Imaging genetics approach to predict progression of Parkinson's diseases.

PubMed

Mansu Kim; Seong-Jin Son; Hyunjin Park

2017-07-01

Imaging genetics is a tool to extract genetic variants associated with both clinical phenotypes and imaging information. The approach can extract additional genetic variants compared to conventional approaches to better investigate various diseased conditions. Here, we applied imaging genetics to study Parkinson's disease (PD). We aimed to extract significant features derived from imaging genetics and neuroimaging. We built a regression model based on extracted significant features combining genetics and neuroimaging to better predict clinical scores of PD progression (i.e. MDS-UPDRS). Our model yielded high correlation (r = 0.697, p <; 0.001) and low root mean squared error (8.36) between predicted and actual MDS-UPDRS scores. Neuroimaging (from 123 I-Ioflupane SPECT) predictors of regression model were computed from independent component analysis approach. Genetic features were computed using image genetics approach based on identified neuroimaging features as intermediate phenotypes. Joint modeling of neuroimaging and genetics could provide complementary information and thus have the potential to provide further insight into the pathophysiology of PD. Our model included newly found neuroimaging features and genetic variants which need further investigation.

Mining hidden data to predict patient prognosis: texture feature extraction and machine learning in mammography

NASA Astrophysics Data System (ADS)

Leighs, J. A.; Halling-Brown, M. D.; Patel, M. N.

2018-03-01

The UK currently has a national breast cancer-screening program and images are routinely collected from a number of screening sites, representing a wealth of invaluable data that is currently under-used. Radiologists evaluate screening images manually and recall suspicious cases for further analysis such as biopsy. Histological testing of biopsy samples confirms the malignancy of the tumour, along with other diagnostic and prognostic characteristics such as disease grade. Machine learning is becoming increasingly popular for clinical image classification problems, as it is capable of discovering patterns in data otherwise invisible. This is particularly true when applied to medical imaging features; however clinical datasets are often relatively small. A texture feature extraction toolkit has been developed to mine a wide range of features from medical images such as mammograms. This study analysed a dataset of 1,366 radiologist-marked, biopsy-proven malignant lesions obtained from the OPTIMAM Medical Image Database (OMI-DB). Exploratory data analysis methods were employed to better understand extracted features. Machine learning techniques including Classification and Regression Trees (CART), ensemble methods (e.g. random forests), and logistic regression were applied to the data to predict the disease grade of the analysed lesions. Prediction scores of up to 83% were achieved; sensitivity and specificity of the models trained have been discussed to put the results into a clinical context. The results show promise in the ability to predict prognostic indicators from the texture features extracted and thus enable prioritisation of care for patients at greatest risk.

Accurate shade image matching by using a smartphone camera.

PubMed

Tam, Weng-Kong; Lee, Hsi-Jian

2017-04-01

Dental shade matching by using digital images may be feasible when suitable color features are properly manipulated. Separating the color features into feature spaces facilitates favorable matching. We propose using support vector machines (SVM), which are outstanding classifiers, in shade classification. A total of 1300 shade tab images were captured using a smartphone camera with auto-mode settings and no flash. The images were shot at angled distances of 14-20cm from a shade guide at a clinic equipped with light tubes that produced a 4000K color temperature. The Group 1 samples comprised 1040 tab images, for which the shade guide was randomly positioned in the clinic, and the Group 2 samples comprised 260 tab images, for which the shade guide had a fixed position in the clinic. Rectangular content was cropped manually on each shade tab image and further divided into 10×2 blocks. The color features extracted from the blocks were described using a feature vector. The feature vectors in each group underwent SVM training and classification by using the "leave-one-out" strategy. The top one and three accuracies of Group 1 were 0.86 and 0.98, respectively, and those of Group 2 were 0.97 and 1.00, respectively. This study provides a feasible technique for dental shade classification that uses the camera of a mobile device. The findings reveal that the proposed SVM classification might outperform the shade-matching results of previous studies that have performed similarity measurements of ΔE levels or used an S, a*, b* feature set. Copyright © 2016 Japan Prosthodontic Society. Published by Elsevier Ltd. All rights reserved.

Image-based modeling of tumor shrinkage in head and neck radiation therapy1

PubMed Central

Chao, Ming; Xie, Yaoqin; Moros, Eduardo G.; Le, Quynh-Thu; Xing, Lei

2010-01-01

Purpose: Understanding the kinetics of tumor growth∕shrinkage represents a critical step in quantitative assessment of therapeutics and realization of adaptive radiation therapy. This article presents a novel framework for image-based modeling of tumor change and demonstrates its performance with synthetic images and clinical cases. Methods: Due to significant tumor tissue content changes, similarity-based models are not suitable for describing the process of tumor volume changes. Under the hypothesis that tissue features in a tumor volume or at the boundary region are partially preserved, the kinetic change was modeled in two steps: (1) Autodetection of homologous tissue features shared by two input images using the scale invariance feature transformation (SIFT) method; and (2) establishment of a voxel-to-voxel correspondence between the images for the remaining spatial points by interpolation. The correctness of the tissue feature correspondence was assured by a bidirectional association procedure, where SIFT features were mapped from template to target images and reversely. A series of digital phantom experiments and five head and neck clinical cases were used to assess the performance of the proposed technique. Results: The proposed technique can faithfully identify the known changes introduced when constructing the digital phantoms. The subsequent feature-guided thin plate spline calculation reproduced the “ground truth” with accuracy better than 1.5 mm. For the clinical cases, the new algorithm worked reliably for a volume change as large as 30%. Conclusions: An image-based tumor kinetic algorithm was developed to model the tumor response to radiation therapy. The technique provides a practical framework for future application in adaptive radiation therapy. PMID:20527569

A Simplified Approach to Encephalitis and Its Mimics: Key Clinical Decision Points in the Setting of Specific Imaging Abnormalities.

PubMed

McKnight, Colin D; Kelly, Aine M; Petrou, Myria; Nidecker, Anna E; Lorincz, Matthew T; Altaee, Duaa K; Gebarski, Stephen S; Foerster, Bradley

2017-06-01

Infectious encephalitis is a relatively common cause of morbidity and mortality. Treatment of infectious encephalitis with antiviral medication can be highly effective when administered promptly. Clinical mimics of encephalitis arise from a broad range of pathologic processes, including toxic, metabolic, neoplastic, autoimmune, and cardiovascular etiologies. These mimics need to be rapidly differentiated from infectious encephalitis to appropriately manage the correct etiology; however, the many overlapping signs of these various entities present a challenge to accurate diagnosis. A systematic approach that considers both the clinical manifestations and the imaging findings of infectious encephalitis and its mimics can contribute to more accurate and timely diagnosis. Following an institutional review board approval, a health insurance portability and accountability act (HIPAA)-compliant search of our institutional imaging database (teaching files) was conducted to generate a list of adult and pediatric patients who presented between January 1, 1995 and October 10, 2013 for imaging to evaluate possible cases of encephalitis. Pertinent medical records, including clinical notes as well as surgical and pathology reports, were reviewed and correlated with imaging findings. Clinical and imaging findings were combined to generate useful flowcharts designed to assist in distinguishing infectious encephalitis from its mimics. Key imaging features were reviewed and were placed in the context of the provided flowcharts. Four flowcharts were presented based on the primary anatomic site of imaging abnormality: group 1: temporal lobe; group 2: cerebral cortex; group 3: deep gray matter; and group 4: white matter. An approach that combines features on clinical presentation was then detailed. Imaging examples were used to demonstrate similarities and key differences. Early recognition of infectious encephalitis is critical, but can be quite complex due to diverse pathologies and overlapping features. Synthesis of both the clinical and imaging features of infectious encephalitis and its mimics is critical to a timely and accurate diagnosis. The use of the flowcharts presented in this article can further enable both clinicians and radiologists to more confidently differentiate encephalitis from its mimics and improve patient care. Copyright © 2017 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Bayesian network interface for assisting radiology interpretation and education

NASA Astrophysics Data System (ADS)

Duda, Jeffrey; Botzolakis, Emmanuel; Chen, Po-Hao; Mohan, Suyash; Nasrallah, Ilya; Rauschecker, Andreas; Rudie, Jeffrey; Bryan, R. Nick; Gee, James; Cook, Tessa

2018-03-01

In this work, we present the use of Bayesian networks for radiologist decision support during clinical interpretation. This computational approach has the advantage of avoiding incorrect diagnoses that result from known human cognitive biases such as anchoring bias, framing effect, availability bias, and premature closure. To integrate Bayesian networks into clinical practice, we developed an open-source web application that provides diagnostic support for a variety of radiology disease entities (e.g., basal ganglia diseases, bone lesions). The Clinical tool presents the user with a set of buttons representing clinical and imaging features of interest. These buttons are used to set the value for each observed feature. As features are identified, the conditional probabilities for each possible diagnosis are updated in real time. Additionally, using sensitivity analysis, the interface may be set to inform the user which remaining imaging features provide maximum discriminatory information to choose the most likely diagnosis. The Case Submission tools allow the user to submit a validated case and the associated imaging features to a database, which can then be used for future tuning/testing of the Bayesian networks. These submitted cases are then reviewed by an assigned expert using the provided QC tool. The Research tool presents users with cases with previously labeled features and a chosen diagnosis, for the purpose of performance evaluation. Similarly, the Education page presents cases with known features, but provides real time feedback on feature selection.

