent to the contour. In this work, 139 CT images with normal appearing livers collected for radiotherapy treatment planning were used to construct the library. The CT images within the library were first registered to each other using affine registration. The nonlinear narrow shell was generated alongside the object contours of registered images. Matching voxels were selected inside common narrow shell image features of a library case and a new case using a speed-up robust features (SURF) strategy. A deformable registration was then performed using a thin plate splines (TPS) technique. The contour associated with the library case was propagated automatically onto the new image by exploiting the deformation field vectors. The liver contour was finally obtained by employing level set based energy optimization within the narrow shell. The performance of the proposed method was evaluated by comparing quantitatively the auto-segmentation results with that delineated by physicians. A novel atlas-based segmentation technique with inclusion of neighborhood image features through the introduction of a narrow-shell surrounding the target objects was established. Application of the technique to 30 liver cases suggested that the technique was capable to reliably segment liver cases from CT, 4D-CT, and CBCT images with little human interaction. The accuracy and speed of the proposed method are quantitatively validated by comparing automatic segmentation results with the manual delineation results. The Jaccard similarity metric between the automatically generated liver contours obtained by the proposed method and the physician delineated results are on an average 90%-96% for planning images. Incorporation of image features into the library contours improves the currently available atlas-based auto-contouring techniques and provides a clinically practical solution for auto-segmentation. The proposed mountainous narrow shell atlas based method can achieve efficient automatic liver propagation for CT, 4D-CT and CBCT images with following treatment planning and should find widespread application in future treatment planning systems.

Classification of ground glass opacity lesion characteristic based on texture feature using lung CT image

NASA Astrophysics Data System (ADS)

Sebatubun, M. M.; Haryawan, C.; Windarta, B.

2018-03-01

Lung cancer causes a high mortality rate in the world than any other cancers. That can be minimised if the symptoms and cancer cells have been detected early. One of the techniques used to detect lung cancer is by computed tomography (CT) scan. CT scan images have been used in this study to identify one of the lesion characteristics named ground glass opacity (GGO). It has been used to determine the level of malignancy of the lesion. There were three phases in identifying GGO: image cropping, feature extraction using grey level co-occurrence matrices (GLCM) and classification using Naïve Bayes Classifier. In order to improve the classification results, the most significant feature was sought by feature selection using gain ratio evaluation. Based on the results obtained, the most significant features could be identified by using feature selection method used in this research. The accuracy rate increased from 83.33% to 91.67%, the sensitivity from 82.35% to 94.11% and the specificity from 84.21% to 89.47%.

Contrast-enhanced CT features of hepatoblastoma: Can we predict histopathology?

PubMed

Baheti, Akshay D; Luana Stanescu, A; Li, Ning; Chapman, Teresa

Hepatoblastoma is the most common hepatic malignancy occurring in the pediatric population. Intratumoral cellular behavior varies, and the small-cell undifferentiated histopathology carries a poorer prognosis than other tissue subtypes. Neoadjuvant chemotherapy is recommended for this tumor subtype prior to surgical resection in most cases. Early identification of tumors with poor prognosis could have a significant clinical impact. Objective The aim of this work was to identify imaging features of small-cell undifferentiated subtype hepatoblastoma that can help distinguish this subtype from more favorable tumors and potentially guide the clinical management. We also sought to characterize contrast-enhanced CT (CECT) features of hepatoblastoma that correlate with metastatic disease and patient outcome. Our study included 34 patients (24 males, 10 females) with a mean age of 16months (range: 0-46months) with surgically confirmed hepatoblastoma and available baseline abdominal imaging by CECT. Clinical data and CT abdominal images were retrospectively analyzed. Five tumors with small-cell undifferentiated components were identified. All of these tumors demonstrated irregular margins on CT imaging. Advanced PRETEXT stage, vascular invasion and irregular margins were associated with metastatic disease and decreased survival. Capsular retraction was also significantly associated with decreased survival. Irregular tumor margins demonstrated statistically significant association with the presence of small-cell undifferentiated components. No other imaging feature showed statistically significant association. Tumor margin irregularity, vascular invasion, capsular retraction, and PRETEXT stage correlate with worse patient outcomes. Irregular tumor margin was the only imaging feature significantly associated with more aggressive tumor subtype. Copyright © 2017 Elsevier Inc. All rights reserved.

NOTE: An innovative phantom for quantitative and qualitative investigation of advanced x-ray imaging technologies

NASA Astrophysics Data System (ADS)

Chiarot, C. B.; Siewerdsen, J. H.; Haycocks, T.; Moseley, D. J.; Jaffray, D. A.

2005-11-01

Development, characterization, and quality assurance of advanced x-ray imaging technologies require phantoms that are quantitative and well suited to such modalities. This note reports on the design, construction, and use of an innovative phantom developed for advanced imaging technologies (e.g., multi-detector CT and the numerous applications of flat-panel detectors in dual-energy imaging, tomosynthesis, and cone-beam CT) in diagnostic and image-guided procedures. The design addresses shortcomings of existing phantoms by incorporating criteria satisfied by no other single phantom: (1) inserts are fully 3D—spherically symmetric rather than cylindrical; (2) modules are quantitative, presenting objects of known size and contrast for quality assurance and image quality investigation; (3) features are incorporated in ideal and semi-realistic (anthropomorphic) contexts; and (4) the phantom allows devices to be inserted and manipulated in an accessible module (right lung). The phantom consists of five primary modules: (1) head, featuring contrast-detail spheres approximate to brain lesions; (2) left lung, featuring contrast-detail spheres approximate to lung modules; (3) right lung, an accessible hull in which devices may be placed and manipulated; (4) liver, featuring conrast-detail spheres approximate to metastases; and (5) abdomen/pelvis, featuring simulated kidneys, colon, rectum, bladder, and prostate. The phantom represents a two-fold evolution in design philosophy—from 2D (cylindrically symmetric) to fully 3D, and from exclusively qualitative or quantitative to a design accommodating quantitative study within an anatomical context. It has proven a valuable tool in investigations throughout our institution, including low-dose CT, dual-energy radiography, and cone-beam CT for image-guided radiation therapy and surgery.

Deep-learning derived features for lung nodule classification with limited datasets

NASA Astrophysics Data System (ADS)

Thammasorn, P.; Wu, W.; Pierce, L. A.; Pipavath, S. N.; Lampe, P. D.; Houghton, A. M.; Haynor, D. R.; Chaovalitwongse, W. A.; Kinahan, P. E.

2018-02-01

Only a few percent of indeterminate nodules found in lung CT images are cancer. However, enabling earlier diagnosis is important to avoid invasive procedures or long-time surveillance to those benign nodules. We are evaluating a classification framework using radiomics features derived with a machine learning approach from a small data set of indeterminate CT lung nodule images. We used a retrospective analysis of 194 cases with pulmonary nodules in the CT images with or without contrast enhancement from lung cancer screening clinics. The nodules were contoured by a radiologist and texture features of the lesion were calculated. In addition, sematic features describing shape were categorized. We also explored a Multiband network, a feature derivation path that uses a modified convolutional neural network (CNN) with a Triplet Network. This was trained to create discriminative feature representations useful for variable-sized nodule classification. The diagnostic accuracy was evaluated for multiple machine learning algorithms using texture, shape, and CNN features. In the CT contrast-enhanced group, the texture or semantic shape features yielded an overall diagnostic accuracy of 80%. Use of a standard deep learning network in the framework for feature derivation yielded features that substantially underperformed compared to texture and/or semantic features. However, the proposed Multiband approach of feature derivation produced results similar in diagnostic accuracy to the texture and semantic features. While the Multiband feature derivation approach did not outperform the texture and/or semantic features, its equivalent performance indicates promise for future improvements to increase diagnostic accuracy. Importantly, the Multiband approach adapts readily to different size lesions without interpolation, and performed well with relatively small amount of training data.

SEGMENTING CT PROSTATE IMAGES USING POPULATION AND PATIENT-SPECIFIC STATISTICS FOR RADIOTHERAPY.

PubMed

Feng, Qianjin; Foskey, Mark; Tang, Songyuan; Chen, Wufan; Shen, Dinggang

2009-08-07

This paper presents a new deformable model using both population and patient-specific statistics to segment the prostate from CT images. There are two novelties in the proposed method. First, a modified scale invariant feature transform (SIFT) local descriptor, which is more distinctive than general intensity and gradient features, is used to characterize the image features. Second, an online training approach is used to build the shape statistics for accurately capturing intra-patient variation, which is more important than inter-patient variation for prostate segmentation in clinical radiotherapy. Experimental results show that the proposed method is robust and accurate, suitable for clinical application.

SEGMENTING CT PROSTATE IMAGES USING POPULATION AND PATIENT-SPECIFIC STATISTICS FOR RADIOTHERAPY

PubMed Central

Feng, Qianjin; Foskey, Mark; Tang, Songyuan; Chen, Wufan; Shen, Dinggang

2010-01-01

This paper presents a new deformable model using both population and patient-specific statistics to segment the prostate from CT images. There are two novelties in the proposed method. First, a modified scale invariant feature transform (SIFT) local descriptor, which is more distinctive than general intensity and gradient features, is used to characterize the image features. Second, an online training approach is used to build the shape statistics for accurately capturing intra-patient variation, which is more important than inter-patient variation for prostate segmentation in clinical radiotherapy. Experimental results show that the proposed method is robust and accurate, suitable for clinical application. PMID:21197416

Integrating dimension reduction and out-of-sample extension in automated classification of ex vivo human patellar cartilage on phase contrast X-ray computed tomography.

PubMed

Nagarajan, Mahesh B; Coan, Paola; Huber, Markus B; Diemoz, Paul C; Wismüller, Axel

2015-01-01

Phase contrast X-ray computed tomography (PCI-CT) has been demonstrated as a novel imaging technique that can visualize human cartilage with high spatial resolution and soft tissue contrast. Different textural approaches have been previously investigated for characterizing chondrocyte organization on PCI-CT to enable classification of healthy and osteoarthritic cartilage. However, the large size of feature sets extracted in such studies motivates an investigation into algorithmic feature reduction for computing efficient feature representations without compromising their discriminatory power. For this purpose, geometrical feature sets derived from the scaling index method (SIM) were extracted from 1392 volumes of interest (VOI) annotated on PCI-CT images of ex vivo human patellar cartilage specimens. The extracted feature sets were subject to linear and non-linear dimension reduction techniques as well as feature selection based on evaluation of mutual information criteria. The reduced feature set was subsequently used in a machine learning task with support vector regression to classify VOIs as healthy or osteoarthritic; classification performance was evaluated using the area under the receiver-operating characteristic (ROC) curve (AUC). Our results show that the classification performance achieved by 9-D SIM-derived geometric feature sets (AUC: 0.96 ± 0.02) can be maintained with 2-D representations computed from both dimension reduction and feature selection (AUC values as high as 0.97 ± 0.02). Thus, such feature reduction techniques can offer a high degree of compaction to large feature sets extracted from PCI-CT images while maintaining their ability to characterize the underlying chondrocyte patterns.

Prognostic Value and Reproducibility of Pretreatment CT Texture Features in Stage III Non-Small Cell Lung Cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fried, David V.; Graduate School of Biomedical Sciences, The University of Texas Health Science Center at Houston, Houston, Texas; Tucker, Susan L.

2014-11-15

Purpose: To determine whether pretreatment CT texture features can improve patient risk stratification beyond conventional prognostic factors (CPFs) in stage III non-small cell lung cancer (NSCLC). Methods and Materials: We retrospectively reviewed 91 cases with stage III NSCLC treated with definitive chemoradiation therapy. All patients underwent pretreatment diagnostic contrast enhanced computed tomography (CE-CT) followed by 4-dimensional CT (4D-CT) for treatment simulation. We used the average-CT and expiratory (T50-CT) images from the 4D-CT along with the CE-CT for texture extraction. Histogram, gradient, co-occurrence, gray tone difference, and filtration-based techniques were used for texture feature extraction. Penalized Cox regression implementing cross-validation wasmore » used for covariate selection and modeling. Models incorporating texture features from the 33 image types and CPFs were compared to those with models incorporating CPFs alone for overall survival (OS), local-regional control (LRC), and freedom from distant metastases (FFDM). Predictive Kaplan-Meier curves were generated using leave-one-out cross-validation. Patients were stratified based on whether their predicted outcome was above or below the median. Reproducibility of texture features was evaluated using test-retest scans from independent patients and quantified using concordance correlation coefficients (CCC). We compared models incorporating the reproducibility seen on test-retest scans to our original models and determined the classification reproducibility. Results: Models incorporating both texture features and CPFs demonstrated a significant improvement in risk stratification compared to models using CPFs alone for OS (P=.046), LRC (P=.01), and FFDM (P=.005). The average CCCs were 0.89, 0.91, and 0.67 for texture features extracted from the average-CT, T50-CT, and CE-CT, respectively. Incorporating reproducibility within our models yielded 80.4% (±3.7% SD), 78.3% (±4.0% SD), and 78.8% (±3.9% SD) classification reproducibility in terms of OS, LRC, and FFDM, respectively. Conclusions: Pretreatment tumor texture may provide prognostic information beyond that obtained from CPFs. Models incorporating feature reproducibility achieved classification rates of ∼80%. External validation would be required to establish texture as a prognostic factor.« less

Automated Analysis of CT Images for the Inspection of Hardwood Logs

Treesearch

Harbin Li; A. Lynn Abbott; Daniel L. Schmoldt

1996-01-01

This paper investigates several classifiers for labeling internal features of hardwood logs using computed tomography (CT) images. A primary motivation is to locate and classify internal defects so that an optimal cutting strategy can be chosen. Previous work has relied on combinations of low-level processing, image segmentation, autoregressive texture modeling, and...

Deep feature classification of angiomyolipoma without visible fat and renal cell carcinoma in abdominal contrast-enhanced CT images with texture image patches and hand-crafted feature concatenation.

PubMed

Lee, Hansang; Hong, Helen; Kim, Junmo; Jung, Dae Chul

2018-04-01

To develop an automatic deep feature classification (DFC) method for distinguishing benign angiomyolipoma without visible fat (AMLwvf) from malignant clear cell renal cell carcinoma (ccRCC) from abdominal contrast-enhanced computer tomography (CE CT) images. A dataset including 80 abdominal CT images of 39 AMLwvf and 41 ccRCC patients was used. We proposed a DFC method for differentiating the small renal masses (SRM) into AMLwvf and ccRCC using the combination of hand-crafted and deep features, and machine learning classifiers. First, 71-dimensional hand-crafted features (HCF) of texture and shape were extracted from the SRM contours. Second, 1000-4000-dimensional deep features (DF) were extracted from the ImageNet pretrained deep learning model with the SRM image patches. In DF extraction, we proposed the texture image patches (TIP) to emphasize the texture information inside the mass in DFs and reduce the mass size variability. Finally, the two features were concatenated and the random forest (RF) classifier was trained on these concatenated features to classify the types of SRMs. The proposed method was tested on our dataset using leave-one-out cross-validation and evaluated using accuracy, sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV), and area under receiver operating characteristics curve (AUC). In experiments, the combinations of four deep learning models, AlexNet, VGGNet, GoogleNet, and ResNet, and four input image patches, including original, masked, mass-size, and texture image patches, were compared and analyzed. In qualitative evaluation, we observed the change in feature distributions between the proposed and comparative methods using tSNE method. In quantitative evaluation, we evaluated and compared the classification results, and observed that (a) the proposed HCF + DF outperformed HCF-only and DF-only, (b) AlexNet showed generally the best performances among the CNN models, and (c) the proposed TIPs not only achieved the competitive performances among the input patches, but also steady performance regardless of CNN models. As a result, the proposed method achieved the accuracy of 76.6 ± 1.4% for the proposed HCF + DF with AlexNet and TIPs, which improved the accuracy by 6.6%p and 8.3%p compared to HCF-only and DF-only, respectively. The proposed shape features and TIPs improved the HCFs and DFs, respectively, and the feature concatenation further enhanced the quality of features for differentiating AMLwvf from ccRCC in abdominal CE CT images. © 2018 American Association of Physicists in Medicine.

Texture-preserved penalized weighted least-squares reconstruction of low-dose CT image via image segmentation and high-order MRF modeling

NASA Astrophysics Data System (ADS)

Han, Hao; Zhang, Hao; Wei, Xinzhou; Moore, William; Liang, Zhengrong

2016-03-01

In this paper, we proposed a low-dose computed tomography (LdCT) image reconstruction method with the help of prior knowledge learning from previous high-quality or normal-dose CT (NdCT) scans. The well-established statistical penalized weighted least squares (PWLS) algorithm was adopted for image reconstruction, where the penalty term was formulated by a texture-based Gaussian Markov random field (gMRF) model. The NdCT scan was firstly segmented into different tissue types by a feature vector quantization (FVQ) approach. Then for each tissue type, a set of tissue-specific coefficients for the gMRF penalty was statistically learnt from the NdCT image via multiple-linear regression analysis. We also proposed a scheme to adaptively select the order of gMRF model for coefficients prediction. The tissue-specific gMRF patterns learnt from the NdCT image were finally used to form an adaptive MRF penalty for the PWLS reconstruction of LdCT image. The proposed texture-adaptive PWLS image reconstruction algorithm was shown to be more effective to preserve image textures than the conventional PWLS image reconstruction algorithm, and we further demonstrated the gain of high-order MRF modeling for texture-preserved LdCT PWLS image reconstruction.

CT and MR Imaging Diagnosis and Staging of Hepatocellular Carcinoma: Part I. Development, Growth, and Spread: Key Pathologic and Imaging Aspects

PubMed Central

Choi, Jin-Young; Lee, Jeong-Min

2014-01-01

Computed tomography (CT) and magnetic resonance (MR) imaging play critical roles in the diagnosis and staging of hepatocellular carcinoma (HCC). The first article of this two-part review discusses key concepts of HCC development, growth, and spread, emphasizing those features with imaging correlates and hence most relevant to radiologists; state-of-the-art CT and MR imaging technique with extracellular and hepatobiliary contrast agents; and the imaging appearance of precursor nodules that eventually may transform into overt HCC. © RSNA, 2014 PMID:25153274

Hard x-ray micro-tomography of a human head post-mortem as a gold standard to compare x-ray modalities

NASA Astrophysics Data System (ADS)

Dalstra, M.; Schulz, G.; Dagassan-Berndt, D.; Verna, C.; Müller-Gerbl, M.; Müller, B.

2016-10-01

An entire human head obtained at autopsy was micro-CT scanned in a nano/micro-CT scanner in a 6-hour long session. Despite the size of the head, it could still be scanned with a pixel size of 70 μm. The aim of this study was to obtain an optimal quality 3D data-set to be used as baseline control in a larger study comparing the image quality of various cone beam CT systems currently used in dentistry. The image quality of the micro-CT scans was indeed better than the ones of the clinical imaging modalities, both with regard to noise and streak artifacts due to metal dental implants. Bony features in the jaws, like the trabecular architecture and the thin wall of the alveolar bone were clearly visible. Therefore, the 3D micro-CT data-set can be used as the gold standard for linear, angular, and volumetric measurements of anatomical features in and around the oral cavity when comparing clinical imaging modalities.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kuo, J; Su, K; Department of Radiology, University Hospitals Case Medical Center, Case Western Reserve University, Cleveland, Ohio

Purpose: Accurate and robust photon attenuation derived from MR is essential for PET/MR and MR-based radiation treatment planning applications. Although the fuzzy C-means (FCM) algorithm has been applied for pseudo-CT generation, the input feature combination and the number of clusters have not been optimized. This study aims to optimize both for clinically practical pseudo-CT generation. Methods: Nine volunteers were recruited. A 190-second, single-acquisition UTE-mDixon with 25% (angular) sampling and 3D radial readout was performed to acquire three primitive MR features at TEs of 0.1, 1.5, and 2.8 ms: the free-induction-decay (FID), the first and the second echo images. Three derivedmore » images, Dixon-fat and Dixon-water generated by two-point Dixon water/fat separation, and R2* (1/T2*) map, were also created. To identify informative inputs for generating a pseudo-CT image volume, all 63 combinations, choosing one to six of the feature images, were used as inputs to FCM for pseudo-CT generation. Further, the number of clusters was varied from four to seven to find the optimal approach. Mean prediction deviation (MPD), mean absolute prediction deviation (MAPD), and correlation coefficient (R) of different combinations were compared for feature selection. Results: Among the 63 feature combinations, the four that resulted in the best MAPD and R were further compared along with the set containing all six features. The results suggested that R2* and Dixon-water are the most informative features. Further, including FID also improved the performance of pseudo-CT generation. Consequently, the set containing FID, Dixon-water, and R2* resulted in the most accurate, robust pseudo-CT when the number of cluster equals to five (5C). The clusters were interpreted as air, fat, bone, brain, and fluid. The six-cluster Result additionally included bone marrow. Conclusion: The results suggested that FID, Dixon-water, R2* are the most important features. The findings can be used to facilitate pseudo-CT generation for unsupervised clustering. Please note that the project was completed with partial funding from the Ohio Department of Development grant TECH 11-063 and a sponsored research agreement with Philips Healthcare that is managed by Case Western Reserve University. As noted in the affiliations, some of the authors are Philips employees.« less

TBIdoc: 3D content-based CT image retrieval system for traumatic brain injury

NASA Astrophysics Data System (ADS)

Li, Shimiao; Gong, Tianxia; Wang, Jie; Liu, Ruizhe; Tan, Chew Lim; Leong, Tze Yun; Pang, Boon Chuan; Lim, C. C. Tchoyoson; Lee, Cheng Kiang; Tian, Qi; Zhang, Zhuo

2010-03-01

Traumatic brain injury (TBI) is a major cause of death and disability. Computed Tomography (CT) scan is widely used in the diagnosis of TBI. Nowadays, large amount of TBI CT data is stacked in the hospital radiology department. Such data and the associated patient information contain valuable information for clinical diagnosis and outcome prediction. However, current hospital database system does not provide an efficient and intuitive tool for doctors to search out cases relevant to the current study case. In this paper, we present the TBIdoc system: a content-based image retrieval (CBIR) system which works on the TBI CT images. In this web-based system, user can query by uploading CT image slices from one study, retrieval result is a list of TBI cases ranked according to their 3D visual similarity to the query case. Specifically, cases of TBI CT images often present diffuse or focal lesions. In TBIdoc system, these pathological image features are represented as bin-based binary feature vectors. We use the Jaccard-Needham measure as the similarity measurement. Based on these, we propose a 3D similarity measure for computing the similarity score between two series of CT slices. nDCG is used to evaluate the system performance, which shows the system produces satisfactory retrieval results. The system is expected to improve the current hospital data management in TBI and to give better support for the clinical decision-making process. It may also contribute to the computer-aided education in TBI.

Contourlet textual features: improving the diagnosis of solitary pulmonary nodules in two dimensional CT images.

PubMed

Wang, Jingjing; Sun, Tao; Gao, Ni; Menon, Desmond Dev; Luo, Yanxia; Gao, Qi; Li, Xia; Wang, Wei; Zhu, Huiping; Lv, Pingxin; Liang, Zhigang; Tao, Lixin; Liu, Xiangtong; Guo, Xiuhua

2014-01-01

To determine the value of contourlet textural features obtained from solitary pulmonary nodules in two dimensional CT images used in diagnoses of lung cancer. A total of 6,299 CT images were acquired from 336 patients, with 1,454 benign pulmonary nodule images from 84 patients (50 male, 34 female) and 4,845 malignant from 252 patients (150 male, 102 female). Further to this, nineteen patient information categories, which included seven demographic parameters and twelve morphological features, were also collected. A contourlet was used to extract fourteen types of textural features. These were then used to establish three support vector machine models. One comprised a database constructed of nineteen collected patient information categories, another included contourlet textural features and the third one contained both sets of information. Ten-fold cross-validation was used to evaluate the diagnosis results for the three databases, with sensitivity, specificity, accuracy, the area under the curve (AUC), precision, Youden index, and F-measure were used as the assessment criteria. In addition, the synthetic minority over-sampling technique (SMOTE) was used to preprocess the unbalanced data. Using a database containing textural features and patient information, sensitivity, specificity, accuracy, AUC, precision, Youden index, and F-measure were: 0.95, 0.71, 0.89, 0.89, 0.92, 0.66, and 0.93 respectively. These results were higher than results derived using the database without textural features (0.82, 0.47, 0.74, 0.67, 0.84, 0.29, and 0.83 respectively) as well as the database comprising only textural features (0.81, 0.64, 0.67, 0.72, 0.88, 0.44, and 0.85 respectively). Using the SMOTE as a pre-processing procedure, new balanced database generated, including observations of 5,816 benign ROIs and 5,815 malignant ROIs, and accuracy was 0.93. Our results indicate that the combined contourlet textural features of solitary pulmonary nodules in CT images with patient profile information could potentially improve the diagnosis of lung cancer.

A study on using texture analysis methods for identifying lobar fissure regions in isotropic CT images.

PubMed

Wei, Q; Hu, Y

2009-01-01

The major hurdle for segmenting lung lobes in computed tomographic (CT) images is to identify fissure regions, which encase lobar fissures. Accurate identification of these regions is difficult due to the variable shape and appearance of the fissures, along with the low contrast and high noise associated with CT images. This paper studies the effectiveness of two texture analysis methods - the gray level co-occurrence matrix (GLCM) and the gray level run length matrix (GLRLM) - in identifying fissure regions from isotropic CT image stacks. To classify GLCM and GLRLM texture features, we applied a feed-forward back-propagation neural network and achieved the best classification accuracy utilizing 16 quantized levels for computing the GLCM and GLRLM texture features and 64 neurons in the input/hidden layers of the neural network. Tested on isotropic CT image stacks of 24 patients with the pathologic lungs, we obtained accuracies of 86% and 87% for identifying fissure regions using the GLCM and GLRLM methods, respectively. These accuracies compare favorably with surgeons/radiologists' accuracy of 80% for identifying fissure regions in clinical settings. This shows promising potential for segmenting lung lobes using the GLCM and GLRLM methods.

Dictionary learning-based CT detection of pulmonary nodules

NASA Astrophysics Data System (ADS)

Wu, Panpan; Xia, Kewen; Zhang, Yanbo; Qian, Xiaohua; Wang, Ge; Yu, Hengyong

2016-10-01

Segmentation of lung features is one of the most important steps for computer-aided detection (CAD) of pulmonary nodules with computed tomography (CT). However, irregular shapes, complicated anatomical background and poor pulmonary nodule contrast make CAD a very challenging problem. Here, we propose a novel scheme for feature extraction and classification of pulmonary nodules through dictionary learning from training CT images, which does not require accurately segmented pulmonary nodules. Specifically, two classification-oriented dictionaries and one background dictionary are learnt to solve a two-category problem. In terms of the classification-oriented dictionaries, we calculate sparse coefficient matrices to extract intrinsic features for pulmonary nodule classification. The support vector machine (SVM) classifier is then designed to optimize the performance. Our proposed methodology is evaluated with the lung image database consortium and image database resource initiative (LIDC-IDRI) database, and the results demonstrate that the proposed strategy is promising.

Validation of CBCT for the computation of textural biomarkers

NASA Astrophysics Data System (ADS)

Paniagua, Beatriz; Ruellas, Antonio C.; Benavides, Erika; Marron, Steve; Wolford, Larry; Cevidanes, Lucia

2015-03-01

Osteoarthritis (OA) is associated with significant pain and 42.6% of patients with TMJ disorders present with evidence of TMJ OA. However, OA diagnosis and treatment remain controversial, since there are no clear symptoms of the disease. The subchondral bone in the TMJ is believed to play a major role in the progression of OA. We hypothesize that the textural imaging biomarkers computed in high resolution Conebeam CT (hr- CBCT) and μCT scans are comparable. The purpose of this study is to test the feasibility of computing textural imaging biomarkers in-vivo using hr-CBCT, compared to those computed in μCT scans as our Gold Standard. Specimens of condylar bones obtained from condylectomies were scanned using μCT and hr- CBCT. Nine different textural imaging biomarkers (four co-occurrence features and five run-length features) from each pair of μCT and hr-CBCT were computed and compared. Pearson correlation coefficients were computed to compare textural biomarkers values of μCT and hr-CBCT. Four of the nine computed textural biomarkers showed a strong positive correlation between biomarkers computed in μCT and hr-CBCT. Higher correlations in Energy and Contrast, and in GLN (grey-level non-uniformity) and RLN (run length non-uniformity) indicate quantitative texture features can be computed reliably in hr-CBCT, when compared with μCT. The textural imaging biomarkers computed in-vivo hr-CBCT have captured the structure, patterns, contrast between neighboring regions and uniformity of healthy and/or pathologic subchondral bone. The ability to quantify bone texture non-invasively now makes it possible to evaluate the progression of subchondral bone alterations, in TMJ OA.

Validation of CBCT for the computation of textural biomarkers

PubMed Central

Paniagua, Beatriz; Ruellas, Antonio Carlos; Benavides, Erika; Marron, Steve; Woldford, Larry; Cevidanes, Lucia

2015-01-01

Osteoarthritis (OA) is associated with significant pain and 42.6% of patients with TMJ disorders present with evidence of TMJ OA. However, OA diagnosis and treatment remain controversial, since there are no clear symptoms of the disease. The subchondral bone in the TMJ is believed to play a major role in the progression of OA. We hypothesize that the textural imaging biomarkers computed in high resolution Conebeam CT (hr-CBCT) and μCT scans are comparable. The purpose of this study is to test the feasibility of computing textural imaging biomarkers in-vivo using hr-CBCT, compared to those computed in μCT scans as our Gold Standard. Specimens of condylar bones obtained from condylectomies were scanned using μCT and hr-CBCT. Nine different textural imaging biomarkers (four co-occurrence features and five run-length features) from each pair of μCT and hr-CBCT were computed and compared. Pearson correlation coefficients were computed to compare textural biomarkers values of μCT and hr-CBCT. Four of the nine computed textural biomarkers showed a strong positive correlation between biomarkers computed in μCT and hr-CBCT. Higher correlations in Energy and Contrast, and in GLN (grey-level non-uniformity) and RLN (run length non-uniformity) indicate quantitative texture features can be computed reliably in hr-CBCT, when compared with μCT. The textural imaging biomarkers computed in-vivo hr-CBCT have captured the structure, patterns, contrast between neighboring regions and uniformity of healthy and/or pathologic subchondral bone. The ability to quantify bone texture non-invasively now makes it possible to evaluate the progression of subchondral bone alterations, in TMJ OA. PMID:26085710

Validation of CBCT for the computation of textural biomarkers.

PubMed

Paniagua, Beatriz; Ruellas, Antonio Carlos; Benavides, Erika; Marron, Steve; Woldford, Larry; Cevidanes, Lucia

2015-03-17

Osteoarthritis (OA) is associated with significant pain and 42.6% of patients with TMJ disorders present with evidence of TMJ OA. However, OA diagnosis and treatment remain controversial, since there are no clear symptoms of the disease. The subchondral bone in the TMJ is believed to play a major role in the progression of OA. We hypothesize that the textural imaging biomarkers computed in high resolution Conebeam CT (hr-CBCT) and μCT scans are comparable. The purpose of this study is to test the feasibility of computing textural imaging biomarkers in-vivo using hr-CBCT, compared to those computed in μCT scans as our Gold Standard. Specimens of condylar bones obtained from condylectomies were scanned using μCT and hr-CBCT. Nine different textural imaging biomarkers (four co-occurrence features and five run-length features) from each pair of μCT and hr-CBCT were computed and compared. Pearson correlation coefficients were computed to compare textural biomarkers values of μCT and hr-CBCT. Four of the nine computed textural biomarkers showed a strong positive correlation between biomarkers computed in μCT and hr-CBCT. Higher correlations in Energy and Contrast, and in GLN (grey-level non-uniformity) and RLN (run length non-uniformity) indicate quantitative texture features can be computed reliably in hr-CBCT, when compared with μCT. The textural imaging biomarkers computed in-vivo hr-CBCT have captured the structure, patterns, contrast between neighboring regions and uniformity of healthy and/or pathologic subchondral bone. The ability to quantify bone texture non-invasively now makes it possible to evaluate the progression of subchondral bone alterations, in TMJ OA.

SU-D-BRA-07: A Phantom Study to Assess the Variability in Radiomics Features Extracted From Cone-Beam CT Images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fave, X; Fried, D; UT Health Science Center Graduate School of Biomedical Sciences, Houston, TX

2015-06-15

Purpose: Several studies have demonstrated the prognostic potential for texture features extracted from CT images of non-small cell lung cancer (NSCLC) patients. The purpose of this study was to determine if these features could be extracted with high reproducibility from cone-beam CT (CBCT) images in order for features to be easily tracked throughout a patient’s treatment. Methods: Two materials in a radiomics phantom, designed to approximate NSCLC tumor texture, were used to assess the reproducibility of 26 features. This phantom was imaged on 9 CBCT scanners, including Elekta and Varian machines. Thoracic and head imaging protocols were acquired on eachmore » machine. CBCT images from 27 NSCLC patients imaged using the thoracic protocol on Varian machines were obtained for comparison. The variance for each texture measured from these patients was compared to the variance in phantom values for different manufacturer/protocol subsets. Levene’s test was used to identify features which had a significantly smaller variance in the phantom scans versus the patient data. Results: Approximately half of the features (13/26 for material1 and 15/26 for material2) had a significantly smaller variance (p<0.05) between Varian thoracic scans of the phantom compared to patient scans. Many of these same features remained significant for the head scans on Varian (12/26 and 8/26). However, when thoracic scans from Elekta and Varian were combined, only a few features were still significant (4/26 and 5/26). Three features (skewness, coarsely filtered mean and standard deviation) were significant in almost all manufacturer/protocol subsets. Conclusion: Texture features extracted from CBCT images of a radiomics phantom are reproducible and show significantly less variation than the same features measured from patient images when images from the same manufacturer or with similar parameters are used. Reproducibility between CBCT scanners may be high enough to allow the extraction of meaningful texture values for patients. This project was funded in part by the Cancer Prevention Research Institute of Texas (CPRIT). Xenia Fave is a recipient of the American Association of Physicists in Medicine Graduate Fellowship.« less

Survival Prediction in Pancreatic Ductal Adenocarcinoma by Quantitative Computed Tomography Image Analysis.

PubMed

Attiyeh, Marc A; Chakraborty, Jayasree; Doussot, Alexandre; Langdon-Embry, Liana; Mainarich, Shiana; Gönen, Mithat; Balachandran, Vinod P; D'Angelica, Michael I; DeMatteo, Ronald P; Jarnagin, William R; Kingham, T Peter; Allen, Peter J; Simpson, Amber L; Do, Richard K

2018-04-01

Pancreatic cancer is a highly lethal cancer with no established a priori markers of survival. Existing nomograms rely mainly on post-resection data and are of limited utility in directing surgical management. This study investigated the use of quantitative computed tomography (CT) features to preoperatively assess survival for pancreatic ductal adenocarcinoma (PDAC) patients. A prospectively maintained database identified consecutive chemotherapy-naive patients with CT angiography and resected PDAC between 2009 and 2012. Variation in CT enhancement patterns was extracted from the tumor region using texture analysis, a quantitative image analysis tool previously described in the literature. Two continuous survival models were constructed, with 70% of the data (training set) using Cox regression, first based only on preoperative serum cancer antigen (CA) 19-9 levels and image features (model A), and then on CA19-9, image features, and the Brennan score (composite pathology score; model B). The remaining 30% of the data (test set) were reserved for independent validation. A total of 161 patients were included in the analysis. Training and test sets contained 113 and 48 patients, respectively. Quantitative image features combined with CA19-9 achieved a c-index of 0.69 [integrated Brier score (IBS) 0.224] on the test data, while combining CA19-9, imaging, and the Brennan score achieved a c-index of 0.74 (IBS 0.200) on the test data. We present two continuous survival prediction models for resected PDAC patients. Quantitative analysis of CT texture features is associated with overall survival. Further work includes applying the model to an external dataset to increase the sample size for training and to determine its applicability.

Intraosseous mucoepidermoid carcinoma: a review of the diagnostic imaging features of four jaw cases.

PubMed

Chan, K C; Pharoah, M; Lee, L; Weinreb, I; Perez-Ordonez, B

2013-01-01

The purpose of this case series is to present the common features of intraosseous mucoepidermoid carcinoma (IMC) of the jaws in plain film and CT imaging. Two oral and maxillofacial radiologists reviewed and characterized the common features of four biopsy-proven cases of IMC in the jaws in plain film and CT imaging obtained from the files of the Department of Oral Radiology, Faculty of Dentistry, University of Toronto, Toronto, Canada. The common features are a well-defined sclerotic periphery, the presence of internal amorphous sclerotic bone and numerous small loculations, lack of septae bordering many of the loculations, and expansion and perforation of the outer cortical plate with extension into surrounding soft tissue. Other characteristics include tooth displacement and root resorption. The four cases of IMC reviewed have common imaging characteristics. All cases share some diagnostic imaging features with other multilocular-appearing entities of the jaws. However, the presence of amorphous sclerotic bone and malignant characteristics can be useful in the differential diagnosis.

Intra-tumour 18F-FDG uptake heterogeneity decreases the reliability on target volume definition with positron emission tomography/computed tomography imaging.

PubMed

Dong, Xinzhe; Wu, Peipei; Sun, Xiaorong; Li, Wenwu; Wan, Honglin; Yu, Jinming; Xing, Ligang

2015-06-01

This study aims to explore whether the intra-tumour (18) F-fluorodeoxyglucose (FDG) uptake heterogeneity affects the reliability of target volume definition with FDG positron emission tomography/computed tomography (PET/CT) imaging for nonsmall cell lung cancer (NSCLC) and squamous cell oesophageal cancer (SCEC). Patients with NSCLC (n = 50) or SCEC (n = 50) who received (18)F-FDG PET/CT scanning before treatments were included in this retrospective study. Intra-tumour FDG uptake heterogeneity was assessed by visual scoring, the coefficient of variation (COV) of the standardised uptake value (SUV) and the image texture feature (entropy). Tumour volumes (gross tumour volume (GTV)) were delineated on the CT images (GTV(CT)), the fused PET/CT images (GTV(PET-CT)) and the PET images, using a threshold at 40% SUV(max) (GTV(PET40%)) or the SUV cut-off value of 2.5 (GTV(PET2.5)). The correlation between the FDG uptake heterogeneity parameters and the differences in tumour volumes among GTV(CT), GTV(PET-CT), GTV(PET40%) and GTV(PET2.5) was analysed. For both NSCLC and SCEC, obvious correlations were found between uptake heterogeneity, SUV or tumour volumes. Three types of heterogeneity parameters were consistent and closely related to each other. Substantial differences between the four methods of GTV definition were found. The differences between the GTV correlated significantly with PET heterogeneity defined with the visual score, the COV or the textural feature-entropy for NSCLC and SCEC. In tumours with a high FDG uptake heterogeneity, a larger GTV delineation difference was found. Advance image segmentation algorithms dealing with tracer uptake heterogeneity should be incorporated into the treatment planning system. © 2015 The Royal Australian and New Zealand College of Radiologists.

The Role of 18F-FDG PET/CT Integrated Imaging in Distinguishing Malignant from Benign Pleural Effusion.

PubMed

Sun, Yajuan; Yu, Hongjuan; Ma, Jingquan; Lu, Peiou

2016-01-01

The aim of our study was to evaluate the role of 18F-FDG PET/CT integrated imaging in differentiating malignant from benign pleural effusion. A total of 176 patients with pleural effusion who underwent 18F-FDG PET/CT examination to differentiate malignancy from benignancy were retrospectively researched. The images of CT imaging, 18F-FDG PET imaging and 18F-FDG PET/CT integrated imaging were visually analyzed. The suspected malignant effusion was characterized by the presence of nodular or irregular pleural thickening on CT imaging. Whereas on PET imaging, pleural 18F-FDG uptake higher than mediastinal activity was interpreted as malignant effusion. Images of 18F-FDG PET/CT integrated imaging were interpreted by combining the morphologic feature of pleura on CT imaging with the degree and form of pleural 18F-FDG uptake on PET imaging. One hundred and eight patients had malignant effusion, including 86 with pleural metastasis and 22 with pleural mesothelioma, whereas 68 patients had benign effusion. The sensitivities of CT imaging, 18F-FDG PET imaging and 18F-FDG PET/CT integrated imaging in detecting malignant effusion were 75.0%, 91.7% and 93.5%, respectively, which were 69.8%, 91.9% and 93.0% in distinguishing metastatic effusion. The sensitivity of 18F-FDG PET/CT integrated imaging in detecting malignant effusion was higher than that of CT imaging (p = 0.000). For metastatic effusion, 18F-FDG PET imaging had higher sensitivity (p = 0.000) and better diagnostic consistency with 18F-FDG PET/CT integrated imaging compared with CT imaging (Kappa = 0.917 and Kappa = 0.295, respectively). The specificities of CT imaging, 18F-FDG PET imaging and 18F-FDG PET/CT integrated imaging were 94.1%, 63.2% and 92.6% in detecting benign effusion. The specificities of CT imaging and 18F-FDG PET/CT integrated imaging were higher than that of 18F-FDG PET imaging (p = 0.000 and p = 0.000, respectively), and CT imaging had better diagnostic consistency with 18F-FDG PET/CT integrated imaging compared with 18F-FDG PET imaging (Kappa = 0.881 and Kappa = 0.240, respectively). 18F-FDG PET/CT integrated imaging is a more reliable modality in distinguishing malignant from benign pleural effusion than 18F-FDG PET imaging and CT imaging alone. For image interpretation of 18F-FDG PET/CT integrated imaging, the PET and CT portions play a major diagnostic role in identifying metastatic effusion and benign effusion, respectively.

Localized thin-section CT with radiomics feature extraction and machine learning to classify early-detected pulmonary nodules from lung cancer screening

NASA Astrophysics Data System (ADS)

Tu, Shu-Ju; Wang, Chih-Wei; Pan, Kuang-Tse; Wu, Yi-Cheng; Wu, Chen-Te

2018-03-01

Lung cancer screening aims to detect small pulmonary nodules and decrease the mortality rate of those affected. However, studies from large-scale clinical trials of lung cancer screening have shown that the false-positive rate is high and positive predictive value is low. To address these problems, a technical approach is greatly needed for accurate malignancy differentiation among these early-detected nodules. We studied the clinical feasibility of an additional protocol of localized thin-section CT for further assessment on recalled patients from lung cancer screening tests. Our approach of localized thin-section CT was integrated with radiomics features extraction and machine learning classification which was supervised by pathological diagnosis. Localized thin-section CT images of 122 nodules were retrospectively reviewed and 374 radiomics features were extracted. In this study, 48 nodules were benign and 74 malignant. There were nine patients with multiple nodules and four with synchronous multiple malignant nodules. Different machine learning classifiers with a stratified ten-fold cross-validation were used and repeated 100 times to evaluate classification accuracy. Of the image features extracted from the thin-section CT images, 238 (64%) were useful in differentiating between benign and malignant nodules. These useful features include CT density (p  =  0.002 518), sigma (p  =  0.002 781), uniformity (p  =  0.032 41), and entropy (p  =  0.006 685). The highest classification accuracy was 79% by the logistic classifier. The performance metrics of this logistic classification model was 0.80 for the positive predictive value, 0.36 for the false-positive rate, and 0.80 for the area under the receiver operating characteristic curve. Our approach of direct risk classification supervised by the pathological diagnosis with localized thin-section CT and radiomics feature extraction may support clinical physicians in determining truly malignant nodules and therefore reduce problems in lung cancer screening.

Localized thin-section CT with radiomics feature extraction and machine learning to classify early-detected pulmonary nodules from lung cancer screening.

PubMed

Tu, Shu-Ju; Wang, Chih-Wei; Pan, Kuang-Tse; Wu, Yi-Cheng; Wu, Chen-Te

2018-03-14

Lung cancer screening aims to detect small pulmonary nodules and decrease the mortality rate of those affected. However, studies from large-scale clinical trials of lung cancer screening have shown that the false-positive rate is high and positive predictive value is low. To address these problems, a technical approach is greatly needed for accurate malignancy differentiation among these early-detected nodules. We studied the clinical feasibility of an additional protocol of localized thin-section CT for further assessment on recalled patients from lung cancer screening tests. Our approach of localized thin-section CT was integrated with radiomics features extraction and machine learning classification which was supervised by pathological diagnosis. Localized thin-section CT images of 122 nodules were retrospectively reviewed and 374 radiomics features were extracted. In this study, 48 nodules were benign and 74 malignant. There were nine patients with multiple nodules and four with synchronous multiple malignant nodules. Different machine learning classifiers with a stratified ten-fold cross-validation were used and repeated 100 times to evaluate classification accuracy. Of the image features extracted from the thin-section CT images, 238 (64%) were useful in differentiating between benign and malignant nodules. These useful features include CT density (p  =  0.002 518), sigma (p  =  0.002 781), uniformity (p  =  0.032 41), and entropy (p  =  0.006 685). The highest classification accuracy was 79% by the logistic classifier. The performance metrics of this logistic classification model was 0.80 for the positive predictive value, 0.36 for the false-positive rate, and 0.80 for the area under the receiver operating characteristic curve. Our approach of direct risk classification supervised by the pathological diagnosis with localized thin-section CT and radiomics feature extraction may support clinical physicians in determining truly malignant nodules and therefore reduce problems in lung cancer screening.

Image processing based detection of lung cancer on CT scan images

NASA Astrophysics Data System (ADS)

Abdillah, Bariqi; Bustamam, Alhadi; Sarwinda, Devvi

2017-10-01

In this paper, we implement and analyze the image processing method for detection of lung cancer. Image processing techniques are widely used in several medical problems for picture enhancement in the detection phase to support the early medical treatment. In this research we proposed a detection method of lung cancer based on image segmentation. Image segmentation is one of intermediate level in image processing. Marker control watershed and region growing approach are used to segment of CT scan image. Detection phases are followed by image enhancement using Gabor filter, image segmentation, and features extraction. From the experimental results, we found the effectiveness of our approach. The results show that the best approach for main features detection is watershed with masking method which has high accuracy and robust.

Apply radiomics approach for early stage prognostic evaluation of ovarian cancer patients: a preliminary study

NASA Astrophysics Data System (ADS)

Danala, Gopichandh; Wang, Yunzhi; Thai, Theresa; Gunderson, Camille; Moxley, Katherine; Moore, Kathleen; Mannel, Robert; Liu, Hong; Zheng, Bin; Qiu, Yuchen

2017-03-01

Predicting metastatic tumor response to chemotherapy at early stage is critically important for improving efficacy of clinical trials of testing new chemotherapy drugs. However, using current response evaluation criteria in solid tumors (RECIST) guidelines only yields a limited accuracy to predict tumor response. In order to address this clinical challenge, we applied Radiomics approach to develop a new quantitative image analysis scheme, aiming to accurately assess the tumor response to new chemotherapy treatment, for the advanced ovarian cancer patients. During the experiment, a retrospective dataset containing 57 patients was assembled, each of which has two sets of CT images: pre-therapy and 4-6 week follow up CT images. A Radiomics based image analysis scheme was then applied on these images, which is composed of three steps. First, the tumors depicted on the CT images were segmented by a hybrid tumor segmentation scheme. Then, a total of 115 features were computed from the segmented tumors, which can be grouped as 1) volume based features; 2) density based features; and 3) wavelet features. Finally, an optimal feature cluster was selected based on the single feature performance and an equal-weighed fusion rule was applied to generate the final predicting score. The results demonstrated that the single feature achieved an area under the receiver operating characteristic curve (AUC) of 0.838+/-0.053. This investigation demonstrates that the Radiomic approach may have the potential in the development of high accuracy predicting model for early stage prognostic assessment of ovarian cancer patients.

Body Imaging

NASA Technical Reports Server (NTRS)

1990-01-01

Magnetic Resonance Imaging (MRI) and Computer-aided Tomography (CT) images are often complementary. In most cases, MRI is good for viewing soft tissue but not bone, while CT images are good for bone but not always good for soft tissue discrimination. Physicians and engineers in the Department of Radiology at the University of Michigan Hospitals are developing a technique for combining the best features of MRI and CT scans to increase the accuracy of discriminating one type of body tissue from another. One of their research tools is a computer program called HICAP. The program can be used to distinguish between healthy and diseased tissue in body images.

An approach for quantitative image quality analysis for CT

NASA Astrophysics Data System (ADS)

Rahimi, Amir; Cochran, Joe; Mooney, Doug; Regensburger, Joe

2016-03-01

An objective and standardized approach to assess image quality of Compute Tomography (CT) systems is required in a wide variety of imaging processes to identify CT systems appropriate for a given application. We present an overview of the framework we have developed to help standardize and to objectively assess CT image quality for different models of CT scanners used for security applications. Within this framework, we have developed methods to quantitatively measure metrics that should correlate with feature identification, detection accuracy and precision, and image registration capabilities of CT machines and to identify strengths and weaknesses in different CT imaging technologies in transportation security. To that end we have designed, developed and constructed phantoms that allow for systematic and repeatable measurements of roughly 88 image quality metrics, representing modulation transfer function, noise equivalent quanta, noise power spectra, slice sensitivity profiles, streak artifacts, CT number uniformity, CT number consistency, object length accuracy, CT number path length consistency, and object registration. Furthermore, we have developed a sophisticated MATLAB based image analysis tool kit to analyze CT generated images of phantoms and report these metrics in a format that is standardized across the considered models of CT scanners, allowing for comparative image quality analysis within a CT model or between different CT models. In addition, we have developed a modified sparse principal component analysis (SPCA) method to generate a modified set of PCA components as compared to the standard principal component analysis (PCA) with sparse loadings in conjunction with Hotelling T2 statistical analysis method to compare, qualify, and detect faults in the tested systems.

A Bayesian framework for early risk prediction in traumatic brain injury

NASA Astrophysics Data System (ADS)

Chaganti, Shikha; Plassard, Andrew J.; Wilson, Laura; Smith, Miya A.; Patel, Mayur B.; Landman, Bennett A.

2016-03-01

Early detection of risk is critical in determining the course of treatment in traumatic brain injury (TBI). Computed tomography (CT) acquired at admission has shown latent prognostic value in prior studies; however, no robust clinical risk predictions have been achieved based on the imaging data in large-scale TBI analysis. The major challenge lies in the lack of consistent and complete medical records for patients, and an inherent bias associated with the limited number of patients samples with high-risk outcomes in available TBI datasets. Herein, we propose a Bayesian framework with mutual information-based forward feature selection to handle this type of data. Using multi-atlas segmentation, 154 image-based features (capturing intensity, volume and texture) were computed over 22 ROIs in 1791 CT scans. These features were combined with 14 clinical parameters and converted into risk likelihood scores using Bayes modeling. We explore the prediction power of the image features versus the clinical measures for various risk outcomes. The imaging data alone were more predictive of outcomes than the clinical data (including Marshall CT classification) for discharge disposition with an area under the curve of 0.81 vs. 0.67, but less predictive than clinical data for discharge Glasgow Coma Scale (GCS) score with an area under the curve of 0.65 vs. 0.85. However, in both cases, combining imaging and clinical data increased the combined area under the curve with 0.86 for discharge disposition and 0.88 for discharge GCS score. In conclusion, CT data have meaningful prognostic value for TBI patients beyond what is captured in clinical measures and the Marshall CT classification.

A systematic approach to vertebral hemangioma.

PubMed

Gaudino, Simona; Martucci, Matia; Colantonio, Raffaella; Lozupone, Emilio; Visconti, Emiliano; Leone, Antonio; Colosimo, Cesare

2015-01-01

Vertebral hemangiomas (VHs) are a frequent and often incidental finding on computed tomography (CT) and magnetic resonance (MR) imaging of the spine. When their imaging appearance is "typical" (coarsened vertical trabeculae on radiographic and CT images, hyperintensity on T1- and T2-weighted MR images), the radiological diagnosis is straightforward. Nonetheless, VHs might also display an "atypical" appearance on MR imaging because of their histological features (amount of fat, vessels, and interstitial edema). Although the majority of VHs are asymptomatic and quiescent lesions, they can exhibit active behaviors, including growing quickly, extending beyond the vertebral body, and invading the paravertebral and/or epidural space with possible compression of the spinal cord and/or nerve roots ("aggressive" VHs). These "atypical" and "aggressive" VHs are a radiological challenge since they can mimic primary bony malignancies or metastases. CT plays a central role in the workup of atypical VHs, being the most appropriate imaging modality to highlight the polka-dot appearance that is representative of them. When aggressive VHs are suspected, both CT and MR are needed. MR is the best imaging modality to characterize the epidural and/or soft-tissue component, helping in the differential diagnosis. Angiography is a useful imaging adjunct for evaluating and even treating aggressive VHs. The primary objectives of this review article are to summarize the clinical, pathological, and imaging features of VHs, as well as the treatment options, and to provide a practical guide for the differential diagnosis, focusing on the rationale assessment of the findings from radiography, CT, and MR imaging.

WE-E-17A-05: Complementary Prognostic Value of CT and 18F-FDG PET Non-Small Cell Lung Cancer Tumor Heterogeneity Features Quantified Through Texture Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Desseroit, M; Cheze Le Rest, C; Tixier, F

2014-06-15

Purpose: Previous studies have shown that CT or 18F-FDG PET intratumor heterogeneity features computed using texture analysis may have prognostic value in Non-Small Cell Lung Cancer (NSCLC), but have been mostly investigated separately. The purpose of this study was to evaluate the potential added value with respect to prognosis regarding the combination of non-enhanced CT and 18F-FDG PET heterogeneity textural features on primary NSCLC tumors. Methods: One hundred patients with non-metastatic NSCLC (stage I–III), treated with surgery and/or (chemo)radiotherapy, that underwent staging 18F-FDG PET/CT images, were retrospectively included. Morphological tumor volumes were semi-automatically delineated on non-enhanced CT using 3D SlicerTM.more » Metabolically active tumor volumes (MATV) were automatically delineated on PET using the Fuzzy Locally Adaptive Bayesian (FLAB) method. Intratumoral tissue density and FDG uptake heterogeneities were quantified using texture parameters calculated from co-occurrence, difference, and run-length matrices. In addition to these textural features, first order histogram-derived metrics were computed on the whole morphological CT tumor volume, as well as on sub-volumes corresponding to fine, medium or coarse textures determined through various levels of LoG-filtering. Association with survival regarding all extracted features was assessed using Cox regression for both univariate and multivariate analysis. Results: Several PET and CT heterogeneity features were prognostic factors of overall survival in the univariate analysis. CT histogram-derived kurtosis and uniformity, as well as Low Grey-level High Run Emphasis (LGHRE), and PET local entropy were independent prognostic factors. Combined with stage and MATV, they led to a powerful prognostic model (p<0.0001), with median survival of 49 vs. 12.6 months and a hazard ratio of 3.5. Conclusion: Intratumoral heterogeneity quantified through textural features extracted from both CT and FDG PET images have complementary and independent prognostic value in NSCLC.« less

Integrating Dimension Reduction and Out-of-Sample Extension in Automated Classification of Ex Vivo Human Patellar Cartilage on Phase Contrast X-Ray Computed Tomography

PubMed Central

Nagarajan, Mahesh B.; Coan, Paola; Huber, Markus B.; Diemoz, Paul C.; Wismüller, Axel

2015-01-01

Phase contrast X-ray computed tomography (PCI-CT) has been demonstrated as a novel imaging technique that can visualize human cartilage with high spatial resolution and soft tissue contrast. Different textural approaches have been previously investigated for characterizing chondrocyte organization on PCI-CT to enable classification of healthy and osteoarthritic cartilage. However, the large size of feature sets extracted in such studies motivates an investigation into algorithmic feature reduction for computing efficient feature representations without compromising their discriminatory power. For this purpose, geometrical feature sets derived from the scaling index method (SIM) were extracted from 1392 volumes of interest (VOI) annotated on PCI-CT images of ex vivo human patellar cartilage specimens. The extracted feature sets were subject to linear and non-linear dimension reduction techniques as well as feature selection based on evaluation of mutual information criteria. The reduced feature set was subsequently used in a machine learning task with support vector regression to classify VOIs as healthy or osteoarthritic; classification performance was evaluated using the area under the receiver-operating characteristic (ROC) curve (AUC). Our results show that the classification performance achieved by 9-D SIM-derived geometric feature sets (AUC: 0.96 ± 0.02) can be maintained with 2-D representations computed from both dimension reduction and feature selection (AUC values as high as 0.97 ± 0.02). Thus, such feature reduction techniques can offer a high degree of compaction to large feature sets extracted from PCI-CT images while maintaining their ability to characterize the underlying chondrocyte patterns. PMID:25710875

A fully automatic end-to-end method for content-based image retrieval of CT scans with similar liver lesion annotations.

PubMed

Spanier, A B; Caplan, N; Sosna, J; Acar, B; Joskowicz, L

2018-01-01

The goal of medical content-based image retrieval (M-CBIR) is to assist radiologists in the decision-making process by retrieving medical cases similar to a given image. One of the key interests of radiologists is lesions and their annotations, since the patient treatment depends on the lesion diagnosis. Therefore, a key feature of M-CBIR systems is the retrieval of scans with the most similar lesion annotations. To be of value, M-CBIR systems should be fully automatic to handle large case databases. We present a fully automatic end-to-end method for the retrieval of CT scans with similar liver lesion annotations. The input is a database of abdominal CT scans labeled with liver lesions, a query CT scan, and optionally one radiologist-specified lesion annotation of interest. The output is an ordered list of the database CT scans with the most similar liver lesion annotations. The method starts by automatically segmenting the liver in the scan. It then extracts a histogram-based features vector from the segmented region, learns the features' relative importance, and ranks the database scans according to the relative importance measure. The main advantages of our method are that it fully automates the end-to-end querying process, that it uses simple and efficient techniques that are scalable to large datasets, and that it produces quality retrieval results using an unannotated CT scan. Our experimental results on 9 CT queries on a dataset of 41 volumetric CT scans from the 2014 Image CLEF Liver Annotation Task yield an average retrieval accuracy (Normalized Discounted Cumulative Gain index) of 0.77 and 0.84 without/with annotation, respectively. Fully automatic end-to-end retrieval of similar cases based on image information alone, rather that on disease diagnosis, may help radiologists to better diagnose liver lesions.

SU-E-I-68: Practical Considerations On Implementation of the Image Gently Pediatric CT Protocols

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, J; Adams, C; Lumby, C

Purpose: One limitation associated with the Image Gently pediatric CT protocols is practical implementation of the recommended manual techniques. Inconsistency as a result of different practice is a possibility among technologist. An additional concern is the added risk of data error that would result in over or underexposure. The Automatic Exposure Control (AEC) features automatically reduce radiation for children. However, they do not work efficiently for the patients of very small size and relative large size. This study aims to implement the Image Gently pediatric CT protocols in the practical setting while maintaining the use of AEC features for pediatricmore » patients of varying size. Methods: Anthropomorphological abdomen phantoms were scanned in a CT scanner using the Image Gently pediatric protocols, the AEC technique with a fixed adult baseline, and automatic protocols with various baselines. The baselines were adjusted corresponding to patient age, weight and posterioranterior thickness to match the Image Gently pediatric CT manual techniques. CTDIvol was recorded for each examination. Image noise was measured and recorded for image quality comparison. Clinical images were evaluated by pediatric radiologists. Results: By adjusting vendor default baselines used in the automatic techniques, radiation dose and image quality can match those of the Image Gently manual techniques. In practice, this can be achieved by dividing pediatric patients into three major groups for technologist reference: infant, small child, and large child. Further division can be done but will increase the number of CT protocols. For each group, AEC can efficiently adjust acquisition techniques for children. This implementation significantly overcomes the limitation of the Image Gently manual techniques. Conclusion: Considering the effectiveness in clinical practice, Image Gently Pediatric CT protocols can be implemented in accordance with AEC techniques, with adjusted baselines, to achieve the goal of providing the most appropriate radiation dose for pediatric patients of varying sizes.« less

Fully Convolutional Architecture for Low-Dose CT Image Noise Reduction

NASA Astrophysics Data System (ADS)

Badretale, S.; Shaker, F.; Babyn, P.; Alirezaie, J.

2017-10-01

One of the critical topics in medical low-dose Computed Tomography (CT) imaging is how best to maintain image quality. As the quality of images decreases with lowering the X-ray radiation dose, improving image quality is extremely important and challenging. We have proposed a novel approach to denoise low-dose CT images. Our algorithm learns directly from an end-to-end mapping from the low-dose Computed Tomography images for denoising the normal-dose CT images. Our method is based on a deep convolutional neural network with rectified linear units. By learning various low-level to high-level features from a low-dose image the proposed algorithm is capable of creating a high-quality denoised image. We demonstrate the superiority of our technique by comparing the results with two other state-of-the-art methods in terms of the peak signal to noise ratio, root mean square error, and a structural similarity index.

Evaluation of chemotherapy response in ovarian cancer treatment using quantitative CT image biomarkers: a preliminary study

NASA Astrophysics Data System (ADS)

Qiu, Yuchen; Tan, Maxine; McMeekin, Scott; Thai, Theresa; Moore, Kathleen; Ding, Kai; Liu, Hong; Zheng, Bin

2015-03-01

The purpose of this study is to identify and apply quantitative image biomarkers for early prediction of the tumor response to the chemotherapy among the ovarian cancer patients participated in the clinical trials of testing new drugs. In the experiment, we retrospectively selected 30 cases from the patients who participated in Phase I clinical trials of new drug or drug agents for ovarian cancer treatment. Each case is composed of two sets of CT images acquired pre- and post-treatment (4-6 weeks after starting treatment). A computer-aided detection (CAD) scheme was developed to extract and analyze the quantitative image features of the metastatic tumors previously tracked by the radiologists using the standard Response Evaluation Criteria in Solid Tumors (RECIST) guideline. The CAD scheme first segmented 3-D tumor volumes from the background using a hybrid tumor segmentation scheme. Then, for each segmented tumor, CAD computed three quantitative image features including the change of tumor volume, tumor CT number (density) and density variance. The feature changes were calculated between the matched tumors tracked on the CT images acquired pre- and post-treatments. Finally, CAD predicted patient's 6-month progression-free survival (PFS) using a decision-tree based classifier. The performance of the CAD scheme was compared with the RECIST category. The result shows that the CAD scheme achieved a prediction accuracy of 76.7% (23/30 cases) with a Kappa coefficient of 0.493, which is significantly higher than the performance of RECIST prediction with a prediction accuracy and Kappa coefficient of 60% (17/30) and 0.062, respectively. This study demonstrated the feasibility of analyzing quantitative image features to improve the early predicting accuracy of the tumor response to the new testing drugs or therapeutic methods for the ovarian cancer patients.

[Quantitative Evaluation of Metal Artifacts on CT Images on the Basis of Statistics of Extremes].

PubMed

Kitaguchi, Shigetoshi; Imai, Kuniharu; Ueda, Suguru; Hashimoto, Naomi; Hattori, Shouta; Saika, Takahiro; Ono, Yoshifumi

2016-05-01

It is well-known that metal artifacts have a harmful effect on the image quality of computed tomography (CT) images. However, the physical property remains still unknown. In this study, we investigated the relationship between metal artifacts and tube currents using statistics of extremes. A commercially available phantom for measuring CT dose index 160 mm in diameter was prepared and a brass rod 13 mm in diameter was placed at the centerline of the phantom. This phantom was used as a target object to evaluate metal artifacts and was scanned using an area detector CT scanner with various tube currents under a constant tube voltage of 120 kV. Sixty parallel line segments with a length of 100 pixels were placed to cross metal artifacts on CT images and the largest difference between two adjacent CT values in each of 60 CT value profiles of these line segments was employed as a feature variable for measuring metal artifacts; these feature variables were analyzed on the basis of extreme value theory. The CT value variation induced by metal artifacts was statistically characterized by Gumbel distribution, which was one of the extreme value distributions; namely, metal artifacts have the same statistical characteristic as streak artifacts. Therefore, Gumbel evaluation method makes it possible to analyze not only streak artifacts but also metal artifacts. Furthermore, the location parameter in Gumbel distribution was shown to be in inverse proportion to the square root of a tube current. This result suggested that metal artifacts have the same dose dependence as image noises.

Phase contrast imaging X-ray computed tomography: quantitative characterization of human patellar cartilage matrix with topological and geometrical features

NASA Astrophysics Data System (ADS)

Nagarajan, Mahesh B.; Coan, Paola; Huber, Markus B.; Diemoz, Paul C.; Wismüller, Axel

2014-03-01

Current assessment of cartilage is primarily based on identification of indirect markers such as joint space narrowing and increased subchondral bone density on x-ray images. In this context, phase contrast CT imaging (PCI-CT) has recently emerged as a novel imaging technique that allows a direct examination of chondrocyte patterns and their correlation to osteoarthritis through visualization of cartilage soft tissue. This study investigates the use of topological and geometrical approaches for characterizing chondrocyte patterns in the radial zone of the knee cartilage matrix in the presence and absence of osteoarthritic damage. For this purpose, topological features derived from Minkowski Functionals and geometric features derived from the Scaling Index Method (SIM) were extracted from 842 regions of interest (ROI) annotated on PCI-CT images of healthy and osteoarthritic specimens of human patellar cartilage. The extracted features were then used in a machine learning task involving support vector regression to classify ROIs as healthy or osteoarthritic. Classification performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC). The best classification performance was observed with high-dimensional geometrical feature vectors derived from SIM (0.95 ± 0.06) which outperformed all Minkowski Functionals (p < 0.001). These results suggest that such quantitative analysis of chondrocyte patterns in human patellar cartilage matrix involving SIM-derived geometrical features can distinguish between healthy and osteoarthritic tissue with high accuracy.

Pulmonary embolism detection using localized vessel-based features in dual energy CT

NASA Astrophysics Data System (ADS)

Dicente Cid, Yashin; Depeursinge, Adrien; Foncubierta Rodríguez, Antonio; Platon, Alexandra; Poletti, Pierre-Alexandre; Müller, Henning

2015-03-01

Pulmonary embolism (PE) affects up to 600,000 patients and contributes to at least 100,000 deaths every year in the United States alone. Diagnosis of PE can be difficult as most symptoms are unspecific and early diagnosis is essential for successful treatment. Computed Tomography (CT) images can show morphological anomalies that suggest the existence of PE. Various image-based procedures have been proposed for improving computer-aided diagnosis of PE. We propose a novel method for detecting PE based on localized vessel-based features computed in Dual Energy CT (DECT) images. DECT provides 4D data indexed by the three spatial coordinates and the energy level. The proposed features encode the variation of the Hounsfield Units across the different levels and the CT attenuation related to the amount of iodine contrast in each vessel. A local classification of the vessels is obtained through the classification of these features. Moreover, the localization of the vessel in the lung provides better comparison between patients. Results show that the simple features designed are able to classify pulmonary embolism patients with an AUC (area under the receiver operating curve) of 0.71 on a lobe basis. Prior segmentation of the lung lobes is not necessary because an automatic atlas-based segmentation obtains similar AUC levels (0.65) for the same dataset. The automatic atlas reaches 0.80 AUC in a larger dataset with more control cases.

The Role of 18F-FDG PET/CT Integrated Imaging in Distinguishing Malignant from Benign Pleural Effusion

PubMed Central

Sun, Yajuan; Yu, Hongjuan; Ma, Jingquan

2016-01-01

Objective The aim of our study was to evaluate the role of 18F-FDG PET/CT integrated imaging in differentiating malignant from benign pleural effusion. Methods A total of 176 patients with pleural effusion who underwent 18F-FDG PET/CT examination to differentiate malignancy from benignancy were retrospectively researched. The images of CT imaging, 18F-FDG PET imaging and 18F-FDG PET/CT integrated imaging were visually analyzed. The suspected malignant effusion was characterized by the presence of nodular or irregular pleural thickening on CT imaging. Whereas on PET imaging, pleural 18F-FDG uptake higher than mediastinal activity was interpreted as malignant effusion. Images of 18F-FDG PET/CT integrated imaging were interpreted by combining the morphologic feature of pleura on CT imaging with the degree and form of pleural 18F-FDG uptake on PET imaging. Results One hundred and eight patients had malignant effusion, including 86 with pleural metastasis and 22 with pleural mesothelioma, whereas 68 patients had benign effusion. The sensitivities of CT imaging, 18F-FDG PET imaging and 18F-FDG PET/CT integrated imaging in detecting malignant effusion were 75.0%, 91.7% and 93.5%, respectively, which were 69.8%, 91.9% and 93.0% in distinguishing metastatic effusion. The sensitivity of 18F-FDG PET/CT integrated imaging in detecting malignant effusion was higher than that of CT imaging (p = 0.000). For metastatic effusion, 18F-FDG PET imaging had higher sensitivity (p = 0.000) and better diagnostic consistency with 18F-FDG PET/CT integrated imaging compared with CT imaging (Kappa = 0.917 and Kappa = 0.295, respectively). The specificities of CT imaging, 18F-FDG PET imaging and 18F-FDG PET/CT integrated imaging were 94.1%, 63.2% and 92.6% in detecting benign effusion. The specificities of CT imaging and 18F-FDG PET/CT integrated imaging were higher than that of 18F-FDG PET imaging (p = 0.000 and p = 0.000, respectively), and CT imaging had better diagnostic consistency with 18F-FDG PET/CT integrated imaging compared with 18F-FDG PET imaging (Kappa = 0.881 and Kappa = 0.240, respectively). Conclusion 18F-FDG PET/CT integrated imaging is a more reliable modality in distinguishing malignant from benign pleural effusion than 18F-FDG PET imaging and CT imaging alone. For image interpretation of 18F-FDG PET/CT integrated imaging, the PET and CT portions play a major diagnostic role in identifying metastatic effusion and benign effusion, respectively. PMID:27560933

Neural network fusion: a novel CT-MR aortic aneurysm image segmentation method

NASA Astrophysics Data System (ADS)

Wang, Duo; Zhang, Rui; Zhu, Jin; Teng, Zhongzhao; Huang, Yuan; Spiga, Filippo; Du, Michael Hong-Fei; Gillard, Jonathan H.; Lu, Qingsheng; Liò, Pietro

2018-03-01

Medical imaging examination on patients usually involves more than one imaging modalities, such as Computed Tomography (CT), Magnetic Resonance (MR) and Positron Emission Tomography(PET) imaging. Multimodal imaging allows examiners to benefit from the advantage of each modalities. For example, for Abdominal Aortic Aneurysm, CT imaging shows calcium deposits in the aorta clearly while MR imaging distinguishes thrombus and soft tissues better.1 Analysing and segmenting both CT and MR images to combine the results will greatly help radiologists and doctors to treat the disease. In this work, we present methods on using deep neural network models to perform such multi-modal medical image segmentation. As CT image and MR image of the abdominal area cannot be well registered due to non-affine deformations, a naive approach is to train CT and MR segmentation network separately. However, such approach is time-consuming and resource-inefficient. We propose a new approach to fuse the high-level part of the CT and MR network together, hypothesizing that neurons recognizing the high level concepts of Aortic Aneurysm can be shared across multiple modalities. Such network is able to be trained end-to-end with non-registered CT and MR image using shorter training time. Moreover network fusion allows a shared representation of Aorta in both CT and MR images to be learnt. Through experiments we discovered that for parts of Aorta showing similar aneurysm conditions, their neural presentations in neural network has shorter distances. Such distances on the feature level is helpful for registering CT and MR image.

Ovarian torsion: diagnostic features on CT and MRI with pathologic correlation.

PubMed

Duigenan, Shauna; Oliva, Esther; Lee, Susanna I

2012-02-01

The CT and MRI features of ovarian torsion are illustrated with gross pathologic correlation. Ovarian enlargement with or without an underlying mass is the finding most frequently associated with torsion, but it is nonspecific. A twisted pedicle, although not often detected on imaging, is pathognomonic when seen. Subacute ovarian hemorrhage and abnormal enhancement is usually seen, and both features show characteristic patterns on CT and MRI. Ipsilateral uterine deviation can also be seen. Diagnostic pitfalls that may mimic ovarian torsion and observations for discriminating them are discussed.

Cross-sectional anatomy, computed tomography and magnetic resonance imaging of the head of common dolphin (Delphinus delphis) and striped dolphin (Stenella coeruleoalba).

PubMed

Alonso-Farré, J M; Gonzalo-Orden, M; Barreiro-Vázquez, J D; Barreiro-Lois, A; André, M; Morell, M; Llarena-Reino, M; Monreal-Pawlowsky, T; Degollada, E

2015-02-01

Computed tomography (CT) and low-field magnetic resonance imaging (MRI) were used to scan seven by-caught dolphin cadavers, belonging to two species: four common dolphins (Delphinus delphis) and three striped dolphins (Stenella coeruleoalba). CT and MRI were obtained with the animals in ventral recumbency. After the imaging procedures, six dolphins were frozen at -20°C and sliced in the same position they were examined. Not only CT and MRI scans, but also cross sections of the heads were obtained in three body planes: transverse (slices of 1 cm thickness) in three dolphins, sagittal (5 cm thickness) in two dolphins and dorsal (5 cm thickness) in two dolphins. Relevant anatomical structures were identified and labelled on each cross section, obtaining a comprehensive bi-dimensional topographical anatomy guide of the main features of the common and the striped dolphin head. Furthermore, the anatomical cross sections were compared with their corresponding CT and MRI images, allowing an imaging identification of most of the anatomical features. CT scans produced an excellent definition of the bony and air-filled structures, while MRI allowed us to successfully identify most of the soft tissue structures in the dolphin's head. This paper provides a detailed anatomical description of the head structures of common and striped dolphins and compares anatomical cross sections with CT and MRI scans, becoming a reference guide for the interpretation of imaging studies. © 2014 Blackwell Verlag GmbH.

Stability of deep features across CT scanners and field of view using a physical phantom

NASA Astrophysics Data System (ADS)

Paul, Rahul; Shafiq-ul-Hassan, Muhammad; Moros, Eduardo G.; Gillies, Robert J.; Hall, Lawrence O.; Goldgof, Dmitry B.

2018-02-01

Radiomics is the process of analyzing radiological images by extracting quantitative features for monitoring and diagnosis of various cancers. Analyzing images acquired from different medical centers is confounded by many choices in acquisition, reconstruction parameters and differences among device manufacturers. Consequently, scanning the same patient or phantom using various acquisition/reconstruction parameters as well as different scanners may result in different feature values. To further evaluate this issue, in this study, CT images from a physical radiomic phantom were used. Recent studies showed that some quantitative features were dependent on voxel size and that this dependency could be reduced or removed by the appropriate normalization factor. Deep features extracted from a convolutional neural network, may also provide additional features for image analysis. Using a transfer learning approach, we obtained deep features from three convolutional neural networks pre-trained on color camera images. An we examination of the dependency of deep features on image pixel size was done. We found that some deep features were pixel size dependent, and to remove this dependency we proposed two effective normalization approaches. For analyzing the effects of normalization, a threshold has been used based on the calculated standard deviation and average distance from a best fit horizontal line among the features' underlying pixel size before and after normalization. The inter and intra scanner dependency of deep features has also been evaluated.

Radiomics-based features for pattern recognition of lung cancer histopathology and metastases.

PubMed

Ferreira Junior, José Raniery; Koenigkam-Santos, Marcel; Cipriano, Federico Enrique Garcia; Fabro, Alexandre Todorovic; Azevedo-Marques, Paulo Mazzoncini de

2018-06-01

lung cancer is the leading cause of cancer-related deaths in the world, and its poor prognosis varies markedly according to tumor staging. Computed tomography (CT) is the imaging modality of choice for lung cancer evaluation, being used for diagnosis and clinical staging. Besides tumor stage, other features, like histopathological subtype, can also add prognostic information. In this work, radiomics-based CT features were used to predict lung cancer histopathology and metastases using machine learning models. local image datasets of confirmed primary malignant pulmonary tumors were retrospectively evaluated for testing and validation. CT images acquired with same protocol were semiautomatically segmented. Tumors were characterized by clinical features and computer attributes of intensity, histogram, texture, shape, and volume. Three machine learning classifiers used up to 100 selected features to perform the analysis. radiomics-based features yielded areas under the receiver operating characteristic curve of 0.89, 0.97, and 0.92 at testing and 0.75, 0.71, and 0.81 at validation for lymph nodal metastasis, distant metastasis, and histopathology pattern recognition, respectively. the radiomics characterization approach presented great potential to be used in a computational model to aid lung cancer histopathological subtype diagnosis as a "virtual biopsy" and metastatic prediction for therapy decision support without the necessity of a whole-body imaging scanning. Copyright © 2018 Elsevier B.V. All rights reserved.

Can technical characteristics predict clinical performance in PET/CT imaging? A correlation study for thyroid cancer diagnosis

NASA Astrophysics Data System (ADS)

Kallergi, Maria; Menychtas, Dimitrios; Georgakopoulos, Alexandros; Pianou, Nikoletta; Metaxas, Marinos; Chatziioannou, Sofia

2013-03-01

The purpose of this study was to determine whether image characteristics could be used to predict the outcome of ROC studies in PET/CT imaging. Patients suspected for recurrent thyroid cancer underwent a standard whole body (WB) examination and an additional high-resolution head-and-neck (HN) F18-FDG PET/CT scan. The value of the latter was determined with an ROC study, the results of which showed that the WB+HN combination was better than WB alone for thyroid cancer detection and diagnosis. Following the ROC experiment, the WB and HN images of confirmed benign or malignant thyroid disease were analyzed and first and second order textural features were determined. Features included minimum, mean, and maximum intensity, as well as contrast in regions of interest encircling the thyroid lesions. Lesion size and standard uptake values (SUV) were also determined. Bivariate analysis was applied to determine relationships between WB and HN features and between observer ROC responses and the various feature values. The two sets showed significant associations in the values of SUV, contrast, and lesion size. They were completely different when the intensities were considered; no relationship was found between the WB minimum, maximum, and mean ROI values and their HN counterparts. SUV and contrast were the strongest predictors of ROC performance on PET/CT examinations of thyroid cancer. The high resolution HN images seem to enhance these relationships but without a single dramatic effect as was projected from the ROC results. A combination of features from both WB and HN datasets may possibly be a more robust predictor of ROC performance.

Automated detection of pulmonary nodules in PET/CT images: Ensemble false-positive reduction using a convolutional neural network technique

DOE Office of Scientific and Technical Information (OSTI.GOV)

Teramoto, Atsushi, E-mail: teramoto@fujita-hu.ac.jp; Fujita, Hiroshi; Yamamuro, Osamu

Purpose: Automated detection of solitary pulmonary nodules using positron emission tomography (PET) and computed tomography (CT) images shows good sensitivity; however, it is difficult to detect nodules in contact with normal organs, and additional efforts are needed so that the number of false positives (FPs) can be further reduced. In this paper, the authors propose an improved FP-reduction method for the detection of pulmonary nodules in PET/CT images by means of convolutional neural networks (CNNs). Methods: The overall scheme detects pulmonary nodules using both CT and PET images. In the CT images, a massive region is first detected using anmore » active contour filter, which is a type of contrast enhancement filter that has a deformable kernel shape. Subsequently, high-uptake regions detected by the PET images are merged with the regions detected by the CT images. FP candidates are eliminated using an ensemble method; it consists of two feature extractions, one by shape/metabolic feature analysis and the other by a CNN, followed by a two-step classifier, one step being rule based and the other being based on support vector machines. Results: The authors evaluated the detection performance using 104 PET/CT images collected by a cancer-screening program. The sensitivity in detecting candidates at an initial stage was 97.2%, with 72.8 FPs/case. After performing the proposed FP-reduction method, the sensitivity of detection was 90.1%, with 4.9 FPs/case; the proposed method eliminated approximately half the FPs existing in the previous study. Conclusions: An improved FP-reduction scheme using CNN technique has been developed for the detection of pulmonary nodules in PET/CT images. The authors’ ensemble FP-reduction method eliminated 93% of the FPs; their proposed method using CNN technique eliminates approximately half the FPs existing in the previous study. These results indicate that their method may be useful in the computer-aided detection of pulmonary nodules using PET/CT images.« less

Quantitative analysis of adipose tissue on chest CT to predict primary graft dysfunction in lung transplant recipients: a novel optimal biomarker approach

NASA Astrophysics Data System (ADS)

Tong, Yubing; Udupa, Jayaram K.; Wang, Chuang; Wu, Caiyun; Pednekar, Gargi; Restivo, Michaela D.; Lederer, David J.; Christie, Jason D.; Torigian, Drew A.

2018-02-01

In this study, patients who underwent lung transplantation are categorized into two groups of successful (positive) or failed (negative) transplantations according to primary graft dysfunction (PGD), i.e., acute lung injury within 72 hours of lung transplantation. Obesity or being underweight is associated with an increased risk of PGD. Adipose quantification and characterization via computed tomography (CT) imaging is an evolving topic of interest. However, very little research of PGD prediction using adipose quantity or characteristics derived from medical images has been performed. The aim of this study is to explore image-based features of thoracic adipose tissue on pre-operative chest CT to distinguish between the above two groups of patients. 140 unenhanced chest CT images from three lung transplant centers (Columbia, Penn, and Duke) are included in this study. 124 patients are in the successful group and 16 in failure group. Chest CT slices at the T7 and T8 vertebral levels are captured to represent the thoracic fat burden by using a standardized anatomic space (SAS) approach. Fat (subcutaneous adipose tissue (SAT)/ visceral adipose tissue (VAT)) intensity and texture properties (1142 in total) for each patient are collected, and then an optimal feature set is selected to maximize feature independence and separation between the two groups. Leave-one-out and leave-ten-out crossvalidation strategies are adopted to test the prediction ability based on those selected features all of which came from VAT texture properties. Accuracy of prediction (ACC), sensitivity (SEN), specificity (SPE), and area under the curve (AUC) of 0.87/0.97, 0.87/0.97, 0.88/1.00, and 0.88/0.99, respectively are achieved by the method. The optimal feature set includes only 5 features (also all from VAT), which might suggest that thoracic VAT plays a more important role than SAT in predicting PGD in lung transplant recipients.

TU-G-201-01: What Therapy Physicists Need to Know About CT and PET/CT: Terminology and Latest Developments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hua, C.

This session will update therapeutic physicists on technological advancements and radiation oncology features of commercial CT, MRI, and PET/CT imaging systems. Also described are physicists’ roles in every stage of equipment selection, purchasing, and operation, including defining specifications, evaluating vendors, making recommendations, and optimal and safe use of imaging equipment in radiation oncology environment. The first presentation defines important terminology of CT and PET/CT followed by a review of latest innovations, such as metal artifact reduction, statistical iterative reconstruction, radiation dose management, tissue classification by dual energy CT and spectral CT, improvement in spatial resolution and sensitivity in PET, andmore » potentials of PET/MR. We will also discuss important technical specifications and items in CT and PET/CT purchasing quotes and their impacts. The second presentation will focus on key components in the request for proposal for a MRI simulator and how to evaluate vendor proposals. MRI safety issues in radiation Oncology, including MRI scanner Zones (4-zone design), will be discussed. Basic MR terminologies, important functionalities, and advanced features, which are relevant to radiation therapy, will be discussed. In the third presentation, justification of imaging systems for radiation oncology, considerations in room design and construction in a RO department, shared use with diagnostic radiology, staffing needs and training, clinical/research use cases and implementation, will be discussed. The emphasis will be on understanding and bridging the differences between diagnostic and radiation oncology installations, building consensus amongst stakeholders for purchase and use, and integrating imaging technologies into the radiation oncology environment. Learning Objectives: Learn the latest innovations of major imaging systems relevant to radiation therapy Be able to describe important technical specifications of CT, MRI, and PET/CT Understand the process of budget request, equipment justification, comparisons of technical specifications, site visits, vendor selection, and contract development.« less

TU-G-201-00: Imaging Equipment Specification and Selection in Radiation Oncology Departments

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

This session will update therapeutic physicists on technological advancements and radiation oncology features of commercial CT, MRI, and PET/CT imaging systems. Also described are physicists’ roles in every stage of equipment selection, purchasing, and operation, including defining specifications, evaluating vendors, making recommendations, and optimal and safe use of imaging equipment in radiation oncology environment. The first presentation defines important terminology of CT and PET/CT followed by a review of latest innovations, such as metal artifact reduction, statistical iterative reconstruction, radiation dose management, tissue classification by dual energy CT and spectral CT, improvement in spatial resolution and sensitivity in PET, andmore » potentials of PET/MR. We will also discuss important technical specifications and items in CT and PET/CT purchasing quotes and their impacts. The second presentation will focus on key components in the request for proposal for a MRI simulator and how to evaluate vendor proposals. MRI safety issues in radiation Oncology, including MRI scanner Zones (4-zone design), will be discussed. Basic MR terminologies, important functionalities, and advanced features, which are relevant to radiation therapy, will be discussed. In the third presentation, justification of imaging systems for radiation oncology, considerations in room design and construction in a RO department, shared use with diagnostic radiology, staffing needs and training, clinical/research use cases and implementation, will be discussed. The emphasis will be on understanding and bridging the differences between diagnostic and radiation oncology installations, building consensus amongst stakeholders for purchase and use, and integrating imaging technologies into the radiation oncology environment. Learning Objectives: Learn the latest innovations of major imaging systems relevant to radiation therapy Be able to describe important technical specifications of CT, MRI, and PET/CT Understand the process of budget request, equipment justification, comparisons of technical specifications, site visits, vendor selection, and contract development.« less

Magnetic resonance imaging and computed tomography characteristics of renal cell carcinoma associated with Xp11.2 translocation/TFE3 gene fusion.

PubMed

Wang, Wei; Ding, Jianhui; Li, Yuan; Wang, Chaofu; Zhou, Liangping; Zhu, Hui; Peng, Weijun

2014-01-01

To characterize Xp11.2 translocation renal cell carcinoma (RCC) using magnetic resonance imaging (MRI) and computed tomography (CT). This study retrospectively collected the MRI and CT data of twelve patients with Xp11.2 translocation RCC confirmed by pathology. Nine cases underwent dynamic contrast-enhanced MRI (DCE-MRI) and 6 cases underwent CT, of which 3 cases underwent MRI and CT simultaneously. The MRI and CT findings were analyzed in regard to tumor position, size, hemorrhagic, cystic or necrotic components, calcification, tumor density, signal intensity and enhancement features. The age of the 12 patients ranged from 13 to 46 years (mean age: 23 years). T2WI revealed heterogeneous intensity, hyper-intensity, and slight hypo-intensity in 6 cases, 2 cases, and 1 case, respectively. On DCE-MR images, mild, moderate, and marked rim enhancement of the tumor in the corticomedullary phase (CMP) were observed in 1, 6, and 2 cases, respectively. The tumor parenchyma showed iso-attenuation (n = 4) or slight hyper-attenuation (n = 1) compared to the normal renal cortex on non-contrast CT images. Imaging findings were suggestive of hemorrhage (n = 4) or necrosis (n = 8) in the tumors, and there was evidence of calcification in 8 cases by CT (n = 3) and pathology (n = 8). On dynamic contrast-enhanced CT images, 3 cases and 1 case manifested moderate and strong CMP enhancement, respectively. Nine tumors by MRI and 4 tumors by CT showed prolonged enhancement. Three neoplasms presented at stage I, 2 at stage II, 3 at stage III, and 4 at stage IV according the 2010 AJCC staging criteria. XP11.2 translocation RCC should be considered when a child or young adult patient presents with a renal tumor with heterogeneous features such as hemorrhage, necrosis, cystic changes, and calcification on CT and MRI and/or is accompanied by metastatic evidence.

Magnetic Resonance Imaging and Computed Tomography Characteristics of Renal Cell Carcinoma Associated with Xp11.2 Translocation/TFE3 Gene Fusion

PubMed Central

Li, Yuan; Wang, Chaofu; Zhou, Liangping; Zhu, Hui; Peng, Weijun

2014-01-01

Purpose To characterize Xp11.2 translocation renal cell carcinoma (RCC) using magnetic resonance imaging (MRI) and computed tomography (CT). Methods This study retrospectively collected the MRI and CT data of twelve patients with Xp11.2 translocation RCC confirmed by pathology. Nine cases underwent dynamic contrast-enhanced MRI (DCE-MRI) and 6 cases underwent CT, of which 3 cases underwent MRI and CT simultaneously. The MRI and CT findings were analyzed in regard to tumor position, size, hemorrhagic, cystic or necrotic components, calcification, tumor density, signal intensity and enhancement features. Results The age of the 12 patients ranged from 13 to 46 years (mean age: 23 years). T2WI revealed heterogeneous intensity, hyper-intensity, and slight hypo-intensity in 6 cases, 2 cases, and 1 case, respectively. On DCE-MR images, mild, moderate, and marked rim enhancement of the tumor in the corticomedullary phase (CMP) were observed in 1, 6, and 2 cases, respectively. The tumor parenchyma showed iso-attenuation (n = 4) or slight hyper-attenuation (n = 1) compared to the normal renal cortex on non-contrast CT images. Imaging findings were suggestive of hemorrhage (n = 4) or necrosis (n = 8) in the tumors, and there was evidence of calcification in 8 cases by CT (n = 3) and pathology (n = 8). On dynamic contrast-enhanced CT images, 3 cases and 1 case manifested moderate and strong CMP enhancement, respectively. Nine tumors by MRI and 4 tumors by CT showed prolonged enhancement. Three neoplasms presented at stage I, 2 at stage II, 3 at stage III, and 4 at stage IV according the 2010 AJCC staging criteria. Conclusions XP11.2 translocation RCC should be considered when a child or young adult patient presents with a renal tumor with heterogeneous features such as hemorrhage, necrosis, cystic changes, and calcification on CT and MRI and/or is accompanied by metastatic evidence. PMID:24926688

Precision of quantitative computed tomography texture analysis using image filtering: A phantom study for scanner variability.

PubMed

Yasaka, Koichiro; Akai, Hiroyuki; Mackin, Dennis; Court, Laurence; Moros, Eduardo; Ohtomo, Kuni; Kiryu, Shigeru

2017-05-01

Quantitative computed tomography (CT) texture analyses for images with and without filtration are gaining attention to capture the heterogeneity of tumors. The aim of this study was to investigate how quantitative texture parameters using image filtering vary among different computed tomography (CT) scanners using a phantom developed for radiomics studies.A phantom, consisting of 10 different cartridges with various textures, was scanned under 6 different scanning protocols using four CT scanners from four different vendors. CT texture analyses were performed for both unfiltered images and filtered images (using a Laplacian of Gaussian spatial band-pass filter) featuring fine, medium, and coarse textures. Forty-five regions of interest were placed for each cartridge (x) in a specific scan image set (y), and the average of the texture values (T(x,y)) was calculated. The interquartile range (IQR) of T(x,y) among the 6 scans was calculated for a specific cartridge (IQR(x)), while the IQR of T(x,y) among the 10 cartridges was calculated for a specific scan (IQR(y)), and the median IQR(y) was then calculated for the 6 scans (as the control IQR, IQRc). The median of their quotient (IQR(x)/IQRc) among the 10 cartridges was defined as the variability index (VI).The VI was relatively small for the mean in unfiltered images (0.011) and for standard deviation (0.020-0.044) and entropy (0.040-0.044) in filtered images. Skewness and kurtosis in filtered images featuring medium and coarse textures were relatively variable across different CT scanners, with VIs of 0.638-0.692 and 0.430-0.437, respectively.Various quantitative CT texture parameters are robust and variable among different scanners, and the behavior of these parameters should be taken into consideration.

Lab-based x-ray nanoCT imaging

NASA Astrophysics Data System (ADS)

Müller, Mark; Allner, Sebastian; Ferstl, Simone; Dierolf, Martin; Tuohimaa, Tomi; Pfeiffer, Franz

2017-03-01

Due to the recent development of transmission X-ray tubes with very small focal spot sizes, laboratory-based CT imaging with sub-micron resolutions is nowadays possible. We recently developed a novel X-ray nanoCT setup featuring a prototype nanofocus X-ray source and a single-photon counting detector. The system is based on mere geometrical magnification and can reach resolutions of 200 nm. To demonstrate the potential of the nanoCT system for biomedical applications we show high resolution nanoCT data of a small piece of human tooth comprising coronal dentin. The reconstructed CT data clearly visualize the dentin tubules within the tooth piece.

Relationship between trabecular texture features of CT images and an amount of bone cement volume injection in percutaneous vertebroplasty

NASA Astrophysics Data System (ADS)

Tack, Gye Rae; Choi, Hyung Guen; Shin, Kyu-Chul; Lee, Sung J.

2001-06-01

Percutaneous vertebroplasty is a surgical procedure that was introduced for the treatment of compression fracture of the vertebrae. This procedure includes puncturing vertebrae and filling with polymethylmethacrylate (PMMA). Recent studies have shown that the procedure could provide structural reinforcement for the osteoporotic vertebrae while being minimally invasive and safe with immediate pain relief. However, treatment failures due to disproportionate PMMA volume injection have been reported as one of complications in vertebroplasty. It is believed that control of PMMA volume is one of the most critical factors that can reduce the incidence of complications. In this study, appropriate amount of PMMA volume was assessed based on the imaging data of a given patient under the following hypotheses: (1) a relationship can be drawn between the volume of PMMA injection and textural features of the trabecular bone in preoperative CT images and (2) the volume of PMMA injection can be estimated based on 3D reconstruction of postoperative CT images. Gray-level run length analysis was used to determine the textural features of the trabecular bone. The width of trabecular (T-texture) and the width of intertrabecular spaces (I-texture) were calculated. The correlation between PMMA volume and textural features of patient's CT images was also examined to evaluate the appropriate PMMA amount. Results indicated that there was a strong correlation between the actual PMMA injection volume and the area of the intertrabecular space and that of trabecular bone calculated from the CT image (correlation coefficient, requals0.96 and requals-0.95, respectively). T- texture (requals-0.93) did correlate better with the actual PMMA volume more than the I-texture (requals0.57). Therefore, it was demonstrated that appropriate PMMA injection volume could be predicted based on the textural analysis for better clinical management of the osteoporotic spine.

A comparative study of new and current methods for dental micro-CT image denoising

PubMed Central

Lashgari, Mojtaba; Qin, Jie; Swain, Michael

2016-01-01

Objectives: The aim of the current study was to evaluate the application of two advanced noise-reduction algorithms for dental micro-CT images and to implement a comparative analysis of the performance of new and current denoising algorithms. Methods: Denoising was performed using gaussian and median filters as the current filtering approaches and the block-matching and three-dimensional (BM3D) method and total variation method as the proposed new filtering techniques. The performance of the denoising methods was evaluated quantitatively using contrast-to-noise ratio (CNR), edge preserving index (EPI) and blurring indexes, as well as qualitatively using the double-stimulus continuous quality scale procedure. Results: The BM3D method had the best performance with regard to preservation of fine textural features (CNREdge), non-blurring of the whole image (blurring index), the clinical visual score in images with very fine features and the overall visual score for all types of images. On the other hand, the total variation method provided the best results with regard to smoothing of images in texture-free areas (CNRTex-free) and in preserving the edges and borders of image features (EPI). Conclusions: The BM3D method is the most reliable technique for denoising dental micro-CT images with very fine textural details, such as shallow enamel lesions, in which the preservation of the texture and fine features is of the greatest importance. On the other hand, the total variation method is the technique of choice for denoising images without very fine textural details in which the clinician or researcher is interested mainly in anatomical features and structural measurements. PMID:26764583

CT texture features of liver parenchyma for predicting development of metastatic disease and overall survival in patients with colorectal cancer.

PubMed

Lee, Scott J; Zea, Ryan; Kim, David H; Lubner, Meghan G; Deming, Dustin A; Pickhardt, Perry J

2018-04-01

To determine if identifiable hepatic textural features are present at abdominal CT in patients with colorectal cancer (CRC) prior to the development of CT-detectable hepatic metastases. Four filtration-histogram texture features (standard deviation, skewness, entropy and kurtosis) were extracted from the liver parenchyma on portal venous phase CT images at staging and post-treatment surveillance. Surveillance scans corresponded to the last scan prior to the development of CT-detectable CRC liver metastases in 29 patients (median time interval, 6 months), and these were compared with interval-matched surveillance scans in 60 CRC patients who did not develop liver metastases. Predictive models of liver metastasis-free survival and overall survival were built using regularised Cox proportional hazards regression. Texture features did not significantly differ between cases and controls. For Cox models using all features as predictors, all coefficients were shrunk to zero, suggesting no association between any CT texture features and outcomes. Prognostic indices derived from entropy features at surveillance CT incorrectly classified patients into risk groups for future liver metastases (p < 0.001). On surveillance CT scans immediately prior to the development of CRC liver metastases, we found no evidence suggesting that changes in identifiable hepatic texture features were predictive of their development. • No correlation between liver texture features and metastasis-free survival was observed. • Liver texture features incorrectly classified patients into risk groups for liver metastases. • Standardised texture analysis workflows need to be developed to improve research reproducibility.

An application of Chan-Vese method used to determine the ROI area in CT lung screening

NASA Astrophysics Data System (ADS)

Prokop, Paweł; Surtel, Wojciech

2016-09-01

The article presents two approaches of determining the ROI area in CT lung screening. First approach is based on a classic method of framing the image in order to determine the ROI by using a MaZda tool. Second approach is based on segmentation of CT images of the lungs and reducing the redundant information from the image. Of the two approaches of an Active Contour, it was decided to choose the Chan-Vese method. In order to determine the effectiveness of the approach, it was performed an analysis of received ROI texture and extraction of textural features. In order to determine the effectiveness of the method, it was performed an analysis of the received ROI textures and extraction of the texture features, by using a Mazda tool. The results were compared and presented in the form of the radar graphs. The second approach proved to be effective and appropriate and consequently it is used for further analysis of CT images, in the computer-aided diagnosis of sarcoidosis.

Can CT imaging features of ground-glass opacity predict invasiveness? A meta-analysis.

PubMed

Dai, Jian; Yu, Guoyou; Yu, Jianqiang

2018-04-01

A meta-analysis was conducted to investigate the diagnostic performance of computed tomography (CT) imaging features of ground-glass opacity (GGO) to predict invasiveness. Two reviewers independently searched PubMed, Medline, Web of Science, Cochrane Embase and CNKI for relevant studies. CT imaging signs of bubble lucency, speculation, lobulated margin, and pleural indentation were used as diagnostic references to discriminate pre-invasive and invasive disease. The sensitivity, specificity, diagnostic odds ratio (DOR), summary receiver operating characteristic (SROC) curves, and the area under the SROC curve (AUC) were calculated to evaluate diagnostic efficiency. Twelve studies were finally included. Diagnostic performance ranged from 0.41 to 0.52 for sensitivity and 0.56 to 0.63 for specificity. The diagnostic positive and negative likelihood ratios ranged from 1.03 to 2.13 and 0.52 to 1.05, respectively. The DORs of the GGO CT features for discriminating invasive disease ranged from 1.02 to 4.00. The area under the ROC curve was also low, with a range of 0.60 to 0.67 for discriminating pre-invasive and invasive disease. The diagnostic value of a single CT imaging sign of GGO, such as bubble lucency, speculation, lobulated margin, or pleural indentation is limited for discriminating pre-invasive and invasive disease because of low sensitivity, specificity, and AUC. © 2018 The Authors. Thoracic Cancer published by China Lung Oncology Group and John Wiley & Sons Australia, Ltd.

A digital model individual template and CT-guided 125I seed implants for malignant tumors of the head and neck.

PubMed

Huang, Ming-Wei; Liu, Shu-Ming; Zheng, Lei; Shi, Yan; Zhang, Jie; Li, Yan-Sheng; Yu, Guang-Yan; Zhang, Jian-Guo

2012-11-01

To enhance the accuracy of radioactive seed implants in the head and neck, a digital model individual template, containing information simultaneously on needle pathway and facial features, was designed to guide implantation with CT imaging. Thirty-one patients with recurrent and local advanced malignant tumors of head and neck after prior surgery and radiotherapy were involved in this study. Before (125)I implants, patients received CT scans based on 0.75mm thickness. And the brachytherapy treatment planning system (BTPS) software was used to make the implantation plan based on the CT images. Mimics software and Geomagic software were used to read the data containing CT images and implantation plan, and to design the individual template. Then the individual template containing the information of needle pathway and face features simultaneously was made through rapid prototyping (RP) technique. All patients received (125)I seeds interstitial implantation under the guide of the individual template and CT. The individual templates were positioned easily and accurately, and were stable. After implants, treatment quality evaluation was made by CT and TPS. The seeds and dosages distribution (D(90),V(100),V(150)) were well meet the treatment requirement. Clinical practice confirms that this approach can facilitate easier and more accurate implantation.

Contourlet Textual Features: Improving the Diagnosis of Solitary Pulmonary Nodules in Two Dimensional CT Images

PubMed Central

Wang, Jingjing; Sun, Tao; Gao, Ni; Menon, Desmond Dev; Luo, Yanxia; Gao, Qi; Li, Xia; Wang, Wei; Zhu, Huiping; Lv, Pingxin; Liang, Zhigang; Tao, Lixin; Liu, Xiangtong; Guo, Xiuhua

2014-01-01

Objective To determine the value of contourlet textural features obtained from solitary pulmonary nodules in two dimensional CT images used in diagnoses of lung cancer. Materials and Methods A total of 6,299 CT images were acquired from 336 patients, with 1,454 benign pulmonary nodule images from 84 patients (50 male, 34 female) and 4,845 malignant from 252 patients (150 male, 102 female). Further to this, nineteen patient information categories, which included seven demographic parameters and twelve morphological features, were also collected. A contourlet was used to extract fourteen types of textural features. These were then used to establish three support vector machine models. One comprised a database constructed of nineteen collected patient information categories, another included contourlet textural features and the third one contained both sets of information. Ten-fold cross-validation was used to evaluate the diagnosis results for the three databases, with sensitivity, specificity, accuracy, the area under the curve (AUC), precision, Youden index, and F-measure were used as the assessment criteria. In addition, the synthetic minority over-sampling technique (SMOTE) was used to preprocess the unbalanced data. Results Using a database containing textural features and patient information, sensitivity, specificity, accuracy, AUC, precision, Youden index, and F-measure were: 0.95, 0.71, 0.89, 0.89, 0.92, 0.66, and 0.93 respectively. These results were higher than results derived using the database without textural features (0.82, 0.47, 0.74, 0.67, 0.84, 0.29, and 0.83 respectively) as well as the database comprising only textural features (0.81, 0.64, 0.67, 0.72, 0.88, 0.44, and 0.85 respectively). Using the SMOTE as a pre-processing procedure, new balanced database generated, including observations of 5,816 benign ROIs and 5,815 malignant ROIs, and accuracy was 0.93. Conclusion Our results indicate that the combined contourlet textural features of solitary pulmonary nodules in CT images with patient profile information could potentially improve the diagnosis of lung cancer. PMID:25250576

Evaluation of correlation between CT image features and ERCC1 protein expression in assessing lung cancer prognosis

NASA Astrophysics Data System (ADS)

Tan, Maxine; Emaminejad, Nastaran; Qian, Wei; Sun, Shenshen; Kang, Yan; Guan, Yubao; Lure, Fleming; Zheng, Bin

2014-03-01

Stage I non-small-cell lung cancers (NSCLC) usually have favorable prognosis. However, high percentage of NSCLC patients have cancer relapse after surgery. Accurately predicting cancer prognosis is important to optimally treat and manage the patients to minimize the risk of cancer relapse. Studies have shown that an excision repair crosscomplementing 1 (ERCC1) gene was a potentially useful genetic biomarker to predict prognosis of NSCLC patients. Meanwhile, studies also found that chronic obstructive pulmonary disease (COPD) was highly associated with lung cancer prognosis. In this study, we investigated and evaluated the correlations between COPD image features and ERCC1 gene expression. A database involving 106 NSCLC patients was used. Each patient had a thoracic CT examination and ERCC1 genetic test. We applied a computer-aided detection scheme to segment and quantify COPD image features. A logistic regression method and a multilayer perceptron network were applied to analyze the correlation between the computed COPD image features and ERCC1 protein expression. A multilayer perceptron network (MPN) was also developed to test performance of using COPD-related image features to predict ERCC1 protein expression. A nine feature based logistic regression analysis showed the average COPD feature values in the low and high ERCC1 protein expression groups are significantly different (p < 0.01). Using a five-fold cross validation method, the MPN yielded an area under ROC curve (AUC = 0.669±0.053) in classifying between the low and high ERCC1 expression cases. The study indicates that CT phenotype features are associated with the genetic tests, which may provide supplementary information to help improve accuracy in assessing prognosis of NSCLC patients.

Texture classification of lung computed tomography images

NASA Astrophysics Data System (ADS)

Pheng, Hang See; Shamsuddin, Siti M.

2013-03-01

Current development of algorithms in computer-aided diagnosis (CAD) scheme is growing rapidly to assist the radiologist in medical image interpretation. Texture analysis of computed tomography (CT) scans is one of important preliminary stage in the computerized detection system and classification for lung cancer. Among different types of images features analysis, Haralick texture with variety of statistical measures has been used widely in image texture description. The extraction of texture feature values is essential to be used by a CAD especially in classification of the normal and abnormal tissue on the cross sectional CT images. This paper aims to compare experimental results using texture extraction and different machine leaning methods in the classification normal and abnormal tissues through lung CT images. The machine learning methods involve in this assessment are Artificial Immune Recognition System (AIRS), Naive Bayes, Decision Tree (J48) and Backpropagation Neural Network. AIRS is found to provide high accuracy (99.2%) and sensitivity (98.0%) in the assessment. For experiments and testing purpose, publicly available datasets in the Reference Image Database to Evaluate Therapy Response (RIDER) are used as study cases.

Computer-Aided Diagnostic (CAD) Scheme by Use of Contralateral Subtraction Technique

NASA Astrophysics Data System (ADS)

Nagashima, Hiroyuki; Harakawa, Tetsumi

We developed a computer-aided diagnostic (CAD) scheme for detection of subtle image findings of acute cerebral infarction in brain computed tomography (CT) by using a contralateral subtraction technique. In our computerized scheme, the lateral inclination of image was first corrected automatically by rotating and shifting. The contralateral subtraction image was then derived by subtraction of reversed image from original image. Initial candidates for acute cerebral infarctions were identified using the multiple-thresholding and image filtering techniques. As the 1st step for removing false positive candidates, fourteen image features were extracted in each of the initial candidates. Halfway candidates were detected by applying the rule-based test with these image features. At the 2nd step, five image features were extracted using the overlapping scale with halfway candidates in interest slice and upper/lower slice image. Finally, acute cerebral infarction candidates were detected by applying the rule-based test with five image features. The sensitivity in the detection for 74 training cases was 97.4% with 3.7 false positives per image. The performance of CAD scheme for 44 testing cases had an approximate result to training cases. Our CAD scheme using the contralateral subtraction technique can reveal suspected image findings of acute cerebral infarctions in CT images.

Automatic co-segmentation of lung tumor based on random forest in PET-CT images

NASA Astrophysics Data System (ADS)

Jiang, Xueqing; Xiang, Dehui; Zhang, Bin; Zhu, Weifang; Shi, Fei; Chen, Xinjian

2016-03-01

In this paper, a fully automatic method is proposed to segment the lung tumor in clinical 3D PET-CT images. The proposed method effectively combines PET and CT information to make full use of the high contrast of PET images and superior spatial resolution of CT images. Our approach consists of three main parts: (1) initial segmentation, in which spines are removed in CT images and initial connected regions achieved by thresholding based segmentation in PET images; (2) coarse segmentation, in which monotonic downhill function is applied to rule out structures which have similar standardized uptake values (SUV) to the lung tumor but do not satisfy a monotonic property in PET images; (3) fine segmentation, random forests method is applied to accurately segment the lung tumor by extracting effective features from PET and CT images simultaneously. We validated our algorithm on a dataset which consists of 24 3D PET-CT images from different patients with non-small cell lung cancer (NSCLC). The average TPVF, FPVF and accuracy rate (ACC) were 83.65%, 0.05% and 99.93%, respectively. The correlation analysis shows our segmented lung tumor volumes has strong correlation ( average 0.985) with the ground truth 1 and ground truth 2 labeled by a clinical expert.

Developing a radiomics framework for classifying non-small cell lung carcinoma subtypes

NASA Astrophysics Data System (ADS)

Yu, Dongdong; Zang, Yali; Dong, Di; Zhou, Mu; Gevaert, Olivier; Fang, Mengjie; Shi, Jingyun; Tian, Jie

2017-03-01

Patient-targeted treatment of non-small cell lung carcinoma (NSCLC) has been well documented according to the histologic subtypes over the past decade. In parallel, recent development of quantitative image biomarkers has recently been highlighted as important diagnostic tools to facilitate histological subtype classification. In this study, we present a radiomics analysis that classifies the adenocarcinoma (ADC) and squamous cell carcinoma (SqCC). We extract 52-dimensional, CT-based features (7 statistical features and 45 image texture features) to represent each nodule. We evaluate our approach on a clinical dataset including 324 ADCs and 110 SqCCs patients with CT image scans. Classification of these features is performed with four different machine-learning classifiers including Support Vector Machines with Radial Basis Function kernel (RBF-SVM), Random forest (RF), K-nearest neighbor (KNN), and RUSBoost algorithms. To improve the classifiers' performance, optimal feature subset is selected from the original feature set by using an iterative forward inclusion and backward eliminating algorithm. Extensive experimental results demonstrate that radiomics features achieve encouraging classification results on both complete feature set (AUC=0.89) and optimal feature subset (AUC=0.91).

SU-E-J-256: Predicting Metastasis-Free Survival of Rectal Cancer Patients Treated with Neoadjuvant Chemo-Radiotherapy by Data-Mining of CT Texture Features of Primary Lesions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhong, H; Wang, J; Shen, L

Purpose: The purpose of this study is to investigate the relationship between computed tomographic (CT) texture features of primary lesions and metastasis-free survival for rectal cancer patients; and to develop a datamining prediction model using texture features. Methods: A total of 220 rectal cancer patients treated with neoadjuvant chemo-radiotherapy (CRT) were enrolled in this study. All patients underwent CT scans before CRT. The primary lesions on the CT images were delineated by two experienced oncologists. The CT images were filtered by Laplacian of Gaussian (LoG) filters with different filter values (1.0–2.5: from fine to coarse). Both filtered and unfiltered imagesmore » were analyzed using Gray-level Co-occurrence Matrix (GLCM) texture analysis with different directions (transversal, sagittal, and coronal). Totally, 270 texture features with different species, directions and filter values were extracted. Texture features were examined with Student’s t-test for selecting predictive features. Principal Component Analysis (PCA) was performed upon the selected features to reduce the feature collinearity. Artificial neural network (ANN) and logistic regression were applied to establish metastasis prediction models. Results: Forty-six of 220 patients developed metastasis with a follow-up time of more than 2 years. Sixtyseven texture features were significantly different in t-test (p<0.05) between patients with and without metastasis, and 12 of them were extremely significant (p<0.001). The Area-under-the-curve (AUC) of ANN was 0.72, and the concordance index (CI) of logistic regression was 0.71. The predictability of ANN was slightly better than logistic regression. Conclusion: CT texture features of primary lesions are related to metastasisfree survival of rectal cancer patients. Both ANN and logistic regression based models can be developed for prediction.« less

SU-F-R-20: Image Texture Features Correlate with Time to Local Failure in Lung SBRT Patients

DOE Office of Scientific and Technical Information (OSTI.GOV)

Andrews, M; Abazeed, M; Woody, N

Purpose: To explore possible correlation between CT image-based texture and histogram features and time-to-local-failure in early stage non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiotherapy (SBRT).Methods and Materials: From an IRB-approved lung SBRT registry for patients treated between 2009–2013 we selected 48 (20 male, 28 female) patients with local failure. Median patient age was 72.3±10.3 years. Mean time to local failure was 15 ± 7.1 months. Physician-contoured gross tumor volumes (GTV) on the planning CT images were processed and 3D gray-level co-occurrence matrix (GLCM) based texture and histogram features were calculated in Matlab. Data were exported tomore » R and a multiple linear regression model was used to examine the relationship between texture features and time-to-local-failure. Results: Multiple linear regression revealed that entropy (p=0.0233, multiple R2=0.60) from GLCM-based texture analysis and the standard deviation (p=0.0194, multiple R2=0.60) from the histogram-based features were statistically significantly correlated with the time-to-local-failure. Conclusion: Image-based texture analysis can be used to predict certain aspects of treatment outcomes of NSCLC patients treated with SBRT. We found entropy and standard deviation calculated for the GTV on the CT images displayed a statistically significant correlation with and time-to-local-failure in lung SBRT patients.« less

Systematic Review and Meta-Analysis of CT Features for Differentiating Complicated and Uncomplicated Appendicitis.

PubMed

Kim, Hae Young; Park, Ji Hoon; Lee, Yoon Jin; Lee, Sung Soo; Jeon, Jong-June; Lee, Kyoung Ho

2018-04-01

Purpose To perform a systematic review and meta-analysis to identify computed tomographic (CT) features for differentiating complicated appendicitis in patients suspected of having appendicitis and to summarize their diagnostic accuracy. Materials and Methods Studies on diagnostic accuracy of CT features for differentiating complicated appendicitis (perforated or gangrenous appendicitis) in patients suspected of having appendicitis were searched in Ovid-MEDLINE, EMBASE, and the Cochrane Library. Overlapping descriptors used in different studies to denote the same image finding were subsumed under a single CT feature. Pooled diagnostic accuracy of the CT features was calculated by using a bivariate random effects model. CT features with pooled diagnostic odds ratios with 95% confidence intervals not including 1 were considered as informative. Results Twenty-three studies were included, and 184 overlapping descriptors for various CT findings were subsumed under 14 features. Of these, 10 features were informative for complicated appendicitis. There was a general tendency for these features to show relatively high specificity but low sensitivity. Extraluminal appendicolith, abscess, appendiceal wall enhancement defect, extraluminal air, ileus, periappendiceal fluid collection, ascites, intraluminal air, and intraluminal appendicolith showed pooled specificity greater than 70% (range, 74%-100%), but sensitivity was limited (range, 14%-59%). Periappendiceal fat stranding was the only feature that showed high sensitivity (94%; 95% confidence interval: 86%, 98%) but low specificity (40%; 95% confidence interval, 23%, 60%). Conclusion Ten informative CT features for differentiating complicated appendicitis were identified in this study, nine of which showed high specificity, but low sensitivity. © RSNA, 2017 Online supplemental material is available for this article.

CT and MRI Findings in Cerebral Aspergilloma.

PubMed

Gärtner, Friederike; Forstenpointner, Julia; Ertl-Wagner, Birgit; Hooshmand, Babak; Riedel, Christian; Jansen, Olav

2017-11-20

Purpose  Invasive aspergillosis usually affects immunocompromised patients. It carries a high risk of morbidity and mortality and usually has a nonspecific clinical presentation. Early diagnosis is essential in order to start effective treatment and improve clinical outcome. Materials and Methods  In a retrospective search of the PACS databases from two medical centers, we identified 9 patients with histologically proven cerebral aspergilloma. We systematically analyzed CT and MRI imaging findings to identify typical imaging appearances of cerebral aspergilloma. Results  CT did not show a typical appearance of the aspergillomas. In 100 % (9/9) there was a rim-attenuated diffusion restriction on MRI imaging. Multiple hypointense layers in the aspergillus wall, especially on the internal side, were detected in 100 % on T2-weighted imaging (9/9). Aspergillomas were T1-hypointense in 66 % of cases (6/9) and partly T1-hyperintense in 33 % (3/9). In 78 % (7/9) of cases, a rim-attenuated diffusion restriction was detected after contrast agent application. Conclusion  Nine cases were identified. Whereas CT features were less typical, we observed the following imaging features on MRI: A strong, rim-attenuated diffusion restriction (9/9); onion layer-like hypointense zones, in particular in the innermost part of the abscess wall on T2-weighted images (9/9). Enhancement of the lesion border was present in the majority of the cases (7/9). Key points   · There are typical MRI imaging features of aspergillomas.. · However, these findings could be affected by the immune status of the patient.. · Swift identification of aspergilloma imaging patterns is essential to allow for adequate therapeutic decision making.. Citation Format · Gärtner F, Forstenpointner J, Ertl-Wagner B et al. CT and MRI Findings in Cerebral Aspergilloma. Fortschr Röntgenstr 2017; DOI: 10.1055/s-0043-120766. © Georg Thieme Verlag KG Stuttgart · New York.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mitera, Gunita, E-mail: Gunita.Mitera@Sunnybrook.ca; Probyn, Linda; Ford, Michael

Purpose: To correlate computed tomography (CT) imaging features of spinal metastases with pain relief after radiotherapy (RT). Methods and Materials: Thirty-three patients receiving computed tomography (CT)-simulated RT for spinal metastases in an outpatient palliative RT clinic from January 2007 to October 2008 were retrospectively reviewed. Forty spinal metastases were evaluated. Pain response was rated using the International Bone Metastases Consensus Working Party endpoints. Three musculoskeletal radiologists and two orthopaedic surgeons evaluated CT features, including osseous and soft tissue tumor extent, presence of a pathologic fracture, severity of vertebral height loss, and presence of kyphosis. Results: The mean patient age wasmore » 69 years; 24 were men and 9 were women. The mean worst pain score was 7/10, and the mean total daily oral morphine equivalent was 77.3 mg. Treatment doses included 8 Gy in one fraction (22/33), 20 Gy in five fractions (10/33), and 20 Gy in eight fractions (1/33). The CT imaging appearance of spinal metastases included vertebral body involvement (40/40), pedicle involvement (23/40), and lamina involvement (18/40). Soft tissue component (10/40) and nerve root compression (9/40) were less common. Pathologic fractures existed in 11/40 lesions, with resultant vertebral body height loss in 10/40 and kyphosis in 2/40 lesions. At months 1, 2, and 3 after RT, 18%, 69%, and 70% of patients experienced pain relief. Pain response was observed with various CT imaging features. Conclusions: Pain response after RT did not differ in patients with and without pathologic fracture, kyphosis, or any other CT features related to extent of tumor involvement. All patients with painful spinal metastases may benefit from palliative RT.« less

n-SIFT: n-dimensional scale invariant feature transform.

PubMed

Cheung, Warren; Hamarneh, Ghassan

2009-09-01

We propose the n-dimensional scale invariant feature transform (n-SIFT) method for extracting and matching salient features from scalar images of arbitrary dimensionality, and compare this method's performance to other related features. The proposed features extend the concepts used for 2-D scalar images in the computer vision SIFT technique for extracting and matching distinctive scale invariant features. We apply the features to images of arbitrary dimensionality through the use of hyperspherical coordinates for gradients and multidimensional histograms to create the feature vectors. We analyze the performance of a fully automated multimodal medical image matching technique based on these features, and successfully apply the technique to determine accurate feature point correspondence between pairs of 3-D MRI images and dynamic 3D + time CT data.

Initial experience in treating lung cancer with helical tomotherapy

PubMed Central

Yartsev, S; Dar, AR; Woodford, C; Wong, E; Bauman, G; Van Dyk, J

2007-01-01

Helical tomotherapy is a new form of image-guided radiation therapy that combines features of a linear accelerator and a helical computed tomography (CT) scanner. Megavoltage CT (MVCT) data allow the verification and correction of patient setup on the couch by comparison and image registration with the kilovoltage CT multi-slice images used for treatment planning. An 84-year-old male patient with Stage III bulky non-small cell lung cancer was treated on a Hi-ART II tomotherapy unit. Daily MVCT imaging was useful for setup corrections and signaled the need to adapt the delivery plan when the patient’s anatomy changed significantly. PMID:21614260

Imaging diagnosis--temporomandibular joint dysplasia in a Basset Hound.

PubMed

Lerer, Assaf; Chalmers, Heather J; Moens, Noel M M; Mackenzie, Shawn D; Kry, Kristin

2014-01-01

A 5-month-old intact male Basset Hound presented for evaluation of pain and crepitation during manipulation of the temporomandibular joint, worse on the right side. A computed tomography (CT) scan of the head was performed. The CT images demonstrated the osseous features of temporomandibular joint dysplasia and facilitated a 3D reconstruction, which allowed better visualization of the dysplastic features. The patient responded to conservative management with a tape muzzle with no recurrence reported by the owner 6 months after presentation. © 2013 American College of Veterinary Radiology.

SU-E-J-261: The Importance of Appropriate Image Preprocessing to Augment the Information of Radiomics Image Features

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, L; Fried, D; Fave, X

Purpose: To investigate how different image preprocessing techniques, their parameters, and the different boundary handling techniques can augment the information of features and improve feature’s differentiating capability. Methods: Twenty-seven NSCLC patients with a solid tumor volume and no visually obvious necrotic regions in the simulation CT images were identified. Fourteen of these patients had a necrotic region visible in their pre-treatment PET images (necrosis group), and thirteen had no visible necrotic region in the pre-treatment PET images (non-necrosis group). We investigated how image preprocessing can impact the ability of radiomics image features extracted from the CT to differentiate between twomore » groups. It is expected the histogram in the necrosis group is more negatively skewed, and the uniformity from the necrosis group is less. Therefore, we analyzed two first order features, skewness and uniformity, on the image inside the GTV in the intensity range [−20HU, 180HU] under the combination of several image preprocessing techniques: (1) applying the isotropic Gaussian or anisotropic diffusion smoothing filter with a range of parameter(Gaussian smoothing: size=11, sigma=0:0.1:2.3; anisotropic smoothing: iteration=4, kappa=0:10:110); (2) applying the boundaryadapted Laplacian filter; and (3) applying the adaptive upper threshold for the intensity range. A 2-tailed T-test was used to evaluate the differentiating capability of CT features on pre-treatment PT necrosis. Result: Without any preprocessing, no differences in either skewness or uniformity were observed between two groups. After applying appropriate Gaussian filters (sigma>=1.3) or anisotropic filters(kappa >=60) with the adaptive upper threshold, skewness was significantly more negative in the necrosis group(p<0.05). By applying the boundary-adapted Laplacian filtering after the appropriate Gaussian filters (0.5 <=sigma<=1.1) or anisotropic filters(20<=kappa <=50), the uniformity was significantly lower in the necrosis group (p<0.05). Conclusion: Appropriate selection of image preprocessing techniques allows radiomics features to extract more useful information and thereby improve prediction models based on these features.« less

TU-G-201-02: An MRI Simulator From Proposal to Operation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cao, Y.

2015-06-15

This session will update therapeutic physicists on technological advancements and radiation oncology features of commercial CT, MRI, and PET/CT imaging systems. Also described are physicists’ roles in every stage of equipment selection, purchasing, and operation, including defining specifications, evaluating vendors, making recommendations, and optimal and safe use of imaging equipment in radiation oncology environment. The first presentation defines important terminology of CT and PET/CT followed by a review of latest innovations, such as metal artifact reduction, statistical iterative reconstruction, radiation dose management, tissue classification by dual energy CT and spectral CT, improvement in spatial resolution and sensitivity in PET, andmore » potentials of PET/MR. We will also discuss important technical specifications and items in CT and PET/CT purchasing quotes and their impacts. The second presentation will focus on key components in the request for proposal for a MRI simulator and how to evaluate vendor proposals. MRI safety issues in radiation Oncology, including MRI scanner Zones (4-zone design), will be discussed. Basic MR terminologies, important functionalities, and advanced features, which are relevant to radiation therapy, will be discussed. In the third presentation, justification of imaging systems for radiation oncology, considerations in room design and construction in a RO department, shared use with diagnostic radiology, staffing needs and training, clinical/research use cases and implementation, will be discussed. The emphasis will be on understanding and bridging the differences between diagnostic and radiation oncology installations, building consensus amongst stakeholders for purchase and use, and integrating imaging technologies into the radiation oncology environment. Learning Objectives: Learn the latest innovations of major imaging systems relevant to radiation therapy Be able to describe important technical specifications of CT, MRI, and PET/CT Understand the process of budget request, equipment justification, comparisons of technical specifications, site visits, vendor selection, and contract development.« less

4D CT sorting based on patient internal anatomy

NASA Astrophysics Data System (ADS)

Li, Ruijiang; Lewis, John H.; Cerviño, Laura I.; Jiang, Steve B.

2009-08-01

Respiratory motion during free-breathing computed tomography (CT) scan may cause significant errors in target definition for tumors in the thorax and upper abdomen. A four-dimensional (4D) CT technique has been widely used for treatment simulation of thoracic and abdominal cancer radiotherapy. The current 4D CT techniques require retrospective sorting of the reconstructed CT slices oversampled at the same couch position. Most sorting methods depend on external surrogates of respiratory motion recorded by extra instruments. However, respiratory signals obtained from these external surrogates may not always accurately represent the internal target motion, especially when irregular breathing patterns occur. We have proposed a new sorting method based on multiple internal anatomical features for multi-slice CT scan acquired in the cine mode. Four features are analyzed in this study, including the air content, lung area, lung density and body area. We use a measure called spatial coherence to select the optimal internal feature at each couch position and to generate the respiratory signals for 4D CT sorting. The proposed method has been evaluated for ten cancer patients (eight with thoracic cancer and two with abdominal cancer). For nine patients, the respiratory signals generated from the combined internal features are well correlated to those from external surrogates recorded by the real-time position management (RPM) system (average correlation: 0.95 ± 0.02), which is better than any individual internal measures at 95% confidence level. For these nine patients, the 4D CT images sorted by the combined internal features are almost identical to those sorted by the RPM signal. For one patient with an irregular breathing pattern, the respiratory signals given by the combined internal features do not correlate well with those from RPM (correlation: 0.68 ± 0.42). In this case, the 4D CT image sorted by our method presents fewer artifacts than that from the RPM signal. Our 4D CT internal sorting method eliminates the need of externally recorded surrogates of respiratory motion. It is an automatic, accurate, robust, cost efficient and yet simple method and therefore can be readily implemented in clinical settings.

Measurement of small lesions near metallic implants with mega-voltage cone beam CT

NASA Astrophysics Data System (ADS)

Grigorescu, Violeta; Prevrhal, Sven; Pouliot, Jean

2008-03-01

Metallic objects severely limit diagnostic CT imaging because of their high X-ray attenuation in the diagnostic energy range. In contrast, radiation therapy linear accelerators now offer CT imaging with X-ray energies in the megavolt range, where the attenuation coefficients of metals are significantly lower. We hypothesized that Mega electron-Voltage Cone-Beam CT (MVCT) implemented on a radiation therapy linear accelerator can detect and quantify small features in the vicinity of metallic implants with accuracy comparable to clinical Kilo electron-Voltage CT (KVCT) for imaging. Our test application was detection of osteolytic lesions formed near the metallic stem of a hip prosthesis, a condition of severe concern in hip replacement surgery. Both MVCT and KVCT were used to image a phantom containing simulated osteolytic bone lesions centered around a Chrome-Cobalt hip prosthesis stem with hemispherical lesions with sizes and densities ranging from 0.5 to 4 mm radius and 0 to 500 mg•cm -3, respectively. Images for both modalities were visually graded to establish lower limits of lesion visibility as a function of their size. Lesion volumes and mean density were determined and compared to reference values. Volume determination errors were reduced from 34%, on KVCT, to 20% for all lesions on MVCT, and density determination errors were reduced from 71% on KVCT to 10% on MVCT. Localization and quantification of lesions was improved with MVCT imaging. MVCT offers a viable alternative to clinical CT in cases where accurate 3D imaging of small features near metallic hardware is critical. These results need to be extended to other metallic objects of different composition and geometry.

TH-E-17A-01: Internal Respiratory Surrogate for 4D CT Using Fourier Transform and Anatomical Features

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hui, C; Suh, Y; Robertson, D

Purpose: To develop a novel algorithm to generate internal respiratory signals for sorting of four-dimensional (4D) computed tomography (CT) images. Methods: The proposed algorithm extracted multiple time resolved features as potential respiratory signals. These features were taken from the 4D CT images and its Fourier transformed space. Several low-frequency locations in the Fourier space and selected anatomical features from the images were used as potential respiratory signals. A clustering algorithm was then used to search for the group of appropriate potential respiratory signals. The chosen signals were then normalized and averaged to form the final internal respiratory signal. Performance ofmore » the algorithm was tested in 50 4D CT data sets and results were compared with external signals from the real-time position management (RPM) system. Results: In almost all cases, the proposed algorithm generated internal respiratory signals that visibly matched the external respiratory signals from the RPM system. On average, the end inspiration times calculated by the proposed algorithm were within 0.1 s of those given by the RPM system. Less than 3% of the calculated end inspiration times were more than one time frame away from those given by the RPM system. In 3 out of the 50 cases, the proposed algorithm generated internal respiratory signals that were significantly smoother than the RPM signals. In these cases, images sorted using the internal respiratory signals showed fewer artifacts in locations corresponding to the discrepancy in the internal and external respiratory signals. Conclusion: We developed a robust algorithm that generates internal respiratory signals from 4D CT images. In some cases, it even showed the potential to outperform the RPM system. The proposed algorithm is completely automatic and generally takes less than 2 min to process. It can be easily implemented into the clinic and can potentially replace the use of external surrogates.« less

TU-AB-202-05: GPU-Based 4D Deformable Image Registration Using Adaptive Tetrahedral Mesh Modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhong, Z; Zhuang, L; Gu, X

Purpose: Deformable image registration (DIR) has been employed today as an automated and effective segmentation method to transfer tumor or organ contours from the planning image to daily images, instead of manual segmentation. However, the computational time and accuracy of current DIR approaches are still insufficient for online adaptive radiation therapy (ART), which requires real-time and high-quality image segmentation, especially in a large datasets of 4D-CT images. The objective of this work is to propose a new DIR algorithm, with fast computational speed and high accuracy, by using adaptive feature-based tetrahedral meshing and GPU-based parallelization. Methods: The first step ismore » to generate the adaptive tetrahedral mesh based on the image features of a reference phase of 4D-CT, so that the deformation can be well captured and accurately diffused from the mesh vertices to voxels of the image volume. Subsequently, the deformation vector fields (DVF) and other phases of 4D-CT can be obtained by matching each phase of the target 4D-CT images with the corresponding deformed reference phase. The proposed 4D DIR method is implemented on GPU, resulting in significantly increasing the computational efficiency due to its parallel computing ability. Results: A 4D NCAT digital phantom was used to test the efficiency and accuracy of our method. Both the image and DVF results show that the fine structures and shapes of lung are well preserved, and the tumor position is well captured, i.e., 3D distance error is 1.14 mm. Compared to the previous voxel-based CPU implementation of DIR, such as demons, the proposed method is about 160x faster for registering a 10-phase 4D-CT with a phase dimension of 256×256×150. Conclusion: The proposed 4D DIR method uses feature-based mesh and GPU-based parallelism, which demonstrates the capability to compute both high-quality image and motion results, with significant improvement on the computational speed.« less

Significance of the impact of motion compensation on the variability of PET image features

NASA Astrophysics Data System (ADS)

Carles, M.; Bach, T.; Torres-Espallardo, I.; Baltas, D.; Nestle, U.; Martí-Bonmatí, L.

2018-03-01

In lung cancer, quantification by positron emission tomography/computed tomography (PET/CT) imaging presents challenges due to respiratory movement. Our primary aim was to study the impact of motion compensation implied by retrospectively gated (4D)-PET/CT on the variability of PET quantitative parameters. Its significance was evaluated by comparison with the variability due to (i) the voxel size in image reconstruction and (ii) the voxel size in image post-resampling. The method employed for feature extraction was chosen based on the analysis of (i) the effect of discretization of the standardized uptake value (SUV) on complementarity between texture features (TF) and conventional indices, (ii) the impact of the segmentation method on the variability of image features, and (iii) the variability of image features across the time-frame of 4D-PET. Thirty-one PET-features were involved. Three SUV discretization methods were applied: a constant width (SUV resolution) of the resampling bin (method RW), a constant number of bins (method RN) and RN on the image obtained after histogram equalization (method EqRN). The segmentation approaches evaluated were 40% of SUVmax and the contrast oriented algorithm (COA). Parameters derived from 4D-PET images were compared with values derived from the PET image obtained for (i) the static protocol used in our clinical routine (3D) and (ii) the 3D image post-resampled to the voxel size of the 4D image and PET image derived after modifying the reconstruction of the 3D image to comprise the voxel size of the 4D image. Results showed that TF complementarity with conventional indices was sensitive to the SUV discretization method. In the comparison of COA and 40% contours, despite the values not being interchangeable, all image features showed strong linear correlations (r  >  0.91, p\\ll 0.001 ). Across the time-frames of 4D-PET, all image features followed a normal distribution in most patients. For our patient cohort, the compensation of tumor motion did not have a significant impact on the quantitative PET parameters. The variability of PET parameters due to voxel size in image reconstruction was more significant than variability due to voxel size in image post-resampling. In conclusion, most of the parameters (apart from the contrast of neighborhood matrix) were robust to the motion compensation implied by 4D-PET/CT. The impact on parameter variability due to the voxel size in image reconstruction and in image post-resampling could not be assumed to be equivalent.

Intra-operative adjustment of standard planes in C-arm CT image data.

PubMed

Brehler, Michael; Görres, Joseph; Franke, Jochen; Barth, Karl; Vetter, Sven Y; Grützner, Paul A; Meinzer, Hans-Peter; Wolf, Ivo; Nabers, Diana

2016-03-01

With the help of an intra-operative mobile C-arm CT, medical interventions can be verified and corrected, avoiding the need for a post-operative CT and a second intervention. An exact adjustment of standard plane positions is necessary for the best possible assessment of the anatomical regions of interest but the mobility of the C-arm causes the need for a time-consuming manual adjustment. In this article, we present an automatic plane adjustment at the example of calcaneal fractures. We developed two feature detection methods (2D and pseudo-3D) based on SURF key points and also transferred the SURF approach to 3D. Combined with an atlas-based registration, our algorithm adjusts the standard planes of the calcaneal C-arm images automatically. The robustness of the algorithms is evaluated using a clinical data set. Additionally, we tested the algorithm's performance for two registration approaches, two resolutions of C-arm images and two methods for metal artifact reduction. For the feature extraction, the novel 3D-SURF approach performs best. As expected, a higher resolution ([Formula: see text] voxel) leads also to more robust feature points and is therefore slightly better than the [Formula: see text] voxel images (standard setting of device). Our comparison of two different artifact reduction methods and the complete removal of metal in the images shows that our approach is highly robust against artifacts and the number and position of metal implants. By introducing our fast algorithmic processing pipeline, we developed the first steps for a fully automatic assistance system for the assessment of C-arm CT images.

Point spread function modeling and image restoration for cone-beam CT

NASA Astrophysics Data System (ADS)

Zhang, Hua; Huang, Kui-Dong; Shi, Yi-Kai; Xu, Zhe

2015-03-01

X-ray cone-beam computed tomography (CT) has such notable features as high efficiency and precision, and is widely used in the fields of medical imaging and industrial non-destructive testing, but the inherent imaging degradation reduces the quality of CT images. Aimed at the problems of projection image degradation and restoration in cone-beam CT, a point spread function (PSF) modeling method is proposed first. The general PSF model of cone-beam CT is established, and based on it, the PSF under arbitrary scanning conditions can be calculated directly for projection image restoration without the additional measurement, which greatly improved the application convenience of cone-beam CT. Secondly, a projection image restoration algorithm based on pre-filtering and pre-segmentation is proposed, which can make the edge contours in projection images and slice images clearer after restoration, and control the noise in the equivalent level to the original images. Finally, the experiments verified the feasibility and effectiveness of the proposed methods. Supported by National Science and Technology Major Project of the Ministry of Industry and Information Technology of China (2012ZX04007021), Young Scientists Fund of National Natural Science Foundation of China (51105315), Natural Science Basic Research Program of Shaanxi Province of China (2013JM7003) and Northwestern Polytechnical University Foundation for Fundamental Research (JC20120226, 3102014KYJD022)

Diagnostic Imaging and workup of Malignant Pleural Mesothelioma.

PubMed

Cardinale, Luciano; Ardissone, Francesco; Gned, Dario; Sverzellati, Nicola; Piacibello, Edoardo; Veltri, Andrea

2017-08-23

Malignant pleural mesothelioma is the most frequent primary neoplasm of the pleura and its incidence is still increasing.This tumor has a strong association with exposure to occupational or environmental asbestos, often after a long latent period of 30-40 years.Plain chest radiography (CXR) is usually the first-line radiologic examination, but the radiographic findings are nonspecific due to its limited contrast resolution and they need to be complemented by other imaging modalities such as computed tomography (CT), magnetic resonance Imaging (MRI), Positron emission tomography-computed tomography (PET-CT) and ultrasound (US).The aim of this paper is to describe the imaging  features of this malignancy, underlining the peculiarity of CXR, CT, MRI, PET-CT and US and also focusing on diagnostic workup, based on the literature evidence and according to our experience.

Robust hepatic vessel segmentation using multi deep convolution network

NASA Astrophysics Data System (ADS)

Kitrungrotsakul, Titinunt; Han, Xian-Hua; Iwamoto, Yutaro; Foruzan, Amir Hossein; Lin, Lanfen; Chen, Yen-Wei

2017-03-01

Extraction of blood vessels of the organ is a challenging task in the area of medical image processing. It is really difficult to get accurate vessel segmentation results even with manually labeling by human being. The difficulty of vessels segmentation is the complicated structure of blood vessels and its large variations that make them hard to recognize. In this paper, we present deep artificial neural network architecture to automatically segment the hepatic vessels from computed tomography (CT) image. We proposed novel deep neural network (DNN) architecture for vessel segmentation from a medical CT volume, which consists of three deep convolution neural networks to extract features from difference planes of CT data. The three networks have share features at the first convolution layer but will separately learn their own features in the second layer. All three networks will join again at the top layer. To validate effectiveness and efficiency of our proposed method, we conduct experiments on 12 CT volumes which training data are randomly generate from 5 CT volumes and 7 using for test. Our network can yield an average dice coefficient 0.830, while 3D deep convolution neural network can yield around 0.7 and multi-scale can yield only 0.6.

Incremental Role of Mammography in the Evaluation of Gynecomastia in Men Who Have Undergone Chest CT.

PubMed

Sonnenblick, Emily B; Salvatore, Mary; Szabo, Janet; Lee, Karen A; Margolies, Laurie R

2016-08-01

The purpose of this study was to determine whether additional breast imaging is clinically valuable in the evaluation of patients with gynecomastia incidentally observed on CT of the chest. In a retrospective analysis, 62 men were identified who had a mammographic diagnosis of gynecomastia and had also undergone CT within 8 months (median, 2 months). We compared the imaging findings of both modalities and correlated them with the clinical outcome. Gynecomastia was statistically significantly larger on mammograms than on CT images; however, there was a high level of concordance in morphologic features and distribution of gynecomastia between mammography and CT. In only one case was gynecomastia evident on mammographic but not CT images, owing to cachexia. Two of the 62 men had ductal carcinoma, which was obscured by gynecomastia. Both of these patients had symptoms suggesting malignancy. The appearance of gynecomastia on CT scans and mammograms was highly correlated. Mammography performed within 8 months of CT is unlikely to reveal cancer unless there is a suspicious clinical finding or a breast mass eccentric to the nipple. Men with clinical symptoms of gynecomastia do not need additional imaging with mammography to confirm the diagnosis if they have undergone recent cross-sectional imaging.

Change descriptors for determining nodule malignancy in national lung screening trial CT screening images

NASA Astrophysics Data System (ADS)

Geiger, Benjamin; Hawkins, Samuel; Hall, Lawrence O.; Goldgof, Dmitry B.; Balagurunathan, Yoganand; Gatenby, Robert A.; Gillies, Robert J.

2016-03-01

Pulmonary nodules are effectively diagnosed in CT scans, but determining their malignancy has been a challenge. The rate of change of the volume of a pulmonary nodule is known to be a prognostic factor for cancer development. In this study, we propose that other changes in imaging characteristics are similarly informative. We examined the combination of image features across multiple CT scans, taken from the National Lung Screening Trial, with individual scans of the same patient separated by approximately one year. By subtracting the values of existing features in multiple scans for the same patient, we were able to improve the ability of existing classification algorithms to determine whether a nodule will become malignant. We trained each classifier on 83 nodules determined to be malignant by biopsy and 172 nodules determined to be benign by their clinical stability through two years of no change; classifiers were tested on 77 malignant and 144 benign nodules, using a set of features that in a test-retest experiment were shown to be stable. An accuracy of 83.71% and AUC of 0.814 were achieved with the Random Forests classifier on a subset of features determined to be stable via test-retest reproducibility analysis, further reduced with the Correlation-based Feature Selection algorithm.

Automated system for lung nodules classification based on wavelet feature descriptor and support vector machine.

PubMed

Madero Orozco, Hiram; Vergara Villegas, Osslan Osiris; Cruz Sánchez, Vianey Guadalupe; Ochoa Domínguez, Humberto de Jesús; Nandayapa Alfaro, Manuel de Jesús

2015-02-12

Lung cancer is a leading cause of death worldwide; it refers to the uncontrolled growth of abnormal cells in the lung. A computed tomography (CT) scan of the thorax is the most sensitive method for detecting cancerous lung nodules. A lung nodule is a round lesion which can be either non-cancerous or cancerous. In the CT, the lung cancer is observed as round white shadow nodules. The possibility to obtain a manually accurate interpretation from CT scans demands a big effort by the radiologist and might be a fatiguing process. Therefore, the design of a computer-aided diagnosis (CADx) system would be helpful as a second opinion tool. The stages of the proposed CADx are: a supervised extraction of the region of interest to eliminate the shape differences among CT images. The Daubechies db1, db2, and db4 wavelet transforms are computed with one and two levels of decomposition. After that, 19 features are computed from each wavelet sub-band. Then, the sub-band and attribute selection is performed. As a result, 11 features are selected and combined in pairs as inputs to the support vector machine (SVM), which is used to distinguish CT images containing cancerous nodules from those not containing nodules. The clinical data set used for experiments consists of 45 CT scans from ELCAP and LIDC. For the training stage 61 CT images were used (36 with cancerous lung nodules and 25 without lung nodules). The system performance was tested with 45 CT scans (23 CT scans with lung nodules and 22 without nodules), different from that used for training. The results obtained show that the methodology successfully classifies cancerous nodules with a diameter from 2 mm to 30 mm. The total preciseness obtained was 82%; the sensitivity was 90.90%, whereas the specificity was 73.91%. The CADx system presented is competitive with other literature systems in terms of sensitivity. The system reduces the complexity of classification by not performing the typical segmentation stage of most CADx systems. Additionally, the novelty of the algorithm is the use of a wavelet feature descriptor.

Diagnostic imaging of solitary tumors of the spine: what to do and say.

PubMed

Rodallec, Mathieu H; Feydy, Antoine; Larousserie, Frédérique; Anract, Philippe; Campagna, Raphaël; Babinet, Antoine; Zins, Marc; Drapé, Jean-Luc

2008-01-01

Metastatic disease, myeloma, and lymphoma are the most common malignant spinal tumors. Hemangioma is the most common benign tumor of the spine. Other primary osseous lesions of the spine are more unusual but may exhibit characteristic imaging features that can help the radiologist develop a differential diagnosis. Radiologic evaluation of a patient who presents with osseous vertebral lesions often includes radiography, computed tomography (CT), and magnetic resonance (MR) imaging. Because of the complex anatomy of the vertebrae, CT is more useful than conventional radiography for evaluating lesion location and analyzing bone destruction and condensation. The diagnosis of spinal tumors is based on patient age, topographic features of the tumor, and lesion pattern as seen at CT and MR imaging. A systematic approach is useful for recognizing tumors of the spine with characteristic features such as bone island, osteoid osteoma, osteochondroma, chondrosarcoma, vertebral angioma, and aneurysmal bone cyst. In the remaining cases, the differential diagnosis may include other primary spinal tumors, vertebral metastases and major nontumoral lesions simulating a vertebral tumor, Paget disease, spondylitis, echinococcal infection, and aseptic osteitis. In many cases, vertebral biopsy is warranted to guide treatment.

An artificial neural networks approach for assessment treatment response in oncological patients using PET/CT images.

PubMed

Nogueira, Mariana A; Abreu, Pedro H; Martins, Pedro; Machado, Penousal; Duarte, Hugo; Santos, João

2017-02-13

Positron Emission Tomography - Computed Tomography (PET/CT) imaging is the basis for the evaluation of response-to-treatment of several oncological diseases. In practice, such evaluation is manually performed by specialists, which is rather complex and time-consuming. Evaluation measures have been proposed, but with questionable reliability. The usage of before and after-treatment image descriptors of the lesions for treatment response evaluation is still a territory to be explored. In this project, Artificial Neural Network approaches were implemented to automatically assess treatment response of patients suffering from neuroendocrine tumors and Hodgkyn lymphoma, based on image features extracted from PET/CT. The results show that the considered set of features allows for the achievement of very high classification performances, especially when data is properly balanced. After synthetic data generation and PCA-based dimensionality reduction to only two components, LVQNN assured classification accuracies of 100%, 100%, 96.3% and 100% regarding the 4 response-to-treatment classes.

Bladder Cancer Treatment Response Assessment in CT using Radiomics with Deep-Learning.

PubMed

Cha, Kenny H; Hadjiiski, Lubomir; Chan, Heang-Ping; Weizer, Alon Z; Alva, Ajjai; Cohan, Richard H; Caoili, Elaine M; Paramagul, Chintana; Samala, Ravi K

2017-08-18

Cross-sectional X-ray imaging has become the standard for staging most solid organ malignancies. However, for some malignancies such as urinary bladder cancer, the ability to accurately assess local extent of the disease and understand response to systemic chemotherapy is limited with current imaging approaches. In this study, we explored the feasibility that radiomics-based predictive models using pre- and post-treatment computed tomography (CT) images might be able to distinguish between bladder cancers with and without complete chemotherapy responses. We assessed three unique radiomics-based predictive models, each of which employed different fundamental design principles ranging from a pattern recognition method via deep-learning convolution neural network (DL-CNN), to a more deterministic radiomics feature-based approach and then a bridging method between the two, utilizing a system which extracts radiomics features from the image patterns. Our study indicates that the computerized assessment using radiomics information from the pre- and post-treatment CT of bladder cancer patients has the potential to assist in assessment of treatment response.

Computer-aided diagnosis for phase-contrast X-ray computed tomography: quantitative characterization of human patellar cartilage with high-dimensional geometric features.

PubMed

Nagarajan, Mahesh B; Coan, Paola; Huber, Markus B; Diemoz, Paul C; Glaser, Christian; Wismüller, Axel

2014-02-01

Phase-contrast computed tomography (PCI-CT) has shown tremendous potential as an imaging modality for visualizing human cartilage with high spatial resolution. Previous studies have demonstrated the ability of PCI-CT to visualize (1) structural details of the human patellar cartilage matrix and (2) changes to chondrocyte organization induced by osteoarthritis. This study investigates the use of high-dimensional geometric features in characterizing such chondrocyte patterns in the presence or absence of osteoarthritic damage. Geometrical features derived from the scaling index method (SIM) and statistical features derived from gray-level co-occurrence matrices were extracted from 842 regions of interest (ROI) annotated on PCI-CT images of ex vivo human patellar cartilage specimens. These features were subsequently used in a machine learning task with support vector regression to classify ROIs as healthy or osteoarthritic; classification performance was evaluated using the area under the receiver-operating characteristic curve (AUC). SIM-derived geometrical features exhibited the best classification performance (AUC, 0.95 ± 0.06) and were most robust to changes in ROI size. These results suggest that such geometrical features can provide a detailed characterization of the chondrocyte organization in the cartilage matrix in an automated and non-subjective manner, while also enabling classification of cartilage as healthy or osteoarthritic with high accuracy. Such features could potentially serve as imaging markers for evaluating osteoarthritis progression and its response to different therapeutic intervention strategies.

Quantitative imaging features of pretreatment CT predict volumetric response to chemotherapy in patients with colorectal liver metastases.

PubMed

Creasy, John M; Midya, Abhishek; Chakraborty, Jayasree; Adams, Lauryn B; Gomes, Camilla; Gonen, Mithat; Seastedt, Kenneth P; Sutton, Elizabeth J; Cercek, Andrea; Kemeny, Nancy E; Shia, Jinru; Balachandran, Vinod P; Kingham, T Peter; Allen, Peter J; DeMatteo, Ronald P; Jarnagin, William R; D'Angelica, Michael I; Do, Richard K G; Simpson, Amber L

2018-06-19

This study investigates whether quantitative image analysis of pretreatment CT scans can predict volumetric response to chemotherapy for patients with colorectal liver metastases (CRLM). Patients treated with chemotherapy for CRLM (hepatic artery infusion (HAI) combined with systemic or systemic alone) were included in the study. Patients were imaged at baseline and approximately 8 weeks after treatment. Response was measured as the percentage change in tumour volume from baseline. Quantitative imaging features were derived from the index hepatic tumour on pretreatment CT, and features statistically significant on univariate analysis were included in a linear regression model to predict volumetric response. The regression model was constructed from 70% of data, while 30% were reserved for testing. Test data were input into the trained model. Model performance was evaluated with mean absolute prediction error (MAPE) and R 2 . Clinicopatholologic factors were assessed for correlation with response. 157 patients were included, split into training (n = 110) and validation (n = 47) sets. MAPE from the multivariate linear regression model was 16.5% (R 2 = 0.774) and 21.5% in the training and validation sets, respectively. Stratified by HAI utilisation, MAPE in the validation set was 19.6% for HAI and 25.1% for systemic chemotherapy alone. Clinical factors associated with differences in median tumour response were treatment strategy, systemic chemotherapy regimen, age and KRAS mutation status (p < 0.05). Quantitative imaging features extracted from pretreatment CT are promising predictors of volumetric response to chemotherapy in patients with CRLM. Pretreatment predictors of response have the potential to better select patients for specific therapies. • Colorectal liver metastases (CRLM) are downsized with chemotherapy but predicting the patients that will respond to chemotherapy is currently not possible. • Heterogeneity and enhancement patterns of CRLM can be measured with quantitative imaging. • Prediction model constructed that predicts volumetric response with 20% error suggesting that quantitative imaging holds promise to better select patients for specific treatments.

Feasibility of ultrasound imaging of osteochondral defects in the ankle: a clinical pilot study.

PubMed

Kok, A C; Terra, M P; Muller, S; Askeland, C; van Dijk, C N; Kerkhoffs, G M M J; Tuijthof, G J M

2014-10-01

Talar osteochondral defects (OCDs) are imaged using magnetic resonance imaging (MRI) or computed tomography (CT). For extensive follow-up, ultrasound might be a fast, non-invasive alternative that images both bone and cartilage. In this study the potential of ultrasound, as compared with CT, in the imaging and grading of OCDs is explored. On the basis of prior CT scans, nine ankles of patients without OCDs and nine ankles of patients with anterocentral OCDs were selected and classified using the Loomer CT classification. A blinded expert skeletal radiologist imaged all ankles with ultrasound and recorded the presence of OCDs. Similarly to CT, ultrasound revealed typical morphologic OCD features, for example, cortex irregularities and loose fragments. Cartilage disruptions, Loomer grades IV (displaced fragment) and V (cyst with fibrous roof), were visible as well. This study encourages further research on the use of ultrasound as a follow-up imaging modality for OCDs located anteriorly or centrally on the talar dome. Copyright © 2014 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

TU-C-12A-12: Differentiating Bone Lesions and Degenerative Joint Disease in NaF PET/CT Scans Using Machine Learning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Perk, T; Bradshaw, T; Muzahir, S

2014-06-15

Purpose: [F-18]NaF PET can be used to image bone metastases; however, tracer uptake in degenerative joint disease (DJD) often appears similar to metastases. This study aims to develop and compare different machine learning algorithms to automatically identify regions of [F-18]NaF scans that correspond to DJD. Methods: 10 metastatic prostate cancer patients received whole body [F-18]NaF PET/CT scans prior to treatment. Image segmentation resulted in 852 ROIs, 69 of which were identified by a nuclear medicine physician as DJD. For all ROIs, various PET and CT textural features were computed. ROIs were divided into training and testing sets used to trainmore » eight different machine learning classifiers. Classifiers were evaluated based on receiver operating characteristics area under the curve (AUC), sensitivity, specificity, and positive predictive value (PPV). We also assessed the added value of including CT features in addition to PET features for training classifiers. Results: The training set consisted of 37 DJD ROIs with 475 non-DJD ROIs, and the testing set consisted of 32 DJD ROIs with 308 non-DJD ROIs. Of all classifiers, generalized linear models (GLM), decision forests (DF), and support vector machines (SVM) had the best performance. AUCs of GLM (0.929), DF (0.921), and SVM (0.889) were significantly higher than the other models (p<0.001). GLM and DF, overall, had the best sensitivity, specificity, and PPV, and gave a significantly better performance (p<0.01) than all other models. PET/CT GLM classifiers had higher AUC than just PET or just CT. GLMs built using PET/CT information had superior or comparable sensitivities, specificities and PPVs to just PET or just CT. Conclusion: Machine learning algorithms trained with PET/CT features were able to identify some cases of DJD. GLM outperformed the other classification algorithms. Using PET and CT information together was shown to be superior to using PET or CT features alone. Research supported by the Prostate Cancer Foundation.« less

Volumetric characterization of human patellar cartilage matrix on phase contrast x-ray computed tomography

NASA Astrophysics Data System (ADS)

Abidin, Anas Z.; Nagarajan, Mahesh B.; Checefsky, Walter A.; Coan, Paola; Diemoz, Paul C.; Hobbs, Susan K.; Huber, Markus B.; Wismüller, Axel

2015-03-01

Phase contrast X-ray computed tomography (PCI-CT) has recently emerged as a novel imaging technique that allows visualization of cartilage soft tissue, subsequent examination of chondrocyte patterns, and their correlation to osteoarthritis. Previous studies have shown that 2D texture features are effective at distinguishing between healthy and osteoarthritic regions of interest annotated in the radial zone of cartilage matrix on PCI-CT images. In this study, we further extend the texture analysis to 3D and investigate the ability of volumetric texture features at characterizing chondrocyte patterns in the cartilage matrix for purposes of classification. Here, we extracted volumetric texture features derived from Minkowski Functionals and gray-level co-occurrence matrices (GLCM) from 496 volumes of interest (VOI) annotated on PCI-CT images of human patellar cartilage specimens. The extracted features were then used in a machine-learning task involving support vector regression to classify ROIs as healthy or osteoarthritic. Classification performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC). The best classification performance was observed with GLCM features correlation (AUC = 0.83 +/- 0.06) and homogeneity (AUC = 0.82 +/- 0.07), which significantly outperformed all Minkowski Functionals (p < 0.05). These results suggest that such quantitative analysis of chondrocyte patterns in human patellar cartilage matrix involving GLCM-derived statistical features can distinguish between healthy and osteoarthritic tissue with high accuracy.

Three dimensional image correlation of CT, MR, and PET studies in radiotherapy treatment planning of brain tumors.

PubMed

Schad, L R; Boesecke, R; Schlegel, W; Hartmann, G H; Sturm, V; Strauss, L G; Lorenz, W J

1987-01-01

A treatment planning system for stereotactic convergent beam irradiation of deeply localized brain tumors is reported. The treatment technique consists of several moving field irradiations in noncoplanar planes at a linear accelerator facility. Using collimated narrow beams, a high concentration of dose within small volumes with a dose gradient of 10-15%/mm was obtained. The dose calculation was based on geometrical information of multiplanar CT or magnetic resonance (MR) imaging data. The patient's head was fixed in a stereotactic localization system, which is usable at CT, MR, and positron emission tomography (PET) installations. Special computer programs for correction of the geometrical MR distortions allowed a precise correlation of the different imaging modalities. The therapist can use combinations of CT, MR, and PET data for defining target volume. For instance, the superior soft tissue contrast of MR coupled with the metabolic features of PET may be a useful addition in the radiation treatment planning process. Furthermore, other features such as calculated dose distribution to critical structures can also be transferred from one set of imaging data to another and can be displayed as three-dimensional shaded structures.

In vivo tumor-targeted dual-modal fluorescence/CT imaging using a nanoprobe co-loaded with an aggregation-induced emission dye and gold nanoparticles.

PubMed

Zhang, Jimei; Li, Chan; Zhang, Xu; Huo, Shuaidong; Jin, Shubin; An, Fei-Fei; Wang, Xiaodan; Xue, Xiangdong; Okeke, C I; Duan, Guiyun; Guo, Fengguang; Zhang, Xiaohong; Hao, Jifu; Wang, Paul C; Zhang, Jinchao; Liang, Xing-Jie

2015-02-01

As an intensely studied computed tomography (CT) contrast agent, gold nanoparticle has been suggested to be combined with fluorescence imaging modality to offset the low sensitivity of CT. However, the strong quenching of gold nanoparticle on fluorescent dyes requires complicated design and shielding to overcome. Herein, we report a unique nanoprobe (M-NPAPF-Au) co-loading an aggregation-induced emission (AIE) red dye and gold nanoparticles into DSPE-PEG(2000) micelles for dual-modal fluorescence/CT imaging. The nanoprobe was prepared based on a facile method of "one-pot ultrasonic emulsification". Surprisingly, in the micelles system, fluorescence dye (NPAPF) efficiently overcame the strong fluorescence quenching of shielding-free gold nanoparticles and retained the crucial AIE feature. In vivo studies demonstrated the nanoprobe had superior tumor-targeting ability, excellent fluorescence and CT imaging effects. The totality of present studies clearly indicates the significant potential application of M-NPAPF-Au as a dual-modal non-invasive fluorescence/X-ray CT nanoprobe for in vivo tumor-targeted imaging and diagnosis. Copyright © 2014 Elsevier Ltd. All rights reserved.

Quantitative micro-CT based coronary artery profiling using interactive local thresholding and cylindrical coordinates.

PubMed

Panetta, Daniele; Pelosi, Gualtiero; Viglione, Federica; Kusmic, Claudia; Terreni, Marianna; Belcari, Nicola; Guerra, Alberto Del; Athanasiou, Lambros; Exarchos, Themistoklis; Fotiadis, Dimitrios I; Filipovic, Nenad; Trivella, Maria Giovanna; Salvadori, Piero A; Parodi, Oberdan

2015-01-01

Micro-CT is an established imaging technique for high-resolution non-destructive assessment of vascular samples, which is gaining growing interest for investigations of atherosclerotic arteries both in humans and in animal models. However, there is still a lack in the definition of micro-CT image metrics suitable for comprehensive evaluation and quantification of features of interest in the field of experimental atherosclerosis (ATS). A novel approach to micro-CT image processing for profiling of coronary ATS is described, providing comprehensive visualization and quantification of contrast agent-free 3D high-resolution reconstruction of full-length artery walls. Accelerated coronary ATS has been induced by high fat cholesterol-enriched diet in swine and left coronary artery (LCA) harvested en bloc for micro-CT scanning and histologic processing. A cylindrical coordinate system has been defined on the image space after curved multiplanar reformation of the coronary vessel for the comprehensive visualization of the main vessel features such as wall thickening and calcium content. A novel semi-automatic segmentation procedure based on 2D histograms has been implemented and the quantitative results validated by histology. The potentiality of attenuation-based micro-CT at low kV to reliably separate arterial wall layers from adjacent tissue as well as identify wall and plaque contours and major tissue components has been validated by histology. Morphometric indexes from histological data corresponding to several micro-CT slices have been derived (double observer evaluation at different coronary ATS stages) and highly significant correlations (R2 > 0.90) evidenced. Semi-automatic morphometry has been validated by double observer manual morphometry of micro-CT slices and highly significant correlations were found (R2 > 0.92). The micro-CT methodology described represents a handy and reliable tool for quantitative high resolution and contrast agent free full length coronary wall profiling, able to assist atherosclerotic vessels morphometry in a preclinical experimental model of coronary ATS and providing a link between in vivo imaging and histology.

SU-F-R-46: Predicting Distant Failure in Lung SBRT Using Multi-Objective Radiomics Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, Z; Folkert, M; Iyengar, P

2016-06-15

Purpose: To predict distant failure in lung stereotactic body radiation therapy (SBRT) in early stage non-small cell lung cancer (NSCLC) by using a new multi-objective radiomics model. Methods: Currently, most available radiomics models use the overall accuracy as the objective function. However, due to data imbalance, a single object may not reflect the performance of a predictive model. Therefore, we developed a multi-objective radiomics model which considers both sensitivity and specificity as the objective functions simultaneously. The new model is used to predict distant failure in lung SBRT using 52 patients treated at our institute. Quantitative imaging features of PETmore » and CT as well as clinical parameters are utilized to build the predictive model. Image features include intensity features (9), textural features (12) and geometric features (8). Clinical parameters for each patient include demographic parameters (4), tumor characteristics (8), treatment faction schemes (4) and pretreatment medicines (6). The modelling procedure consists of two steps: extracting features from segmented tumors in PET and CT; and selecting features and training model parameters based on multi-objective. Support Vector Machine (SVM) is used as the predictive model, while a nondominated sorting-based multi-objective evolutionary computation algorithm II (NSGA-II) is used for solving the multi-objective optimization. Results: The accuracy for PET, clinical, CT, PET+clinical, PET+CT, CT+clinical, PET+CT+clinical are 71.15%, 84.62%, 84.62%, 85.54%, 82.69%, 84.62%, 86.54%, respectively. The sensitivities for the above seven combinations are 41.76%, 58.33%, 50.00%, 50.00%, 41.67%, 41.67%, 58.33%, while the specificities are 80.00%, 92.50%, 90.00%, 97.50%, 92.50%, 97.50%, 97.50%. Conclusion: A new multi-objective radiomics model for predicting distant failure in NSCLC treated with SBRT was developed. The experimental results show that the best performance can be obtained by combining all features.« less

Three modality image registration of brain SPECT/CT and MR images for quantitative analysis of dopamine transporter imaging

NASA Astrophysics Data System (ADS)

Yamaguchi, Yuzuho; Takeda, Yuta; Hara, Takeshi; Zhou, Xiangrong; Matsusako, Masaki; Tanaka, Yuki; Hosoya, Kazuhiko; Nihei, Tsutomu; Katafuchi, Tetsuro; Fujita, Hiroshi

2016-03-01

Important features in Parkinson's disease (PD) are degenerations and losses of dopamine neurons in corpus striatum. 123I-FP-CIT can visualize activities of the dopamine neurons. The activity radio of background to corpus striatum is used for diagnosis of PD and Dementia with Lewy Bodies (DLB). The specific activity can be observed in the corpus striatum on SPECT images, but the location and the shape of the corpus striatum on SPECT images only are often lost because of the low uptake. In contrast, MR images can visualize the locations of the corpus striatum. The purpose of this study was to realize a quantitative image analysis for the SPECT images by using image registration technique with brain MR images that can determine the region of corpus striatum. In this study, the image fusion technique was used to fuse SPECT and MR images by intervening CT image taken by SPECT/CT. The mutual information (MI) for image registration between CT and MR images was used for the registration. Six SPECT/CT and four MR scans of phantom materials are taken by changing the direction. As the results of the image registrations, 16 of 24 combinations were registered within 1.3mm. By applying the approach to 32 clinical SPECT/CT and MR cases, all of the cases were registered within 0.86mm. In conclusions, our registration method has a potential in superimposing MR images on SPECT images.

Volumetric quantitative characterization of human patellar cartilage with topological and geometrical features on phase-contrast X-ray computed tomography.

PubMed

Nagarajan, Mahesh B; Coan, Paola; Huber, Markus B; Diemoz, Paul C; Wismüller, Axel

2015-11-01

Phase-contrast X-ray computed tomography (PCI-CT) has attracted significant interest in recent years for its ability to provide significantly improved image contrast in low absorbing materials such as soft biological tissue. In the research context of cartilage imaging, previous studies have demonstrated the ability of PCI-CT to visualize structural details of human patellar cartilage matrix and capture changes to chondrocyte organization induced by osteoarthritis. This study evaluates the use of geometrical and topological features for volumetric characterization of such chondrocyte patterns in the presence (or absence) of osteoarthritic damage. Geometrical features derived from the scaling index method (SIM) and topological features derived from Minkowski Functionals were extracted from 1392 volumes of interest (VOI) annotated on PCI-CT images of ex vivo human patellar cartilage specimens. These features were subsequently used in a machine learning task with support vector regression to classify VOIs as healthy or osteoarthritic; classification performance was evaluated using the area under the receiver operating characteristic curve (AUC). Our results show that the classification performance of SIM-derived geometrical features (AUC: 0.90 ± 0.09) is significantly better than Minkowski Functionals volume (AUC: 0.54 ± 0.02), surface (AUC: 0.72 ± 0.06), mean breadth (AUC: 0.74 ± 0.06) and Euler characteristic (AUC: 0.78 ± 0.04) (p < 10(-4)). These results suggest that such geometrical features can provide a detailed characterization of the chondrocyte organization in the cartilage matrix in an automated manner, while also enabling classification of cartilage as healthy or osteoarthritic with high accuracy. Such features could potentially serve as diagnostic imaging markers for evaluating osteoarthritis progression and its response to different therapeutic intervention strategies.

Automatic machine learning based prediction of cardiovascular events in lung cancer screening data

NASA Astrophysics Data System (ADS)

de Vos, Bob D.; de Jong, Pim A.; Wolterink, Jelmer M.; Vliegenthart, Rozemarijn; Wielingen, Geoffrey V. F.; Viergever, Max A.; Išgum, Ivana

2015-03-01

Calcium burden determined in CT images acquired in lung cancer screening is a strong predictor of cardiovascular events (CVEs). This study investigated whether subjects undergoing such screening who are at risk of a CVE can be identified using automatic image analysis and subject characteristics. Moreover, the study examined whether these individuals can be identified using solely image information, or if a combination of image and subject data is needed. A set of 3559 male subjects undergoing Dutch-Belgian lung cancer screening trial was included. Low-dose non-ECG synchronized chest CT images acquired at baseline were analyzed (1834 scanned in the University Medical Center Groningen, 1725 in the University Medical Center Utrecht). Aortic and coronary calcifications were identified using previously developed automatic algorithms. A set of features describing number, volume and size distribution of the detected calcifications was computed. Age of the participants was extracted from image headers. Features describing participants' smoking status, smoking history and past CVEs were obtained. CVEs that occurred within three years after the imaging were used as outcome. Support vector machine classification was performed employing different feature sets using sets of only image features, or a combination of image and subject related characteristics. Classification based solely on the image features resulted in the area under the ROC curve (Az) of 0.69. A combination of image and subject features resulted in an Az of 0.71. The results demonstrate that subjects undergoing lung cancer screening who are at risk of CVE can be identified using automatic image analysis. Adding subject information slightly improved the performance.