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

FFDM image quality assessment using computerized image texture analysis

NASA Astrophysics Data System (ADS)

Berger, Rachelle; Carton, Ann-Katherine; Maidment, Andrew D. A.; Kontos, Despina

2010-04-01

Quantitative measures of image quality (IQ) are routinely obtained during the evaluation of imaging systems. These measures, however, do not necessarily correlate with the IQ of the actual clinical images, which can also be affected by factors such as patient positioning. No quantitative method currently exists to evaluate clinical IQ. Therefore, we investigated the potential of using computerized image texture analysis to quantitatively assess IQ. Our hypothesis is that image texture features can be used to assess IQ as a measure of the image signal-to-noise ratio (SNR). To test feasibility, the "Rachel" anthropomorphic breast phantom (Model 169, Gammex RMI) was imaged with a Senographe 2000D FFDM system (GE Healthcare) using 220 unique exposure settings (target/filter, kVs, and mAs combinations). The mAs were varied from 10%-300% of that required for an average glandular dose (AGD) of 1.8 mGy. A 2.5cm2 retroareolar region of interest (ROI) was segmented from each image. The SNR was computed from the ROIs segmented from images linear with dose (i.e., raw images) after flat-field and off-set correction. Image texture features of skewness, coarseness, contrast, energy, homogeneity, and fractal dimension were computed from the Premium ViewTM postprocessed image ROIs. Multiple linear regression demonstrated a strong association between the computed image texture features and SNR (R2=0.92, p<=0.001). When including kV, target and filter as additional predictor variables, a stronger association with SNR was observed (R2=0.95, p<=0.001). The strong associations indicate that computerized image texture analysis can be used to measure image SNR and potentially aid in automating IQ assessment as a component of the clinical workflow. Further work is underway to validate our findings in larger clinical datasets.

New clinical opportunities for retinal vascular imaging: adaptive optics to OCT angiography

NASA Astrophysics Data System (ADS)

Rosen, Richard; Chui, Toco; Weitz, Rishard; Dubra, Alfredo; Carroll, Joseph; Garcia, Patricia; Pinhas, Alexander; Scripsema, Nicole; Mo, Shelley; Agemy, Steven; Krawitz, Brian

2018-03-01

As techniques of retinal imaging have evolved, anatomic features that were only assessable in the laboratory have become available in the clinic for patient care. The retinal capillaries were initially described on microscope sections in the pathology laboratory. As optical methods have advanced these features have become part of the routine clinical landscape inspected daily by physicians. This paper briefly traces the evolution of these techniques and shows how they fit into the modern diagnostic armamentarium of ophthalmic retinal care.

Thermography based prescreening software tool for veterinary clinics

NASA Astrophysics Data System (ADS)

Dahal, Rohini; Umbaugh, Scott E.; Mishra, Deependra; Lama, Norsang; Alvandipour, Mehrdad; Umbaugh, David; Marino, Dominic J.; Sackman, Joseph

2017-05-01

Under development is a clinical software tool which can be used in the veterinary clinics as a prescreening tool for these pathologies: anterior cruciate ligament (ACL) disease, bone cancer and feline hyperthyroidism. Currently, veterinary clinical practice uses several imaging techniques including radiology, computed tomography (CT), and magnetic resonance imaging (MRI). But, harmful radiation involved during imaging, expensive equipment setup, excessive time consumption and the need for a cooperative patient during imaging, are major drawbacks of these techniques. In veterinary procedures, it is very difficult for animals to remain still for the time periods necessary for standard imaging without resorting to sedation - which creates another set of complexities. Therefore, clinical application software integrated with a thermal imaging system and the algorithms with high sensitivity and specificity for these pathologies, can address the major drawbacks of the existing imaging techniques. A graphical user interface (GUI) has been created to allow ease of use for the clinical technician. The technician inputs an image, enters patient information, and selects the camera view associated with the image and the pathology to be diagnosed. The software will classify the image using an optimized classification algorithm that has been developed through thousands of experiments. Optimal image features are extracted and the feature vector is then used in conjunction with the stored image database for classification. Classification success rates as high as 88% for bone cancer, 75% for ACL and 90% for feline hyperthyroidism have been achieved. The software is currently undergoing preliminary clinical testing.

A standardised protocol for texture feature analysis of endoscopic images in gynaecological cancer.

PubMed

Neofytou, Marios S; Tanos, Vasilis; Pattichis, Marios S; Pattichis, Constantinos S; Kyriacou, Efthyvoulos C; Koutsouris, Dimitris D

2007-11-29

In the development of tissue classification methods, classifiers rely on significant differences between texture features extracted from normal and abnormal regions. Yet, significant differences can arise due to variations in the image acquisition method. For endoscopic imaging of the endometrium, we propose a standardized image acquisition protocol to eliminate significant statistical differences due to variations in: (i) the distance from the tissue (panoramic vs close up), (ii) difference in viewing angles and (iii) color correction. We investigate texture feature variability for a variety of targets encountered in clinical endoscopy. All images were captured at clinically optimum illumination and focus using 720 x 576 pixels and 24 bits color for: (i) a variety of testing targets from a color palette with a known color distribution, (ii) different viewing angles, (iv) two different distances from a calf endometrial and from a chicken cavity. Also, human images from the endometrium were captured and analysed. For texture feature analysis, three different sets were considered: (i) Statistical Features (SF), (ii) Spatial Gray Level Dependence Matrices (SGLDM), and (iii) Gray Level Difference Statistics (GLDS). All images were gamma corrected and the extracted texture feature values were compared against the texture feature values extracted from the uncorrected images. Statistical tests were applied to compare images from different viewing conditions so as to determine any significant differences. For the proposed acquisition procedure, results indicate that there is no significant difference in texture features between the panoramic and close up views and between angles. For a calibrated target image, gamma correction provided an acquired image that was a significantly better approximation to the original target image. In turn, this implies that the texture features extracted from the corrected images provided for better approximations to the original images. Within the proposed protocol, for human ROIs, we have found that there is a large number of texture features that showed significant differences between normal and abnormal endometrium. This study provides a standardized protocol for avoiding any significant texture feature differences that may arise due to variability in the acquisition procedure or the lack of color correction. After applying the protocol, we have found that significant differences in texture features will only be due to the fact that the features were extracted from different types of tissue (normal vs abnormal).

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.

TU-D-207B-02: Delta-Radiomics: The Prognostic Value of Therapy-Induced Changes in Radiomics Features for Stage III Non-Small Cell Lung Cancer Patients

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

Fave, X; Court, L; UT Health Science Center, Graduate School of Biomedical Sciences, Houston, TX

Purpose: To determine how radiomics features change during radiation therapy and whether those changes (delta-radiomics features) can improve prognostic models built with clinical factors. Methods: 62 radiomics features, including histogram, co-occurrence, run-length, gray-tone difference, and shape features, were calculated from pretreatment and weekly intra-treatment CTs for 107 stage III NSCLC patients (5–9 images per patient). Image preprocessing for each feature was determined using the set of pretreatment images: bit-depth resample and/or a smoothing filter were tested for their impact on volume-correlation and significance of each feature in univariate cox regression models to maximize their information content. Next, the optimized featuresmore » were calculated from the intratreatment images and tested in linear mixed-effects models to determine which features changed significantly with dose-fraction. The slopes in these significant features were defined as delta-radiomics features. To test their prognostic potential multivariate cox regression models were fitted, first using only clinical features and then clinical+delta-radiomics features for overall-survival, local-recurrence, and distant-metastases. Leave-one-out cross validation was used for model-fitting and patient predictions. Concordance indices(c-index) and p-values for the log-rank test with patients stratified at the median were calculated. Results: Approximately one-half of the 62 optimized features required no preprocessing, one-fourth required smoothing, and one-fourth required smoothing and resampling. From these, 54 changed significantly during treatment. For overall-survival, the c-index improved from 0.52 for clinical factors alone to 0.62 for clinical+delta-radiomics features. For distant-metastases, the c-index improved from 0.53 to 0.58, while for local-recurrence it did not improve. Patient stratification significantly improved (p-value<0.05) for overallsurvival and distant-metastases when delta-radiomics features were included. The delta-radiomics versions of autocorrelation, kurtosis, and compactness were selected most frequently in leave-one-out iterations. Conclusion: Weekly changes in radiomics features can potentially be used to evaluate treatment response and predict patient outcomes. High-risk patients could be recommended for dose escalation or consolidation chemotherapy. This project was funded in part by grants from the National Cancer Institute (NCI) and the Cancer Prevention Research Institute of Texas (CPRIT).« less

Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels.