TU-AB-BRA-10: Prognostic Value of Intra-Radiation Treatment FDG-PET and CT Imaging Features in Locally Advanced Head and Neck Cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Song, J; Pollom, E; Durkee, B

2015-06-15

Purpose: To predict response to radiation treatment using computational FDG-PET and CT images in locally advanced head and neck cancer (HNC). Methods: 68 patients with State III-IVB HNC treated with chemoradiation were included in this retrospective study. For each patient, we analyzed primary tumor and lymph nodes on PET and CT scans acquired both prior to and during radiation treatment, which led to 8 combinations of image datasets. From each image set, we extracted high-throughput, radiomic features of the following types: statistical, morphological, textural, histogram, and wavelet, resulting in a total of 437 features. We then performed unsupervised redundancy removalmore » and stability test on these features. To avoid over-fitting, we trained a logistic regression model with simultaneous feature selection based on least absolute shrinkage and selection operator (LASSO). To objectively evaluate the prediction ability, we performed 5-fold cross validation (CV) with 50 random repeats of stratified bootstrapping. Feature selection and model training was solely conducted on the training set and independently validated on the holdout test set. Receiver operating characteristic (ROC) curve of the pooled Result and the area under the ROC curve (AUC) was calculated as figure of merit. Results: For predicting local-regional recurrence, our model built on pre-treatment PET of lymph nodes achieved the best performance (AUC=0.762) on 5-fold CV, which compared favorably with node volume and SUVmax (AUC=0.704 and 0.449, p<0.001). Wavelet coefficients turned out to be the most predictive features. Prediction of distant recurrence showed a similar trend, in which pre-treatment PET features of lymph nodes had the highest AUC of 0.705. Conclusion: The radiomics approach identified novel imaging features that are predictive to radiation treatment response. If prospectively validated in larger cohorts, they could aid in risk-adaptive treatment of HNC.« less

Assessment of the dose reduction potential of a model-based iterative reconstruction algorithm using a task-based performance metrology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Samei, Ehsan, E-mail: samei@duke.edu; Richard, Samuel

2015-01-15

Purpose: Different computed tomography (CT) reconstruction techniques offer different image quality attributes of resolution and noise, challenging the ability to compare their dose reduction potential against each other. The purpose of this study was to evaluate and compare the task-based imaging performance of CT systems to enable the assessment of the dose performance of a model-based iterative reconstruction (MBIR) to that of an adaptive statistical iterative reconstruction (ASIR) and a filtered back projection (FBP) technique. Methods: The ACR CT phantom (model 464) was imaged across a wide range of mA setting on a 64-slice CT scanner (GE Discovery CT750 HD,more » Waukesha, WI). Based on previous work, the resolution was evaluated in terms of a task-based modulation transfer function (MTF) using a circular-edge technique and images from the contrast inserts located in the ACR phantom. Noise performance was assessed in terms of the noise-power spectrum (NPS) measured from the uniform section of the phantom. The task-based MTF and NPS were combined with a task function to yield a task-based estimate of imaging performance, the detectability index (d′). The detectability index was computed as a function of dose for two imaging tasks corresponding to the detection of a relatively small and a relatively large feature (1.5 and 25 mm, respectively). The performance of MBIR in terms of the d′ was compared with that of ASIR and FBP to assess its dose reduction potential. Results: Results indicated that MBIR exhibits a variability spatial resolution with respect to object contrast and noise while significantly reducing image noise. The NPS measurements for MBIR indicated a noise texture with a low-pass quality compared to the typical midpass noise found in FBP-based CT images. At comparable dose, the d′ for MBIR was higher than those of FBP and ASIR by at least 61% and 19% for the small feature and the large feature tasks, respectively. Compared to FBP and ASIR, MBIR indicated a 46%–84% dose reduction potential, depending on task, without compromising the modeled detection performance. Conclusions: The presented methodology based on ACR phantom measurements extends current possibilities for the assessment of CT image quality under the complex resolution and noise characteristics exhibited with statistical and iterative reconstruction algorithms. The findings further suggest that MBIR can potentially make better use of the projections data to reduce CT dose by approximately a factor of 2. Alternatively, if the dose held unchanged, it can improve image quality by different levels for different tasks.« less

SU-C-BRA-04: Automated Segmentation of Head-And-Neck CT Images for Radiotherapy Treatment Planning Via Multi-Atlas Machine Learning (MAML)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ren, X; Gao, H; Sharp, G

Purpose: Accurate image segmentation is a crucial step during image guided radiation therapy. This work proposes multi-atlas machine learning (MAML) algorithm for automated segmentation of head-and-neck CT images. Methods: As the first step, the algorithm utilizes normalized mutual information as similarity metric, affine registration combined with multiresolution B-Spline registration, and then fuses together using the label fusion strategy via Plastimatch. As the second step, the following feature selection strategy is proposed to extract five feature components from reference or atlas images: intensity (I), distance map (D), box (B), center of gravity (C) and stable point (S). The box feature Bmore » is novel. It describes a relative position from each point to minimum inscribed rectangle of ROI. The center-of-gravity feature C is the 3D Euclidean distance from a sample point to the ROI center of gravity, and then S is the distance of the sample point to the landmarks. Then, we adopt random forest (RF) in Scikit-learn, a Python module integrating a wide range of state-of-the-art machine learning algorithms as classifier. Different feature and atlas strategies are used for different ROIs for improved performance, such as multi-atlas strategy with reference box for brainstem, and single-atlas strategy with reference landmark for optic chiasm. Results: The algorithm was validated on a set of 33 CT images with manual contours using a leave-one-out cross-validation strategy. Dice similarity coefficients between manual contours and automated contours were calculated: the proposed MAML method had an improvement from 0.79 to 0.83 for brainstem and 0.11 to 0.52 for optic chiasm with respect to multi-atlas segmentation method (MA). Conclusion: A MAML method has been proposed for automated segmentation of head-and-neck CT images with improved performance. It provides the comparable result in brainstem and the improved result in optic chiasm compared with MA. Xuhua Ren and Hao Gao were partially supported by the NSFC (#11405105), the 973 Program (#2015CB856000), and the Shanghai Pujiang Talent Program (#14PJ1404500).« less

SU-F-R-22: Malignancy Classification for Small Pulmonary Nodules with Radiomics and Logistic Regression

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, W; Tu, S

Purpose: We conducted a retrospective study of Radiomics research for classifying malignancy of small pulmonary nodules. A machine learning algorithm of logistic regression and open research platform of Radiomics, IBEX (Imaging Biomarker Explorer), were used to evaluate the classification accuracy. Methods: The training set included 100 CT image series from cancer patients with small pulmonary nodules where the average diameter is 1.10 cm. These patients registered at Chang Gung Memorial Hospital and received a CT-guided operation of lung cancer lobectomy. The specimens were classified by experienced pathologists with a B (benign) or M (malignant). CT images with slice thickness ofmore » 0.625 mm were acquired from a GE BrightSpeed 16 scanner. The study was formally approved by our institutional internal review board. Nodules were delineated and 374 feature parameters were extracted from IBEX. We first used the t-test and p-value criteria to study which feature can differentiate between group B and M. Then we implemented a logistic regression algorithm to perform nodule malignancy classification. 10-fold cross-validation and the receiver operating characteristic curve (ROC) were used to evaluate the classification accuracy. Finally hierarchical clustering analysis, Spearman rank correlation coefficient, and clustering heat map were used to further study correlation characteristics among different features. Results: 238 features were found differentiable between group B and M based on whether their statistical p-values were less than 0.05. A forward search algorithm was used to select an optimal combination of features for the best classification and 9 features were identified. Our study found the best accuracy of classifying malignancy was 0.79±0.01 with the 10-fold cross-validation. The area under the ROC curve was 0.81±0.02. Conclusion: Benign nodules may be treated as a malignant tumor in low-dose CT and patients may undergo unnecessary surgeries or treatments. Our study may help radiologists to differentiate nodule malignancy for low-dose CT.« less

Computed tomography synthesis from magnetic resonance images in the pelvis using multiple random forests and auto-context features

NASA Astrophysics Data System (ADS)

Andreasen, Daniel; Edmund, Jens M.; Zografos, Vasileios; Menze, Bjoern H.; Van Leemput, Koen

2016-03-01

In radiotherapy treatment planning that is only based on magnetic resonance imaging (MRI), the electron density information usually obtained from computed tomography (CT) must be derived from the MRI by synthesizing a so-called pseudo CT (pCT). This is a non-trivial task since MRI intensities are neither uniquely nor quantitatively related to electron density. Typical approaches involve either a classification or regression model requiring specialized MRI sequences to solve intensity ambiguities, or an atlas-based model necessitating multiple registrations between atlases and subject scans. In this work, we explore a machine learning approach for creating a pCT of the pelvic region from conventional MRI sequences without using atlases. We use a random forest provided with information about local texture, edges and spatial features derived from the MRI. This helps to solve intensity ambiguities. Furthermore, we use the concept of auto-context by sequentially training a number of classification forests to create and improve context features, which are finally used to train a regression forest for pCT prediction. We evaluate the pCT quality in terms of the voxel-wise error and the radiologic accuracy as measured by water-equivalent path lengths. We compare the performance of our method against two baseline pCT strategies, which either set all MRI voxels in the subject equal to the CT value of water, or in addition transfer the bone volume from the real CT. We show an improved performance compared to both baseline pCTs suggesting that our method may be useful for MRI-only radiotherapy.

An improved method for pancreas segmentation using SLIC and interactive region merging

NASA Astrophysics Data System (ADS)

Zhang, Liyuan; Yang, Huamin; Shi, Weili; Miao, Yu; Li, Qingliang; He, Fei; He, Wei; Li, Yanfang; Zhang, Huimao; Mori, Kensaku; Jiang, Zhengang

2017-03-01

Considering the weak edges in pancreas segmentation, this paper proposes a new solution which integrates more features of CT images by combining SLIC superpixels and interactive region merging. In the proposed method, Mahalanobis distance is first utilized in SLIC method to generate better superpixel images. By extracting five texture features and one gray feature, the similarity measure between two superpixels becomes more reliable in interactive region merging. Furthermore, object edge blocks are accurately addressed by re-segmentation merging process. Applying the proposed method to four cases of abdominal CT images, we segment pancreatic tissues to verify the feasibility and effectiveness. The experimental results show that the proposed method can make segmentation accuracy increase to 92% on average. This study will boost the application process of pancreas segmentation for computer-aided diagnosis system.

Subsolid pulmonary nodules: imaging evaluation and strategic management.

PubMed

Godoy, Myrna C B; Sabloff, Bradley; Naidich, David P

2012-07-01

Given the higher rate of malignancy of subsolid pulmonary nodules and the considerably lower growth rate of ground-glass nodules (GGNs), dedicated standardized guidelines for management of these nodules have been proposed, including long-term low-dose computed tomography (CT) follow-up (≥3 years). Physicians must be familiar with the strategic management of subsolid pulmonary nodules, and should be able to identify imaging features that suggest invasive adenocarcinoma requiring a more aggressive management. Low-dose CT screening studies for early detection of lung cancer have increased our knowledge of pulmonary nodules, and in particular our understanding of the strong although imperfect correlation of the subsolid pulmonary nodules, including pure GGNs and part-solid nodules, with the spectrum of preinvasive to invasive lung adenocarcinoma. Serial CT imaging has shown stepwise progression in a subset of these nodules, characterized by increase in size and density of pure GGNs and development of a solid component, the latter usually indicating invasive adenocarcinoma. There is close correlation between the CT features of subsolid nodules (SSNs) and the spectrum of lung adenocarcinoma. Standardized guidelines are suggested for management of SSNs.

Plaque imaging with CT—a comprehensive review on coronary CT angiography based risk assessment

PubMed Central

Kolossváry, Márton; Szilveszter, Bálint; Merkely, Béla

2017-01-01

CT based technologies have evolved considerably in recent years. Coronary CT angiography (CTA) provides robust assessment of coronary artery disease (CAD). Early coronary CTA imaging—as a gate-keeper of invasive angiography—has focused on the presence of obstructive stenosis. Coronary CTA is currently the only non-invasive imaging modality for the evaluation of non-obstructive CAD, which has been shown to contribute to adverse cardiac events. Importantly, improved spatial resolution of CT scanners and novel image reconstruction algorithms enable the quantification and characterization of atherosclerotic plaques. State-of-the-art CT imaging can therefore reliably assess the extent of CAD and differentiate between various plaque features. Recent studies have demonstrated the incremental prognostic value of adverse plaque features over luminal stenosis. Comprehensive coronary plaque assessment holds potential to significantly improve individual risk assessment incorporating adverse plaque characteristics, the extent and severity of atherosclerotic plaque burden. As a result, several coronary CTA based composite risk scores have been proposed recently to determine patients at high risk for adverse events. Coronary CTA became a promising modality for the evaluation of functional significance of coronary lesions using CT derived fractional flow reserve (FFR-CT) and/or rest/dynamic myocardial CT perfusion. This could lead to substantial reduction in unnecessary invasive catheterization procedures and provide information on ischemic burden of CAD. Discordance between the degree of stenosis and ischemia has been recognized in clinical landmark trials using invasive FFR. Both lesion stenosis and composition are possibly related to myocardial ischemia. The evaluation of lesion-specific ischemia using combined functional and morphological plaque information could ultimately improve the diagnostic performance of CTA and thus patient care. In this review we aimed to summarize current evidence on comprehensive coronary artery plaque assessment using coronary CTA. PMID:29255692

Tissue segmentation of computed tomography images using a Random Forest algorithm: a feasibility study

NASA Astrophysics Data System (ADS)

Polan, Daniel F.; Brady, Samuel L.; Kaufman, Robert A.

2016-09-01

There is a need for robust, fully automated whole body organ segmentation for diagnostic CT. This study investigates and optimizes a Random Forest algorithm for automated organ segmentation; explores the limitations of a Random Forest algorithm applied to the CT environment; and demonstrates segmentation accuracy in a feasibility study of pediatric and adult patients. To the best of our knowledge, this is the first study to investigate a trainable Weka segmentation (TWS) implementation using Random Forest machine-learning as a means to develop a fully automated tissue segmentation tool developed specifically for pediatric and adult examinations in a diagnostic CT environment. Current innovation in computed tomography (CT) is focused on radiomics, patient-specific radiation dose calculation, and image quality improvement using iterative reconstruction, all of which require specific knowledge of tissue and organ systems within a CT image. The purpose of this study was to develop a fully automated Random Forest classifier algorithm for segmentation of neck-chest-abdomen-pelvis CT examinations based on pediatric and adult CT protocols. Seven materials were classified: background, lung/internal air or gas, fat, muscle, solid organ parenchyma, blood/contrast enhanced fluid, and bone tissue using Matlab and the TWS plugin of FIJI. The following classifier feature filters of TWS were investigated: minimum, maximum, mean, and variance evaluated over a voxel radius of 2 n , (n from 0 to 4), along with noise reduction and edge preserving filters: Gaussian, bilateral, Kuwahara, and anisotropic diffusion. The Random Forest algorithm used 200 trees with 2 features randomly selected per node. The optimized auto-segmentation algorithm resulted in 16 image features including features derived from maximum, mean, variance Gaussian and Kuwahara filters. Dice similarity coefficient (DSC) calculations between manually segmented and Random Forest algorithm segmented images from 21 patient image sections, were analyzed. The automated algorithm produced segmentation of seven material classes with a median DSC of 0.86  ±  0.03 for pediatric patient protocols, and 0.85  ±  0.04 for adult patient protocols. Additionally, 100 randomly selected patient examinations were segmented and analyzed, and a mean sensitivity of 0.91 (range: 0.82-0.98), specificity of 0.89 (range: 0.70-0.98), and accuracy of 0.90 (range: 0.76-0.98) were demonstrated. In this study, we demonstrate that this fully automated segmentation tool was able to produce fast and accurate segmentation of the neck and trunk of the body over a wide range of patient habitus and scan parameters.

Robustness of Radiomic Features in [11C]Choline and [18F]FDG PET/CT Imaging of Nasopharyngeal Carcinoma: Impact of Segmentation and Discretization.

PubMed

Lu, Lijun; Lv, Wenbing; Jiang, Jun; Ma, Jianhua; Feng, Qianjin; Rahmim, Arman; Chen, Wufan

2016-12-01

Radiomic features are increasingly utilized to evaluate tumor heterogeneity in PET imaging and to enable enhanced prediction of therapy response and outcome. An important ingredient to success in translation of radiomic features to clinical reality is to quantify and ascertain their robustness. In the present work, we studied the impact of segmentation and discretization on 88 radiomic features in 2-deoxy-2-[ 18 F]fluoro-D-glucose ([ 18 F]FDG) and [ 11 C]methyl-choline ([ 11 C]choline) positron emission tomography/X-ray computed tomography (PET/CT) imaging of nasopharyngeal carcinoma. Forty patients underwent [ 18 F]FDG PET/CT scans. Of these, nine patients were imaged on a different day utilizing [ 11 C]choline PET/CT. Tumors were delineated using reference manual segmentation by the consensus of three expert physicians, using 41, 50, and 70 % maximum standardized uptake value (SUV max ) threshold with background correction, Nestle's method, and watershed and region growing methods, and then discretized with fixed bin size (0.05, 0.1, 0.2, 0.5, and 1) in units of SUV. A total of 88 features, including 21 first-order intensity features, 10 shape features, and 57 second- and higher-order textural features, were extracted from the tumors. The robustness of the features was evaluated via the intraclass correlation coefficient (ICC) for seven kinds of segmentation methods (involving all 88 features) and five kinds of discretization bin size (involving the 57 second- and higher-order features). Forty-four (50 %) and 55 (63 %) features depicted ICC ≥0.8 with respect to segmentation as obtained from [ 18 F]FDG and [ 11 C]choline, respectively. Thirteen (23 %) and 12 (21 %) features showed ICC ≥0.8 with respect to discretization as obtained from [ 18 F]FDG and [ 11 C]choline, respectively. Six features were obtained from both [ 18 F]FDG and [ 11 C]choline having ICC ≥0.8 for both segmentation and discretization, five of which were gray-level co-occurrence matrix (GLCM) features (SumEntropy, Entropy, DifEntropy, Homogeneity1, and Homogeneity2) and one of which was an neighborhood gray-tone different matrix (NGTDM) feature (Coarseness). Discretization generated larger effects on features than segmentation in both tracers. Features extracted from [ 11 C]choline were more robust than [ 18 F]FDG for segmentation. Discretization had very similar effects on features extracted from both tracers.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Galavis, P; Friedman, K; Chandarana, H

Purpose: Radiomics involves the extraction of texture features from different imaging modalities with the purpose of developing models to predict patient treatment outcomes. The purpose of this study is to investigate texture feature reproducibility across [18F]FDG PET/CT and [18F]FDG PET/MR imaging in patients with primary malignancies. Methods: Twenty five prospective patients with solid tumors underwent clinical [18F]FDG PET/CT scan followed by [18F]FDG PET/MR scans. In all patients the lesions were identified using nuclear medicine reports. The images were co-registered and segmented using an in-house auto-segmentation method. Fifty features, based on the intensity histogram, second and high order matrices, were extractedmore » from the segmented regions from both image data sets. One-way random-effects ANOVA model of the intra-class correlation coefficient (ICC) was used to establish texture feature correlations between both data sets. Results: Fifty features were classified based on their ICC values, which were found in the range from 0.1 to 0.86, in three categories: high, intermediate, and low. Ten features extracted from second and high-order matrices showed large ICC ≥ 0.70. Seventeen features presented intermediate 0.5 ≤ ICC ≤ 0.65 and the remaining twenty three presented low ICC ≤ 0.45. Conclusion: Features with large ICC values could be reliable candidates for quantification as they lead to similar results from both imaging modalities. Features with small ICC indicates a lack of correlation. Therefore, the use of these features as a quantitative measure will lead to different assessments of the same lesion depending on the imaging modality from where they are extracted. This study shows the importance of the need for further investigation and standardization of features across multiple imaging modalities.« less

Computed tomography in children with community-acquired pneumonia.

PubMed

Andronikou, Savvas; Goussard, Pierre; Sorantin, Erich

2017-10-01

Diagnostic imaging plays a significant role in both the diagnosis and treatment of complications of pneumonia in children and chest radiography is the imaging modality of choice. Computed tomography (CT) on the other hand, is not currently a first-line imaging tool for children with suspected uncomplicated community-acquired pneumonia and is largely reserved for when complications of pneumonia are suspected or there is difficulty in differentiating pneumonia from other pathology. This review outlines the situations where CT needs to be considered in children with pneumonia, describes the imaging features of the parenchymal and pleural complications of pneumonia, discusses how CT may have a wider role in developing countries where human immunodeficiency virus (HIV) and tuberculosis are prevalent, makes note of the role of CT scanning for identifying missed foreign body aspiration and, lastly, addresses radiation concerns.

Recent advances in 3D computed tomography techniques for simulation and navigation in hepatobiliary pancreatic surgery.

PubMed

Uchida, Masafumi

2014-04-01

A few years ago it could take several hours to complete a 3D image using a 3D workstation. Thanks to advances in computer science, obtaining results of interest now requires only a few minutes. Many recent 3D workstations or multimedia computers are equipped with onboard 3D virtual patient modeling software, which enables patient-specific preoperative assessment and virtual planning, navigation, and tool positioning. Although medical 3D imaging can now be conducted using various modalities, including computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and ultrasonography (US) among others, the highest quality images are obtained using CT data, and CT images are now the most commonly used source of data for 3D simulation and navigation image. If the 2D source image is bad, no amount of 3D image manipulation in software will provide a quality 3D image. In this exhibition, the recent advances in CT imaging technique and 3D visualization of the hepatobiliary and pancreatic abnormalities are featured, including scan and image reconstruction technique, contrast-enhanced techniques, new application of advanced CT scan techniques, and new virtual reality simulation and navigation imaging. © 2014 Japanese Society of Hepato-Biliary-Pancreatic Surgery.

Accurate registration of temporal CT images for pulmonary nodules detection

NASA Astrophysics Data System (ADS)

Yan, Jichao; Jiang, Luan; Li, Qiang

2017-02-01

Interpretation of temporal CT images could help the radiologists to detect some subtle interval changes in the sequential examinations. The purpose of this study was to develop a fully automated scheme for accurate registration of temporal CT images for pulmonary nodule detection. Our method consisted of three major registration steps. Firstly, affine transformation was applied in the segmented lung region to obtain global coarse registration images. Secondly, B-splines based free-form deformation (FFD) was used to refine the coarse registration images. Thirdly, Demons algorithm was performed to align the feature points extracted from the registered images in the second step and the reference images. Our database consisted of 91 temporal CT cases obtained from Beijing 301 Hospital and Shanghai Changzheng Hospital. The preliminary results showed that approximately 96.7% cases could obtain accurate registration based on subjective observation. The subtraction images of the reference images and the rigid and non-rigid registered images could effectively remove the normal structures (i.e. blood vessels) and retain the abnormalities (i.e. pulmonary nodules). This would be useful for the screening of lung cancer in our future study.

Differential diagnosis of CT focal liver lesions using texture features, feature selection and ensemble driven classifiers.

PubMed

Mougiakakou, Stavroula G; Valavanis, Ioannis K; Nikita, Alexandra; Nikita, Konstantina S

2007-09-01

The aim of the present study is to define an optimally performing computer-aided diagnosis (CAD) architecture for the classification of liver tissue from non-enhanced computed tomography (CT) images into normal liver (C1), hepatic cyst (C2), hemangioma (C3), and hepatocellular carcinoma (C4). To this end, various CAD architectures, based on texture features and ensembles of classifiers (ECs), are comparatively assessed. Number of regions of interests (ROIs) corresponding to C1-C4 have been defined by experienced radiologists in non-enhanced liver CT images. For each ROI, five distinct sets of texture features were extracted using first order statistics, spatial gray level dependence matrix, gray level difference method, Laws' texture energy measures, and fractal dimension measurements. Two different ECs were constructed and compared. The first one consists of five multilayer perceptron neural networks (NNs), each using as input one of the computed texture feature sets or its reduced version after genetic algorithm-based feature selection. The second EC comprised five different primary classifiers, namely one multilayer perceptron NN, one probabilistic NN, and three k-nearest neighbor classifiers, each fed with the combination of the five texture feature sets or their reduced versions. The final decision of each EC was extracted by using appropriate voting schemes, while bootstrap re-sampling was utilized in order to estimate the generalization ability of the CAD architectures based on the available relatively small-sized data set. The best mean classification accuracy (84.96%) is achieved by the second EC using a fused feature set, and the weighted voting scheme. The fused feature set was obtained after appropriate feature selection applied to specific subsets of the original feature set. The comparative assessment of the various CAD architectures shows that combining three types of classifiers with a voting scheme, fed with identical feature sets obtained after appropriate feature selection and fusion, may result in an accurate system able to assist differential diagnosis of focal liver lesions from non-enhanced CT images.

Image-based metal artifact reduction in x-ray computed tomography utilizing local anatomical similarity

NASA Astrophysics Data System (ADS)

Dong, Xue; Yang, Xiaofeng; Rosenfield, Jonathan; Elder, Eric; Dhabaan, Anees

2017-03-01

X-ray computed tomography (CT) is widely used in radiation therapy treatment planning in recent years. However, metal implants such as dental fillings and hip prostheses can cause severe bright and dark streaking artifacts in reconstructed CT images. These artifacts decrease image contrast and degrade HU accuracy, leading to inaccuracies in target delineation and dose calculation. In this work, a metal artifact reduction method is proposed based on the intrinsic anatomical similarity between neighboring CT slices. Neighboring CT slices from the same patient exhibit similar anatomical features. Exploiting this anatomical similarity, a gamma map is calculated as a weighted summation of relative HU error and distance error for each pixel in an artifact-corrupted CT image relative to a neighboring, artifactfree image. The minimum value in the gamma map for each pixel is used to identify an appropriate pixel from the artifact-free CT slice to replace the corresponding artifact-corrupted pixel. With the proposed method, the mean CT HU error was reduced from 360 HU and 460 HU to 24 HU and 34 HU on head and pelvis CT images, respectively. Dose calculation accuracy also improved, as the dose difference was reduced from greater than 20% to less than 4%. Using 3%/3mm criteria, the gamma analysis failure rate was reduced from 23.25% to 0.02%. An image-based metal artifact reduction method is proposed that replaces corrupted image pixels with pixels from neighboring CT slices free of metal artifacts. This method is shown to be capable of suppressing streaking artifacts, thereby improving HU and dose calculation accuracy.

Use of a Diagnostic Score to Prioritize Computed Tomographic (CT) Imaging for Patients Suspected of Ischemic Stroke Who May Benefit from Thrombolytic Therapy

PubMed Central

Bots, Michiel L.; Selvarajah, Sharmini; Kappelle, L. Jaap; Abdul Aziz, Zariah; Sidek, Norsima Nazifah; Vaartjes, Ilonca

2016-01-01

Background A shortage of computed tomographic (CT) machines in low and middle income countries often results in delayed CT imaging for patients suspected of a stroke. Yet, time constraint is one of the most important aspects for patients with an ischemic stroke to benefit from thrombolytic therapy. We set out to assess whether application of the Siriraj Stroke Score is able to assist physicians in prioritizing patients with a high probability of having an ischemic stroke for urgent CT imaging. Methods From the Malaysian National Neurology Registry, we selected patients aged 18 years and over with clinical features suggesting of a stroke, who arrived in the hospital 4.5 hours or less from ictus. The prioritization of receiving CT imaging was left to the discretion of the treating physician. We applied the Siriraj Stroke Score to all patients, refitted the score and defined a cut-off value to best distinguish an ischemic stroke from a hemorrhagic stroke. Results Of the 2176 patients included, 73% had an ischemic stroke. Only 33% of the ischemic stroke patients had CT imaging within 4.5 hours. The median door-to-scan time for these patients was 4 hours (IQR: 1;16). With the recalibrated score, it would have been possible to prioritize 95% (95% CI: 94%–96%) of patients with an ischemic stroke for urgent CT imaging. Conclusions In settings where CT imaging capacity is limited, we propose the use of the Siriraj Stroke Score to prioritize patients with a probable ischemic stroke for urgent CT imaging. PMID:27768752

Time-Adjusted Internal Target Volume: A Novel Approach Focusing on Heterogeneity of Tumor Motion Based on 4-Dimensional Computed Tomography Imaging for Radiation Therapy Planning of Lung Cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nishibuchi, Ikuno; Department of Radiation Oncology, Hiroshima Prefectural Hospital, Hiroshima; Kimura, Tomoki, E-mail: tkkimura@hiroshima-u.ac.jp

2014-08-01

Purpose: To consider nonuniform tumor motion within the internal target volume (ITV) by defining time-adjusted ITV (TTV), a volume designed to include heterogeneity of tumor existence on the basis of 4-dimensional computed tomography (4D-CT). Methods and Materials: We evaluated 30 lung cancer patients. Breath-hold CT (BH-CT) and free-breathing 4D-CT scans were acquired for each patient. The tumors were manually delineated using a lung CT window setting (window, 1600 HU; level, −300 HU). Tumor in BH-CT images was defined as gross tumor volume (GTV), and the sum of tumors in 4D-CT images was defined as ITV-4D. The TTV images were generatedmore » from the 4D-CT datasets, and the tumor existence probability within ITV-4D was calculated. We calculated the TTV{sub 80} value, which is the percentage of the volume with a tumor existence probability that exceeded 80% on ITV-4D. Several factors that affected the TTV{sub 80} value, such as the ITV-4D/GTV ratio or tumor centroid deviation, were evaluated. Results: Time-adjusted ITV images were acquired for all patients, and tumor respiratory motion heterogeneity was visualized. The median (range) ITV-4D/GTV ratio and median tumor centroid deviation were 1.6 (1.0-4.1) and 6.3 mm (0.1-30.3 mm), respectively. The median TTV{sub 80} value was 43.3% (2.9-98.7%). Strong correlations were observed between the TTV{sub 80} value and the ITV-4D/GTV ratio (R=−0.71) and tumor centroid deviation (R=−0.72). The TTV images revealed the tumor motion pattern features within ITV. Conclusions: The TTV images reflected nonuniform tumor motion, and they revealed the tumor motion pattern features, suggesting that the TTV concept may facilitate various aspects of radiation therapy planning of lung cancer while incorporating respiratory motion in the future.« less

Pediatric chest imaging.

PubMed

Gross, G W

1992-10-01

The highlight of recent articles published on pediatric chest imaging is the potential advantage of digital imaging of the infant's chest. Digital chest imaging allows accurate determination of functional residual capacity as well as manipulation of the image to highlight specific anatomic features. Reusable photostimulable phosphor imaging systems provide wide imaging latitude and lower patient dose. In addition, digital radiology permits multiple remote-site viewing on monitor displays. Several excellent reviews of the imaging features of various thoracic abnormalities and the application of newer imaging modalities, such as ultrafast CT and MR imaging to the pediatric chest, are additional highlights.

CT and MR imaging features in phosphaturic mesenchymal tumor-mixed connective tissue: A case report

PubMed Central

Shi, Zhenshan; Deng, Yiqiong; Li, Xiumei; Li, Yueming; Cao, Dairong; Coossa, Vikash Sahadeo

2018-01-01

Phosphaturic mesenchymal tumor-mixed connective tissue (PMT-MCT) is rare and usually benign and slow-growing. The majority of these tumors is associated with sporadic tumor-induced osteomalacia (TIO) or rickets, affect middle-aged individuals and are located in the extremities. Previous imaging studies often focused on seeking the causative tumors of TIO, not on the radiological features of these tumors, especially magnetic resonance imaging (MRI) features. PMT-MCT remains a largely misdiagnosed, ignored or unknown entity by most radiologists and clinicians. In the present case report, a review of the known literature of PMT-MCT was conducted and the CT and MRI findings from three patient cases were described for diagnosing the small subcutaneous tumor. Typical MRI appearances of PMT-MCT were isointense relative to the muscles on T1-weighted imaging, and markedly hyperintense on T2-weighted imaging containing variably flow voids, with markedly heterogeneous/homogenous enhancement on post contrast T1-weighted fat-suppression imaging. Short time inversion recovery was demonstrated to be the optimal sequence in localizing the tumor. PMID:29552133

CT and MR imaging features in phosphaturic mesenchymal tumor-mixed connective tissue: A case report.

PubMed

Shi, Zhenshan; Deng, Yiqiong; Li, Xiumei; Li, Yueming; Cao, Dairong; Coossa, Vikash Sahadeo

2018-04-01

Phosphaturic mesenchymal tumor-mixed connective tissue (PMT-MCT) is rare and usually benign and slow-growing. The majority of these tumors is associated with sporadic tumor-induced osteomalacia (TIO) or rickets, affect middle-aged individuals and are located in the extremities. Previous imaging studies often focused on seeking the causative tumors of TIO, not on the radiological features of these tumors, especially magnetic resonance imaging (MRI) features. PMT-MCT remains a largely misdiagnosed, ignored or unknown entity by most radiologists and clinicians. In the present case report, a review of the known literature of PMT-MCT was conducted and the CT and MRI findings from three patient cases were described for diagnosing the small subcutaneous tumor. Typical MRI appearances of PMT-MCT were isointense relative to the muscles on T1-weighted imaging, and markedly hyperintense on T2-weighted imaging containing variably flow voids, with markedly heterogeneous/homogenous enhancement on post contrast T1-weighted fat-suppression imaging. Short time inversion recovery was demonstrated to be the optimal sequence in localizing the tumor.

4D-CT motion estimation using deformable image registration and 5D respiratory motion modeling.

PubMed

Yang, Deshan; Lu, Wei; Low, Daniel A; Deasy, Joseph O; Hope, Andrew J; El Naqa, Issam

2008-10-01

Four-dimensional computed tomography (4D-CT) imaging technology has been developed for radiation therapy to provide tumor and organ images at the different breathing phases. In this work, a procedure is proposed for estimating and modeling the respiratory motion field from acquired 4D-CT imaging data and predicting tissue motion at the different breathing phases. The 4D-CT image data consist of series of multislice CT volume segments acquired in ciné mode. A modified optical flow deformable image registration algorithm is used to compute the image motion from the CT segments to a common full volume 3D-CT reference. This reference volume is reconstructed using the acquired 4D-CT data at the end-of-exhalation phase. The segments are optimally aligned to the reference volume according to a proposed a priori alignment procedure. The registration is applied using a multigrid approach and a feature-preserving image downsampling maxfilter to achieve better computational speed and higher registration accuracy. The registration accuracy is about 1.1 +/- 0.8 mm for the lung region according to our verification using manually selected landmarks and artificially deformed CT volumes. The estimated motion fields are fitted to two 5D (spatial 3D+tidal volume+airflow rate) motion models: forward model and inverse model. The forward model predicts tissue movements and the inverse model predicts CT density changes as a function of tidal volume and airflow rate. A leave-one-out procedure is used to validate these motion models. The estimated modeling prediction errors are about 0.3 mm for the forward model and 0.4 mm for the inverse model.

Automatic lung tumor segmentation on PET/CT images using fuzzy Markov random field model.

PubMed

Guo, Yu; Feng, Yuanming; Sun, Jian; Zhang, Ning; Lin, Wang; Sa, Yu; Wang, Ping

2014-01-01

The combination of positron emission tomography (PET) and CT images provides complementary functional and anatomical information of human tissues and it has been used for better tumor volume definition of lung cancer. This paper proposed a robust method for automatic lung tumor segmentation on PET/CT images. The new method is based on fuzzy Markov random field (MRF) model. The combination of PET and CT image information is achieved by using a proper joint posterior probability distribution of observed features in the fuzzy MRF model which performs better than the commonly used Gaussian joint distribution. In this study, the PET and CT simulation images of 7 non-small cell lung cancer (NSCLC) patients were used to evaluate the proposed method. Tumor segmentations with the proposed method and manual method by an experienced radiation oncologist on the fused images were performed, respectively. Segmentation results obtained with the two methods were similar and Dice's similarity coefficient (DSC) was 0.85 ± 0.013. It has been shown that effective and automatic segmentations can be achieved with this method for lung tumors which locate near other organs with similar intensities in PET and CT images, such as when the tumors extend into chest wall or mediastinum.

3D Riesz-wavelet based Covariance descriptors for texture classification of lung nodule tissue in CT.

PubMed

Cirujeda, Pol; Muller, Henning; Rubin, Daniel; Aguilera, Todd A; Loo, Billy W; Diehn, Maximilian; Binefa, Xavier; Depeursinge, Adrien

2015-01-01

In this paper we present a novel technique for characterizing and classifying 3D textured volumes belonging to different lung tissue types in 3D CT images. We build a volume-based 3D descriptor, robust to changes of size, rigid spatial transformations and texture variability, thanks to the integration of Riesz-wavelet features within a Covariance-based descriptor formulation. 3D Riesz features characterize the morphology of tissue density due to their response to changes in intensity in CT images. These features are encoded in a Covariance-based descriptor formulation: this provides a compact and flexible representation thanks to the use of feature variations rather than dense features themselves and adds robustness to spatial changes. Furthermore, the particular symmetric definite positive matrix form of these descriptors causes them to lay in a Riemannian manifold. Thus, descriptors can be compared with analytical measures, and accurate techniques from machine learning and clustering can be adapted to their spatial domain. Additionally we present a classification model following a "Bag of Covariance Descriptors" paradigm in order to distinguish three different nodule tissue types in CT: solid, ground-glass opacity, and healthy lung. The method is evaluated on top of an acquired dataset of 95 patients with manually delineated ground truth by radiation oncology specialists in 3D, and quantitative sensitivity and specificity values are presented.

CT, MRI and PET imaging in peritoneal malignancy

PubMed Central

Sahdev, Anju; Reznek, Rodney H.

2011-01-01

Abstract Imaging plays a vital role in the evaluation of patients with suspected or proven peritoneal malignancy. Nevertheless, despite significant advances in imaging technology and protocols, assessment of peritoneal pathology remains challenging. The combination of complex peritoneal anatomy, an extensive surface area that may host tumour deposits and the considerable overlap of imaging appearances of various peritoneal diseases often makes interpretation difficult. Contrast-enhanced multidetector computed tomography (MDCT) remains the most versatile tool in the imaging of peritoneal malignancy. However, conventional and emerging magnetic resonance imaging (MRI) and positron emission tomography (PET)/CT techniques offer significant advantages over MDCT in detection and surveillance. This article reviews established and new techniques in CT, MRI and PET imaging in both primary and secondary peritoneal malignancies and provides an overview of peritoneal anatomy, function and modes of disease dissemination with illustration of common sites and imaging features of peritoneal malignancy. PMID:21865109

SU-F-R-21: The Stability of Radiomics Features On 4D FDG-PET/CT Images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ma, C

2016-06-15

Purpose: The aim of our study was to perform a stability analysis of 4D PET-derived features in non-small cell lung carcinoma (NSCLC) based on six different respiratory phases. Methods: The 4D FDG-PET/CT respiratory phases were labeled as T0%, T17%, T33%,T50%, T67%, T83% phases, with the T0% phase approximately corresponding to the normal end-inspiration. Lesions were manually delineated based on fused PET-CT, using a standardized clinical delineation protocol. Six texture parameters were analyzed. Results: Results showed that the majority of assessed features had a low stability such as Homogeneity (0.385–0.416), Dissimilarity (3.707–3.861), Angular two moments (0.013–0.019), Contrast (39.782–49.562), Entropy(4.683–5.002) and Inversemore » differential moment (0.317–0.362) on different respiratory phases. Conclusion: This study suggest that further research of quantitative PET imaging features is warranted with respect to respiratory motion.« less

Generation of brain pseudo-CTs using an undersampled, single-acquisition UTE-mDixon pulse sequence and unsupervised clustering

DOE Office of Scientific and Technical Information (OSTI.GOV)

Su, Kuan-Hao; Hu, Lingzhi; Traughber, Melanie

Purpose: MR-based pseudo-CT has an important role in MR-based radiation therapy planning and PET attenuation correction. The purpose of this study is to establish a clinically feasible approach, including image acquisition, correction, and CT formation, for pseudo-CT generation of the brain using a single-acquisition, undersampled ultrashort echo time (UTE)-mDixon pulse sequence. Methods: Nine patients were recruited for this study. For each patient, a 190-s, undersampled, single acquisition UTE-mDixon sequence of the brain was acquired (TE = 0.1, 1.5, and 2.8 ms). A novel method of retrospective trajectory correction of the free induction decay (FID) signal was performed based on point-spreadmore » functions of three external MR markers. Two-point Dixon images were reconstructed using the first and second echo data (TE = 1.5 and 2.8 ms). R2{sup ∗} images (1/T2{sup ∗}) were then estimated and were used to provide bone information. Three image features, i.e., Dixon-fat, Dixon-water, and R2{sup ∗}, were used for unsupervised clustering. Five tissue clusters, i.e., air, brain, fat, fluid, and bone, were estimated using the fuzzy c-means (FCM) algorithm. A two-step, automatic tissue-assignment approach was proposed and designed according to the prior information of the given feature space. Pseudo-CTs were generated by a voxelwise linear combination of the membership functions of the FCM. A low-dose CT was acquired for each patient and was used as the gold standard for comparison. Results: The contrast and sharpness of the FID images were improved after trajectory correction was applied. The mean of the estimated trajectory delay was 0.774 μs (max: 1.350 μs; min: 0.180 μs). The FCM-estimated centroids of different tissue types showed a distinguishable pattern for different tissues, and significant differences were found between the centroid locations of different tissue types. Pseudo-CT can provide additional skull detail and has low bias and absolute error of estimated CT numbers of voxels (−22 ± 29 HU and 130 ± 16 HU) when compared to low-dose CT. Conclusions: The MR features generated by the proposed acquisition, correction, and processing methods may provide representative clustering information and could thus be used for clinical pseudo-CT generation.« less

Application of volume rendering technique (VRT) for musculoskeletal imaging.

PubMed

Darecki, Rafał

2002-10-30

A review of the applications of volume rendering technique in musculoskeletal three-dimensional imaging from CT data. General features, potential and indications for applying the method are presented.

Classification of micro-CT images using 3D characterization of bone canal patterns in human osteogenesis imperfecta

NASA Astrophysics Data System (ADS)

Abidin, Anas Z.; Jameson, John; Molthen, Robert; Wismüller, Axel

2017-03-01

Few studies have analyzed the microstructural properties of bone in cases of Osteogenenis Imperfecta (OI), or `brittle bone disease'. Current approaches mainly focus on bone mineral density measurements as an indirect indicator of bone strength and quality. It has been shown that bone strength would depend not only on composition but also structural organization. This study aims to characterize 3D structure of the cortical bone in high-resolution micro CT images. A total of 40 bone fragments from 28 subjects (13 with OI and 15 healthy controls) were imaged using micro tomography using a synchrotron light source (SRµCT). Minkowski functionals - volume, surface, curvature, and Euler characteristics - describing the topological organization of the bone were computed from the images. The features were used in a machine learning task to classify between healthy and OI bone. The best classification performance (mean AUC - 0.96) was achieved with a combined 4-dimensional feature of all Minkowski functionals. Individually, the best feature performance was seen using curvature (mean AUC - 0.85), which characterizes the edges within a binary object. These results show that quantitative analysis of cortical bone microstructure, in a computer-aided diagnostics framework, can be used to distinguish between healthy and OI bone with high accuracy.

Investigation of Carbon Fiber Architecture in Braided Composites Using X-Ray CT Inspection

NASA Technical Reports Server (NTRS)

Rhoads, Daniel J.; Miller, Sandi G.; Roberts, Gary D.; Rauser, Richard W.; Golovaty, Dmitry; Wilber, J. Patrick; Espanol, Malena I.

2017-01-01

During the fabrication of braided carbon fiber composite materials, process variations occur which affect the fiber architecture. Quantitative measurements of local and global fiber architecture variations are needed to determine the potential effect of process variations on mechanical properties of the cured composite. Although non-destructive inspection via X-ray CT imaging is a promising approach, difficulties in quantitative analysis of the data arise due to the similar densities of the material constituents. In an effort to gain more quantitative information about features related to fiber architecture, methods have been explored to improve the details that can be captured by X-ray CT imaging. Metal-coated fibers and thin veils are used as inserts to extract detailed information about fiber orientations and inter-ply behavior from X-ray CT images.

Acute gastrointestinal vaso-occlusive ischemia in sickle cell disease: CT imaging features and clinical outcome.

PubMed

Gardner, Carly S; Jaffe, Tracy A

2016-03-01

The purpose of this study was to determine the incidence, specific imaging features, and outcome of gastrointestinal vaso-occlusive ischemia (GVOI) in sickle cell patients undergoing CT for acute abdominal pain. This HIPAA-compliant, IRB-approved retrospective study evaluated sickle cell patients with an abdominal pain crisis and acute gastrointestinal abnormalities on CT from 1/2006 to 1/2014. CT findings were divided into those compatible and incompatible with bowel ischemia or clinical diagnosis of GVOI. Two abdominal radiologists (1, 13 years' experience) reviewed the CTs for specific imaging features of ischemia. Clinical laboratory values (lactate, WBC) and outcome were recorded. Descriptive statistics and Wilcoxon-Mann-Whitney two-sample rank-sum test were performed. Of 217 CTs, 33 had acute gastrointestinal abnormalities: 75% (25/33) consistent with ischemia and clinical GVOI. Complications of ischemia occurred in 16% (4/25): ileus (50%), perforation (25%), and pneumatosis (25%). In uncomplicated cases, all had bowel wall thickening: segmental 52% (11/21) or diffuse 48% (10/21). The colon was commonly involved (76%, 16/21), particularly the ascending (57%, 12/21). Most abnormalities (52%, 11/21) were in the superior mesenteric artery distribution. Average lactate (4.3 ± 4.0 mmol/L, p = 0.02) and WBC count (20.1 ± 10.4, ×1000 cells/μL, p = 0.01) were significantly higher in GVOI. Overall mortality in patients with GVOI was 17% (3/18). GVOI is an important feature of the acute abdominal crisis in patients with sickle cell disease and can be seen in up to 75% of patients with abnormal bowel findings on CT. The diagnosis should be strongly considered in sickle cell patients with CT findings of diffuse or segmental bowel wall thickening, particularly involving the colon.

CT, MRI, and 18F-FDG PET/CT findings of malignant peripheral nerve sheath tumor of the head and neck.

PubMed

Kim, Ha Youn; Hwang, Ji Young; Kim, Hyung-Jin; Kim, Yi Kyung; Cha, Jihoon; Park, Gyeong Min; Kim, Sung Tae

2017-10-01

Background Malignant peripheral nerve sheath tumor (MPNST) is a highly malignant tumor and rarely occurs in the head and neck. Purpose To describe the imaging features of MPNST of the head and neck. Material and Methods We retrospectively analyzed computed tomography (CT; n = 14), magnetic resonance imaging (MRI; n = 16), and 18 F-FDG PET/CT (n = 5) imaging features of 18 MPNSTs of the head and neck in 17 patients. Special attention was paid to determine the nerve of origin from which the tumor might have arisen. Results All lesions were well-defined (n = 3) or ill-defined (n = 15) masses (mean, 6.1 cm). Lesions were at various locations but most commonly the neck (n = 8), followed by the intracranial cavity (n = 3), paranasal sinus (n = 2), and orbit (n = 2). The nerve of origin was inferred for 11 lesions: seven in the neck, two in the orbit, one in the cerebellopontine angle, and one on the parietal scalp. Attenuation, signal intensity, and enhancement pattern of the lesions on CT and MRI were non-specific. Necrosis/hemorrhage/cystic change within the lesion was considered to be present on images in 13 and bone change in nine. On 18 F-FDG PET/CT images, all five lesions demonstrated various hypermetabolic foci with maximum standard uptake value (SUV max ) from 3.2 to 14.6 (mean, 7.16 ± 4.57). Conclusion MPNSTs can arise from various locations in the head and neck. Though non-specific, a mass with an ill-defined margin along the presumed course of the cranial nerves may aid the diagnosis of MPSNT in the head and neck.

Uric acid versus non-uric acid urinary stones: differentiation with single energy CT texture analysis.

PubMed

Zhang, G-M-Y; Sun, H; Shi, B; Xu, M; Xue, H-D; Jin, Z-Y

2018-05-21

To evaluate the accuracy of computed tomography (CT) texture analysis (TA) to differentiate uric acid (UA) stones from non-UA stones on unenhanced CT in patients with urinary calculi with ex vivo Fourier transform infrared spectroscopy (FTIR) as the reference standard. Fourteen patients with 18 UA stones and 31 patients with 32 non-UA stones were included. All the patients had preoperative CT evaluation and subsequent surgical removal of the stones. CTTA was performed on CT images using commercially available research software. Each texture feature was evaluated using the non-parametric Mann-Whitney test. Receiver operating characteristic (ROC) curves were created and the area under the ROC curve (AUC) was calculated for texture parameters that were significantly different. The features were used to train support vector machine (SVM) classifiers. Diagnostic accuracy was evaluated. Compared to non-UA stones, UA stones had significantly lower mean, standard deviation and mean of positive pixels but higher kurtosis (p<0.001) on both unfiltered and filtered texture scales. There were no significant differences in entropy or skewness between UA and non-UA stones. The average SVM accuracy of texture features for differentiating UA from non-UA stones ranged from 88% to 92% (after 10-fold cross validation). A model incorporating standard deviation, skewness, and kurtosis from unfiltered texture scale images resulted in an AUC of 0.965±00.029 with a sensitivity of 94.4% and specificity of 93.7%. CTTA can be used to accurately differentiate UA stones from non-UA stones in vivo using unenhanced CT images. Copyright © 2018 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Accuracy of computed tomographic features in differentiating intestinal tuberculosis from Crohn's disease: a systematic review with meta-analysis.

PubMed

Kedia, Saurabh; Sharma, Raju; Sreenivas, Vishnubhatla; Madhusudhan, Kumble Seetharama; Sharma, Vishal; Bopanna, Sawan; Pratap Mouli, Venigalla; Dhingra, Rajan; Yadav, Dawesh Prakash; Makharia, Govind; Ahuja, Vineet

2017-04-01

Abdominal computed tomography (CT) can noninvasively image the entire gastrointestinal tract and assess extraintestinal features that are important in differentiating Crohn's disease (CD) and intestinal tuberculosis (ITB). The present meta-analysis pooled the results of all studies on the role of CT abdomen in differentiating between CD and ITB. We searched PubMed and Embase for all publications in English that analyzed the features differentiating between CD and ITB on abdominal CT. The features included comb sign, necrotic lymph nodes, asymmetric bowel wall thickening, skip lesions, fibrofatty proliferation, mural stratification, ileocaecal area, long segment, and left colonic involvements. Sensitivity, specificity, positive and negative likelihood ratios, and diagnostic odds ratio (DOR) were calculated for all the features. Symmetric receiver operating characteristic curve was plotted for features present in >3 studies. Heterogeneity and publication bias was assessed and sensitivity analysis was performed by excluding studies that compared features on conventional abdominal CT instead of CT enterography (CTE). We included 6 studies (4 CTE, 1 conventional abdominal CT, and 1 CTE+conventional abdominal CT) involving 417 and 195 patients with CD and ITB, respectively. Necrotic lymph nodes had the highest diagnostic accuracy (sensitivity, 23%; specificity, 100%; DOR, 30.2) for ITB diagnosis, and comb sign (sensitivity, 82%; specificity, 81%; DOR, 21.5) followed by skip lesions (sensitivity, 86%; specificity, 74%; DOR, 16.5) had the highest diagnostic accuracy for CD diagnosis. On sensitivity analysis, the diagnostic accuracy of other features excluding asymmetric bowel wall thickening remained similar. Necrotic lymph nodes and comb sign on abdominal CT had the best diagnostic accuracy in differentiating CD and ITB.

Automated labeling of log features in CT imagery of multiple hardwood species

Treesearch

Daniel L. Schmoldt; Jing He; A. Lynn Abbott

2000-01-01

Before noninvasive scanning, e.g., computed tomography (CT), becomes feasible in industrial saw-mill operations, we need a procedure that can automatically interpret scan information in order to provide the saw operator with information necessary to make proper sawing decisions. To this end, we have worked to develop an approach for automatic analysis of CT images of...

Radiological Findings in a case of Advance staged Mesothelioma

PubMed Central

Aziz, Fahad

2009-01-01

Chest X Ray is the initial screening test for the mesothelioma like all other the chest diseases. But computed tomography (CT) is the imaging technique of choice for charactering pleural masses. CT also gives important information regarding invasion of the chest wall and surrounding structures. Certain CT features help differentiate benign from malignant processes. This short article highlights the salient CT appearance of mesothelioma; the most common pleural tumor. PMID:22263002

An extended algebraic reconstruction technique (E-ART) for dual spectral CT.

PubMed

Zhao, Yunsong; Zhao, Xing; Zhang, Peng

2015-03-01

Compared with standard computed tomography (CT), dual spectral CT (DSCT) has many advantages for object separation, contrast enhancement, artifact reduction, and material composition assessment. But it is generally difficult to reconstruct images from polychromatic projections acquired by DSCT, because of the nonlinear relation between the polychromatic projections and the images to be reconstructed. This paper first models the DSCT reconstruction problem as a nonlinear system problem; and then extend the classic ART method to solve the nonlinear system. One feature of the proposed method is its flexibility. It fits for any scanning configurations commonly used and does not require consistent rays for different X-ray spectra. Another feature of the proposed method is its high degree of parallelism, which means that the method is suitable for acceleration on GPUs (graphic processing units) or other parallel systems. The method is validated with numerical experiments from simulated noise free and noisy data. High quality images are reconstructed with the proposed method from the polychromatic projections of DSCT. The reconstructed images are still satisfactory even if there are certain errors in the estimated X-ray spectra.

Ultrasound, elastography, and fluorodeoxyglucose positron emission tomography/computed tomography imaging in Riedel's thyroiditis: report of two cases.

PubMed

Slman, Rouba; Monpeyssen, Hervé; Desarnaud, Serge; Haroche, Julien; Fediaevsky, Laurence Du Pasquier; Fabrice, Menegaux; Seret-Begue, Dominique; Amoura, Zahir; Aurengo, André; Leenhardt, Laurence

2011-07-01

Riedel's thyroiditis (RT) is a rare disease characterized by a chronic inflammatory lesion of the thyroid gland with invasion by a dense fibrosis. Publications of the imaging features of RT are scarce. To our knowledge, ultrasound elastography (USE) findings have not been previously reported. Therefore, we describe two patients with RT who were imaged with ultrasonography (US), USE, and fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT). Two women were referred for a large, hard goiter with compressive symptoms (dyspnea and dysphagia); in one patient, the goiter was associated with retroperitoneal fibrosis. In both cases, RT was confirmed by surgical biopsy with pathological examination. Thyroid US imaging was performed with a US scan and a 10-13 MHz linear transducer. The hardness of the tissues was analyzed using transient USE (ShearWave, Aixplorer-SuperSonic Imagine). PET/CT scanning was performed with a Philips Gemini GXL camera (GE Medical Systems). In the first patient, US examination revealed a compressive multinodular goiter with large solid hypoechoic and poorly vascularized areas adjacent to the nodules. The predominant right nodule was hypoechoic with irregular margins. The second patient had a hypoechoic goiter with large bilateral hypoechoic areas. In both cases, an unusual feature was observed: the presence of tissue surrounding the primitive carotid artery, associated with thrombi of the internal jugular vein. Further, USE showed heterogeneity in the stiffness values of the thyroid parenchyma varying between 21 kPa and 281 kPa. FDG-PET/CT imaging showed uptake foci in the thyroid gland. In both cases, US showed a decrease in the thyroid gland volume and the disappearance of encasement of the neck vasculature in response to corticosteroid treatment. In contrast, the FDG-PET/CT features remained unchanged. US features, such as vascular encasement and improvement under corticosteroid treatment, seem to be specific to this rare disease. For the first time, USE documents the hardness of RT tissues. Apart from the FDG-PET/CT findings that merit further investigation, US and USE prove useful tools in the assessment of such a rare disease.

[Analysis of clinical and imaging characteristics of infectious sacroiliac arthritis and review of literatures].

PubMed

Wang, Gang; Wang, Yanyan; Zhu, Jian; Jin, Jingyu; Zhao, Zheng; Zhang, Jianglin; Huang, Feng

2015-05-01

To study the clinical and imaging characteristics of patients with infectious sacroiliac arthritis. Twenty-one patients diagnosed with infectious sacroiliac arthritis were analyzed retrospectively between 2000 and 2014. The chief complaint was pain in hip and lumbosacral area. Their clinical features, laboratory tests and pathological examination results as well as CT/MRI/PET-CT images were evaluated. There were nine males and thirteen females eighteen (85.7%) patients had unilateral sacroiliac joint involvement. Among these patients, three were diagnosed with brucellosis sacroiliac arthritis (BSI), eight patients with tuberculosis sacroiliac arthritis (TSI), and ten patients with non-brucellosis and non-tuberculosis infectious sacroiliac arthritis (ISI). For those patients with non-brucellosis and non-tuberculosis infectious sacroiliac arthritis, white blood cell count, erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) were dramatically increased. Twelve patients were diagnosed pathologically including 6 ISI, 2 BSI and 4 TSI. Twelve patients and seventeen patients were scanned by CT and MRI respectively. Two patients undertook PET-CT examination. Antibiotic therapy showed significant therapeutic effects in all patients. Infectious sacroiliac arthritis patients with hip or lumbosacral pain as the chief complaint can be easily misdiagnosed as spondyloarthritis. Comprehensive analysis of clinical features, imaging and laboratory findings is essential for accurate diagnosis.

Registration of PET and CT images based on multiresolution gradient of mutual information demons algorithm for positioning esophageal cancer patients.

PubMed

Jin, Shuo; Li, Dengwang; Wang, Hongjun; Yin, Yong

2013-01-07

Accurate registration of 18F-FDG PET (positron emission tomography) and CT (computed tomography) images has important clinical significance in radiation oncology. PET and CT images are acquired from (18)F-FDG PET/CT scanner, but the two acquisition processes are separate and take a long time. As a result, there are position errors in global and deformable errors in local caused by respiratory movement or organ peristalsis. The purpose of this work was to implement and validate a deformable CT to PET image registration method in esophageal cancer to eventually facilitate accurate positioning the tumor target on CT, and improve the accuracy of radiation therapy. Global registration was firstly utilized to preprocess position errors between PET and CT images, achieving the purpose of aligning these two images on the whole. Demons algorithm, based on optical flow field, has the features of fast process speed and high accuracy, and the gradient of mutual information-based demons (GMI demons) algorithm adds an additional external force based on the gradient of mutual information (GMI) between two images, which is suitable for multimodality images registration. In this paper, GMI demons algorithm was used to achieve local deformable registration of PET and CT images, which can effectively reduce errors between internal organs. In addition, to speed up the registration process, maintain its robustness, and avoid the local extremum, multiresolution image pyramid structure was used before deformable registration. By quantitatively and qualitatively analyzing cases with esophageal cancer, the registration scheme proposed in this paper can improve registration accuracy and speed, which is helpful for precisely positioning tumor target and developing the radiation treatment planning in clinical radiation therapy application.

Registration of PET and CT images based on multiresolution gradient of mutual information demons algorithm for positioning esophageal cancer patients

PubMed Central

Jin, Shuo; Li, Dengwang; Yin, Yong

2013-01-01

Accurate registration of  18F−FDG PET (positron emission tomography) and CT (computed tomography) images has important clinical significance in radiation oncology. PET and CT images are acquired from  18F−FDG PET/CT scanner, but the two acquisition processes are separate and take a long time. As a result, there are position errors in global and deformable errors in local caused by respiratory movement or organ peristalsis. The purpose of this work was to implement and validate a deformable CT to PET image registration method in esophageal cancer to eventually facilitate accurate positioning the tumor target on CT, and improve the accuracy of radiation therapy. Global registration was firstly utilized to preprocess position errors between PET and CT images, achieving the purpose of aligning these two images on the whole. Demons algorithm, based on optical flow field, has the features of fast process speed and high accuracy, and the gradient of mutual information‐based demons (GMI demons) algorithm adds an additional external force based on the gradient of mutual information (GMI) between two images, which is suitable for multimodality images registration. In this paper, GMI demons algorithm was used to achieve local deformable registration of PET and CT images, which can effectively reduce errors between internal organs. In addition, to speed up the registration process, maintain its robustness, and avoid the local extremum, multiresolution image pyramid structure was used before deformable registration. By quantitatively and qualitatively analyzing cases with esophageal cancer, the registration scheme proposed in this paper can improve registration accuracy and speed, which is helpful for precisely positioning tumor target and developing the radiation treatment planning in clinical radiation therapy application. PACS numbers: 87.57.nj, 87.57.Q‐, 87.57.uk PMID:23318381

[Imaging origins and characteristics analysis of acute and chronic aspiration pneumonia].

PubMed

Wang, Kang; Li, Ming; Wang, Xiongbiao; Qin, Jianmin; Wang, Zhi; Zhao, Zehua; Qin, Le; Hua, Yanqing

2014-11-11

To discuss about the pathologic and imaging origins and characteristics of CT scaning and X-ray radiography for acute and chronic aspiration pneumonia. Imaging data from 30 patients with aspiration pneumonia were retrospectively analyzed, CT scaning was performed in 27 patients, which PMVR reconstruction was performed in 21 cases;3 exammed by X-ray with 2 used by esophagography. Opaque bodies were detected in trachea by CT scaning in 12 patients.7 patients in acute phase rapidly developed into acute respiratory distress syndrome(ARDS). CT signs of 30 patients with acute and chronic aspiration pneumonia included: centrilobular nodules were detected in 2 cases with acute phase, 4 cases with subacute phase and 4 cases with chronic phase; the imaging of ground glass opacity were detected in 9 cases with acute phase, 2 cases with subacute phase and 3 cases with chronic phase; the imaging of bronchiectasis was detected in 8 cases with chronic phase, which mucilage embolism was detected in 3 of 8 cases; the imaging of atelectasis was detected in 6 cases with chronic phase; the imaging of sheeted consolidation was detected in 5 cases with chronic phase, 8 case with acute phase; the imaging of interstitial fibrosis was detected in 3 cases with chronic phase. Lesions of inferior lobe of right lung were detected in 9 cases with chronic phase, 4 cases with subacute phase, 11 case with acute phase;lesions of inferior lobe of left lung were detected in 6 cases with chronic phase and 3 cases with subacute group, 11 case with acute phase. The imaging features of acute and chronic aspiration pneumonia overlap with GGO and centrilobular nodules in every group. While the imaging features of atelectasis, bronchiectasis or mucilage embolism are found in chronic phase. The chest CT scaning may accurately evaluate the dynamic change of aspiration pneumonia.

Computerized scheme for vertebra detection in CT scout image

NASA Astrophysics Data System (ADS)

Guo, Wei; Chen, Qiang; Zhou, Hanxun; Zhang, Guodong; Cong, Lin; Li, Qiang

2016-03-01

Our purposes are to develop a vertebra detection scheme for automated scan planning, which would assist radiological technologists in their routine work for the imaging of vertebrae. Because the orientations of vertebrae were various, and the Haar-like features were only employed to represent the subject on the vertical, horizontal, or diagonal directions, we rotated the CT scout image seven times to make the vertebrae roughly horizontal in least one of the rotated images. Then, we employed Adaboost learning algorithm to construct a strong classifier for the vertebra detection by use of Haar-like features, and combined the detection results with the overlapping region according to the number of times they were detected. Finally, most of the false positives were removed by use of the contextual relationship between them. The detection scheme was evaluated on a database with 76 CT scout image. Our detection scheme reported 1.65 false positives per image at a sensitivity of 94.3% for initial detection of vertebral candidates, and then the performance of detection was improved to 0.95 false positives per image at a sensitivity of 98.6% for the further steps of false positive reduction. The proposed scheme achieved a high performance for the detection of vertebrae with different orientations.

Multidetector CT of musculoskeletal disease in the pediatric patient: principles, techniques, and clinical applications.

PubMed

Fayad, Laura M; Johnson, Pamela; Fishman, Elliot K

2005-01-01

Computed tomography (CT) plays an important role in the evaluation of musculoskeletal disease in the pediatric patient. With the advent of high-performance 16-section multidetector CT, images can be produced with subsecond gantry rotation times and with submillimeter acquisition, which yields true isotropic high-resolution volume data sets; these features are not attainable with older spiral CT technology. Such capabilities are particularly helpful in the evaluation of pediatric patients by virtually eliminating the need for sedation and minimizing dependence on patient cooperation. The role of three-dimensional (3D) volume imaging in the evaluation of pediatric musculoskeletal disease continues to evolve, with this technique becoming increasingly important in detection and characterization of lesions as well as in decisions about patient care. Specific designs and protocols for multidetector CT studies can be selected to minimize radiation dose to the patient. Principal clinical applications of 3D CT in evaluation of the pediatric musculoskeletal system include developmental abnormalities, trauma, neoplasms, and postoperative imaging.

Thin-Section CT Features of Idiopathic Pulmonary Fibrosis Correlated with Micro-CT and Histologic Analysis.

PubMed

Mai, Cindy; Verleden, Stijn E; McDonough, John E; Willems, Stijn; De Wever, Walter; Coolen, Johan; Dubbeldam, Adriana; Van Raemdonck, Dirk E; Verbeken, Eric K; Verleden, Geert M; Hogg, James C; Vanaudenaerde, Bart M; Wuyts, Wim A; Verschakelen, Johny A

2017-04-01

Purpose To elucidate the underlying lung changes responsible for the computed tomographic (CT) features of idiopathic pulmonary fibrosis (IPF) and to gain insight into the way IPF proceeds through the lungs and progresses over time. Materials and Methods Micro-CT studies of tissue cores obtained from explant lungs were examined and were correlated 1:1 with a CT study obtained immediately before transplantation. Samples for histologic analysis were obtained from selected cores. Results In areas with no or minimal abnormalities on CT images, small areas of increased attenuation located in or near the interlobular septa can be seen on micro-CT studies. In more involved lung areas, the number of opacities increases and opacities enlarge and approach each other along the interlobular septa, causing a fine reticular pattern on CT images. Simultaneously, air-containing structures in and around these opacities arise, corresponding with small cysts on CT images. Honeycombing is caused by a progressive increase in the number and size of these cystic structures and tissue opacities that gradually extend toward the centrilobular region and finally replace the entire lobule. At histologic analysis, the small islands of increased attenuation very likely correspond with fibroblastic foci. Near these fibroblastic foci, an abnormal adjacency of alveolar walls was seen, suggesting alveolar collapse. In later stages, normal lung tissue is replaced by a large amount of young collagen, as seen in patients with advanced fibrosis. Conclusion Fibrosis and cyst formation in patients with IPF seem to start at the periphery of the pulmonary lobule and progressively extend toward the core of this anatomic lung unit. Evidence was found that alveolar collapse might already be present in an early stage when there is only little pulmonary fibrosis. © RSNA, 2016.

Thin-Section CT Features of Idiopathic Pulmonary Fibrosis Correlated with Micro-CT and Histologic Analysis

PubMed Central

Mai, Cindy; Verleden, Stijn E.; McDonough, John E.; Willems, Stijn; De Wever, Walter; Coolen, Johan; Dubbeldam, Adriana; Van Raemdonck, Dirk E.; Verbeken, Eric K.; Verleden, Geert M.; Hogg, James C.; Vanaudenaerde, Bart M.; Wuyts, Wim A.

2017-01-01

Purpose To elucidate the underlying lung changes responsible for the computed tomographic (CT) features of idiopathic pulmonary fibrosis (IPF) and to gain insight into the way IPF proceeds through the lungs and progresses over time. Materials and Methods Micro-CT studies of tissue cores obtained from explant lungs were examined and were correlated 1:1 with a CT study obtained immediately before transplantation. Samples for histologic analysis were obtained from selected cores. Results In areas with no or minimal abnormalities on CT images, small areas of increased attenuation located in or near the interlobular septa can be seen on micro-CT studies. In more involved lung areas, the number of opacities increases and opacities enlarge and approach each other along the interlobular septa, causing a fine reticular pattern on CT images. Simultaneously, air-containing structures in and around these opacities arise, corresponding with small cysts on CT images. Honeycombing is caused by a progressive increase in the number and size of these cystic structures and tissue opacities that gradually extend toward the centrilobular region and finally replace the entire lobule. At histologic analysis, the small islands of increased attenuation very likely correspond with fibroblastic foci. Near these fibroblastic foci, an abnormal adjacency of alveolar walls was seen, suggesting alveolar collapse. In later stages, normal lung tissue is replaced by a large amount of young collagen, as seen in patients with advanced fibrosis. Conclusion Fibrosis and cyst formation in patients with IPF seem to start at the periphery of the pulmonary lobule and progressively extend toward the core of this anatomic lung unit. Evidence was found that alveolar collapse might already be present in an early stage when there is only little pulmonary fibrosis. © RSNA, 2016 PMID:27715655

Non-rigid registration between 3D ultrasound and CT images of the liver based on intensity and gradient information

NASA Astrophysics Data System (ADS)

Lee, Duhgoon; Nam, Woo Hyun; Lee, Jae Young; Ra, Jong Beom

2011-01-01

In order to utilize both ultrasound (US) and computed tomography (CT) images of the liver concurrently for medical applications such as diagnosis and image-guided intervention, non-rigid registration between these two types of images is an essential step, as local deformation between US and CT images exists due to the different respiratory phases involved and due to the probe pressure that occurs in US imaging. This paper introduces a voxel-based non-rigid registration algorithm between the 3D B-mode US and CT images of the liver. In the proposed algorithm, to improve the registration accuracy, we utilize the surface information of the liver and gallbladder in addition to the information of the vessels inside the liver. For an effective correlation between US and CT images, we treat those anatomical regions separately according to their characteristics in US and CT images. Based on a novel objective function using a 3D joint histogram of the intensity and gradient information, vessel-based non-rigid registration is followed by surface-based non-rigid registration in sequence, which improves the registration accuracy. The proposed algorithm is tested for ten clinical datasets and quantitative evaluations are conducted. Experimental results show that the registration error between anatomical features of US and CT images is less than 2 mm on average, even with local deformation due to different respiratory phases and probe pressure. In addition, the lesion registration error is less than 3 mm on average with a maximum of 4.5 mm that is considered acceptable for clinical applications.

Applying quantitative adiposity feature analysis models to predict benefit of bevacizumab-based chemotherapy in ovarian cancer patients

NASA Astrophysics Data System (ADS)

Wang, Yunzhi; Qiu, Yuchen; Thai, Theresa; More, Kathleen; Ding, Kai; Liu, Hong; Zheng, Bin

2016-03-01

How to rationally identify epithelial ovarian cancer (EOC) patients who will benefit from bevacizumab or other antiangiogenic therapies is a critical issue in EOC treatments. The motivation of this study is to quantitatively measure adiposity features from CT images and investigate the feasibility of predicting potential benefit of EOC patients with or without receiving bevacizumab-based chemotherapy treatment using multivariate statistical models built based on quantitative adiposity image features. A dataset involving CT images from 59 advanced EOC patients were included. Among them, 32 patients received maintenance bevacizumab after primary chemotherapy and the remaining 27 patients did not. We developed a computer-aided detection (CAD) scheme to automatically segment subcutaneous fat areas (VFA) and visceral fat areas (SFA) and then extracted 7 adiposity-related quantitative features. Three multivariate data analysis models (linear regression, logistic regression and Cox proportional hazards regression) were performed respectively to investigate the potential association between the model-generated prediction results and the patients' progression-free survival (PFS) and overall survival (OS). The results show that using all 3 statistical models, a statistically significant association was detected between the model-generated results and both of the two clinical outcomes in the group of patients receiving maintenance bevacizumab (p<0.01), while there were no significant association for both PFS and OS in the group of patients without receiving maintenance bevacizumab. Therefore, this study demonstrated the feasibility of using quantitative adiposity-related CT image features based statistical prediction models to generate a new clinical marker and predict the clinical outcome of EOC patients receiving maintenance bevacizumab-based chemotherapy.

Characterization of healthy and osteoarthritic chondrocyte cell patterns on phase contrast CT images of the knee cartilage matrix

NASA Astrophysics Data System (ADS)

Nagarajan, Mahesh B.; Coan, Paola; Huber, Markus B.; Yang, Chien-Chun; Glaser, Christian; Reiser, Maximilian F.; Wismüller, Axel

2012-03-01

The current approach to evaluating cartilage degeneration at the knee joint requires visualization of the joint space on radiographic images where indirect cues such as joint space narrowing serve as markers for osteoarthritis. A recent novel approach to visualizing the knee cartilage matrix using phase contrast CT imaging (PCI-CT) was shown to allow direct examination of chondrocyte cell patterns and their subsequent correlation to osteoarthritis. This study aims to characterize chondrocyte cell patterns in the radial zone of the knee cartilage matrix in the presence and absence of osteoarthritic damage through both gray-level co-occurrence matrix (GLCM) derived texture features as well as Minkowski Functionals (MF). Thirteen GLCM and three MF texture features were extracted from 404 regions of interest (ROI) annotated on PCI images of healthy and osteoarthritic specimens of knee cartilage. These texture features were then used in a machine learning task to classify ROIs as healthy or osteoarthritic. A fuzzy k-nearest neighbor classifier was used and its performance was evaluated using the area under the ROC curve (AUC). The best classification performance was observed with the MF features 'perimeter' and 'Euler characteristic' and with GLCM correlation features (f3 and f13). With the experimental conditions used in this study, both Minkowski Functionals and GLCM achieved a high classification performance (AUC value of 0.97) in the task of distinguishing between health and osteoarthritic ROIs. These results show that such quantitative analysis of chondrocyte patterns in the knee cartilage matrix can distinguish between healthy and osteoarthritic tissue with high accuracy.

Temporal Dermoid Cyst with Unusual Imaging Appearance: Case Report.

PubMed

Abderrahmen, Khansa; Bouhoula, Asma; Aouidj, Lasaad; Jemel, Hafedh

2016-01-01

Intracranial dermoid cysts are benign, slow growing tumors derived from ectopic inclusions of epithelial cells during closure of neural tube. These lesions, accounting for less than 1% of intracranial tumors, have characteristic computed tomography (CT) and magnetic resonance imaging (MRI) appearances that generally permits preoperative diagnosis. However, the radiologic features are uncommon and the cyst can be easily misdiagnosed with other tumors in rare cases. Herein, we report a case of a left temporoparietal dermoid cyst in a 48-year-old woman that was peroperatively and histopathologically proven but not advocated on CT and MRI. Clinical, radiological and histopathological features of a dermoid cyst are reviewed.

The value of nodal information in predicting lung cancer relapse using 4DPET/4DCT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Heyse, E-mail: heyse.li@mail.utoronto.ca; Becker, Nathan; Raman, Srinivas

2015-08-15

Purpose: There is evidence that computed tomography (CT) and positron emission tomography (PET) imaging metrics are prognostic and predictive in nonsmall cell lung cancer (NSCLC) treatment outcomes. However, few studies have explored the use of standardized uptake value (SUV)-based image features of nodal regions as predictive features. The authors investigated and compared the use of tumor and node image features extracted from the radiotherapy target volumes to predict relapse in a cohort of NSCLC patients undergoing chemoradiation treatment. Methods: A prospective cohort of 25 patients with locally advanced NSCLC underwent 4DPET/4DCT imaging for radiation planning. Thirty-seven image features were derivedmore » from the CT-defined volumes and SUVs of the PET image from both the tumor and nodal target regions. The machine learning methods of logistic regression and repeated stratified five-fold cross-validation (CV) were used to predict local and overall relapses in 2 yr. The authors used well-known feature selection methods (Spearman’s rank correlation, recursive feature elimination) within each fold of CV. Classifiers were ranked on their Matthew’s correlation coefficient (MCC) after CV. Area under the curve, sensitivity, and specificity values are also presented. Results: For predicting local relapse, the best classifier found had a mean MCC of 0.07 and was composed of eight tumor features. For predicting overall relapse, the best classifier found had a mean MCC of 0.29 and was composed of a single feature: the volume greater than 0.5 times the maximum SUV (N). Conclusions: The best classifier for predicting local relapse had only tumor features. In contrast, the best classifier for predicting overall relapse included a node feature. Overall, the methods showed that nodes add value in predicting overall relapse but not local relapse.« less

Comprehensive Modeling and Visualization of Cardiac Anatomy and Physiology from CT Imaging and Computer Simulations

PubMed Central

Sun, Peng; Zhou, Haoyin; Ha, Seongmin; Hartaigh, Bríain ó; Truong, Quynh A.; Min, James K.

2016-01-01

In clinical cardiology, both anatomy and physiology are needed to diagnose cardiac pathologies. CT imaging and computer simulations provide valuable and complementary data for this purpose. However, it remains challenging to gain useful information from the large amount of high-dimensional diverse data. The current tools are not adequately integrated to visualize anatomic and physiologic data from a complete yet focused perspective. We introduce a new computer-aided diagnosis framework, which allows for comprehensive modeling and visualization of cardiac anatomy and physiology from CT imaging data and computer simulations, with a primary focus on ischemic heart disease. The following visual information is presented: (1) Anatomy from CT imaging: geometric modeling and visualization of cardiac anatomy, including four heart chambers, left and right ventricular outflow tracts, and coronary arteries; (2) Function from CT imaging: motion modeling, strain calculation, and visualization of four heart chambers; (3) Physiology from CT imaging: quantification and visualization of myocardial perfusion and contextual integration with coronary artery anatomy; (4) Physiology from computer simulation: computation and visualization of hemodynamics (e.g., coronary blood velocity, pressure, shear stress, and fluid forces on the vessel wall). Substantially, feedback from cardiologists have confirmed the practical utility of integrating these features for the purpose of computer-aided diagnosis of ischemic heart disease. PMID:26863663

Differential diagnosis of breast masses in South Korean premenopausal women using diffuse optical spectroscopic imaging

NASA Astrophysics Data System (ADS)

Leproux, Anaïs; Kim, You Me; Min, Jun Won; McLaren, Christine E.; Chen, Wen-Pin; O'Sullivan, Thomas D.; Lee, Seung-ha; Chung, Phil-Sang; Tromberg, Bruce J.

2016-07-01

Young patients with dense breasts have a relatively low-positive biopsy rate for breast cancer (˜1 in 7). South Korean women have higher breast density than Westerners. We investigated the benefit of using a functional and metabolic imaging technique, diffuse optical spectroscopic imaging (DOSI), to help the standard of care imaging tools to distinguish benign from malignant lesions in premenopausal Korean women. DOSI uses near-infrared light to measure breast tissue composition by quantifying tissue concentrations of water (ctH2O), bulk lipid (ctLipid), deoxygenated (ctHHb), and oxygenated (ctHbO2) hemoglobin. DOSI spectral signatures specific to abnormal tissue and absent in healthy tissue were also used to form a malignancy index. This study included 19 premenopausal subjects (average age 41±9), corresponding to 11 benign and 10 malignant lesions. Elevated lesion to normal ratio of ctH2O, ctHHb, ctHbO2, total hemoglobin (THb=ctHHb+ctHbO2), and tissue optical index (ctHHb×ctH2O/ctLipid) were observed in the malignant lesions compared to the benign lesions (p<0.02). THb and malignancy index were the two best single predictors of malignancy, with >90% sensitivity and specificity. Malignant lesions showed significantly higher metabolism and perfusion than benign lesions. DOSI spectral features showed high discriminatory power for distinguishing malignant and benign lesions in dense breasts of the Korean population.

Characterization of PET/CT images using texture analysis: the past, the present… any future?

PubMed

Hatt, Mathieu; Tixier, Florent; Pierce, Larry; Kinahan, Paul E; Le Rest, Catherine Cheze; Visvikis, Dimitris

2017-01-01

After seminal papers over the period 2009 - 2011, the use of texture analysis of PET/CT images for quantification of intratumour uptake heterogeneity has received increasing attention in the last 4 years. Results are difficult to compare due to the heterogeneity of studies and lack of standardization. There are also numerous challenges to address. In this review we provide critical insights into the recent development of texture analysis for quantifying the heterogeneity in PET/CT images, identify issues and challenges, and offer recommendations for the use of texture analysis in clinical research. Numerous potentially confounding issues have been identified, related to the complex workflow for the calculation of textural features, and the dependency of features on various factors such as acquisition, image reconstruction, preprocessing, functional volume segmentation, and methods of establishing and quantifying correspondences with genomic and clinical metrics of interest. A lack of understanding of what the features may represent in terms of the underlying pathophysiological processes and the variability of technical implementation practices makes comparing results in the literature challenging, if not impossible. Since progress as a field requires pooling results, there is an urgent need for standardization and recommendations/guidelines to enable the field to move forward. We provide a list of correct formulae for usual features and recommendations regarding implementation. Studies on larger cohorts with robust statistical analysis and machine learning approaches are promising directions to evaluate the potential of this approach.

3D intrathoracic region definition and its application to PET-CT analysis

NASA Astrophysics Data System (ADS)

Cheirsilp, Ronnarit; Bascom, Rebecca; Allen, Thomas W.; Higgins, William E.

2014-03-01

Recently developed integrated PET-CT scanners give co-registered multimodal data sets that offer complementary three-dimensional (3D) digital images of the chest. PET (positron emission tomography) imaging gives highly specific functional information of suspect cancer sites, while CT (X-ray computed tomography) gives associated anatomical detail. Because the 3D CT and PET scans generally span the body from the eyes to the knees, accurate definition of the intrathoracic region is vital for focusing attention to the central-chest region. In this way, diagnostically important regions of interest (ROIs), such as central-chest lymph nodes and cancer nodules, can be more efficiently isolated. We propose a method for automatic segmentation of the intrathoracic region from a given co-registered 3D PET-CT study. Using the 3D CT scan as input, the method begins by finding an initial intrathoracic region boundary for a given 2D CT section. Next, active contour analysis, driven by a cost function depending on local image gradient, gradient-direction, and contour shape features, iteratively estimates the contours spanning the intrathoracic region on neighboring 2D CT sections. This process continues until the complete region is defined. We next present an interactive system that employs the segmentation method for focused 3D PET-CT chest image analysis. A validation study over a series of PET-CT studies reveals that the segmentation method gives a Dice index accuracy of less than 98%. In addition, further results demonstrate the utility of the method for focused 3D PET-CT chest image analysis, ROI definition, and visualization.

The cheating liver: imaging of focal steatosis and fatty sparing.

PubMed

Dioguardi Burgio, Marco; Bruno, Onorina; Agnello, Francesco; Torrisi, Chiara; Vernuccio, Federica; Cabibbo, Giuseppe; Soresi, Maurizio; Petta, Salvatore; Calamia, Mauro; Papia, Giovanni; Gambino, Angelo; Ricceri, Viola; Midiri, Massimo; Lagalla, Roberto; Brancatelli, Giuseppe

2016-06-01

Focal steatosis and fatty sparing are a frequent finding in liver imaging, and can mimic solid lesions. Liver regional variations in the degree of fat accumulation can be related to vascular anomalies, metabolic disorders, use of certain drugs or coexistence of hepatic masses. CT and MRI are the modalities of choice for the noninvasive diagnosis of hepatic steatosis. Knowledge of CT and MRI appearance of focal steatosis and fatty sparing is crucial for an accurate diagnosis, and to rule-out other pathologic processes. This paper will review the CT and MRI techniques for the diagnosis of hepatic steatosis and the CT and MRI features of common and uncommon causes of focal steatosis and fatty sparing.

Applying a radiomics approach to predict prognosis of lung cancer patients

NASA Astrophysics Data System (ADS)

Emaminejad, Nastaran; Yan, Shiju; Wang, Yunzhi; Qian, Wei; Guan, Yubao; Zheng, Bin

2016-03-01

Radiomics is an emerging technology to decode tumor phenotype based on quantitative analysis of image features computed from radiographic images. In this study, we applied Radiomics concept to investigate the association among the CT image features of lung tumors, which are either quantitatively computed or subjectively rated by radiologists, and two genomic biomarkers namely, protein expression of the excision repair cross-complementing 1 (ERCC1) genes and a regulatory subunit of ribonucleotide reductase (RRM1), in predicting disease-free survival (DFS) of lung cancer patients after surgery. An image dataset involving 94 patients was used. Among them, 20 had cancer recurrence within 3 years, while 74 patients remained DFS. After tumor segmentation, 35 image features were computed from CT images. Using the Weka data mining software package, we selected 10 non-redundant image features. Applying a SMOTE algorithm to generate synthetic data to balance case numbers in two DFS ("yes" and "no") groups and a leave-one-case-out training/testing method, we optimized and compared a number of machine learning classifiers using (1) quantitative image (QI) features, (2) subjective rated (SR) features, and (3) genomic biomarkers (GB). Data analyses showed relatively lower correlation among the QI, SR and GB prediction results (with Pearson correlation coefficients < 0.5 including between ERCC1 and RRM1 biomarkers). By using area under ROC curve as an assessment index, the QI, SR and GB based classifiers yielded AUC = 0.89+/-0.04, 0.73+/-0.06 and 0.76+/-0.07, respectively, which showed that all three types of features had prediction power (AUC>0.5). Among them, using QI yielded the highest performance.

TU-H-CAMPUS-IeP3-01: Simultaneous PET Restoration and PET/CT Co-Segmentation Using a Variational Method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, L; Tan, S; Lu, W

Purpose: PET images are usually blurred due to the finite spatial resolution, while CT images suffer from low contrast. Segment a tumor from either a single PET or CT image is thus challenging. To make full use of the complementary information between PET and CT, we propose a novel variational method for simultaneous PET image restoration and PET/CT images co-segmentation. Methods: The proposed model was constructed based on the Γ-convergence approximation of Mumford-Shah (MS) segmentation model for PET/CT co-segmentation. Moreover, a PET de-blur process was integrated into the MS model to improve the segmentation accuracy. An interaction edge constraint termmore » over the two modalities were specially designed to share the complementary information. The energy functional was iteratively optimized using an alternate minimization (AM) algorithm. The performance of the proposed method was validated on ten lung cancer cases and five esophageal cancer cases. The ground truth were manually delineated by an experienced radiation oncologist using the complementary visual features of PET and CT. The segmentation accuracy was evaluated by Dice similarity index (DSI) and volume error (VE). Results: The proposed method achieved an expected restoration result for PET image and satisfactory segmentation results for both PET and CT images. For lung cancer dataset, the average DSI (0.72) increased by 0.17 and 0.40 than single PET and CT segmentation. For esophageal cancer dataset, the average DSI (0.85) increased by 0.07 and 0.43 than single PET and CT segmentation. Conclusion: The proposed method took full advantage of the complementary information from PET and CT images. This work was supported in part by the National Cancer Institute Grants R01CA172638. Shan Tan and Laquan Li were supported in part by the National Natural Science Foundation of China, under Grant Nos. 60971112 and 61375018.« less

Computed tomographic features of apical infection of equine maxillary cheek teeth: a retrospective study of 49 horses.

PubMed

Bühler, M; Fürst, A; Lewis, F I; Kummer, M; Ohlerth, S

2014-07-01

Computed tomographic (CT) studies evaluating the relevance of individual CT features of apical infection in maxillary cheek teeth are lacking. To study the prevalence and relationship of single CT features in horses with and without clinical evidence of apical infection in maxillary cheek teeth. Retrospective case-control study. Multislice CT scans of the head of 49 horses were evaluated retrospectively. Changes of the infundibulum, pulp, root, lamina dura, periodontal space and alveolar bone in maxillary cheek teeth were recorded. Single CT changes were much more prevalent in the 28 horses with clinical signs. However, infundibular changes and a nondetectable lamina dura were also common in the 21 horses without clinical evidence of apical infection. Computed tomographic abnormalities of the pulp, root, periapical bone and periodontal space and the presence of a tooth fracture were significantly related. Infundibular changes were not associated with other CT signs of apical infection. Although nondetectable lamina dura was the most frequent CT change in all teeth in both studied groups, it was most commonly a solitary feature in otherwise normal teeth. Apical infections, defined as ≥3 CT changes, occurred mainly in the 108/208, 109/209 and 110/210 (Triadan numbers) and were found only in horses with clinical evidence of apical infection, except in one horse without clinical signs that had one affected root. Combined CT changes of the pulp, root, lamina dura, periapical bone and periodontal space and the presence of a tooth fracture appear to be reliable features to diagnose apical infection in maxillary cheek teeth. As a solitary feature, a nondetectable lamina dura should be interpreted cautiously and may even be considered normal due to its minor thickness and/or too low resolution of the imaging modality. © 2013 EVJ Ltd.

Evaluation of non-rigid registration parameters for atlas-based segmentation of CT images of human cochlea

NASA Astrophysics Data System (ADS)

Elfarnawany, Mai; Alam, S. Riyahi; Agrawal, Sumit K.; Ladak, Hanif M.

2017-02-01

Cochlear implant surgery is a hearing restoration procedure for patients with profound hearing loss. In this surgery, an electrode is inserted into the cochlea to stimulate the auditory nerve and restore the patient's hearing. Clinical computed tomography (CT) images are used for planning and evaluation of electrode placement, but their low resolution limits the visualization of internal cochlear structures. Therefore, high resolution micro-CT images are used to develop atlas-based segmentation methods to extract these nonvisible anatomical features in clinical CT images. Accurate registration of the high and low resolution CT images is a prerequisite for reliable atlas-based segmentation. In this study, we evaluate and compare different non-rigid B-spline registration parameters using micro-CT and clinical CT images of five cadaveric human cochleae. The varying registration parameters are cost function (normalized correlation (NC), mutual information and mean square error), interpolation method (linear, windowed-sinc and B-spline) and sampling percentage (1%, 10% and 100%). We compare the registration results visually and quantitatively using the Dice similarity coefficient (DSC), Hausdorff distance (HD) and absolute percentage error in cochlear volume. Using MI or MSE cost functions and linear or windowed-sinc interpolation resulted in visually undesirable deformation of internal cochlear structures. Quantitatively, the transforms using 100% sampling percentage yielded the highest DSC and smallest HD (0.828+/-0.021 and 0.25+/-0.09mm respectively). Therefore, B-spline registration with cost function: NC, interpolation: B-spline and sampling percentage: moments 100% can be the foundation of developing an optimized atlas-based segmentation algorithm of intracochlear structures in clinical CT images.

Classification of Hepatic Lesions From CT Images Using Texture Features and Neural Networks

DTIC Science & Technology

2001-10-25

ROI’s) taken from non-enhanced CT images of normal liver, hepatic cysts, hemangiomas, and hepatocellular carcinomas (a total of 147 samples), have been...disease”. The third NN classifies “other disease” into hemangiomas and hepatocellular carcinomas . In order to enhance the performance of the...and hepatocellular carcinoma (C4). II. METHODOLOGY The proposed diagnostic system is presented in Fig. 1. It consists of two levels: the

Carotid plaque characterization using CT and MRI scans for synergistic image analysis

NASA Astrophysics Data System (ADS)

Getzin, Matthew; Xu, Yiqin; Rao, Arhant; Madi, Saaussan; Bahadur, Ali; Lennartz, Michelle R.; Wang, Ge

2014-09-01

Noninvasive determination of plaque vulnerability has been a holy grail of medical imaging. Despite advances in tomographic technologies , there is currently no effective way to identify vulnerable atherosclerotic plaques with high sensitivity and specificity. Computed tomography (CT) and magnetic resonance imaging (MRI) are widely used, but neither provides sufficient information of plaque properties. Thus, we are motivated to combine CT and MRI imaging to determine if the composite information can better reflect the histological determination of plaque vulnerability. Two human endarterectomy specimens (1 symptomatic carotid and 1 stable femoral) were imaged using Scanco Medical Viva CT40 and Bruker Pharmascan 16cm 7T Horizontal MRI / MRS systems. μCT scans were done at 55 kVp and tube current of 70 mA. Samples underwent RARE-VTR and MSME pulse sequences to measure T1, T2 values, and proton density. The specimens were processed for histology and scored for vulnerability using the American Heart Association criteria. Single modality-based analyses were performed through segmentation of key imaging biomarkers (i.e. calcification and lumen), image registration, measurement of fibrous capsule, and multi-component T1 and T2 decay modeling. Feature differences were analyzed between the unstable and stable controls, symptomatic carotid and femoral plaque, respectively. By building on the techniques used in this study, synergistic CT+MRI analysis may provide a promising solution for plaque characterization in vivo.

Lung texture in serial thoracic CT scans: Assessment of change introduced by image registration1

PubMed Central

Cunliffe, Alexandra R.; Al-Hallaq, Hania A.; Labby, Zacariah E.; Pelizzari, Charles A.; Straus, Christopher; Sensakovic, William F.; Ludwig, Michelle; Armato, Samuel G.

2012-01-01

Purpose: The aim of this study was to quantify the effect of four image registration methods on lung texture features extracted from serial computed tomography (CT) scans obtained from healthy human subjects. Methods: Two chest CT scans acquired at different time points were collected retrospectively for each of 27 patients. Following automated lung segmentation, each follow-up CT scan was registered to the baseline scan using four algorithms: (1) rigid, (2) affine, (3) B-splines deformable, and (4) demons deformable. The registration accuracy for each scan pair was evaluated by measuring the Euclidean distance between 150 identified landmarks. On average, 1432 spatially matched 32 × 32-pixel region-of-interest (ROI) pairs were automatically extracted from each scan pair. First-order, fractal, Fourier, Laws’ filter, and gray-level co-occurrence matrix texture features were calculated in each ROI, for a total of 140 features. Agreement between baseline and follow-up scan ROI feature values was assessed by Bland–Altman analysis for each feature; the range spanned by the 95% limits of agreement of feature value differences was calculated and normalized by the average feature value to obtain the normalized range of agreement (nRoA). Features with small nRoA were considered “registration-stable.” The normalized bias for each feature was calculated from the feature value differences between baseline and follow-up scans averaged across all ROIs in every patient. Because patients had “normal” chest CT scans, minimal change in texture feature values between scan pairs was anticipated, with the expectation of small bias and narrow limits of agreement. Results: Registration with demons reduced the Euclidean distance between landmarks such that only 9% of landmarks were separated by ≥1 mm, compared with rigid (98%), affine (95%), and B-splines (90%). Ninety-nine of the 140 (71%) features analyzed yielded nRoA > 50% for all registration methods, indicating that the majority of feature values were perturbed following registration. Nineteen of the features (14%) had nRoA < 15% following demons registration, indicating relative feature value stability. Student's t-tests showed that the nRoA of these 19 features was significantly larger when rigid, affine, or B-splines registration methods were used compared with demons registration. Demons registration yielded greater normalized bias in feature value change than B-splines registration, though this difference was not significant (p = 0.15). Conclusions: Demons registration provided higher spatial accuracy between matched anatomic landmarks in serial CT scans than rigid, affine, or B-splines algorithms. Texture feature changes calculated in healthy lung tissue from serial CT scans were smaller following demons registration compared with all other algorithms. Though registration altered the values of the majority of texture features, 19 features remained relatively stable after demons registration, indicating their potential for detecting pathologic change in serial CT scans. Combined use of accurate deformable registration using demons and texture analysis may allow for quantitative evaluation of local changes in lung tissue due to disease progression or treatment response. PMID:22894392

Nanoparticle Contrast Agents for Computed Tomography: A Focus on Micelles

PubMed Central

Cormode, David P.; Naha, Pratap C.; Fayad, Zahi A.

2014-01-01

Computed tomography (CT) is an X-ray based whole body imaging technique that is widely used in medicine. Clinically approved contrast agents for CT are iodinated small molecules or barium suspensions. Over the past seven years there has been a great increase in the development of nanoparticles as CT contrast agents. Nanoparticles have several advantages over small molecule CT contrast agents, such as long blood-pool residence times, and the potential for cell tracking and targeted imaging applications. Furthermore, there is a need for novel CT contrast agents, due to the growing population of renally impaired patients and patients hypersensitive to iodinated contrast. Micelles and lipoproteins, a micelle-related class of nanoparticle, have notably been adapted as CT contrast agents. In this review we discuss the principles of CT image formation and the generation of CT contrast. We discuss the progress in developing non-targeted, targeted and cell tracking nanoparticle CT contrast agents. We feature agents based on micelles and used in conjunction with spectral CT. The large contrast agent doses needed will necessitate careful toxicology studies prior to clinical translation. However, the field has seen tremendous advances in the past decade and we expect many more advances to come in the next decade. PMID:24470293

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hu, P; Wang, J; Zhong, H

Purpose: To evaluate the reproducibility of radiomics features by repeating computed tomographic (CT) scans in rectal cancer. To choose stable radiomics features for rectal cancer. Methods: 40 rectal cancer patients were enrolled in this study, each of whom underwent two CT scans within average 8.7 days (5 days to 17 days), before any treatment was delivered. The rectal gross tumor volume (GTV) was distinguished and segmented by an experienced oncologist in both CTs. Totally, more than 2000 radiomics features were defined in this study, which were divided into four groups (I: GLCM, II: GLRLM III: Wavelet GLCM and IV: Waveletmore » GLRLM). For each group, five types of features were extracted (Max slice: features from the largest slice of target images, Max value: features from all slices of target images and choose the maximum value, Min value: minimum value of features for all slices, Average value: average value of features for all slices, Matrix sum: all slices of target images translate into GLCM and GLRLM matrices and superpose all matrices, then extract features from the superposed matrix). Meanwhile a LOG (Laplace of Gauss) filter with different parameters was applied to these images. Concordance correlation coefficients (CCC) and inter-class correlation coefficients (ICC) were calculated to assess the reproducibility. Results: 403 radiomics features were extracted from each type of patients’ medical images. Features of average type are the most reproducible. Different filters have little effect for radiomics features. For the average type features, 253 out of 403 features (62.8%) showed high reproducibility (ICC≥0.8), 133 out of 403 features (33.0%) showed medium reproducibility (0.8≥ICC≥0.5) and 17 out of 403 features (4.2%) showed low reproducibility (ICC≥0.5). Conclusion: The average type radiomics features are the most stable features in rectal cancer. Further analysis of these features of rectal cancer can be warranted for treatment monitoring and prognosis prediction.« less

Accuracy and variability of texture-based radiomics features of lung lesions across CT imaging conditions

NASA Astrophysics Data System (ADS)

Zheng, Yuese; Solomon, Justin; Choudhury, Kingshuk; Marin, Daniele; Samei, Ehsan

2017-03-01

Texture analysis for lung lesions is sensitive to changing imaging conditions but these effects are not well understood, in part, due to a lack of ground-truth phantoms with realistic textures. The purpose of this study was to explore the accuracy and variability of texture features across imaging conditions by comparing imaged texture features to voxel-based 3D printed textured lesions for which the true values are known. The seven features of interest were based on the Grey Level Co-Occurrence Matrix (GLCM). The lesion phantoms were designed with three shapes (spherical, lobulated, and spiculated), two textures (homogenous and heterogeneous), and two sizes (diameter < 1.5 cm and 1.5 cm < diameter < 3 cm), resulting in 24 lesions (with a second replica of each). The lesions were inserted into an anthropomorphic thorax phantom (Multipurpose Chest Phantom N1, Kyoto Kagaku) and imaged using a commercial CT system (GE Revolution) at three CTDI levels (0.67, 1.42, and 5.80 mGy), three reconstruction algorithms (FBP, IR-2, IR-4), four reconstruction kernel types (standard, soft, edge), and two slice thicknesses (0.6 mm and 5 mm). Another repeat scan was performed. Texture features from these images were extracted and compared to the ground truth feature values by percent relative error. The variability across imaging conditions was calculated by standard deviation across a certain imaging condition for all heterogeneous lesions. The results indicated that the acquisition method has a significant influence on the accuracy and variability of extracted features and as such, feature quantities are highly susceptible to imaging parameter choices. The most influential parameters were slice thickness and reconstruction kernels. Thin slice thickness and edge reconstruction kernel overall produced more accurate and more repeatable results. Some features (e.g., Contrast) were more accurately quantified under conditions that render higher spatial frequencies (e.g., thinner slice thickness and sharp kernels), while others (e.g., Homogeneity) showed more accurate quantification under conditions that render smoother images (e.g., higher dose and smoother kernels). Care should be exercised is relating texture features between cases of varied acquisition protocols, with need to cross calibration dependent on the feature of interest.

Comparative evaluation of the cadaveric, radiographic and computed tomographic anatomy of the heads of green iguana (Iguana iguana), common tegu (Tupinambis merianae) and bearded dragon (Pogona vitticeps).

PubMed

Banzato, Tommaso; Selleri, Paolo; Veladiano, Irene A; Martin, Andrea; Zanetti, Emanuele; Zotti, Alessandro

2012-05-11

Radiology and computed tomography are the most commonly available diagnostic tools for the diagnosis of pathologies affecting the head and skull in veterinary practice. Nevertheless, accurate interpretation of radiographic and CT studies requires a thorough knowledge of the gross and the cross-sectional anatomy. Despite the increasing success of reptiles as pets, only a few reports over their normal imaging features are currently available. The aim of this study is to describe the normal cadaveric, radiographic and computed tomographic features of the heads of the green iguana, tegu and bearded dragon. 6 adult green iguanas, 4 tegus, 3 bearded dragons, and, the adult cadavers of: 4 green iguana, 4 tegu, 4 bearded dragon were included in the study. 2 cadavers were dissected following a stratigraphic approach and 2 cadavers were cross-sectioned for each species. These latter specimens were stored in a freezer (-20°C) until completely frozen. Transversal sections at 5 mm intervals were obtained by means of an electric band-saw. Each section was cleaned and photographed on both sides. Radiographs of the head of each subject were obtained. Pre- and post- contrast computed tomographic studies of the head were performed on all the live animals. CT images were displayed in both bone and soft tissue windows. Individual anatomic structures were first recognised and labelled on the anatomic images and then matched on radiographs and CT images. Radiographic and CT images of the skull provided good detail of the bony structures in all species. In CT contrast medium injection enabled good detail of the soft tissues to be obtained in the iguana whereas only the eye was clearly distinguishable from the remaining soft tissues in both the tegu and the bearded dragon. The results provide an atlas of the normal anatomical and in vivo radiographic and computed tomographic features of the heads of lizards, and this may be useful in interpreting any imaging modality involving these species.

Comparative evaluation of the cadaveric, radiographic and computed tomographic anatomy of the heads of green iguana (Iguana iguana) , common tegu ( Tupinambis merianae) and bearded dragon ( Pogona vitticeps)

PubMed Central

2012-01-01

Background Radiology and computed tomography are the most commonly available diagnostic tools for the diagnosis of pathologies affecting the head and skull in veterinary practice. Nevertheless, accurate interpretation of radiographic and CT studies requires a thorough knowledge of the gross and the cross-sectional anatomy. Despite the increasing success of reptiles as pets, only a few reports over their normal imaging features are currently available. The aim of this study is to describe the normal cadaveric, radiographic and computed tomographic features of the heads of the green iguana, tegu and bearded dragon. Results 6 adult green iguanas, 4 tegus, 3 bearded dragons, and, the adult cadavers of : 4 green iguana, 4 tegu, 4 bearded dragon were included in the study. 2 cadavers were dissected following a stratigraphic approach and 2 cadavers were cross-sectioned for each species. These latter specimens were stored in a freezer (−20°C) until completely frozen. Transversal sections at 5 mm intervals were obtained by means of an electric band-saw. Each section was cleaned and photographed on both sides. Radiographs of the head of each subject were obtained. Pre- and post- contrast computed tomographic studies of the head were performed on all the live animals. CT images were displayed in both bone and soft tissue windows. Individual anatomic structures were first recognised and labelled on the anatomic images and then matched on radiographs and CT images. Radiographic and CT images of the skull provided good detail of the bony structures in all species. In CT contrast medium injection enabled good detail of the soft tissues to be obtained in the iguana whereas only the eye was clearly distinguishable from the remaining soft tissues in both the tegu and the bearded dragon. Conclusions The results provide an atlas of the normal anatomical and in vivo radiographic and computed tomographic features of the heads of lizards, and this may be useful in interpreting any imaging modality involving these species. PMID:22578088

Internal respiratory surrogate in multislice 4D CT using a combination of Fourier transform and anatomical features

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hui, Cheukkai; Suh, Yelin; Robertson, Daniel

Purpose: The purpose of this study was to develop a novel algorithm to create a robust internal respiratory signal (IRS) for retrospective sorting of four-dimensional (4D) computed tomography (CT) images. Methods: The proposed algorithm combines information from the Fourier transform of the CT images and from internal anatomical features to form the IRS. The algorithm first extracts potential respiratory signals from low-frequency components in the Fourier space and selected anatomical features in the image space. A clustering algorithm then constructs groups of potential respiratory signals with similar temporal oscillation patterns. The clustered group with the largest number of similar signalsmore » is chosen to form the final IRS. To evaluate the performance of the proposed algorithm, the IRS was computed and compared with the external respiratory signal from the real-time position management (RPM) system on 80 patients. Results: In 72 (90%) of the 4D CT data sets tested, the IRS computed by the authors’ proposed algorithm matched with the RPM signal based on their normalized cross correlation. For these data sets with matching respiratory signals, the average difference between the end inspiration times (Δt{sub ins}) in the IRS and RPM signal was 0.11 s, and only 2.1% of Δt{sub ins} were more than 0.5 s apart. In the eight (10%) 4D CT data sets in which the IRS and the RPM signal did not match, the average Δt{sub ins} was 0.73 s in the nonmatching couch positions, and 35.4% of them had a Δt{sub ins} greater than 0.5 s. At couch positions in which IRS did not match the RPM signal, a correlation-based metric indicated poorer matching of neighboring couch positions in the RPM-sorted images. This implied that, when IRS did not match the RPM signal, the images sorted using the IRS showed fewer artifacts than the clinical images sorted using the RPM signal. Conclusions: The authors’ proposed algorithm can generate robust IRSs that can be used for retrospective sorting of 4D CT data. The algorithm is completely automatic and requires very little processing time. The algorithm is cost efficient and can be easily adopted for everyday clinical use.« less

3D-SIFT-Flow for atlas-based CT liver image segmentation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xu, Yan, E-mail: xuyan04@gmail.com; Xu, Chenchao, E-mail: chenchaoxu33@gmail.com; Kuang, Xiao, E-mail: kuangxiao.ace@gmail.com

Purpose: In this paper, the authors proposed a new 3D registration algorithm, 3D-scale invariant feature transform (SIFT)-Flow, for multiatlas-based liver segmentation in computed tomography (CT) images. Methods: In the registration work, the authors developed a new registration method that takes advantage of dense correspondence using the informative and robust SIFT feature. The authors computed the dense SIFT features for the source image and the target image and designed an objective function to obtain the correspondence between these two images. Labeling of the source image was then mapped to the target image according to the former correspondence, resulting in accurate segmentation.more » In the fusion work, the 2D-based nonparametric label transfer method was extended to 3D for fusing the registered 3D atlases. Results: Compared with existing registration algorithms, 3D-SIFT-Flow has its particular advantage in matching anatomical structures (such as the liver) that observe large variation/deformation. The authors observed consistent improvement over widely adopted state-of-the-art registration methods such as ELASTIX, ANTS, and multiatlas fusion methods such as joint label fusion. Experimental results of liver segmentation on the MICCAI 2007 Grand Challenge are encouraging, e.g., Dice overlap ratio 96.27% ± 0.96% by our method compared with the previous state-of-the-art result of 94.90% ± 2.86%. Conclusions: Experimental results show that 3D-SIFT-Flow is robust for segmenting the liver from CT images, which has large tissue deformation and blurry boundary, and 3D label transfer is effective and efficient for improving the registration accuracy.« less

HoDOr: histogram of differential orientations for rigid landmark tracking in medical images

NASA Astrophysics Data System (ADS)

Tiwari, Abhishek; Patwardhan, Kedar Anil

2018-03-01

Feature extraction plays a pivotal role in pattern recognition and matching. An ideal feature should be invariant to image transformations such as translation, rotation, scaling, etc. In this work, we present a novel rotation-invariant feature, which is based on Histogram of Oriented Gradients (HOG). We compare performance of the proposed approach with the HOG feature on 2D phantom data, as well as 3D medical imaging data. We have used traditional histogram comparison measures such as Bhattacharyya distance and Normalized Correlation Coefficient (NCC) to assess efficacy of the proposed approach under effects of image rotation. In our experiments, the proposed feature performs 40%, 20%, and 28% better than the HOG feature on phantom (2D), Computed Tomography (CT-3D), and Ultrasound (US-3D) data for image matching, and landmark tracking tasks respectively.

CT diagnosis of a clinically unsuspected acute appendicitis complicating infectious mononucleosis.

PubMed

Zissin, R; Brautbar, O; Shapiro-Feinberg, M

2001-01-01

Acute appendicitis is a rare complication of infectious mononucleosis (IM). We describe a patient with IM and splenic rupture with a computerized tomography (CT) diagnosis of acute appendicitis during the acute phase of the infectious disease. Diagnostic imaging features of acute appendicitis were found on an abdominal CT performed for the evaluation of postoperative fever. Histologic examination confirmed the CT diagnosis of the clinically unsuspected acute appendicitis. Our case is unique both for the rarity of this complication and the lack of clinical symptoms.

A Method for Extracting Suspected Parotid Lesions in CT Images using Feature-based Segmentation and Active Contours based on Stationary Wavelet Transform

NASA Astrophysics Data System (ADS)

Wu, T. Y.; Lin, S. F.

2013-10-01

Automatic suspected lesion extraction is an important application in computer-aided diagnosis (CAD). In this paper, we propose a method to automatically extract the suspected parotid regions for clinical evaluation in head and neck CT images. The suspected lesion tissues in low contrast tissue regions can be localized with feature-based segmentation (FBS) based on local texture features, and can be delineated with accuracy by modified active contour models (ACM). At first, stationary wavelet transform (SWT) is introduced. The derived wavelet coefficients are applied to derive the local features for FBS, and to generate enhanced energy maps for ACM computation. Geometric shape features (GSFs) are proposed to analyze each soft tissue region segmented by FBS; the regions with higher similarity GSFs with the lesions are extracted and the information is also applied as the initial conditions for fine delineation computation. Consequently, the suspected lesions can be automatically localized and accurately delineated for aiding clinical diagnosis. The performance of the proposed method is evaluated by comparing with the results outlined by clinical experts. The experiments on 20 pathological CT data sets show that the true-positive (TP) rate on recognizing parotid lesions is about 94%, and the dimension accuracy of delineation results can also approach over 93%.

Recent micro-CT scanner developments at UGCT

NASA Astrophysics Data System (ADS)

Dierick, Manuel; Van Loo, Denis; Masschaele, Bert; Van den Bulcke, Jan; Van Acker, Joris; Cnudde, Veerle; Van Hoorebeke, Luc

2014-04-01

This paper describes two X-ray micro-CT scanners which were recently developed to extend the experimental possibilities of microtomography research at the Centre for X-ray Tomography (www.ugct.ugent.be) of the Ghent University (Belgium). The first scanner, called Nanowood, is a wide-range CT scanner with two X-ray sources (160 kVmax) and two detectors, resolving features down to 0.4 μm in small samples, but allowing samples up to 35 cm to be scanned. This is a sample size range of 3 orders of magnitude, making this scanner well suited for imaging multi-scale materials such as wood, stone, etc. Besides the traditional cone-beam acquisition, Nanowood supports helical acquisition, and it can generate images with significant phase-contrast contributions. The second scanner, known as the Environmental micro-CT scanner (EMCT), is a gantry based micro-CT scanner with variable magnification for scanning objects which are not easy to rotate in a standard micro-CT scanner, for example because they are physically connected to external experimental hardware such as sensor wiring, tubing or others. This scanner resolves 5 μm features, covers a field-of-view of about 12 cm wide with an 80 cm vertical travel range. Both scanners will be extensively described and characterized, and their potential will be demonstrated with some key application results.

Three-dimensional visualization of the microvasculature of bile duct ligation-induced liver fibrosis in rats by x-ray phase-contrast imaging computed tomography

NASA Astrophysics Data System (ADS)

Xuan, Ruijiao; Zhao, Xinyan; Hu, Doudou; Jian, Jianbo; Wang, Tailing; Hu, Chunhong

2015-07-01

X-ray phase-contrast imaging (PCI) can substantially enhance contrast, and is particularly useful in differentiating biological soft tissues with small density differences. Combined with computed tomography (CT), PCI-CT enables the acquisition of accurate microstructures inside biological samples. In this study, liver microvasculature was visualized without contrast agents in vitro with PCI-CT using liver fibrosis samples induced by bile duct ligation (BDL) in rats. The histological section examination confirmed the correspondence of CT images with the microvascular morphology of the samples. By means of the PCI-CT and three-dimensional (3D) visualization technique, 3D microvascular structures in samples from different stages of liver fibrosis were clearly revealed. Different types of blood vessels, including portal veins and hepatic veins, in addition to ductular proliferation and bile ducts, could be distinguished with good sensitivity, excellent specificity and excellent accuracy. The study showed that PCI-CT could assess the morphological changes in liver microvasculature that result from fibrosis and allow characterization of the anatomical and pathological features of the microvasculature. With further development of PCI-CT technique, it may become a novel noninvasive imaging technique for the auxiliary analysis of liver fibrosis.

SU-F-R-24: Identifying Prognostic Imaging Biomarkers in Early Stage Lung Cancer Using Radiomics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zeng, X; Wu, J; Cui, Y

2016-06-15

Purpose: Patients diagnosed with early stage lung cancer have favorable outcomes when treated with surgery or stereotactic radiotherapy. However, a significant proportion (∼20%) of patients will develop metastatic disease and eventually die of the disease. The purpose of this work is to identify quantitative imaging biomarkers from CT for predicting overall survival in early stage lung cancer. Methods: In this institutional review board-approved HIPPA-compliant retrospective study, we retrospectively analyzed the diagnostic CT scans of 110 patients with early stage lung cancer. Data from 70 patients were used for training/discovery purposes, while those of remaining 40 patients were used for independentmore » validation. We extracted 191 radiomic features, including statistical, histogram, morphological, and texture features. Cox proportional hazard regression model, coupled with the least absolute shrinkage and selection operator (LASSO), was used to predict overall survival based on the radiomic features. Results: The optimal prognostic model included three image features from the Law’s feature and wavelet texture. In the discovery cohort, this model achieved a concordance index or CI=0.67, and it separated the low-risk from high-risk groups in predicting overall survival (hazard ratio=2.72, log-rank p=0.007). In the independent validation cohort, this radiomic signature achieved a CI=0.62, and significantly stratified the low-risk and high-risk groups in terms of overall survival (hazard ratio=2.20, log-rank p=0.042). Conclusion: We identified CT imaging characteristics associated with overall survival in early stage lung cancer. If prospectively validated, this could potentially help identify high-risk patients who might benefit from adjuvant systemic therapy.« less

Optimization of CT image reconstruction algorithms for the lung tissue research consortium (LTRC)

NASA Astrophysics Data System (ADS)

McCollough, Cynthia; Zhang, Jie; Bruesewitz, Michael; Bartholmai, Brian

2006-03-01

To create a repository of clinical data, CT images and tissue samples and to more clearly understand the pathogenetic features of pulmonary fibrosis and emphysema, the National Heart, Lung, and Blood Institute (NHLBI) launched a cooperative effort known as the Lung Tissue Resource Consortium (LTRC). The CT images for the LTRC effort must contain accurate CT numbers in order to characterize tissues, and must have high-spatial resolution to show fine anatomic structures. This study was performed to optimize the CT image reconstruction algorithms to achieve these criteria. Quantitative analyses of phantom and clinical images were conducted. The ACR CT accreditation phantom containing five regions of distinct CT attenuations (CT numbers of approximately -1000 HU, -80 HU, 0 HU, 130 HU and 900 HU), and a high-contrast spatial resolution test pattern, was scanned using CT systems from two manufacturers (General Electric (GE) Healthcare and Siemens Medical Solutions). Phantom images were reconstructed using all relevant reconstruction algorithms. Mean CT numbers and image noise (standard deviation) were measured and compared for the five materials. Clinical high-resolution chest CT images acquired on a GE CT system for a patient with diffuse lung disease were reconstructed using BONE and STANDARD algorithms and evaluated by a thoracic radiologist in terms of image quality and disease extent. The clinical BONE images were processed with a 3 x 3 x 3 median filter to simulate a thicker slice reconstructed in smoother algorithms, which have traditionally been proven to provide an accurate estimation of emphysema extent in the lungs. Using a threshold technique, the volume of emphysema (defined as the percentage of lung voxels having a CT number lower than -950 HU) was computed for the STANDARD, BONE, and BONE filtered. The CT numbers measured in the ACR CT Phantom images were accurate for all reconstruction kernels for both manufacturers. As expected, visual evaluation of the spatial resolution bar patterns demonstrated that the BONE (GE) and B46f (Siemens) showed higher spatial resolution compared to the STANDARD (GE) or B30f (Siemens) reconstruction algorithms typically used for routine body CT imaging. Only the sharper images were deemed clinically acceptable for the evaluation of diffuse lung disease (e.g. emphysema). Quantitative analyses of the extent of emphysema in patient data showed the percent volumes above the -950 HU threshold as 9.4% for the BONE reconstruction, 5.9% for the STANDARD reconstruction, and 4.7% for the BONE filtered images. Contrary to the practice of using standard resolution CT images for the quantitation of diffuse lung disease, these data demonstrate that a single sharp reconstruction (BONE/B46f) should be used for both the qualitative and quantitative evaluation of diffuse lung disease. The sharper reconstruction images, which are required for diagnostic interpretation, provide accurate CT numbers over the range of -1000 to +900 HU and preserve the fidelity of small structures in the reconstructed images. A filtered version of the sharper images can be accurately substituted for images reconstructed with smoother kernels for comparison to previously published results.

Automated detection of pulmonary nodules in CT images with support vector machines

NASA Astrophysics Data System (ADS)

Liu, Lu; Liu, Wanyu; Sun, Xiaoming

2008-10-01

Many methods have been proposed to avoid radiologists fail to diagnose small pulmonary nodules. Recently, support vector machines (SVMs) had received an increasing attention for pattern recognition. In this paper, we present a computerized system aimed at pulmonary nodules detection; it identifies the lung field, extracts a set of candidate regions with a high sensitivity ratio and then classifies candidates by the use of SVMs. The Computer Aided Diagnosis (CAD) system presented in this paper supports the diagnosis of pulmonary nodules from Computed Tomography (CT) images as inflammation, tuberculoma, granuloma..sclerosing hemangioma, and malignant tumor. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of SVMs classifiers. The achieved classification performance was 100%, 92.75% and 90.23% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis.

Imaging Features of Radiofrequency Ablation with Heat-Deployed Liposomal Doxorubicin in Hepatic Tumors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hong, Cheng William, E-mail: williamhongcheng@gmail.com; Chow, Lucy, E-mail: lucychow282@gmail.com; Turkbey, Evrim B., E-mail: evrimbengi@yahoo.com

2016-03-15

IntroductionThe imaging features of unresectable hepatic malignancies in patients who underwent radiofrequency ablation (RFA) in combination with lyso-thermosensitive liposomal doxorubicin (LTLD) were determined.Materials and MethodsA phase I dose escalation study combining RFA with LTLD was performed with peri- and post- procedural CT and MRI. Imaging features were analyzed and measured in terms of ablative zone size and surrounding penumbra size. The dynamic imaging appearance was described qualitatively immediately following the procedure and at 1-month follow-up. The control group receiving liver RFA without LTLD was compared to the study group in terms of imaging features and post-ablative zone size dynamics atmore » follow-up.ResultsPost-treatment scans of hepatic lesions treated with RFA and LTLD have distinctive imaging characteristics when compared to those treated with RFA alone. The addition of LTLD resulted in a regular or smooth enhancing rim on T1W MRI which often correlated with increased attenuation on CT. The LTLD-treated ablation zones were stable or enlarged at follow-up four weeks later in 69 % of study subjects as opposed to conventional RFA where the ablation zone underwent involution compared to imaging acquired immediately after the procedure.ConclusionThe imaging features following RFA with LTLD were different from those after standard RFA and can mimic residual or recurrent tumor. Knowledge of the subtle findings between the two groups can help avoid misinterpretation and proper identification of treatment failure in this setting. Increased size of the LTLD-treated ablation zone after RFA suggests the ongoing drug-induced biological effects.« less

Group-wise feature-based registration of CT and ultrasound images of spine

NASA Astrophysics Data System (ADS)

Rasoulian, Abtin; Mousavi, Parvin; Hedjazi Moghari, Mehdi; Foroughi, Pezhman; Abolmaesumi, Purang

2010-02-01

Registration of pre-operative CT and freehand intra-operative ultrasound of lumbar spine could aid surgeons in the spinal needle injection which is a common procedure for pain management. Patients are always in a supine position during the CT scan, and in the prone or sitting position during the intervention. This leads to a difference in the spinal curvature between the two imaging modalities, which means a single rigid registration cannot be used for all of the lumbar vertebrae. In this work, a method for group-wise registration of pre-operative CT and intra-operative freehand 2-D ultrasound images of the lumbar spine is presented. The approach utilizes a pointbased registration technique based on the unscented Kalman filter, taking as input segmented vertebrae surfaces in both CT and ultrasound data. Ultrasound images are automatically segmented using a dynamic programming approach, while the CT images are semi-automatically segmented using thresholding. Since the curvature of the spine is different between the pre-operative and the intra-operative data, the registration approach is designed to simultaneously align individual groups of points segmented from each vertebra in the two imaging modalities. A biomechanical model is used to constrain the vertebrae transformation parameters during the registration and to ensure convergence. The mean target registration error achieved for individual vertebrae on five spine phantoms generated from CT data of patients, is 2.47 mm with standard deviation of 1.14 mm.

An Efficient Pipeline for Abdomen Segmentation in CT Images.

PubMed

Koyuncu, Hasan; Ceylan, Rahime; Sivri, Mesut; Erdogan, Hasan

2018-04-01

Computed tomography (CT) scans usually include some disadvantages due to the nature of the imaging procedure, and these handicaps prevent accurate abdomen segmentation. Discontinuous abdomen edges, bed section of CT, patient information, closeness between the edges of the abdomen and CT, poor contrast, and a narrow histogram can be regarded as the most important handicaps that occur in abdominal CT scans. Currently, one or more handicaps can arise and prevent technicians obtaining abdomen images through simple segmentation techniques. In other words, CT scans can include the bed section of CT, a patient's diagnostic information, low-quality abdomen edges, low-level contrast, and narrow histogram, all in one scan. These phenomena constitute a challenge, and an efficient pipeline that is unaffected by handicaps is required. In addition, analysis such as segmentation, feature selection, and classification has meaning for a real-time diagnosis system in cases where the abdomen section is directly used with a specific size. A statistical pipeline is designed in this study that is unaffected by the handicaps mentioned above. Intensity-based approaches, morphological processes, and histogram-based procedures are utilized to design an efficient structure. Performance evaluation is realized in experiments on 58 CT images (16 training, 16 test, and 26 validation) that include the abdomen and one or more disadvantage(s). The first part of the data (16 training images) is used to detect the pipeline's optimum parameters, while the second and third parts are utilized to evaluate and to confirm the segmentation performance. The segmentation results are presented as the means of six performance metrics. Thus, the proposed method achieves remarkable average rates for training/test/validation of 98.95/99.36/99.57% (jaccard), 99.47/99.67/99.79% (dice), 100/99.91/99.91% (sensitivity), 98.47/99.23/99.85% (specificity), 99.38/99.63/99.87% (classification accuracy), and 98.98/99.45/99.66% (precision). In summary, a statistical pipeline performing the task of abdomen segmentation is achieved that is not affected by the disadvantages, and the most detailed abdomen segmentation study is performed for the use before organ and tumor segmentation, feature extraction, and classification.

PET/CT: underlying physics, instrumentation, and advances.

PubMed

Torres Espallardo, I

Since it was first introduced, the main goal of PET/CT has been to provide both PET and CT images with high clinical quality and to present them to radiologists and specialists in nuclear medicine as a fused, perfectly aligned image. The use of fused PET and CT images quickly became routine in clinical practice, showing the great potential of these hybrid scanners. Thanks to this success, manufacturers have gone beyond considering CT as a mere attenuation corrector for PET, concentrating instead on design high performance PET and CT scanners with more interesting features. Since the first commercial PET/CT scanner became available in 2001, both the PET component and the CT component have improved immensely. In the case of PET, faster scintillation crystals with high stopping power such as LYSO crystals have enabled more sensitive devices to be built, making it possible to reduce the number of undesired coincidence events and to use time of flight (TOF) techniques. All these advances have improved lesion detection, especially in situations with very noisy backgrounds. Iterative reconstruction methods, together with the corrections carried out during the reconstruction and the use of the point-spread function, have improved image quality. In parallel, CT instrumentation has also improved significantly, and 64- and 128-row detectors have been incorporated into the most modern PET/CT scanners. This makes it possible to obtain high quality diagnostic anatomic images in a few seconds that both enable the correction of PET attenuation and provide information for diagnosis. Furthermore, nowadays nearly all PET/CT scanners have a system that modulates the dose of radiation that the patient is exposed to in the CT study in function of the region scanned. This article reviews the underlying physics of PET and CT imaging separately, describes the changes in the instrumentation and standard protocols in a combined PET/CT system, and finally points out the most important advances in this hybrid imaging modality. Copyright © 2016 SERAM. Publicado por Elsevier España, S.L.U. All rights reserved.

Computed tomography imaging features of hepatic perivascular epithelioid cell tumor: A case report and literature review.

PubMed

Han, Xu; Sun, Mei-Yu; Liu, Jing-Hong; Zhang, Xiao-Yan; Wang, Meng-Yao; Fan, Rui; Qamar, Sahrish

2017-12-01

Perivascular epithelioid cell tumor (PEComa) is a rare tumor which is most frequently found in uterus. The tumor arising from liver is extremely uncommon. A 36-year-old female with abdominal distention, cramps, and low-grade fever for over 15 days. The patient had a history of gastric adenocarcinoma with ovarian, celiac lymph nodes, and retroperitoneal lymph nodes metastases. Computed tomography (CT) imaging demonstrated an ill-defined heterogeneous hypo-dense mass in segment 8 (S8) of the liver. Contrast-enhanced CT imaging showed marked enhancement in arterial phase, mild-to-moderate enhancement in portal and equilibrium phases. Tumor-feeding artery was demonstrated from the right hepatic artery by the three-dimensional reconstruction images. Biopsy was performed, and a diagnosis of PEComa was rendered. No intervention for this tumor before liver biopsy. We present a rare case of hepatic PEComa. The information we provided is useful for summarizing the CT features of this kind of tumors. It should be included in differential diagnoses from common hypervascular neoplasms of liver. The final diagnosis is established on histopathological and immunohistochemical studies that are the "gold standard."

Computed tomography imaging features of hepatic perivascular epithelioid cell tumor

PubMed Central

Han, Xu; Sun, Mei-Yu; Liu, Jing-Hong; Zhang, Xiao-Yan; Wang, Meng-Yao; Fan, Rui; Qamar, Sahrish

2017-01-01

Abstract Rationale: Perivascular epithelioid cell tumor (PEComa) is a rare tumor which is most frequently found in uterus. The tumor arising from liver is extremely uncommon. Patient concerns: A 36-year-old female with abdominal distention, cramps, and low-grade fever for over 15 days. The patient had a history of gastric adenocarcinoma with ovarian, celiac lymph nodes, and retroperitoneal lymph nodes metastases. Diagnoses: Computed tomography (CT) imaging demonstrated an ill-defined heterogeneous hypo-dense mass in segment 8 (S8) of the liver. Contrast-enhanced CT imaging showed marked enhancement in arterial phase, mild-to-moderate enhancement in portal and equilibrium phases. Tumor-feeding artery was demonstrated from the right hepatic artery by the three-dimensional reconstruction images. Biopsy was performed, and a diagnosis of PEComa was rendered. Interventions: No intervention for this tumor before liver biopsy. Lessons: We present a rare case of hepatic PEComa. The information we provided is useful for summarizing the CT features of this kind of tumors. It should be included in differential diagnoses from common hypervascular neoplasms of liver. The final diagnosis is established on histopathological and immunohistochemical studies that are the “gold standard.” PMID:29245304

SU-E-I-43: Pediatric CT Dose and Image Quality Optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stevens, G; Singh, R

2014-06-01

Purpose: To design an approach to optimize radiation dose and image quality for pediatric CT imaging, and to evaluate expected performance. Methods: A methodology was designed to quantify relative image quality as a function of CT image acquisition parameters. Image contrast and image noise were used to indicate expected conspicuity of objects, and a wide-cone system was used to minimize scan time for motion avoidance. A decision framework was designed to select acquisition parameters as a weighted combination of image quality and dose. Phantom tests were used to acquire images at multiple techniques to demonstrate expected contrast, noise and dose.more » Anthropomorphic phantoms with contrast inserts were imaged on a 160mm CT system with tube voltage capabilities as low as 70kVp. Previously acquired clinical images were used in conjunction with simulation tools to emulate images at different tube voltages and currents to assess human observer preferences. Results: Examination of image contrast, noise, dose and tube/generator capabilities indicates a clinical task and object-size dependent optimization. Phantom experiments confirm that system modeling can be used to achieve the desired image quality and noise performance. Observer studies indicate that clinical utilization of this optimization requires a modified approach to achieve the desired performance. Conclusion: This work indicates the potential to optimize radiation dose and image quality for pediatric CT imaging. In addition, the methodology can be used in an automated parameter selection feature that can suggest techniques given a limited number of user inputs. G Stevens and R Singh are employees of GE Healthcare.« less

CT imaging of ovarian yolk sac tumor with emphasis on differential diagnosis

PubMed Central

Li, Yang-Kang; Zheng, Yu; Lin, Jian-Bang; Xu, Gui-Xiao; Cai, Ai-Qun; Zhou, Xiu-Guo; Zhang, Guo-Jun

2015-01-01

Ovarian yolk sac tumors (YSTs) are rare neoplasms. No radiological study has been done to compare the imaging findings between this type of tumor and other ovarian tumors. Here we analyzed the CT findings of 11 pathologically proven ovarian YSTs and compared their imaging findings with 18 other types of ovarian tumors in the same age range. Patient age, tumor size, tumor shape, ascites and metastasis of two groups did not differ significantly (P > 0.05). A mixed solid-cystic nature, intratumoral hemorrhage, marked enhancement and dilated intratumoral vessel of two groups differed significantly (P < 0.05). The area under the ROC curve of four significant CT features was 0.679, 0.707, 0.705, and 1.000, respectively. Multivariate logistic regression analysis identified two independent signs of YST: intratumoral hemorrhage and marked enhancement. Our results show that certain suggestive CT signs that may be valuable for improving the accuracy of imaging diagnosis of YST and may be helpful in distinguishing YST from other ovarian tumors. PMID:26074455

Quantification of organ motion based on an adaptive image-based scale invariant feature method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Paganelli, Chiara; Peroni, Marta; Baroni, Guido

2013-11-15

Purpose: The availability of corresponding landmarks in IGRT image series allows quantifying the inter and intrafractional motion of internal organs. In this study, an approach for the automatic localization of anatomical landmarks is presented, with the aim of describing the nonrigid motion of anatomo-pathological structures in radiotherapy treatments according to local image contrast.Methods: An adaptive scale invariant feature transform (SIFT) was developed from the integration of a standard 3D SIFT approach with a local image-based contrast definition. The robustness and invariance of the proposed method to shape-preserving and deformable transforms were analyzed in a CT phantom study. The application ofmore » contrast transforms to the phantom images was also tested, in order to verify the variation of the local adaptive measure in relation to the modification of image contrast. The method was also applied to a lung 4D CT dataset, relying on manual feature identification by an expert user as ground truth. The 3D residual distance between matches obtained in adaptive-SIFT was then computed to verify the internal motion quantification with respect to the expert user. Extracted corresponding features in the lungs were used as regularization landmarks in a multistage deformable image registration (DIR) mapping the inhale vs exhale phase. The residual distances between the warped manual landmarks and their reference position in the inhale phase were evaluated, in order to provide a quantitative indication of the registration performed with the three different point sets.Results: The phantom study confirmed the method invariance and robustness properties to shape-preserving and deformable transforms, showing residual matching errors below the voxel dimension. The adapted SIFT algorithm on the 4D CT dataset provided automated and accurate motion detection of peak to peak breathing motion. The proposed method resulted in reduced residual errors with respect to standard SIFT, providing a motion description comparable to expert manual identification, as confirmed by DIR.Conclusions: The application of the method to a 4D lung CT patient dataset demonstrated adaptive-SIFT potential as an automatic tool to detect landmarks for DIR regularization and internal motion quantification. Future works should include the optimization of the computational cost and the application of the method to other anatomical sites and image modalities.« less

Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks

PubMed Central

Ibragimov, Bulat; Xing, Lei

2017-01-01

Purpose Accurate segmentation of organs-at-risks (OARs) is the key step for efficient planning of radiation therapy for head and neck (HaN) cancer treatment. In the work, we proposed the first deep learning-based algorithm, for segmentation of OARs in HaN CT images, and compared its performance against state-of-the-art automated segmentation algorithms, commercial software and inter-observer variability. Methods Convolutional neural networks (CNNs) – a concept from the field of deep learning – were used to study consistent intensity patterns of OARs from training CT images and to segment the OAR in a previously unseen test CT image. For CNN training, we extracted a representative number of positive intensity patches around voxels that belong to the OAR of interest in training CT images, and negative intensity patches around voxels that belong to the surrounding structures. These patches then passed through a sequence of CNN layers that captured local image features such as corners, end-points and edges, and combined them into more complex high-order features that can efficiently describe the OAR. The trained network was applied to classify voxels in a region of interest in the test image where the corresponding OAR is expected to be located. We then smoothed the obtained classification results by using Markov random fields algorithm. We finally extracted the largest connected component of the smoothed voxels classified as the OAR by CNN, performed dilate-erode operations to remov cavities of the component, which resulted in segmentation of the OAR in the test image. Results The performance of CNNs was validated on segmentation of spinal cord, mandible, parotid glands, submandibular glands, larynx, pharynx, eye globes, optic nerves and optic chiasm using 50 CT images. The obtained segmentation results varied from 37.4% Dice coefficient (DSC) for chiasm to 89.5% DSC for mandible. We also analyzed the performance of state-of-the-art algorithms and commercial software reported in the literature, and observed that CNNs demonstrate similar or superior performance on segmentation of spinal cord, mandible, parotid glands, larynx, pharynx, eye globes and optic nerves, but inferior performance on segmentation of submandibular glands and optic chiasm. Conclusion We concluded that convolution neural networks can accurately segment most of OARs using a representative database of 50 HaN CT images. At the same time, inclusion of additional information, e.g. MR images, may be beneficial for some OARs with poorly-visible boundaries. PMID:28205307

Computer-aided diagnosis in phase contrast imaging X-ray computed tomography for quantitative characterization of ex vivo human patellar cartilage.

PubMed

Nagarajan, Mahesh B; Coan, Paola; Huber, Markus B; Diemoz, Paul C; Glaser, Christian; Wismuller, Axel

2013-10-01

Visualization of ex vivo human patellar cartilage matrix through the phase contrast imaging X-ray computed tomography (PCI-CT) has been previously demonstrated. Such studies revealed osteoarthritis-induced changes to chondrocyte organization in the radial zone. This study investigates the application of texture analysis to characterizing such chondrocyte patterns in the presence and absence of osteoarthritic damage. Texture features derived from Minkowski functionals (MF) and gray-level co-occurrence matrices (GLCM) were extracted from 842 regions of interest (ROI) annotated on PCI-CT images of ex vivo human patellar cartilage specimens. These texture features were subsequently used in a machine learning task with support vector regression to classify ROIs as healthy or osteoarthritic; classification performance was evaluated using the area under the receiver operating characteristic curve (AUC). The best classification performance was observed with the MF features perimeter (AUC: 0.94 ±0.08 ) and "Euler characteristic" (AUC: 0.94 ±0.07 ), and GLCM-derived feature "Correlation" (AUC: 0.93 ±0.07). These results suggest that such texture features can provide a detailed characterization of the chondrocyte organization in the cartilage matrix, enabling classification of cartilage as healthy or osteoarthritic with high accuracy.

Texture classification of normal tissues in computed tomography using Gabor filters

NASA Astrophysics Data System (ADS)

Dettori, Lucia; Bashir, Alia; Hasemann, Julie

2007-03-01

The research presented in this article is aimed at developing an automated imaging system for classification of normal tissues in medical images obtained from Computed Tomography (CT) scans. Texture features based on a bank of Gabor filters are used to classify the following tissues of interests: liver, spleen, kidney, aorta, trabecular bone, lung, muscle, IP fat, and SQ fat. The approach consists of three steps: convolution of the regions of interest with a bank of 32 Gabor filters (4 frequencies and 8 orientations), extraction of two Gabor texture features per filter (mean and standard deviation), and creation of a Classification and Regression Tree-based classifier that automatically identifies the various tissues. The data set used consists of approximately 1000 DIACOM images from normal chest and abdominal CT scans of five patients. The regions of interest were labeled by expert radiologists. Optimal trees were generated using two techniques: 10-fold cross-validation and splitting of the data set into a training and a testing set. In both cases, perfect classification rules were obtained provided enough images were available for training (~65%). All performance measures (sensitivity, specificity, precision, and accuracy) for all regions of interest were at 100%. This significantly improves previous results that used Wavelet, Ridgelet, and Curvelet texture features, yielding accuracy values in the 85%-98% range The Gabor filters' ability to isolate features at different frequencies and orientations allows for a multi-resolution analysis of texture essential when dealing with, at times, very subtle differences in the texture of tissues in CT scans.

Evaluation of deformable image registration and a motion model in CT images with limited features.

PubMed

Liu, F; Hu, Y; Zhang, Q; Kincaid, R; Goodman, K A; Mageras, G S

2012-05-07

Deformable image registration (DIR) is increasingly used in radiotherapy applications and provides the basis for a previously described model of patient-specific respiratory motion. We examine the accuracy of a DIR algorithm and a motion model with respiration-correlated CT (RCCT) images of software phantom with known displacement fields, physical deformable abdominal phantom with implanted fiducials in the liver and small liver structures in patient images. The motion model is derived from a principal component analysis that relates volumetric deformations with the motion of the diaphragm or fiducials in the RCCT. Patient data analysis compares DIR with rigid registration as ground truth: the mean ± standard deviation 3D discrepancy of liver structure centroid positions is 2.0 ± 2.2 mm. DIR discrepancy in the software phantom is 3.8 ± 2.0 mm in lung and 3.7 ± 1.8 mm in abdomen; discrepancies near the chest wall are larger than indicated by image feature matching. Marker's 3D discrepancy in the physical phantom is 3.6 ± 2.8 mm. The results indicate that visible features in the images are important for guiding the DIR algorithm. Motion model accuracy is comparable to DIR, indicating that two principal components are sufficient to describe DIR-derived deformation in these datasets.

Prediction of Late Postoperative Hemorrhage after Whipple Procedure Using Computed Tomography Performed During Early Postoperative Period.

PubMed

Han, Ga Jin; Kim, Suk; Lee, Nam Kyung; Kim, Chang Won; Seo, Hyeong Il; Kim, Hyun Sung; Kim, Tae Un

2018-01-01

Postpancreatectomy hemorrhage (PPH) is an uncommon but serious complication of Whipple surgery. To evaluate the radiologic features associated with late PPH at the first postoperative follow up CT, before bleeding. To evaluate the radiological features associated with late PPH at the first follow-up CT, two radiologists retrospectively reviewed the initial postoperative follow-up CT images of 151 patients, who had undergone Whipple surgery. Twenty patients showed PPH due to vascular problem or anastomotic ulcer. The research compared CT and clinical findings of 20 patients with late PPH and 131 patients without late PPH, including presence of suggestive feature of pancreatic fistula (presence of air at fluid along pancreaticojejunostomy [PJ]), abscess (fluid collection with an enhancing rim or gas), fluid along hepaticojejunostomy or PJ, the density of ascites, and the size of visible gastroduodenal artery (GDA) stump. CT findings including pancreatic fistula, abscess, and large GDA stump were associated with PPH on univariate analysis ( p ≤ 0.009). On multivariate analysis, radiological features suggestive of a pancreatic fistula, abscess, and a GDA stump > 4.45 mm were associated with PPH ( p ≤ 0.031). Early postoperative CT findings including GDA stump size larger than 4.45 mm, fluid collection with an enhancing rim or gas, and air at fluid along PJ, could predict late PPH.

Imaging and Clinicopathologic Features of Esophageal Gastrointestinal Stromal Tumors

PubMed Central

Winant, Abbey J.; Gollub, Marc J.; Shia, Jinru; Antonescu, Christina; Bains, Manjit S.; Levine, Marc S.

2016-01-01

OBJECTIVE The purpose of this article is to describe the imaging and clinicopathologic characteristics of esophageal gastrointestinal stromal tumors (GISTs) and to emphasize the features that differentiate esophageal GISTs from esophageal leiomyomas. MATERIALS AND METHODS A pathology database search identified all surgically resected or biopsied esophageal GISTs, esophageal leiomyomas, and esophageal leiomyosarcomas from 1994 to 2012. Esophageal GISTs were included only if imaging studies (including CT, fluoroscopic, or 18F-FDG PET/CT scans) and clinical data were available. RESULTS Nineteen esophageal mesenchymal tumors were identified, including eight esophageal GISTs (42%), 10 esophageal leiomyomas (53%), and one esophageal leiomyosarcoma (5%). Four patients (50%) with esophageal GIST had symptoms, including dysphagia in three (38%), cough in one (13%), and chest pain in one (13%). One esophageal GIST appeared on barium study as a smooth submucosal mass. All esophageal GISTs appeared on CT as well-marginated predominantly distal lesions, isoattenuating to muscle, that moderately enhanced after IV contrast agent administration. Compared with esophageal leiomyomas, esophageal GISTs tended to be more distal, larger, and more heterogeneous and showed greater IV enhancement on CT. All esophageal GISTs showed marked avidity (mean maximum standardized uptake value, 16) on PET scans. All esophageal GISTs were positive for c-KIT (a cell-surface transmembrane tyrosine kinase also known as CD117) and CD34. On histopathology, six esophageal GISTs (75%) were of the spindle pattern and two (25%) were of a mixed spindle and epithelioid pattern. Five esophageal GISTs had exon 11 mutations (with imatinib sensitivity). Clinical outcome correlated with treatment strategy (resection plus adjuvant therapy or resection alone) rather than risk stratification. CONCLUSION Esophageal GISTs are unusual but clinically important mesenchymal neoplasms. Although esophageal GISTs and esophageal leiomyomas had overlapping imaging features, esophageal GISTs tended to be more distal, larger, more heterogeneous, and more enhancing on CT and were markedly FDG avid on PET. Given their malignant potential, esophageal GISTs should be included in the differential diagnosis of intramural esophageal neoplasms. PMID:25055264

Markerless motion estimation for motion-compensated clinical brain imaging

NASA Astrophysics Data System (ADS)

Kyme, Andre Z.; Se, Stephen; Meikle, Steven R.; Fulton, Roger R.

2018-05-01

Motion-compensated brain imaging can dramatically reduce the artifacts and quantitative degradation associated with voluntary and involuntary subject head motion during positron emission tomography (PET), single photon emission computed tomography (SPECT) and computed tomography (CT). However, motion-compensated imaging protocols are not in widespread clinical use for these modalities. A key reason for this seems to be the lack of a practical motion tracking technology that allows for smooth and reliable integration of motion-compensated imaging protocols in the clinical setting. We seek to address this problem by investigating the feasibility of a highly versatile optical motion tracking method for PET, SPECT and CT geometries. The method requires no attached markers, relying exclusively on the detection and matching of distinctive facial features. We studied the accuracy of this method in 16 volunteers in a mock imaging scenario by comparing the estimated motion with an accurate marker-based method used in applications such as image guided surgery. A range of techniques to optimize performance of the method were also studied. Our results show that the markerless motion tracking method is highly accurate (<2 mm discrepancy against a benchmarking system) on an ethnically diverse range of subjects and, moreover, exhibits lower jitter and estimation of motion over a greater range than some marker-based methods. Our optimization tests indicate that the basic pose estimation algorithm is very robust but generally benefits from rudimentary background masking. Further marginal gains in accuracy can be achieved by accounting for non-rigid motion of features. Efficiency gains can be achieved by capping the number of features used for pose estimation provided that these features adequately sample the range of head motion encountered in the study. These proof-of-principle data suggest that markerless motion tracking is amenable to motion-compensated brain imaging and holds good promise for a practical implementation in clinical PET, SPECT and CT systems.

Fast algorithm for probabilistic bone edge detection (FAPBED)

NASA Astrophysics Data System (ADS)

Scepanovic, Danilo; Kirshtein, Joshua; Jain, Ameet K.; Taylor, Russell H.

2005-04-01

The registration of preoperative CT to intra-operative reality systems is a crucial step in Computer Assisted Orthopedic Surgery (CAOS). The intra-operative sensors include 3D digitizers, fiducials, X-rays and Ultrasound (US). FAPBED is designed to process CT volumes for registration to tracked US data. Tracked US is advantageous because it is real time, noninvasive, and non-ionizing, but it is also known to have inherent inaccuracies which create the need to develop a framework that is robust to various uncertainties, and can be useful in US-CT registration. Furthermore, conventional registration methods depend on accurate and absolute segmentation. Our proposed probabilistic framework addresses the segmentation-registration duality, wherein exact segmentation is not a prerequisite to achieve accurate registration. In this paper, we develop a method for fast and automatic probabilistic bone surface (edge) detection in CT images. Various features that influence the likelihood of the surface at each spatial coordinate are combined using a simple probabilistic framework, which strikes a fair balance between a high-level understanding of features in an image and the low-level number crunching of standard image processing techniques. The algorithm evaluates different features for detecting the probability of a bone surface at each voxel, and compounds the results of these methods to yield a final, low-noise, probability map of bone surfaces in the volume. Such a probability map can then be used in conjunction with a similar map from tracked intra-operative US to achieve accurate registration. Eight sample pelvic CT scans were used to extract feature parameters and validate the final probability maps. An un-optimized fully automatic Matlab code runs in five minutes per CT volume on average, and was validated by comparison against hand-segmented gold standards. The mean probability assigned to nonzero surface points was 0.8, while nonzero non-surface points had a mean value of 0.38 indicating clear identification of surface points on average. The segmentation was also sufficiently crisp, with a full width at half maximum (FWHM) value of 1.51 voxels.

Computerized PET/CT image analysis in the evaluation of tumour response to therapy

PubMed Central

Wang, J; Zhang, H H

2015-01-01

Current cancer therapy strategy is mostly population based, however, there are large differences in tumour response among patients. It is therefore important for treating physicians to know individual tumour response. In recent years, many studies proposed the use of computerized positron emission tomography/CT image analysis in the evaluation of tumour response. Results showed that computerized analysis overcame some major limitations of current qualitative and semiquantitative analysis and led to improved accuracy. In this review, we summarize these studies in four steps of the analysis: image registration, tumour segmentation, image feature extraction and response evaluation. Future works are proposed and challenges described. PMID:25723599

Clinics in diagnostic imaging (174). L5 vertebral superior facet osteoblastoma (OB).

PubMed

Subramanian, Manickam; Chou, Hong; Chokkappan, Kabilan; Peh, Wilfred Cg

2017-02-01

A 25-year-old man presented with chronic low back pain and occasional radiation to the right lower limb. Magnetic resonance imaging and computed tomography (CT) of the lumbar spine showed an osteolytic expansile lesion with a central sclerotic nidus in the right superior facet of the L5 vertebra and surrounding marrow oedema. The diagnosis of osteoblastoma was made based on imaging findings and confirmed after CT-guided biopsy. Radiofrequency ablation of the lesion was successfully performed. The patient tolerated the procedure well and showed symptomatic relief. The imaging features and management of osteoblastoma are discussed. Copyright: © Singapore Medical Association.

Medical Image Fusion Based on Feature Extraction and Sparse Representation

PubMed Central

Wei, Gao; Zongxi, Song

2017-01-01

As a novel multiscale geometric analysis tool, sparse representation has shown many advantages over the conventional image representation methods. However, the standard sparse representation does not take intrinsic structure and its time complexity into consideration. In this paper, a new fusion mechanism for multimodal medical images based on sparse representation and decision map is proposed to deal with these problems simultaneously. Three decision maps are designed including structure information map (SM) and energy information map (EM) as well as structure and energy map (SEM) to make the results reserve more energy and edge information. SM contains the local structure feature captured by the Laplacian of a Gaussian (LOG) and EM contains the energy and energy distribution feature detected by the mean square deviation. The decision map is added to the normal sparse representation based method to improve the speed of the algorithm. Proposed approach also improves the quality of the fused results by enhancing the contrast and reserving more structure and energy information from the source images. The experiment results of 36 groups of CT/MR, MR-T1/MR-T2, and CT/PET images demonstrate that the method based on SR and SEM outperforms five state-of-the-art methods. PMID:28321246

SU-E-J-265: Feasibility Study of Texture Analysis for Prognosis of Local Tumor Recurrence Within 5-Years for Pharyngeal-Laryngeal Carcinoma Patients Received Radiotherapy Treatment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, W; Tu, S

Purpose: Pharyngeal and laryngeal carcinomas (PLC) are among the top leading cancers in Asian populations. Typically the tumor may recur and progress in a short period of time if radiotherapy fails to deliver a successful treatment. Here we used image texture features extracted from images of computed tomography (CT) planning and conducted a retrospective study to evaluate whether texture analysis is a feasible approach to predict local tumor recurrence for PLC patients received radiotherapy treatment. Methods: CT planning images of 100 patients with PLC treated by radiotherapy at our facility between 2001 and 2010 are collected. These patients were receivedmore » two separate CT scans, before and mid-course of the treatment delivery. Before the radiotherapy, a CT scanning was used for the first treatment planning. A total of 30 fractions were used in the treatment and patients were scanned with a second CT around the end of the fifteenth delivery for an adaptive treatment planning. Only patients who were treated with intensity modulated radiation therapy and RapidArc were selected. Treatment planning software of Eclipse was used. The changes of texture parameters between two CT acquisitions were computed to determine whether they were correlated to the local tumor recurrence. The following texture parameters were used in the preliminary assessment: mean, variance, standard deviation, skewness, kurtosis, energy, entropy, inverse difference moment, cluster shade, inertia, cluster prominence, gray-level co-occurrence matrix, and gray-level run-length matrix. The study was reviewed and approved by the committee of our institutional review board. Results: Our calculations suggested the following texture parameters were correlated with the local tumor recurrence: skewness, kurtosis, entropy, and inertia (p<0.0.05). Conclusion: The preliminary results were positive. However some works remain crucial to be completed, including addition of texture parameters for different image features, sensitivity of tumor segmentation variations, and effect of image filtering.« less

Evaluation of the OSC-TV iterative reconstruction algorithm for cone-beam optical CT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Matenine, Dmitri, E-mail: dmitri.matenine.1@ulaval.ca; Mascolo-Fortin, Julia, E-mail: julia.mascolo-fortin.1@ulaval.ca; Goussard, Yves, E-mail: yves.goussard@polymtl.ca

Purpose: The present work evaluates an iterative reconstruction approach, namely, the ordered subsets convex (OSC) algorithm with regularization via total variation (TV) minimization in the field of cone-beam optical computed tomography (optical CT). One of the uses of optical CT is gel-based 3D dosimetry for radiation therapy, where it is employed to map dose distributions in radiosensitive gels. Model-based iterative reconstruction may improve optical CT image quality and contribute to a wider use of optical CT in clinical gel dosimetry. Methods: This algorithm was evaluated using experimental data acquired by a cone-beam optical CT system, as well as complementary numericalmore » simulations. A fast GPU implementation of OSC-TV was used to achieve reconstruction times comparable to those of conventional filtered backprojection. Images obtained via OSC-TV were compared with the corresponding filtered backprojections. Spatial resolution and uniformity phantoms were scanned and respective reconstructions were subject to evaluation of the modulation transfer function, image uniformity, and accuracy. The artifacts due to refraction and total signal loss from opaque objects were also studied. Results: The cone-beam optical CT data reconstructions showed that OSC-TV outperforms filtered backprojection in terms of image quality, thanks to a model-based simulation of the photon attenuation process. It was shown to significantly improve the image spatial resolution and reduce image noise. The accuracy of the estimation of linear attenuation coefficients remained similar to that obtained via filtered backprojection. Certain image artifacts due to opaque objects were reduced. Nevertheless, the common artifact due to the gel container walls could not be eliminated. Conclusions: The use of iterative reconstruction improves cone-beam optical CT image quality in many ways. The comparisons between OSC-TV and filtered backprojection presented in this paper demonstrate that OSC-TV can potentially improve the rendering of spatial features and reduce cone-beam optical CT artifacts.« less

Evaluation of the OSC-TV iterative reconstruction algorithm for cone-beam optical CT.

PubMed

Matenine, Dmitri; Mascolo-Fortin, Julia; Goussard, Yves; Després, Philippe

2015-11-01

The present work evaluates an iterative reconstruction approach, namely, the ordered subsets convex (OSC) algorithm with regularization via total variation (TV) minimization in the field of cone-beam optical computed tomography (optical CT). One of the uses of optical CT is gel-based 3D dosimetry for radiation therapy, where it is employed to map dose distributions in radiosensitive gels. Model-based iterative reconstruction may improve optical CT image quality and contribute to a wider use of optical CT in clinical gel dosimetry. This algorithm was evaluated using experimental data acquired by a cone-beam optical CT system, as well as complementary numerical simulations. A fast GPU implementation of OSC-TV was used to achieve reconstruction times comparable to those of conventional filtered backprojection. Images obtained via OSC-TV were compared with the corresponding filtered backprojections. Spatial resolution and uniformity phantoms were scanned and respective reconstructions were subject to evaluation of the modulation transfer function, image uniformity, and accuracy. The artifacts due to refraction and total signal loss from opaque objects were also studied. The cone-beam optical CT data reconstructions showed that OSC-TV outperforms filtered backprojection in terms of image quality, thanks to a model-based simulation of the photon attenuation process. It was shown to significantly improve the image spatial resolution and reduce image noise. The accuracy of the estimation of linear attenuation coefficients remained similar to that obtained via filtered backprojection. Certain image artifacts due to opaque objects were reduced. Nevertheless, the common artifact due to the gel container walls could not be eliminated. The use of iterative reconstruction improves cone-beam optical CT image quality in many ways. The comparisons between OSC-TV and filtered backprojection presented in this paper demonstrate that OSC-TV can potentially improve the rendering of spatial features and reduce cone-beam optical CT artifacts.

Review methods for image segmentation from computed tomography images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mamat, Nurwahidah; Rahman, Wan Eny Zarina Wan Abdul; Soh, Shaharuddin Cik

Image segmentation is a challenging process in order to get the accuracy of segmentation, automation and robustness especially in medical images. There exist many segmentation methods that can be implemented to medical images but not all methods are suitable. For the medical purposes, the aims of image segmentation are to study the anatomical structure, identify the region of interest, measure tissue volume to measure growth of tumor and help in treatment planning prior to radiation therapy. In this paper, we present a review method for segmentation purposes using Computed Tomography (CT) images. CT images has their own characteristics that affectmore » the ability to visualize anatomic structures and pathologic features such as blurring of the image and visual noise. The details about the methods, the goodness and the problem incurred in the methods will be defined and explained. It is necessary to know the suitable segmentation method in order to get accurate segmentation. This paper can be a guide to researcher to choose the suitable segmentation method especially in segmenting the images from CT scan.« less

Predicting Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer with Textural Features Derived from Pretreatment 18F-FDG PET/CT Imaging.

PubMed

Beukinga, Roelof J; Hulshoff, Jan B; van Dijk, Lisanne V; Muijs, Christina T; Burgerhof, Johannes G M; Kats-Ugurlu, Gursah; Slart, Riemer H J A; Slump, Cornelis H; Mul, Véronique E M; Plukker, John Th M

2017-05-01

Adequate prediction of tumor response to neoadjuvant chemoradiotherapy (nCRT) in esophageal cancer (EC) patients is important in a more personalized treatment. The current best clinical method to predict pathologic complete response is SUV max in 18 F-FDG PET/CT imaging. To improve the prediction of response, we constructed a model to predict complete response to nCRT in EC based on pretreatment clinical parameters and 18 F-FDG PET/CT-derived textural features. Methods: From a prospectively maintained single-institution database, we reviewed 97 consecutive patients with locally advanced EC and a pretreatment 18 F-FDG PET/CT scan between 2009 and 2015. All patients were treated with nCRT (carboplatin/paclitaxel/41.4 Gy) followed by esophagectomy. We analyzed clinical, geometric, and pretreatment textural features extracted from both 18 F-FDG PET and CT. The current most accurate prediction model with SUV max as a predictor variable was compared with 6 different response prediction models constructed using least absolute shrinkage and selection operator regularized logistic regression. Internal validation was performed to estimate the model's performances. Pathologic response was defined as complete versus incomplete response (Mandard tumor regression grade system 1 vs. 2-5). Results: Pathologic examination revealed 19 (19.6%) complete and 78 (80.4%) incomplete responders. Least absolute shrinkage and selection operator regularization selected the clinical parameters: histologic type and clinical T stage, the 18 F-FDG PET-derived textural feature long run low gray level emphasis, and the CT-derived textural feature run percentage. Introducing these variables to a logistic regression analysis showed areas under the receiver-operating-characteristic curve (AUCs) of 0.78 compared with 0.58 in the SUV max model. The discrimination slopes were 0.17 compared with 0.01, respectively. After internal validation, the AUCs decreased to 0.74 and 0.54, respectively. Conclusion: The predictive values of the constructed models were superior to the standard method (SUV max ). These results can be considered as an initial step in predicting tumor response to nCRT in locally advanced EC. Further research in refining the predictive value of these models is needed to justify omission of surgery. © 2017 by the Society of Nuclear Medicine and Molecular Imaging.

Evaluation of the BreastSimulator software platform for breast tomography

NASA Astrophysics Data System (ADS)

Mettivier, G.; Bliznakova, K.; Sechopoulos, I.; Boone, J. M.; Di Lillo, F.; Sarno, A.; Castriconi, R.; Russo, P.

2017-08-01

The aim of this work was the evaluation of the software BreastSimulator, a breast x-ray imaging simulation software, as a tool for the creation of 3D uncompressed breast digital models and for the simulation and the optimization of computed tomography (CT) scanners dedicated to the breast. Eight 3D digital breast phantoms were created with glandular fractions in the range 10%-35%. The models are characterised by different sizes and modelled realistic anatomical features. X-ray CT projections were simulated for a dedicated cone-beam CT scanner and reconstructed with the FDK algorithm. X-ray projection images were simulated for 5 mono-energetic (27, 32, 35, 43 and 51 keV) and 3 poly-energetic x-ray spectra typically employed in current CT scanners dedicated to the breast (49, 60, or 80 kVp). Clinical CT images acquired from two different clinical breast CT scanners were used for comparison purposes. The quantitative evaluation included calculation of the power-law exponent, β, from simulated and real breast tomograms, based on the power spectrum fitted with a function of the spatial frequency, f, of the form S(f)  =  α/f   β . The breast models were validated by comparison against clinical breast CT and published data. We found that the calculated β coefficients were close to that of clinical CT data from a dedicated breast CT scanner and reported data in the literature. In evaluating the software package BreastSimulator to generate breast models suitable for use with breast CT imaging, we found that the breast phantoms produced with the software tool can reproduce the anatomical structure of real breasts, as evaluated by calculating the β exponent from the power spectral analysis of simulated images. As such, this research tool might contribute considerably to the further development, testing and optimisation of breast CT imaging techniques.

CT Scans of Cores Metadata, Barrow, Alaska 2015

DOE Data Explorer

Katie McKnight; Tim Kneafsey; Craig Ulrich

2015-03-11

Individual ice cores were collected from Barrow Environmental Observatory in Barrow, Alaska, throughout 2013 and 2014. Cores were drilled along different transects to sample polygonal features (i.e. the trough, center and rim of high, transitional and low center polygons). Most cores were drilled around 1 meter in depth and a few deep cores were drilled around 3 meters in depth. Three-dimensional images of the frozen cores were constructed using a medical X-ray computed tomography (CT) scanner. TIFF files can be uploaded to ImageJ (an open-source imaging software) to examine soil structure and densities within each core.

[Features of maxillary and mandibular nerves imaging during stem regional blockades. From paresthesia to 3D-CT guidance].

PubMed

Zaytsev, A Yu; Nazaryan, D N; Kim, S Yu; Dubrovin, K V; Svetlov, V A; Khovrin, V V

2014-01-01

There are difficulties in procedure of regional block of 2 and 3 brunches of the trigeminal nerve despite availability of many different methods of nerves imaging. The difficulties are connected with complex anatomy structure. Neurostimulation not always effective and as a rule, is accompanied with wrong interpretation of movement response on stimulation. The changing of the tactics on paraesthesia search improves the situation. The use of new methods of nerves imaging (3D-CT) also allows decreasing the frequency of fails during procedure of regional block of the brunches of the trigeminal nerve.

Dynamic updating atlas for heart segmentation with a nonlinear field-based model.

PubMed

Cai, Ken; Yang, Rongqian; Yue, Hongwei; Li, Lihua; Ou, Shanxing; Liu, Feng

2017-09-01

Segmentation of cardiac computed tomography (CT) images is an effective method for assessing the dynamic function of the heart and lungs. In the atlas-based heart segmentation approach, the quality of segmentation usually relies upon atlas images, and the selection of those reference images is a key step. The optimal goal in this selection process is to have the reference images as close to the target image as possible. This study proposes an atlas dynamic update algorithm using a scheme of nonlinear deformation field. The proposed method is based on the features among double-source CT (DSCT) slices. The extraction of these features will form a base to construct an average model and the created reference atlas image is updated during the registration process. A nonlinear field-based model was used to effectively implement a 4D cardiac segmentation. The proposed segmentation framework was validated with 14 4D cardiac CT sequences. The algorithm achieved an acceptable accuracy (1.0-2.8 mm). Our proposed method that combines a nonlinear field-based model and dynamic updating atlas strategies can provide an effective and accurate way for whole heart segmentation. The success of the proposed method largely relies on the effective use of the prior knowledge of the atlas and the similarity explored among the to-be-segmented DSCT sequences. Copyright © 2016 John Wiley & Sons, Ltd.

Recurrent Temporal Bone Tenosynovial Giant Cell Tumor with Chondroid Metaplasia: the Use of Imaging to Assess Recurrence

PubMed Central

Pina, Sofia; Fernandez, Maria; Maya, Silvia; Garcia, Roberto A.; Noor, Ali; Pawha, Puneet S.; Som, Peter M.

2014-01-01

Summary Tenosynovial giant cell tumor (TGCT) is a benign proliferative lesion of unclear etiology. It is predominantly monoarticular and involves the synovium of the joint, tendon sheath, and bursa. TGCT of the temporomandibular joint (TMJ) is rare and aggressive resulting in destruction of surrounding structures. The diagnosis may be suggested by imaging, mainly by the MR features and PET/CT, and confirmed by histopathology. We describe the case of a 50-year-old man who presented with right-sided hearing loss, tinnitus and TMJ pain. Pathology revealed tenosynovial giant cell tumor with chondroid metaplasia. Six years later he developed a recurrence, which was documented to our knowledge for the first time with CT, MR and FDG PET/CT imaging. PMID:24571839

Image-guided decision support system for pulmonary nodule classification in 3D thoracic CT images

NASA Astrophysics Data System (ADS)

Kawata, Yoshiki; Niki, Noboru; Ohmatsu, Hironobu; Kusumoto, Masahiro; Kakinuma, Ryutaro; Mori, Kiyoshi; Yamada, Kozo; Nishiyama, Hiroyuki; Eguchi, Kenji; Kaneko, Masahiro; Moriyama, Noriyuki

2004-05-01

The purpose of this study is to develop an image-guided decision support system that assists decision-making in clinical differential diagnosis of pulmonary nodules. This approach retrieves and displays nodules that exhibit morphological and internal profiles consistent to the nodule in question. It uses a three-dimensional (3-D) CT image database of pulmonary nodules for which diagnosis is known. In order to build the system, there are following issues that should be solved: 1) to categorize the nodule database with respect to morphological and internal features, 2) to quickly search nodule images similar to an indeterminate nodule from a large database, and 3) to reveal malignancy likelihood computed by using similar nodule images. Especially, the first problem influences the design of other issues. The successful categorization of nodule pattern might lead physicians to find important cues that characterize benign and malignant nodules. This paper focuses on an approach to categorize the nodule database with respect to nodule shape and CT density patterns inside nodule.

SU-E-J-251: Incorporation of Pre-Therapy 18F-FDG Uptake with CT Texture Features in a Predictive Model for Radiation Pneumonitis Development

DOE Office of Scientific and Technical Information (OSTI.GOV)

Anthony, G; Cunliffe, A; Armato, S

2015-06-15

Purpose: To determine whether the addition of standardized uptake value (SUV) statistical variables to CT lung texture features can improve a predictive model of radiation pneumonitis (RP) development in patients undergoing radiation therapy. Methods: Anonymized data from 96 esophageal cancer patients (18 RP-positive cases of Grade ≥ 2) were retrospectively collected including pre-therapy PET/CT scans, pre-/posttherapy diagnostic CT scans and RP status. Twenty texture features (firstorder, fractal, Laws’ filter and gray-level co-occurrence matrix) were calculated from diagnostic CT scans and compared in anatomically matched regions of the lung. The mean, maximum, standard deviation, and 50th–95th percentiles of the SUV valuesmore » for all lung voxels in the corresponding PET scans were acquired. For each texture feature, a logistic regression-based classifier consisting of (1) the average change in that texture feature value between the pre- and post-therapy CT scans and (2) the pre-therapy SUV standard deviation (SUV{sub SD}) was created. The RP-classification performance of each logistic regression model was compared to the performance of its texture feature alone by computing areas under the receiver operating characteristic curves (AUCs). T-tests were performed to determine whether the mean AUC across texture features changed significantly when SUV{sub SD} was added to the classifier. Results: The AUC for single-texturefeature classifiers ranged from 0.58–0.81 in high-dose (≥ 30 Gy) regions of the lungs and from 0.53–0.71 in low-dose (< 10 Gy) regions. Adding SUVSD in a logistic regression model using a 50/50 data partition for training and testing significantly increased the mean AUC by 0.08, 0.06 and 0.04 in the low-, medium- and high-dose regions, respectively. Conclusion: Addition of SUVSD from a pre-therapy PET scan to a single CT-based texture feature improves RP-classification performance on average. These findings demonstrate the potential for more accurate prediction of RP using information from multiple imaging modalities. Supported, in part, by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health under grant number T32 EB002103; SGA receives royalties and licensing fees through the University of Chicago for computer-aided diagnosis technology. HA receives royalties through the University of Chicago for computer-aided diagnosis technology.« less

Quantifying the impact of respiratory-gated 4D CT acquisition on thoracic image quality: a digital phantom study.

PubMed

Bernatowicz, K; Keall, P; Mishra, P; Knopf, A; Lomax, A; Kipritidis, J

2015-01-01

Prospective respiratory-gated 4D CT has been shown to reduce tumor image artifacts by up to 50% compared to conventional 4D CT. However, to date no studies have quantified the impact of gated 4D CT on normal lung tissue imaging, which is important in performing dose calculations based on accurate estimates of lung volume and structure. To determine the impact of gated 4D CT on thoracic image quality, the authors developed a novel simulation framework incorporating a realistic deformable digital phantom driven by patient tumor motion patterns. Based on this framework, the authors test the hypothesis that respiratory-gated 4D CT can significantly reduce lung imaging artifacts. Our simulation framework synchronizes the 4D extended cardiac torso (XCAT) phantom with tumor motion data in a quasi real-time fashion, allowing simulation of three 4D CT acquisition modes featuring different levels of respiratory feedback: (i) "conventional" 4D CT that uses a constant imaging and couch-shift frequency, (ii) "beam paused" 4D CT that interrupts imaging to avoid oversampling at a given couch position and respiratory phase, and (iii) "respiratory-gated" 4D CT that triggers acquisition only when the respiratory motion fulfills phase-specific displacement gating windows based on prescan breathing data. Our framework generates a set of ground truth comparators, representing the average XCAT anatomy during beam-on for each of ten respiratory phase bins. Based on this framework, the authors simulated conventional, beam-paused, and respiratory-gated 4D CT images using tumor motion patterns from seven lung cancer patients across 13 treatment fractions, with a simulated 5.5 cm(3) spherical lesion. Normal lung tissue image quality was quantified by comparing simulated and ground truth images in terms of overall mean square error (MSE) intensity difference, threshold-based lung volume error, and fractional false positive/false negative rates. Averaged across all simulations and phase bins, respiratory-gating reduced overall thoracic MSE by 46% compared to conventional 4D CT (p ∼ 10(-19)). Gating leads to small but significant (p < 0.02) reductions in lung volume errors (1.8%-1.4%), false positives (4.0%-2.6%), and false negatives (2.7%-1.3%). These percentage reductions correspond to gating reducing image artifacts by 24-90 cm(3) of lung tissue. Similar to earlier studies, gating reduced patient image dose by up to 22%, but with scan time increased by up to 135%. Beam paused 4D CT did not significantly impact normal lung tissue image quality, but did yield similar dose reductions as for respiratory-gating, without the added cost in scanning time. For a typical 6 L lung, respiratory-gated 4D CT can reduce image artifacts affecting up to 90 cm(3) of normal lung tissue compared to conventional acquisition. This image improvement could have important implications for dose calculations based on 4D CT. Where image quality is less critical, beam paused 4D CT is a simple strategy to reduce imaging dose without sacrificing acquisition time.