PubMed

Soltaninejad, Mohammadreza; Yang, Guang; Lambrou, Tryphon; Allinson, Nigel; Jones, Timothy L; Barrick, Thomas R; Howe, Franklyn A; Ye, Xujiong

2018-04-01

Accurate segmentation of brain tumour in magnetic resonance images (MRI) is a difficult task due to various tumour types. Using information and features from multimodal MRI including structural MRI and isotropic (p) and anisotropic (q) components derived from the diffusion tensor imaging (DTI) may result in a more accurate analysis of brain images. We propose a novel 3D supervoxel based learning method for segmentation of tumour in multimodal MRI brain images (conventional MRI and DTI). Supervoxels are generated using the information across the multimodal MRI dataset. For each supervoxel, a variety of features including histograms of texton descriptor, calculated using a set of Gabor filters with different sizes and orientations, and first order intensity statistical features are extracted. Those features are fed into a random forests (RF) classifier to classify each supervoxel into tumour core, oedema or healthy brain tissue. The method is evaluated on two datasets: 1) Our clinical dataset: 11 multimodal images of patients and 2) BRATS 2013 clinical dataset: 30 multimodal images. For our clinical dataset, the average detection sensitivity of tumour (including tumour core and oedema) using multimodal MRI is 86% with balanced error rate (BER) 7%; while the Dice score for automatic tumour segmentation against ground truth is 0.84. The corresponding results of the BRATS 2013 dataset are 96%, 2% and 0.89, respectively. The method demonstrates promising results in the segmentation of brain tumour. Adding features from multimodal MRI images can largely increase the segmentation accuracy. The method provides a close match to expert delineation across all tumour grades, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management. Copyright © 2018 Elsevier B.V. All rights reserved.

NCI Workshop Report: Clinical and Computational Requirements for Correlating Imaging Phenotypes with Genomics Signatures.

PubMed

Colen, Rivka; Foster, Ian; Gatenby, Robert; Giger, Mary Ellen; Gillies, Robert; Gutman, David; Heller, Matthew; Jain, Rajan; Madabhushi, Anant; Madhavan, Subha; Napel, Sandy; Rao, Arvind; Saltz, Joel; Tatum, James; Verhaak, Roeland; Whitman, Gary

2014-10-01

The National Cancer Institute (NCI) Cancer Imaging Program organized two related workshops on June 26-27, 2013, entitled "Correlating Imaging Phenotypes with Genomics Signatures Research" and "Scalable Computational Resources as Required for Imaging-Genomics Decision Support Systems." The first workshop focused on clinical and scientific requirements, exploring our knowledge of phenotypic characteristics of cancer biological properties to determine whether the field is sufficiently advanced to correlate with imaging phenotypes that underpin genomics and clinical outcomes, and exploring new scientific methods to extract phenotypic features from medical images and relate them to genomics analyses. The second workshop focused on computational methods that explore informatics and computational requirements to extract phenotypic features from medical images and relate them to genomics analyses and improve the accessibility and speed of dissemination of existing NIH resources. These workshops linked clinical and scientific requirements of currently known phenotypic and genotypic cancer biology characteristics with imaging phenotypes that underpin genomics and clinical outcomes. The group generated a set of recommendations to NCI leadership and the research community that encourage and support development of the emerging radiogenomics research field to address short-and longer-term goals in cancer research.

Quantitative radiomic profiling of glioblastoma represents transcriptomic expression.

PubMed

Kong, Doo-Sik; Kim, Junhyung; Ryu, Gyuha; You, Hye-Jin; Sung, Joon Kyung; Han, Yong Hee; Shin, Hye-Mi; Lee, In-Hee; Kim, Sung-Tae; Park, Chul-Kee; Choi, Seung Hong; Choi, Jeong Won; Seol, Ho Jun; Lee, Jung-Il; Nam, Do-Hyun

2018-01-19

Quantitative imaging biomarkers have increasingly emerged in the field of research utilizing available imaging modalities. We aimed to identify good surrogate radiomic features that can represent genetic changes of tumors, thereby establishing noninvasive means for predicting treatment outcome. From May 2012 to June 2014, we retrospectively identified 65 patients with treatment-naïve glioblastoma with available clinical information from the Samsung Medical Center data registry. Preoperative MR imaging data were obtained for all 65 patients with primary glioblastoma. A total of 82 imaging features including first-order statistics, volume, and size features, were semi-automatically extracted from structural and physiologic images such as apparent diffusion coefficient and perfusion images. Using commercially available software, NordicICE, we performed quantitative imaging analysis and collected the dataset composed of radiophenotypic parameters. Unsupervised clustering methods revealed that the radiophenotypic dataset was composed of three clusters. Each cluster represented a distinct molecular classification of glioblastoma; classical type, proneural and neural types, and mesenchymal type. These clusters also reflected differential clinical outcomes. We found that extracted imaging signatures does not represent copy number variation and somatic mutation. Quantitative radiomic features provide a potential evidence to predict molecular phenotype and treatment outcome. Radiomic profiles represents transcriptomic phenotypes more well.

Radiomic biomarkers from PET/CT multi-modality fusion images for the prediction of immunotherapy response in advanced non-small cell lung cancer patients

NASA Astrophysics Data System (ADS)

Mu, Wei; Qi, Jin; Lu, Hong; Schabath, Matthew; Balagurunathan, Yoganand; Tunali, Ilke; Gillies, Robert James

2018-02-01

Purpose: Investigate the ability of using complementary information provided by the fusion of PET/CT images to predict immunotherapy response in non-small cell lung cancer (NSCLC) patients. Materials and methods: We collected 64 patients diagnosed with primary NSCLC treated with anti PD-1 checkpoint blockade. Using PET/CT images, fused images were created following multiple methodologies, resulting in up to 7 different images for the tumor region. Quantitative image features were extracted from the primary image (PET/CT) and the fused images, which included 195 from primary images and 1235 features from the fusion images. Three clinical characteristics were also analyzed. We then used support vector machine (SVM) classification models to identify discriminant features that predict immunotherapy response at baseline. Results: A SVM built with 87 fusion features and 13 primary PET/CT features on validation dataset had an accuracy and area under the ROC curve (AUROC) of 87.5% and 0.82, respectively, compared to a model built with 113 original PET/CT features on validation dataset 78.12% and 0.68. Conclusion: The fusion features shows better ability to predict immunotherapy response prediction compared to individual image features.

Brain Morphometry Using Anatomical Magnetic Resonance Imaging

ERIC Educational Resources Information Center

Bansal, Ravi; Gerber, Andrew J.; Peterson, Bradley S.

2008-01-01

The efficacy of anatomical magnetic resonance imaging (MRI) in studying the morphological features of various regions of the brain is described, also providing the steps used in the processing and studying of the images. The ability to correlate these features with several clinical and psychological measures can help in using anatomical MRI to…

Association between dynamic features of breast DCE-MR imaging and clinical response of neoadjuvant chemotherapy: a preliminary analysis

NASA Astrophysics Data System (ADS)

Huang, Lijuan; Fan, Ming; Li, Lihua; Zhang, Juan; Shao, Guoliang; Zheng, Bin

2016-03-01

Neoadjuvant chemotherapy (NACT) is being used increasingly in the management of patients with breast cancer for systemically reducing the size of primary tumor before surgery in order to improve survival. The clinical response of patients to NACT is correlated with reduced or abolished of their primary tumor, which is important for treatment in the next stage. Recently, the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is used for evaluation of the response of patients to NACT. To measure this correlation, we extracted the dynamic features from the DCE- MRI and performed association analysis between these features and the clinical response to NACT. In this study, 59 patients are screened before NATC, of which 47 are complete or partial response, and 12 are no response. We segmented the breast areas depicted on each MR image by a computer-aided diagnosis (CAD) scheme, registered images acquired from the sequential MR image scan series, and calculated eighteen features extracted from DCE-MRI. We performed SVM with the 18 features for classification between patients of response and no response. Furthermore, 6 of the 18 features are selected to refine the classification by using Genetic Algorithm. The accuracy, sensitivity and specificity are 87%, 95.74% and 50%, respectively. The calculated area under a receiver operating characteristic (ROC) curve is 0.79+/-0.04. This study indicates that the features of DCE-MRI of breast cancer are associated with the response of NACT. Therefore, our method could be helpful for evaluation of NACT in treatment of breast cancer.

Ultrasonographic evaluation of acute pelvic pain in pregnant and postpartum period.

PubMed

Park, Sung Bin; Han, Byoung Hee; Lee, Young Ho

2017-04-22

Acute pelvic pain in pregnant and postpartum patients presents diagnostic and therapeutic challenges. The interpretation of imaging findings in these patients is influenced by the knowledge of the physiological changes that occur during the pregnant and postpartum period, as well as by the clinical history. Ultrasonography remains the primary imaging investigation of the pregnant and postpartum women. This article describes the causes and imaging features of acute pelvic pain in pregnant and postpartum period and suggests characteristics to such diseases, focusing on the ultrasonography features that allow one to arrive at the corrective diagnosis. Knowledge of the clinical settings and imaging features of acute pelvic pain in pregnant and postpartum period can lead to accurate diagnosis and appropriate management of the condition.

"Radio-oncomics" : The potential of radiomics in radiation oncology.

PubMed

Peeken, Jan Caspar; Nüsslin, Fridtjof; Combs, Stephanie E

2017-10-01

Radiomics, a recently introduced concept, describes quantitative computerized algorithm-based feature extraction from imaging data including computer tomography (CT), magnetic resonance imaging (MRT), or positron-emission tomography (PET) images. For radiation oncology it offers the potential to significantly influence clinical decision-making and thus therapy planning and follow-up workflow. After image acquisition, image preprocessing, and defining regions of interest by structure segmentation, algorithms are applied to calculate shape, intensity, texture, and multiscale filter features. By combining multiple features and correlating them with clinical outcome, prognostic models can be created. Retrospective studies have proposed radiomics classifiers predicting, e. g., overall survival, radiation treatment response, distant metastases, or radiation-related toxicity. Besides, radiomics features can be correlated with genomic information ("radiogenomics") and could be used for tumor characterization. Distinct patterns based on data-based as well as genomics-based features will influence radiation oncology in the future. Individualized treatments in terms of dose level adaption and target volume definition, as well as other outcome-related parameters will depend on radiomics and radiogenomics. By integration of various datasets, the prognostic power can be increased making radiomics a valuable part of future precision medicine approaches. This perspective demonstrates the evidence for the radiomics concept in radiation oncology. The necessity of further studies to integrate radiomics classifiers into clinical decision-making and the radiation therapy workflow is emphasized.

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.

[Clinical features and magnetic resonance imaging evaluation of encephalopathy in high-risk late preterm infants].

PubMed

Zhu, Yan; Zhang, Ke; Hu, Lan; Xiao, Mi-Li; Li, Zhi-Hua; Chen, Chao

2017-05-01

To investigate the risk factors, clinical features, and magnetic resonance imaging (MRI) changes of encephalopathy in high-risk late preterm infants. Head MRI scan was performed for late preterm infants with high-risk factors for brain injury who were hospitalized between January 2009 and December 2014. The risk factors, clinical features, and head MRI features of encephalopathy in late preterm infants were analyzed. A total of 1 007 late preterm infants underwent MRI scan, among whom 313 (31.1%) had imaging features in accordance with the features of encephalopathy of prematurity. Of all infants, 76.7% had white matter damage. There was no association between the development of encephalopathy and gestational age in late preterm infants, but the detection rate of encephalopathy gradually increased with the increasing birth weight (P<0.05). The logistic regression analysis showed that a history of resuscitation was an independent risk factor for encephalopathy of prematurity (P<0.01). Encephalopathy of prematurity is commonly seen in high-risk late preterm infants, especially white matter damage. A history of resuscitation is an independent risk factor for encephalopathy in late preterm infants.

Space Technology - Game Changing Development NASA Facts: Autonomous Medical Operations

NASA Technical Reports Server (NTRS)

Thompson, David E.

2018-01-01

The AMO (Autonomous Medical Operations) Project is working extensively to train medical models on the reliability and confidence of computer-aided interpretation of ultrasound images in various clinical settings, and of various anatomical structures. AI (Artificial Intelligence) algorithms recognize and classify features in the ultrasound images, and these are compared to those features that clinicians use to diagnose diseases. The acquisition of clinically validated image assessment and the use of the AI algorithms constitutes fundamental baseline for a Medical Decision Support System that will advise crew on long-duration, remote missions.

Clinical skin imaging using color spatial frequency domain imaging (Conference Presentation)

NASA Astrophysics Data System (ADS)

Yang, Bin; Lesicko, John; Moy, Austin J.; Reichenberg, Jason; Tunnell, James W.

2016-02-01

Skin diseases are typically associated with underlying biochemical and structural changes compared with normal tissues, which alter the optical properties of the skin lesions, such as tissue absorption and scattering. Although widely used in dermatology clinics, conventional dermatoscopes don't have the ability to selectively image tissue absorption and scattering, which may limit its diagnostic power. Here we report a novel clinical skin imaging technique called color spatial frequency domain imaging (cSFDI) which enhances contrast by rendering color spatial frequency domain (SFD) image at high spatial frequency. Moreover, by tuning spatial frequency, we can obtain both absorption weighted and scattering weighted images. We developed a handheld imaging system specifically for clinical skin imaging. The flexible configuration of the system allows for better access to skin lesions in hard-to-reach regions. A total of 48 lesions from 31 patients were imaged under 470nm, 530nm and 655nm illumination at a spatial frequency of 0.6mm^(-1). The SFD reflectance images at 470nm, 530nm and 655nm were assigned to blue (B), green (G) and red (R) channels to render a color SFD image. Our results indicated that color SFD images at f=0.6mm-1 revealed properties that were not seen in standard color images. Structural features were enhanced and absorption features were reduced, which helped to identify the sources of the contrast. This imaging technique provides additional insights into skin lesions and may better assist clinical diagnosis.

Computer-Based Image Analysis for Plus Disease Diagnosis in Retinopathy of Prematurity: Performance of the "i-ROP" System and Image Features Associated With Expert Diagnosis.

PubMed

Ataer-Cansizoglu, Esra; Bolon-Canedo, Veronica; Campbell, J Peter; Bozkurt, Alican; Erdogmus, Deniz; Kalpathy-Cramer, Jayashree; Patel, Samir; Jonas, Karyn; Chan, R V Paul; Ostmo, Susan; Chiang, Michael F

2015-11-01

We developed and evaluated the performance of a novel computer-based image analysis system for grading plus disease in retinopathy of prematurity (ROP), and identified the image features, shapes, and sizes that best correlate with expert diagnosis. A dataset of 77 wide-angle retinal images from infants screened for ROP was collected. A reference standard diagnosis was determined for each image by combining image grading from 3 experts with the clinical diagnosis from ophthalmoscopic examination. Manually segmented images were cropped into a range of shapes and sizes, and a computer algorithm was developed to extract tortuosity and dilation features from arteries and veins. Each feature was fed into our system to identify the set of characteristics that yielded the highest-performing system compared to the reference standard, which we refer to as the "i-ROP" system. Among the tested crop shapes, sizes, and measured features, point-based measurements of arterial and venous tortuosity (combined), and a large circular cropped image (with radius 6 times the disc diameter), provided the highest diagnostic accuracy. The i-ROP system achieved 95% accuracy for classifying preplus and plus disease compared to the reference standard. This was comparable to the performance of the 3 individual experts (96%, 94%, 92%), and significantly higher than the mean performance of 31 nonexperts (81%). This comprehensive analysis of computer-based plus disease suggests that it may be feasible to develop a fully-automated system based on wide-angle retinal images that performs comparably to expert graders at three-level plus disease discrimination. Computer-based image analysis, using objective and quantitative retinal vascular features, has potential to complement clinical ROP diagnosis by ophthalmologists.

Discriminative feature representation: an effective postprocessing solution to low dose CT imaging

NASA Astrophysics Data System (ADS)

Chen, Yang; Liu, Jin; Hu, Yining; Yang, Jian; Shi, Luyao; Shu, Huazhong; Gui, Zhiguo; Coatrieux, Gouenou; Luo, Limin

2017-03-01

This paper proposes a concise and effective approach termed discriminative feature representation (DFR) for low dose computerized tomography (LDCT) image processing, which is currently a challenging problem in medical imaging field. This DFR method assumes LDCT images as the superposition of desirable high dose CT (HDCT) 3D features and undesirable noise-artifact 3D features (the combined term of noise and artifact features induced by low dose scan protocols), and the decomposed HDCT features are used to provide the processed LDCT images with higher quality. The target HDCT features are solved via the DFR algorithm using a featured dictionary composed by atoms representing HDCT features and noise-artifact features. In this study, the featured dictionary is efficiently built using physical phantom images collected from the same CT scanner as the target clinical LDCT images to process. The proposed DFR method also has good robustness in parameter setting for different CT scanner types. This DFR method can be directly applied to process DICOM formatted LDCT images, and has good applicability to current CT systems. Comparative experiments with abdomen LDCT data validate the good performance of the proposed approach. This research was supported by National Natural Science Foundation under grants (81370040, 81530060), the Fundamental Research Funds for the Central Universities, and the Qing Lan Project in Jiangsu Province.