Investigating CT to CBCT image registration for head and neck proton therapy as a tool for daily dose recalculation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Landry, Guillaume, E-mail: g.landry@lmu.de; Nijhuis, Reinoud; Thieke, Christian

2015-03-15

Purpose: Intensity modulated proton therapy (IMPT) of head and neck (H and N) cancer patients may be improved by plan adaptation. The decision to adapt the treatment plan based on a dose recalculation on the current anatomy requires a diagnostic quality computed tomography (CT) scan of the patient. As gantry-mounted cone beam CT (CBCT) scanners are currently being offered by vendors, they may offer daily or weekly updates of patient anatomy. CBCT image quality may not be sufficient for accurate proton dose calculation and it is likely necessary to perform CBCT CT number correction. In this work, the authors investigatedmore » deformable image registration (DIR) of the planning CT (pCT) to the CBCT to generate a virtual CT (vCT) to be used for proton dose recalculation. Methods: Datasets of six H and N cancer patients undergoing photon intensity modulated radiation therapy were used in this study to validate the vCT approach. Each dataset contained a CBCT acquired within 3 days of a replanning CT (rpCT), in addition to a pCT. The pCT and rpCT were delineated by a physician. A Morphons algorithm was employed in this work to perform DIR of the pCT to CBCT following a rigid registration of the two images. The contours from the pCT were deformed using the vector field resulting from DIR to yield a contoured vCT. The DIR accuracy was evaluated with a scale invariant feature transform (SIFT) algorithm comparing automatically identified matching features between vCT and CBCT. The rpCT was used as reference for evaluation of the vCT. The vCT and rpCT CT numbers were converted to stopping power ratio and the water equivalent thickness (WET) was calculated. IMPT dose distributions from treatment plans optimized on the pCT were recalculated with a Monte Carlo algorithm on the rpCT and vCT for comparison in terms of gamma index, dose volume histogram (DVH) statistics as well as proton range. The DIR generated contours on the vCT were compared to physician-drawn contours on the rpCT. Results: The DIR accuracy was better than 1.4 mm according to the SIFT evaluation. The mean WET differences between vCT (pCT) and rpCT were below 1 mm (2.6 mm). The amount of voxels passing 3%/3 mm gamma criteria were above 95% for the vCT vs rpCT. When using the rpCT contour set to derive DVH statistics from dose distributions calculated on the rpCT and vCT the differences, expressed in terms of 30 fractions of 2 Gy, were within [−4, 2 Gy] for parotid glands (D{sub mean}), spinal cord (D{sub 2%}), brainstem (D{sub 2%}), and CTV (D{sub 95%}). When using DIR generated contours for the vCT, those differences ranged within [−8, 11 Gy]. Conclusions: In this work, the authors generated CBCT based stopping power distributions using DIR of the pCT to a CBCT scan. DIR accuracy was below 1.4 mm as evaluated by the SIFT algorithm. Dose distributions calculated on the vCT agreed well to those calculated on the rpCT when using gamma index evaluation as well as DVH statistics based on the same contours. The use of DIR generated contours introduced variability in DVH statistics.« less

New auto-segment method of cerebral hemorrhage

NASA Astrophysics Data System (ADS)

Wang, Weijiang; Shen, Tingzhi; Dang, Hua

2007-12-01

A novel method for Computerized tomography (CT) cerebral hemorrhage (CH) image automatic segmentation is presented in the paper, which uses expert system that models human knowledge about the CH automatic segmentation problem. The algorithm adopts a series of special steps and extracts some easy ignored CH features which can be found by statistic results of mass real CH images, such as region area, region CT number, region smoothness and some statistic CH region relationship. And a seven steps' extracting mechanism will ensure these CH features can be got correctly and efficiently. By using these CH features, a decision tree which models the human knowledge about the CH automatic segmentation problem has been built and it will ensure the rationality and accuracy of the algorithm. Finally some experiments has been taken to verify the correctness and reasonable of the automatic segmentation, and the good correct ratio and fast speed make it possible to be widely applied into practice.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fried, D; Meier, J; Mawlawi, O

Purpose: Use a NEMA-IEC PET phantom to assess the robustness of FDG-PET-based radiomics features to changes in reconstruction parameters across different scanners. Methods: We scanned a NEMA-IEC PET phantom on 3 different scanners (GE Discovery VCT, GE Discovery 710, and Siemens mCT) using a FDG source-to-background ratio of 10:1. Images were retrospectively reconstructed using different iterations (2–3), subsets (21–24), Gaussian filter widths (2, 4, 6mm), and matrix sizes (128,192,256). The 710 and mCT used time-of-flight and point-spread-functions in reconstruction. The axial-image through the center of the 6 active spheres was used for analysis. A region-of-interest containing all spheres was ablemore » to simulate a heterogeneous lesion due to partial volume effects. Maximum voxel deviations from all retrospectively reconstructed images (18 per scanner) was compared to our standard clinical protocol. PET Images from 195 non-small cell lung cancer patients were used to compare feature variation. The ratio of a feature’s standard deviation from the patient cohort versus the phantom images was calculated to assess for feature robustness. Results: Across all images, the percentage of voxels differing by <1SUV and <2SUV ranged from 61–92% and 88–99%, respectively. Voxel-voxel similarity decreased when using higher resolution image matrices (192/256 versus 128) and was comparable across scanners. Taking the ratio of patient and phantom feature standard deviation was able to identify features that were not robust to changes in reconstruction parameters (e.g. co-occurrence correlation). Metrics found to be reasonably robust (standard deviation ratios > 3) were observed for routinely used SUV metrics (e.g. SUVmean and SUVmax) as well as some radiomics features (e.g. co-occurrence contrast, co-occurrence energy, standard deviation, and uniformity). Similar standard deviation ratios were observed across scanners. Conclusions: Our method enabled a comparison of feature variability across scanners and was able to identify features that were not robust to changes in reconstruction parameters.« less

Optical differentiation between malignant and benign lymphadenopathy by grey scale texture analysis of endobronchial ultrasound convex probe images.

PubMed

Nguyen, Phan; Bashirzadeh, Farzad; Hundloe, Justin; Salvado, Olivier; Dowson, Nicholas; Ware, Robert; Masters, Ian Brent; Bhatt, Manoj; Kumar, Aravind Ravi; Fielding, David

2012-03-01

Morphologic and sonographic features of endobronchial ultrasound (EBUS) convex probe images are helpful in predicting metastatic lymph nodes. Grey scale texture analysis is a well-established methodology that has been applied to ultrasound images in other fields of medicine. The aim of this study was to determine if this methodology could differentiate between benign and malignant lymphadenopathy of EBUS images. Lymph nodes from digital images of EBUS procedures were manually mapped to obtain a region of interest and were analyzed in a prediction set. The regions of interest were analyzed for the following grey scale texture features in MATLAB (version 7.8.0.347 [R2009a]): mean pixel value, difference between maximal and minimal pixel value, SEM pixel value, entropy, correlation, energy, and homogeneity. Significant grey scale texture features were used to assess a validation set compared with fluoro-D-glucose (FDG)-PET-CT scan findings where available. Fifty-two malignant nodes and 48 benign nodes were in the prediction set. Malignant nodes had a greater difference in the maximal and minimal pixel values, SEM pixel value, entropy, and correlation, and a lower energy (P < .0001 for all values). Fifty-one lymph nodes were in the validation set; 44 of 51 (86.3%) were classified correctly. Eighteen of these lymph nodes also had FDG-PET-CT scan assessment, which correctly classified 14 of 18 nodes (77.8%), compared with grey scale texture analysis, which correctly classified 16 of 18 nodes (88.9%). Grey scale texture analysis of EBUS convex probe images can be used to differentiate malignant and benign lymphadenopathy. Preliminary results are comparable to FDG-PET-CT scan.

Nonlocal low-rank and sparse matrix decomposition for spectral CT reconstruction

NASA Astrophysics Data System (ADS)

Niu, Shanzhou; Yu, Gaohang; Ma, Jianhua; Wang, Jing

2018-02-01

Spectral computed tomography (CT) has been a promising technique in research and clinics because of its ability to produce improved energy resolution images with narrow energy bins. However, the narrow energy bin image is often affected by serious quantum noise because of the limited number of photons used in the corresponding energy bin. To address this problem, we present an iterative reconstruction method for spectral CT using nonlocal low-rank and sparse matrix decomposition (NLSMD), which exploits the self-similarity of patches that are collected in multi-energy images. Specifically, each set of patches can be decomposed into a low-rank component and a sparse component, and the low-rank component represents the stationary background over different energy bins, while the sparse component represents the rest of the different spectral features in individual energy bins. Subsequently, an effective alternating optimization algorithm was developed to minimize the associated objective function. To validate and evaluate the NLSMD method, qualitative and quantitative studies were conducted by using simulated and real spectral CT data. Experimental results show that the NLSMD method improves spectral CT images in terms of noise reduction, artifact suppression and resolution preservation.

Positioning accuracy in a registration-free CT-based navigation system

NASA Astrophysics Data System (ADS)

Brandenberger, D.; Birkfellner, W.; Baumann, B.; Messmer, P.; Huegli, R. W.; Regazzoni, P.; Jacob, A. L.

2007-12-01

In order to maintain overall navigation accuracy established by a calibration procedure in our CT-based registration-free navigation system, the CT scanner has to repeatedly generate identical volume images of a target at the same coordinates. We tested the positioning accuracy of the prototype of an advanced workplace for image-guided surgery (AWIGS) which features an operating table capable of direct patient transfer into a CT scanner. Volume images (N = 154) of a specialized phantom were analysed for translational shifting after various table translations. Variables included added weight and phantom position on the table. The navigation system's calibration accuracy was determined (bias 2.1 mm, precision ± 0.7 mm, N = 12). In repeated use, a bias of 3.0 mm and a precision of ± 0.9 mm (N = 10) were maintainable. Instances of translational image shifting were related to the table-to-CT scanner docking mechanism. A distance scaling error when altering the table's height was detected. Initial prototype problems visible in our study causing systematic errors were resolved by repeated system calibrations between interventions. We conclude that the accuracy achieved is sufficient for a wide range of clinical applications in surgery and interventional radiology.

Pathologic and Radiologic Correlation of Adult Cystic Lung Disease: A Comprehensive Review

PubMed Central

Parimi, Vamsi; Taddonio, Michale; Kane, Joshua Robert; Yeldandi, Anjana

2017-01-01

The presence of pulmonary parenchymal cysts on computed tomography (CT) imaging presents a significant diagnostic challenge. The diverse range of possible etiologies can usually be differentiated based on the clinical setting and radiologic features. In fact, the advent of high-resolution CT has facilitated making a diagnosis solely on analysis of CT image patterns, thus averting the need for a biopsy. While it is possible to make a fairly specific diagnosis during early stages of disease evolution by its characteristic radiological presentation, distinct features may progress to temporally converge into relatively nonspecific radiologic presentations sometimes necessitating histological examination to make a diagnosis. The aim of this review study is to provide both the pathologist and the radiologist with an overview of the diseases most commonly associated with cystic lung lesions primarily in adults by illustration and description of pathologic and radiologic features of each entity. Brief descriptions and characteristic radiologic features of the various disease entities are included and illustrative examples are provided for the common majority of them. In this article, we also classify pulmonary cystic disease with an emphasis on the pathophysiology behind cyst formation in an attempt to elucidate the characteristics of similar cystic appearances seen in various disease entities. PMID:28270943

Micro-CT image-derived metrics quantify arterial wall distensibility reduction in a rat model of pulmonary hypertension

NASA Astrophysics Data System (ADS)

Johnson, Roger H.; Karau, Kelly L.; Molthen, Robert C.; Haworth, Steven T.; Dawson, Christopher A.

2000-04-01

We developed methods to quantify arterial structural and mechanical properties in excised rat lungs and applied them to investigate the distensibility decrease accompanying chronic hypoxia-induced pulmonary hypertension. Lungs of control and hypertensive (three weeks 11% O2) animals were excised and a contrast agent introduced before micro-CT imaging with a special purpose scanner. For each lung, four 3D image data sets were obtained, each at a different intra-arterial contrast agent pressure. Vessel segment diameters and lengths were measured at all levels in the arterial tree hierarchy, and these data used to generate features sensitive to distensibility changes. Results indicate that measurements obtained from 3D micro-CT images can be used to quantify vessel biomechanical properties in this rat model of pulmonary hypertension and that distensibility is reduced by exposure to chronic hypoxia. Mechanical properties can be assessed in a localized fashion and quantified in a spatially-resolved way or as a single parameter describing the tree as a whole. Micro-CT is a nondestructive way to rapidly assess structural and mechanical properties of arteries in small animal organs maintained in a physiological state. Quantitative features measured by this method may provide valuable insights into the mechanisms causing the elevated pressures in pulmonary hypertension of differing etiologies and should become increasingly valuable tools in the study of complex phenotypes in small-animal models of important diseases such as hypertension.

WE-AB-204-06: Pseudo-CT Generation Using Undersampled, Single-Acquisition UTE-MDixon and Direct-Mapping Artificial Neural Networks for MR-Based Attenuation Correction and Radiation Therapy Planning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Su, K; Kuo, J; Department of Radiology, University Hospitals Case Medical Center, Case Western Reserve University, Cleveland, Ohio

Purpose: Emerging technologies such as dedicated PET/MRI and MR-therapy systems require robust and clinically practical methods for determining photon attenuation. Herein, we propose using novel MR acquisition methods and processing for the generation of pseudo-CTs. Methods: A single acquisition, 190-second UTE-mDixon sequence with 25% (angular) sampling density and 3D radial readout was performed on nine volunteers. Three water-filled tubes were placed in the FOV for trajectory-delay correction. The MR data were reconstructed to generate three primitive images acquired at TEs of 0.1, 1.5 and 2.8 ms. In addition, three derived MR images were generated, i.e. two-point Dixon water/fat separation andmore » R2* (1/T2*) map. Furthermore, two spatial features, i.e. local binary pattern (S-1) and relative spatial coordinates (S-2), were incorporated. A direct-mapping operator was generated using Artificial Neural Networks (ANNs) for transforming the MR features to a pseudo-CT. CT images served as the training data and, using a leave-one-out method, for performance evaluation using mean prediction deviation (MPD), mean absolute prediction deviation (MAPD), and correlation coefficient (R). Results: The errors between measured CT and pseudo-CT declined dramatically when the spatial features, i.e. S-1 and S-2, were included. The MPD, MAPD, and R were, respectively, 5±57 HU, 141±41 HU, and 0.815±0.066 for results generated by the ANN trained without the spatial features and were 32±26 HU, 115±18 HU, and 0.869±0.035 with the spatial features. The estimation errors of the pseudo-CT were smaller when both the S-1 and S-2 were used together than when either the S-1 or the S-2 was used. Pseudo-CT generation (256×256×256 voxels) by ANN took < 0.5 s using a computer having an Intel i7 3.4GHz CPU and 16 GB RAM. Conclusion: The proposed direct-mapping ANN approach is a technically accurate, clinically practical method for pseudo-CT generation and can potentially help improve the accuracy of MR-AC and MR-RTP applications. Please note that the project was completed with partial funding from the Ohio Department of Development grant TECH 11-063 and a sponsored research agreement with Philips Healthcare that is managed by Case Western Reserve University. As noted in the affiliations, some of the authors are Philips employees.« less

Micro-CTvlab: A web based virtual gallery of biological specimens using X-ray microtomography (micro-CT).

PubMed

Keklikoglou, Kleoniki; Faulwetter, Sarah; Chatzinikolaou, Eva; Michalakis, Nikitas; Filiopoulou, Irene; Minadakis, Nikos; Panteri, Emmanouela; Perantinos, George; Gougousis, Alexandros; Arvanitidis, Christos

2016-01-01

During recent years, X-ray microtomography (micro-CT) has seen an increasing use in biological research areas, such as functional morphology, taxonomy, evolutionary biology and developmental research. Micro-CT is a technology which uses X-rays to create sub-micron resolution images of external and internal features of specimens. These images can then be rendered in a three-dimensional space and used for qualitative and quantitative 3D analyses. However, the online exploration and dissemination of micro-CT datasets are rarely made available to the public due to their large size and a lack of dedicated online platforms for the interactive manipulation of 3D data. Here, the development of a virtual micro-CT laboratory (Micro-CT vlab ) is described, which can be used by everyone who is interested in digitisation methods and biological collections and aims at making the micro-CT data exploration of natural history specimens freely available over the internet. The Micro-CT vlab offers to the user virtual image galleries of various taxa which can be displayed and downloaded through a web application. With a few clicks, accurate, detailed and three-dimensional models of species can be studied and virtually dissected without destroying the actual specimen. The data and functions of the Micro-CT vlab can be accessed either on a normal computer or through a dedicated version for mobile devices.

Missed rib fractures on evaluation of initial chest CT for trauma patients: pattern analysis and diagnostic value of coronal multiplanar reconstruction images with multidetector row CT.

PubMed

Cho, S H; Sung, Y M; Kim, M S

2012-10-01

The objective of this study was to review the prevalence and radiological features of rib fractures missed on initial chest CT evaluation, and to examine the diagnostic value of additional coronal images in a large series of trauma patients. 130 patients who presented to an emergency room for blunt chest trauma underwent multidetector row CT of the thorax within the first hour during their stay, and had follow-up CT or bone scans as diagnostic gold standards. Images were evaluated on two separate occasions: once with axial images and once with both axial and coronal images. The detection rates of missed rib fractures were compared between readings using a non-parametric method of clustered data. In the cases of missed rib fractures, the shapes, locations and associated fractures were evaluated. 58 rib fractures were missed with axial images only and 52 were missed with both axial and coronal images (p=0.088). The most common shape of missed rib fractures was buckled (56.9%), and the anterior arc (55.2%) was most commonly involved. 21 (36.2%) missed rib fractures had combined fractures on the same ribs, and 38 (65.5%) were accompanied by fracture on neighbouring ribs. Missed rib fractures are not uncommon, and radiologists should be familiar with buckle fractures, which are frequently missed. Additional coronal imagescan be helpful in the diagnosis of rib fractures that are not seen on axial images.

The adaptive statistical iterative reconstruction-V technique for radiation dose reduction in abdominal CT: comparison with the adaptive statistical iterative reconstruction technique.

PubMed

Kwon, Heejin; Cho, Jinhan; Oh, Jongyeong; Kim, Dongwon; Cho, Junghyun; Kim, Sanghyun; Lee, Sangyun; Lee, Jihyun

2015-10-01

To investigate whether reduced radiation dose abdominal CT images reconstructed with adaptive statistical iterative reconstruction V (ASIR-V) compromise the depiction of clinically competent features when compared with the currently used routine radiation dose CT images reconstructed with ASIR. 27 consecutive patients (mean body mass index: 23.55 kg m(-2) underwent CT of the abdomen at two time points. At the first time point, abdominal CT was scanned at 21.45 noise index levels of automatic current modulation at 120 kV. Images were reconstructed with 40% ASIR, the routine protocol of Dong-A University Hospital. At the second time point, follow-up scans were performed at 30 noise index levels. Images were reconstructed with filtered back projection (FBP), 40% ASIR, 30% ASIR-V, 50% ASIR-V and 70% ASIR-V for the reduced radiation dose. Both quantitative and qualitative analyses of image quality were conducted. The CT dose index was also recorded. At the follow-up study, the mean dose reduction relative to the currently used common radiation dose was 35.37% (range: 19-49%). The overall subjective image quality and diagnostic acceptability of the 50% ASIR-V scores at the reduced radiation dose were nearly identical to those recorded when using the initial routine-dose CT with 40% ASIR. Subjective ratings of the qualitative analysis revealed that of all reduced radiation dose CT series reconstructed, 30% ASIR-V and 50% ASIR-V were associated with higher image quality with lower noise and artefacts as well as good sharpness when compared with 40% ASIR and FBP. However, the sharpness score at 70% ASIR-V was considered to be worse than that at 40% ASIR. Objective image noise for 50% ASIR-V was 34.24% and 46.34% which was lower than 40% ASIR and FBP. Abdominal CT images reconstructed with ASIR-V facilitate radiation dose reductions of to 35% when compared with the ASIR. This study represents the first clinical research experiment to use ASIR-V, the newest version of iterative reconstruction. Use of the ASIR-V algorithm decreased image noise and increased image quality when compared with the ASIR and FBP methods. These results suggest that high-quality low-dose CT may represent a new clinical option.

The adaptive statistical iterative reconstruction-V technique for radiation dose reduction in abdominal CT: comparison with the adaptive statistical iterative reconstruction technique

PubMed Central

Cho, Jinhan; Oh, Jongyeong; Kim, Dongwon; Cho, Junghyun; Kim, Sanghyun; Lee, Sangyun; Lee, Jihyun

2015-01-01

Objective: To investigate whether reduced radiation dose abdominal CT images reconstructed with adaptive statistical iterative reconstruction V (ASIR-V) compromise the depiction of clinically competent features when compared with the currently used routine radiation dose CT images reconstructed with ASIR. Methods: 27 consecutive patients (mean body mass index: 23.55 kg m−2 underwent CT of the abdomen at two time points. At the first time point, abdominal CT was scanned at 21.45 noise index levels of automatic current modulation at 120 kV. Images were reconstructed with 40% ASIR, the routine protocol of Dong-A University Hospital. At the second time point, follow-up scans were performed at 30 noise index levels. Images were reconstructed with filtered back projection (FBP), 40% ASIR, 30% ASIR-V, 50% ASIR-V and 70% ASIR-V for the reduced radiation dose. Both quantitative and qualitative analyses of image quality were conducted. The CT dose index was also recorded. Results: At the follow-up study, the mean dose reduction relative to the currently used common radiation dose was 35.37% (range: 19–49%). The overall subjective image quality and diagnostic acceptability of the 50% ASIR-V scores at the reduced radiation dose were nearly identical to those recorded when using the initial routine-dose CT with 40% ASIR. Subjective ratings of the qualitative analysis revealed that of all reduced radiation dose CT series reconstructed, 30% ASIR-V and 50% ASIR-V were associated with higher image quality with lower noise and artefacts as well as good sharpness when compared with 40% ASIR and FBP. However, the sharpness score at 70% ASIR-V was considered to be worse than that at 40% ASIR. Objective image noise for 50% ASIR-V was 34.24% and 46.34% which was lower than 40% ASIR and FBP. Conclusion: Abdominal CT images reconstructed with ASIR-V facilitate radiation dose reductions of to 35% when compared with the ASIR. Advances in knowledge: This study represents the first clinical research experiment to use ASIR-V, the newest version of iterative reconstruction. Use of the ASIR-V algorithm decreased image noise and increased image quality when compared with the ASIR and FBP methods. These results suggest that high-quality low-dose CT may represent a new clinical option. PMID:26234823

Investigation of 3D histograms of oriented gradients for image-based registration of CT with interventional CBCT

NASA Astrophysics Data System (ADS)

Trimborn, Barbara; Wolf, Ivo; Abu-Sammour, Denis; Henzler, Thomas; Schad, Lothar R.; Zöllner, Frank G.

2017-03-01

Image registration of preprocedural contrast-enhanced CTs to intraprocedual cone-beam computed tomography (CBCT) can provide additional information for interventional liver oncology procedures such as transcatheter arterial chemoembolisation (TACE). In this paper, a novel similarity metric for gradient-based image registration is proposed. The metric relies on the patch-based computation of histograms of oriented gradients (HOG) building the basis for a feature descriptor. The metric was implemented in a framework for rigid 3D-3D-registration of pre-interventional CT with intra-interventional CBCT data obtained during the workflow of a TACE. To evaluate the performance of the new metric, the capture range was estimated based on the calculation of the mean target registration error and compared to the results obtained with a normalized cross correlation metric. The results show that 3D HOG feature descriptors are suitable as image-similarity metric and that the novel metric can compete with established methods in terms of registration accuracy

Processing of CT images for analysis of diffuse lung disease in the lung tissue research consortium

NASA Astrophysics Data System (ADS)

Karwoski, Ronald A.; Bartholmai, Brian; Zavaletta, Vanessa A.; Holmes, David; Robb, Richard A.

2008-03-01

The goal of Lung Tissue Resource Consortium (LTRC) is to improve the management of diffuse lung diseases through a better understanding of the biology of Chronic Obstructive Pulmonary Disease (COPD) and fibrotic interstitial lung disease (ILD) including Idiopathic Pulmonary Fibrosis (IPF). Participants are subjected to a battery of tests including tissue biopsies, physiologic testing, clinical history reporting, and CT scanning of the chest. The LTRC is a repository from which investigators can request tissue specimens and test results as well as semi-quantitative radiology reports, pathology reports, and automated quantitative image analysis results from the CT scan data performed by the LTRC core laboratories. The LTRC Radiology Core Laboratory (RCL), in conjunction with the Biomedical Imaging Resource (BIR), has developed novel processing methods for comprehensive characterization of pulmonary processes on volumetric high-resolution CT scans to quantify how these diseases manifest in radiographic images. Specifically, the RCL has implemented a semi-automated method for segmenting the anatomical regions of the lungs and airways. In these anatomic regions, automated quantification of pathologic features of disease including emphysema volumes and tissue classification are performed using both threshold techniques and advanced texture measures to determine the extent and location of emphysema, ground glass opacities, "honeycombing" (HC) and "irregular linear" or "reticular" pulmonary infiltrates and normal lung. Wall thickness measurements of the trachea, and its branches to the 3 rd and limited 4 th order are also computed. The methods for processing, segmentation and quantification are described. The results are reviewed and verified by an expert radiologist following processing and stored in the public LTRC database for use by pulmonary researchers. To date, over 1200 CT scans have been processed by the RCL and the LTRC project is on target for recruitment of the 2200 patients with 1800 CT scans in the repository for the 5-year effort. Ongoing analysis of the results in the LTRC database by the LTRC participating institutions and outside investigators are underway to look at the clinical and physiological significance of the imaging features of these diseases and correlate these findings with quality of life and other important prognostic indicators of severity. In the future, the quantitative measures of disease may have greater utility by showing correlation with prognosis, disease severity and other physiological parameters. These imaging features may provide non-invasive alternative endpoints or surrogate markers to alleviate the need for tissue biopsy or provide an accurate means to monitor rate of disease progression or response to therapy.

SU-D-206-03: Segmentation Assisted Fast Iterative Reconstruction Method for Cone-Beam CT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, P; Mao, T; Gong, S

2016-06-15

Purpose: Total Variation (TV) based iterative reconstruction (IR) methods enable accurate CT image reconstruction from low-dose measurements with sparse projection acquisition, due to the sparsifiable feature of most CT images using gradient operator. However, conventional solutions require large amount of iterations to generate a decent reconstructed image. One major reason is that the expected piecewise constant property is not taken into consideration at the optimization starting point. In this work, we propose an iterative reconstruction method for cone-beam CT (CBCT) using image segmentation to guide the optimization path more efficiently on the regularization term at the beginning of the optimizationmore » trajectory. Methods: Our method applies general knowledge that one tissue component in the CT image contains relatively uniform distribution of CT number. This general knowledge is incorporated into the proposed reconstruction using image segmentation technique to generate the piecewise constant template on the first-pass low-quality CT image reconstructed using analytical algorithm. The template image is applied as an initial value into the optimization process. Results: The proposed method is evaluated on the Shepp-Logan phantom of low and high noise levels, and a head patient. The number of iterations is reduced by overall 40%. Moreover, our proposed method tends to generate a smoother reconstructed image with the same TV value. Conclusion: We propose a computationally efficient iterative reconstruction method for CBCT imaging. Our method achieves a better optimization trajectory and a faster convergence behavior. It does not rely on prior information and can be readily incorporated into existing iterative reconstruction framework. Our method is thus practical and attractive as a general solution to CBCT iterative reconstruction. This work is supported by the Zhejiang Provincial Natural Science Foundation of China (Grant No. LR16F010001), National High-tech R&D Program for Young Scientists by the Ministry of Science and Technology of China (Grant No. 2015AA020917).« less

Could texture features from preoperative CT image be used for predicting occult peritoneal carcinomatosis in patients with advanced gastric cancer?

PubMed

Kim, Hae Young; Kim, Young Hoon; Yun, Gabin; Chang, Won; Lee, Yoon Jin; Kim, Bohyoung

2018-01-01

To retrospectively investigate whether texture features obtained from preoperative CT images of advanced gastric cancer (AGC) patients could be used for the prediction of occult peritoneal carcinomatosis (PC) detected during operation. 51 AGC patients with occult PC detected during operation from January 2009 to December 2012 were included as occult PC group. For the control group, other 51 AGC patients without evidence of distant metastasis including PC, and whose clinical T and N stage could be matched to those of the patients of the occult PC group, were selected from the period of January 2011 to July 2012. Each group was divided into test (n = 41) and validation cohort (n = 10). Demographic and clinical data of these patients were acquired from the hospital database. Texture features including average, standard deviation, kurtosis, skewness, entropy, correlation, and contrast were obtained from manually drawn region of interest (ROI) over the omentum on the axial CT image showing the omentum at its largest cross sectional area. After using Fisher's exact and Wilcoxon signed-rank test for comparison of the clinical and texture features between the two groups of the test cohort, conditional logistic regression analysis was performed to determine significant independent predictor for occult PC. Using the optimal cut-off value from receiver operating characteristic (ROC) analysis for the significant variables, diagnostic sensitivity and specificity were determined in the test cohort. The cut-off value of the significant variables obtained from the test cohort was then applied to the validation cohort. Bonferroni correction was used to adjust P value for multiple comparisons. Between the two groups, there was no significant difference in the clinical features. Regarding the texture features, the occult PC group showed significantly higher average, entropy, standard deviation, and significantly lower correlation (P value < 0.004 for all). Conditional logistic regression analysis demonstrated that entropy was significant independent predictor for occult PC. When the cut-off value of entropy (> 7.141) was applied to the validation cohort, sensitivity and specificity for the prediction of occult PC were 80% and 90%, respectively. For AGC patients whose PC cannot be detected with routine imaging such as CT, texture analysis may be a useful adjunct for the prediction of occult PC.

Which patellofemoral joint imaging features are associated with patellofemoral pain? Systematic review and meta-analysis.

PubMed

Drew, B T; Redmond, A C; Smith, T O; Penny, F; Conaghan, P G

2016-02-01

To review the association between patellofemoral joint (PFJ) imaging features and patellofemoral pain (PFP). A systematic review of the literature from AMED, CiNAHL, Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, PEDro, EMBASE and SPORTDiscus was undertaken from their inception to September 2014. Studies were eligible if they used magnetic resonance imaging (MRI), computed tomography (CT), ultrasound (US) or X-ray (XR) to compare PFJ features between a PFP group and an asymptomatic control group in people <45 years of age. A pooled meta-analysis was conducted and data was interpreted using a best evidence synthesis. Forty studies (all moderate to high quality) describing 1043 people with PFP and 839 controls were included. Two features were deemed to have a large standardised mean difference (SMD) based on meta-analysis: an increased MRI bisect offset at 0° knee flexion under load (0.99; 95% CI: 0.49, 1.49) and an increased CT congruence angle at 15° knee flexion, both under load (1.40 95% CI: 0.04, 2.76) and without load (1.24; 95% CI: 0.37, 2.12). A medium SMD was identified for MRI patella tilt and patellofemoral contact area. Limited evidence was found to support the association of other imaging features with PFP. A sensitivity analysis showed an increase in the SMD for patella bisect offset at 0° knee flexion (1.91; 95% CI: 1.31, 2.52) and patella tilt at 0° knee flexion (0.99; 95% CI: 0.47, 1.52) under full weight bearing. Certain PFJ imaging features were associated with PFP. Future interventional strategies may be targeted at these features. CRD 42014009503. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Abdomen disease diagnosis in CT images using flexiscale curvelet transform and improved genetic algorithm.

PubMed

Sethi, Gaurav; Saini, B S

2015-12-01

This paper presents an abdomen disease diagnostic system based on the flexi-scale curvelet transform, which uses different optimal scales for extracting features from computed tomography (CT) images. To optimize the scale of the flexi-scale curvelet transform, we propose an improved genetic algorithm. The conventional genetic algorithm assumes that fit parents will likely produce the healthiest offspring that leads to the least fit parents accumulating at the bottom of the population, reducing the fitness of subsequent populations and delaying the optimal solution search. In our improved genetic algorithm, combining the chromosomes of a low-fitness and a high-fitness individual increases the probability of producing high-fitness offspring. Thereby, all of the least fit parent chromosomes are combined with high fit parent to produce offspring for the next population. In this way, the leftover weak chromosomes cannot damage the fitness of subsequent populations. To further facilitate the search for the optimal solution, our improved genetic algorithm adopts modified elitism. The proposed method was applied to 120 CT abdominal images; 30 images each of normal subjects, cysts, tumors and stones. The features extracted by the flexi-scale curvelet transform were more discriminative than conventional methods, demonstrating the potential of our method as a diagnostic tool for abdomen diseases.

Anthropomorphic thorax phantom for cardio-respiratory motion simulation in tomographic imaging

NASA Astrophysics Data System (ADS)

Bolwin, Konstantin; Czekalla, Björn; Frohwein, Lynn J.; Büther, Florian; Schäfers, Klaus P.

2018-02-01

Patient motion during medical imaging using techniques such as computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), or single emission computed tomography (SPECT) is well known to degrade images, leading to blurring effects or severe artifacts. Motion correction methods try to overcome these degrading effects. However, they need to be validated under realistic conditions. In this work, a sophisticated anthropomorphic thorax phantom is presented that combines several aspects of a simulator for cardio-respiratory motion. The phantom allows us to simulate various types of cardio-respiratory motions inside a human-like thorax, including features such as inflatable lungs, beating left ventricular myocardium, respiration-induced motion of the left ventricle, moving lung lesions, and moving coronary artery plaques. The phantom is constructed to be MR-compatible. This means that we can not only perform studies in PET, SPECT and CT, but also inside an MRI system. The technical features of the anthropomorphic thorax phantom Wilhelm are presented with regard to simulating motion effects in hybrid emission tomography and radiotherapy. This is supplemented by a study on the detectability of small coronary plaque lesions in PET/CT under the influence of cardio-respiratory motion, and a study on the accuracy of left ventricular blood volumes.

Quantitative Segmentation of Fluorescence Microscopy Images of Heterogeneous Tissue: Application to the Detection of Residual Disease in Tumor Margins

PubMed Central

Mueller, Jenna L.; Harmany, Zachary T.; Mito, Jeffrey K.; Kennedy, Stephanie A.; Kim, Yongbaek; Dodd, Leslie; Geradts, Joseph; Kirsch, David G.; Willett, Rebecca M.; Brown, J. Quincy; Ramanujam, Nimmi

2013-01-01

Purpose To develop a robust tool for quantitative in situ pathology that allows visualization of heterogeneous tissue morphology and segmentation and quantification of image features. Materials and Methods Tissue excised from a genetically engineered mouse model of sarcoma was imaged using a subcellular resolution microendoscope after topical application of a fluorescent anatomical contrast agent: acriflavine. An algorithm based on sparse component analysis (SCA) and the circle transform (CT) was developed for image segmentation and quantification of distinct tissue types. The accuracy of our approach was quantified through simulations of tumor and muscle images. Specifically, tumor, muscle, and tumor+muscle tissue images were simulated because these tissue types were most commonly observed in sarcoma margins. Simulations were based on tissue characteristics observed in pathology slides. The potential clinical utility of our approach was evaluated by imaging excised margins and the tumor bed in a cohort of mice after surgical resection of sarcoma. Results Simulation experiments revealed that SCA+CT achieved the lowest errors for larger nuclear sizes and for higher contrast ratios (nuclei intensity/background intensity). For imaging of tumor margins, SCA+CT effectively isolated nuclei from tumor, muscle, adipose, and tumor+muscle tissue types. Differences in density were correctly identified with SCA+CT in a cohort of ex vivo and in vivo images, thus illustrating the diagnostic potential of our approach. Conclusion The combination of a subcellular-resolution microendoscope, acriflavine staining, and SCA+CT can be used to accurately isolate nuclei and quantify their density in anatomical images of heterogeneous tissue. PMID:23824589

Quantitative Segmentation of Fluorescence Microscopy Images of Heterogeneous Tissue: Application to the Detection of Residual Disease in Tumor Margins.

PubMed

Mueller, Jenna L; Harmany, Zachary T; Mito, Jeffrey K; Kennedy, Stephanie A; Kim, Yongbaek; Dodd, Leslie; Geradts, Joseph; Kirsch, David G; Willett, Rebecca M; Brown, J Quincy; Ramanujam, Nimmi

2013-01-01

To develop a robust tool for quantitative in situ pathology that allows visualization of heterogeneous tissue morphology and segmentation and quantification of image features. TISSUE EXCISED FROM A GENETICALLY ENGINEERED MOUSE MODEL OF SARCOMA WAS IMAGED USING A SUBCELLULAR RESOLUTION MICROENDOSCOPE AFTER TOPICAL APPLICATION OF A FLUORESCENT ANATOMICAL CONTRAST AGENT: acriflavine. An algorithm based on sparse component analysis (SCA) and the circle transform (CT) was developed for image segmentation and quantification of distinct tissue types. The accuracy of our approach was quantified through simulations of tumor and muscle images. Specifically, tumor, muscle, and tumor+muscle tissue images were simulated because these tissue types were most commonly observed in sarcoma margins. Simulations were based on tissue characteristics observed in pathology slides. The potential clinical utility of our approach was evaluated by imaging excised margins and the tumor bed in a cohort of mice after surgical resection of sarcoma. Simulation experiments revealed that SCA+CT achieved the lowest errors for larger nuclear sizes and for higher contrast ratios (nuclei intensity/background intensity). For imaging of tumor margins, SCA+CT effectively isolated nuclei from tumor, muscle, adipose, and tumor+muscle tissue types. Differences in density were correctly identified with SCA+CT in a cohort of ex vivo and in vivo images, thus illustrating the diagnostic potential of our approach. The combination of a subcellular-resolution microendoscope, acriflavine staining, and SCA+CT can be used to accurately isolate nuclei and quantify their density in anatomical images of heterogeneous tissue.

Automatic recognition of 3D GGO CT imaging signs through the fusion of hybrid resampling and layer-wise fine-tuning CNNs.

PubMed

Han, Guanghui; Liu, Xiabi; Zheng, Guangyuan; Wang, Murong; Huang, Shan

2018-06-06

Ground-glass opacity (GGO) is a common CT imaging sign on high-resolution CT, which means the lesion is more likely to be malignant compared to common solid lung nodules. The automatic recognition of GGO CT imaging signs is of great importance for early diagnosis and possible cure of lung cancers. The present GGO recognition methods employ traditional low-level features and system performance improves slowly. Considering the high-performance of CNN model in computer vision field, we proposed an automatic recognition method of 3D GGO CT imaging signs through the fusion of hybrid resampling and layer-wise fine-tuning CNN models in this paper. Our hybrid resampling is performed on multi-views and multi-receptive fields, which reduces the risk of missing small or large GGOs by adopting representative sampling panels and processing GGOs with multiple scales simultaneously. The layer-wise fine-tuning strategy has the ability to obtain the optimal fine-tuning model. Multi-CNN models fusion strategy obtains better performance than any single trained model. We evaluated our method on the GGO nodule samples in publicly available LIDC-IDRI dataset of chest CT scans. The experimental results show that our method yields excellent results with 96.64% sensitivity, 71.43% specificity, and 0.83 F1 score. Our method is a promising approach to apply deep learning method to computer-aided analysis of specific CT imaging signs with insufficient labeled images. Graphical abstract We proposed an automatic recognition method of 3D GGO CT imaging signs through the fusion of hybrid resampling and layer-wise fine-tuning CNN models in this paper. Our hybrid resampling reduces the risk of missing small or large GGOs by adopting representative sampling panels and processing GGOs with multiple scales simultaneously. The layer-wise fine-tuning strategy has ability to obtain the optimal fine-tuning model. Our method is a promising approach to apply deep learning method to computer-aided analysis of specific CT imaging signs with insufficient labeled images.

A prospective randomized multicentre study of the impact of gallium-68 prostate-specific membrane antigen (PSMA) PET/CT imaging for staging high-risk prostate cancer prior to curative-intent surgery or radiotherapy (proPSMA study): clinical trial protocol.

PubMed

Hofman, Michael S; Murphy, Declan G; Williams, Scott G; Nzenza, Tatenda; Herschtal, Alan; Lourenco, Richard De Abreu; Bailey, Dale L; Budd, Ray; Hicks, Rodney J; Francis, Roslyn J; Lawrentschuk, Nathan

2018-05-03

Accurate staging of patients with prostate cancer (PCa) is important for therapeutic decision-making. Relapse after surgery or radiotherapy of curative intent is not uncommon and, in part, represents a failure of staging with current diagnostic imaging techniques to detect disease spread. Prostate-specific membrane antigen (PSMA) positron-emission tomography (PET)/computed tomography (CT) is a new whole-body scanning technique that enables visualization of PCa with high contrast. The hypotheses of this study are that: (i) PSMA-PET/CT has improved diagnostic performance compared with conventional imaging; (ii) PSMA-PET/CT should be used as a first-line diagnostic test for staging; (iii) the improved diagnostic performance of PSMA-PET/CT will result in significant management impact; and (iv) there are economic benefits if PSMA-PET/CT is incorporated into the management algorithm. The proPSMA trial is a prospective, multicentre study in which patients with untreated high-risk PCa will be randomized to gallium-68-PSMA-11 PET/CT or conventional imaging, consisting of CT of the abdomen/pelvis and bone scintigraphy with single-photon emission CT/CT. Patients eligible for inclusion are those with newly diagnosed PCa with select high-risk features, defined as International Society of Urological Pathology grade group ≥3 (primary Gleason grade 4, or any Gleason grade 5), prostate-specific antigen level ≥20 ng/mL or clinical stage ≥T3. Patients with negative, equivocal or oligometastatic disease on first line-imaging will cross over to receive the other imaging arm. The primary objective is to compare the accuracy of PSMA-PET/CT with that of conventional imaging for detecting nodal or distant metastatic disease. Histopathological, imaging and clinical follow-up at 6 months will define the primary endpoint according to a predefined scoring system. Secondary objectives include comparing management impact, the number of equivocal studies, the incremental value of second-line imaging in patients who cross over, the cost of each imaging strategy, radiation exposure, inter-observer agreement and safety of PSMA-PET/CT. Longer-term follow-up will also assess the prognostic value of a negative PSMA-PET/CT. This trial will provide data to establish whether PSMA-PET/CT should replace conventional imaging in the primary staging of select high-risk localized PCa, or whether it should be used to provide incremental diagnostic information in selected cases. © 2018 The Authors BJU International © 2018 BJU International Published by John Wiley & Sons Ltd.

X-ray phase contrast tomography by tracking near field speckle

PubMed Central

Wang, Hongchang; Berujon, Sebastien; Herzen, Julia; Atwood, Robert; Laundy, David; Hipp, Alexander; Sawhney, Kawal

2015-01-01

X-ray imaging techniques that capture variations in the x-ray phase can yield higher contrast images with lower x-ray dose than is possible with conventional absorption radiography. However, the extraction of phase information is often more difficult than the extraction of absorption information and requires a more sophisticated experimental arrangement. We here report a method for three-dimensional (3D) X-ray phase contrast computed tomography (CT) which gives quantitative volumetric information on the real part of the refractive index. The method is based on the recently developed X-ray speckle tracking technique in which the displacement of near field speckle is tracked using a digital image correlation algorithm. In addition to differential phase contrast projection images, the method allows the dark-field images to be simultaneously extracted. After reconstruction, compared to conventional absorption CT images, the 3D phase CT images show greatly enhanced contrast. This new imaging method has advantages compared to other X-ray imaging methods in simplicity of experimental arrangement, speed of measurement and relative insensitivity to beam movements. These features make the technique an attractive candidate for material imaging such as in-vivo imaging of biological systems containing soft tissue. PMID:25735237

Imaging patterns and focal lesions in fatty liver: a pictorial review.

PubMed

Venkatesh, Sudhakar K; Hennedige, Tiffany; Johnson, Geoffrey B; Hough, David M; Fletcher, Joel G

2017-05-01

Non-alcoholic fatty liver disease is the most common cause of chronic liver disease and affects nearly one-third of US population. With the increasing trend of obesity in the population, associated fatty change in the liver will be a common feature observed in imaging studies. Fatty liver causes changes in liver parenchyma appearance on imaging modalities including ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI) and may affect the imaging characteristics of focal liver lesions (FLLs). The imaging characteristics of FLLs were classically described in a non-fatty liver. In addition, focal fatty change and focal fat sparing may also simulate FLLs. Knowledge of characteristic patterns of fatty change in the liver (diffuse, geographical, focal, subcapsular, and perivascular) and their impact on the detection and characterization of FLL is therefore important. In general, fatty change may improve detection of FLLs on MRI using fat suppression sequences, but may reduce sensitivity on a single-phase (portal venous) CT and conventional ultrasound. In patients with fatty liver, MRI is generally superior to ultrasound and CT for detection and characterization of FLL. In this pictorial essay, we describe the imaging patterns of fatty change in the liver and its effect on detection and characterization of FLLs on ultrasound, CT, MRI, and PET.

A feature alignment score for online cone-beam CT-based image-guided radiotherapy for prostate cancer.

PubMed

Hargrave, Catriona; Deegan, Timothy; Poulsen, Michael; Bednarz, Tomasz; Harden, Fiona; Mengersen, Kerrie

2018-05-17

To develop a method for scoring online cone-beam CT (CBCT)-to-planning CT image feature alignment to inform prostate image-guided radiotherapy (IGRT) decision-making. The feasibility of incorporating volume variation metric thresholds predictive of delivering planned dose into weighted functions, was investigated. Radiation therapists and radiation oncologists participated in workshops where they reviewed prostate CBCT-IGRT case examples and completed a paper-based survey of image feature matching practices. For 36 prostate cancer patients, one daily CBCT was retrospectively contoured then registered with their plan to simulate delivered dose if (a) no online setup corrections and (b) online image alignment and setup corrections, were performed. Survey results were used to select variables for inclusion in classification and regression tree (CART) and boosted regression trees (BRT) modeling of volume variation metric thresholds predictive of delivering planned dose to the prostate, proximal seminal vesicles (PSV), bladder, and rectum. Weighted functions incorporating the CART and BRT results were used to calculate a score of individual tumor and organ at risk image feature alignment (FAS TV _ OAR ). Scaled and weighted FAS TV _ OAR were then used to calculate a score of overall treatment compliance (FAS global ) for a given CBCT-planning CT registration. The FAS TV _ OAR were assessed for sensitivity, specificity, and predictive power. FAS global thresholds indicative of high, medium, or low overall treatment plan compliance were determined using coefficients from multiple linear regression analysis. Thirty-two participants completed the prostate CBCT-IGRT survey. While responses demonstrated consensus of practice for preferential ranking of planning CT and CBCT match features in the presence of deformation and rotation, variation existed in the specified thresholds for observed volume differences requiring patient repositioning or repeat bladder and bowel preparation. The CART and BRT modeling indicated that for a given registration, a Dice similarity coefficient >0.80 and >0.60 for the prostate and PSV, respectively, and a maximum Hausdorff distance <8.0 mm for both structures were predictive of delivered dose ± 5% of planned dose. A normalized volume difference <1.0 and a CBCT anterior rectum wall >1.0 mm anterior to the planning CT anterior rectum wall were predictive of delivered dose >5% of planned rectum dose. A normalized volume difference <0.88, and a CBCT bladder wall >13.5 mm inferior and >5.0 mm posterior to the planning CT bladder were predictive of delivered dose >5% of planned bladder dose. A FAS TV _ OAR >0 is indicative of delivery of planned dose. For calculated FAS TV _ OAR for the prostate, PSV, bladder, and rectum using test data, sensitivity was 0.56, 0.75, 0.89, and 1.00, respectively; specificity 0.90, 0.94, 0.59, and 1.00, respectively; positive predictive power 0.90, 0.86, 0.53, and 1.00, respectively; and negative predictive power 0.56, 0.89, 0.91, and 1.00, respectively. Thresholds for the calculated FAS global of were low <60, medium 60-80, and high >80, with a 27% misclassification rate for the test data. A FAS global incorporating nested FAS TV _ OAR and volume variation metric thresholds predictive of treatment plan compliance was developed, offering an alternative to pretreatment dose calculations to assess treatment delivery accuracy. © 2018 American Association of Physicists in Medicine.

Medical image retrieval system using multiple features from 3D ROIs

NASA Astrophysics Data System (ADS)

Lu, Hongbing; Wang, Weiwei; Liao, Qimei; Zhang, Guopeng; Zhou, Zhiming

2012-02-01

Compared to a retrieval using global image features, features extracted from regions of interest (ROIs) that reflect distribution patterns of abnormalities would benefit more for content-based medical image retrieval (CBMIR) systems. Currently, most CBMIR systems have been designed for 2D ROIs, which cannot reflect 3D anatomical features and region distribution of lesions comprehensively. To further improve the accuracy of image retrieval, we proposed a retrieval method with 3D features including both geometric features such as Shape Index (SI) and Curvedness (CV) and texture features derived from 3D Gray Level Co-occurrence Matrix, which were extracted from 3D ROIs, based on our previous 2D medical images retrieval system. The system was evaluated with 20 volume CT datasets for colon polyp detection. Preliminary experiments indicated that the integration of morphological features with texture features could improve retrieval performance greatly. The retrieval result using features extracted from 3D ROIs accorded better with the diagnosis from optical colonoscopy than that based on features from 2D ROIs. With the test database of images, the average accuracy rate for 3D retrieval method was 76.6%, indicating its potential value in clinical application.

Automatic Scoring of Multiple Semantic Attributes With Multi-Task Feature Leverage: A Study on Pulmonary Nodules in CT Images.

PubMed

Sihong Chen; Jing Qin; Xing Ji; Baiying Lei; Tianfu Wang; Dong Ni; Jie-Zhi Cheng

2017-03-01

The gap between the computational and semantic features is the one of major factors that bottlenecks the computer-aided diagnosis (CAD) performance from clinical usage. To bridge this gap, we exploit three multi-task learning (MTL) schemes to leverage heterogeneous computational features derived from deep learning models of stacked denoising autoencoder (SDAE) and convolutional neural network (CNN), as well as hand-crafted Haar-like and HoG features, for the description of 9 semantic features for lung nodules in CT images. We regard that there may exist relations among the semantic features of "spiculation", "texture", "margin", etc., that can be explored with the MTL. The Lung Image Database Consortium (LIDC) data is adopted in this study for the rich annotation resources. The LIDC nodules were quantitatively scored w.r.t. 9 semantic features from 12 radiologists of several institutes in U.S.A. By treating each semantic feature as an individual task, the MTL schemes select and map the heterogeneous computational features toward the radiologists' ratings with cross validation evaluation schemes on the randomly selected 2400 nodules from the LIDC dataset. The experimental results suggest that the predicted semantic scores from the three MTL schemes are closer to the radiologists' ratings than the scores from single-task LASSO and elastic net regression methods. The proposed semantic attribute scoring scheme may provide richer quantitative assessments of nodules for better support of diagnostic decision and management. Meanwhile, the capability of the automatic association of medical image contents with the clinical semantic terms by our method may also assist the development of medical search engine.

Acceleration of fluoro-CT reconstruction for a mobile C-Arm on GPU and FPGA hardware: a simulation study

NASA Astrophysics Data System (ADS)

Xue, Xinwei; Cheryauka, Arvi; Tubbs, David

2006-03-01

CT imaging in interventional and minimally-invasive surgery requires high-performance computing solutions that meet operational room demands, healthcare business requirements, and the constraints of a mobile C-arm system. The computational requirements of clinical procedures using CT-like data are increasing rapidly, mainly due to the need for rapid access to medical imagery during critical surgical procedures. The highly parallel nature of Radon transform and CT algorithms enables embedded computing solutions utilizing a parallel processing architecture to realize a significant gain of computational intensity with comparable hardware and program coding/testing expenses. In this paper, using a sample 2D and 3D CT problem, we explore the programming challenges and the potential benefits of embedded computing using commodity hardware components. The accuracy and performance results obtained on three computational platforms: a single CPU, a single GPU, and a solution based on FPGA technology have been analyzed. We have shown that hardware-accelerated CT image reconstruction can be achieved with similar levels of noise and clarity of feature when compared to program execution on a CPU, but gaining a performance increase at one or more orders of magnitude faster. 3D cone-beam or helical CT reconstruction and a variety of volumetric image processing applications will benefit from similar accelerations.

Prognostic importance of pleural attachment status measured by pretreatment CT images in patients with stage IA lung adenocarcinoma: measurement of the ratio of the interface between nodule and neighboring pleura to nodule surface area

NASA Astrophysics Data System (ADS)

Kawata, Y.; Niki, N.; Kusumoto, M.; Ohmatsu, H.; Aokage, K.; Ishii, G.; Matsumoto, Y.; Tsuchida, T.; Eguchi, K.; Kaneko, M.

2018-02-01

Screening for lung cancer with low-dose computed tomography (CT) has led to increased recognition of small lung cancers and is expected to increase the rate of detection of early-stage lung cancer. Major concerns in the implementation of the CT screening of large populations include determining the appropriate management of pulmonary nodules found on a scan. The identification of patients with early-stage lung cancer who have a higher risk for relapse and who require more aggressive surveillance has been a target of intense investigation. This study was performed to investigate whether image features of internal intensity in combination with surrounding structure characteristics are associated with an increased risk of relapse in patients with stage IA lung adenocarcinoma. We focused on pleural attachment status which is one of morphological characteristics associated with prognosis in three-dimensional thoracic CT images.

Abdominal multi-organ CT segmentation using organ correlation graph and prediction-based shape and location priors.

PubMed

Okada, Toshiyuki; Linguraru, Marius George; Hori, Masatoshi; Summers, Ronald M; Tomiyama, Noriyuki; Sato, Yoshinobu

2013-01-01

The paper addresses the automated segmentation of multiple organs in upper abdominal CT data. We propose a framework of multi-organ segmentation which is adaptable to any imaging conditions without using intensity information in manually traced training data. The features of the framework are as follows: (1) the organ correlation graph (OCG) is introduced, which encodes the spatial correlations among organs inherent in human anatomy; (2) the patient-specific organ shape and location priors obtained using OCG enable the estimation of intensity priors from only target data and optionally a number of untraced CT data of the same imaging condition as the target data. The proposed methods were evaluated through segmentation of eight abdominal organs (liver, spleen, left and right kidney, pancreas, gallbladder, aorta, and inferior vena cava) from 86 CT data obtained by four imaging conditions at two hospitals. The performance was comparable to the state-of-the-art method using intensity priors constructed from manually traced data.

PET imaging: implications for the future of therapy monitoring with PET/CT in oncology.

PubMed

Tomasi, Giampaolo; Rosso, Lula

2012-10-01

Among the methods based on molecular imaging, the measure of the tracer uptake variation between a baseline and follow-up scan with the SUV and [(18)F]FDG-PET/CT is a very powerful tool for assessing response to treatment in oncology. However, the development of new targeted therapeutics and tissue pharmacokinetic evaluation of existing ones are increasingly requiring therapy monitoring with alternative tracers and indicators. In parallel, the potential predictive and prognostic value of other image-derived parameters, such as tumour volume and textural features, relating to tumoral heterogeneity, has recently emerged from several works. Copyright © 2012 Elsevier Ltd. All rights reserved.

Colloid Adenocarcinoma of the Lung: CT and PET/CT Findings in Seven Patients.

PubMed

Kim, Han Kyul; Han, Joungho; Franks, Teri J; Lee, Kyung Soo; Kim, Tae Jung; Choi, Joon Young; Zo, Jaeil

2018-05-24

We aimed to assess CT and 18 F-FDG PET/CT findings of colloid adenocarcinoma of the lung in seven patients. From 2010 to 2017, seven patients with surgically proven colloid adenocarcinoma of the lung were identified. CT (both enhanced and unenhanced) and PET/CT findings were analyzed, and the imaging features were compared with histopathologic reports. Clinical and demographic features were also analyzed. In all cases except one, tumors showed low attenuation on unenhanced CT scans, ranging in attenuation from -16.5 to 20.7 HU (median, 9.2 HU). After contrast medium injection, enhancement was scant, so net enhancement ranged from 0.4 to 29.0 HU (median, 4.1 HU). All tumors had a lobulated contour. Stippled calcifications within the tumor were seen in one patient. The maximum standardized uptake value of tumors on PET/CT ranged from 1.5 to 4.5 (median, 3.5). In six of seven patients, FDG accumulation was seen in the tumor walls (n = 3, curvilinear uptake) or in both the tumor walls and tumor septa (n = 3, crisscross uptake). Six patients were alive without recurrence after a median follow-up period of 2.3 years (range, 2 months to 5 years). In one patient, who was alive at follow-up 4 years after imaging and had received adjuvant concurrent chemoradiation therapy after lobectomy, recurrent disease was detected 6 months after completion of the therapy. On CT, pulmonary colloid adenocarcinomas present as lobulated homogeneously low-attenuation tumors. At PET, curvilinear or crisscross FDG uptake is seen within the tumor where tumor cells are lining the walls or septal structures.

Primary Hepatic Malignant Fibrous Histiocytoma on PET/CT.

PubMed

Liu, Yachao; Xu, Baixuan

2018-06-01

Malignant fibrous histiocytoma is mainly presented in extremities, less commonly in posterior peritoneum, but primary presented in liver is very rare and often with a poor prognosis because of its high aggression. The features of clinical presentations and images are variable and the pre-operative diagnosis is difficult. Here, we report a primary hepatic malignant fibrous histiocytoma patient with no distant metastasis showed on pre-operative F-FDG PET/CT, however with many metastases showed on the post-operative F-FDG PET/CT.

Unusual Bone Superscan, MIBG Superscan, and 68Ga DOTATATE PET/CT in Metastatic Pheochromocytoma.

PubMed

Tan, Teik Hin; Wong, Teck Huat; Hassan, Siti Zarina Amir; Lee, Boon Nang

2015-11-01

A 17-year-old adolescent boy with biochemically raised 2-hour urinary metanephrine and normetanephrine as well as CT findings of retroperitoneal soft tissue mass and bony metastases was referred for further assessment. Apart from Ga DOTATATE PET/CT evaluation, pretargeted systemic radionuclide therapy assessment with I-MIBG scintigraphy showed unusual phenomenon of MIBG superscan. Postsurgically, restaging Tc-MDP bone scintigraphy showed typical bone superscan features. The MIBG superscan was better delineated on post-I-MIBG therapy images.

Automated segmentations of skin, soft-tissue, and skeleton, from torso CT images

NASA Astrophysics Data System (ADS)

Zhou, Xiangrong; Hara, Takeshi; Fujita, Hiroshi; Yokoyama, Ryujiro; Kiryu, Takuji; Hoshi, Hiroaki

2004-05-01

We have been developing a computer-aided diagnosis (CAD) scheme for automatically recognizing human tissue and organ regions from high-resolution torso CT images. We show some initial results for extracting skin, soft-tissue and skeleton regions. 139 patient cases of torso CT images (male 92, female 47; age: 12-88) were used in this study. Each case was imaged with a common protocol (120kV/320mA) and covered the whole torso with isotopic spatial resolution of about 0.63 mm and density resolution of 12 bits. A gray-level thresholding based procedure was applied to separate the human body from background. The density and distance features to body surface were used to determine the skin, and separate soft-tissue from the others. A 3-D region growing based method was used to extract the skeleton. We applied this system to the 139 cases and found that the skin, soft-tissue and skeleton regions were recognized correctly for 93% of the patient cases. The accuracy of segmentation results was acceptable by evaluating the results slice by slice. This scheme will be included in CAD systems for detecting and diagnosing the abnormal lesions in multi-slice torso CT images.

Colitis detection on abdominal CT scans by rich feature hierarchies

NASA Astrophysics Data System (ADS)

Liu, Jiamin; Lay, Nathan; Wei, Zhuoshi; Lu, Le; Kim, Lauren; Turkbey, Evrim; Summers, Ronald M.

2016-03-01

Colitis is inflammation of the colon due to neutropenia, inflammatory bowel disease (such as Crohn disease), infection and immune compromise. Colitis is often associated with thickening of the colon wall. The wall of a colon afflicted with colitis is much thicker than normal. For example, the mean wall thickness in Crohn disease is 11-13 mm compared to the wall of the normal colon that should measure less than 3 mm. Colitis can be debilitating or life threatening, and early detection is essential to initiate proper treatment. In this work, we apply high-capacity convolutional neural networks (CNNs) to bottom-up region proposals to detect potential colitis on CT scans. Our method first generates around 3000 category-independent region proposals for each slice of the input CT scan using selective search. Then, a fixed-length feature vector is extracted from each region proposal using a CNN. Finally, each region proposal is classified and assigned a confidence score with linear SVMs. We applied the detection method to 260 images from 26 CT scans of patients with colitis for evaluation. The detection system can achieve 0.85 sensitivity at 1 false positive per image.

Accuracy of UTE-MRI-based patient setup for brain cancer radiation therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Yingli; Cao, Minsong; Kaprealian, Tania

2016-01-15

Purpose: Radiation therapy simulations solely based on MRI have advantages compared to CT-based approaches. One feature readily available from computed tomography (CT) that would need to be reproduced with MR is the ability to compute digitally reconstructed radiographs (DRRs) for comparison against on-board radiographs commonly used for patient positioning. In this study, the authors generate MR-based bone images using a single ultrashort echo time (UTE) pulse sequence and quantify their 3D and 2D image registration accuracy to CT and radiographic images for treatments in the cranium. Methods: Seven brain cancer patients were scanned at 1.5 T using a radial UTEmore » sequence. The sequence acquired two images at two different echo times. The two images were processed using an in-house software to generate the UTE bone images. The resultant bone images were rigidly registered to simulation CT data and the registration error was determined using manually annotated landmarks as references. DRRs were created based on UTE-MRI and registered to simulated on-board images (OBIs) and actual clinical 2D oblique images from ExacTrac™. Results: UTE-MRI resulted in well visualized cranial, facial, and vertebral bones that quantitatively matched the bones in the CT images with geometric measurement errors of less than 1 mm. The registration error between DRRs generated from 3D UTE-MRI and the simulated 2D OBIs or the clinical oblique x-ray images was also less than 1 mm for all patients. Conclusions: UTE-MRI-based DRRs appear to be promising for daily patient setup of brain cancer radiotherapy with kV on-board imaging.« less

Accuracy of Dual-Energy Virtual Monochromatic CT Numbers: Comparison between the Single-Source Projection-Based and Dual-Source Image-Based Methods.

PubMed

Ueguchi, Takashi; Ogihara, Ryota; Yamada, Sachiko

2018-03-21

To investigate the accuracy of dual-energy virtual monochromatic computed tomography (CT) numbers obtained by two typical hardware and software implementations: the single-source projection-based method and the dual-source image-based method. A phantom with different tissue equivalent inserts was scanned with both single-source and dual-source scanners. A fast kVp-switching feature was used on the single-source scanner, whereas a tin filter was used on the dual-source scanner. Virtual monochromatic CT images of the phantom at energy levels of 60, 100, and 140 keV were obtained by both projection-based (on the single-source scanner) and image-based (on the dual-source scanner) methods. The accuracy of virtual monochromatic CT numbers for all inserts was assessed by comparing measured values to their corresponding true values. Linear regression analysis was performed to evaluate the dependency of measured CT numbers on tissue attenuation, method, and their interaction. Root mean square values of systematic error over all inserts at 60, 100, and 140 keV were approximately 53, 21, and 29 Hounsfield unit (HU) with the single-source projection-based method, and 46, 7, and 6 HU with the dual-source image-based method, respectively. Linear regression analysis revealed that the interaction between the attenuation and the method had a statistically significant effect on the measured CT numbers at 100 and 140 keV. There were attenuation-, method-, and energy level-dependent systematic errors in the measured virtual monochromatic CT numbers. CT number reproducibility was comparable between the two scanners, and CT numbers had better accuracy with the dual-source image-based method at 100 and 140 keV. Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Radiomics in Oncological PET/CT: Clinical Applications.

PubMed

Lee, Jeong Won; Lee, Sang Mi

2018-06-01

18 F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) is widely used for staging, evaluating treatment response, and predicting prognosis in malignant diseases. FDG uptake and volumetric PET parameters such as metabolic tumor volume have been used and are still used as conventional PET parameters to assess biological characteristics of tumors. However, in recent years, additional features derived from PET images by computational processing have been found to reflect intratumoral heterogeneity, which is related to biological tumor features, and to provide additional predictive and prognostic information, which leads to the concept of radiomics. In this review, we focus on recent clinical studies of malignant diseases that investigated intratumoral heterogeneity on PET/CT, and we discuss its clinical role in various cancers.

Ring-enhancement pattern on contrast-enhanced CT predicts adenosquamous carcinoma of the pancreas: a matched case-control study.

PubMed

Imaoka, Hiroshi; Shimizu, Yasuhiro; Mizuno, Nobumasa; Hara, Kazuo; Hijioka, Susumu; Tajika, Masahiro; Tanaka, Tsutomu; Ishihara, Makoto; Ogura, Takeshi; Obayashi, Tomohiko; Shinagawa, Akihide; Sakaguchi, Masafumi; Yamaura, Hidekazu; Kato, Mina; Niwa, Yasumasa; Yamao, Kenji

2014-01-01

Adenosquamous carcinoma of the pancreas (ASC) is a rare malignant neoplasm of the pancreas, exhibiting both glandular and squamous differentiation. However, little is known about its imaging features. This study examined the imaging features of pancreatic ASC. We evaluated images of contrast-enhanced computed tomography (CT) and endoscopic ultrasonography (EUS). As controls, solid pancreatic neoplasms matched in a 2:1 ratio to ASC cases for age, sex and tumor location were also evaluated. Twenty-three ASC cases were examined, and 46 solid pancreatic neoplasms (43 pancreatic ductal adenocarcinomas, two pancreatic neuroendocrine tumors and one acinar cell carcinoma) were matched as controls. Univariate analysis demonstrated significant differences in the outline and vascularity of tumors on contrast-enhanced CT in the ASC and control groups (P < 0.001 and P < 0.001, respectively). A smooth outline, cystic changes, and the ring-enhancement pattern on contrast-enhanced CT were seen to have significant predictive powers by stepwise forward logistic regression analysis (P = 0.044, P = 0.010, and P = 0.001, respectively). Of the three, the ring-enhancement pattern was the most useful, and its predictive diagnostic sensitivity, specificity, positive predictive value and negative predictive value for diagnosis of ASC were 65.2%, 89.6%, 75.0% and 84.3%, respectively. These results demonstrate that presence of the ring-enhancement pattern on contrast-enhanced CT is the most useful predictive factor for ASC. Copyright © 2014 IAP and EPC. Published by Elsevier B.V. All rights reserved.

Applying microCT and 3D visualization to Jurassic silicified conifer seed cones: A virtual advantage over thin-sectioning.

PubMed

Gee, Carole T

2013-11-01

As an alternative to conventional thin-sectioning, which destroys fossil material, high-resolution X-ray computed tomography (also called microtomography or microCT) integrated with scientific visualization, three-dimensional (3D) image segmentation, size analysis, and computer animation is explored as a nondestructive method of imaging the internal anatomy of 150-million-year-old conifer seed cones from the Late Jurassic Morrison Formation, USA, and of recent and other fossil cones. • MicroCT was carried out on cones using a General Electric phoenix v|tome|x s 240D, and resulting projections were processed with visualization software to produce image stacks of serial single sections for two-dimensional (2D) visualization, 3D segmented reconstructions with targeted structures in color, and computer animations. • If preserved in differing densities, microCT produced images of internal fossil tissues that showed important characters such as seed phyllotaxy or number of seeds per cone scale. Color segmentation of deeply embedded seeds highlighted the arrangement of seeds in spirals. MicroCT of recent cones was even more effective. • This is the first paper on microCT integrated with 3D segmentation and computer animation applied to silicified seed cones, which resulted in excellent 2D serial sections and segmented 3D reconstructions, revealing features requisite to cone identification and understanding of strobilus construction.

Relative location prediction in CT scan images using convolutional neural networks.

PubMed

Guo, Jiajia; Du, Hongwei; Zhu, Jianyue; Yan, Ting; Qiu, Bensheng

2018-07-01

Relative location prediction in computed tomography (CT) scan images is a challenging problem. Many traditional machine learning methods have been applied in attempts to alleviate this problem. However, the accuracy and speed of these methods cannot meet the requirement of medical scenario. In this paper, we propose a regression model based on one-dimensional convolutional neural networks (CNN) to determine the relative location of a CT scan image both quickly and precisely. In contrast to other common CNN models that use a two-dimensional image as an input, the input of this CNN model is a feature vector extracted by a shape context algorithm with spatial correlation. Normalization via z-score is first applied as a pre-processing step. Then, in order to prevent overfitting and improve model's performance, 20% of the elements of the feature vectors are randomly set to zero. This CNN model consists primarily of three one-dimensional convolutional layers, three dropout layers and two fully-connected layers with appropriate loss functions. A public dataset is employed to validate the performance of the proposed model using a 5-fold cross validation. Experimental results demonstrate an excellent performance of the proposed model when compared with contemporary techniques, achieving a median absolute error of 1.04 cm and mean absolute error of 1.69 cm. The time taken for each relative location prediction is approximately 2 ms. Results indicate that the proposed CNN method can contribute to a quick and accurate relative location prediction in CT scan images, which can improve efficiency of the medical picture archiving and communication system in the future. Copyright © 2018 Elsevier B.V. All rights reserved.

[Differential diagnosis between renal cell carcinoma associated with XP11.2 translocation/TFE gene fusion and papillary renal cell carcinoma based on CT and MRI findings].

PubMed

Zhu, Qingqiang; Zhu, Wenrong; Wu, Jingtao; Fu, Jianxiong; Chen, Wenxin; Wang, Zhongqiu

2014-05-20

To comparative study of CT and MRI appearances in renal cell carcinoma associated with XP11.2 translocation/TFE gene fusion (XP11.2 RCC) and papillary renal cell carcinoma (PRCC). 12 patients with XP11.2 RCC and 18 patients with PRCC were retrospectively studied, and the data was analyzed by AVONA and chi-square text. 12 patients with XP11.2 RCC and 18 patients with PRCC, cystic components (2 vs 11, P < 0.05), calcification (0 vs 6, P < 0.05), hemorrhage (9 vs 5, P < 0.05), homogeneous enhancement (10 vs 7, P < 0.05) and had lymph node (3 vs 0) or hepatic metastasis (1vs 0) (P < 0.05). On unenhanced CT, the density of XP11.2 RCC was greater than PRCC, normal renal cortex or medulla (P < 0.05). Their degree of enhancement were less than normal renal cortex on all enhanced phases (P < 0.05). The enhancement degree of XP11.2 RCC was higher than PRCC (on all phases) and renal medulla (on cortical and medullary phase) (P < 0.05), but less than normal renal medulla on the delayed phase (P < 0.05). The enhancement degree of PRCC was lower than renal medulla on all phases (P < 0.05). The XP11.2 RCC was isointense on T1-weighted imaging, hypointense on T2-weighted imaging. The PRCC was isointense or hypointense on T1-weighted imaging, isointense on T2-weighted imaging. The CT and MRI could show imagings features of XP11.2 RCC and PRCC, and these features were helpful in predicting a specific subtype of renal cell carcinoma.

Renal carcinomas associated with Xp11.2 translocations/TFE3 gene fusions: findings on MRI and computed tomography imaging.

PubMed

Liu, Kefu; Xie, Ping; Peng, Weijun; Zhou, Zhengrong

2014-08-01

To retrospectively analyze MRI and computed tomographic (CT) findings from renal carcinomas associated with Xp11.2 translocations/TFE3 gene fusions (Xp11-RCC). Institutional review board permission was obtained to review patient medical records, and the requirement for informed consent was waved . The clinical and MRI/CT features of five cases with Xp11-RCC that were confirmed by pathology were analyzed retrospectively. The image characteristics included the lesion location and size, contribution of cystic and solid components, intratumoral necrosis or hemorrhage, invasion of perinephric tissue and renal sinus, lymphadenopathy, major venous or arterial vascular invasion, pattern of the tumor growth, intratumor calcification and lipids, homogeneity of SI on T2-weighted images, attenuation and SI of the mass with respect to the normal renal cortex on precontrast and contrasted CT/MRI images, tumor SIs, tumor attenuations and tumor-to-cortex indices, homogeneity of enhancement on the contrasted images. The mean age was 32 years (range, 15-47 years). Most patients (4/5) were women. All tumors showed a cortical location. The average tumor size was 9 cm (range, 4-18 cm). Four tumors comprised a predominantly solid lesion with focal necrosis, and one tumor comprised a solid lesion with significant necrosis. All tumors showed intertumor hemorrhage, infiltrative growth and invasion of the perirenal adipose/renal sinus. Four cases showed retroperitoneal lymphadenopathy, of which one case showed simultaneous mediastinal and supraclavicular lymphadenopathy. All tumors from four cases showed mild hyperintensity on T1-weighted MRI images, and three tumors showed hypointensity on T2-weighted MRI images relative to the renal cortex except for 1 tumor that showed significant hemorrhage and a relative hyperintensity. For 3 cases who were imaged with CT, two tumors imaged using nonenhanced CT images showed mild hyperdensity relative to the renal cortex. Calcification was noted in all three tumors. All tumors showed mild, persistent enhancement. Typical Xp11-RCC manifests as an advanced, solid renal mass with mild persistent enhancement, a prevalence of intertumor hemorrhage/calcification, and a cortical epicenter location. The predilection for children and young adults is a useful clinical feature when confirming a diagnosis of Xp11-RCC. © 2013 Wiley Periodicals, Inc.

Washout of hepatocellular carcinoma on portal venous phase of multidetector computed tomography in a pre-transplant population.

PubMed

Kitzing, Yu Xuan; Ng, Bernard H K; Kitzing, Bjoern; Waugh, Richard; Kench, James G; Strasser, Simone I; McCormack, Samuel

2015-12-01

Washout is an important diagnostic imaging feature of hepatocellular carcinoma (HCC) on computed tomography (CT). The primary aim of this study is to evaluate the prevalence and the interobserver variation in the detection of portal venous phase (PVP) washout of HCCs using CT in a transplant population. The secondary aim is to evaluate factors influencing the detection of PVP washout. Forty-five patients who underwent CT liver imaging within the 60 days before transplantation had viable HCCs confirmed on pathology. Two radiologists retrospectively reviewed the images for HCCs including features of arterial enhancement and PVP washout. Clinical data, peak kilovoltage, imaging features of portal hypertension, region of interest attenuation measurements of the individual lesions, background liver parenchyma and portal vein were obtained. Liver to lesion attenuation ratio was also calculated. Statistical analysis was performed. The two readers identified 50 arterially enhancing HCCs in 45 patients. In consensus, the two readers identified washout in 60% of the HCCs with a substantial interobserver agreement (kappa = 0.633). PVP washout was associated with larger lesion size, increased background liver parenchyma attenuation, increased liver to lesion attenuation ratio, increased portal vein attenuation and hepatitis B viral status (P = 0.027, 0.008, 0.014, 0.017 and 0.037 respectively). In our transplant population, portal venous phase washout was seen in 60% of the hypervascular HCCs. Factors influencing the presence of PVP washout include lesion size as well as the liver and portal vein attenuation reflective of the portal haemodynamics. © 2015 The Royal Australian and New Zealand College of Radiologists.

Quantifying the impact of respiratory-gated 4D CT acquisition on thoracic image quality: A digital phantom study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bernatowicz, K., E-mail: kingab@student.ethz.ch; Knopf, A.; Lomax, A.

Purpose: Prospective respiratory-gated 4D CT has been shown to reduce tumor image artifacts by up to 50% compared to conventional 4D CT. However, to date no studies have quantified the impact of gated 4D CT on normal lung tissue imaging, which is important in performing dose calculations based on accurate estimates of lung volume and structure. To determine the impact of gated 4D CT on thoracic image quality, the authors developed a novel simulation framework incorporating a realistic deformable digital phantom driven by patient tumor motion patterns. Based on this framework, the authors test the hypothesis that respiratory-gated 4D CTmore » can significantly reduce lung imaging artifacts. Methods: Our simulation framework synchronizes the 4D extended cardiac torso (XCAT) phantom with tumor motion data in a quasi real-time fashion, allowing simulation of three 4D CT acquisition modes featuring different levels of respiratory feedback: (i) “conventional” 4D CT that uses a constant imaging and couch-shift frequency, (ii) “beam paused” 4D CT that interrupts imaging to avoid oversampling at a given couch position and respiratory phase, and (iii) “respiratory-gated” 4D CT that triggers acquisition only when the respiratory motion fulfills phase-specific displacement gating windows based on prescan breathing data. Our framework generates a set of ground truth comparators, representing the average XCAT anatomy during beam-on for each of ten respiratory phase bins. Based on this framework, the authors simulated conventional, beam-paused, and respiratory-gated 4D CT images using tumor motion patterns from seven lung cancer patients across 13 treatment fractions, with a simulated 5.5 cm{sup 3} spherical lesion. Normal lung tissue image quality was quantified by comparing simulated and ground truth images in terms of overall mean square error (MSE) intensity difference, threshold-based lung volume error, and fractional false positive/false negative rates. Results: Averaged across all simulations and phase bins, respiratory-gating reduced overall thoracic MSE by 46% compared to conventional 4D CT (p ∼ 10{sup −19}). Gating leads to small but significant (p < 0.02) reductions in lung volume errors (1.8%–1.4%), false positives (4.0%–2.6%), and false negatives (2.7%–1.3%). These percentage reductions correspond to gating reducing image artifacts by 24–90 cm{sup 3} of lung tissue. Similar to earlier studies, gating reduced patient image dose by up to 22%, but with scan time increased by up to 135%. Beam paused 4D CT did not significantly impact normal lung tissue image quality, but did yield similar dose reductions as for respiratory-gating, without the added cost in scanning time. Conclusions: For a typical 6 L lung, respiratory-gated 4D CT can reduce image artifacts affecting up to 90 cm{sup 3} of normal lung tissue compared to conventional acquisition. This image improvement could have important implications for dose calculations based on 4D CT. Where image quality is less critical, beam paused 4D CT is a simple strategy to reduce imaging dose without sacrificing acquisition time.« less

A segmentation and point-matching enhanced efficient deformable image registration method for dose accumulation between HDR CT images

NASA Astrophysics Data System (ADS)

Zhen, Xin; Chen, Haibin; Yan, Hao; Zhou, Linghong; Mell, Loren K.; Yashar, Catheryn M.; Jiang, Steve; Jia, Xun; Gu, Xuejun; Cervino, Laura

2015-04-01

Deformable image registration (DIR) of fractional high-dose-rate (HDR) CT images is challenging due to the presence of applicators in the brachytherapy image. Point-to-point correspondence fails because of the undesired deformation vector fields (DVF) propagated from the applicator region (AR) to the surrounding tissues, which can potentially introduce significant DIR errors in dose mapping. This paper proposes a novel segmentation and point-matching enhanced efficient DIR (named SPEED) scheme to facilitate dose accumulation among HDR treatment fractions. In SPEED, a semi-automatic seed point generation approach is developed to obtain the incremented fore/background point sets to feed the random walks algorithm, which is used to segment and remove the AR, leaving empty AR cavities in the HDR CT images. A feature-based ‘thin-plate-spline robust point matching’ algorithm is then employed for AR cavity surface points matching. With the resulting mapping, a DVF defining on each voxel is estimated by B-spline approximation, which serves as the initial DVF for the subsequent Demons-based DIR between the AR-free HDR CT images. The calculated DVF via Demons combined with the initial one serve as the final DVF to map doses between HDR fractions. The segmentation and registration accuracy are quantitatively assessed by nine clinical HDR cases from three gynecological cancer patients. The quantitative analysis and visual inspection of the DIR results indicate that SPEED can suppress the impact of applicator on DIR, and accurately register HDR CT images as well as deform and add interfractional HDR doses.

A segmentation and point-matching enhanced efficient deformable image registration method for dose accumulation between HDR CT images.

PubMed

Zhen, Xin; Chen, Haibin; Yan, Hao; Zhou, Linghong; Mell, Loren K; Yashar, Catheryn M; Jiang, Steve; Jia, Xun; Gu, Xuejun; Cervino, Laura

2015-04-07

Deformable image registration (DIR) of fractional high-dose-rate (HDR) CT images is challenging due to the presence of applicators in the brachytherapy image. Point-to-point correspondence fails because of the undesired deformation vector fields (DVF) propagated from the applicator region (AR) to the surrounding tissues, which can potentially introduce significant DIR errors in dose mapping. This paper proposes a novel segmentation and point-matching enhanced efficient DIR (named SPEED) scheme to facilitate dose accumulation among HDR treatment fractions. In SPEED, a semi-automatic seed point generation approach is developed to obtain the incremented fore/background point sets to feed the random walks algorithm, which is used to segment and remove the AR, leaving empty AR cavities in the HDR CT images. A feature-based 'thin-plate-spline robust point matching' algorithm is then employed for AR cavity surface points matching. With the resulting mapping, a DVF defining on each voxel is estimated by B-spline approximation, which serves as the initial DVF for the subsequent Demons-based DIR between the AR-free HDR CT images. The calculated DVF via Demons combined with the initial one serve as the final DVF to map doses between HDR fractions. The segmentation and registration accuracy are quantitatively assessed by nine clinical HDR cases from three gynecological cancer patients. The quantitative analysis and visual inspection of the DIR results indicate that SPEED can suppress the impact of applicator on DIR, and accurately register HDR CT images as well as deform and add interfractional HDR doses.

Missed rib fractures on evaluation of initial chest CT for trauma patients: pattern analysis and diagnostic value of coronal multiplanar reconstruction images with multidetector row CT

PubMed Central

Cho, S H; Sung, Y M; Kim, M S

2012-01-01

Objective The objective of this study was to review the prevalence and radiological features of rib fractures missed on initial chest CT evaluation, and to examine the diagnostic value of additional coronal images in a large series of trauma patients. Methods 130 patients who presented to an emergency room for blunt chest trauma underwent multidetector row CT of the thorax within the first hour during their stay, and had follow-up CT or bone scans as diagnostic gold standards. Images were evaluated on two separate occasions: once with axial images and once with both axial and coronal images. The detection rates of missed rib fractures were compared between readings using a non-parametric method of clustered data. In the cases of missed rib fractures, the shapes, locations and associated fractures were evaluated. Results 58 rib fractures were missed with axial images only and 52 were missed with both axial and coronal images (p=0.088). The most common shape of missed rib fractures was buckled (56.9%), and the anterior arc (55.2%) was most commonly involved. 21 (36.2%) missed rib fractures had combined fractures on the same ribs, and 38 (65.5%) were accompanied by fracture on neighbouring ribs. Conclusion Missed rib fractures are not uncommon, and radiologists should be familiar with buckle fractures, which are frequently missed. Additional coronal imagescan be helpful in the diagnosis of rib fractures that are not seen on axial images. PMID:22514102

TU-H-CAMPUS-JeP2-03: Machine-Learning-Based Delineation Framework of GTV Regions of Solid and Ground Glass Opacity Lung Tumors at Datasets of Planning CT and PET/CT Images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ikushima, K; Arimura, H; Jin, Z

Purpose: In radiation treatment planning, delineation of gross tumor volume (GTV) is very important, because the GTVs affect the accuracies of radiation therapy procedure. To assist radiation oncologists in the delineation of GTV regions while treatment planning for lung cancer, we have proposed a machine-learning-based delineation framework of GTV regions of solid and ground glass opacity (GGO) lung tumors following by optimum contour selection (OCS) method. Methods: Our basic idea was to feed voxel-based image features around GTV contours determined by radiation oncologists into a machine learning classifier in the training step, after which the classifier produced the degree ofmore » GTV for each voxel in the testing step. Ten data sets of planning CT and PET/CT images were selected for this study. The support vector machine (SVM), which learned voxel-based features which include voxel value and magnitudes of image gradient vector that obtained from each voxel in the planning CT and PET/CT images, extracted initial GTV regions. The final GTV regions were determined using the OCS method that was able to select a global optimum object contour based on multiple active delineations with a level set method around the GTV. To evaluate the results of proposed framework for ten cases (solid:6, GGO:4), we used the three-dimensional Dice similarity coefficient (DSC), which denoted the degree of region similarity between the GTVs delineated by radiation oncologists and the proposed framework. Results: The proposed method achieved an average three-dimensional DSC of 0.81 for ten lung cancer patients, while a standardized uptake value-based method segmented GTV regions with the DSC of 0.43. The average DSCs for solid and GGO were 0.84 and 0.76, respectively, obtained by the proposed framework. Conclusion: The proposed framework with the support vector machine may be useful for assisting radiation oncologists in delineating solid and GGO lung tumors.« less

Textural features of pretreatment 18F-FDG PET/CT images: prognostic significance in patients with advanced T-stage oropharyngeal squamous cell carcinoma.

PubMed

Cheng, Nai-Ming; Fang, Yu-Hua Dean; Chang, Joseph Tung-Chieh; Huang, Chung-Guei; Tsan, Din-Li; Ng, Shu-Hang; Wang, Hung-Ming; Lin, Chien-Yu; Liao, Chun-Ta; Yen, Tzu-Chen

2013-10-01

Previous studies have shown that total lesion glycolysis (TLG) may serve as a prognostic indicator in oropharyngeal squamous cell carcinoma (OPSCC). We sought to investigate whether the textural features of pretreatment (18)F-FDG PET/CT images can provide any additional prognostic information over TLG and clinical staging in patients with advanced T-stage OPSCC. We retrospectively analyzed the pretreatment (18)F-FDG PET/CT images of 70 patients with advanced T-stage OPSCC who had completed concurrent chemoradiotherapy, bioradiotherapy, or radiotherapy with curative intent. All of the patients had data on human papillomavirus (HPV) infection and were followed up for at least 24 mo or until death. A standardized uptake value (SUV) of 2.5 was taken as a cutoff for tumor boundary. The textural features of pretreatment (18)F-FDG PET/CT images were extracted from histogram analysis (SUV variance and SUV entropy), normalized gray-level cooccurrence matrix (uniformity, entropy, dissimilarity, contrast, homogeneity, inverse different moment, and correlation), and neighborhood gray-tone difference matrix (coarseness, contrast, busyness, complexity, and strength). Receiver-operating-characteristic curves were used to identify the optimal cutoff values for the textural features and TLG. Thirteen patients were HPV-positive. Multivariate Cox regression analysis showed that age, tumor TLG, and uniformity were independently associated with progression-free survival (PFS) and disease-specific survival (DSS). TLG, uniformity, and HPV positivity were significantly associated with overall survival (OS). A prognostic scoring system based on TLG and uniformity was derived. Patients who presented with TLG > 121.9 g and uniformity ≤ 0.138 experienced significantly worse PFS, DSS, and OS rates than those without (P < 0.001, < 0.001, and 0.002, respectively). Patients with TLG > 121.9 g or uniformity ≤ 0.138 were further divided according to age, and different PFS and DSS were observed. Uniformity extracted from the normalized gray-level cooccurrence matrix represents an independent prognostic predictor in patients with advanced T-stage OPSCC. A scoring system was developed and may serve as a risk-stratification strategy for guiding therapy.