Classifying Acute Ischemic Stroke Onset Time using Deep Imaging Features

PubMed Central

Ho, King Chung; Speier, William; El-Saden, Suzie; Arnold, Corey W.

2017-01-01

Models have been developed to predict stroke outcomes (e.g., mortality) in attempt to provide better guidance for stroke treatment. However, there is little work in developing classification models for the problem of unknown time-since-stroke (TSS), which determines a patient’s treatment eligibility based on a clinical defined cutoff time point (i.e., <4.5hrs). In this paper, we construct and compare machine learning methods to classify TSS<4.5hrs using magnetic resonance (MR) imaging features. We also propose a deep learning model to extract hidden representations from the MR perfusion-weighted images and demonstrate classification improvement by incorporating these additional imaging features. Finally, we discuss a strategy to visualize the learned features from the proposed deep learning model. The cross-validation results show that our best classifier achieved an area under the curve of 0.68, which improves significantly over current clinical methods (0.58), demonstrating the potential benefit of using advanced machine learning methods in TSS classification. PMID:29854156

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.

Soft-Tissue Infections and Their Imaging Mimics: From Cellulitis to Necrotizing Fasciitis.

PubMed

Hayeri, Mohammad Reza; Ziai, Pouya; Shehata, Monda L; Teytelboym, Oleg M; Huang, Brady K

2016-10-01

Infection of the musculoskeletal system can be associated with high mortality and morbidity if not promptly and accurately diagnosed. These infections are generally diagnosed and managed clinically; however, clinical and laboratory findings sometimes lack sensitivity and specificity, and a definite diagnosis may not be possible. In uncertain situations, imaging is frequently performed to confirm the diagnosis, evaluate the extent of the disease, and aid in treatment planning. In particular, cross-sectional imaging, including computed tomography and magnetic resonance imaging, provides detailed anatomic information in the evaluation of soft tissues due to their inherent high spatial and contrast resolution. Imaging findings of soft-tissue infections can be nonspecific and can have different appearances depending on the depth and anatomic extent of tissue involvement. Although many imaging features of infectious disease can overlap with noninfectious processes, imaging can help establish the diagnosis when combined with the clinical history and laboratory findings. Radiologists should be familiar with the spectrum of imaging findings of soft-tissue infections to better aid the referring physician in managing these patients. The aim of this article is to review the spectrum of soft-tissue infections using a systematic anatomic compartment approach. We discuss the clinical features of soft-tissue infections, their imaging findings with emphasis on cross-sectional imaging, their potential mimics, and clinical management. © RSNA, 2016.

Automatic detection of diabetic retinopathy features in ultra-wide field retinal images

NASA Astrophysics Data System (ADS)

Levenkova, Anastasia; Sowmya, Arcot; Kalloniatis, Michael; Ly, Angelica; Ho, Arthur

2017-03-01

Diabetic retinopathy (DR) is a major cause of irreversible vision loss. DR screening relies on retinal clinical signs (features). Opportunities for computer-aided DR feature detection have emerged with the development of Ultra-WideField (UWF) digital scanning laser technology. UWF imaging covers 82% greater retinal area (200°), against 45° in conventional cameras3 , allowing more clinically relevant retinopathy to be detected4 . UWF images also provide a high resolution of 3078 x 2702 pixels. Currently DR screening uses 7 overlapping conventional fundus images, and the UWF images provide similar results1,4. However, in 40% of cases, more retinopathy was found outside the 7-field ETDRS) fields by UWF and in 10% of cases, retinopathy was reclassified as more severe4 . This is because UWF imaging allows examination of both the central retina and more peripheral regions, with the latter implicated in DR6 . We have developed an algorithm for automatic recognition of DR features, including bright (cotton wool spots and exudates) and dark lesions (microaneurysms and blot, dot and flame haemorrhages) in UWF images. The algorithm extracts features from grayscale (green "red-free" laser light) and colour-composite UWF images, including intensity, Histogram-of-Gradient and Local binary patterns. Pixel-based classification is performed with three different classifiers. The main contribution is the automatic detection of DR features in the peripheral retina. The method is evaluated by leave-one-out cross-validation on 25 UWF retinal images with 167 bright lesions, and 61 other images with 1089 dark lesions. The SVM classifier performs best with AUC of 94.4% / 95.31% for bright / dark lesions.

Review of quantitative ultrasound: envelope statistics and backscatter coefficient imaging and contributions to diagnostic ultrasound

PubMed Central

Oelze, Michael L.; Mamou, Jonathan

2017-01-01

Conventional medical imaging technologies, including ultrasound, have continued to improve over the years. For example, in oncology, medical imaging is characterized by high sensitivity, i.e., the ability to detect anomalous tissue features, but the ability to classify these tissue features from images often lacks specificity. As a result, a large number of biopsies of tissues with suspicious image findings are performed each year with a vast majority of these biopsies resulting in a negative finding. To improve specificity of cancer imaging, quantitative imaging techniques can play an important role. Conventional ultrasound B-mode imaging is mainly qualitative in nature. However, quantitative ultrasound (QUS) imaging can provide specific numbers related to tissue features that can increase the specificity of image findings leading to improvements in diagnostic ultrasound. QUS imaging techniques can encompass a wide variety of techniques including spectral-based parameterization, elastography, shear wave imaging, flow estimation and envelope statistics. Currently, spectral-based parameterization and envelope statistics are not available on most conventional clinical ultrasound machines. However, in recent years QUS techniques involving spectral-based parameterization and envelope statistics have demonstrated success in many applications, providing additional diagnostic capabilities. Spectral-based techniques include the estimation of the backscatter coefficient, estimation of attenuation, and estimation of scatterer properties such as the correlation length associated with an effective scatterer diameter and the effective acoustic concentration of scatterers. Envelope statistics include the estimation of the number density of scatterers and quantification of coherent to incoherent signals produced from the tissue. Challenges for clinical application include correctly accounting for attenuation effects and transmission losses and implementation of QUS on clinical devices. Successful clinical and pre-clinical applications demonstrating the ability of QUS to improve medical diagnostics include characterization of the myocardium during the cardiac cycle, cancer detection, classification of solid tumors and lymph nodes, detection and quantification of fatty liver disease, and monitoring and assessment of therapy. PMID:26761606

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

Aghaei, Faranak; Tan, Maxine; Liu, Hong

Purpose: To identify a new clinical marker based on quantitative kinetic image features analysis and assess its feasibility to predict tumor response to neoadjuvant chemotherapy. Methods: The authors assembled a dataset involving breast MR images acquired from 68 cancer patients before undergoing neoadjuvant chemotherapy. Among them, 25 patients had complete response (CR) and 43 had partial and nonresponse (NR) to chemotherapy based on the response evaluation criteria in solid tumors. The authors developed a computer-aided detection scheme to segment breast areas and tumors depicted on the breast MR images and computed a total of 39 kinetic image features from bothmore » tumor and background parenchymal enhancement regions. The authors then applied and tested two approaches to classify between CR and NR cases. The first one analyzed each individual feature and applied a simple feature fusion method that combines classification results from multiple features. The second approach tested an attribute selected classifier that integrates an artificial neural network (ANN) with a wrapper subset evaluator, which was optimized using a leave-one-case-out validation method. Results: In the pool of 39 features, 10 yielded relatively higher classification performance with the areas under receiver operating characteristic curves (AUCs) ranging from 0.61 to 0.78 to classify between CR and NR cases. Using a feature fusion method, the maximum AUC = 0.85 ± 0.05. Using the ANN-based classifier, AUC value significantly increased to 0.96 ± 0.03 (p < 0.01). Conclusions: This study demonstrated that quantitative analysis of kinetic image features computed from breast MR images acquired prechemotherapy has potential to generate a useful clinical marker in predicting tumor response to chemotherapy.« less

A phantom design for assessment of detectability in PET imaging

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

Wollenweber, Scott D., E-mail: scott.wollenweber@g

2016-09-15

Purpose: The primary clinical role of positron emission tomography (PET) imaging is the detection of anomalous regions of {sup 18}F-FDG uptake, which are often indicative of malignant lesions. The goal of this work was to create a task-configurable fillable phantom for realistic measurements of detectability in PET imaging. Design goals included simplicity, adjustable feature size, realistic size and contrast levels, and inclusion of a lumpy (i.e., heterogeneous) background. Methods: The detection targets were hollow 3D-printed dodecahedral nylon features. The exostructure sphere-like features created voids in a background of small, solid non-porous plastic (acrylic) spheres inside a fillable tank. The featuresmore » filled at full concentration while the background concentration was reduced due to filling only between the solid spheres. Results: Multiple iterations of feature size and phantom construction were used to determine a configuration at the limit of detectability for a PET/CT system. A full-scale design used a 20 cm uniform cylinder (head-size) filled with a fixed pattern of features at a contrast of approximately 3:1. Known signal-present and signal-absent PET sub-images were extracted from multiple scans of the same phantom and with detectability in a challenging (i.e., useful) range. These images enabled calculation and comparison of the quantitative observer detectability metrics between scanner designs and image reconstruction methods. The phantom design has several advantages including filling simplicity, wall-less contrast features, the control of the detectability range via feature size, and a clinically realistic lumpy background. Conclusions: This phantom provides a practical method for testing and comparison of lesion detectability as a function of imaging system, acquisition parameters, and image reconstruction methods and parameters.« less

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.

Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image.

PubMed

Singh, Anushikha; Dutta, Malay Kishore; ParthaSarathi, M; Uher, Vaclav; Burget, Radim

2016-02-01

Glaucoma is a disease of the retina which is one of the most common causes of permanent blindness worldwide. This paper presents an automatic image processing based method for glaucoma diagnosis from the digital fundus image. In this paper wavelet feature extraction has been followed by optimized genetic feature selection combined with several learning algorithms and various parameter settings. Unlike the existing research works where the features are considered from the complete fundus or a sub image of the fundus, this work is based on feature extraction from the segmented and blood vessel removed optic disc to improve the accuracy of identification. The experimental results presented in this paper indicate that the wavelet features of the segmented optic disc image are clinically more significant in comparison to features of the whole or sub fundus image in the detection of glaucoma from fundus image. Accuracy of glaucoma identification achieved in this work is 94.7% and a comparison with existing methods of glaucoma detection from fundus image indicates that the proposed approach has improved accuracy of classification. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Successful treatment of suspected organizing pneumonia in a patient without typical imaging and pathological characteristic: A case report.

PubMed

Ailing, Liu; Ning, Xu; Tao, Qu; Aijun, Li

2017-01-01

Organizing pneumonia (OP) is a clinicopathological entity characterized by granulation tissue plugs in the lumen of small airways, alveolar ducts, and alveoli. Diagnosis of OP needs the combination of clinical features, imaging and pathology. But it occurs often that there are no typical pathological features to support the diagnosis, which poses a challenge for clinicians' diagnosis and treatment. We diagnosed a case of OP without typical imaging and pathological characteristic and treated successfully. Finally we confirmed the pathological diagnosis. Not every OP case is supported by pathological evidence and typical imaging changes. It is important for us to judge and decide the diagnosis according to clinical experience.

Clinics in diagnostic imaging (179). Severe rhabdomyolysis complicated by myonecrosis.

PubMed

Kok, Shi Xian Shawn; Tan, Tien Jin

2017-08-01

A 32-year-old man presented to the emergency department with severe right lower limb pain and swelling of three days' duration. He had multiple prior admissions for recurrent seizures and suicide attempts. Markedly elevated serum creatine kinase levels and urine myoglobinuria were consistent with a diagnosis of rhabdomyolysis. Initial magnetic resonance imaging of the right lower limb revealed diffuse muscle oedema and features of myositis in the gluteal muscles and the adductor, anterior and posterior compartments of the thigh. Follow-up magnetic resonance imaging performed 11 days later showed interval development of areas of myonecrosis and haemorrhage. The causes, clinical presentation and imaging features of rhabdomyolysis are discussed. Copyright: © Singapore Medical Association.

A Clinical Decision Support System Using Ultrasound Textures and Radiologic Features to Distinguish Metastasis From Tumor-Free Cervical Lymph Nodes in Patients With Papillary Thyroid Carcinoma.

PubMed

Abbasian Ardakani, Ali; Reiazi, Reza; Mohammadi, Afshin

2018-03-30

This study investigated the potential of a clinical decision support approach for the classification of metastatic and tumor-free cervical lymph nodes (LNs) in papillary thyroid carcinoma on the basis of radiologic and textural analysis through ultrasound (US) imaging. In this research, 170 metastatic and 170 tumor-free LNs were examined by the proposed clinical decision support method. To discover the difference between the groups, US imaging was used for the extraction of radiologic and textural features. The radiologic features in the B-mode scans included the echogenicity, margin, shape, and presence of microcalcification. To extract the textural features, a wavelet transform was applied. A support vector machine classifier was used to classify the LNs. In the training set data, a combination of radiologic and textural features represented the best performance with sensitivity, specificity, accuracy, and area under the curve (AUC) values of 97.14%, 98.57%, 97.86%, and 0.994, respectively, whereas the classification based on radiologic and textural features alone yielded lower performance, with AUCs of 0.964 and 0.922. On testing the data set, the proposed model could classify the tumor-free and metastatic LNs with an AUC of 0.952, which corresponded to sensitivity, specificity, and accuracy of 93.33%, 96.66%, and 95.00%. The clinical decision support method based on textural and radiologic features has the potential to characterize LNs via 2-dimensional US. Therefore, it can be used as a supplementary technique in daily clinical practice to improve radiologists' understanding of conventional US imaging for characterizing LNs. © 2018 by the American Institute of Ultrasound in Medicine.

Detection of relationships among multi-modal brain imaging meta-features via information flow.

PubMed

Miller, Robyn L; Vergara, Victor M; Calhoun, Vince D

2018-01-15

Neuroscientists and clinical researchers are awash in data from an ever-growing number of imaging and other bio-behavioral modalities. This flow of brain imaging data, taken under resting and various task conditions, combines with available cognitive measures, behavioral information, genetic data plus other potentially salient biomedical and environmental information to create a rich but diffuse data landscape. The conditions being studied with brain imaging data are often extremely complex and it is common for researchers to employ more than one imaging, behavioral or biological data modality (e.g., genetics) in their investigations. While the field has advanced significantly in its approach to multimodal data, the vast majority of studies still ignore joint information among two or more features or modalities. We propose an intuitive framework based on conditional probabilities for understanding information exchange between features in what we are calling a feature meta-space; that is, a space consisting of many individual featurae spaces. Features can have any dimension and can be drawn from any data source or modality. No a priori assumptions are made about the functional form (e.g., linear, polynomial, exponential) of captured inter-feature relationships. We demonstrate the framework's ability to identify relationships between disparate features of varying dimensionality by applying it to a large multi-site, multi-modal clinical dataset, balance between schizophrenia patients and controls. In our application it exposes both expected (previously observed) relationships, and novel relationships rarely considered investigated by clinical researchers. To the best of our knowledge there is not presently a comparably efficient way to capture relationships of indeterminate functional form between features of arbitrary dimension and type. We are introducing this method as an initial foray into a space that remains relatively underpopulated. The framework we propose is powerful, intuitive and very efficiently provides a high-level overview of a massive data space. In our application it exposes both expected relationships and relationships very rarely considered worth investigating by clinical researchers. Copyright © 2017 Elsevier B.V. All rights reserved.

Fourier domain image fusion for differential X-ray phase-contrast breast imaging.

PubMed

Coello, Eduardo; Sperl, Jonathan I; Bequé, Dirk; Benz, Tobias; Scherer, Kai; Herzen, Julia; Sztrókay-Gaul, Anikó; Hellerhoff, Karin; Pfeiffer, Franz; Cozzini, Cristina; Grandl, Susanne

2017-04-01

X-Ray Phase-Contrast (XPC) imaging is a novel technology with a great potential for applications in clinical practice, with breast imaging being of special interest. This work introduces an intuitive methodology to combine and visualize relevant diagnostic features, present in the X-ray attenuation, phase shift and scattering information retrieved in XPC imaging, using a Fourier domain fusion algorithm. The method allows to present complementary information from the three acquired signals in one single image, minimizing the noise component and maintaining visual similarity to a conventional X-ray image, but with noticeable enhancement in diagnostic features, details and resolution. Radiologists experienced in mammography applied the image fusion method to XPC measurements of mastectomy samples and evaluated the feature content of each input and the fused image. This assessment validated that the combination of all the relevant diagnostic features, contained in the XPC images, was present in the fused image as well. Copyright © 2017 Elsevier B.V. All rights reserved.

Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

PubMed Central

Aerts, Hugo J. W. L.; Velazquez, Emmanuel Rios; Leijenaar, Ralph T. H.; Parmar, Chintan; Grossmann, Patrick; Cavalho, Sara; Bussink, Johan; Monshouwer, René; Haibe-Kains, Benjamin; Rietveld, Derek; Hoebers, Frank; Rietbergen, Michelle M.; Leemans, C. René; Dekker, Andre; Quackenbush, John; Gillies, Robert J.; Lambin, Philippe

2014-01-01

Human cancers exhibit strong phenotypic differences that can be visualized noninvasively by medical imaging. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. Here we present a radiomic analysis of 440 features quantifying tumour image intensity, shape and texture, which are extracted from computed tomography data of 1,019 patients with lung or head-and-neck cancer. We find that a large number of radiomic features have prognostic power in independent data sets of lung and head-and-neck cancer patients, many of which were not identified as significant before. Radiogenomics analysis reveals that a prognostic radiomic signature, capturing intratumour heterogeneity, is associated with underlying gene-expression patterns. These data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. This may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost. PMID:24892406

Imaging in the assessment and management of athletic pubalgia.

PubMed

Robinson, Philip; Bhat, Vineet; English, Bryan

2011-02-01

This article reviews the clinical, anatomical, and biomechanical basis of pubalgia and relates it to the potential imaging findings and subsequent management. Although the magnetic resonance imaging features typically seen in symptomatic athletes are emphasized, this condition remains a complex clinical problem, and treatment addressing the functional rehabilitation of the entire region is highlighted. © Thieme Medical Publishers.

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.

Three-Dimensional Cataract Crystalline Lens Imaging With Swept-Source Optical Coherence Tomography.

PubMed

de Castro, Alberto; Benito, Antonio; Manzanera, Silvestre; Mompeán, Juan; Cañizares, Belén; Martínez, David; Marín, Jose María; Grulkowski, Ireneusz; Artal, Pablo

2018-02-01

To image, describe, and characterize different features visible in the crystalline lens of older adults with and without cataract when imaged three-dimensionally with a swept-source optical coherence tomography (SS-OCT) system. We used a new SS-OCT laboratory prototype designed to enhance the visualization of the crystalline lens and imaged the entire anterior segment of both eyes in two groups of participants: patients scheduled to undergo cataract surgery, n = 17, age range 36 to 91 years old, and volunteers without visual complains, n = 14, age range 20 to 81 years old. Pre-cataract surgery patients were also clinically graded according to the Lens Opacification Classification System III. The three-dimensional location and shape of the visible opacities were compared with the clinical grading. Hypo- and hyperreflective features were visible in the lens of all pre-cataract surgery patients and in some of the older adults in the volunteer group. When the clinical examination revealed cortical or subcapsular cataracts, hyperreflective features were visible either in the cortex parallel to the surfaces of the lens or in the posterior pole. Other type of opacities that appeared as hyporeflective localized features were identified in the cortex of the lens. The OCT signal in the nucleus of the crystalline lens correlated with the nuclear cataract clinical grade. A dedicated OCT is a useful tool to study in vivo the subtle opacities in the cataractous crystalline lens, revealing its position and size three-dimensionally. The use of these images allows obtaining more detailed information on the age-related changes leading to cataract.

Imaging of gout: an overview.

PubMed

Dalbeth, Nicola; Doyle, Anthony J

2012-12-01

The diverse clinical states and sites of pathology in gout provide challenges when considering the features apparent on imaging. Ideally, an imaging modality should capture all aspects of disease including monosodium urate crystal deposition, acute inflammation, tophus, tissue remodelling and complications of disease. The modalities used in gout include conventional radiography, ultrasonography, magnetic resonance imaging, computed tomography and dual-energy computed tomography. This review discusses the role of each of these imaging modalities in gout, focussing on the imaging characteristics, role in gout diagnosis and role for disease monitoring. Ultrasonography and dual-energy computed tomography are particularly promising methods for both non-invasive diagnosis and monitoring of disease. The observation that ultrasonographic appearances of monosodium urate crystal deposition can be observed in patients with hyperuricaemia but no other clinical features of gout raises important questions about disease definitions. Copyright © 2012 Elsevier Ltd. All rights reserved.

Clinics in diagnostic imaging (163). Transient lateral patellar dislocation with trochlear dysplasia

PubMed Central

Zhang, Junwei; Lee, Chin Hwee

2015-01-01

A 14-year-old girl presented with left knee pain and swelling after an injury. Magnetic resonance (MR) imaging showed a transient lateral patellar dislocation with patellar osteochondral fracture, medial patellofemoral ligament tear and underlying femoral trochlear dysplasia. Open reduction and internal fixation of the osteochondral fracture, plication of the medial patellar retinaculum and lateral release were performed. As lateral patellar dislocation is often clinically unsuspected, an understanding of its characteristic imaging features is important in making the diagnosis. Knowledge of the various predisposing factors for patellar instability may also influence the choice of surgical management. We also discuss signs of acute injury and chronic instability observed on MR imaging, and the imaging features of anatomical variants that predispose an individual to lateral patellar dislocation. Treatment options and postsurgical imaging appearances are also briefly described. PMID:26512145

[Report of a case with Joubert syndrome and literature review].

PubMed

Yi, Ya-hui; Li, Gang; Lu, Zhong-lie; Zhou, Jian-sheng; Yao, Zhen-wei; Wang, Peng-fei; Yao, Jin-xiang

2011-12-01

To explore the clinical feature, imaging and their diagnostic value for Joubert syndrome (JS). The clinical data, imaging feature, and 31 references from China Biomedical literature database (CBMdise) were reviewed and analyzed. The age of onset of 32 patients including male 20 and female 12 ranged from 3 days to 6 years (mean 2.2 years). All the 32 patients with Joubert syndrome showed "slow growth" and "reduced muscle tension", 26 cases (81.3%) showed "gasp for breath", 26 cases (81.3%) showed "unusual motion of eyeball", 2 cases (6.3%) showed additional fingers (toes), 6 cases (18.8%) showed stretching tongue with agape. The typical imaging features of Joubert syndrome included "molar tooth sign", "midline cleavage" between cerebellar hemispheres and "bat-wing" like fourth ventricle, all the 32 patients with Joubert syndrome showed "midline cleavage", "molar tooth sign" was present in 29 cases (90.1%), and "bat-wing" like fourth ventricle in 30 cases (93.8%). Joubert syndrome is a rare congenital brain malformation. The typical clinical manifestations included "gasp for breath", "reduced tension of muscle", "slow growth" and "unusual motion of eyeball", and at the same time the patients had the following typical imaging features of brain: "molar tooth sign", "midline cleavage" and "bat-wing" like fourth ventricle.

Radiological and endoscopic imaging methods in the management of cystic pancreatic neoplasms.

PubMed

Aslan, Ahmet; Inan, Ibrahim; Orman, Süleyman; Aslan, Mine; Acar, Murat

2017-01-01

The management of cystic pancreatic neoplasm (CPN) is a clinical dilemma because of its clinical presentations and malignant potential. Surgery is the best treatment choice ; however, pancreatic surgery still has high complication rates, even in experienced centers. Imaging methods have a definitive role in the management of CPN and computed tomography, magnetic resonance imaging, and endoscopic ultrasonography are the preferred methods since they can reveal the suspicious features for malignancy. Therefore, radiologists, gastroenterologists, endoscopists, and surgeons should be aware of the common features of CPN, its discrete presentations on imaging methods, and the limitations of these modalities in the management of the disease. This study aims to review the radiological and endoscopic imaging methods used for the management of CPN. © Acta Gastro-Enterologica Belgica.

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.

Magnetic resonance imaging features of complex Chiari malformation variant of Chiari 1 malformation.

PubMed

Moore, Hannah E; Moore, Kevin R

2014-11-01

Complex Chiari malformation is a subgroup of Chiari 1 malformation with distinct imaging features. Children with complex Chiari malformation are reported to have a more severe clinical phenotype and sometimes require more extensive surgical treatment than those with uncomplicated Chiari 1 malformation. We describe reported MR imaging features of complex Chiari malformation and evaluate the utility of craniometric parameters and qualitative anatomical observations for distinguishing complex Chiari malformation from uncomplicated Chiari 1 malformation. We conducted a retrospective search of the institutional imaging database using the keywords "Chiari" and "Chiari 1" to identify children imaged during the 2006-2011 time period. Children with Chiari 2 malformation were excluded after imaging review. We used the first available diagnostic brain or cervical spine MR study for data measurement. Standard measurements and observations were made of obex level (mm), cerebellar tonsillar descent (mm), perpendicular distance to basion-C2 line (pB-C2, mm), craniocervical angle (degrees), clivus length, and presence or absence of syringohydromyelia, basilar invagination and congenital craniovertebral junction osseous anomalies. After imaging review, we accessed the institutional health care clinical database to determine whether each subject clinically met criteria for Chiari 1 malformation or complex Chiari malformation. Obex level and craniocervical angle measurements showed statistically significant differences between the populations with complex Chiari malformation and uncomplicated Chiari 1 malformation. Cerebellar tonsillar descent and perpendicular distance to basion-C2 line measurements trended toward but did not meet statistical significance. Odontoid retroflexion, craniovertebral junction osseous anomalies, and syringohydromyelia were all observed proportionally more often in children with complex Chiari malformation than in those with Chiari 1 malformation. Characteristic imaging features of complex Chiari malformation, especially obex level, permit its distinction from the more common uncomplicated Chiari 1 malformation.