An improved high order texture features extraction method with application to pathological diagnosis of colon lesions for CT colonography

NASA Astrophysics Data System (ADS)

Song, Bowen; Zhang, Guopeng; Lu, Hongbing; Wang, Huafeng; Han, Fangfang; Zhu, Wei; Liang, Zhengrong

2014-03-01

Differentiation of colon lesions according to underlying pathology, e.g., neoplastic and non-neoplastic, is of fundamental importance for patient management. Image intensity based textural features have been recognized as a useful biomarker for the differentiation task. In this paper, we introduce high order texture features, beyond the intensity, such as gradient and curvature, for that task. Based on the Haralick texture analysis method, we introduce a virtual pathological method to explore the utility of texture features from high order differentiations, i.e., gradient and curvature, of the image intensity distribution. The texture features were validated on database consisting of 148 colon lesions, of which 35 are non-neoplastic lesions, using the random forest classifier and the merit of area under the curve (AUC) of the receiver operating characteristics. The results show that after applying the high order features, the AUC was improved from 0.8069 to 0.8544 in differentiating non-neoplastic lesion from neoplastic ones, e.g., hyperplastic polyps from tubular adenomas, tubulovillous adenomas and adenocarcinomas. The experimental results demonstrated that texture features from the higher order images can significantly improve the classification accuracy in pathological differentiation of colorectal lesions. The gain in differentiation capability shall increase the potential of computed tomography (CT) colonography for colorectal cancer screening by not only detecting polyps but also classifying them from optimal polyp management for the best outcome in personalized medicine.

Competitive Advantage of PET/MRI

PubMed Central

Jadvar, Hossein; Colletti, Patrick M.

2013-01-01

Multimodality imaging has made great strides in the imaging evaluation of patients with a variety of diseases. Positron emission tomography/computed tomography (PET/CT) is now established as the imaging modality of choice in many clinical conditions, particularly in oncology. While the initial development of combined PET/magnetic resonance imaging (PET/MRI) was in the preclinical arena, hybrid PET/MR scanners are now available for clinical use. PET/MRI combines the unique features of MRI including excellent soft tissue contrast, diffusion-weighted imaging, dynamic contrast-enhanced imaging, fMRI and other specialized sequences as well as MR spectroscopy with the quantitative physiologic information that is provided by PET. Most evidence for the potential clinical utility of PET/MRI is based on studies performed with side-by-side comparison or software-fused MRI and PET images. Data on distinctive utility of hybrid PET/MRI are rapidly emerging. There are potential competitive advantages of PET/MRI over PET/CT. In general, PET/MRI may be preferred over PET/CT where the unique features of MRI provide more robust imaging evaluation in certain clinical settings. The exact role and potential utility of simultaneous data acquisition in specific research and clinical settings will need to be defined. It may be that simultaneous PET/MRI will be best suited for clinical situations that are disease-specific, organ-specific, related to diseases of the children or in those patients undergoing repeated imaging for whom cumulative radiation dose must be kept as low as reasonably achievable. PET/MRI also offers interesting opportunities for use of dual modality probes. Upon clear definition of clinical utility, other important and practical issues related to business operational model, clinical workflow and reimbursement will also be resolved. PMID:23791129

Competitive advantage of PET/MRI.

PubMed

Jadvar, Hossein; Colletti, Patrick M

2014-01-01

Multimodality imaging has made great strides in the imaging evaluation of patients with a variety of diseases. Positron emission tomography/computed tomography (PET/CT) is now established as the imaging modality of choice in many clinical conditions, particularly in oncology. While the initial development of combined PET/magnetic resonance imaging (PET/MRI) was in the preclinical arena, hybrid PET/MR scanners are now available for clinical use. PET/MRI combines the unique features of MRI including excellent soft tissue contrast, diffusion-weighted imaging, dynamic contrast-enhanced imaging, fMRI and other specialized sequences as well as MR spectroscopy with the quantitative physiologic information that is provided by PET. Most evidence for the potential clinical utility of PET/MRI is based on studies performed with side-by-side comparison or software-fused MRI and PET images. Data on distinctive utility of hybrid PET/MRI are rapidly emerging. There are potential competitive advantages of PET/MRI over PET/CT. In general, PET/MRI may be preferred over PET/CT where the unique features of MRI provide more robust imaging evaluation in certain clinical settings. The exact role and potential utility of simultaneous data acquisition in specific research and clinical settings will need to be defined. It may be that simultaneous PET/MRI will be best suited for clinical situations that are disease-specific, organ-specific, related to diseases of the children or in those patients undergoing repeated imaging for whom cumulative radiation dose must be kept as low as reasonably achievable. PET/MRI also offers interesting opportunities for use of dual modality probes. Upon clear definition of clinical utility, other important and practical issues related to business operational model, clinical workflow and reimbursement will also be resolved. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

[Features of Acquired Immunodeficiency Syndrome-related Lymphoma on (18)F-fluorodeoxyglucose Positron Emission Tomography/Computed Tomography].

PubMed

Niu, Na; Zhu, Zhao-hui; Ma, Yan-ru; Xing, Hai-qun; Li, Fang

2015-10-01

To analyze the imaging features of (18)F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography(PET)/computed tomography (CT) in acquired immune deficiency syndrome-related lymphoma (ARL) patients correlated with their clinical signs, symptoms, and treatments. Five ARL patients underwent ¹⁸F-FDG PET/CT at Peking Union Medical College Hospital from October 2008 to January 2013. Two patients received two additional follow-up studies 6 months later. Among these 5 patients, ¹⁸FDG-PET/CT helped in diagnosis of two patient and changed therapeutic strategy in other two patients. In two patients underwent ¹⁸F-FDG PET/CT brain scans, low-metabolism lesion was newly found in cerebral cortex. Of 4 patients receiving highly active antiretroviral therapy, PET/CT also demonstrated diffusely elevated ¹⁸F-FDG uptake in subcutaneous adipose tissue in two patients. ¹⁸F-FDG PET/CT is a highly useful tool in the diagnosis and treatment of ARL patients, in particular in the identification of associated encephalopathy and lipodystrophy.

Quantitative Prediction of Stone Fragility From Routine Dual Energy CT: Ex vivo proof of Feasibility.

PubMed

Ferrero, Andrea; Montoya, Juan C; Vaughan, Lisa E; Huang, Alice E; McKeag, Ian O; Enders, Felicity T; Williams, James C; McCollough, Cynthia H

2016-12-01

Previous studies have demonstrated a qualitative relationship between stone fragility and internal stone morphology. The goal of this study was to quantify morphologic features from dual-energy computed tomography (CT) images and assess their relationship to stone fragility. Thirty-three calcified urinary stones were scanned with micro-CT. Next, they were placed within torso-shaped water phantoms and scanned with the dual-energy CT stone composition protocol in routine use at our institution. Mixed low- and high-energy images were used to measure volume, surface roughness, and 12 metrics describing internal morphology for each stone. The ratios of low- to high-energy CT numbers were also measured. Subsequent to imaging, stone fragility was measured by disintegrating each stone in a controlled ex vivo experiment using an ultrasonic lithotripter and recording the time to comminution. A multivariable linear regression model was developed to predict time to comminution. The average stone volume was 300 mm 3 (range: 134-674 mm 3 ). The average comminution time measured ex vivo was 32 seconds (range: 7-115 seconds). Stone volume, dual-energy CT number ratio, and surface roughness were found to have the best combined predictive ability to estimate comminution time (adjusted R 2  = 0.58). The predictive ability of mixed dual-energy CT images, without use of the dual-energy CT number ratio, to estimate comminution time was slightly inferior, with an adjusted R 2 of 0.54. Dual-energy CT number ratios, volume, and morphologic metrics may provide a method for predicting stone fragility, as measured by time to comminution from ultrasonic lithotripsy. Copyright © 2016 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Quantitative prediction of stone fragility from routine single and dual energy CT: proof of feasibility

PubMed Central

Ferrero, Andrea; Montoya, Juan C.; Vaughan, Lisa E.; Huang, Alice E.; McKeag, Ian O.; Enders, Felicity T.; Williams, James C.; McCollough, Cynthia H.

2016-01-01

Rationale and Objectives Previous studies have demonstrated a qualitative relationship between stone fragility and internal stone morphology. The goal of this study was to quantify morphological features from dual-energy CT images and assess their relationship to stone fragility. Materials and Methods Thirty-three calcified urinary stones were scanned with micro CT. Next, they were placed within torso-shaped water phantoms and scanned with the dual-energy CT stone composition protocol in routine use at our institution. Mixed low-and high-energy images were used to measure volume, surface roughness, and 12 metrics describing internal morphology for each stone. The ratios of low- to high-energy CT numbers were also measured. Subsequent to imaging, stone fragility was measured by disintegrating each stone in a controlled ex vivo experiment using an ultrasonic lithotripter and recording the time to comminution. A multivariable linear regression model was developed to predict time to comminution. Results The average stone volume was 300 mm3 (range 134–674 mm3). The average comminution time measured ex vivo was 32 s (range 7–115 s). Stone volume, dual-energy CT number ratio and surface roughness were found to have the best combined predictive ability to estimate comminution time (adjusted R2= 0.58). The predictive ability of mixed dual-energy CT images, without use of the dual-energy CT number ratio, to estimate comminution time was slightly inferior, with an adjusted R2 of 0.54. Conclusion Dual-energy CT number ratios, volume, and morphological metrics may provide a method for predicting stone fragility, as measured by time to comminution from ultrasonic lithotripsy. PMID:27717761

Deep neural networks for automatic detection of osteoporotic vertebral fractures on CT scans.

PubMed

Tomita, Naofumi; Cheung, Yvonne Y; Hassanpour, Saeed

2018-07-01

Osteoporotic vertebral fractures (OVFs) are prevalent in older adults and are associated with substantial personal suffering and socio-economic burden. Early diagnosis and treatment of OVFs are critical to prevent further fractures and morbidity. However, OVFs are often under-diagnosed and under-reported in computed tomography (CT) exams as they can be asymptomatic at an early stage. In this paper, we present and evaluate an automatic system that can detect incidental OVFs in chest, abdomen, and pelvis CT examinations at the level of practicing radiologists. Our OVF detection system leverages a deep convolutional neural network (CNN) to extract radiological features from each slice in a CT scan. These extracted features are processed through a feature aggregation module to make the final diagnosis for the full CT scan. In this work, we explored different methods for this feature aggregation, including the use of a long short-term memory (LSTM) network. We trained and evaluated our system on 1432 CT scans, comprised of 10,546 two-dimensional (2D) images in sagittal view. Our system achieved an accuracy of 89.2% and an F1 score of 90.8% based on our evaluation on a held-out test set of 129 CT scans, which were established as reference standards through standard semiquantitative and quantitative methods. The results of our system matched the performance of practicing radiologists on this test set in real-world clinical circumstances. We expect the proposed system will assist and improve OVF diagnosis in clinical settings by pre-screening routine CT examinations and flagging suspicious cases prior to review by radiologists. Copyright © 2018 Elsevier Ltd. All rights reserved.

MO-DE-207A-02: A Feature-Preserving Image Reconstruction Method for Improved Pancreaticlesion Classification in Diagnostic CT Imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xu, J; Tsui, B; Noo, F

Purpose: To develop a feature-preserving model based image reconstruction (MBIR) method that improves performance in pancreatic lesion classification at equal or reduced radiation dose. Methods: A set of pancreatic lesion models was created with both benign and premalignant lesion types. These two classes of lesions are distinguished by their fine internal structures; their delineation is therefore crucial to the task of pancreatic lesion classification. To reduce image noise while preserving the features of the lesions, we developed a MBIR method with curvature-based regularization. The novel regularization encourages formation of smooth surfaces that model both the exterior shape and the internalmore » features of pancreatic lesions. Given that the curvature depends on the unknown image, image reconstruction or denoising becomes a non-convex optimization problem; to address this issue an iterative-reweighting scheme was used to calculate and update the curvature using the image from the previous iteration. Evaluation was carried out with insertion of the lesion models into the pancreas of a patient CT image. Results: Visual inspection was used to compare conventional TV regularization with our curvature-based regularization. Several penalty-strengths were considered for TV regularization, all of which resulted in erasing portions of the septation (thin partition) in a premalignant lesion. At matched noise variance (50% noise reduction in the patient stomach region), the connectivity of the septation was well preserved using the proposed curvature-based method. Conclusion: The curvature-based regularization is able to reduce image noise while simultaneously preserving the lesion features. This method could potentially improve task performance for pancreatic lesion classification at equal or reduced radiation dose. The result is of high significance for longitudinal surveillance studies of patients with pancreatic cysts, which may develop into pancreatic cancer. The Senior Author receives financial support from Siemens GmbH Healthcare.« less

Application of PET/CT in treatment response evaluation and recurrence prediction in patients with newly-diagnosed multiple myeloma

PubMed Central

Li, Ying; Liu, Junru; Huang, Beihui; Chen, Meilan; Diao, Xiangwen; Li, Juan

2017-01-01

Multiple myeloma (MM) causes osteolytic lesions which can be detected by 18F-fluorodeoxyglucose positron emission tomography/Computed tomography (18F-FDG PET/CT). We prospectively involve 96 Newly diagnosed MM to take PET/CT scan at scheduled treatment time (figure 1), and 18F-FDG uptake of lesion was measured by SUVmax and T/Mmax. All MM patients took bortezomib based chemotherapy as induction and received ASCT and maintenance. All clinical features were analyzed with the PET/CT image changes, and some relationships between treatment response and FDG uptakes changes were found: Osteolytic lesions of MM uptakes higher FDG than healthy volunteers, and this trend is more obvious in extramedullary lesions. Compared to X-ray, PET/CT was more sensitive both in discoering bone as well as extramedullary lesions. In newly diagnosed MM, several adverse clinical factors were related to high FDG uptakes of bone lesions. Bone lesion FDG uptakes of MM with P53 mutation or with hypodiploidy and complex karyotype were also higher than those without such changes. In treatment response, PET/CT showed higher sensitivity in detecting tumor residual disease than immunofixation electrophoresis. But in relapse prediction, it might show false positive disease recurrences and the imaging changes might be influenced by infections and hemoglobulin levels. Conclusion: PET/CT is sensitive in discovering meduallary and extrameduallary lesions of MM, and the 18F-FDG uptake of lesions are related with clinical indictors and biological features of plasma cells. In evaluating treatment response and survival, PET/CT showed its superiority. But in predicting relapse or refractory, it may show false positive results. PMID:27556189

Validating automatic semantic annotation of anatomy in DICOM CT images

NASA Astrophysics Data System (ADS)

Pathak, Sayan D.; Criminisi, Antonio; Shotton, Jamie; White, Steve; Robertson, Duncan; Sparks, Bobbi; Munasinghe, Indeera; Siddiqui, Khan

2011-03-01

In the current health-care environment, the time available for physicians to browse patients' scans is shrinking due to the rapid increase in the sheer number of images. This is further aggravated by mounting pressure to become more productive in the face of decreasing reimbursement. Hence, there is an urgent need to deliver technology which enables faster and effortless navigation through sub-volume image visualizations. Annotating image regions with semantic labels such as those derived from the RADLEX ontology can vastly enhance image navigation and sub-volume visualization. This paper uses random regression forests for efficient, automatic detection and localization of anatomical structures within DICOM 3D CT scans. A regression forest is a collection of decision trees which are trained to achieve direct mapping from voxels to organ location and size in a single pass. This paper focuses on comparing automated labeling with expert-annotated ground-truth results on a database of 50 highly variable CT scans. Initial investigations show that regression forest derived localization errors are smaller and more robust than those achieved by state-of-the-art global registration approaches. The simplicity of the algorithm's context-rich visual features yield typical runtimes of less than 10 seconds for a 5123 voxel DICOM CT series on a single-threaded, single-core machine running multiple trees; each tree taking less than a second. Furthermore, qualitative evaluation demonstrates that using the detected organs' locations as index into the image volume improves the efficiency of the navigational workflow in all the CT studies.

Lung lobe modeling and segmentation with individualized surface meshes

NASA Astrophysics Data System (ADS)

Blaffert, Thomas; Barschdorf, Hans; von Berg, Jens; Dries, Sebastian; Franz, Astrid; Klinder, Tobias; Lorenz, Cristian; Renisch, Steffen; Wiemker, Rafael

2008-03-01

An automated segmentation of lung lobes in thoracic CT images is of interest for various diagnostic purposes like the quantification of emphysema or the localization of tumors within the lung. Although the separating lung fissures are visible in modern multi-slice CT-scanners, their contrast in the CT-image often does not separate the lobes completely. This makes it impossible to build a reliable segmentation algorithm without additional information. Our approach uses general anatomical knowledge represented in a geometrical mesh model to construct a robust lobe segmentation, which even gives reasonable estimates of lobe volumes if fissures are not visible at all. The paper describes the generation of the lung model mesh including lobes by an average volume model, its adaptation to individual patient data using a special fissure feature image, and a performance evaluation over a test data set showing an average segmentation accuracy of 1 to 3 mm.

Advancements in automated tissue segmentation pipeline for contrast-enhanced CT scans of adult and pediatric patients

NASA Astrophysics Data System (ADS)

Somasundaram, Elanchezhian; Kaufman, Robert; Brady, Samuel

2017-03-01

The development of a random forests machine learning technique is presented for fully-automated neck, chest, abdomen, and pelvis tissue segmentation of CT images using Trainable WEKA (Waikato Environment for Knowledge Analysis) Segmentation (TWS) plugin of FIJI (ImageJ, NIH). The use of a single classifier model to segment six tissue classes (lung, fat, muscle, solid organ, blood/contrast agent, bone) in the CT images is studied. An automated unbiased scheme to sample pixels from the training images and generate a balanced training dataset over the seven classes is also developed. Two independent training datasets are generated from a pool of 4 adult (>55 kg) and 3 pediatric patients (<=55 kg) with 7 manually contoured slices for each patient. Classifier training investigated 28 image filters comprising a total of 272 features. Highly correlated and insignificant features are eliminated using Correlated Feature Subset (CFS) selection with Best First Search (BFS) algorithms in WEKA. The 2 training models (from the 2 training datasets) had 74 and 71 input training features, respectively. The study also investigated the effect of varying the number of trees (25, 50, 100, and 200) in the random forest algorithm. The performance of the 2 classifier models are evaluated on inter-patient intra-slice, intrapatient inter-slice and inter-patient inter-slice test datasets. The Dice similarity coefficients (DSC) and confusion matrices are used to understand the performance of the classifiers across the tissue segments. The effect of number of features in the training input on the performance of the classifiers for tissue classes with less than optimal DSC values is also studied. The average DSC values for the two training models on the inter-patient intra-slice test data are: 0.98, 0.89, 0.87, 0.79, 0.68, and 0.84, for lung, fat, muscle, solid organ, blood/contrast agent, and bone, respectively. The study demonstrated that a robust segmentation accuracy for lung, muscle and fat tissue classes. For solid-organ, blood/contrast and bone, the performance of the segmentation pipeline improved significantly by using the advanced capabilities of WEKA. However, further improvements are needed to reduce the noise in the segmentation.

Selecting a CT scanner for cardiac imaging: the heart of the matter.

PubMed

Lewis, Maria A; Pascoal, Ana; Keevil, Stephen F; Lewis, Cornelius A

2016-09-01

Coronary angiography to assess the presence and degree of arterial stenosis is an examination now routinely performed on CT scanners. Although developments in CT technology over recent years have made great strides in improving the diagnostic accuracy of this technique, patients with certain characteristics can still be "difficult to image". The various groups will benefit from different technological enhancements depending on the type of challenge they present. Good temporal and spatial resolution, wide longitudinal (z-axis) detector coverage and high X-ray output are the key requirements of a successful CT coronary angiography (CTCA) scan. The requirement for optimal patient dose is a given. The different scanner models recommended for CTCA all excel in different aspects. The specification data presented here for these scanners and the explanation of the impact of the different features should help in making a more informed decision when selecting a scanner for CTCA.

Multilevel image recognition using discriminative patches and kernel covariance descriptor

NASA Astrophysics Data System (ADS)

Lu, Le; Yao, Jianhua; Turkbey, Evrim; Summers, Ronald M.

2014-03-01

Computer-aided diagnosis of medical images has emerged as an important tool to objectively improve the performance, accuracy and consistency for clinical workflow. To computerize the medical image diagnostic recognition problem, there are three fundamental problems: where to look (i.e., where is the region of interest from the whole image/volume), image feature description/encoding, and similarity metrics for classification or matching. In this paper, we exploit the motivation, implementation and performance evaluation of task-driven iterative, discriminative image patch mining; covariance matrix based descriptor via intensity, gradient and spatial layout; and log-Euclidean distance kernel for support vector machine, to address these three aspects respectively. To cope with often visually ambiguous image patterns for the region of interest in medical diagnosis, discovery of multilabel selective discriminative patches is desired. Covariance of several image statistics summarizes their second order interactions within an image patch and is proved as an effective image descriptor, with low dimensionality compared with joint statistics and fast computation regardless of the patch size. We extensively evaluate two extended Gaussian kernels using affine-invariant Riemannian metric or log-Euclidean metric with support vector machines (SVM), on two medical image classification problems of degenerative disc disease (DDD) detection on cortical shell unwrapped CT maps and colitis detection on CT key images. The proposed approach is validated with promising quantitative results on these challenging tasks. Our experimental findings and discussion also unveil some interesting insights on the covariance feature composition with or without spatial layout for classification and retrieval, and different kernel constructions for SVM. This will also shed some light on future work using covariance feature and kernel classification for medical image analysis.

Quantifying metal-induced susceptibility artifacts of the instrumented spine at 1.5T using fast-spin echo and 3D-multispectral MRI.

PubMed

Kaushik, S Sivaram; Karr, Robin; Runquist, Matthew; Marszalkowski, Cathy; Sharma, Abhishiek; Rand, Scott D; Maiman, Dennis; Koch, Kevin M

2017-01-01

To evaluate magnetic resonance imaging (MRI) artifacts near metallic spinal instrumentation using both conventional metal artifact reduction sequences (MARS) and 3D multispectral imaging sequences (3D-MSI). Both MARS and 3D-MSI images were acquired in 10 subjects with titanium spinal hardware on a 1.5T GE 450W scanner. Clinical computed tomography (CT) images were used to measure the volume of the implant using seed-based region growing. Using 30-40 landmarks, the MARS and 3D-MSI images were coregistered to the CT images. Three independent users manually segmented the artifact volume from both MR sequences. For five L-spine subjects, one user independently segmented the nerve root in both MARS and 3D-MSI images. For all 10 subjects, the measured artifact volume for the 3D-MSI images closely matched that of the CT implant volume (absolute error: 4.3 ± 2.0 cm 3 ). The MARS artifact volume was ∼8-fold higher than that of the 3D-MSI images (30.7 ± 20.2, P = 0.002). The average nerve root volume for the MARS images was 24 ± 7.3% lower than the 3D-MSI images (P = 0.06). Compared to 3D-MSI images, the higher-resolution MARS images may help study features farther away from the implant surface. However, the MARS images retained substantial artifacts in the slice-dimension that result in a larger artifact volume. These artifacts have the potential to obscure physiologically relevant features, and can be mitigated with 3D-MSI sequences. Hence, MR study protocols may benefit with the inclusion both MARS and 3D-MSI sequences to accurately study pathology near the spine. 2 J. Magn. Reson. Imaging 2017;45:51-58. © 2016 International Society for Magnetic Resonance in Medicine.

High pitch third generation dual-source CT: Coronary and Cardiac Visualization on Routine Chest CT

PubMed Central

Sandfort, Veit; Ahlman, Mark; Jones, Elizabeth; Selwaness, Mariana; Chen, Marcus; Folio, Les; Bluemke, David A.

2016-01-01

Background Chest CT scans are frequently performed in radiology departments but have not previously contained detailed depiction of cardiac structures. Objectives To evaluate myocardial and coronary visualization on high-pitch non-gated CT of the chest using 3rd generation dual-source computed tomography (CT). Methods Cardiac anatomy of patients who had 3rd generation, non-gated high pitch contrast enhanced chest CT and who also had prior conventional (low pitch) chest CT as part of a chest abdomen pelvis exam was evaluated. Cardiac image features were scored by reviewers blinded to diagnosis and pitch. Paired analysis was performed. Results 3862 coronary segments and 2220 cardiac structures were evaluated by two readers in 222 CT scans. Most patients (97.2%) had chest CT for oncologic evaluation. The median pitch was 2.34 (IQR 2.05, 2.65) in high pitch and 0.8 (IQR 0.8, 0.8) in low pitch scans (p<0.001). High pitch CT showed higher image visualization scores for all cardiovascular structures compared with conventional pitch scans (p<0.0001). Coronary arteries were visualized in 9 coronary segments per exam in high pitch scans versus 2 segments for conventional pitch (p<0.0001). Radiation exposure was lower in the high pitch group compared with the conventional pitch group (median CTDIvol 10.83 vs. 12.36 mGy and DLP 790 vs. 827 mGycm respectively, p <0.01 for both) with comparable image noise (p=0.43). Conclusion Myocardial structure and coronary arteries are frequently visualized on non-gated 3rd generation chest CT. These results raise the question of whether the heart and coronary arteries should be routinely interpreted on routine chest CT that is otherwise obtained for non-cardiac indications. PMID:27133589

Iodine and freeze-drying enhanced high-resolution MicroCT imaging for reconstructing 3D intraneural topography of human peripheral nerve fascicles.

PubMed

Yan, Liwei; Guo, Yongze; Qi, Jian; Zhu, Qingtang; Gu, Liqiang; Zheng, Canbin; Lin, Tao; Lu, Yutong; Zeng, Zitao; Yu, Sha; Zhu, Shuang; Zhou, Xiang; Zhang, Xi; Du, Yunfei; Yao, Zhi; Lu, Yao; Liu, Xiaolin

2017-08-01

The precise annotation and accurate identification of the topography of fascicles to the end organs are prerequisites for studying human peripheral nerves. In this study, we present a feasible imaging method that acquires 3D high-resolution (HR) topography of peripheral nerve fascicles using an iodine and freeze-drying (IFD) micro-computed tomography (microCT) method to greatly increase the contrast of fascicle images. The enhanced microCT imaging method can facilitate the reconstruction of high-contrast HR fascicle images, fascicle segmentation and extraction, feature analysis, and the tracing of fascicle topography to end organs, which define fascicle functions. The complex intraneural aggregation and distribution of fascicles is typically assessed using histological techniques or MR imaging to acquire coarse axial three-dimensional (3D) maps. However, the disadvantages of histological techniques (static, axial manual registration, and data instability) and MR imaging (low-resolution) limit these applications in reconstructing the topography of nerve fascicles. Thus, enhanced microCT is a new technique for acquiring 3D intraneural topography of the human peripheral nerve fascicles both to improve our understanding of neurobiological principles and to guide accurate repair in the clinic. Additionally, 3D microstructure data can be used as a biofabrication model, which in turn can be used to fabricate scaffolds to repair long nerve gaps. Copyright © 2017 Elsevier B.V. All rights reserved.

SU-F-R-07: Radiomics of CT Features and Associations and Correlation with Outcomes Following Lung SBRT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schreibmann, E; Iwinski Sutter, A; Whitaker, D

Objective: To investigate the prognostic significance of image gradients and in predicting clinical outcomes in a patients with non-small cell lung cancer treated with stereotactic body radiotherapy (SBRT) on 71 patients with 83 treated lesions. Methods: The records of patients treated with lung SBRT were retrospectively reviewed. When applicable, SBRT target volumes were modified to exclude any overlap with pleura, chestwall, or mediastinum. The ITK software package was utilized to generate quantitative measures of image intensity, inhomogeneity, shape morphology and first and second-order CT textures. Multivariate and univariate models containing CT features were generated to assess associations with clinicopathologic factors.more » Results: On univariate analysis, tumor size (HR 0.54, p=0.045) sumHU (HR 0.31, p=0.044) and short run grey level emphasis STD (HR 0.22, p=0.019) were associated with regional failure-free survival; meanHU (HR 0.30, p=0.035), long run emphasis (HR 0.21, p=0.011) and long run low grey level emphasis (HR 0.14, p=0.005) was associated with distant failure-free survival (DFFS). No features were significant on multivariate modeling however long run low grey level emphasis had a hazard ratio of 0.12 (p=0.061) for DFFS. Adenocarcinoma and squamous cell carcinoma differed with respect to long run emphasis STD (p=0.024), short run low grey level emphasis STD (p<0.001), and long run low grey level emphasis STD (p=0.024). Multivariate modeling of texture features associated with tumor histology was used to estimate histologies of 18 lesions treated without histologic confirmation. Of these, MVA suggested the same histology as a prior metachronous lung malignancy in 3/7 patients. Conclusion: Extracting radiomics features on clinical datasets was feasible with the ITK package with minimal effort to identify pre-treatment quantitative CT features with prognostic factors for distant control after lung SBRT.« less

Extraction of 3D Femur Neck Trabecular Bone Architecture from Clinical CT Images in Osteoporotic Evaluation: a Novel Framework.

PubMed

Sapthagirivasan, V; Anburajan, M; Janarthanam, S

2015-08-01

The early detection of osteoporosis risk enhances the lifespan and quality of life of an individual. A reasonable in-vivo assessment of trabecular bone strength at the proximal femur helps to evaluate the fracture risk and henceforth, to understand the associated structural dynamics on occurrence of osteoporosis. The main aim of our study was to develop a framework to automatically determine the trabecular bone strength from clinical femur CT images and thereby to estimate its correlation with BMD. All the 50 studied south Indian female subjects aged 30 to 80 years underwent CT and DXA measurements at right femur region. Initially, the original CT slices were intensified and active contour model was utilised for the extraction of the neck region. After processing through a novel process called trabecular enrichment approach (TEA), the three dimensional (3D) trabecular features were extracted. The extracted 3D trabecular features, such as volume fraction (VF), solidity of delta points (SDP) and boundness, demonstrated a significant correlation with femoral neck bone mineral density (r = 0.551, r = 0.432, r = 0.552 respectively) at p < 0.001. The higher area under the curve values of the extracted features (VF: 85.3 %; 95CI: 68.2-100 %, SDP: 82.1 %; 95CI: 65.1-98.9 % and boundness: 90.4 %; 95CI: 78.7-100 %) were observed. The findings suggest that the proposed framework with TEA method would be useful for spotting women vulnerable to osteoporotic risk.

Magnetic resonance characteristics and susceptibility weighted imaging of the brain in gadolinium encephalopathy.

PubMed

Samardzic, Dejan; Thamburaj, Krishnamoorthy

2015-01-01

To report the brain imaging features on magnetic resonance imaging (MRI) in inadvertent intrathecal gadolinium administration. A 67-year-old female with gadolinium encephalopathy from inadvertent high dose intrathecal gadolinium administration during an epidural steroid injection was studied with multisequence 3T MRI. T1-weighted imaging shows pseudo-T2 appearance with diffusion of gadolinium into the brain parenchyma, olivary bodies, and membranous labyrinth. Nulling of cerebrospinal fluid (CSF) signal is absent on fluid attenuation recovery (FLAIR). Susceptibility-weighted imaging (SWI) demonstrates features similar to subarachnoid hemorrhage. CT may demonstrate a pseudo-cerebral edema pattern given the high attenuation characteristics of gadolinium. Intrathecal gadolinium demonstrates characteristic imaging features on MRI of the brain and may mimic subarachnoid hemorrhage on susceptibility-weighted imaging. Identifying high dose gadolinium within the CSF spaces on MRI is essential to avoid diagnostic and therapeutic errors. Copyright © 2013 by the American Society of Neuroimaging.

The use of postmortem computed tomography in the diagnosis of intentional medication overdose.

PubMed

Burke, Michael P; O'Donnell, Chris; Bassed, Richard

2012-09-01

The recognition of a well defined basal layer of radio dense material on the postmortem computed tomography (CT) images, in the setting of typical scene findings of an intentional medication overdose and unremarkable external examination of the deceased's body can, in certain circumstances, permit such cases to be managed without routine full autopsy examination. Preliminary toxicological analysis can be targeted to such cases to provide further supportive evidence of intentional medication overdose. In cases where the scene findings are ambiguous or have been contaminated the postmortem CT images may alert the pathologist of the possibility of overdose in an otherwise apparently natural death. We reviewed 61 cases of documented intentional therapeutic medication overdose and 61 control cases. In the majority of the cases of confirmed intentional therapeutic medication overdose the CT images showed no diagnostic features. However, in many cases a well defined basal layer of radio-opaque material was clearly seen to line the gastric mucosa. The postmortem CT pattern which we believe to be highly suggestive of intentional medication overdose must be differentiated from other causes of increased radio density in the stomach which include CT artefacts.

Can CT and MR Shape and Textural Features Differentiate Benign Versus Malignant Pleural Lesions?

PubMed

Pena, Elena; Ojiaku, MacArinze; Inacio, Joao R; Gupta, Ashish; Macdonald, D Blair; Shabana, Wael; Seely, Jean M; Rybicki, Frank J; Dennie, Carole; Thornhill, Rebecca E

2017-10-01

The study aimed to identify a radiomic approach based on CT and or magnetic resonance (MR) features (shape and texture) that may help differentiate benign versus malignant pleural lesions, and to assess if the radiomic model may improve confidence and accuracy of radiologists with different subspecialty backgrounds. Twenty-nine patients with pleural lesions studied on both contrast-enhanced CT and MR imaging were reviewed retrospectively. Three texture and three shape features were extracted. Combinations of features were used to generate logistic regression models using histopathology as outcome. Two thoracic and two abdominal radiologists evaluated their degree of confidence in malignancy. Diagnostic accuracy of radiologists was determined using contingency tables. Cohen's kappa coefficient was used to assess inter-reader agreement. Using optimal threshold criteria, sensitivity, specificity, and accuracy of each feature and combination of features were obtained and compared to the accuracy and confidence of radiologists. The CT model that best discriminated malignant from benign lesions revealed an AUC CT  = 0.92 ± 0.05 (P < 0.0001). The most discriminative MR model showed an AUC MR  = 0.87 ± 0.09 (P < 0.0001). The CT model was compared to the diagnostic confidence of all radiologists and the model outperformed both abdominal radiologists (P < 0.002), whereas the top discriminative MR model outperformed one of the abdominal radiologists (P = 0.02). The most discriminative MR model was more accurate than one abdominal (P = 0.04) and one thoracic radiologist (P = 0.02). Quantitative textural and shape analysis may help distinguish malignant from benign lesions. A radiomics-based approach may increase diagnostic confidence of abdominal radiologists on CT and MR and may potentially improve radiologists' accuracy in the assessment of pleural lesions characterized by MR. Copyright © 2017 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Unenhanced Computed Tomography to Visualize Hollow Viscera and/or Mesenteric Injury After Blunt Abdominal Trauma: A Single-Institution Experience.

PubMed

Yang, Xu-Yang; Wei, Ming-Tian; Jin, Cheng-Wu; Wang, Meng; Wang, Zi-Qiang

2016-03-01

To identify and describe the major features of unenhanced computed tomography (CT) images of blunt hollow viscera and/or mesenteric injury (BHVI/MI) and to determine the value of unenhanced CT in the diagnosis of BHVI/MI. This retrospective study included 151 patients who underwent unenhanced CT before laparotomy for blunt abdominal trauma between January 2011 and December 2013. According to surgical observations, patients were classified as having BHVI/MI (n = 73) or not (n = 78). Sensitivity, specificity, P values, and likelihood ratios were calculated by comparing CT findings between the 2 groups. Six significant CT findings (P < 0.05) for BHVI/MI were identified and their sensitivity and specificity values determined, as follows: bowel wall thickening (39.7%, 96.2%), mesentery thickening (46.6%, 88.5%), mesenteric fat infiltration (12.3%, 98.7%), peritoneal fat infiltration (31.5%, 87.1%), parietal peritoneum thickening (30.1%, 85.9%), and intra- or retro-peritoneal air (34.2%, 96.2%). Unenhanced CT scan was useful as an initial assessment tool for BHVI/MI after blunt abdominal trauma. Six key features on CT were correlated with BHVI/MI.

An automated landmark-based elastic registration technique for large deformation recovery from 4-D CT lung images

NASA Astrophysics Data System (ADS)

Negahdar, Mohammadreza; Zacarias, Albert; Milam, Rebecca A.; Dunlap, Neal; Woo, Shiao Y.; Amini, Amir A.

2012-03-01

The treatment plan evaluation for lung cancer patients involves pre-treatment and post-treatment volume CT imaging of the lung. However, treatment of the tumor volume lung results in structural changes to the lung during the course of treatment. In order to register the pre-treatment volume to post-treatment volume, there is a need to find robust and homologous features which are not affected by the radiation treatment along with a smooth deformation field. Since airways are well-distributed in the entire lung, in this paper, we propose use of airway tree bifurcations for registration of the pre-treatment volume to the post-treatment volume. A dedicated and automated algorithm has been developed that finds corresponding airway bifurcations in both images. To derive the 3-D deformation field, a B-spline transformation model guided by mutual information similarity metric was used to guarantee the smoothness of the transformation while combining global information from bifurcation points. Therefore, the approach combines both global statistical intensity information with local image feature information. Since during normal breathing, the lung undergoes large nonlinear deformations, it is expected that the proposed method would also be applicable to large deformation registration between maximum inhale and maximum exhale images in the same subject. The method has been evaluated by registering 3-D CT volumes at maximum exhale data to all the other temporal volumes in the POPI-model data.

Renal carcinomas associated with Xp11.2 translocations: are CT findings suggestive of the diagnosis?

PubMed

He, J; Huan, Y; Qiao, Q; Zhang, J; Zhang, J S

2014-01-01

The purpose of the present study was to summarize the computed tomography (CT) features of renal carcinomas associated with Xp11.2 translocations, and determine whether the diagnosis can be reliably deduced from imaging findings. Radiological studies of six patients (aged from 9-29 years) with renal carcinoma associated with Xp11.2 translocations were retrospectively analysed. The tumours varied in size from 3.3-11 cm (mean 5.4 cm). Unenhanced CT and cortical, medullary, and pelvic-phase contrast-enhanced CT imaging was undertaken in all cases. Unenhanced CT revealed that tumours had a relatively increased radiodensity (4/6, ranged from 45-60 HU) and suggested the possibility of diffuse haemorrhage. Three of the six cases showed irregular and boundary calcification of the lesion. Contrast-enhanced CT showed relatively well demarcated tumours with heterogeneous enhancement (6/6). Prolonged enhancement of tumours might be a common sign (6/6) in Xp11.2 translocations. Three out of the six cases were combined with retroperitoneal lymph nodes metastasis. Renal carcinomas associated with Xp11.2 translocations should be considered, particularly in children and young patients, when the lesion has calcification and is hyper-dense on unenhanced CT, and has prolonged enhancement on contrast-enhanced images. Copyright © 2013 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Computer-aided diagnosis of liver tumors on computed tomography images.

PubMed

Chang, Chin-Chen; Chen, Hong-Hao; Chang, Yeun-Chung; Yang, Ming-Yang; Lo, Chung-Ming; Ko, Wei-Chun; Lee, Yee-Fan; Liu, Kao-Lang; Chang, Ruey-Feng

2017-07-01

Liver cancer is the tenth most common cancer in the USA, and its incidence has been increasing for several decades. Early detection, diagnosis, and treatment of the disease are very important. Computed tomography (CT) is one of the most common and robust imaging techniques for the detection of liver cancer. CT scanners can provide multiple-phase sequential scans of the whole liver. In this study, we proposed a computer-aided diagnosis (CAD) system to diagnose liver cancer using the features of tumors obtained from multiphase CT images. A total of 71 histologically-proven liver tumors including 49 benign and 22 malignant lesions were evaluated with the proposed CAD system to evaluate its performance. Tumors were identified by the user and then segmented using a region growing algorithm. After tumor segmentation, three kinds of features were obtained for each tumor, including texture, shape, and kinetic curve. The texture was quantified using 3 dimensional (3-D) texture data of the tumor based on the grey level co-occurrence matrix (GLCM). Compactness, margin, and an elliptic model were used to describe the 3-D shape of the tumor. The kinetic curve was established from each phase of tumor and represented as variations in density between each phase. Backward elimination was used to select the best combination of features, and binary logistic regression analysis was used to classify the tumors with leave-one-out cross validation. The accuracy and sensitivity for the texture were 71.82% and 68.18%, respectively, which were better than for the shape and kinetic curve under closed specificity. Combining all of the features achieved the highest accuracy (58/71, 81.69%), sensitivity (18/22, 81.82%), and specificity (40/49, 81.63%). The Az value of combining all features was 0.8713. Combining texture, shape, and kinetic curve features may be able to differentiate benign from malignant tumors in the liver using our proposed CAD system. Copyright © 2017 Elsevier B.V. All rights reserved.

Clinics in diagnostic imaging (171). Caecal volvulus with underlying intestinal malrotation.

PubMed

Ooi, Su Kai Gideon; Tan, Tien Jin; Ngu, James Chi Yong

2016-11-01

A 46-year-old Chinese woman with a history of cholecystectomy and appendicectomy presented to the emergency department with symptoms of intestinal obstruction. Physical examination revealed central abdominal tenderness but no clinical features of peritonism. Plain radiography of the abdomen revealed a grossly distended large bowel loop with the long axis extending from the right lower abdomen toward the epigastrium, and an intraluminal air-fluid level. These findings were suspicious for an acute caecal volvulus, which was confirmed on subsequent contrast-enhanced computed tomography (CT) of the abdomen and pelvis. CT demonstrated an abnormal positional relationship between the superior mesenteric vein and artery, indicative of an underlying intestinal malrotation. This case highlights the utility of preoperative imaging in establishing the diagnosis of an uncommon cause of bowel obstruction. It also shows the importance of recognising the characteristic imaging features early, so as to ensure appropriate and expedient management, thus reducing patient morbidity arising from complications. Copyright: © Singapore Medical Association.

Nearest neighbor 3D segmentation with context features

NASA Astrophysics Data System (ADS)

Hristova, Evelin; Schulz, Heinrich; Brosch, Tom; Heinrich, Mattias P.; Nickisch, Hannes

2018-03-01

Automated and fast multi-label segmentation of medical images is challenging and clinically important. This paper builds upon a supervised machine learning framework that uses training data sets with dense organ annotations and vantage point trees to classify voxels in unseen images based on similarity of binary feature vectors extracted from the data. Without explicit model knowledge, the algorithm is applicable to different modalities and organs, and achieves high accuracy. The method is successfully tested on 70 abdominal CT and 42 pelvic MR images. With respect to ground truth, an average Dice overlap score of 0.76 for the CT segmentation of liver, spleen and kidneys is achieved. The mean score for the MR delineation of bladder, bones, prostate and rectum is 0.65. Additionally, we benchmark several variations of the main components of the method and reduce the computation time by up to 47% without significant loss of accuracy. The segmentation results are - for a nearest neighbor method - surprisingly accurate, robust as well as data and time efficient.

Controversies in imaging of hepatocellular carcinoma: multidetector CT (MDCT)

PubMed Central

Silverman, Paul M; Szklaruk, Janio

2005-01-01

Primary hepatocellular carcinoma (HCC) is a significant tumor worldwide and represents the most common primary hepatic neoplasm. Staging criteria are important for appreciation of timely work up of these neoplasms in contradiction with surgical colleagues. This article demonstrates the appearance of HCC on multiphasic, multidetector CT (MDCT) and relates these findings to current staging criteria. The variable appearance on different planes of contrast is critical to appreciate in staging this neoplasm. The hypervascular nature of the primary tumor makes MDCT and three-phase imaging a critical feature in the detection and characterization of this tumor. This is especially critical in the patients who are candidates for surgical resection. Additionally, MDCT has allowed arterial phase imaging to define the vascular supply of the tumor. An accurate representation of the size and number of lesions is critical in not only the initial staging but also the follow-up of hepatocellular carcinoma. The post-treatment features including the appearance post-surgically and after radiofrequency ablation can be well appreciated on MDCT. PMID:16361147

Geometrical study on two tilting arcs based exact cone-beam CT for breast imaging

NASA Astrophysics Data System (ADS)

Zeng, Kai; Yu, Hengyong; Fajardo, Laurie L.; Wang, Ge

2006-08-01

Breast cancer is the second leading cause of cancer death in women in the United States. Currently, X-ray mammography is the method of choice for screening and diagnosing breast cancer. However, this 2D projective modality is far from perfect; with up to 17% breast cancer going unidentified. Over past several years, there has been an increasing interest in cone-beam CT for breast imaging. However, previous methods utilizing cone-beam CT only produce approximate reconstructions. Following Katsevich's recent work, we propose a new scanning mode and associated exact cone-beam CT method for breast imaging. In our design, cone-beam scans are performed along two tilting arcs for collection of a sufficient amount of data for exact reconstruction. In our Katsevich-type algorithm, conebeam data is filtered in a shift-invariant fashion and then backprojected in 3D for the final reconstruction. This approach has several desirable features. First, it allows data truncation unavoidable in practice. Second, it optimizes image quality for quantitative analysis. Third, it is efficient for sequential/parallel computation. Furthermore, we analyze the reconstruction region and the detection window in detail, which are important for numerical implementation.

Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans

NASA Astrophysics Data System (ADS)

Cheng, Jie-Zhi; Ni, Dong; Chou, Yi-Hong; Qin, Jing; Tiu, Chui-Mei; Chang, Yeun-Chung; Huang, Chiun-Sheng; Shen, Dinggang; Chen, Chung-Ming

2016-04-01

This paper performs a comprehensive study on the deep-learning-based computer-aided diagnosis (CADx) for the differential diagnosis of benign and malignant nodules/lesions by avoiding the potential errors caused by inaccurate image processing results (e.g., boundary segmentation), as well as the classification bias resulting from a less robust feature set, as involved in most conventional CADx algorithms. Specifically, the stacked denoising auto-encoder (SDAE) is exploited on the two CADx applications for the differentiation of breast ultrasound lesions and lung CT nodules. The SDAE architecture is well equipped with the automatic feature exploration mechanism and noise tolerance advantage, and hence may be suitable to deal with the intrinsically noisy property of medical image data from various imaging modalities. To show the outperformance of SDAE-based CADx over the conventional scheme, two latest conventional CADx algorithms are implemented for comparison. 10 times of 10-fold cross-validations are conducted to illustrate the efficacy of the SDAE-based CADx algorithm. The experimental results show the significant performance boost by the SDAE-based CADx algorithm over the two conventional methods, suggesting that deep learning techniques can potentially change the design paradigm of the CADx systems without the need of explicit design and selection of problem-oriented features.

Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans.

PubMed

Cheng, Jie-Zhi; Ni, Dong; Chou, Yi-Hong; Qin, Jing; Tiu, Chui-Mei; Chang, Yeun-Chung; Huang, Chiun-Sheng; Shen, Dinggang; Chen, Chung-Ming

2016-04-15

This paper performs a comprehensive study on the deep-learning-based computer-aided diagnosis (CADx) for the differential diagnosis of benign and malignant nodules/lesions by avoiding the potential errors caused by inaccurate image processing results (e.g., boundary segmentation), as well as the classification bias resulting from a less robust feature set, as involved in most conventional CADx algorithms. Specifically, the stacked denoising auto-encoder (SDAE) is exploited on the two CADx applications for the differentiation of breast ultrasound lesions and lung CT nodules. The SDAE architecture is well equipped with the automatic feature exploration mechanism and noise tolerance advantage, and hence may be suitable to deal with the intrinsically noisy property of medical image data from various imaging modalities. To show the outperformance of SDAE-based CADx over the conventional scheme, two latest conventional CADx algorithms are implemented for comparison. 10 times of 10-fold cross-validations are conducted to illustrate the efficacy of the SDAE-based CADx algorithm. The experimental results show the significant performance boost by the SDAE-based CADx algorithm over the two conventional methods, suggesting that deep learning techniques can potentially change the design paradigm of the CADx systems without the need of explicit design and selection of problem-oriented features.

Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans

PubMed Central

Cheng, Jie-Zhi; Ni, Dong; Chou, Yi-Hong; Qin, Jing; Tiu, Chui-Mei; Chang, Yeun-Chung; Huang, Chiun-Sheng; Shen, Dinggang; Chen, Chung-Ming

2016-01-01

This paper performs a comprehensive study on the deep-learning-based computer-aided diagnosis (CADx) for the differential diagnosis of benign and malignant nodules/lesions by avoiding the potential errors caused by inaccurate image processing results (e.g., boundary segmentation), as well as the classification bias resulting from a less robust feature set, as involved in most conventional CADx algorithms. Specifically, the stacked denoising auto-encoder (SDAE) is exploited on the two CADx applications for the differentiation of breast ultrasound lesions and lung CT nodules. The SDAE architecture is well equipped with the automatic feature exploration mechanism and noise tolerance advantage, and hence may be suitable to deal with the intrinsically noisy property of medical image data from various imaging modalities. To show the outperformance of SDAE-based CADx over the conventional scheme, two latest conventional CADx algorithms are implemented for comparison. 10 times of 10-fold cross-validations are conducted to illustrate the efficacy of the SDAE-based CADx algorithm. The experimental results show the significant performance boost by the SDAE-based CADx algorithm over the two conventional methods, suggesting that deep learning techniques can potentially change the design paradigm of the CADx systems without the need of explicit design and selection of problem-oriented features. PMID:27079888

Pulsed excitation terahertz tomography - multiparametric approach

NASA Astrophysics Data System (ADS)

Lopato, Przemyslaw

2018-04-01

This article deals with pulsed excitation terahertz computed tomography (THz CT). Opposite to x-ray CT, where just a single value (pixel) is obtained, in case of pulsed THz CT the time signal is acquired for each position. Recorded waveform can be parametrized - many features carrying various information about examined structure can be calculated. Based on this, multiparametric reconstruction algorithm was proposed: inverse Radon transform based reconstruction is applied for each parameter and then fusion of results is utilized. Performance of the proposed imaging scheme was experimentally verified using dielectric phantoms.

Automatic lung nodule classification with radiomics approach

NASA Astrophysics Data System (ADS)

Ma, Jingchen; Wang, Qian; Ren, Yacheng; Hu, Haibo; Zhao, Jun

2016-03-01

Lung cancer is the first killer among the cancer deaths. Malignant lung nodules have extremely high mortality while some of the benign nodules don't need any treatment .Thus, the accuracy of diagnosis between benign or malignant nodules diagnosis is necessary. Notably, although currently additional invasive biopsy or second CT scan in 3 months later may help radiologists to make judgments, easier diagnosis approaches are imminently needed. In this paper, we propose a novel CAD method to distinguish the benign and malignant lung cancer from CT images directly, which can not only improve the efficiency of rumor diagnosis but also greatly decrease the pain and risk of patients in biopsy collecting process. Briefly, according to the state-of-the-art radiomics approach, 583 features were used at the first step for measurement of nodules' intensity, shape, heterogeneity and information in multi-frequencies. Further, with Random Forest method, we distinguish the benign nodules from malignant nodules by analyzing all these features. Notably, our proposed scheme was tested on all 79 CT scans with diagnosis data available in The Cancer Imaging Archive (TCIA) which contain 127 nodules and each nodule is annotated by at least one of four radiologists participating in the project. Satisfactorily, this method achieved 82.7% accuracy in classification of malignant primary lung nodules and benign nodules. We believe it would bring much value for routine lung cancer diagnosis in CT imaging and provide improvement in decision-support with much lower cost.

Solid pseudopapillary neoplasm of the pancreas: CT imaging features and radiologic-pathologic correlation.

PubMed

Anil, Gopinathan; Zhang, Junwei; Al Hamar, Nawal Ebrahim; Nga, Min En

2017-01-01

We aimed to evaluate the imaging features of solid pseudopapillary neoplasm (SPN) of the pancreas with an emphasis on radiologic-pathologic correlation. Ten patients (all female; mean age, 32 years) with histologic or cytologic diagnosis of SPN encountered between January 2007 and December 2013 were included in this study. Preoperative computed tomography (CT) images were reviewed for location, attenuation, enhancement pattern, margin, shape, size, morphology, presence of capsule and calcification. CT appearances were correlated with histopathologic findings. Tumors in the distal pancreatic body and tail had a tendency to be larger (mean size 12.6 cm vs. 4.0 cm). Six of the nine tumors that were resected had a fibrous pseudocapsule at histology, five of which could be identified on CT scan. Eight lesions had mixed hypoenhancing solid components and cystic areas corresponding to tumor necrosis and hemorrhage. The two smallest lesions were purely solid and nonencapsulated. Varied patterns of calcification were seen in four tumors. Three of the four pancreatic tail tumors invaded the spleen. At a median follow-up of 53 months, there was no evidence of recurrence in the nine patients who underwent surgical resection of the tumor. A mixed solid and cystic pancreatic mass in a young woman is suggestive of SPN. However, smaller lesions may be completely solid. Splenic invasion can occur in pancreatic tail SPNs; however, in this series it did not adversely affect the long-term outcome.

Mutual information based feature selection for medical image retrieval

NASA Astrophysics Data System (ADS)

Zhi, Lijia; Zhang, Shaomin; Li, Yan

2018-04-01

In this paper, authors propose a mutual information based method for lung CT image retrieval. This method is designed to adapt to different datasets and different retrieval task. For practical applying consideration, this method avoids using a large amount of training data. Instead, with a well-designed training process and robust fundamental features and measurements, the method in this paper can get promising performance and maintain economic training computation. Experimental results show that the method has potential practical values for clinical routine application.

Classification of pulmonary nodules in lung CT images using shape and texture features

NASA Astrophysics Data System (ADS)

Dhara, Ashis Kumar; Mukhopadhyay, Sudipta; Dutta, Anirvan; Garg, Mandeep; Khandelwal, Niranjan; Kumar, Prafulla

2016-03-01

Differentiation of malignant and benign pulmonary nodules is important for prognosis of lung cancer. In this paper, benign and malignant nodules are classified using support vector machine. Several shape-based and texture-based features are used to represent the pulmonary nodules in the feature space. A semi-automated technique is used for nodule segmentation. Relevant features are selected for efficient representation of nodules in the feature space. The proposed scheme and the competing technique are evaluated on a data set of 542 nodules of Lung Image Database Consortium and Image Database Resource Initiative. The nodules with composite rank of malignancy "1","2" are considered as benign and "4","5" are considered as malignant. Area under the receiver operating characteristics curve is 0:9465 for the proposed method. The proposed method outperforms the competing technique.

Knowledge of medical imaging radiation dose and risk among doctors.

PubMed

Brown, Nicholas; Jones, Lee

2013-02-01

The growth of computed tomography (CT) and nuclear medicine (NM) scans has revolutionised healthcare but also greatly increased population radiation doses. Overuse of diagnostic radiation is becoming a feature of medical practice, leading to possible unnecessary radiation exposures and lifetime-risks of developing cancer. Doctors across all medical specialties and experience levels were surveyed to determine their knowledge of radiation doses and potential risks associated with some diagnostic imaging. A survey relating to knowledge and understanding of medical imaging radiation was distributed to doctors at 14 major Queensland public hospitals, as well as fellows and trainees in radiology, emergency medicine and general practice. From 608 valid responses, only 17.3% correctly estimated the radiation dose from CT scans and almost 1 in 10 incorrectly believed that CT radiation is not associated with any increased lifetime risk of developing cancer. There is a strong inverse relationship between a clinician's experience and their knowledge of CT radiation dose and risks, even among radiologists. More than a third (35.7%) of doctors incorrectly believed that typical NM imaging either does not use ionising radiation or emits doses equal to or less than a standard chest radiograph. Knowledge of CT and NM radiation doses is poor across all specialties, and there is a significant inverse relationship between experience and awareness of CT dose and risk. Despite having a poor understanding of these concepts, most doctors claim to consider them prior to requesting scans and when discussing potential risks with patients. © 2012 The Authors. Journal of Medical Imaging and Radiation Oncology © 2012 The Royal Australian and New Zealand College of Radiologists.

3D documentation and visualization of external injury findings by integration of simple photography in CT/MRI data sets (IprojeCT).

PubMed

Campana, Lorenzo; Breitbeck, Robert; Bauer-Kreuz, Regula; Buck, Ursula

2016-05-01

This study evaluated the feasibility of documenting patterned injury using three dimensions and true colour photography without complex 3D surface documentation methods. This method is based on a generated 3D surface model using radiologic slice images (CT) while the colour information is derived from photographs taken with commercially available cameras. The external patterned injuries were documented in 16 cases using digital photography as well as highly precise photogrammetry-supported 3D structured light scanning. The internal findings of these deceased were recorded using CT and MRI. For registration of the internal with the external data, two different types of radiographic markers were used and compared. The 3D surface model generated from CT slice images was linked with the photographs, and thereby digital true-colour 3D models of the patterned injuries could be created (Image projection onto CT/IprojeCT). In addition, these external models were merged with the models of the somatic interior. We demonstrated that 3D documentation and visualization of external injury findings by integration of digital photography in CT/MRI data sets is suitable for the 3D documentation of individual patterned injuries to a body. Nevertheless, this documentation method is not a substitution for photogrammetry and surface scanning, especially when the entire bodily surface is to be recorded in three dimensions including all external findings, and when precise data is required for comparing highly detailed injury features with the injury-inflicting tool.

Prediction of outcome using pretreatment 18F-FDG PET/CT and MRI radiomics in locally advanced cervical cancer treated with chemoradiotherapy.

PubMed

Lucia, François; Visvikis, Dimitris; Desseroit, Marie-Charlotte; Miranda, Omar; Malhaire, Jean-Pierre; Robin, Philippe; Pradier, Olivier; Hatt, Mathieu; Schick, Ulrike

2018-05-01

The aim of this study is to determine if radiomics features from 18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (MRI) images could contribute to prognoses in cervical cancer. One hundred and two patients (69 for training and 33 for testing) with locally advanced cervical cancer (LACC) receiving chemoradiotherapy (CRT) from 08/2010 to 12/2016 were enrolled in this study. 18 F-FDG PET/CT and MRI examination [T1, T2, T1C, diffusion-weighted imaging (DWI)] were performed for each patient before CRT. Primary tumor volumes were delineated with the fuzzy locally adaptive Bayesian algorithm in the PET images and with 3D Slicer™ in the MRI images. Radiomics features (intensity, shape, and texture) were extracted and their prognostic value was compared with clinical parameters for recurrence-free and locoregional control. In the training cohort, median follow-up was 3.0 years (range, 0.43-6.56 years) and relapse occurred in 36% of patients. In univariate analysis, FIGO stage (I-II vs. III-IV) and metabolic response (complete vs. non-complete) were probably associated with outcome without reaching statistical significance, contrary to several radiomics features from both PET and MRI sequences. Multivariate analysis in training test identified Grey Level Non Uniformity GLRLM in PET and Entropy GLCM in ADC maps from DWI MRI as independent prognostic factors. These had significantly higher prognostic power than clinical parameters, as evaluated in the testing cohort with accuracy of 94% for predicting recurrence and 100% for predicting lack of loco-regional control (versus ~50-60% for clinical parameters). In LACC treated with CRT, radiomics features such as EntropyGLCM and GLNUGLRLM from functional imaging DWI-MRI and PET, respectively, are independent predictors of recurrence and loco-regional control with significantly higher prognostic power than usual clinical parameters. Further research is warranted for their validation, which may justify more aggressive treatment in patients identified with high probability of recurrence.

Technical Note: Characterization of custom 3D printed multimodality imaging phantoms.

PubMed

Bieniosek, Matthew F; Lee, Brian J; Levin, Craig S

2015-10-01

Imaging phantoms are important tools for researchers and technicians, but they can be costly and difficult to customize. Three dimensional (3D) printing is a widely available rapid prototyping technique that enables the fabrication of objects with 3D computer generated geometries. It is ideal for quickly producing customized, low cost, multimodal, reusable imaging phantoms. This work validates the use of 3D printed phantoms by comparing CT and PET scans of a 3D printed phantom and a commercial "Micro Deluxe" phantom. This report also presents results from a customized 3D printed PET/MRI phantom, and a customized high resolution imaging phantom with sub-mm features. CT and PET scans of a 3D printed phantom and a commercial Micro Deluxe (Data Spectrum Corporation, USA) phantom with 1.2, 1.6, 2.4, 3.2, 4.0, and 4.8 mm diameter hot rods were acquired. The measured PET and CT rod sizes, activities, and attenuation coefficients were compared. A PET/MRI scan of a custom 3D printed phantom with hot and cold rods was performed, with photon attenuation and normalization measurements performed with a separate 3D printed normalization phantom. X-ray transmission scans of a customized two level high resolution 3D printed phantom with sub-mm features were also performed. Results show very good agreement between commercial and 3D printed micro deluxe phantoms with less than 3% difference in CT measured rod diameter, less than 5% difference in PET measured rod diameter, and a maximum of 6.2% difference in average rod activity from a 10 min, 333 kBq/ml (9 μCi/ml) Siemens Inveon (Siemens Healthcare, Germany) PET scan. In all cases, these differences were within the measurement uncertainties of our setups. PET/MRI scans successfully identified 3D printed hot and cold rods on PET and MRI modalities. X-ray projection images of a 3D printed high resolution phantom identified features as small as 350 μm wide. This work shows that 3D printed phantoms can be functionally equivalent to commercially available phantoms. They are a viable option for quickly distributing and fabricating low cost, customized phantoms.

Preoperative Computerized Tomography and Magnetic Resonance Imaging of the Pancreas Predicts Pancreatic Mass and Functional Outcomes After Total Pancreatectomy and Islet Autotransplant

PubMed Central

Young, Michael C.; Theis, Jake R.; Hodges, James S.; Dunn, Ty B.; Pruett, Timothy L.; Chinnakotla, Srinath; Walker, Sidney P.; Freeman, Martin L.; Trikudanathan, Guru; Arain, Mustafa; Robertson, R. Paul; Wilhelm, Joshua J.; Schwarzenberg, Sarah J.; Bland, Barbara; Beilman, Gregory J.; Bellin, Melena D.

2015-01-01

Objectives About two-thirds of patients will remain on insulin therapy after total pancreatectomy with islet autotransplant (TPIAT) for chronic pancreatitis. We investigated the relationship between measured pancreas volume on computerized tomography (CT) or magnetic resonance imaging (MRI), and features of chronic pancreatiits on imaging, with subsequent islet isolation and diabetes outcomes. Methods CT or MRI was reviewed for pancreas volume (Vitrea software), and presence or absence of calcifications, atrophy, and dilated pancreatic duct in 97 patients undergoing TPIAT. Relationship between these features and: (1) islet mass isolated and (2) diabetes status at 1 year post-TPAIT were evaluated. Results Pancreas volume correlated with islet mass measured as total islet equivalents (r=0.50, p<0.0001). Mean islet equivalents was reduced by more than half if any one of calcifications, atrophy, or ductal dilatation were observed. Pancreatic calcifications increased the odds of insulin dependence 4.0 fold (1.1, 15). Collectively, the pancreas volume and 3 imaging features strongly associated with 1 year insulin use (p=0.07), islet graft failure (p=0.003), Hemoglobin A1c (p=0.0004), fasting glucose (p=0.027), and fasting C-peptide level (p=0.008). Conclusions Measures of pancreatic parenchymal destruction on imaging, including smaller pancreas volume and calcifications associate strongly with impaired islet mass and 1 year diabetes outcomes. PMID:26745861

Automatic Delineation of the Myocardial Wall from CT Images via Shape Segmentation and Variational Region Growing

PubMed Central

Zhu, Liangjia; Gao, Yi; Appia, Vikram; Yezzi, Anthony; Arepalli, Chesnal; Faber, Tracy; Stillman, Arthur; Tannenbaum, Allen

2014-01-01

Prognosis and diagnosis of cardiac diseases frequently require quantitative evaluation of the ventricle volume, mass, and ejection fraction. The delineation of the myocardial wall is involved in all of these evaluations, which is a challenging task due to large variations in myocardial shapes and image quality. In this work, we present an automatic method for extracting the myocardial wall of the left and right ventricles from cardiac CT images. In the method, the left and right ventricles are located sequentially, in which each ventricle is detected by first identifying the endocardium and then segmenting the epicardium. To this end, the endocardium is localized by utilizing its geometric features obtained on-line from a CT image. After that, a variational region-growing model is employed to extract the epicardium of the ventricles. In particular, the location of the endocardium of the left ventricle is determined via using an active contour model on the blood-pool surface. To localize the right ventricle, the active contour model is applied on a heart surface extracted based on the left ventricle segmentation result. The robustness and accuracy of the proposed approach is demonstrated by experimental results from 33 human and 12 pig CT images. PMID:23744658

Improving Low-dose Cardiac CT Images based on 3D Sparse Representation

NASA Astrophysics Data System (ADS)

Shi, Luyao; Hu, Yining; Chen, Yang; Yin, Xindao; Shu, Huazhong; Luo, Limin; Coatrieux, Jean-Louis

2016-03-01

Cardiac computed tomography (CCT) is a reliable and accurate tool for diagnosis of coronary artery diseases and is also frequently used in surgery guidance. Low-dose scans should be considered in order to alleviate the harm to patients caused by X-ray radiation. However, low dose CT (LDCT) images tend to be degraded by quantum noise and streak artifacts. In order to improve the cardiac LDCT image quality, a 3D sparse representation-based processing (3D SR) is proposed by exploiting the sparsity and regularity of 3D anatomical features in CCT. The proposed method was evaluated by a clinical study of 14 patients. The performance of the proposed method was compared to the 2D spares representation-based processing (2D SR) and the state-of-the-art noise reduction algorithm BM4D. The visual assessment, quantitative assessment and qualitative assessment results show that the proposed approach can lead to effective noise/artifact suppression and detail preservation. Compared to the other two tested methods, 3D SR method can obtain results with image quality most close to the reference standard dose CT (SDCT) images.

TU-F-CAMPUS-I-05: Investigation of An EMCCD Detector with Variable Gain in a Micro-CT System

DOE Office of Scientific and Technical Information (OSTI.GOV)

Krishnakumar, S Bysani; Ionita, C; Rudin, S

Purpose: To investigate the performance of a newly built Electron Multiplying Charged Coupled Device (EMCCD) based Micro-CT system, with variable detector gain, using a phantom containing contrast agent of different concentrations. Methods: We built a micro- CT system with an EMCCD having 8 microns pixels and on-chip variable gain. We tested the system using a phantom containing five tubes filled with different iodine contrast solutions (30% to 70%). First, we scanned the phantom using various x-ray exposures values at 40 kVp and constant detector gain. Next, for the same tube currents, the detector gain was increased to maintain the airmore » value of the projection image constant. A standard FDK algorithm was used to reconstruct the data. Performance was analyzed by comparing the signal-to-noise ratio (SNR) measurements for increased gain with those for the low constant gain at each exposure. Results: The high detector gain reconstructed data SNR was always greater than the low gain data SNR for all x-ray settings and for all iodine features. The largest increases were observed for low contrast features, 30% iodine concentration, where the SNR improvement approached 2. Conclusion: One of the first implementations of an EMCCD based micro- CT system was presented and used to image a phantom with various iodine solution concentrations. The analysis of the reconstructed volumes showed a significant improvement of the SNR especially for low contrast features. The unique on-chip gain feature is a substantial benefit allowing the use of the system at very low x-ray exposures per frame.Partial support: NIH grant R01EB002873 and Toshiba Medical Systems Corp. Partial support: NIH grant R01EB002873 and Toshiba Medical Systems Corp.« less

Face recognition using slow feature analysis and contourlet transform

NASA Astrophysics Data System (ADS)

Wang, Yuehao; Peng, Lingling; Zhe, Fuchuan

2018-04-01

In this paper we propose a novel face recognition approach based on slow feature analysis (SFA) in contourlet transform domain. This method firstly use contourlet transform to decompose the face image into low frequency and high frequency part, and then takes technological advantages of slow feature analysis for facial feature extraction. We named the new method combining the slow feature analysis and contourlet transform as CT-SFA. The experimental results on international standard face database demonstrate that the new face recognition method is effective and competitive.

MO-DE-207B-11: Reliability of PET/CT Radiomics Features in Functional and Morphological Components of NSCLC Lesions: A Repeatability Analysis in a Prospective Multicenter Cohort

DOE Office of Scientific and Technical Information (OSTI.GOV)

Desseroit, M; EE DACTIM, CHU de Poitiers, Poitiers; Tixier, F

2016-06-15

Purpose: The goal of this study was to evaluate the repeatability of radiomics features (intensity, shape and heterogeneity) in both PET and low-dose CT components of test-retest FDG-PET/CT images in a prospective multicenter cohort of 74 NSCLC patients from ACRIN 6678 and a similar Merck trial. Methods: Seventy-four patients with stage III-IV NCSLC were prospectively included. The primary tumor and up to 3 additional lesions per patient were analyzed. The Fuzzy Locally Adaptive Bayesian algorithm was used to automatically delineate metabolically active volume (MAV) in PET. The 3D SlicerTM software was exploited to delineate anatomical volumes (AV) in CT. Tenmore » intensity first-order features, as well as 26 textural features and four 3D shape descriptors were calculated from tumour volumes in both modalities. The repeatability of each metric was assessed by Bland-Altman analysis. Results: One hundred and five lesions (primary tumors and nodal or distant metastases) were delineated and characterized. The MAV and AV determination had a repeatability of −1.4±11.0% and −1.2±18.7% respectively. Several shape and heterogeneity features were found to be highly or moderately repeatable (e.g., sphericity, co-occurrence entropy or intensity size-zone matrix zone percentage), whereas others were confirmed as unreliable with much higher variability (more than twice that of the corresponding volume determination). Conclusion: Our results in this large multicenter cohort with more than 100 measurements confirm the PET findings in previous studies (with <30 lesions). In addition, our study is the first to explore the repeatability of radiomics features in the low-dose CT component of PET/CT acquisitions (previous studies considered dosimetry CT, CE-CT or CBCT). Several features were identified as reliable in both PET and CT components and could be used to build prognostic models. This work has received a French government support granted to the CominLabs excellence laboratory and managed by the National Research Agency in the “Investing for the Future” program under reference ANR-10-LABX-07-01, and support from the city of Brest.« less

Systemic mastocytosis: CT and US features of abdominal manifestations.

PubMed

Avila, N A; Ling, A; Worobec, A S; Mican, J M; Metcalfe, D D

1997-02-01

To study the imaging findings in patients with systemic mastocytosis and to correlate the findings with the severity of disease on the basis of an established classification system. Pathologic findings, when available, were correlated with imaging findings. Computed tomographic (CT) and ultrasound (US) scans and corresponding pathologic findings, when available, were retrospectively reviewed in 27 patients with systemic mastocytosis. Only five (19%) of the patients in our series had normal abdominal CT and/or US examination results. Common abdominal imaging findings associated with systemic mastocytosis were hepatosplenomegaly, retroperitoneal adenopathy, periportal adenopathy, mesenteric adenopathy, thickening of the omentum and the mesentery, and ascites. Less common findings included hepatofugal portal venous flow, Budd-Chiari syndrome, cavernous transformation of the portal vein, ovarian mass, and complications such as chloroma. The findings were more common in patients with category II and those with category III disease. Abdominal findings at CT and US are common in patients with systemic mastocytosis. Although the findings in patients with systemic mastocytosis are not specific to the disease, they are useful in directing further studies for diagnostic confirmation and in estimating the extent of systemic involvement.

Potential Bone to Implant Contact Area of Short Versus Standard Implants: An In Vitro Micro-Computed Tomography Analysis.

PubMed

Quaranta, Alessandro; DʼIsidoro, Orlando; Bambini, Fabrizio; Putignano, Angelo

2016-02-01

To compare the available potential bone-implant contact (PBIC) area of standard and short dental implants by micro-computed tomography (μCT) assessment. Three short implants with different diameters (4.5 × 6 mm, 4.1 × 7 mm, and 4.1 × 6 mm) and 2 standard implants (3.5 × 10 mm and 3.3 × 9 mm) with diverse design and surface features were scanned with μCT. Cross-sectional images were obtained. Image data were manually processed to find the plane that corresponds to the most coronal contact point between the crestal bone and implant. The available PBIC was calculated for each sample. Later on, the cross-sectional slices were processed by a 3-dimensional (3D) software, and 3D images of each sample were used for descriptive analysis and display the microtopography and macrotopography. The wide-diameter short implant (4.5 × 6 mm) showed the higher PBIC (210.89 mm) value followed by the standard (178.07 mm and 185.37 mm) and short implants (130.70 mm and 110.70 mm). Wide-diameter short implants show a surface area comparable with standard implants. Micro-CT analysis is a promising technique to evaluate surface area in dental implants with different macrodesign, microdesign, and surface features.

Synthesized interstitial lung texture for use in anthropomorphic computational phantoms

NASA Astrophysics Data System (ADS)

Becchetti, Marc F.; Solomon, Justin B.; Segars, W. Paul; Samei, Ehsan

2016-04-01

A realistic model of the anatomical texture from the pulmonary interstitium was developed with the goal of extending the capability of anthropomorphic computational phantoms (e.g., XCAT, Duke University), allowing for more accurate image quality assessment. Contrast-enhanced, high dose, thorax images for a healthy patient from a clinical CT system (Discovery CT750HD, GE healthcare) with thin (0.625 mm) slices and filtered back- projection (FBP) were used to inform the model. The interstitium which gives rise to the texture was defined using 24 volumes of interest (VOIs). These VOIs were selected manually to avoid vasculature, bronchi, and bronchioles. A small scale Hessian-based line filter was applied to minimize the amount of partial-volumed supernumerary vessels and bronchioles within the VOIs. The texture in the VOIs was characterized using 8 Haralick and 13 gray-level run length features. A clustered lumpy background (CLB) model with added noise and blurring to match CT system was optimized to resemble the texture in the VOIs using a genetic algorithm with the Mahalanobis distance as a similarity metric between the texture features. The most similar CLB model was then used to generate the interstitial texture to fill the lung. The optimization improved the similarity by 45%. This will substantially enhance the capabilities of anthropomorphic computational phantoms, allowing for more realistic CT simulations.

Understanding bone responses in B-mode ultrasound images and automatic bone surface extraction using a Bayesian probabilistic framework

NASA Astrophysics Data System (ADS)

Jain, Ameet K.; Taylor, Russell H.

2004-04-01

The registration of preoperative CT to intra-operative reality systems is a crucial step in Computer Assisted Orthopedic Surgery (CAOS). The intra-operative sensors include 3D digitizers, fiducials, X-rays and Ultrasound (US). Although US has many advantages over others, tracked US for Orthopedic Surgery has been researched by only a few authors. An important factor limiting the accuracy of tracked US to CT registration (1-3mm) has been the difficulty in determining the exact location of the bone surfaces in the US images (the response could range from 2-4mm). Thus it is crucial to localize the bone surface accurately from these images. Moreover conventional US imaging systems are known to have certain inherent inaccuracies, mainly due to the fact that the imaging model is assumed planar. This creates the need to develop a bone segmentation framework that can couple information from various post-processed spatially separated US images (of the bone) to enhance the localization of the bone surface. In this paper we discuss the various reasons that cause inherent uncertainties in the bone surface localization (in B-mode US images) and suggest methods to account for these. We also develop a method for automatic bone surface detection. To do so, we account objectively for the high-level understanding of the various bone surface features visible in typical US images. A combination of these features would finally decide the surface position. We use a Bayesian probabilistic framework, which strikes a fair balance between high level understanding from features in an image and the low level number crunching of standard image processing techniques. It also provides us with a mathematical approach that facilitates combining multiple images to augment the bone surface estimate.

Applying microCT and 3D visualization to Jurassic silicified conifer seed cones: A virtual advantage over thin-sectioning1

PubMed Central

Gee, Carole T.

2013-01-01

• Premise of the study: As an alternative to conventional thin-sectioning, which destroys fossil material, high-resolution X-ray computed tomography (also called microtomography or microCT) integrated with scientific visualization, three-dimensional (3D) image segmentation, size analysis, and computer animation is explored as a nondestructive method of imaging the internal anatomy of 150-million-year-old conifer seed cones from the Late Jurassic Morrison Formation, USA, and of recent and other fossil cones. • Methods: MicroCT was carried out on cones using a General Electric phoenix v|tome|x s 240D, and resulting projections were processed with visualization software to produce image stacks of serial single sections for two-dimensional (2D) visualization, 3D segmented reconstructions with targeted structures in color, and computer animations. • Results: If preserved in differing densities, microCT produced images of internal fossil tissues that showed important characters such as seed phyllotaxy or number of seeds per cone scale. Color segmentation of deeply embedded seeds highlighted the arrangement of seeds in spirals. MicroCT of recent cones was even more effective. • Conclusions: This is the first paper on microCT integrated with 3D segmentation and computer animation applied to silicified seed cones, which resulted in excellent 2D serial sections and segmented 3D reconstructions, revealing features requisite to cone identification and understanding of strobilus construction. PMID:25202495

Assessment of ultrasonography and computed tomography for the evaluation of unilateral orbital disease in dogs.

PubMed

Boroffka, Susanne A E B; Verbruggen, Anne-Marie; Grinwis, Guy C M; Voorhout, George; Barthez, Paul Y

2007-03-01

To describe clinical, ultrasonographic, and computed tomographic (CT) features of confirmed neoplastic and nonneoplastic disease in dogs with unilateral orbital diseases, determine criteria to differentiate between the 2 conditions, and assess the relative value of ultrasonography and CT for the differential diagnosis of these 2 conditions. Prospective study. 29 dogs with unilateral neoplastic orbital disease and 16 dogs with unilateral nonneoplastic orbital disease. Clinical history and results of physical and ophthalmologic examinations were recorded. Ultrasonographic and CT images were evaluated, and discriminating factors were identified to differentiate neoplastic from nonneoplastic diseases. Diagnostic value of ultrasonography and CT was assessed. Dogs with neoplastic disease were significantly older; had clinical signs for a longer time before initial examination; had more progressive onset of clinical signs; and more frequently had protrusion of the nictitating membrane, fever, and anorexia. The most discriminating factor for both imaging modalities was delineation of the margins (odds ratio was 41.7 for ultrasonography and 45 for CT), with neoplastic lesions clearly delineated more often. Ultrasonographically, neoplastic lesions were more frequently hypoechoic and homogeneous, with indentation of the globe and bone involvement evident more frequently than for nonneoplastic lesions. Mineralization was detected only with neoplasia. Fluctuant fluid was seen more frequently in dogs with nonneoplastic disease. Computed tomography more frequently revealed extraorbital involvement. Diagnostic value was similar for both imaging modalities. Ultrasonography and CT are valuable imaging modalities to assist in differentiating neoplastic from nonneoplastic unilateral orbital disease in dogs.

PET/CT with 18F Fluorocholine as an Imaging Biomarker for Chronic Liver Disease: A Preliminary Radiopathologic Correspondence Study in Patients with Liver Cancer.

PubMed

Kwee, Sandi A; Wong, Linda; Chan, Owen T M; Kalathil, Sumodh; Tsai, Naoky

2018-04-01

Purpose To determine the relationship between hepatic uptake at preoperative fluorine 18 ( 18 F) fluorocholine combined positron emission tomography (PET) and computed tomography (CT) and the histopathologic features of chronic liver disease in patients with Child-Pugh class A or B disease who are undergoing hepatic resection for liver cancer. Materials and Methods Forty-eight patients with resectable liver tumors underwent preoperative 18 F fluorocholine PET/CT. Mean liver standardized uptake value (SUV mean ) measurements were obtained from PET images, while histologic indexes of inflammation and fibrosis were applied to nontumor liver tissue from resection specimens. Effects of histopathologic features on liver SUV mean were examined with analysis of variance. Results Liver SUV mean ranged from 4.3 to 11.6, correlating significantly with Knodell histologic activity index (ρ = -0.81, P < .001) and several clinical indexes of liver disease severity. Liver SUV mean also differed significantly across groups stratified by necroinflammatory severity and Metavir fibrosis stage (P < . 001). The area under the receiver operating characteristic curve for 18 F fluorocholine PET/CT detecting Metavir fibrosis stage F1 or higher was 0.89 ± 0.05, with an odds-ratio of 3.03 (95% confidence interval: 1.59, 5.88) and sensitivity and specificity of 82% and 93%, respectively. Conclusion Correlations found in patients undergoing hepatic resection for liver cancer between liver 18 F fluorocholine uptake and histopathologic indexes of liver fibrosis and inflammation support the use of 18 F fluorocholine PET/CT as a potential imaging biomarker for chronic liver disease. © RSNA, 2018.

Gestational Trophoblastic Disease: A Multimodality Imaging Approach with Impact on Diagnosis and Management

PubMed Central

Ramani, Subhash; Thakur, Meenkashi

2014-01-01

Gestational trophoblastic disease is a condition of uncertain etiology, comprised of hydatiform mole (complete and partial), invasive mole, choriocarcinoma, and placental site trophoblastic tumor. It arises from abnormal proliferation of trophoblastic tissue. Early diagnosis of gestational trophoblastic disease and its potential complications is important for timely and successful management of the condition with preservation of fertility. Initial diagnosis is based on a multimodality approach: encompassing clinical features, serial quantitative β-hCG titers, and pelvic ultrasonography. Pelvic magnetic resonance imaging (MRI) is sometimes used as a problem-solving tool to assess the depth of myometrial invasion and extrauterine disease spread in equivocal and complicated cases. Chest radiography, body computed tomography (CT), and brain MRI have been recommended as investigative tools for overall disease staging. Angiography has a role in management of disease complications and metastases. Efficacy of PET (positron emission tomography) and PET/CT in the evaluation of recurrent or metastatic disease has not been adequately investigated yet. This paper discusses the imaging features of gestational trophoblastic disease on various imaging modalities and the role of different imaging techniques in the diagnosis and management of this entity. PMID:25126425

Contribution of postmortem multidetector CT scanning to identification of the deceased in a mass disaster: Experience gained from the 2009 Victorian bushfires.

PubMed

O'Donnell, C; Iino, M; Mansharan, K; Leditscke, J; Woodford, N

2011-02-25

CT scanning of the deceased is an established technique performed on all individuals admitted to VIFM over the last 5 years. It is used primarily to assist pathologists in determining cause and manner of death but is also invaluable for identification of unknown deceased individuals where traditional methods are not possible. Based on this experience, CT scanning was incorporated into phase 2 of the Institute's DVI process for the 2009 Victorian bushfires. All deceased individuals and fragmented remains admitted to the mortuary were CT scanned in their body bags using established protocols. Images were reviewed by 2 teams of 2 radiologists experienced in forensic imaging and the findings transcribed onto a data sheet constructed specifically for the DVI exercise. The contents of 255 body bags were examined in the 28 days following the fires. 164 missing persons were included in the DVI process with 163 deceased individuals eventually identified. CT contributed to this identification in 161 persons. In 2 cases, radiologists were unable to recognize commingled remains. CT was utilized in the initial triage of each bag's contents. If radiological evaluation determined that bodies were incomplete then this information was provided to search teams who revisited the scenes of death. CT was helpful in differentiation of human from non-human remains in 8 bags, recognition of human/animal commingling in 10 bags and human commingling in 6 bags. In 61% of cases gender was able to be determined on CT using a novel technique of genitalia detection and in all but 2 cases this was correct. Age range was able to be determined on CT in 94% with an accuracy of 76%. Specific identification features detected on CT included the presence of disease (14 disease entities in 13 cases), medical devices (26 devices in 19 cases) and 274 everyday metallic items associated with the remains of 135 individuals. CT scanning provided useful information prior to autopsy by flagging likely findings including the presence of non-human remains, at the time of autopsy by assisting in the localization of identifying features in heavily disfigured bodies, and after autopsy by retrospective review of images for clarification of issues that arose at the time of pathologist case review. In view of the success of CT scanning in this mass disaster, DVI administrators should explore the incorporation of CT services into their disaster plans. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Characterizing cartilage microarchitecture on phase-contrast x-ray computed tomography using deep learning with convolutional neural networks

NASA Astrophysics Data System (ADS)

Deng, Botao; Abidin, Anas Z.; D'Souza, Adora M.; Nagarajan, Mahesh B.; Coan, Paola; Wismüller, Axel

2017-03-01

The effectiveness of phase contrast X-ray computed tomography (PCI-CT) in visualizing human patellar cartilage matrix has been demonstrated due to its ability to capture soft tissue contrast on a micrometer resolution scale. Recent studies have shown that off-the-shelf Convolutional Neural Network (CNN) features learned from a nonmedical data set can be used for medical image classification. In this paper, we investigate the ability of features extracted from two different CNNs for characterizing chondrocyte patterns in the cartilage matrix. We obtained features from 842 regions of interest annotated on PCI-CT images of human patellar cartilage using CaffeNet and Inception-v3 Network, which were then used in a machine learning task involving support vector machines with radial basis function kernel to classify the ROIs as healthy or osteoarthritic. Classification performance was evaluated using the area (AUC) under the Receiver Operating Characteristic (ROC) curve. The best classification performance was observed with features from Inception-v3 network (AUC = 0.95), which outperforms features extracted from CaffeNet (AUC = 0.91). These results suggest that such characterization of chondrocyte patterns using features from internal layers of CNNs can be used to distinguish between healthy and osteoarthritic tissue with high accuracy.

SU-D-207-01: Markerless Respiratory Motion Tracking with Contrast Enhanced Thoracic Cone Beam CT Projections

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chao, M; Yuan, Y; Rosenzweig, K

2015-06-15

Purpose: To develop a novel technique to enhance the image contrast of clinical cone beam CT projections and extract respiratory signals based on anatomical motion using the modified Amsterdam Shroud (AS) method to benefit image guided radiation therapy. Methods: Thoracic cone beam CT projections acquired prior to treatment were preprocessed to increase their contrast for better respiratory signal extraction. Air intensity on raw images was firstly estimated and then applied to correct the projections to generate new attenuation images that were subsequently improved with deeper anatomy feature enhancement through taking logarithm operation, derivative along superior-inferior direction, respectively. All pixels onmore » individual post-processed two dimensional images were horizontally summed to one column and all projections were combined side by side to create an AS image from which patient’s respiratory signal was extracted. The impact of gantry rotation on the breathing signal rendering was also investigated. Ten projection image sets from five lung cancer patients acquired with the Varian Onboard Imager on 21iX Clinac (Varian Medical Systems, Palo Alto, CA) were employed to assess the proposed technique. Results: Application of the air correction on raw projections showed that more than an order of magnitude of contrast enhancement was achievable. The typical contrast on the raw projections is around 0.02 while that on attenuation images could greater than 0.5. Clear and stable breathing signal can be reliably extracted from the new images while the uncorrected projection sets failed to yield clear signals most of the time. Conclusion: Anatomy feature plays a key role in yielding breathing signal from the projection images using the AS technique. The air correction process facilitated the contrast enhancement significantly and attenuation images thus obtained provides a practical solution to obtaining markerless breathing motion tracking.« less

Half-Fan-Based Intensity-Weighted Region-of-Interest Imaging for Low-Dose Cone-Beam CT in Image-Guided Radiation Therapy.

PubMed

Yoo, Boyeol; Son, Kihong; Pua, Rizza; Kim, Jinsung; Solodov, Alexander; Cho, Seungryong

2016-10-01

With the increased use of computed tomography (CT) in clinics, dose reduction is the most important feature people seek when considering new CT techniques or applications. We developed an intensity-weighted region-of-interest (IWROI) imaging method in an exact half-fan geometry to reduce the imaging radiation dose to patients in cone-beam CT (CBCT) for image-guided radiation therapy (IGRT). While dose reduction is highly desirable, preserving the high-quality images of the ROI is also important for target localization in IGRT. An intensity-weighting (IW) filter made of copper was mounted in place of a bowtie filter on the X-ray tube unit of an on-board imager (OBI) system such that the filter can substantially reduce radiation exposure to the outer ROI. In addition to mounting the IW filter, the lead-blade collimation of the OBI was adjusted to produce an exact half-fan scanning geometry for a further reduction of the radiation dose. The chord-based rebinned backprojection-filtration (BPF) algorithm in circular CBCT was implemented for image reconstruction, and a humanoid pelvis phantom was used for the IWROI imaging experiment. The IWROI image of the phantom was successfully reconstructed after beam-quality correction, and it was registered to the reference image within an acceptable level of tolerance. Dosimetric measurements revealed that the dose is reduced by approximately 61% in the inner ROI and by 73% in the outer ROI compared to the conventional bowtie filter-based half-fan scan. The IWROI method substantially reduces the imaging radiation dose and provides reconstructed images with an acceptable level of quality for patient setup and target localization. The proposed half-fan-based IWROI imaging technique can add a valuable option to CBCT in IGRT applications.

Deep 3D convolution neural network for CT brain hemorrhage classification

NASA Astrophysics Data System (ADS)

Jnawali, Kamal; Arbabshirani, Mohammad R.; Rao, Navalgund; Patel, Alpen A.

2018-02-01

Intracranial hemorrhage is a critical conditional with the high mortality rate that is typically diagnosed based on head computer tomography (CT) images. Deep learning algorithms, in particular, convolution neural networks (CNN), are becoming the methodology of choice in medical image analysis for a variety of applications such as computer-aided diagnosis, and segmentation. In this study, we propose a fully automated deep learning framework which learns to detect brain hemorrhage based on cross sectional CT images. The dataset for this work consists of 40,367 3D head CT studies (over 1.5 million 2D images) acquired retrospectively over a decade from multiple radiology facilities at Geisinger Health System. The proposed algorithm first extracts features using 3D CNN and then detects brain hemorrhage using the logistic function as the last layer of the network. Finally, we created an ensemble of three different 3D CNN architectures to improve the classification accuracy. The area under the curve (AUC) of the receiver operator characteristic (ROC) curve of the ensemble of three architectures was 0.87. Their results are very promising considering the fact that the head CT studies were not controlled for slice thickness, scanner type, study protocol or any other settings. Moreover, the proposed algorithm reliably detected various types of hemorrhage within the skull. This work is one of the first applications of 3D CNN trained on a large dataset of cross sectional medical images for detection of a critical radiological condition

Machine learning algorithm for automatic detection of CT-identifiable hyperdense lesions associated with traumatic brain injury

NASA Astrophysics Data System (ADS)

Keshavamurthy, Krishna N.; Leary, Owen P.; Merck, Lisa H.; Kimia, Benjamin; Collins, Scott; Wright, David W.; Allen, Jason W.; Brock, Jeffrey F.; Merck, Derek

2017-03-01

Traumatic brain injury (TBI) is a major cause of death and disability in the United States. Time to treatment is often related to patient outcome. Access to cerebral imaging data in a timely manner is a vital component of patient care. Current methods of detecting and quantifying intracranial pathology can be time-consuming and require careful review of 2D/3D patient images by a radiologist. Additional time is needed for image protocoling, acquisition, and processing. These steps often occur in series, adding more time to the process and potentially delaying time-dependent management decisions for patients with traumatic brain injury. Our team adapted machine learning and computer vision methods to develop a technique that rapidly and automatically detects CT-identifiable lesions. Specifically, we use scale invariant feature transform (SIFT)1 and deep convolutional neural networks (CNN)2 to identify important image features that can distinguish TBI lesions from background data. Our learning algorithm is a linear support vector machine (SVM)3. Further, we also employ tools from topological data analysis (TDA) for gleaning insights into the correlation patterns between healthy and pathological data. The technique was validated using 409 CT scans of the brain, acquired via the Progesterone for the Treatment of Traumatic Brain Injury phase III clinical trial (ProTECT_III) which studied patients with moderate to severe TBI4. CT data were annotated by a central radiologist and included patients with positive and negative scans. Additionally, the largest lesion on each positive scan was manually segmented. We reserved 80% of the data for training the SVM and used the remaining 20% for testing. Preliminary results are promising with 92.55% prediction accuracy (sensitivity = 91.15%, specificity = 93.45%), indicating the potential usefulness of this technique in clinical scenarios.

CT findings of persistent pure ground glass opacity: can we predict the invasiveness?

PubMed

Liu, Li-Heng; Liu, Ming; Wei, Ran; Jin, Er-Hu; Liu, Yu-Hui; Xu, Liang; Li, Wen-Wu; Huang, Yong

2015-01-01

To investigate whether CT findings can predict the invasiveness of persistent cancerous pure ground glass opacity (pGGO) by correlating the CT imaging features of persistent pGGO with pathological changes. Ninety five patients with persistent pGGOs were included. Three radiologists evaluated the morphologic features of these pGGOs at high resolution CT (HRCT). Binary logistic regression was used to assess the association between CT findings and histopathological classification (pre-invasive and invasive groups). Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of diameters. A total of 105 pGGOs were identified. Between pre-invasive (atypical adenomatous hyperplasia, AAH, and adenocarcinoma in situ, AIS) and invasive group (minimally invasive adenocarcinoma, MIA and invasive lung adenocarcinomas, ILA), there were significant differences in diameter, spiculation and vessel dilatation (p<0.05). No difference was found in air-bronchogram, bubble- lucency, lobulated-margin, pleural indentation or vascular convergence (p>0.05). The optimal threshold value of the diameters to predict the invasiveness of pGGO was 12.50mm. HRCT features can predict the invasiveness of persistent pGGO. The pGGO with a diameter more than 12.50mm, presences of spiculation and vessel dilatation are important factors to differentiate invasive adenocarcinoma from pre-invasive cancerous lesions.

The use of neuroimaging in the management of chronic headache in children in clinical practice versus clinical practice guidelines.

PubMed

Prpić, Igor; Ahel, Tea; Rotim, Krešimir; Gajski, Domagoj; Vukelić, Petar; Sasso, Antun

2014-12-01

In daily practice, neuroimaging studies are frequently performed for the management of childhood headache. The aim of this study was to determine whether there is significant discrepancy between clinical practice and clinical practice guidelines on the indications for neuroimaging studies. Medical records of children with chronic headache, aged 2 to 18 years and treated at Rijeka University Hospital Center, Kantrida Department of Pediatrics, were retrospectively reviewed. Indications for brain magnetic resonance imaging and computed tomography (MRI/CT) scanning were reviewed and compared with clinical practice guidelines. Brain imaging was performed in 164 (76.3%) of 215 children, MRI in 93 (56.7%) and CT in 71 (43.3%) children. Indications for brain MRI/CT were as follows: anxiety and/or insistence by the child's family (71.3%), presence of associated features suggesting neurologic dysfunction (13.4%), age under 5 years (12.8%) and abnormal neurologic examination (2.4%). The majority of children (71.4%) had normal neuroimaging findings. In the rest of imaging studies (28.1%), MRI/CT revealed different intracerebral/extracerebral findings not influencing changes in headache management. Only one (0.60%) patient required change in headache management after MRI/CT. Study results proved that, despite available evidence-based clinical guidelines, brain imaging in children with chronic headaches is overused, mostly in order to decrease anxiety of the family/patient.

Bone metastases in castration-resistant prostate cancer: associations between morphologic CT patterns, glycolytic activity, and androgen receptor expression on PET and overall survival.

PubMed

Vargas, Hebert Alberto; Wassberg, Cecilia; Fox, Josef J; Wibmer, Andreas; Goldman, Debra A; Kuk, Deborah; Gonen, Mithat; Larson, Steven M; Morris, Michael J; Scher, Howard I; Hricak, Hedvig

2014-04-01

To compare the features of bone metastases at computed tomography (CT) to tracer uptake at fluorine 18 fluorodeoxyglucose (FDG) positron emission tomography (PET) and fluorine 18 16β-fluoro-5-dihydrotestosterone (FDHT) PET and to determine associations between these imaging features and overall survival in men with castration-resistant prostate cancer. This is a retrospective study of 38 patients with castration-resistant prostate cancer. Two readers independently evaluated CT, FDG PET, and FDHT PET features of bone metastases. Associations between imaging findings and overall survival were determined by using univariate Cox proportional hazards regression. In 38 patients, reader 1 detected 881 lesions and reader 2 detected 867 lesions. Attenuation coefficients at CT correlated inversely with FDG (reader 1: r = -0.3007; P < .001; reader 2: r = -0.3147; P < .001) and FDHT (reader 1: r = -0.2680; P = .001; reader 2: r = -0.3656; P < .001) uptake. The number of lesions on CT scans was significantly associated with overall survival (reader 1: hazard ratio [HR], 1.025; P = .05; reader 2: HR, 1.021; P = .04). The numbers of lesions on FDG and FDHT PET scans were significantly associated with overall survival for reader 1 (HR, 1.051-1.109; P < .001) and reader 2 (HR, 1.026-1.082; P ≤ .009). Patients with higher FDHT uptake (lesion with the highest maximum standardized uptake value) had significantly shorter overall survival (reader 1: HR, 1.078; P = .02; reader 2: HR, 1.092; P = .02). FDG uptake intensity was not associated with overall survival (reader 1, P = .65; reader 2, P = .38). In patients with castration-resistant prostate cancer, numbers of bone lesions on CT, FDG PET, and FDHT PET scans and the intensity of FDHT uptake are significantly associated with overall survival. RSNA, 2013

Bone Metastases in Castration-Resistant Prostate Cancer: Associations between Morphologic CT Patterns, Glycolytic Activity, and Androgen Receptor Expression on PET and Overall Survival

PubMed Central

Wassberg, Cecilia; Fox, Josef J.; Wibmer, Andreas; Goldman, Debra A.; Kuk, Deborah; Gonen, Mithat; Larson, Steven M.; Morris, Michael J.; Scher, Howard I.; Hricak, Hedvig

2014-01-01

Purpose To compare the features of bone metastases at computed tomography (CT) to tracer uptake at fluorine 18 fluorodeoxyglucose (FDG) positron emission tomography (PET) and fluorine 18 16β-fluoro-5-dihydrotestosterone (FDHT) PET and to determine associations between these imaging features and overall survival in men with castration-resistant prostate cancer. Materials and Methods This is a retrospective study of 38 patients with castration-resistant prostate cancer. Two readers independently evaluated CT, FDG PET, and FDHT PET features of bone metastases. Associations between imaging findings and overall survival were determined by using univariate Cox proportional hazards regression. Results In 38 patients, reader 1 detected 881 lesions and reader 2 detected 867 lesions. Attenuation coefficients at CT correlated inversely with FDG (reader 1: r = −0.3007; P < .001; reader 2: r = −0.3147; P < .001) and FDHT (reader 1: r = −0.2680; P = .001; reader 2: r = −0.3656; P < .001) uptake. The number of lesions on CT scans was significantly associated with overall survival (reader 1: hazard ratio [HR], 1.025; P = .05; reader 2: HR, 1.021; P = .04). The numbers of lesions on FDG and FDHT PET scans were significantly associated with overall survival for reader 1 (HR, 1.051–1.109; P < .001) and reader 2 (HR, 1.026–1.082; P ≤ .009). Patients with higher FDHT uptake (lesion with the highest maximum standardized uptake value) had significantly shorter overall survival (reader 1: HR, 1.078; P = .02; reader 2: HR, 1.092; P = .02). FDG uptake intensity was not associated with overall survival (reader 1, P = .65; reader 2, P = .38). Conclusion In patients with castration-resistant prostate cancer, numbers of bone lesions on CT, FDG PET, and FDHT PET scans and the intensity of FDHT uptake are significantly associated with overall survival. © RSNA, 2013 PMID:24475817

Severity mapping of the proximal femur: a new method for assessing hip osteoarthritis with computed tomography.

PubMed

Turmezei, T D; Lomas, D J; Hopper, M A; Poole, K E S

2014-10-01

Plain radiography has been the mainstay of imaging assessment in osteoarthritis for over 50 years, but it does have limitations. Here we present the methodology and results of a new technique for identifying, grading, and mapping the severity and spatial distribution of osteoarthritic disease features at the hip in 3D with clinical computed tomography (CT). CT imaging of 456 hips from 230 adult female volunteers (mean age 66 ± 17 years) was reviewed using 3D multiplanar reformatting to identify bone-related radiological features of osteoarthritis, namely osteophytes, subchondral cysts and joint space narrowing. Scoresheets dividing up the femoral head, head-neck region and the joint space were used to register the location and severity of each feature (scored from 0 to 3). Novel 3D cumulative feature severity maps were then created to display where the most severe disease features from each individual were anatomically located across the cohort. Feature severity maps showed a propensity for osteophytes at the inferoposterior and superolateral femoral head-neck junction. Subchondral cysts were a less common and less localised phenomenon. Joint space narrowing <1.5 mm was recorded in at least one sector of 83% of hips, but most frequently in the posterolateral joint space. This is the first description of hip osteoarthritis using unenhanced clinical CT in which we describe the co-localisation of posterior osteophytes and joint space narrowing for the first time. We believe this technique can perform several important roles in future osteoarthritis research, including phenotyping and sensitive disease assessment in 3D. Copyright © 2014 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.

Impact of CT perfusion imaging on the assessment of peripheral chronic pulmonary thromboembolism: clinical experience in 62 patients.

PubMed

Le Faivre, Julien; Duhamel, Alain; Khung, Suonita; Faivre, Jean-Baptiste; Lamblin, Nicolas; Remy, Jacques; Remy-Jardin, Martine

2016-11-01

To evaluate the impact of CT perfusion imaging on the detection of peripheral chronic pulmonary embolisms (CPE). 62 patients underwent a dual-energy chest CT angiographic examination with (a) reconstruction of diagnostic and perfusion images; (b) enabling depiction of vascular features of peripheral CPE on diagnostic images and perfusion defects (20 segments/patient; total: 1240 segments examined). The interpretation of diagnostic images was of two types: (a) standard (i.e., based on cross-sectional images alone) or (b) detailed (i.e., based on cross-sectional images and MIPs). The segment-based analysis showed (a) 1179 segments analyzable on both imaging modalities and 61 segments rated as nonanalyzable on perfusion images; (b) the percentage of diseased segments was increased by 7.2 % when perfusion imaging was compared to the detailed reading of diagnostic images, and by 26.6 % when compared to the standard reading of images. At a patient level, the extent of peripheral CPE was higher on perfusion imaging, with a greater impact when compared to the standard reading of diagnostic images (number of patients with a greater number of diseased segments: n = 45; 72.6 % of the study population). Perfusion imaging allows recognition of a greater extent of peripheral CPE compared to diagnostic imaging. • Dual-energy computed tomography generates standard diagnostic imaging and lung perfusion analysis. • Depiction of CPE on central arteries relies on standard diagnostic imaging. • Detection of peripheral CPE is improved by perfusion imaging.

Calibration and analysis of a multimodal micro-CT and structured light imaging system for the evaluation of excised breast tissue

NASA Astrophysics Data System (ADS)

McClatchy, David M., III; Rizzo, Elizabeth J.; Meganck, Jeff; Kempner, Josh; Vicory, Jared; Wells, Wendy A.; Paulsen, Keith D.; Pogue, Brian W.

2017-12-01

A multimodal micro-computed tomography (CT) and multi-spectral structured light imaging (SLI) system is introduced and systematically analyzed to test its feasibility to aid in margin delineation during breast conserving surgery (BCS). Phantom analysis of the micro-CT yielded a signal-to-noise ratio of 34, a contrast of 1.64, and a minimum detectable resolution of 240 μm for a 1.2 min scan. The SLI system, spanning wavelengths 490 nm to 800 nm and spatial frequencies up to 1.37 mm-1 , was evaluated with aqueous tissue simulating phantoms having variations in particle size distribution, scatter density, and blood volume fraction. The reduced scattering coefficient, μs\\prime and phase function parameter, γ, were accurately recovered over all wavelengths independent of blood volume fractions from 0% to 4%, assuming a flat sample geometry perpendicular to the imaging plane. The resolution of the optical system was tested with a step phantom, from which the modulation transfer function was calculated yielding a maximum resolution of 3.78 cycles per mm. The three dimensional spatial co-registration between the CT and optical imaging space was tested and shown to be accurate within 0.7 mm. A freshly resected breast specimen, with lobular carcinoma, fibrocystic disease, and adipose, was imaged with the system. The micro-CT provided visualization of the tumor mass and its spiculations, and SLI yielded superficial quantification of light scattering parameters for the malignant and benign tissue types. These results appear to be the first demonstration of SLI combined with standard medical tomography for imaging excised tumor specimens. While further investigations are needed to determine and test the spectral, spatial, and CT features required to classify tissue, this study demonstrates the ability of multimodal CT/SLI to quantify, visualize, and spatially navigate breast tumor specimens, which could potentially aid in the assessment of tumor margin status during BCS.

Clinical and imaging features in lung torsion and description of a novel imaging sign.

PubMed

Hammer, Mark M; Madan, Rachna

2018-04-01

We set out to identify the clinical and imaging features seen in lung torsion, a rare but emergent diagnosis leading to vascular compromise of a lobe or entire lung. We retrospectively identified 10 patients with torsion who underwent chest CT. We evaluated each case for the presence of bronchial obstruction and abnormal fissure orientation. In seven patients who underwent contrast-enhanced CTs, we assessed for the presence of the antler sign, a novel sign seen on axial images demonstrating abnormal curvature of the artery and branches originating on one side. Five patients had right middle lobe (RML) torsion after right upper lobectomy, and the remaining occurred following thoracentesis, aortic surgery, or spontaneously. Chest CTs demonstrated bronchial obstruction in eight cases and presence of abnormal fissure orientation in four patients. The antler sign was present in three patients with whole-lung torsion and one patient with lobar torsion; vascular swirling was seen on 3-D images in all seven patients with contrast-enhanced CTs. Lung parenchymal imaging findings in lung torsion may be non-specific. Identification of the antler sign on contrast-enhanced chest CT, in combination with other signs such as bronchial obstruction and abnormal fissure orientation, indicates rotation of the bronchovascular pedicle. The presence of this sign should prompt further evaluation with 3-dimensional reconstructions.

Micro-CTvlab: A web based virtual gallery of biological specimens using X-ray microtomography (micro-CT)

PubMed Central

Faulwetter, Sarah; Chatzinikolaou, Eva; Michalakis, Nikitas; Filiopoulou, Irene; Minadakis, Nikos; Panteri, Emmanouela; Perantinos, George; Gougousis, Alexandros; Arvanitidis, Christos

2016-01-01

Abstract Background During recent years, X-ray microtomography (micro-CT) has seen an increasing use in biological research areas, such as functional morphology, taxonomy, evolutionary biology and developmental research. Micro-CT is a technology which uses X-rays to create sub-micron resolution images of external and internal features of specimens. These images can then be rendered in a three-dimensional space and used for qualitative and quantitative 3D analyses. However, the online exploration and dissemination of micro-CT datasets are rarely made available to the public due to their large size and a lack of dedicated online platforms for the interactive manipulation of 3D data. Here, the development of a virtual micro-CT laboratory (Micro-CTvlab) is described, which can be used by everyone who is interested in digitisation methods and biological collections and aims at making the micro-CT data exploration of natural history specimens freely available over the internet. New information The Micro-CTvlab offers to the user virtual image galleries of various taxa which can be displayed and downloaded through a web application. With a few clicks, accurate, detailed and three-dimensional models of species can be studied and virtually dissected without destroying the actual specimen. The data and functions of the Micro-CTvlab can be accessed either on a normal computer or through a dedicated version for mobile devices. PMID:27956848

Can radiomics features be reproducibly measured from CBCT images for patients with non-small cell lung cancer?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fave, Xenia, E-mail: xjfave@mdanderson.org; Fried, David; Mackin, Dennis

Purpose: Increasing evidence suggests radiomics features extracted from computed tomography (CT) images may be useful in prognostic models for patients with nonsmall cell lung cancer (NSCLC). This study was designed to determine whether such features can be reproducibly obtained from cone-beam CT (CBCT) images taken using medical Linac onboard-imaging systems in order to track them through treatment. Methods: Test-retest CBCT images of ten patients previously enrolled in a clinical trial were retrospectively obtained and used to determine the concordance correlation coefficient (CCC) for 68 different texture features. The volume dependence of each feature was also measured using the Spearman rankmore » correlation coefficient. Features with a high reproducibility (CCC > 0.9) that were not due to volume dependence in the patient test-retest set were further examined for their sensitivity to differences in imaging protocol, level of scatter, and amount of motion by using two phantoms. The first phantom was a texture phantom composed of rectangular cartridges to represent different textures. Features were measured from two cartridges, shredded rubber and dense cork, in this study. The texture phantom was scanned with 19 different CBCT imagers to establish the features’ interscanner variability. The effect of scatter on these features was studied by surrounding the same texture phantom with scattering material (rice and solid water). The effect of respiratory motion on these features was studied using a dynamic-motion thoracic phantom and a specially designed tumor texture insert of the shredded rubber material. The differences between scans acquired with different Linacs and protocols, varying amounts of scatter, and with different levels of motion were compared to the mean intrapatient difference from the test-retest image set. Results: Of the original 68 features, 37 had a CCC >0.9 that was not due to volume dependence. When the Linac manufacturer and imaging protocol were kept consistent, 4–13 of these 37 features passed our criteria for reproducibility more than 50% of the time, depending on the manufacturer-protocol combination. Almost all of the features changed substantially when scatter material was added around the phantom. For the dense cork, 23 features passed in the thoracic scans and 11 features passed in the head scans when the differences between one and two layers of scatter were compared. Using the same test for the shredded rubber, five features passed the thoracic scans and eight features passed the head scans. Motion substantially impacted the reproducibility of the features. With 4 mm of motion, 12 features from the entire volume and 14 features from the center slice measurements were reproducible. With 6–8 mm of motion, three features (Laplacian of Gaussian filtered kurtosis, gray-level nonuniformity, and entropy), from the entire volume and seven features (coarseness, high gray-level run emphasis, gray-level nonuniformity, sum-average, information measure correlation, scaled mean, and entropy) from the center-slice measurements were considered reproducible. Conclusions: Some radiomics features are robust to the noise and poor image quality of CBCT images when the imaging protocol is consistent, relative changes in the features are used, and patients are limited to those with less than 1 cm of motion.« less

Quantitative CT analysis for the preoperative prediction of pathologic grade in pancreatic neuroendocrine tumors

NASA Astrophysics Data System (ADS)

Chakraborty, Jayasree; Pulvirenti, Alessandra; Yamashita, Rikiya; Midya, Abhishek; Gönen, Mithat; Klimstra, David S.; Reidy, Diane L.; Allen, Peter J.; Do, Richard K. G.; Simpson, Amber L.

2018-02-01

Pancreatic neuroendocrine tumors (PanNETs) account for approximately 5% of all pancreatic tumors, affecting one individual per million each year.1 PanNETs are difficult to treat due to biological variability from benign to highly malignant, indolent to very aggressive. The World Health Organization classifies PanNETs into three categories based on cell proliferative rate, usually detected using the Ki67 index and cell morphology: low-grade (G1), intermediate-grade (G2) and high-grade (G3) tumors. Knowledge of grade prior to treatment would select patients for optimal therapy: G1/G2 tumors respond well to somatostatin analogs and targeted or cytotoxic drugs whereas G3 tumors would be targeted with platinum or alkylating agents.2, 3 Grade assessment is based on the pathologic examination of the surgical specimen, biopsy or ne-needle aspiration; however, heterogeneity in the proliferative index can lead to sampling errors.4 Based on studies relating qualitatively assessed shape and enhancement characteristics on CT imaging to tumor grade in PanNET,5 we propose objective classification of PanNET grade with quantitative analysis of CT images. Fifty-five patients were included in our retrospective analysis. A pathologist graded the tumors. Texture and shape-based features were extracted from CT. Random forest and naive Bayes classifiers were compared for the classification of G1/G2 and G3 PanNETs. The best area under the receiver operating characteristic curve (AUC) of 0:74 and accuracy of 71:64% was achieved with texture features. The shape-based features achieved an AUC of 0:70 and accuracy of 78:73%.

A reconstruction method for cone-beam differential x-ray phase-contrast computed tomography.

PubMed

Fu, Jian; Velroyen, Astrid; Tan, Renbo; Zhang, Junwei; Chen, Liyuan; Tapfer, Arne; Bech, Martin; Pfeiffer, Franz

2012-09-10

Most existing differential phase-contrast computed tomography (DPC-CT) approaches are based on three kinds of scanning geometries, described by parallel-beam, fan-beam and cone-beam. Due to the potential of compact imaging systems with magnified spatial resolution, cone-beam DPC-CT has attracted significant interest. In this paper, we report a reconstruction method based on a back-projection filtration (BPF) algorithm for cone-beam DPC-CT. Due to the differential nature of phase contrast projections, the algorithm restrains from differentiation of the projection data prior to back-projection, unlike BPF algorithms commonly used for absorption-based CT data. This work comprises a numerical study of the algorithm and its experimental verification using a dataset measured with a three-grating interferometer and a micro-focus x-ray tube source. Moreover, the numerical simulation and experimental results demonstrate that the proposed method can deal with several classes of truncated cone-beam datasets. We believe that this feature is of particular interest for future medical cone-beam phase-contrast CT imaging applications.

Performance analysis of model based iterative reconstruction with dictionary learning in transportation security CT

NASA Astrophysics Data System (ADS)

Haneda, Eri; Luo, Jiajia; Can, Ali; Ramani, Sathish; Fu, Lin; De Man, Bruno

2016-05-01

In this study, we implement and compare model based iterative reconstruction (MBIR) with dictionary learning (DL) over MBIR with pairwise pixel-difference regularization, in the context of transportation security. DL is a technique of sparse signal representation using an over complete dictionary which has provided promising results in image processing applications including denoising,1 as well as medical CT reconstruction.2 It has been previously reported that DL produces promising results in terms of noise reduction and preservation of structural details, especially for low dose and few-view CT acquisitions.2 A distinguishing feature of transportation security CT is that scanned baggage may contain items with a wide range of material densities. While medical CT typically scans soft tissues, blood with and without contrast agents, and bones, luggage typically contains more high density materials (i.e. metals and glass), which can produce severe distortions such as metal streaking artifacts. Important factors of security CT are the emphasis on image quality such as resolution, contrast, noise level, and CT number accuracy for target detection. While MBIR has shown exemplary performance in the trade-off of noise reduction and resolution preservation, we demonstrate that DL may further improve this trade-off. In this study, we used the KSVD-based DL3 combined with the MBIR cost-minimization framework and compared results to Filtered Back Projection (FBP) and MBIR with pairwise pixel-difference regularization. We performed a parameter analysis to show the image quality impact of each parameter. We also investigated few-view CT acquisitions where DL can show an additional advantage relative to pairwise pixel difference regularization.

SU-D-207B-03: A PET-CT Radiomics Comparison to Predict Distant Metastasis in Lung Adenocarcinoma

DOE Office of Scientific and Technical Information (OSTI.GOV)

Coroller, T; Yip, S; Lee, S

2016-06-15

Purpose: Early prediction of distant metastasis may provide crucial information for adaptive therapy, subsequently improving patient survival. Radiomic features that extracted from PET and CT images have been used for assessing tumor phenotype and predicting clinical outcomes. This study investigates the values of radiomic features in predicting distant metastasis (DM) in non-small cell lung cancer (NSCLC). Methods: A total of 108 patients with stage II–III lung adenocarcinoma were included in this retrospective study. Twenty radiomic features were selected (10 from CT and 10 from PET). Conventional features (metabolic tumor volume, SUV, volume and diameter) were included for comparison. Concordance indexmore » (CI) was used to evaluate features prognostic value. Noether test was used to compute p-value to consider CI significance from random (CI = 0.5) and were adjusted for multiple testing using false rate discovery (FDR). Results: A total of 70 patients had DM (64.8%) with a median time to event of 8.8 months. The median delivered dose was 60 Gy (range 33–68 Gy). None of the conventional features from PET (CI ranged from 0.51 to 0.56) or CT (CI ranged from 0.57 to 0.58) were significant from random. Five radiomics features were significantly prognostic from random for DM (p-values < 0.05). Four were extracted from CT (CI = 0.61 to 0.63, p-value <0.01) and one from PET which was also the most prognostic (CI = 0.64, p-value <0.001). Conclusion: This study demonstrated significant association between radiomic features and DM for patients with locally advanced lung adenocarcinoma. Moreover, conventional (clinically utilized) metrics were not significantly associated with DM. Radiomics can potentially help classify patients at higher risk of DM, allowing clinicians to individualize treatment, such as intensification of chemotherapy) to reduce the risk of DM and improve survival. R.M. has consulting interests with Amgen.« less

MicroCT analysis of a retrieved root restored with a bonded fiber-reinforced composite dowel: a pilot study.

PubMed

Lorenzoni, Fabio Cesar; Bonfante, Estevam A; Bonfante, Gerson; Martins, Leandro M; Witek, Lukasz; Silva, Nelson R F A

2013-08-01

This evaluation aimed to (1) validate micro-computed tomography (microCT) findings using scanning electron microscopy (SEM) imaging, and (2) quantify the volume of voids and the bonded surface area resulting from fiber-reinforced composite (FRC) dowel cementation technique using microCT scanning technology/3D reconstructing software. A fiberglass dowel was cemented in a condemned maxillary lateral incisor prior to its extraction. A microCT scan was performed of the extracted tooth creating a large volume of data in DICOM format. This set of images was imported to image-processing software to inspect the internal architecture of structures. The outer surface and the spatial relationship of dentin, FRC dowel, cement layer, and voids were reconstructed. Three-dimensional spatial architecture of structures and volumetric analysis revealed that 9.89% of the resin cement was composed of voids and that the bonded area between root dentin and cement was 60.63% larger than that between cement and FRC dowel. SEM imaging demonstrated the presence of voids similarly observed using microCT technology (aim 1). MicroCT technology was able to nondestructively measure the volume of voids within the cement layer and the bonded surface area at the root/cement/FRC interfaces (aim 2). The interfaces at the root dentin/cement/dowel represent a timely and relevant topic where several efforts have been conducted in the past few years to understand their inherent features. MicroCT technology combined with 3D reconstruction allows for not only inspecting the internal arrangement rendered by fiberglass adhesively bonded to root dentin, but also estimating the volume of voids and contacted bond area between the dentin and cement layer. © 2013 by the American College of Prosthodontists.

Reconstruction of a time-averaged midposition CT scan for radiotherapy planning of lung cancer patients using deformable registration.

PubMed

Wolthaus, J W H; Sonke, J J; van Herk, M; Damen, E M F

2008-09-01

lower lobe lung tumors move with amplitudes of up to 2 cm due to respiration. To reduce respiration imaging artifacts in planning CT scans, 4D imaging techniques are used. Currently, we use a single (midventilation) frame of the 4D data set for clinical delineation of structures and radiotherapy planning. A single frame, however, often contains artifacts due to breathing irregularities, and is noisier than a conventional CT scan since the exposure per frame is lower. Moreover, the tumor may be displaced from the mean tumor position due to hysteresis. The aim of this work is to develop a framework for the acquisition of a good quality scan representing all scanned anatomy in the mean position by averaging transformed (deformed) CT frames, i.e., canceling out motion. A nonrigid registration method is necessary since motion varies over the lung. 4D and inspiration breath-hold (BH) CT scans were acquired for 13 patients. An iterative multiscale motion estimation technique was applied to the 4D CT scan, similar to optical flow but using image phase (gray-value transitions from bright to dark and vice versa) instead. From the (4D) deformation vector field (DVF) derived, the local mean position in the respiratory cycle was computed and the 4D DVF was modified to deform all structures of the original 4D CT scan to this mean position. A 3D midposition (MidP) CT scan was then obtained by (arithmetic or median) averaging of the deformed 4D CT scan. Image registration accuracy, tumor shape deviation with respect to the BH CT scan, and noise were determined to evaluate the image fidelity of the MidP CT scan and the performance of the technique. Accuracy of the used deformable image registration method was comparable to established automated locally rigid registration and to manual landmark registration (average difference to both methods < 0.5 mm for all directions) for the tumor region. From visual assessment, the registration was good for the clearly visible features (e.g., tumor and diaphragm). The shape of the tumor, with respect to that of the BH CT scan, was better represented by the MidP reconstructions than any of the 4D CT frames (including MidV; reduction of "shape differences" was 66%). The MidP scans contained about one-third the noise of individual 4D CT scan frames. We implemented an accurate method to estimate the motion of structures in a 4D CT scan. Subsequently, a novel method to create a midposition CT scan (time-weighted average of the anatomy) for treatment planning with reduced noise and artifacts was introduced. Tumor shape and position in the MidP CT scan represents that of the BH CT scan better than MidV CT scan and, therefore, was found to be appropriate for treatment planning.

Unsuspected Active Ulcerative Colitis in a Patient With Dermatomyositis: A Rare Association Detected on 18F-FDG PET/CT During the Search for an Occult Malignancy.

PubMed

Rayamajhi, Sampanna Jung; Gorla, Arun Kumar Reddy; Basher, Rajender Kumar; Sood, Ashwani; Mittal, Bhagwant Rai

2017-01-01

Dermatomyositis is an inflammatory myopathy with the characteristic features of skin rash and myopathy. We here present a known case of dermatomyositis evaluated with 18 F-FDG PET/CT for the presence of any occult malignancy. The scan was negative for the presence of any malignancy. However, it revealed multiple intensely FDG avid colonic strictures that were later proven on colonoscopic biopsy to be ulcerative colitis. Also, a well-known association of bilateral sacroilitis was simultaneously demonstrated on the scan. The present case demonstrates that 18 F-FDG PET/CT imaging can serve as a one-stop shop imaging modality in dermatomyositis by facilitating detection of occult primary if any and by providing insight into other rare systemic associations.

Unsuspected Active Ulcerative Colitis in a Patient With Dermatomyositis: A Rare Association Detected on 18F-FDG PET/CT During the Search for an Occult Malignancy

PubMed Central

Rayamajhi, Sampanna Jung; Gorla, Arun Kumar Reddy; Basher, Rajender Kumar; Sood, Ashwani; Mittal, Bhagwant Rai

2017-01-01

Dermatomyositis is an inflammatory myopathy with the characteristic features of skin rash and myopathy. We here present a known case of dermatomyositis evaluated with 18F-FDG PET/CT for the presence of any occult malignancy. The scan was negative for the presence of any malignancy. However, it revealed multiple intensely FDG avid colonic strictures that were later proven on colonoscopic biopsy to be ulcerative colitis. Also, a well-known association of bilateral sacroilitis was simultaneously demonstrated on the scan. The present case demonstrates that 18F-FDG PET/CT imaging can serve as a one-stop shop imaging modality in dermatomyositis by facilitating detection of occult primary if any and by providing insight into other rare systemic associations. PMID:28533643

Towards quantitative imaging: stability of fully automated nodule segmentation across varied dose levels and reconstruction parameters in a low-dose CT screening patient cohort

NASA Astrophysics Data System (ADS)

Wahi-Anwar, M. Wasil; Emaminejad, Nastaran; Hoffman, John; Kim, Grace H.; Brown, Matthew S.; McNitt-Gray, Michael F.

2018-02-01

Quantitative imaging in lung cancer CT seeks to characterize nodules through quantitative features, usually from a region of interest delineating the nodule. The segmentation, however, can vary depending on segmentation approach and image quality, which can affect the extracted feature values. In this study, we utilize a fully-automated nodule segmentation method - to avoid reader-influenced inconsistencies - to explore the effects of varied dose levels and reconstruction parameters on segmentation. Raw projection CT images from a low-dose screening patient cohort (N=59) were reconstructed at multiple dose levels (100%, 50%, 25%, 10%), two slice thicknesses (1.0mm, 0.6mm), and a medium kernel. Fully-automated nodule detection and segmentation was then applied, from which 12 nodules were selected. Dice similarity coefficient (DSC) was used to assess the similarity of the segmentation ROIs of the same nodule across different reconstruction and dose conditions. Nodules at 1.0mm slice thickness and dose levels of 25% and 50% resulted in DSC values greater than 0.85 when compared to 100% dose, with lower dose leading to a lower average and wider spread of DSC values. At 0.6mm, the increased bias and wider spread of DSC values from lowering dose were more pronounced. The effects of dose reduction on DSC for CAD-segmented nodules were similar in magnitude to reducing the slice thickness from 1.0mm to 0.6mm. In conclusion, variation of dose and slice thickness can result in very different segmentations because of noise and image quality. However, there exists some stability in segmentation overlap, as even at 1mm, an image with 25% of the lowdose scan still results in segmentations similar to that seen in a full-dose scan.

Improving image quality in laboratory x-ray phase-contrast imaging

NASA Astrophysics Data System (ADS)

De Marco, F.; Marschner, M.; Birnbacher, L.; Viermetz, M.; Noël, P.; Herzen, J.; Pfeiffer, F.

2017-03-01

Grating-based X-ray phase-contrast (gbPC) is known to provide significant benefits for biomedical imaging. To investigate these benefits, a high-sensitivity gbPC micro-CT setup for small (≍ 5 cm) biological samples has been constructed. Unfortunately, high differential-phase sensitivity leads to an increased magnitude of data processing artifacts, limiting the quality of tomographic reconstructions. Most importantly, processing of phase-stepping data with incorrect stepping positions can introduce artifacts resembling Moiré fringes to the projections. Additionally, the focal spot size of the X-ray source limits resolution of tomograms. Here we present a set of algorithms to minimize artifacts, increase resolution and improve visual impression of projections and tomograms from the examined setup. We assessed two algorithms for artifact reduction: Firstly, a correction algorithm exploiting correlations of the artifacts and differential-phase data was developed and tested. Artifacts were reliably removed without compromising image data. Secondly, we implemented a new algorithm for flatfield selection, which was shown to exclude flat-fields with strong artifacts. Both procedures successfully improved image quality of projections and tomograms. Deconvolution of all projections of a CT scan can minimize blurring introduced by the finite size of the X-ray source focal spot. Application of the Richardson-Lucy deconvolution algorithm to gbPC-CT projections resulted in an improved resolution of phase-contrast tomograms. Additionally, we found that nearest-neighbor interpolation of projections can improve the visual impression of very small features in phase-contrast tomograms. In conclusion, we achieved an increase in image resolution and quality for the investigated setup, which may lead to an improved detection of very small sample features, thereby maximizing the setup's utility.

A simulation-based study on the influence of beam hardening in X-ray computed tomography for dimensional metrology.

PubMed

Lifton, Joseph J; Malcolm, Andrew A; McBride, John W

2015-01-01

X-ray computed tomography (CT) is a radiographic scanning technique for visualising cross-sectional images of an object non-destructively. From these cross-sectional images it is possible to evaluate internal dimensional features of a workpiece which may otherwise be inaccessible to tactile and optical instruments. Beam hardening is a physical process that degrades the quality of CT images and has previously been suggested to influence dimensional measurements. Using a validated simulation tool, the influence of spectrum pre-filtration and beam hardening correction are evaluated for internal and external dimensional measurements. Beam hardening is shown to influence internal and external dimensions in opposition, and to have a greater influence on outer dimensions compared to inner dimensions. The results suggest the combination of spectrum pre-filtration and a local gradient-based surface determination method are able to greatly reduce the influence of beam hardening in X-ray CT for dimensional metrology.

[Analysis of 163 rib fractures by imaging examination].

PubMed

Song, Tian-fu; Wang, Chao-chao

2014-12-01

To explore the applications of imaging examination on rib fracture sites in forensic identification. Features including the sites, numbers of the processed imaging examination and the first radiological technology at diagnosis in 56 cases of rib fractures from 163 injuries were retrospectively analyzed. The detection rate of the rib fractures within 14 days was 65.6%. The initial detection rate of anterior rib fracture proceeded by X-ray was 76.2%, then 90.5% detected at a second time X-ray, while the detection rate of CT was 66.7% and 80.0%, respectively. The initial detec- tion rate of rib fracture in axillary section proceeded by X-ray was 27.6%, then 58.6% detected at a second time X-ray, while the detection rate of CT was 54.3% and 80.4%, respectively. The initial detection rate of posterior rib fracture proceeded by X-ray was 63.6%, then 81.8% detected at a second time X-ray, while the detection rate of CT was 50.0% and 70.0%, respectively. It is important to pay attention to the use of combined imaging examinations and the follow-up results. In the cases of suspicious for rib fracture in axillary section, CT examination is suggested in such false X-ray negative cases.

Diagnostic Accuracy of CT Enterography for Active Inflammatory Terminal Ileal Crohn Disease: Comparison of Full-Dose and Half-Dose Images Reconstructed with FBP and Half-Dose Images with SAFIRE.

PubMed

Gandhi, Namita S; Baker, Mark E; Goenka, Ajit H; Bullen, Jennifer A; Obuchowski, Nancy A; Remer, Erick M; Coppa, Christopher P; Einstein, David; Feldman, Myra K; Kanmaniraja, Devaraju; Purysko, Andrei S; Vahdat, Noushin; Primak, Andrew N; Karim, Wadih; Herts, Brian R

2016-08-01

Purpose To compare the diagnostic accuracy and image quality of computed tomographic (CT) enterographic images obtained at half dose and reconstructed with filtered back projection (FBP) and sinogram-affirmed iterative reconstruction (SAFIRE) with those of full-dose CT enterographic images reconstructed with FBP for active inflammatory terminal or neoterminal ileal Crohn disease. Materials and Methods This retrospective study was compliant with HIPAA and approved by the institutional review board. The requirement to obtain informed consent was waived. Ninety subjects (45 with active terminal ileal Crohn disease and 45 without Crohn disease) underwent CT enterography with a dual-source CT unit. The reference standard for confirmation of active Crohn disease was active terminal ileal Crohn disease based on ileocolonoscopy or established Crohn disease and imaging features of active terminal ileal Crohn disease. Data from both tubes were reconstructed with FBP (100% exposure); data from the primary tube (50% exposure) were reconstructed with FBP and SAFIRE strengths 3 and 4, yielding four datasets per CT enterographic examination. The mean volume CT dose index (CTDIvol) and size-specific dose estimate (SSDE) at full dose were 13.1 mGy (median, 7.36 mGy) and 15.9 mGy (median, 13.06 mGy), respectively, and those at half dose were 6.55 mGy (median, 3.68 mGy) and 7.95 mGy (median, 6.5 mGy). Images were subjectively evaluated by eight radiologists for quality and diagnostic confidence for Crohn disease. Areas under the receiver operating characteristic curves (AUCs) were estimated, and the multireader, multicase analysis of variance method was used to compare reconstruction methods on the basis of a noninferiority margin of 0.05. Results The mean AUCs with half-dose scans (FBP, 0.908; SAFIRE 3, 0.935; SAFIRE 4, 0.924) were noninferior to the mean AUC with full-dose FBP scans (0.908; P < .003). The proportion of images with inferior quality was significantly higher with all half-dose reconstructions than with full-dose FBP (mean proportion: 0.117 for half-dose FBP, 0.054 for half-dose SAFIRE 3, 0.054 for half-dose SAFIRE 4, and 0.017 for full-dose FBP; P < .001). Conclusion The diagnostic accuracy of half-dose CT enterography with FBP and SAFIRE is statistically noninferior to that of full-dose CT enterography for active inflammatory terminal ileal Crohn disease, despite an inferior subjective image quality. (©) RSNA, 2016 Online supplemental material is available for this article.

Sparse representation-based volumetric super-resolution algorithm for 3D CT images of reservoir rocks

NASA Astrophysics Data System (ADS)

Li, Zhengji; Teng, Qizhi; He, Xiaohai; Yue, Guihua; Wang, Zhengyong

2017-09-01

The parameter evaluation of reservoir rocks can help us to identify components and calculate the permeability and other parameters, and it plays an important role in the petroleum industry. Until now, computed tomography (CT) has remained an irreplaceable way to acquire the microstructure of reservoir rocks. During the evaluation and analysis, large samples and high-resolution images are required in order to obtain accurate results. Owing to the inherent limitations of CT, however, a large field of view results in low-resolution images, and high-resolution images entail a smaller field of view. Our method is a promising solution to these data collection limitations. In this study, a framework for sparse representation-based 3D volumetric super-resolution is proposed to enhance the resolution of 3D voxel images of reservoirs scanned with CT. A single reservoir structure and its downgraded model are divided into a large number of 3D cubes of voxel pairs and these cube pairs are used to calculate two overcomplete dictionaries and the sparse-representation coefficients in order to estimate the high frequency component. Future more, to better result, a new feature extract method with combine BM4D together with Laplacian filter are introduced. In addition, we conducted a visual evaluation of the method, and used the PSNR and FSIM to evaluate it qualitatively.

Myocardial strain estimation from CT: towards computer-aided diagnosis on infarction identification

NASA Astrophysics Data System (ADS)

Wong, Ken C. L.; Tee, Michael; Chen, Marcus; Bluemke, David A.; Summers, Ronald M.; Yao, Jianhua

2015-03-01

Regional myocardial strains have the potential for early quantification and detection of cardiac dysfunctions. Although image modalities such as tagged and strain-encoded MRI can provide motion information of the myocardium, they are uncommon in clinical routine. In contrary, cardiac CT images are usually available, but they only provide motion information at salient features such as the cardiac boundaries. To estimate myocardial strains from a CT image sequence, we adopted a cardiac biomechanical model with hyperelastic material properties to relate the motion on the cardiac boundaries to the myocardial deformation. The frame-to-frame displacements of the cardiac boundaries are obtained using B-spline deformable image registration based on mutual information, which are enforced as boundary conditions to the biomechanical model. The system equation is solved by the finite element method to provide the dense displacement field of the myocardium, and the regional values of the three principal strains and the six strains in cylindrical coordinates are computed in terms of the American Heart Association nomenclature. To study the potential of the estimated regional strains on identifying myocardial infarction, experiments were performed on cardiac CT image sequences of ten canines with artificially induced myocardial infarctions. The leave-one-subject-out cross validations show that, by using the optimal strain magnitude thresholds computed from ROC curves, the radial strain and the first principal strain have the best performance.

Image segmentation using joint spatial-intensity-shape features: application to CT lung nodule segmentation

NASA Astrophysics Data System (ADS)

Ye, Xujiong; Siddique, Musib; Douiri, Abdel; Beddoe, Gareth; Slabaugh, Greg

2009-02-01

Automatic segmentation of medical images is a challenging problem due to the complexity and variability of human anatomy, poor contrast of the object being segmented, and noise resulting from the image acquisition process. This paper presents a novel feature-guided method for the segmentation of 3D medical lesions. The proposed algorithm combines 1) a volumetric shape feature (shape index) based on high-order partial derivatives; 2) mean shift clustering in a joint spatial-intensity-shape (JSIS) feature space; and 3) a modified expectation-maximization (MEM) algorithm on the mean shift mode map to merge the neighboring regions (modes). In such a scenario, the volumetric shape feature is integrated into the process of the segmentation algorithm. The joint spatial-intensity-shape features provide rich information for the segmentation of the anatomic structures or lesions (tumors). The proposed method has been evaluated on a clinical dataset of thoracic CT scans that contains 68 nodules. A volume overlap ratio between each segmented nodule and the ground truth annotation is calculated. Using the proposed method, the mean overlap ratio over all the nodules is 0.80. On visual inspection and using a quantitative evaluation, the experimental results demonstrate the potential of the proposed method. It can properly segment a variety of nodules including juxta-vascular and juxta-pleural nodules, which are challenging for conventional methods due to the high similarity of intensities between the nodules and their adjacent tissues. This approach could also be applied to lesion segmentation in other anatomies, such as polyps in the colon.

Neuroradiologic characteristics of astroblastoma and systematic review of the literature: 2 new cases and 125 cases reported in 59 publications.

PubMed

Cunningham, Danielle A; Lowe, Lisa H; Shao, Lei; Acosta, Natasha R

2016-08-01

Astroblastoma is a rare tumor of uncertain origin most commonly presenting in the cerebrum of children and young adults. The literature contains only case reports and small series regarding its radiologic features. This systematic review is the largest study of imaging findings of astroblastoma to date and serves to identify features that might differentiate it from other neoplasms. This study describes the imaging features of astroblastoma based on a systematic review of the literature and two new cases. We conducted a PubMed and Google Scholar database search that identified 59 publications containing 125 cases of pathology-confirmed astroblastoma, and we also added two new cases from our own institution. Data collected include patient age, gender, tumor location, morphology, calcifications and calvarial changes. We recorded findings on CT, MRI, diffusion-weighted imaging (DWI), MR spectroscopy, positron emission tomography (PET) and catheter angiography. Age at diagnosis ranged 0-70 years (mean 18 years; median 14 years). Female-to-male ratio was 8:1. Of 127 cases, 66 reported CT, 78 reported MRI and 47 reported both findings. Not all authors reported all features, but the tumor features reported included supratentorial in 96% (122/127), superficial in 72% (48/67), well-demarcated in 96% (79/82), mixed cystic-solid in 93% (79/85), and enhancing in 99% (78/79). On CT, 84% (26/31) of astroblastomas were hyperattenuated, 73% (27/37) had calcifications and 7 cases reported adjacent calvarial erosion. Astroblastomas were hypointense on T1-W in 58% (26/45) and on T2-W in 50% (23/46) of MRI sequences. Peritumoral edema was present in 80% (40/50) of cases but was typically described as slight. Six cases included DWI findings, with 100% showing restricted diffusion. On MR spectroscopy, 100% (5/5) showed nonspecific tumor spectra with elevated choline and decreased N-acetylaspartate (NAA). PET revealed nonspecific reduced uptake of [F-18] 2-fluoro-2-deoxyglucose ((18)F-FDG) and increased uptake of [11C]-Methionine in 100% (3/3) of cases. Catheter angiography findings (n=12) were variable, including hypervascularity in 67%, arteriovenous shunting in 33% and avascular areas in 25%. Astroblastomas occur most often in adolescent girls. Imaging often shows a supratentorial, superficial, well-defined, cystic-solid enhancing mass. On CT, most are hyperattenuated, have calcifications, and may remodel adjacent bone if superficial. MRI characteristically reveals a hypointense mass on T1-W and T2-W sequences with restricted diffusion. MR spectroscopy, PET and catheter angiography findings are nonspecific.

Neutrosophic segmentation of breast lesions for dedicated breast CT

NASA Astrophysics Data System (ADS)

Lee, Juhun; Nishikawa, Robert M.; Reiser, Ingrid; Boone, John M.

2017-03-01

We proposed the neutrosophic approach for segmenting breast lesions in breast Computer Tomography (bCT) images. The neutrosophic set (NS) considers the nature and properties of neutrality (or indeterminacy), which is neither true nor false. We considered the image noise as an indeterminate component, while treating the breast lesion and other breast areas as true and false components. We first transformed the image into the NS domain. Each voxel in the image can be described as its membership in True, Indeterminate, and False sets. Operations α-mean, β-enhancement, and γ-plateau iteratively smooth and contrast-enhance the image to reduce the noise level of the true set. Once the true image no longer changes, we applied one existing algorithm for bCT images, the RGI segmentation, on the resulting image to segment the breast lesions. We compared the segmentation performance of the proposed method (named as NS-RGI) to that of the regular RGI segmentation. We used a total of 122 breast lesions (44 benign, 78 malignant) of 123 non-contrasted bCT cases. We measured the segmentation performances of the NS-RGI and the RGI using the DICE coefficient. The average DICE value of the NS-RGI was 0.82 (STD: 0.09), while that of the RGI was 0.8 (STD: 0.12). The difference between the two DICE values was statistically significant (paired t test, p-value = 0.0007). We conducted a subsequent feature analysis on the resulting segmentations. The classifier performance for the NS-RGI (AUC = 0.8) improved over that of the RGI (AUC = 0.69, p-value = 0.006).

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, B; Fujita, A; Buch, K

Purpose: To investigate the correlation between texture analysis-based model observer and human observer in the task of diagnosis of ischemic infarct in non-contrast head CT of adults. Methods: Non-contrast head CTs of five patients (2 M, 3 F; 58–83 y) with ischemic infarcts were retro-reconstructed using FBP and Adaptive Statistical Iterative Reconstruction (ASIR) of various levels (10–100%). Six neuro -radiologists reviewed each image and scored image quality for diagnosing acute infarcts by a 9-point Likert scale in a blinded test. These scores were averaged across the observers to produce the average human observer responses. The chief neuro-radiologist placed multiple ROIsmore » over the infarcts. These ROIs were entered into a texture analysis software package. Forty-two features per image, including 11 GLRL, 5 GLCM, 4 GLGM, 9 Laws, and 13 2-D features, were computed and averaged over the images per dataset. The Fisher-coefficient (ratio of between-class variance to in-class variance) was calculated for each feature to identify the most discriminating features from each matrix that separate the different confidence scores most efficiently. The 15 features with the highest Fisher -coefficient were entered into linear multivariate regression for iterative modeling. Results: Multivariate regression analysis resulted in the best prediction model of the confidence scores after three iterations (df=11, F=11.7, p-value<0.0001). The model predicted scores and human observers were highly correlated (R=0.88, R-sq=0.77). The root-mean-square and maximal residual were 0.21 and 0.44, respectively. The residual scatter plot appeared random, symmetric, and unbiased. Conclusion: For diagnosis of ischemic infarct in non-contrast head CT in adults, the predicted image quality scores from texture analysis-based model observer was highly correlated with that of human observers for various noise levels. Texture-based model observer can characterize image quality of low contrast, subtle texture changes in addition to human observers.« less

SU-E-J-254: Evaluating the Role of Mid-Treatment and Post-Treatment FDG-PET/CT in Predicting Progression-Free Survival and Distant Metastasis of Anal Cancer Patients Treated with Chemoradiotherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, H; Wang, J; Chuong, M

2015-06-15

Purpose: To evaluate the role of mid-treatment and post-treatment FDG-PET/CT in predicting progression-free survival (PFS) and distant metastasis (DM) of anal cancer patients treated with chemoradiotherapy (CRT). Methods: 17 anal cancer patients treated with CRT were retrospectively studied. The median prescription dose was 56 Gy (range, 50–62.5 Gy). All patients underwent FDG-PET/CT scans before and after CRT. 16 of the 17 patients had an additional FDG-PET/CT image at 3–5 weeks into the treatment (denoted as mid-treatment FDG-PET/CT). 750 features were extracted from these three sets of scans, which included both traditional PET/CT measures (SUVmax, SUVpeak, tumor diameters, etc.) and spatialtemporalmore » PET/CT features (comprehensively quantify a tumor’s FDG uptake intensity and distribution, spatial variation (texture), geometric property and their temporal changes relative to baseline). 26 clinical parameters (age, gender, TNM stage, histology, GTV dose, etc.) were also analyzed. Advanced analytics including methods to select an optimal set of predictors and a model selection engine, which identifies the most accurate machine learning algorithm for predictive analysis was developed. Results: Comparing baseline + mid-treatment PET/CT set to baseline + posttreatment PET/CT set, 14 predictors were selected from each feature group. Same three clinical parameters (tumor size, T stage and whether 5-FU was held during any cycle of chemotherapy) and two traditional measures (pre- CRT SUVmin and SUVmedian) were selected by both predictor groups. Different mix of spatial-temporal PET/CT features was selected. Using the 14 predictors and Naive Bayes, mid-treatment PET/CT set achieved 87.5% accuracy (2 PFS patients misclassified, all local recurrence and DM patients correctly classified). Post-treatment PET/CT set achieved 94.0% accuracy (all PFS and DM patients correctly predicted, 1 local recurrence patient misclassified) with logistic regression, neural network or support vector machine model. Conclusion: Applying radiomics approach to either midtreatment or post-treatment PET/CT could achieve high accuracy in predicting anal cancer treatment outcomes. This work was supported in part by the National Cancer Institute Grant R01CA172638.« less

Intramodality and intermodality agreement in radiography and computed tomography of equine distal limb fractures.

PubMed

Crijns, C P; Martens, A; Bergman, H-J; van der Veen, H; Duchateau, L; van Bree, H J J; Gielen, I M V L

2014-01-01

Computed tomography (CT) is increasingly accessible in equine referral hospitals. To document the level of agreement within and between radiography and CT in characterising equine distal limb fractures. Retrospective descriptive study. Images from horses that underwent radiographic and CT evaluation for suspected distal limb fractures were reviewed, including 27 horses and 3 negative controls. Using Cohen's kappa and weighted kappa analysis, the level of agreement among 4 observers for a predefined set of diagnostic characteristics for radiography and CT separately and for the level of agreement between the 2 imaging modalities were documented. Both CT and radiography had very good intramodality agreement in identifying fractures, but intermodality agreement was lower. There was good intermodality and intramodality agreement for anatomical localisation and the identification of fracture displacement. Agreement for articular involvement, fracture comminution and fracture fragment number was towards the lower limit of good agreement. There was poor to fair intermodality agreement regarding fracture orientation, fracture width and coalescing cracks; intramodality agreement was higher for CT than for radiography for these features. Further studies, including comparisons with surgical and/or post mortem findings, are required to determine the sensitivity and specificity of CT and radiography in the diagnosis and characterisation of equine distal limb fractures. © 2013 EVJ Ltd.

A concept-based interactive biomedical image retrieval approach using visualness and spatial information

NASA Astrophysics Data System (ADS)

Rahman, Md M.; Antani, Sameer K.; Demner-Fushman, Dina; Thoma, George R.

2015-03-01

This paper presents a novel approach to biomedical image retrieval by mapping image regions to local concepts and represent images in a weighted entropy-based concept feature space. The term concept refers to perceptually distinguishable visual patches that are identified locally in image regions and can be mapped to a glossary of imaging terms. Further, the visual significance (e.g., visualness) of concepts is measured as Shannon entropy of pixel values in image patches and is used to refine the feature vector. Moreover, the system can assist user in interactively select a Region-Of-Interest (ROI) and search for similar image ROIs. Further, a spatial verification step is used as a post-processing step to improve retrieval results based on location information. The hypothesis that such approaches would improve biomedical image retrieval, is validated through experiments on a data set of 450 lung CT images extracted from journal articles from four different collections.

CT-based texture analysis potentially provides prognostic information complementary to interim fdg-pet for patients with hodgkin's and aggressive non-hodgkin's lymphomas.

PubMed

Ganeshan, B; Miles, K A; Babikir, S; Shortman, R; Afaq, A; Ardeshna, K M; Groves, A M; Kayani, I

2017-03-01

The purpose of this study was to investigate the ability of computed tomography texture analysis (CTTA) to provide additional prognostic information in patients with Hodgkin's lymphoma (HL) and high-grade non-Hodgkin's lymphoma (NHL). This retrospective, pilot-study approved by the IRB comprised 45 lymphoma patients undergoing routine 18F-FDG-PET-CT. Progression-free survival (PFS) was determined from clinical follow-up (mean-duration: 40 months; range: 10-62 months). Non-contrast-enhanced low-dose CT images were submitted to CTTA comprising image filtration to highlight features of different sizes followed by histogram-analysis using kurtosis. Prognostic value of CTTA was compared to PET FDG-uptake value, tumour-stage, tumour-bulk, lymphoma-type, treatment-regime, and interim FDG-PET (iPET) status using Kaplan-Meier analysis. Cox regression analysis determined the independence of significantly prognostic imaging and clinical features. A total of 27 patients had aggressive NHL and 18 had HL. Mean PFS was 48.5 months. There was no significant difference in pre-treatment CTTA between the lymphoma sub-types. Kaplan-Meier analysis found pre-treatment CTTA (medium feature scale, p=0.010) and iPET status (p<0.001) to be significant predictors of PFS. Cox analysis revealed that an interaction between pre-treatment CTTA and iPET status was the only independent predictor of PFS (HR: 25.5, 95% CI: 5.4-120, p<0.001). Specifically, pre-treatment CTTA risk stratified patients with negative iPET. CTTA can potentially provide prognostic information complementary to iPET for patients with HL and aggressive NHL. • CT texture-analysis (CTTA) provides prognostic information complementary to interim FDG-PET in Lymphoma. • Pre-treatment CTTA and interim PET status were significant predictors of progression-free survival. • Patients with negative interim PET could be further stratified by pre-treatment CTTA. • Provide precision surveillance where additional imaging reserved for patients at greatest recurrence-risk. • Assists in risk-adapted treatment strategy based on interim PET and CTTA.

Improving Low-dose Cardiac CT Images based on 3D Sparse Representation

PubMed Central

Shi, Luyao; Hu, Yining; Chen, Yang; Yin, Xindao; Shu, Huazhong; Luo, Limin; Coatrieux, Jean-Louis

2016-01-01

Cardiac computed tomography (CCT) is a reliable and accurate tool for diagnosis of coronary artery diseases and is also frequently used in surgery guidance. Low-dose scans should be considered in order to alleviate the harm to patients caused by X-ray radiation. However, low dose CT (LDCT) images tend to be degraded by quantum noise and streak artifacts. In order to improve the cardiac LDCT image quality, a 3D sparse representation-based processing (3D SR) is proposed by exploiting the sparsity and regularity of 3D anatomical features in CCT. The proposed method was evaluated by a clinical study of 14 patients. The performance of the proposed method was compared to the 2D spares representation-based processing (2D SR) and the state-of-the-art noise reduction algorithm BM4D. The visual assessment, quantitative assessment and qualitative assessment results show that the proposed approach can lead to effective noise/artifact suppression and detail preservation. Compared to the other two tested methods, 3D SR method can obtain results with image quality most close to the reference standard dose CT (SDCT) images. PMID:26980176

An intelligent support system for automatic detection of cerebral vascular accidents from brain CT images.

PubMed

Hajimani, Elmira; Ruano, M G; Ruano, A E

2017-07-01

This paper presents a Radial Basis Functions Neural Network (RBFNN) based detection system, for automatic identification of Cerebral Vascular Accidents (CVA) through analysis of Computed Tomographic (CT) images. For the design of a neural network classifier, a Multi Objective Genetic Algorithm (MOGA) framework is used to determine the architecture of the classifier, its corresponding parameters and input features by maximizing the classification precision, while ensuring generalization. This approach considers a large number of input features, comprising first and second order pixel intensity statistics, as well as symmetry/asymmetry information with respect to the ideal mid-sagittal line. Values of specificity of 98% and sensitivity of 98% were obtained, at pixel level, by an ensemble of non-dominated models generated by MOGA, in a set of 150 CT slices (1,867,602pixels), marked by a NeuroRadiologist. This approach also compares favorably at a lesion level with three other published solutions, in terms of specificity (86% compared with 84%), degree of coincidence of marked lesions (89% compared with 77%) and classification accuracy rate (96% compared with 88%). Copyright © 2017. Published by Elsevier B.V.

Post-infectious bronchiolitis obliterans in children: CT features that predict responsiveness to pulse methylprednisolone.

PubMed

Yoon, H M; Lee, J S; Hwang, J-Y; Cho, Y A; Yoon, H-K; Yu, J; Hong, S-J; Yoon, C H

2015-05-01

Intravenous pulse methylprednisolone therapy (IPMT) is an important treatment option for post-infectious obliterative bronchiolitis (OB), although it must be used carefully and only in selected patients because of its drawbacks. This study evaluated whether CT and clinical features of children with post-infectious OB can predict their responsiveness to IPMT. We searched the medical records for patients (less than 18 years of age) who were diagnosed with post-infectious OB between January 2000 and December 2011. 17 children who received IPMT were included in this study. All underwent chest CT before and after IPMT. The radiological features seen on pre-treatment CT were recorded. The air-trapping area percentages on pre- and post-treatment CT images were determined. The nine patients who exhibited decreased air trapping on post-treatment CT scans relative to pre-treatment scans were classed as responders. The patient ages and time from initial pneumonia to IPMT were recorded. All responders and only four non-responders had thickened bronchial walls before treatment (p = 0.029). The two groups did not differ significantly in terms of bronchiolitis, bronchiectasis or the extent of air trapping, although the responders had a significantly shorter median interval between initial pneumonia and IPMT (4 vs 50 months; p = 0.005) and were significantly younger (median, 2.0 vs 7.5 years; p = 0.048). Immediate IPMT may improve the degree of air trapping in children with post-infectious OB if they show a thickened bronchial wall on CT. Children with post-infectious OB may respond favourably to IPMT when pre-treatment CT indicates bronchial-wall thickening.

A platform-independent method to reduce CT truncation artifacts using discriminative dictionary representations.

PubMed

Chen, Yang; Budde, Adam; Li, Ke; Li, Yinsheng; Hsieh, Jiang; Chen, Guang-Hong

2017-01-01

When the scan field of view (SFOV) of a CT system is not large enough to enclose the entire cross-section of the patient, or the patient needs to be positioned partially outside the SFOV for certain clinical applications, truncation artifacts often appear in the reconstructed CT images. Many truncation artifact correction methods perform extrapolations of the truncated projection data based on certain a priori assumptions. The purpose of this work was to develop a novel CT truncation artifact reduction method that directly operates on DICOM images. The blooming of pixel values associated with truncation was modeled using exponential decay functions, and based on this model, a discriminative dictionary was constructed to represent truncation artifacts and nonartifact image information in a mutually exclusive way. The discriminative dictionary consists of a truncation artifact subdictionary and a nonartifact subdictionary. The truncation artifact subdictionary contains 1000 atoms with different decay parameters, while the nonartifact subdictionary contains 1000 independent realizations of Gaussian white noise that are exclusive with the artifact features. By sparsely representing an artifact-contaminated CT image with this discriminative dictionary, the image was separated into a truncation artifact-dominated image and a complementary image with reduced truncation artifacts. The artifact-dominated image was then subtracted from the original image with an appropriate weighting coefficient to generate the final image with reduced artifacts. This proposed method was validated via physical phantom studies and retrospective human subject studies. Quantitative image evaluation metrics including the relative root-mean-square error (rRMSE) and the universal image quality index (UQI) were used to quantify the performance of the algorithm. For both phantom and human subject studies, truncation artifacts at the peripheral region of the SFOV were effectively reduced, revealing soft tissue and bony structure once buried in the truncation artifacts. For the phantom study, the proposed method reduced the relative RMSE from 15% (original images) to 11%, and improved the UQI from 0.34 to 0.80. A discriminative dictionary representation method was developed to mitigate CT truncation artifacts directly in the DICOM image domain. Both phantom and human subject studies demonstrated that the proposed method can effectively reduce truncation artifacts without access to projection data. © 2016 American Association of Physicists in Medicine.

[Virtual endoscopy with a volumetric reconstruction technic: the technical aspects].

PubMed

Pavone, P; Laghi, A; Panebianco, V; Catalano, C; Giura, R; Passariello, R

1998-06-01

We analyze the peculiar technical features of virtual endoscopy obtained with volume rendering. Our preliminary experience is based on virtual endoscopy images from volumetric data acquired with spiral CT (Siemens, Somatom Plus 4) using acquisition protocols standardized for different anatomic areas. Images are reformatted at the CT console, to obtain 1 mm thick contiguous slices, and transferred in DICOM format to an O2 workstation (Silicon Graphics, Mountain View CA, USA) with processor speed of 180 Mhz, 256 Mbyte RAM memory and 4.1 Gbyte hard disk. The software is Vitrea 1.0 (Vital Images, Fairfield, Iowa), running on a Unix platform. Image output is obtained through the Ethernet network to a Macintosh computer and a thermic printer (Kodak 8600 XLS). Diagnostic quality images were obtained in all the cases. Fly-through in the airways allowed correct evaluation of the main bronchi and of the origin of segmentary bronchi. In the vascular district, both carotid strictures and abdominal aortic aneurysms were depicted, with the same accuracy as with conventional reconstruction techniques. In the colon studies, polypoid lesions were correctly depicted in all the cases, with good correlation with endoscopic and double-contrast barium enema findings. In a case of lipoma of the ascending colon, virtual endoscopy allowed to study the colon both cranially and caudally to the lesion. The simultaneous evaluation of axial CT images permitted to characterize the lesion correctly on the basis of its density values. The peculiar feature of volume rendering is the use of the whole information inside the imaging volume to reconstruct three-dimensional images; no threshold values are used and no data are lost as opposite to conventional image reconstruction techniques. The different anatomic structures are visualized modifying the reciprocal opacities, showing the structures of no interest as translucent. The modulation of different opacities is obtained modifying the shape of the opacity curve, either using pre-set curves or in a completely independent way. Other technical features of volume rendering are the perspective evaluation of the objects, color and lighting. In conclusion, volume rendering is a promising technique to elaborate three-dimensional images, offering very realistic endoscopic views. At present, the main limitation is represented by the need of powerful and high-cost workstations.

Comparison of image features calculated in different dimensions for computer-aided diagnosis of lung nodules

NASA Astrophysics Data System (ADS)

Xu, Ye; Lee, Michael C.; Boroczky, Lilla; Cann, Aaron D.; Borczuk, Alain C.; Kawut, Steven M.; Powell, Charles A.

2009-02-01

Features calculated from different dimensions of images capture quantitative information of the lung nodules through one or multiple image slices. Previously published computer-aided diagnosis (CADx) systems have used either twodimensional (2D) or three-dimensional (3D) features, though there has been little systematic analysis of the relevance of the different dimensions and of the impact of combining different dimensions. The aim of this study is to determine the importance of combining features calculated in different dimensions. We have performed CADx experiments on 125 pulmonary nodules imaged using multi-detector row CT (MDCT). The CADx system computed 192 2D, 2.5D, and 3D image features of the lesions. Leave-one-out experiments were performed using five different combinations of features from different dimensions: 2D, 3D, 2.5D, 2D+3D, and 2D+3D+2.5D. The experiments were performed ten times for each group. Accuracy, sensitivity and specificity were used to evaluate the performance. Wilcoxon signed-rank tests were applied to compare the classification results from these five different combinations of features. Our results showed that 3D image features generate the best result compared with other combinations of features. This suggests one approach to potentially reducing the dimensionality of the CADx data space and the computational complexity of the system while maintaining diagnostic accuracy.

CT characteristics of Morel-Lavallée lesions: an under-recognized but significant finding in acute trauma imaging.

PubMed

McKenzie, Gavin A; Niederhauser, Blake D; Collins, Mark S; Howe, Benjamin M

2016-08-01

To highlight the significance and imaging characteristics of Morel-Lavallée (ML) lesions, which have been well characterized on MRI, but are potentially under-recognized on CT. Twenty-eight Morel-Lavallée lesions were identified in 18 patients and were all clinically or surgically confirmed. Lesions were grouped into acute (<3 days), subacute (3-30 days), and chronic (>30 days) at the time of CT imaging. Charts were reviewed to gather patient characteristics, injury patterns, radiologist interpretation, treatment, and outcomes. Sixteen male and 2 female patients with a mean age of 50 years (range 19-80) at the date of their initial evaluation were identified. All patients had significant trauma that accounted for 28 ML lesions, all of which were in a characteristic subcutaneous location overlying the muscular fascial plane. Lesions on CT went through an evolution from hyperdense, poorly or moderately marginated without a pseudocapsule to being hypodense, with internal fat globules or septations and well marginated with a complete enhancing pseudocapsule. Only 1 (4 %) of the ML lesions was suggested and 7 (25 %) lesions were not commented on at all by the interpreting radiologist. Morel-Lavallée lesions are post-traumatic closed, internal, soft-tissue, degloving lesions that are potentially underrecognized on CT. Most acute ML lesions are nonspecific, resembling simple hematomas or contusions. ML lesions evolve as they age with subacute and chronic lesions demonstrating the known features described on MR imaging that should allow for an accurate imaging diagnosis.

A study on automated anatomical labeling to arteries concerning with colon from 3D abdominal CT images

NASA Astrophysics Data System (ADS)

Hoang, Bui Huy; Oda, Masahiro; Jiang, Zhengang; Kitasaka, Takayuki; Misawa, Kazunari; Fujiwara, Michitaka; Mori, Kensaku

2011-03-01

This paper presents an automated anatomical labeling method of arteries extracted from contrasted 3D CT images based on multi-class AdaBoost. In abdominal surgery, understanding of vasculature related to a target organ such as the colon is very important. Therefore, the anatomical structure of blood vessels needs to be understood by computers in a system supporting abdominal surgery. There are several researches on automated anatomical labeling, but there is no research on automated anatomical labeling to arteries concerning with the colon. The proposed method obtains a tree structure of arteries from the artery region and calculates features values of each branch. These feature values are thickness, curvature, direction, and running vectors of branch. Then, candidate arterial names are computed by classifiers that are trained to output artery names. Finally, a global optimization process is applied to the candidate arterial names to determine final names. Target arteries of this paper are nine lower abdominal arteries (AO, LCIA, RCIA, LEIA, REIA, SMA, IMA, LIIA, RIIA). We applied the proposed method to 14 cases of 3D abdominal contrasted CT images, and evaluated the results by leave-one-out scheme. The average precision and recall rates of the proposed method were 87.9% and 93.3%, respectively. The results of this method are applicable for anatomical name display of surgical simulation and computer aided surgery.

Automated anatomical labeling method for abdominal arteries extracted from 3D abdominal CT images

NASA Astrophysics Data System (ADS)

Oda, Masahiro; Hoang, Bui Huy; Kitasaka, Takayuki; Misawa, Kazunari; Fujiwara, Michitaka; Mori, Kensaku

2012-02-01

This paper presents an automated anatomical labeling method of abdominal arteries. In abdominal surgery, understanding of blood vessel structure concerning with a target organ is very important. Branching pattern of blood vessels differs among individuals. It is required to develop a system that can assist understanding of a blood vessel structure and anatomical names of blood vessels of a patient. Previous anatomical labbeling methods for abdominal arteries deal with either of the upper or lower abdominal arteries. In this paper, we present an automated anatomical labeling method of both of the upper and lower abdominal arteries extracted from CT images. We obtain a tree structure of artery regions and calculate feature values for each branch. These feature values include the diameter, curvature, direction, and running vectors of a branch. Target arteries of this method are grouped based on branching conditions. The following processes are separately applied for each group. We compute candidate artery names by using classifiers that are trained to output artery names. A correction process of the candidate anatomical names based on the rule of majority is applied to determine final names. We applied the proposed method to 23 cases of 3D abdominal CT images. Experimental results showed that the proposed method is able to perform nomenclature of entire major abdominal arteries. The recall and the precision rates of labeling are 79.01% and 80.41%, respectively.

Radial gradient and radial deviation radiomic features from pre-surgical CT scans are associated with survival among lung adenocarcinoma patients.

PubMed

Tunali, Ilke; Stringfield, Olya; Guvenis, Albert; Wang, Hua; Liu, Ying; Balagurunathan, Yoganand; Lambin, Philippe; Gillies, Robert J; Schabath, Matthew B

2017-11-10

The goal of this study was to extract features from radial deviation and radial gradient maps which were derived from thoracic CT scans of patients diagnosed with lung adenocarcinoma and assess whether these features are associated with overall survival. We used two independent cohorts from different institutions for training (n= 61) and test (n= 47) and focused our analyses on features that were non-redundant and highly reproducible. To reduce the number of features and covariates into a single parsimonious model, a backward elimination approach was applied. Out of 48 features that were extracted, 31 were eliminated because they were not reproducible or were redundant. We considered 17 features for statistical analysis and identified a final model containing the two most highly informative features that were associated with lung cancer survival. One of the two features, radial deviation outside-border separation standard deviation, was replicated in a test cohort exhibiting a statistically significant association with lung cancer survival (multivariable hazard ratio = 0.40; 95% confidence interval 0.17-0.97). Additionally, we explored the biological underpinnings of these features and found radial gradient and radial deviation image features were significantly associated with semantic radiological features.

Dissimilarity representations in lung parenchyma classification

NASA Astrophysics Data System (ADS)

Sørensen, Lauge; de Bruijne, Marleen

2009-02-01

A good problem representation is important for a pattern recognition system to be successful. The traditional approach to statistical pattern recognition is feature representation. More specifically, objects are represented by a number of features in a feature vector space, and classifiers are built in this representation. This is also the general trend in lung parenchyma classification in computed tomography (CT) images, where the features often are measures on feature histograms. Instead, we propose to build normal density based classifiers in dissimilarity representations for lung parenchyma classification. This allows for the classifiers to work on dissimilarities between objects, which might be a more natural way of representing lung parenchyma. In this context, dissimilarity is defined between CT regions of interest (ROI)s. ROIs are represented by their CT attenuation histogram and ROI dissimilarity is defined as a histogram dissimilarity measure between the attenuation histograms. In this setting, the full histograms are utilized according to the chosen histogram dissimilarity measure. We apply this idea to classification of different emphysema patterns as well as normal, healthy tissue. Two dissimilarity representation approaches as well as different histogram dissimilarity measures are considered. The approaches are evaluated on a set of 168 CT ROIs using normal density based classifiers all showing good performance. Compared to using histogram dissimilarity directly as distance in a emph{k} nearest neighbor classifier, which achieves a classification accuracy of 92.9%, the best dissimilarity representation based classifier is significantly better with a classification accuracy of 97.0% (text{emph{p" border="0" class="imgtopleft"> = 0.046).

Radiomic texture-curvature (RTC) features for precision medicine of patients with rheumatoid arthritis-associated interstitial lung disease

NASA Astrophysics Data System (ADS)

Watari, Chinatsu; Matsuhiro, Mikio; Näppi, Janne J.; Nasirudin, Radin A.; Hironaka, Toru; Kawata, Yoshiki; Niki, Noboru; Yoshida, Hiroyuki

2018-03-01

We investigated the effect of radiomic texture-curvature (RTC) features of lung CT images in the prediction of the overall survival of patients with rheumatoid arthritis-associated interstitial lung disease (RA-ILD). We retrospectively collected 70 RA-ILD patients who underwent thin-section lung CT and serial pulmonary function tests. After the extraction of the lung region, we computed hyper-curvature features that included the principal curvatures, curvedness, bright/dark sheets, cylinders, blobs, and curvature scales for the bronchi and the aerated lungs. We also computed gray-level co-occurrence matrix (GLCM) texture features on the segmented lungs. An elastic-net penalty method was used to select and combine these features with a Cox proportional hazards model for predicting the survival of the patient. Evaluation was performed by use of concordance index (C-index) as a measure of prediction performance. The C-index values of the texture features, hyper-curvature features, and the combination thereof (RTC features) in predicting patient survival was estimated by use of bootstrapping with 2,000 replications, and they were compared with an established clinical prognostic biomarker known as the gender, age, and physiology (GAP) index by means of two-sided t-test. Bootstrap evaluation yielded the following C-index values for the clinical and radiomic features: (a) GAP index: 78.3%; (b) GLCM texture features: 79.6%; (c) hypercurvature features: 80.8%; and (d) RTC features: 86.8%. The RTC features significantly outperformed any of the other predictors (P < 0.001). The Kaplan-Meier survival curves of patients stratified to low- and high-risk groups based on the RTC features showed statistically significant (P < 0.0001) difference. Thus, the RTC features can provide an effective imaging biomarker for predicting the overall survival of patients with RA-ILD.

Statistical-techniques-based computer-aided diagnosis (CAD) using texture feature analysis: application in computed tomography (CT) imaging to fatty liver disease

NASA Astrophysics Data System (ADS)

Chung, Woon-Kwan; Park, Hyong-Hu; Im, In-Chul; Lee, Jae-Seung; Goo, Eun-Hoe; Dong, Kyung-Rae

2012-09-01

This paper proposes a computer-aided diagnosis (CAD) system based on texture feature analysis and statistical wavelet transformation technology to diagnose fatty liver disease with computed tomography (CT) imaging. In the target image, a wavelet transformation was performed for each lesion area to set the region of analysis (ROA, window size: 50 × 50 pixels) and define the texture feature of a pixel. Based on the extracted texture feature values, six parameters (average gray level, average contrast, relative smoothness, skewness, uniformity, and entropy) were determined to calculate the recognition rate for a fatty liver. In addition, a multivariate analysis of the variance (MANOVA) method was used to perform a discriminant analysis to verify the significance of the extracted texture feature values and the recognition rate for a fatty liver. According to the results, each texture feature value was significant for a comparison of the recognition rate for a fatty liver ( p < 0.05). Furthermore, the F-value, which was used as a scale for the difference in recognition rates, was highest in the average gray level, relatively high in the skewness and the entropy, and relatively low in the uniformity, the relative smoothness and the average contrast. The recognition rate for a fatty liver had the same scale as that for the F-value, showing 100% (average gray level) at the maximum and 80% (average contrast) at the minimum. Therefore, the recognition rate is believed to be a useful clinical value for the automatic detection and computer-aided diagnosis (CAD) using the texture feature value. Nevertheless, further study on various diseases and singular diseases will be needed in the future.

Improving lung cancer prognosis assessment by incorporating synthetic minority oversampling technique and score fusion method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yan, Shiju; Qian, Wei; Guan, Yubao

2016-06-15

Purpose: This study aims to investigate the potential to improve lung cancer recurrence risk prediction performance for stage I NSCLS patients by integrating oversampling, feature selection, and score fusion techniques and develop an optimal prediction model. Methods: A dataset involving 94 early stage lung cancer patients was retrospectively assembled, which includes CT images, nine clinical and biological (CB) markers, and outcome of 3-yr disease-free survival (DFS) after surgery. Among the 94 patients, 74 remained DFS and 20 had cancer recurrence. Applying a computer-aided detection scheme, tumors were segmented from the CT images and 35 quantitative image (QI) features were initiallymore » computed. Two normalized Gaussian radial basis function network (RBFN) based classifiers were built based on QI features and CB markers separately. To improve prediction performance, the authors applied a synthetic minority oversampling technique (SMOTE) and a BestFirst based feature selection method to optimize the classifiers and also tested fusion methods to combine QI and CB based prediction results. Results: Using a leave-one-case-out cross-validation (K-fold cross-validation) method, the computed areas under a receiver operating characteristic curve (AUCs) were 0.716 ± 0.071 and 0.642 ± 0.061, when using the QI and CB based classifiers, respectively. By fusion of the scores generated by the two classifiers, AUC significantly increased to 0.859 ± 0.052 (p < 0.05) with an overall prediction accuracy of 89.4%. Conclusions: This study demonstrated the feasibility of improving prediction performance by integrating SMOTE, feature selection, and score fusion techniques. Combining QI features and CB markers and performing SMOTE prior to feature selection in classifier training enabled RBFN based classifier to yield improved prediction accuracy.« less

Mechanically assisted 3D ultrasound for pre-operative assessment and guiding percutaneous treatment of focal liver tumors

NASA Astrophysics Data System (ADS)

Sadeghi Neshat, Hamid; Bax, Jeffery; Barker, Kevin; Gardi, Lori; Chedalavada, Jason; Kakani, Nirmal; Fenster, Aaron

2014-03-01

Image-guided percutaneous ablation is the standard treatment for focal liver tumors deemed inoperable and is commonly used to maintain eligibility for patients on transplant waitlists. Radiofrequency (RFA), microwave (MWA) and cryoablation technologies are all delivered via one or a number of needle-shaped probes inserted directly into the tumor. Planning is mostly based on contrast CT/MRI. While intra-procedural CT is commonly used to confirm the intended probe placement, 2D ultrasound (US) remains the main, and in some centers the only imaging modality used for needle guidance. Corresponding intraoperative 2D US with planning and other intra-procedural imaging modalities is essential for accurate needle placement. However, identification of matching features of interest among these images is often challenging given the limited field-of-view (FOV) and low quality of 2D US images. We have developed a passive tracking arm with a motorized scan-head and software tools to improve guiding capabilities of conventional US by large FOV 3D US scans that provides more anatomical landmarks that can facilitate registration of US with both planning and intra-procedural images. The tracker arm is used to scan the whole liver with a high geometrical accuracy that facilitates multi-modality landmark based image registration. Software tools are provided to assist with the segmentation of the ablation probes and tumors, find the 2D view that best shows the probe(s) from a 3D US image, and to identify the corresponding image from planning CT scans. In this paper, evaluation results from laboratory testing and a phase 1 clinical trial for planning and guiding RFA and MWA procedures using the developed system will be presented. Early clinical results show a comparable performance to intra-procedural CT that suggests 3D US as a cost-effective alternative with no side-effects in centers where CT is not available.

SU-G-IeP4-10: Microimaging for Different Degrees of Human Cavernous Hemangioma of Liver by Using In-Line Phase-Contrast Imaging CT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Duan, J

2016-06-15

Purpose: Cavernous hemangioma of the liver (CHL) is the most common benign solid tumor of the liver. In this study, we quantitative assessment the different degrees of CHL from microscopic viewpoint by using in-line phase-contrast imaging CT (ILPCI-CT). Methods: The experiments were performed at x-ray imaging and biomedical application beamline (BL13W1) of Shanghai Synchrotron Radiation Facility (SSRF) in China. Three typical specimens at different stages, i.e., mild, moderate and severe human CHL were imaged using ILPCI-CT at 16keV without contrast agents. The 3D visualization of different degrees of CHL samples were presented using ILPCI-CT. Additionally, quantitative evaluation of the CHLmore » features, such as the range of hepatic sinusoid equivalent diameters in different degrees of CHL samples, the ratio of the hepatic sinusoid to the CHL tissue, were measured. Results: The planar image clearly displayed the dilated hepatic sinusoids in microns. There was no normal hepatic vascular found in the all CHL samples. Different stages of CHL samples were presented with vivid shapes and stereoscopic effects by using 3D visualization. The equivalent diameters of hepatic sinusoids in three degrees CHL were different. The equivalent diameters of the hepatic sinusoids in mild CHL, range from 60 to 120 µm. The equivalent diameters of the hepatic sinusoids in moderate CHL, range from 65 to 190 µm. The equivalent diameters of the hepatic sinusoids in severe CHL, range from 95 to 215 µm. The ratio of the hepatic sinusoid to the mild, moderate and severe CHL tissue were 3%, 16% and 21%, respectively. Conclusion: The results show that the high degree of sensitivity of the ILPCI-CT technique and demonstrate the feasibility of accurate visualization of different stage human CHL. ILPCI-CT may offers a potential use in non-invasive study and analysis of CHL.« less

Trabecular bone class mapping across resolutions: translating methods from HR-pQCT to clinical CT

NASA Astrophysics Data System (ADS)

Valentinitsch, Alexander; Fischer, Lukas; Patsch, Janina M.; Bauer, Jan; Kainberger, Franz; Langs, Georg; DiFranco, Matthew

2015-03-01

Quantitative assessment of 3D bone microarchitecture in high-resolution peripheral quantitative computed tomography (HR-pQCT) has shown promise in fracture risk assessment and biomechanics, but is limited to the distal radius and tibia. Trabecular microarchitecture classes (TMACs), based on voxel-wise clustering texture and structure tensor features in HRpQCT, is extended in this paper to quantify trabecular bone classes in clinical multi-detector CT (MDCT) images. Our comparison of TMACs in 12 cadaver radii imaged using both HRpQCT and MDCT yields a mean Dice score of up to 0.717+/-0.40 and visually concordant bone quality maps. Further work to develop clinically viable bone quantitative imaging using HR-pQCT validation could have a significant impact on overall bone health assessment.

Accuracy and Utility of Deformable Image Registration in {sup 68}Ga 4D PET/CT Assessment of Pulmonary Perfusion Changes During and After Lung Radiation Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hardcastle, Nicholas, E-mail: nick.hardcastle@gmail.com; Centre for Medical Radiation Physics, University of Wollongong, Wollongong; Hofman, Michael S.

2015-09-01

Purpose: Measuring changes in lung perfusion resulting from radiation therapy dose requires registration of the functional imaging to the radiation therapy treatment planning scan. This study investigates registration accuracy and utility for positron emission tomography (PET)/computed tomography (CT) perfusion imaging in radiation therapy for non–small cell lung cancer. Methods: {sup 68}Ga 4-dimensional PET/CT ventilation-perfusion imaging was performed before, during, and after radiation therapy for 5 patients. Rigid registration and deformable image registration (DIR) using B-splines and Demons algorithms was performed with the CT data to obtain a deformation map between the functional images and planning CT. Contour propagation accuracy andmore » correspondence of anatomic features were used to assess registration accuracy. Wilcoxon signed-rank test was used to determine statistical significance. Changes in lung perfusion resulting from radiation therapy dose were calculated for each registration method for each patient and averaged over all patients. Results: With B-splines/Demons DIR, median distance to agreement between lung contours reduced modestly by 0.9/1.1 mm, 1.3/1.6 mm, and 1.3/1.6 mm for pretreatment, midtreatment, and posttreatment (P<.01 for all), and median Dice score between lung contours improved by 0.04/0.04, 0.05/0.05, and 0.05/0.05 for pretreatment, midtreatment, and posttreatment (P<.001 for all). Distance between anatomic features reduced with DIR by median 2.5 mm and 2.8 for pretreatment and midtreatment time points, respectively (P=.001) and 1.4 mm for posttreatment (P>.2). Poorer posttreatment results were likely caused by posttreatment pneumonitis and tumor regression. Up to 80% standardized uptake value loss in perfusion scans was observed. There was limited change in the loss in lung perfusion between registration methods; however, Demons resulted in larger interpatient variation compared with rigid and B-splines registration. Conclusions: DIR accuracy in the data sets studied was variable depending on anatomic changes resulting from radiation therapy; caution must be exercised when using DIR in regions of low contrast or radiation pneumonitis. Lung perfusion reduces with increasing radiation therapy dose; however, DIR did not translate into significant changes in dose–response assessment.« less

Low dose CT image restoration using a database of image patches

NASA Astrophysics Data System (ADS)

Ha, Sungsoo; Mueller, Klaus

2015-01-01

Reducing the radiation dose in CT imaging has become an active research topic and many solutions have been proposed to remove the significant noise and streak artifacts in the reconstructed images. Most of these methods operate within the domain of the image that is subject to restoration. This, however, poses limitations on the extent of filtering possible. We advocate to take into consideration the vast body of external knowledge that exists in the domain of already acquired medical CT images, since after all, this is what radiologists do when they examine these low quality images. We can incorporate this knowledge by creating a database of prior scans, either of the same patient or a diverse corpus of different patients, to assist in the restoration process. Our paper follows up on our previous work that used a database of images. Using images, however, is challenging since it requires tedious and error prone registration and alignment. Our new method eliminates these problems by storing a diverse set of small image patches in conjunction with a localized similarity matching scheme. We also empirically show that it is sufficient to store these patches without anatomical tags since their statistics are sufficiently strong to yield good similarity matches from the database and as a direct effect, produce image restorations of high quality. A final experiment demonstrates that our global database approach can recover image features that are difficult to preserve with conventional denoising approaches.

Aortic annulus sizing using watershed transform and morphological approach for CT images

NASA Astrophysics Data System (ADS)

Mohammad, Norhasmira; Omar, Zaid; Sahrim, Mus'ab

2018-02-01

Aortic valve disease occurs due to calcification deposits on the area of leaflets within the human heart. It is progressive over time where it can affect the mechanism of the heart valve. To avoid the risk of surgery for vulnerable patients especially senior citizens, a new method has been introduced: Transcatheter Aortic Valve Implantation (TAVI), which places a synthetic catheter within the patient's valve. This entails a procedure of aortic annulus sizing, which requires manual measurement of the scanned images acquired from Computed Tomographic (CT) by experts. The step requires intensive efforts, though human error may still eventually lead to false measurement. In this research, image processing techniques are implemented onto cardiac CT images to achieve an automated and accurate measurement of the heart annulus. The image is first put through pre-processing for noise filtration and image enhancement. Then, a marker image is computed using the combination of opening and closing operations where the foreground image is marked as a feature while the background image is set to zero. Marker image is used to control the watershed transformation and also to prevent oversegmentation. This transformation has the advantage of fast computational and oversegmentation problems, which usually appear with the watershed transform can be solved with the introduction of marker image. Finally, the measurement of aortic annulus from the image data is obtained through morphological operations. Results affirm the approach's ability to achieve accurate annulus measurements compared to conventional techniques.

CT Image Sequence Analysis for Object Recognition - A Rule-Based 3-D Computer Vision System

Treesearch

Dongping Zhu; Richard W. Conners; Daniel L. Schmoldt; Philip A. Araman

1991-01-01

Research is now underway to create a vision system for hardwood log inspection using a knowledge-based approach. In this paper, we present a rule-based, 3-D vision system for locating and identifying wood defects using topological, geometric, and statistical attributes. A number of different features can be derived from the 3-D input scenes. These features and evidence...

Technical Note: Characterization of custom 3D printed multimodality imaging phantoms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bieniosek, Matthew F.; Lee, Brian J.; Levin, Craig S., E-mail: cslevin@stanford.edu

Purpose: Imaging phantoms are important tools for researchers and technicians, but they can be costly and difficult to customize. Three dimensional (3D) printing is a widely available rapid prototyping technique that enables the fabrication of objects with 3D computer generated geometries. It is ideal for quickly producing customized, low cost, multimodal, reusable imaging phantoms. This work validates the use of 3D printed phantoms by comparing CT and PET scans of a 3D printed phantom and a commercial “Micro Deluxe” phantom. This report also presents results from a customized 3D printed PET/MRI phantom, and a customized high resolution imaging phantom withmore » sub-mm features. Methods: CT and PET scans of a 3D printed phantom and a commercial Micro Deluxe (Data Spectrum Corporation, USA) phantom with 1.2, 1.6, 2.4, 3.2, 4.0, and 4.8 mm diameter hot rods were acquired. The measured PET and CT rod sizes, activities, and attenuation coefficients were compared. A PET/MRI scan of a custom 3D printed phantom with hot and cold rods was performed, with photon attenuation and normalization measurements performed with a separate 3D printed normalization phantom. X-ray transmission scans of a customized two level high resolution 3D printed phantom with sub-mm features were also performed. Results: Results show very good agreement between commercial and 3D printed micro deluxe phantoms with less than 3% difference in CT measured rod diameter, less than 5% difference in PET measured rod diameter, and a maximum of 6.2% difference in average rod activity from a 10 min, 333 kBq/ml (9 μCi/ml) Siemens Inveon (Siemens Healthcare, Germany) PET scan. In all cases, these differences were within the measurement uncertainties of our setups. PET/MRI scans successfully identified 3D printed hot and cold rods on PET and MRI modalities. X-ray projection images of a 3D printed high resolution phantom identified features as small as 350 μm wide. Conclusions: This work shows that 3D printed phantoms can be functionally equivalent to commercially available phantoms. They are a viable option for quickly distributing and fabricating low cost, customized phantoms.« less

Geometric correction method for 3d in-line X-ray phase contrast image reconstruction

PubMed Central

2014-01-01

Background Mechanical system with imperfect or misalignment of X-ray phase contrast imaging (XPCI) components causes projection data misplaced, and thus result in the reconstructed slice images of computed tomography (CT) blurred or with edge artifacts. So the features of biological microstructures to be investigated are destroyed unexpectedly, and the spatial resolution of XPCI image is decreased. It makes data correction an essential pre-processing step for CT reconstruction of XPCI. Methods To remove unexpected blurs and edge artifacts, a mathematics model for in-line XPCI is built by considering primary geometric parameters which include a rotation angle and a shift variant in this paper. Optimal geometric parameters are achieved by finding the solution of a maximization problem. And an iterative approach is employed to solve the maximization problem by using a two-step scheme which includes performing a composite geometric transformation and then following a linear regression process. After applying the geometric transformation with optimal parameters to projection data, standard filtered back-projection algorithm is used to reconstruct CT slice images. Results Numerical experiments were carried out on both synthetic and real in-line XPCI datasets. Experimental results demonstrate that the proposed method improves CT image quality by removing both blurring and edge artifacts at the same time compared to existing correction methods. Conclusions The method proposed in this paper provides an effective projection data correction scheme and significantly improves the image quality by removing both blurring and edge artifacts at the same time for in-line XPCI. It is easy to implement and can also be extended to other XPCI techniques. PMID:25069768

Telangiectatic adenoma - computed tomography and magnetic resonance findings: a case report and review of the literature.

PubMed

Takayassu, Tatiana Chinem; Marchiori, Edson; Eiras, Antonio; Cabral, Rafael Ferracini; Cabral, Fernanda Caseira; Batista, Raquel Ribeiro; Zanetti, Gláucia; Dias, Paula Cristina Pereira

2009-01-07

Telangiectatic adenoma is a new classification of a hepatic lesion. It was previously named telangiectatic focal nodular hyperplasia but it is in fact true adenoma with telangiectatic features. We report here a case of telangiectatic adenoma in a 72-year-old woman. The image features are lack of a central scar, a heterogeneous lesion, hyperintensity in T1-weighted MR images, strong hyperintensity in T2-weighted MR images, and persistent contrast enhancement in delayed-phase contrast-enhanced CT or T1-weighted MR images. It is a monoclonal lesion with potential of malignancy. The treatment of telangiectatic adenoma is surgery, the same way as hepatic adenoma. Focal nodular hyperplasia may be managed by clinical follow-up alone.

A rare adult renal neuroblastoma better imaged by 18F-FDG than by 68Ga-dotanoc in the PET/CT scan.

PubMed

Jain, Tarun Kumar; Singh, Sharwan Kumar; Sood, Ashwani; Ashwathanarayama, Abhiram Gj; Basher, Rajender Kumar; Shukla, Jaya; Mittal, Bhagwant Rai

2017-01-01

Primary renal neuroblastoma is an uncommon tumor in children and extremely rare in adults. We present a case of a middle aged female having a large retroperitoneal mass involving the right kidney with features of neuroblastoma on pre-operative histopathology. Whole-body fluorine-18-fluoro-deoxyglucose positron emission tomography ( 18 F-FDG PET/CT) and 68 Ga-dotanoc PET/CT scans performed for staging and therapeutic potential revealed a tracer avid mass replacing the right kidney and also pelvic lymph nodes. The 18 F-FDG PET/CT scan showed better both the primary lesion and the metastases in the pelvic lymph nodes than the 68 Ga-dotanoc scan supporting diagnosis and treatment planning.

Characterization of a novel anthropomorphic plastinated lung phantom

PubMed Central

Yoon, Sungwon; Henry, Robert W.; Bouley, Donna M.; Bennett, N. Robert; Fahrig, Rebecca

2008-01-01

Phantoms are widely used during the development of new imaging systems and algorithms. For development and optimization of new imaging systems such as tomosynthesis, where conventional image quality metrics may not be applicable, a realistic phantom that can be used across imaging systems is desirable. A novel anthropomorphic lung phantom was developed by plastination of an actual pig lung. The plastinated phantom is characterized and compared with reference to in vivo images of the same tissue prior to plastination using high resolution 3D CT. The phantom is stable over time and preserves the anatomical features and relative locations of the in vivo sample. The volumes for different tissue types in the phantom are comparable to the in vivo counterparts, and CT numbers for different tissue types fall within a clinically useful range. Based on the measured CT numbers, the phantom cardiac tissue experienced a 92% decrease in bulk density and the phantom pulmonary tissue experienced a 78% decrease in bulk density compared to their in vivo counterparts. By-products in the phantom from the room temperature vulcanizing silicone and plastination process are also identified. A second generation phantom, which eliminates most of the by-products, is presented. Such anthropomorphic phantoms can be used to evaluate a wide range of novel imaging systems. PMID:19175148

Combined X-ray CT and mass spectrometry for biomedical imaging applications

NASA Astrophysics Data System (ADS)

Schioppa, E., Jr.; Ellis, S.; Bruinen, A. L.; Visser, J.; Heeren, R. M. A.; Uher, J.; Koffeman, E.

2014-04-01

Imaging technologies play a key role in many branches of science, especially in biology and medicine. They provide an invaluable insight into both internal structure and processes within a broad range of samples. There are many techniques that allow one to obtain images of an object. Different techniques are based on the analysis of a particular sample property by means of a dedicated imaging system, and as such, each imaging modality provides the researcher with different information. The use of multimodal imaging (imaging with several different techniques) can provide additional and complementary information that is not possible when employing a single imaging technique alone. In this study, we present for the first time a multi-modal imaging technique where X-ray computerized tomography (CT) is combined with mass spectrometry imaging (MSI). While X-ray CT provides 3-dimensional information regarding the internal structure of the sample based on X-ray absorption coefficients, MSI of thin sections acquired from the same sample allows the spatial distribution of many elements/molecules, each distinguished by its unique mass-to-charge ratio (m/z), to be determined within a single measurement and with a spatial resolution as low as 1 μm or even less. The aim of the work is to demonstrate how molecular information from MSI can be spatially correlated with 3D structural information acquired from X-ray CT. In these experiments, frozen samples are imaged in an X-ray CT setup using Medipix based detectors equipped with a CO2 cooled sample holder. Single projections are pre-processed before tomographic reconstruction using a signal-to-thickness calibration. In the second step, the object is sliced into thin sections (circa 20 μm) that are then imaged using both matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) and secondary ion (SIMS) mass spectrometry, where the spatial distribution of specific molecules within the sample is determined. The combination of two vastly different imaging approaches provides complementary information (i.e., anatomical and molecular distributions) that allows the correlation of distinct structural features with specific molecules distributions leading to unique insights in disease development.

Review of cardiovascular imaging in The Journal of Nuclear Cardiology in 2014: Part 1 of 2: Positron emission tomography, computed tomography, and neuronal imaging.

PubMed

AlJaroudi, Wael A; Hage, Fadi G

2015-06-01

The year 2014 has been an exciting year for the cardiovascular imaging community with significant advances in the realm of nuclear and multimodality cardiac imaging. In this new feature of the Journal of Nuclear Cardiology, we will summarize some of the breakthroughs that were published in the Journal in 2014 in 2 sister articles. This first article will concentrate on publications dealing with cardiac positron emission tomography (PET), computed tomography (CT), and neuronal imaging.

Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures.

PubMed

Larue, Ruben T H M; Defraene, Gilles; De Ruysscher, Dirk; Lambin, Philippe; van Elmpt, Wouter

2017-02-01

Quantitative analysis of tumour characteristics based on medical imaging is an emerging field of research. In recent years, quantitative imaging features derived from CT, positron emission tomography and MR scans were shown to be of added value in the prediction of outcome parameters in oncology, in what is called the radiomics field. However, results might be difficult to compare owing to a lack of standardized methodologies to conduct quantitative image analyses. In this review, we aim to present an overview of the current challenges, technical routines and protocols that are involved in quantitative imaging studies. The first issue that should be overcome is the dependency of several features on the scan acquisition and image reconstruction parameters. Adopting consistent methods in the subsequent target segmentation step is evenly crucial. To further establish robust quantitative image analyses, standardization or at least calibration of imaging features based on different feature extraction settings is required, especially for texture- and filter-based features. Several open-source and commercial software packages to perform feature extraction are currently available, all with slightly different functionalities, which makes benchmarking quite challenging. The number of imaging features calculated is typically larger than the number of patients studied, which emphasizes the importance of proper feature selection and prediction model-building routines to prevent overfitting. Even though many of these challenges still need to be addressed before quantitative imaging can be brought into daily clinical practice, radiomics is expected to be a critical component for the integration of image-derived information to personalize treatment in the future.

Experience in the application of Java Technologies in telemedicine

PubMed Central

Fedyukin, IV; Reviakin, YG; Orlov, OI; Doarn, CR; Harnett, BM; Merrell, RC

2002-01-01

Java language has been demonstrated to be an effective tool in supporting medical image viewing in Russia. This evaluation was completed by obtaining a maximum of 20 images, depending on the client's computer workstation from one patient using a commercially available computer tomography (CT) scanner. The images were compared against standard CT images that were viewed at the site of capture. There was no appreciable difference. The client side is a lightweight component that provides an intuitive interface for end users. Each image is loaded in its own thread and the user can begin work after the first image has been loaded. This feature is especially useful on slow connection speed, 9.6 Kbps for example. The server side, which is implemented by the Java Servlet Engine works more effective than common gateway interface (CGI) programs do. Advantages of the Java Technology place this program on the next level of application development. This paper presents a unique application of Java in telemedicine. PMID:12459045

Saliency U-Net: A regional saliency map-driven hybrid deep learning network for anomaly segmentation

NASA Astrophysics Data System (ADS)

Karargyros, Alex; Syeda-Mahmood, Tanveer

2018-02-01

Deep learning networks are gaining popularity in many medical image analysis tasks due to their generalized ability to automatically extract relevant features from raw images. However, this can make the learning problem unnecessarily harder requiring network architectures of high complexity. In case of anomaly detection, in particular, there is often sufficient regional difference between the anomaly and the surrounding parenchyma that could be easily highlighted through bottom-up saliency operators. In this paper we propose a new hybrid deep learning network using a combination of raw image and such regional maps to more accurately learn the anomalies using simpler network architectures. Specifically, we modify a deep learning network called U-Net using both the raw and pre-segmented images as input to produce joint encoding (contraction) and expansion paths (decoding) in the U-Net. We present results of successfully delineating subdural and epidural hematomas in brain CT imaging and liver hemangioma in abdominal CT images using such network.

Experience in the application of Java Technologies in telemedicine.

PubMed

Fedyukin, IV; Reviakin, YG; Orlov, OI; Doarn, CR; Harnett, BM; Merrell, RC

2002-09-17

Java language has been demonstrated to be an effective tool in supporting medical image viewing in Russia. This evaluation was completed by obtaining a maximum of 20 images, depending on the client's computer workstation from one patient using a commercially available computer tomography (CT) scanner. The images were compared against standard CT images that were viewed at the site of capture. There was no appreciable difference. The client side is a lightweight component that provides an intuitive interface for end users. Each image is loaded in its own thread and the user can begin work after the first image has been loaded. This feature is especially useful on slow connection speed, 9.6 Kbps for example. The server side, which is implemented by the Java Servlet Engine works more effective than common gateway interface (CGI) programs do. Advantages of the Java Technology place this program on the next level of application development. This paper presents a unique application of Java in telemedicine.

Multiple and solitary skeletal muscle metastases on 18F-FDG PET/CT imaging.

PubMed

Nocuń, Anna; Chrapko, Beata

2015-11-01

The aim of this study was to investigate the features and patterns of skeletal muscle metastases (SMM) detected with F-fluorodeoxyglucose (F-FDG) PET/computed tomography (PET/CT). Our database was analyzed for patients with pathologically proven malignancy, who underwent F-FDG PET/CT in our institution. The patients with SMM were included in the study group on the basis of the final diagnosis confirmed by follow-up or histopathology. Images were acquired using a PET/CT system Biograph mCT S(64)-4R. CT was performed without contrast enhancement. The selected group included 31 patients (1.7% of the database, which consisted of 1805 patients). A total of 233 lesions were found. The prevalence of SMM evaluated in specific primary malignancies was the highest in melanoma (6.9%), followed by carcinoma of unknown primary (4.4%), colorectal cancer (4.1%) and lung cancer (2.8%). Three patterns of skeletal muscle metastatic involvement were observed: multiple SMM accompanied by other metastases (64.5%), solitary lesion associated with other metastases (29%) and isolated intramuscular lesions (two cases, 6.5%). Isolated SMM represented recurrence of the malignant disease. In patients with extraskeletal metastases, solitary or multiple SMM did not affect tumor staging. Solitary SMM are less common than multiple on F-FDG PET/CT imaging. SMM are usually associated with other metastases and do not affect tumor staging. The cases of isolated SMM are very rare. Nevertheless, in patients with a diagnosis of malignant disease, a solitary, F-FDG avid intramuscular focus should be suspected to represent metastasis.

Utility of CT after sonography for suspected appendicitis in children: integration of a clinical scoring system with a staged imaging protocol.

PubMed

Srinivasan, Abhay; Servaes, Sabah; Peña, Andrès; Darge, Kassa

2015-02-01

To improve diagnosis of pediatric appendicitis, many institutions have implemented a staged imaging protocol utilizing ultrasonography (US) first and then computed tomography (CT). A substantial number of children with suspected appendicitis undergo CT after US, and the efficient and accurate diagnosis of pediatric appendicitis continues to be challenging. The objective of the study is to characterize the utility of CT following US for diagnosis of pediatric appendicitis, in conjunction with a clinical appendicitis score (AS). Imaging studies of children with suspected appendicitis who underwent CT after US in an imaging protocol were retrospectively reviewed by three radiologists in consensus. Chart review derived the AS (range 0-10) and obtained the patient diagnosis and disposition, and an AS was applied to each patient. Clinical and radiologic data were analyzed to assess the yield of CT after US. Studies of 211 children (mean age 11.3 years) were included. The positive threshold for AS was determined to be 6 out of 10. When AS and US were concordant (N = 140), the sensitivity and specificity of US were similar to CT. When AS and US were discordant (N = 71) and also when AS ≥ 6 (N = 84), subsequent CT showed superior sensitivity and specificity to US alone. In the subset where US showed neither the appendix nor inflammatory change in the right lower quadrant (126/211, 60 % of scans), when AS < 6 (N = 83), the negative predictive value (NPV) of US was 0.98. However, when AS ≥ 6 (N = 43), NPV of US was 0.58, and the positive predictive value of subsequent CT was 1. There was a significant decrease in depiction of the appendix on US with patient weight-to-age ratio of >6 (kg/year, P < 0.001) and after-hours (1700 -0730 hours) performance of US (P < 0.001). Results suggest that the appendicitis score has utility in guiding an imaging protocol and support the contention that non-visualization of the appendix on US is not intrinsically non-diagnostic. There was little benefit to additional CT when AS < 6 and US did not show the appendix or evidence of inflammation; this would have avoided CT in 140/211 (66 %) patients. CT demonstrated benefit when AS ≥ 6, suggesting that cases with AS ≥ 6 and features that limit depiction of the appendix on US may be triaged to CT.

SU-C-207B-06: Comparison of Registration Methods for Modeling Pathologic Response of Esophageal Cancer to Chemoradiation Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Riyahi, S; Choi, W; Bhooshan, N

2016-06-15

Purpose: To compare linear and deformable registration methods for evaluation of tumor response to Chemoradiation therapy (CRT) in patients with esophageal cancer. Methods: Linear and multi-resolution BSpline deformable registration were performed on Pre-Post-CRT CT/PET images of 20 patients with esophageal cancer. For both registration methods, we registered CT using Mean Square Error (MSE) metric, however to register PET we used transformation obtained using Mutual Information (MI) from the same CT due to being multi-modality. Similarity of Warped-CT/PET was quantitatively evaluated using Normalized Mutual Information and plausibility of DF was assessed using inverse consistency Error. To evaluate tumor response four groupsmore » of tumor features were examined: (1) Conventional PET/CT e.g. SUV, diameter (2) Clinical parameters e.g. TNM stage, histology (3)spatial-temporal PET features that describe intensity, texture and geometry of tumor (4)all features combined. Dominant features were identified using 10-fold cross-validation and Support Vector Machine (SVM) was deployed for tumor response prediction while the accuracy was evaluated by ROC Area Under Curve (AUC). Results: Average and standard deviation of Normalized mutual information for deformable registration using MSE was 0.2±0.054 and for linear registration was 0.1±0.026, showing higher NMI for deformable registration. Likewise for MI metric, deformable registration had 0.13±0.035 comparing to linear counterpart with 0.12±0.037. Inverse consistency error for deformable registration for MSE metric was 4.65±2.49 and for linear was 1.32±2.3 showing smaller value for linear registration. The same conclusion was obtained for MI in terms of inverse consistency error. AUC for both linear and deformable registration was 1 showing no absolute difference in terms of response evaluation. Conclusion: Deformable registration showed better NMI comparing to linear registration, however inverse consistency of transformation was lower in linear registration. We do not expect to see significant difference when warping PET images using deformable or linear registration. This work was supported in part by the National Cancer Institute Grants R01CA172638.« less

Postmortem computed tomography findings in suicide victims.

PubMed

Garetier, M; Deloire, L; Dédouit, F; Dumousset, E; Saccardy, C; Ben Salem, D

2017-02-01

Suicide is the eighth cause of mortality in France and the leading cause in people aged between 25 and 34 years. The most common methods of suicide are hanging, self-poisoning with medicines and firearms. Postmortem computed tomography (CT) is a useful adjunct to autopsy to confirm suicide and exclude other causes of death. At autopsy, fractures of the hyoid bone or thyroid cartilage, or both, are found in more than 50% of suicidal hangings. Cervical vertebra fractures are rare and only seen in suicide victims jumping from a great height. Three-dimensional reconstructions from CT data are useful to visualize the ligature mark on the neck. In suicides by firearm, postmortem CT shows entry and exit wounds, parenchymal lesions along the bullet path, as well as projectiles in case of penetrating trauma. However, in the chest and abdomen it is more difficult to identify the path of the projectile. Postmortem CT also shows specific features of suicide by drowning or stabbing, but its use is limited in cases of self-poisoning. The use of postmortem CT is also limited by decomposition and change of body position. This article presents the imaging features seen on postmortem CT according to the method of suicide. Copyright © 2016 Éditions françaises de radiologie. Published by Elsevier Masson SAS. All rights reserved.

Radiological and Radionuclide Imaging of Degenerative Disease of the Facet Joints

PubMed Central

Shur, Natalie; Corrigan, Alexis; Agrawal, Kanhaiyalal; Desai, Amidevi; Gnanasegaran, Gopinath

2015-01-01

The facet joint has been increasingly implicated as a potential source of lower back pain. Diagnosis can be challenging as there is not a direct correlation between facet joint disease and clinical or radiological features. The purpose of this article is to review the diagnosis, treatment, and current imaging modality options in the context of degenerative facet joint disease. We describe each modality in turn with a pictorial review using current evidence. Newer hybrid imaging techniques such as single photon emission computed tomography/computed tomography (SPECT/CT) provide additional information relative to the historic gold standard magnetic resonance imaging. The diagnostic benefits of SPECT/CT include precise localization and characterization of spinal lesions and improved diagnosis for lower back pain. It may have a role in selecting patients for local therapeutic injections, as well as guiding their location with increased precision. PMID:26170560

Fluorodeoxyglucose positron emission tomography for detection of tumor recurrence following radiofrequency ablation in retrospective cohort of stage I lung cancer.

PubMed

Wang, Yingbing; Lanuti, Michael; Bernheim, Adam; Shepard, Jo-Anne O; Sharma, Amita

2018-05-03

The goal of this study was to define patterns for tumor recurrence on PET following RFA, compare time to imaging recurrence by PET versus CT, evaluate whether pre-treatment tumor uptake predicts recurrence and propose an optimal post-RFA surveillance strategy. A retrospective cohort study was performed of biopsy confirmed primary stage I lung cancers treated with RFA. FDG PET and near contemporaneous diagnostic CT imaging pre-ablation, within 30 days post-ablation, and beyond 6 months were independently and retrospectively evaluated for features supportive of recurrence. Time to imaging recurrence by PET (TTR_PET) and by CT (TTR_CT) were determined and compared. FDG avidity of untreated tumors was compared between recurrent and non-recurrent groups. Thirteen recurrences after 72 RFA treatments were confirmed by diagnostic CT. All recurrences were associated with focally intense and increasing FDG uptake beyond 6 months (sensitivity 100%; specificity 98.5%). Mean TTR_PET was 14 months compared to mean TTR_CT of 17 months (not statistically significant). Normalized SUVmax and total lesions glycolysis of lung cancers that recurred after RFA was 4.0 and 6.0, respectively compared to 2.8 and 5.0, respectively for cancers that did not recur (p = .068). A pattern of focally intense and increasing FDG PET uptake has high sensitivity and specificity for detecting recurrent lung cancer following RFA. Surveillance after RFA should include a contrast enhanced diagnostic CT at 1 month to diagnose procedural complications, PET at 6 months as a post-treatment metabolic baseline (with diagnostic CT if PET is abnormal) and alternating diagnostic CTs or PET every 6 months for 2 years.

Dual-phase CT for the assessment of acute vascular injuries in high-energy blunt trauma: the imaging findings and management implications.

PubMed

Iacobellis, Francesca; Ierardi, Anna M; Mazzei, Maria A; Magenta Biasina, Alberto; Carrafiello, Gianpaolo; Nicola, Refky; Scaglione, Mariano

2016-01-01

Acute vascular injuries are the second most common cause of fatalities in patients with multiple traumatic injuries; thus, prompt identification and management is essential for patient survival. Over the past few years, multidetector CT (MDCT) using dual-phase scanning protocol has become the imaging modality of choice in high-energy deceleration traumas. The objective of this article was to review the role of dual-phase MDCT in the identification and management of acute vascular injuries, particularly in the chest and abdomen following multiple traumatic injuries. In addition, this article will provide examples of MDCT features of acute vascular injuries with correlative surgical and interventional findings.

Congenital Adrenal Neuroblastoma With and Without Cystic Change: Differentiating Features With an Emphasis on the of Value of Ultrasound.

PubMed

Hwang, Sook Min; Yoo, So-Young; Kim, Ji Hye; Jeon, Tae Yeon

2016-11-01

The purpose of this study was to compare the features of congenital adrenal neuroblastomas with and without cystic changes and to emphasize the value of ultrasound in the diagnostic evaluation of cystic congenital adrenal neuroblastoma. A total of 41 patients with surgically confirmed congenital adrenal neuroblastoma were enrolled. We divided the patients into two groups according to presence or absence of cystic change in the tumor, as determined from the initial ultrasound findings. Clinical and laboratory findings, disease stage, and patient outcome were investigated with a statistical comparison between the two groups. The imaging findings for cystic congenital adrenal neuroblastoma were reviewed to compare the additional diagnostic value of CT and MRI when paired with ultrasound. There were 22 patients (54%) in the group without cystic changes and 19 patients (46%) in the group with cystic changes. Prenatal detection and absence of metastasis were significantly more common in the cystic group than in the noncystic group (p < 0.05). Sensitivities of tumor marker levels were also significantly lower in the cystic group. Patient outcome was excellent, and there was no significant difference between the groups. With regard to imaging of cystic congenital adrenal neuroblastoma, in the 15 cases in which CT or MRI was paired with ultrasound, no additional diagnostic information was discerned with CT or MRI. Nearly one-half of congenital adrenal neuroblastomas are cystic, and these tumors have clinical and laboratory features that distinguish them from noncystic congenital adrenal neuroblastoma. Diagnostic tests, including CT, MRI, and assessment of tumor markers, have low diagnostic value in the evaluation of cystic congenital adrenal neuroblastoma.

Radiomics biomarkers for accurate tumor progression prediction of oropharyngeal cancer

NASA Astrophysics Data System (ADS)

Hadjiiski, Lubomir; Chan, Heang-Ping; Cha, Kenny H.; Srinivasan, Ashok; Wei, Jun; Zhou, Chuan; Prince, Mark; Papagerakis, Silvana

2017-03-01

Accurate tumor progression prediction for oropharyngeal cancers is crucial for identifying patients who would best be treated with optimized treatment and therefore minimize the risk of under- or over-treatment. An objective decision support system that can merge the available radiomics, histopathologic and molecular biomarkers in a predictive model based on statistical outcomes of previous cases and machine learning may assist clinicians in making more accurate assessment of oropharyngeal tumor progression. In this study, we evaluated the feasibility of developing individual and combined predictive models based on quantitative image analysis from radiomics, histopathology and molecular biomarkers for oropharyngeal tumor progression prediction. With IRB approval, 31, 84, and 127 patients with head and neck CT (CT-HN), tumor tissue microarrays (TMAs) and molecular biomarker expressions, respectively, were collected. For 8 of the patients all 3 types of biomarkers were available and they were sequestered in a test set. The CT-HN lesions were automatically segmented using our level sets based method. Morphological, texture and molecular based features were extracted from CT-HN and TMA images, and selected features were merged by a neural network. The classification accuracy was quantified using the area under the ROC curve (AUC). Test AUCs of 0.87, 0.74, and 0.71 were obtained with the individual predictive models based on radiomics, histopathologic, and molecular features, respectively. Combining the radiomics and molecular models increased the test AUC to 0.90. Combining all 3 models increased the test AUC further to 0.94. This preliminary study demonstrates that the individual domains of biomarkers are useful and the integrated multi-domain approach is most promising for tumor progression prediction.

Detection of melorheostosis in a young lady with upper limb pain on Three Phase Bone Scintigram/SPECT-CT.

PubMed

Hassan, Aamna; Khalid, Madeeha; Khawar, Saquib

2016-01-01

Melorheostosis is a benign, noninheritable bone dysplasia characterized by its classic radiographic features of dense, flowing hyperostosis. It frequently affects one limb, usually the lower extremity and rarely the axial skeleton. A 26-year-old lady with obesity, polycystic ovarian syndrome and scalp dandruff presented with a long standing history of upper extremity pain and inability to adduct the arm completely. A Tc-99m MDP whole body and SPECT/CT scan performed for suspected fibrous dysplasia showed increased radiotracer uptake in densely sclerotic humeral and radial melorheostosis. This case highlighted the role of SPECT/CT imaging in this rare condition.

Analysis of pulmonary pure ground-glass nodule in enhanced dual energy CT imaging for predicting invasive adenocarcinoma: comparing with conventional thin-section CT imaging

PubMed Central

Zhang, Ying; Tang, Jian; Xu, Jianrong

2017-01-01

Background To investigate the value of dual energy computed tomography (DECT) parameters (including iodine concentration and monochromatic CT numbers) for predicting pure ground-glass nodules (pGGNs) of invasive adenocarcinoma (IA). Methods A total of 55 resected pGGNs evaluated with both unenhanced thin-section CT (TSCT) and enhanced DECT scans were included. Correlations between histopathology [adenocarcinoma in situ (AIS), minimally IA (MIA), and IA] and CT scan characteristics were examined. CT scan and clinicodemographic data were investigated by univariate and multivariate analysis to identify features that helped distinguish IA from AIS or MIA. Results Both normalized iodine concentration (NIC) of IA and slope of spectral curve [slope(k)] were not significantly different between IA and AIS or MIA. Size, performance of pleural retraction and enhanced monochromatic CT attenuation values of 120–140 keV were significantly higher for IA. In multivariate regression analysis, size and enhanced monochromatic CT number of 140 keV were independent predictors for IA. Using the two parameters together, the diagnostic capacity of IA could be improved from 0.697 or 0.635 to 0.713. Conclusions DECT could help demonstrate blood supply and indicate invasion extent of pGGNs, and monochromatic CT number of higher energy (especially 140 keV) would be better for diagnosing IA than lower energies. Together with size of pGGNs, the diagnostic capacity of IA could be better. PMID:29312701

Combined texture feature analysis of segmentation and classification of benign and malignant tumour CT slices.

PubMed

Padma, A; Sukanesh, R

2013-01-01

A computer software system is designed for the segmentation and classification of benign from malignant tumour slices in brain computed tomography (CT) images. This paper presents a method to find and select both the dominant run length and co-occurrence texture features of region of interest (ROI) of the tumour region of each slice to be segmented by Fuzzy c means clustering (FCM) and evaluate the performance of support vector machine (SVM)-based classifiers in classifying benign and malignant tumour slices. Two hundred and six tumour confirmed CT slices are considered in this study. A total of 17 texture features are extracted by a feature extraction procedure, and six features are selected using Principal Component Analysis (PCA). This study constructed the SVM-based classifier with the selected features and by comparing the segmentation results with the experienced radiologist labelled ground truth (target). Quantitative analysis between ground truth and segmented tumour is presented in terms of segmentation accuracy, segmentation error and overlap similarity measures such as the Jaccard index. The classification performance of the SVM-based classifier with the same selected features is also evaluated using a 10-fold cross-validation method. The proposed system provides some newly found texture features have an important contribution in classifying benign and malignant tumour slices efficiently and accurately with less computational time. The experimental results showed that the proposed system is able to achieve the highest segmentation and classification accuracy effectiveness as measured by jaccard index and sensitivity and specificity.

Advanced imaging of the macrostructure and microstructure of bone

NASA Technical Reports Server (NTRS)

Genant, H. K.; Gordon, C.; Jiang, Y.; Link, T. M.; Hans, D.; Majumdar, S.; Lang, T. F.

2000-01-01

Noninvasive and/or nondestructive techniques are capable of providing more macro- or microstructural information about bone than standard bone densitometry. Although the latter provides important information about osteoporotic fracture risk, numerous studies indicate that bone strength is only partially explained by bone mineral density. Quantitative assessment of macro- and microstructural features may improve our ability to estimate bone strength. The methods available for quantitatively assessing macrostructure include (besides conventional radiographs) quantitative computed tomography (QCT) and volumetric quantitative computed tomography (vQCT). Methods for assessing microstructure of trabecular bone noninvasively and/or nondestructively include high-resolution computed tomography (hrCT), micro-computed tomography (muCT), high-resolution magnetic resonance (hrMR), and micromagnetic resonance (muMR). vQCT, hrCT and hrMR are generally applicable in vivo; muCT and muMR are principally applicable in vitro. Although considerable progress has been made in the noninvasive and/or nondestructive imaging of the macro- and microstructure of bone, considerable challenges and dilemmas remain. From a technical perspective, the balance between spatial resolution versus sampling size, or between signal-to-noise versus radiation dose or acquisition time, needs further consideration, as do the trade-offs between the complexity and expense of equipment and the availability and accessibility of the methods. The relative merits of in vitro imaging and its ultrahigh resolution but invasiveness versus those of in vivo imaging and its modest resolution but noninvasiveness also deserve careful attention. From a clinical perspective, the challenges for bone imaging include balancing the relative advantages of simple bone densitometry against the more complex architectural features of bone or, similarly, the deeper research requirements against the broader clinical needs. The considerable potential biological differences between the peripheral appendicular skeleton and the central axial skeleton have to be addressed further. Finally, the relative merits of these sophisticated imaging techniques have to be weighed with respect to their applications as diagnostic procedures requiring high accuracy or reliability on one hand and their monitoring applications requiring high precision or reproducibility on the other. Copyright 2000 S. Karger AG, Basel.

Development of an organ-specific insert phantom generated using a 3D printer for investigations of cardiac computed tomography protocols.

PubMed

Abdullah, Kamarul A; McEntee, Mark F; Reed, Warren; Kench, Peter L

2018-04-30

An ideal organ-specific insert phantom should be able to simulate the anatomical features with appropriate appearances in the resultant computed tomography (CT) images. This study investigated a 3D printing technology to develop a novel and cost-effective cardiac insert phantom derived from volumetric CT image datasets of anthropomorphic chest phantom. Cardiac insert volumes were segmented from CT image datasets, derived from an anthropomorphic chest phantom of Lungman N-01 (Kyoto Kagaku, Japan). These segmented datasets were converted to a virtual 3D-isosurface of heart-shaped shell, while two other removable inserts were included using computer-aided design (CAD) software program. This newly designed cardiac insert phantom was later printed by using a fused deposition modelling (FDM) process via a Creatbot DM Plus 3D printer. Then, several selected filling materials, such as contrast media, oil, water and jelly, were loaded into designated spaces in the 3D-printed phantom. The 3D-printed cardiac insert phantom was positioned within the anthropomorphic chest phantom and 30 repeated CT acquisitions performed using a multi-detector scanner at 120-kVp tube potential. Attenuation (Hounsfield Unit, HU) values were measured and compared to the image datasets of real-patient and Catphan ® 500 phantom. The output of the 3D-printed cardiac insert phantom was a solid acrylic plastic material, which was strong, light in weight and cost-effective. HU values of the filling materials were comparable to the image datasets of real-patient and Catphan ® 500 phantom. A novel and cost-effective cardiac insert phantom for anthropomorphic chest phantom was developed using volumetric CT image datasets with a 3D printer. Hence, this suggested the printing methodology could be applied to generate other phantoms for CT imaging studies. © 2018 The Authors. Journal of Medical Radiation Sciences published by John Wiley & Sons Australia, Ltd on behalf of Australian Society of Medical Imaging and Radiation Therapy and New Zealand Institute of Medical Radiation Technology.

Differentiation of deep venous thrombosis from femoral vein mixing artifact on routine abdominopelvic CT.

PubMed

Doshi, Ankur M; Hoffman, David; Kierans, Andrea S; Ream, Justin M; Rosenkrantz, Andrew B

2015-10-01

The objective of this study is to assess the performance of qualitative and quantitative imaging features for the differentiation of deep venous thrombosis (DVT) from mixing artifact on routine portal venous phase abdominopelvic CT. This retrospective study included 40 adult patients with a femoral vein filling defect on portal venous phase CT and a Duplex ultrasound (n = 36) or catheter venogram (n = 4) to confirm presence or absence of DVT. Two radiologists (R1, R2) assessed the femoral veins for various qualitative and quantitative features. 60% of patients were confirmed to have DVT and 40% had mixing artifact. Features with significantly greater frequency in DVT than mixing artifact (all p ≤ 0.006) were central location (R1 90% vs. 28%; R2 96% vs. 31%), sharp margin (R1 83% vs. 28%; R2 96% vs. 31%), venous expansion (R1 48% vs. 6%, R2 56% vs. 6%), and venous wall enhancement (R1 62% vs. 0%; R2 48% vs. 0%). DVT exhibited significantly lower mean attenuation than mixing artifact (R1 42.1 ± 20.2 vs. 57.1 ± 23.6 HU; R2 43.6 ± 19.4 vs. 58.8 ± 23.4 HU, p ≤ 0.031) and a significantly larger difference in vein diameter compared to the contralateral vein (R1 0.4 ± 0.4 vs. 0.1 ± 0.2 cm; R2 0.3 ± 0.4 vs. 0.0 ± 0.1 cm, p ≤ 0.026). At multivariable analysis, central location and sharp margin were significant independent predictors of DVT for both readers (p ≤ 0.013). Awareness of these qualitative and quantitative imaging features may improve radiologists' confidence for differentiating femoral vein DVT and mixing artifact on routine portal venous phase CT. However, given overlap with mixing artifact, larger studies remain warranted.

Cochlear anatomy using micro computed tomography (μCT) imaging

NASA Astrophysics Data System (ADS)

Kim, Namkeun; Yoon, Yongjin; Steele, Charles; Puria, Sunil

2008-02-01

A novel micro computed tomography (μCT) image processing method was implemented to measure anatomical features of the gerbil and chinchilla cochleas, taking into account the bent modailosis axis. Measurements were made of the scala vestibule (SV) area, the scala tympani (SV) area, and the basilar membrane (BM) width using prepared cadaveric temporal bones. 3-D cochlear structures were obtained from the scanned images using a process described in this study. It was necessary to consider the sharp curvature of mododailosis axis near the basal region. The SV and ST areas were calculated from the μCT reconstructions and compared with existing data obtained by Magnetic Resonance Microscopy (MRM), showing both qualitative and quantitative agreement. In addition to this, the width of the BM, which is the distance between the primary and secondary osseous spiral laminae, is calculated for the two animals and compared with previous data from the MRM method. For the gerbil cochlea, which does not have much cartilage in the osseous spiral lamina, the μCT-based BM width measurements show good agreement with previous data. The chinchilla BM, which contains more cartilage in the osseous spiral lamina than the gerbil, shows a large difference in the BM widths between the μCT and MRM methods. The SV area, ST area, and BM width measurements from this study can be used in building an anatomically based mathematical cochlear model.

Dengue encephalitis with predominant cerebellar involvement: report of eight cases with MR and CT imaging features.

PubMed

Hegde, Vinay; Aziz, Zarina; Kumar, Sharath; Bhat, Maya; Prasad, Chandrajit; Gupta, A K; Netravathi, M; Saini, Jitender

2015-03-01

CNS dengue infection is a rare condition and the pattern of brain involvement has not been well described. We report the MR imaging (MRI) features in eight cases of dengue encephalitis. We retrospectively searched cases of dengue encephalitis in which imaging was performed. Eight cases (three men, five women; age range: 8-42 years) diagnosed with dengue encephalitis were included in the study. MR studies were performed on 3-T and 1.5-T MR clinical systems. Two neuroradiologists retrospectively reviewed the MR images and analysed the type of lesions, as well as their distribution and imaging features. All eight cases exhibited MRI abnormalities and the cerebellum was involved in all cases. In addition, MRI signal changes were also noted in the brainstem, thalamus, basal ganglia, internal capsule, insula, mesial temporal lobe, and cortical and cerebral white matter. Areas of susceptibility, diffusion restriction, and patchy post-contrast enhancement were the salient imaging features in our cohort of cases. A pattern of symmetrical cerebellar involvement and presence of microbleeds/haemorrhage may serve as a useful imaging marker and may help in the diagnosis of dengue encephalitis.

Novel CT-based objective imaging biomarkers of long term radiation-induced lung damage.

PubMed

Veiga, Catarina; Landau, David; Devaraj, Anand; Doel, Tom; White, Jared; Ngai, Yenting; Hawkes, David J; McClelland, Jamie R

2018-06-14

and Purpose: Recent improvements in lung cancer survival have spurred an interest in understanding and minimizing long term radiation-induced lung damage (RILD). However, there is still no objective criteria to quantify RILD leading to variable reporting across centres and trials. We propose a set of objective imaging biomarkers to quantify common radiological findings observed 12-months after lung cancer radiotherapy (RT). Baseline and 12-month CT scans of 27 patients from a phase I/II clinical trial of isotoxic chemoradiation were included in this study. To detect and measure the severity of RILD, twelve quantitative imaging biomarkers were developed. These describe basic CT findings including parenchymal change, volume reduction and pleural change. The imaging biomarkers were implemented as semi-automated image analysis pipelines and assessed against visual assessment of the occurrence of each change. The majority of the biomarkers were measurable in each patient. Their continuous nature allows objective scoring of severity for each patient. For each imaging biomarker the cohort was split into two groups according to the presence or absence of the biomarker by visual assessment, testing the hypothesis that the imaging biomarkers were different in these two groups. All features were statistically significant except for rotation of the main bronchus and diaphragmatic curvature. The majority of the biomarkers were not strongly correlated with each other suggesting that each of the biomarkers is measuring a separate element of RILD pathology. We developed objective CT-based imaging biomarkers that quantify the severity of radiological lung damage after RT. These biomarkers are representative of typical radiological findings of RILD. Copyright © 2018. Published by Elsevier Inc.

Variability in CT lung-nodule quantification: Effects of dose reduction and reconstruction methods on density and texture based features.

PubMed

Lo, P; Young, S; Kim, H J; Brown, M S; McNitt-Gray, M F

2016-08-01

To investigate the effects of dose level and reconstruction method on density and texture based features computed from CT lung nodules. This study had two major components. In the first component, a uniform water phantom was scanned at three dose levels and images were reconstructed using four conventional filtered backprojection (FBP) and four iterative reconstruction (IR) methods for a total of 24 different combinations of acquisition and reconstruction conditions. In the second component, raw projection (sinogram) data were obtained for 33 lung nodules from patients scanned as a part of their clinical practice, where low dose acquisitions were simulated by adding noise to sinograms acquired at clinical dose levels (a total of four dose levels) and reconstructed using one FBP kernel and two IR kernels for a total of 12 conditions. For the water phantom, spherical regions of interest (ROIs) were created at multiple locations within the water phantom on one reference image obtained at a reference condition. For the lung nodule cases, the ROI of each nodule was contoured semiautomatically (with manual editing) from images obtained at a reference condition. All ROIs were applied to their corresponding images reconstructed at different conditions. For 17 of the nodule cases, repeat contours were performed to assess repeatability. Histogram (eight features) and gray level co-occurrence matrix (GLCM) based texture features (34 features) were computed for all ROIs. For the lung nodule cases, the reference condition was selected to be 100% of clinical dose with FBP reconstruction using the B45f kernel; feature values calculated from other conditions were compared to this reference condition. A measure was introduced, which the authors refer to as Q, to assess the stability of features across different conditions, which is defined as the ratio of reproducibility (across conditions) to repeatability (across repeat contours) of each feature. The water phantom results demonstrated substantial variability among feature values calculated across conditions, with the exception of histogram mean. Features calculated from lung nodules demonstrated similar results with histogram mean as the most robust feature (Q ≤ 1), having a mean and standard deviation Q of 0.37 and 0.22, respectively. Surprisingly, histogram standard deviation and variance features were also quite robust. Some GLCM features were also quite robust across conditions, namely, diff. variance, sum variance, sum average, variance, and mean. Except for histogram mean, all features have a Q of larger than one in at least one of the 3% dose level conditions. As expected, the histogram mean is the most robust feature in their study. The effects of acquisition and reconstruction conditions on GLCM features vary widely, though trending toward features involving summation of product between intensities and probabilities being more robust, barring a few exceptions. Overall, care should be taken into account for variation in density and texture features if a variety of dose and reconstruction conditions are used for the quantification of lung nodules in CT, otherwise changes in quantification results may be more reflective of changes due to acquisition and reconstruction conditions than in the nodule itself.

Fundamental limits of image registration performance: Effects of image noise and resolution in CT-guided interventions.

PubMed

Ketcha, M D; de Silva, T; Han, R; Uneri, A; Goerres, J; Jacobson, M; Vogt, S; Kleinszig, G; Siewerdsen, J H

2017-02-11

In image-guided procedures, image acquisition is often performed primarily for the task of geometrically registering information from another image dataset, rather than detection / visualization of a particular feature. While the ability to detect a particular feature in an image has been studied extensively with respect to image quality characteristics (noise, resolution) and is an ongoing, active area of research, comparatively little has been accomplished to relate such image quality characteristics to registration performance. To establish such a framework, we derived Cramer-Rao lower bounds (CRLB) for registration accuracy, revealing the underlying dependencies on image variance and gradient strength. The CRLB was analyzed as a function of image quality factors (in particular, dose) for various similarity metrics and compared to registration accuracy using CT images of an anthropomorphic head phantom at various simulated dose levels. Performance was evaluated in terms of root mean square error (RMSE) of the registration parameters. Analysis of the CRLB shows two primary dependencies: 1) noise variance (related to dose); and 2) sum of squared image gradients (related to spatial resolution and image content). Comparison of the measured RMSE to the CRLB showed that the best registration method, RMSE achieved the CRLB to within an efficiency factor of 0.21, and optimal estimators followed the predicted inverse proportionality between registration performance and radiation dose. Analysis of the CRLB for image registration is an important step toward understanding and evaluating an intraoperative imaging system with respect to a registration task. While the CRLB is optimistic in absolute performance, it reveals a basis for relating the performance of registration estimators as a function of noise content and may be used to guide acquisition parameter selection (e.g., dose) for purposes of intraoperative registration.

Fuzzy membership functions for analysis of high-resolution CT images of diffuse pulmonary diseases.

PubMed

Almeida, Eliana; Rangayyan, Rangaraj M; Azevedo-Marques, Paulo M

2015-08-01

We propose the use of fuzzy membership functions to analyze images of diffuse pulmonary diseases (DPDs) based on fractal and texture features. The features were extracted from preprocessed regions of interest (ROIs) selected from high-resolution computed tomography images. The ROIs represent five different patterns of DPDs and normal lung tissue. A Gaussian mixture model (GMM) was constructed for each feature, with six Gaussians modeling the six patterns. Feature selection was performed and the GMMs of the five significant features were used. From the GMMs, fuzzy membership functions were obtained by a probability-possibility transformation and further statistical analysis was performed. An average classification accuracy of 63.5% was obtained for the six classes. For four of the six classes, the classification accuracy was superior to 65%, and the best classification accuracy was 75.5% for one class. The use of fuzzy membership functions to assist in pattern classification is an alternative to deterministic approaches to explore strategies for medical diagnosis.

Higher-order scene statistics of breast images

NASA Astrophysics Data System (ADS)

Abbey, Craig K.; Sohl-Dickstein, Jascha N.; Olshausen, Bruno A.; Eckstein, Miguel P.; Boone, John M.

2009-02-01

Researchers studying human and computer vision have found description and construction of these systems greatly aided by analysis of the statistical properties of naturally occurring scenes. More specifically, it has been found that receptive fields with directional selectivity and bandwidth properties similar to mammalian visual systems are more closely matched to the statistics of natural scenes. It is argued that this allows for sparse representation of the independent components of natural images [Olshausen and Field, Nature, 1996]. These theories have important implications for medical image perception. For example, will a system that is designed to represent the independent components of natural scenes, where objects occlude one another and illumination is typically reflected, be appropriate for X-ray imaging, where features superimpose on one another and illumination is transmissive? In this research we begin to examine these issues by evaluating higher-order statistical properties of breast images from X-ray projection mammography (PM) and dedicated breast computed tomography (bCT). We evaluate kurtosis in responses of octave bandwidth Gabor filters applied to PM and to coronal slices of bCT scans. We find that kurtosis in PM rises and quickly saturates for filter center frequencies with an average value above 0.95. By contrast, kurtosis in bCT peaks near 0.20 cyc/mm with kurtosis of approximately 2. Our findings suggest that the human visual system may be tuned to represent breast tissue more effectively in bCT over a specific range of spatial frequencies.

Evaluation of accelerated iterative x-ray CT image reconstruction using floating point graphics hardware.

PubMed

Kole, J S; Beekman, F J

2006-02-21

Statistical reconstruction methods offer possibilities to improve image quality as compared with analytical methods, but current reconstruction times prohibit routine application in clinical and micro-CT. In particular, for cone-beam x-ray CT, the use of graphics hardware has been proposed to accelerate the forward and back-projection operations, in order to reduce reconstruction times. In the past, wide application of this texture hardware mapping approach was hampered owing to limited intrinsic accuracy. Recently, however, floating point precision has become available in the latest generation commodity graphics cards. In this paper, we utilize this feature to construct a graphics hardware accelerated version of the ordered subset convex reconstruction algorithm. The aims of this paper are (i) to study the impact of using graphics hardware acceleration for statistical reconstruction on the reconstructed image accuracy and (ii) to measure the speed increase one can obtain by using graphics hardware acceleration. We compare the unaccelerated algorithm with the graphics hardware accelerated version, and for the latter we consider two different interpolation techniques. A simulation study of a micro-CT scanner with a mathematical phantom shows that at almost preserved reconstructed image accuracy, speed-ups of a factor 40 to 222 can be achieved, compared with the unaccelerated algorithm, and depending on the phantom and detector sizes. Reconstruction from physical phantom data reconfirms the usability of the accelerated algorithm for practical cases.

Computed tomographic features of idiopathic fibrosing interstitial pneumonia: comparison with pulmonary fibrosis related to collagen vascular disease.

PubMed

Hwang, Jeong-Hwa; Misumi, Shigeki; Sahin, Hakan; Brown, Kevin K; Newell, John D; Lynch, David A

2009-01-01

To compare the computed tomographic (CT) features of idiopathic fibrosing interstitial pneumonia with those of pulmonary fibrosis related to collagen vascular disease (CVD). We reviewed the CT scans of 177 patients with diffuse interstitial pulmonary fibrosis, of which 97 had idiopathic fibrosing interstitial pneumonia and 80 had CVD. The CT images were systematically scored for the presence and extent of pulmonary and extrapulmonary abnormalities. Computed tomographic diagnosis of usual interstitial pneumonia (UIP) or nonspecific interstitial pneumonia (NSIP) was assigned. A CT pattern of UIP was identified in 59 (60.8%) of patients with idiopathic fibrosing interstitial pneumonia compared with 15 (18.7%) of those patients with CVD; conversely, the CT diagnosis of NSIP was made in 51 (64%) of patients with CVD compared with 36 (37%) of patients with idiopathic disease (P < 0.01). In 113 patients who had lung biopsy, the CT diagnoses of UIP and NSIP were concordant with the histologic diagnoses in 36 of 50 patients and 34 of 41 patients, respectively. Pleural effusions, esophageal dilation, and pericardial abnormalities were more frequent in patients with CVD than in patients with idiopathic fibrosing interstitial pneumonia. Compared with patients with CVD, those patients with an idiopathic fibrosing interstitial pneumonia showed a higher prevalence of a UIP pattern and lower prevalence of an NSIP pattern as determined by CT. Identification of coexisting extrapulmonary abnormalities on CT can support a diagnosis of CVD.

SU-F-R-53: CT-Based Radiomics Analysis of Non-Small Cell Lung Cancer Patients Treated with Stereotactic Body Radiation Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huynh, E; Coroller, T; Narayan, V

Purpose: Stereotactic body radiation therapy (SBRT) is the standard of care for medically inoperable non-small cell lung cancer (NSCLC) patients and has demonstrated excellent local control and survival. However, some patients still develop distant metastases and local recurrence, and therefore, there is a clinical need to identify patients at high-risk of disease recurrence. The aim of the current study is to use a radiomics approach to identify imaging biomarkers, based on tumor phenotype, for clinical outcomes in SBRT patients. Methods: Radiomic features were extracted from free breathing computed tomography (CT) images of 113 Stage I-II NSCLC patients treated with SBRT.more » Their association to and prognostic performance for distant metastasis (DM), locoregional recurrence (LRR) and survival was assessed and compared with conventional features (tumor volume and diameter) and clinical parameters (e.g. performance status, overall stage). The prognostic performance was evaluated using the concordance index (CI). Multivariate model performance was evaluated using cross validation. All p-values were corrected for multiple testing using the false discovery rate. Results: Radiomic features were associated with DM (one feature), LRR (one feature) and survival (four features). Conventional features were only associated with survival and one clinical parameter was associated with LRR and survival. One radiomic feature was significantly prognostic for DM (CI=0.670, p<0.1 from random), while none of the conventional and clinical parameters were significant for DM. The multivariate radiomic model had a higher median CI (0.671) for DM than the conventional (0.618) and clinical models (0.617). Conclusion: Radiomic features have potential to be imaging biomarkers for clinical outcomes that conventional imaging metrics and clinical parameters cannot predict in SBRT patients, such as distant metastasis. Development of a radiomics biomarker that can identify patients at high-risk of recurrence could facilitate personalization of their treatment regimen for an optimized clinical outcome. R.M. had consulting interest with Amgen (ended in 2015).« less

Imaging and histological features of central subchondral osteophytes in racehorses with metacarpophalangeal joint osteoarthritis.

PubMed

Olive, J; D'Anjou, M A; Girard, C; Laverty, S; Theoret, C L

2009-12-01

Marginal osteophytes represent a well known component of osteoarthritis in man and animals. Conversely, central subchondral osteophytes (COs), which are commonly present in human knees with osteoarthritis, have not been reported in horses. To describe and compare computed radiography (CR), single-slice computed tomography (CT), 1.5 Tesla magnetic resonance imaging (MRI), and histological features of COs in equine metacarpophalangeal joints with macroscopic evidence of naturally-occurring osteoarthritis. MRI sequences (sagittal spoiled gradient recalled echo [SPGR] with fat saturation, sagittal T2-weighted fast spin echo with fat saturation [T2-FS], dorsal and transverse T1-weighted gradient-recalled echo [GRE], and sagittal T2*-weighted gradient echo with fast imaging employing steady state acquisition [FIESTA]), as well as transverse and reformatted sagittal CTI and 4 computed radiographic (CR) views of 20 paired metacarpophalangeal joints were acquired ex vivo. Following macroscopic evaluation, samples were harvested in predetermined sites of the metacarpal condyle for subsequent histology. The prevalence and detection level of COs was determined for each imaging modality. Abnormalities consistent with COs were clearly depicted on MRI, using the SPGR sequence, in 7/20 (35%) joints. They were identified as a focal hypointense protuberance from the subchondral plate into the cartilage, at the palmarodistal aspect (n=7) and/or at the very dorsal aspect (n=2) of the metacarpal condyle. COs were visible but less obvious in 5 of the 7 joints using FIESTA and reformatted sagittal CT, and were not identifiable on T2-FS, T1-GRE or CR. Microscopically, they consisted of dense bone protruding into the calcified cartilage and disrupting the tidemarks, and they were consistently associated with overlying cartilage defects. Subchondral osteophytes are a feature of osteoarthritis of equine metacarpophalangeal joints and they may be diagnosed using 1.5 Tesla MRI and CT. Central subchondral osteophytes on MRI represent indirect evidence of cartilage damage in horses.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Manohar, N; Cho, S; Reynoso, F

Purpose: To make benchtop x-ray fluorescence computed tomography (XFCT) practical for routine preclinical imaging tasks with gold nanoparticles (GNPs) by deploying, integrating, and characterizing a dedicated high-performance x-ray source and addition of simultaneous micro-CT functionality. Methods: Considerable research effort is currently under way to develop a polychromatic benchtop cone-beam XFCT system capable of imaging GNPs by stimulation and detection of gold K-shell x-ray fluorescence (XRF) photons. Recently, an ad hoc high-power x-ray source was incorporated and used to image the biodistribution of GNPs within a mouse, postmortem. In the current work, a dedicated x-ray source system featuring a liquid-cooled tungsten-targetmore » x-ray tube (max 160 kVp, ∼3 kW power) was deployed. The source was operated at 125 kVp, 24 mA. The tube’s compact dimensions allowed greater flexibility for optimizing both the irradiation and detection geometries. Incident x-rays were shaped by a conical collimator and filtered by 2 mm of tin. A compact “OEM” cadmium-telluride x-ray detector was implemented for detecting XRF/scatter spectra. Additionally, a flat panel detector was installed to allow simultaneous transmission CT imaging. The performance of the system was characterized by determining the detection limit (10-second acquisition time) for inserts filled with water/GNPs at various concentrations (0 and 0.010–1.0 wt%) and embedded in a small-animal-sized phantom. The phantom was loaded with 0.5, 0.3, and 0.1 wt% inserts and imaged using XFCT and simultaneous micro-CT. Results: An unprecedented detection limit of 0.030 wt% was experimentally demonstrated, with a 33% reduction in acquisition time. The reconstructed XFCT image accurately localized the imaging inserts. Micro-CT imaging did not provide enough contrast to distinguish imaging inserts from the phantom under the current conditions. Conclusion: The system is immediately capable of in vivo preclinical XFCT imaging with GNPs. Micro-CT imaging will require optimization of irradiation parameters to improve contrast. Supported by NIH/NCI grant R01CA155446; This investigation was supported by NIH/NCI grant R01CA155446.« less

Comparing the role of shape and texture on staging hepatic fibrosis from medical imaging

NASA Astrophysics Data System (ADS)

Zhang, Xuejun; Louie, Ryan; Liu, Brent J.; Gao, Xin; Tan, Xiaomin; Qu, Xianghe; Long, Liling

2016-03-01

The purpose of this study is to investigate the role of shape and texture in the classification of hepatic fibrosis by selecting the optimal parameters for a better Computer-aided diagnosis (CAD) system. 10 surface shape features are extracted from a standardized profile of liver; while15 texture features calculated from gray level co-occurrence matrix (GLCM) are extracted within an ROI in liver. Each combination of these input subsets is checked by using support vector machine (SVM) with leave-one-case-out method to differentiate fibrosis into two groups: normal or abnormal. The accurate rate value of all 10/15 types number of features is 66.83% by texture, while 85.74% by shape features, respectively. The irregularity of liver shape can demonstrate fibrotic grade efficiently and texture feature of CT image is not recommended to use with shape feature for interpretation of cirrhosis.

CT AND MRI FEATURES OF CAROTID BODY PARAGANGLIOMAS IN 16 DOGS.

PubMed

Mai, Wilfried; Seiler, Gabriela S; Lindl-Bylicki, Britany J; Zwingenberger, Allison L

2015-01-01

Carotid body tumors (paragangliomas) arise from chemoreceptors located at the carotid bifurcation. In imaging studies, this neoplasm may be confused with other neck neoplasms such as thyroid carcinoma. The purpose of this retrospective, cross-sectional study was to describe computed tomographic (CT) and magnetic resonance imaging (MRI) characteristics of confirmed carotid body tumors in a multi-institutional sample of dogs. A total of 16 dogs met inclusion criteria (14 examined using CT and two with MRI). The most common reason for imaging was a palpable cervical mass or respiratory signs (i.e., dyspnea or increased respiratory noises). The most commonly affected breed was Boston terrier (n = 5). Dogs were predominantly male castrated (n = 10) and the median age was 9 years [range 3-14.5]. Most tumors appeared as a large mass centered at the carotid bifurcation, with poor margination in six dogs and discrete margins in ten dogs. Masses were iso- to hypoattenuating to adjacent muscles in CT images and hyperintense to muscles in T1- and T2-weighted MRI. For both CT and MRI, masses typically showed strong and heterogeneous contrast enhancement. There was invasion into the adjacent structures in 9/16 dogs. In six of these nine dogs, the basilar portion of the skull was affected. The external carotid artery was entrapped in seven dogs. There was invasion into the internal jugular vein in three dogs, and into the external jugular, maxillary, and linguo-facial veins in one dog. Imaging characteristics helped explain some clinical presentations such as breathing difficulties, Horner's syndrome, head tilt, or facial nerve paralysis. © 2015 American College of Veterinary Radiology.

Diagnostic Accuracy of Ultrasound, Contrast-enhanced CT, and Conventional MRI for Differentiating Leiomyoma From Leiomyosarcoma.

PubMed

Gaetke-Udager, Kara; McLean, Karen; Sciallis, Andrew P; Alves, Timothy; Maturen, Katherine E; Mervak, Benjamin M; Moore, Andreea G; Wasnik, Ashish P; Erba, Jake; Davenport, Matthew S

2016-10-01

This study aimed to determine whether uterine leiomyoma can be distinguished from uterine leiomyosarcoma on ultrasound (US), computed tomography (CT), and/or magnetic resonance imaging (MRI) without diffusion-weighted imaging. Institutional review board approval was obtained and informed consent was waived for this Health Insurance Portability and Accountability Act-compliant retrospective case-control diagnostic accuracy study. All subjects with resected uterine leiomyosarcoma diagnosed over a 17-year period (1998-2014) at a single institution for whom pre-resection US (n = 10), CT (n = 11), or MRI (n = 7) was available were matched by tumor size and imaging modality with 28 subjects with resected uterine leiomyoma. Six blinded radiologists (three attendings, three residents) assigned 5-point Likert scores for the following features: (1) margins, (2) necrosis, (3) hemorrhage, (4) vascularity, (5) calcifications, (6) heterogeneity, and (7) likelihood of malignancy (primary end point). Mean suspicion scores were calculated and receiver operating characteristic curves were generated. The ability of individual morphologic features to predict malignancy was assessed with logistic regression. Mean suspicion scores were 2.5 ± 1.2 (attendings) and 2.4 ± 1.3 (residents) for leiomyoma, and 2.7 ± 1.3 (attendings) and 2.7 ± 1.4 (residents) for leiomyosarcoma. The areas under the receiver operating characteristic curves (range: 0.330-0.685) were not significantly different from chance, either overall (P = .36-.88) or by any modality (P = .28-.96), for any reader. Reader experience had no effect on diagnostic accuracy. No morphologic parameter was significantly predictive of malignancy (P = .10-.97). Uterine leiomyoma cannot be differentiated accurately from leiomyosarcoma on US, CT, or MRI without diffusion-weighted imaging. Copyright © 2016 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Position tracking of moving liver lesion based on real-time registration between 2D ultrasound and 3D preoperative images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Weon, Chijun; Hyun Nam, Woo; Lee, Duhgoon

Purpose: Registration between 2D ultrasound (US) and 3D preoperative magnetic resonance (MR) (or computed tomography, CT) images has been studied recently for US-guided intervention. However, the existing techniques have some limits, either in the registration speed or the performance. The purpose of this work is to develop a real-time and fully automatic registration system between two intermodal images of the liver, and subsequently an indirect lesion positioning/tracking algorithm based on the registration result, for image-guided interventions. Methods: The proposed position tracking system consists of three stages. In the preoperative stage, the authors acquire several 3D preoperative MR (or CT) imagesmore » at different respiratory phases. Based on the transformations obtained from nonrigid registration of the acquired 3D images, they then generate a 4D preoperative image along the respiratory phase. In the intraoperative preparatory stage, they properly attach a 3D US transducer to the patient’s body and fix its pose using a holding mechanism. They then acquire a couple of respiratory-controlled 3D US images. Via the rigid registration of these US images to the 3D preoperative images in the 4D image, the pose information of the fixed-pose 3D US transducer is determined with respect to the preoperative image coordinates. As feature(s) to use for the rigid registration, they may choose either internal liver vessels or the inferior vena cava. Since the latter is especially useful in patients with a diffuse liver disease, the authors newly propose using it. In the intraoperative real-time stage, they acquire 2D US images in real-time from the fixed-pose transducer. For each US image, they select candidates for its corresponding 2D preoperative slice from the 4D preoperative MR (or CT) image, based on the predetermined pose information of the transducer. The correct corresponding image is then found among those candidates via real-time 2D registration based on a gradient-based similarity measure. Finally, if needed, they obtain the position information of the liver lesion using the 3D preoperative image to which the registered 2D preoperative slice belongs. Results: The proposed method was applied to 23 clinical datasets and quantitative evaluations were conducted. With the exception of one clinical dataset that included US images of extremely low quality, 22 datasets of various liver status were successfully applied in the evaluation. Experimental results showed that the registration error between the anatomical features of US and preoperative MR images is less than 3 mm on average. The lesion tracking error was also found to be less than 5 mm at maximum. Conclusions: A new system has been proposed for real-time registration between 2D US and successive multiple 3D preoperative MR/CT images of the liver and was applied for indirect lesion tracking for image-guided intervention. The system is fully automatic and robust even with images that had low quality due to patient status. Through visual examinations and quantitative evaluations, it was verified that the proposed system can provide high lesion tracking accuracy as well as high registration accuracy, at performance levels which were acceptable for various clinical applications.« less

3D printing for orthopedic applications: from high resolution cone beam CT images to life size physical models

NASA Astrophysics Data System (ADS)

Jackson, Amiee; Ray, Lawrence A.; Dangi, Shusil; Ben-Zikri, Yehuda K.; Linte, Cristian A.

2017-03-01

With increasing resolution in image acquisition, the project explores capabilities of printing toward faithfully reflecting detail and features depicted in medical images. To improve safety and efficiency of orthopedic surgery and spatial conceptualization in training and education, this project focused on generating virtual models of orthopedic anatomy from clinical quality computed tomography (CT) image datasets and manufacturing life-size physical models of the anatomy using 3D printing tools. Beginning with raw micro CT data, several image segmentation techniques including thresholding, edge recognition, and region-growing algorithms available in packages such as ITK-SNAP, MITK, or Mimics, were utilized to separate bone from surrounding soft tissue. After converting the resulting data to a standard 3D printing format, stereolithography (STL), the STL file was edited using Meshlab, Netfabb, and Meshmixer. The editing process was necessary to ensure a fully connected surface (no loose elements), positive volume with manifold geometry (geometry possible in the 3D physical world), and a single, closed shell. The resulting surface was then imported into a "slicing" software to scale and orient for printing on a Flashforge Creator Pro. In printing, relationships between orientation, print bed volume, model quality, material use and cost, and print time were considered. We generated anatomical models of the hand, elbow, knee, ankle, and foot from both low-dose high-resolution cone-beam CT images acquired using the soon to be released scanner developed by Carestream, as well as scaled models of the skeletal anatomy of the arm and leg, together with life-size models of the hand and foot.

SU-E-J-262: Variability in Texture Analysis of Gynecological Tumors in the Context of An 18F-FDG PET Adaptive Protocol

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nawrocki, J; Chino, J; Das, S

Purpose: This study examines the effect on texture analysis due to variable reconstruction of PET images in the context of an adaptive FDG PET protocol for node positive gynecologic cancer patients. By measuring variability in texture features from baseline and intra-treatment PET-CT, we can isolate unreliable texture features due to large variation. Methods: A subset of seven patients with node positive gynecological cancers visible on PET was selected for this study. Prescribed dose varied between 45–50.4Gy, with a 55–70Gy boost to the PET positive nodes. A baseline and intratreatment (between 30–36Gy) PET-CT were obtained on a Siemens Biograph mCT. Eachmore » clinical PET image set was reconstructed 6 times using a TrueX+TOF algorithm with varying iterations and Gaussian filter. Baseline and intra-treatment primary GTVs were segmented using PET Edge (MIM Software Inc., Cleveland, OH), a semi-automatic gradient-based algorithm, on the clinical PET and transferred to the other reconstructed sets. Using an in-house MATLAB program, four 3D texture matrices describing relationships between voxel intensities in the GTV were generated: co-occurrence, run length, size zone, and neighborhood difference. From these, 39 textural features characterizing texture were calculated in addition to SUV histogram features. The percent variability among parameters was first calculated. Each reconstructed texture feature from baseline and intra-treatment per patient was normalized to the clinical baseline scan and compared using the Wilcoxon signed-rank test in order to isolate variations due to reconstruction parameters. Results: For the baseline scans, 13 texture features showed a mean range greater than 10%. For the intra scans, 28 texture features showed a mean range greater than 10%. Comparing baseline to intra scans, 25 texture features showed p <0.05. Conclusion: Variability due to different reconstruction parameters increased with treatment, however, the majority of texture features showed significant changes during treatment independent of reconstruction effects.« less

MO-C-17A-11: A Segmentation and Point Matching Enhanced Deformable Image Registration Method for Dose Accumulation Between HDR CT Images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhen, X; Chen, H; Zhou, L

2014-06-15

Purpose: To propose and validate a novel and accurate deformable image registration (DIR) scheme to facilitate dose accumulation among treatment fractions of high-dose-rate (HDR) gynecological brachytherapy. Method: We have developed a method to adapt DIR algorithms to gynecologic anatomies with HDR applicators by incorporating a segmentation step and a point-matching step into an existing DIR framework. In the segmentation step, random walks algorithm is used to accurately segment and remove the applicator region (AR) in the HDR CT image. A semi-automatic seed point generation approach is developed to obtain the incremented foreground and background point sets to feed the randommore » walks algorithm. In the subsequent point-matching step, a feature-based thin-plate spline-robust point matching (TPS-RPM) algorithm is employed for AR surface point matching. With the resulting mapping, a DVF characteristic of the deformation between the two AR surfaces is generated by B-spline approximation, which serves as the initial DVF for the following Demons DIR between the two AR-free HDR CT images. Finally, the calculated DVF via Demons combined with the initial one serve as the final DVF to map doses between HDR fractions. Results: The segmentation and registration accuracy are quantitatively assessed by nine clinical HDR cases from three gynecological cancer patients. The quantitative results as well as the visual inspection of the DIR indicate that our proposed method can suppress the interference of the applicator with the DIR algorithm, and accurately register HDR CT images as well as deform and add interfractional HDR doses. Conclusions: We have developed a novel and robust DIR scheme that can perform registration between HDR gynecological CT images and yield accurate registration results. This new DIR scheme has potential for accurate interfractional HDR dose accumulation. This work is supported in part by the National Natural ScienceFoundation of China (no 30970866 and no 81301940)« less

Artery and vein segmentation of the cerebral vasculature in 4D CT using a 3D fully convolutional neural network

NASA Astrophysics Data System (ADS)

Meijs, Midas; Manniesing, Rashindra

2018-02-01

Segmentation of the arteries and veins of the cerebral vasculature is important for improved visualization and for the detection of vascular related pathologies including arteriovenous malformations. We propose a 3D fully convolutional neural network (CNN) using a time-to-signal image as input and the distance to the center of gravity of the brain as spatial feature integrated in the final layers of the CNN. The method was trained and validated on 6 and tested on 4 4D CT patient imaging data. The reference standard was acquired by manual annotations by an experienced observer. Quantitative evaluation showed a mean Dice similarity coefficient of 0.94 +/- 0.03 and 0.97 +/- 0.01, a mean absolute volume difference of 4.36 +/- 5.47 % and 1.79 +/- 2.26 % for artery and vein respectively and an overall accuracy of 0.96 +/- 0.02. The average calculation time per volume on the test set was approximately one minute. Our method shows promising results and enables fast and accurate segmentation of arteries and veins in full 4D CT imaging data.

Advances in Pancreatic CT Imaging.

PubMed

Almeida, Renata R; Lo, Grace C; Patino, Manuel; Bizzo, Bernardo; Canellas, Rodrigo; Sahani, Dushyant V

2018-07-01

The purpose of this article is to discuss the advances in CT acquisition and image postprocessing as they apply to imaging the pancreas and to conceptualize the role of radiogenomics and machine learning in pancreatic imaging. CT is the preferred imaging modality for assessment of pancreatic diseases. Recent advances in CT (dual-energy CT, CT perfusion, CT volumetry, and radiogenomics) and emerging computational algorithms (machine learning) have the potential to further increase the value of CT in pancreatic imaging.

Brain CT image similarity retrieval method based on uncertain location graph.

PubMed

Pan, Haiwei; Li, Pengyuan; Li, Qing; Han, Qilong; Feng, Xiaoning; Gao, Linlin

2014-03-01

A number of brain computed tomography (CT) images stored in hospitals that contain valuable information should be shared to support computer-aided diagnosis systems. Finding the similar brain CT images from the brain CT image database can effectively help doctors diagnose based on the earlier cases. However, the similarity retrieval for brain CT images requires much higher accuracy than the general images. In this paper, a new model of uncertain location graph (ULG) is presented for brain CT image modeling and similarity retrieval. According to the characteristics of brain CT image, we propose a novel method to model brain CT image to ULG based on brain CT image texture. Then, a scheme for ULG similarity retrieval is introduced. Furthermore, an effective index structure is applied to reduce the searching time. Experimental results reveal that our method functions well on brain CT images similarity retrieval with higher accuracy and efficiency.

Computed tomographic features of canine nonparenchymal hemangiosarcoma.

PubMed

Fukuda, Shoko; Kobayashi, Tetsuya; Robertson, Ian D; Oshima, Fukiko; Fukazawa, Eri; Nakano, Yuko; Ono, Shin; Thrall, Donald E

2014-01-01

The purpose of this retrospective study was to describe pre- and postcontrast computed tomographic (CT) characteristics of confirmed nonparenchymal hemangiosarcoma in a group of dogs. Medical records were searched during the period of July 2003 and October 2011 and dogs with histologically confirmed nonparenchymal hemangiosarcoma and pre- and postcontrast CT images were recruited. Two observers recorded a consensus opinion for the following CT characteristics for each dog: largest transverse tumor diameter, number of masses, general tumor shape, character of the tumor margin, precontrast appearance, presence of dystrophic calcification, presence of postcontrast enhancement, pattern of postcontrast enhancement, presence of regional lymphadenopathy, and presence of associated cavitary fluid. A total of 17 dogs met inclusion criteria. Tumors were located in the nasal cavity, muscle, mandible, mesentery, subcutaneous tissue, and retroperitoneal space. Computed tomographic features of nonparenchymal hemangiosarcoma were similar to those of other soft tissue sarcomas, with most tumors being heterogeneous in precontrast images, invasive into adjacent tissue, and heterogeneously contrast enhancing. One unexpected finding was the presence of intense foci of contrast enhancement in 13 of the 17 tumors (76%). This appearance, which is not typical of other soft tissue sarcomas, was consistent with contrast medium residing in vascular channels. Findings indicated that there were no unique distinguishing CT characteristics for nonparenchymal hemangiosarcoma in dogs; however, the presence of highly attenuating foci of contrast enhancement may warrant further investigation in prospective diagnostic sensitivity and treatment outcome studies. © 2014 American College of Veterinary Radiology.