Cholangiocarcinoma: classification, diagnosis, staging, imaging features, and management.

PubMed

Oliveira, Irai S; Kilcoyne, Aoife; Everett, Jamie M; Mino-Kenudson, Mari; Harisinghani, Mukesh G; Ganesan, Karthik

2017-06-01

Cholangiocarcinoma is a relatively uncommon malignant neoplasm with poor prognosis. The distinction between extrahepatic and intrahepatic subtypes is important as epidemiological features, biologic and pathologic characteristics, and clinical course are different for both entities. This review study focuses on the role imaging plays in the diagnosis, classification, staging, and post-treatment assessment of cholangiocarcinoma.

Prototypes for Content-Based Image Retrieval in Clinical Practice

PubMed Central

Depeursinge, Adrien; Fischer, Benedikt; Müller, Henning; Deserno, Thomas M

2011-01-01

Content-based image retrieval (CBIR) has been proposed as key technology for computer-aided diagnostics (CAD). This paper reviews the state of the art and future challenges in CBIR for CAD applied to clinical practice. We define applicability to clinical practice by having recently demonstrated the CBIR system on one of the CAD demonstration workshops held at international conferences, such as SPIE Medical Imaging, CARS, SIIM, RSNA, and IEEE ISBI. From 2009 to 2011, the programs of CADdemo@CARS and the CAD Demonstration Workshop at SPIE Medical Imaging were sought for the key word “retrieval” in the title. The systems identified were analyzed and compared according to the hierarchy of gaps for CBIR systems. In total, 70 software demonstrations were analyzed. 5 systems were identified meeting the criterions. The fields of application are (i) bone age assessment, (ii) bone fractures, (iii) interstitial lung diseases, and (iv) mammography. Bridging the particular gaps of semantics, feature extraction, feature structure, and evaluation have been addressed most frequently. In specific application domains, CBIR technology is available for clinical practice. While system development has mainly focused on bridging content and feature gaps, performance and usability have become increasingly important. The evaluation must be based on a larger set of reference data, and workflow integration must be achieved before CBIR-CAD is really established in clinical practice. PMID:21892374

Breast MRI radiogenomics: Current status and research implications.

PubMed

Grimm, Lars J

2016-06-01

Breast magnetic resonance imaging (MRI) radiogenomics is an emerging area of research that has the potential to directly influence clinical practice. Clinical MRI scanners today are capable of providing excellent temporal and spatial resolution, which allows extraction of numerous imaging features via human extraction approaches or complex computer vision algorithms. Meanwhile, advances in breast cancer genetics research has resulted in the identification of promising genes associated with cancer outcomes. In addition, validated genomic signatures have been developed that allow categorization of breast cancers into distinct molecular subtypes as well as predict the risk of cancer recurrence and response to therapy. Current radiogenomics research has been directed towards exploratory analysis of individual genes, understanding tumor biology, and developing imaging surrogates to genetic analysis with the long-term goal of developing a meaningful tool for clinical care. The background of breast MRI radiogenomics research, image feature extraction techniques, approaches to radiogenomics research, and promising areas of investigation are reviewed. J. Magn. Reson. Imaging 2016;43:1269-1278. © 2015 Wiley Periodicals, Inc.

Review of Quantitative Ultrasound: Envelope Statistics and Backscatter Coefficient Imaging and Contributions to Diagnostic Ultrasound.

PubMed

Oelze, Michael L; Mamou, Jonathan

2016-02-01

Conventional medical imaging technologies, including ultrasound, have continued to improve over the years. For example, in oncology, medical imaging is characterized by high sensitivity, i.e., the ability to detect anomalous tissue features, but the ability to classify these tissue features from images often lacks specificity. As a result, a large number of biopsies of tissues with suspicious image findings are performed each year with a vast majority of these biopsies resulting in a negative finding. To improve specificity of cancer imaging, quantitative imaging techniques can play an important role. Conventional ultrasound B-mode imaging is mainly qualitative in nature. However, quantitative ultrasound (QUS) imaging can provide specific numbers related to tissue features that can increase the specificity of image findings leading to improvements in diagnostic ultrasound. QUS imaging can encompass a wide variety of techniques including spectral-based parameterization, elastography, shear wave imaging, flow estimation, and envelope statistics. Currently, spectral-based parameterization and envelope statistics are not available on most conventional clinical ultrasound machines. However, in recent years, QUS techniques involving spectral-based parameterization and envelope statistics have demonstrated success in many applications, providing additional diagnostic capabilities. Spectral-based techniques include the estimation of the backscatter coefficient (BSC), estimation of attenuation, and estimation of scatterer properties such as the correlation length associated with an effective scatterer diameter (ESD) and the effective acoustic concentration (EAC) of scatterers. Envelope statistics include the estimation of the number density of scatterers and quantification of coherent to incoherent signals produced from the tissue. Challenges for clinical application include correctly accounting for attenuation effects and transmission losses and implementation of QUS on clinical devices. Successful clinical and preclinical applications demonstrating the ability of QUS to improve medical diagnostics include characterization of the myocardium during the cardiac cycle, cancer detection, classification of solid tumors and lymph nodes, detection and quantification of fatty liver disease, and monitoring and assessment of therapy.

Glioma grading using cell nuclei morphologic features in digital pathology images

NASA Astrophysics Data System (ADS)

Reza, Syed M. S.; Iftekharuddin, Khan M.

2016-03-01

This work proposes a computationally efficient cell nuclei morphologic feature analysis technique to characterize the brain gliomas in tissue slide images. In this work, our contributions are two-fold: 1) obtain an optimized cell nuclei segmentation method based on the pros and cons of the existing techniques in literature, 2) extract representative features by k-mean clustering of nuclei morphologic features to include area, perimeter, eccentricity, and major axis length. This clustering based representative feature extraction avoids shortcomings of extensive tile [1] [2] and nuclear score [3] based methods for brain glioma grading in pathology images. Multilayer perceptron (MLP) is used to classify extracted features into two tumor types: glioblastoma multiforme (GBM) and low grade glioma (LGG). Quantitative scores such as precision, recall, and accuracy are obtained using 66 clinical patients' images from The Cancer Genome Atlas (TCGA) [4] dataset. On an average ~94% accuracy from 10 fold crossvalidation confirms the efficacy of the proposed method.

Novel histopathologic feature identified through image analysis augments stage II colorectal cancer clinical reporting

PubMed Central

Caie, Peter D.; Zhou, Ying; Turnbull, Arran K.; Oniscu, Anca; Harrison, David J.

2016-01-01

A number of candidate histopathologic factors show promise in identifying stage II colorectal cancer (CRC) patients at a high risk of disease-specific death, however they can suffer from low reproducibility and none have replaced classical pathologic staging. We developed an image analysis algorithm which standardized the quantification of specific histopathologic features and exported a multi-parametric feature-set captured without bias. The image analysis algorithm was executed across a training set (n = 50) and the resultant big data was distilled through decision tree modelling to identify the most informative parameters to sub-categorize stage II CRC patients. The most significant, and novel, parameter identified was the ‘sum area of poorly differentiated clusters’ (AreaPDC). This feature was validated across a second cohort of stage II CRC patients (n = 134) (HR = 4; 95% CI, 1.5– 11). Finally, the AreaPDC was integrated with the significant features within the clinical pathology report, pT stage and differentiation, into a novel prognostic index (HR = 7.5; 95% CI, 3–18.5) which improved upon current clinical staging (HR = 4.26; 95% CI, 1.7– 10.3). The identification of poorly differentiated clusters as being highly significant in disease progression presents evidence to suggest that these features could be the source of novel targets to decrease the risk of disease specific death. PMID:27322148

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.

Segmentation of prostate biopsy needles in transrectal ultrasound images

NASA Astrophysics Data System (ADS)

Krefting, Dagmar; Haupt, Barbara; Tolxdorff, Thomas; Kempkensteffen, Carsten; Miller, Kurt

2007-03-01

Prostate cancer is the most common cancer in men. Tissue extraction at different locations (biopsy) is the gold-standard for diagnosis of prostate cancer. These biopsies are commonly guided by transrectal ultrasound imaging (TRUS). Exact location of the extracted tissue within the gland is desired for more specific diagnosis and provides better therapy planning. While the orientation and the position of the needle within clinical TRUS image are limited, the appearing length and visibility of the needle varies strongly. Marker lines are present and tissue inhomogeneities and deflection artefacts may appear. Simple intensity, gradient oder edge-detecting based segmentation methods fail. Therefore a multivariate statistical classificator is implemented. The independent feature model is built by supervised learning using a set of manually segmented needles. The feature space is spanned by common binary object features as size and eccentricity as well as imaging-system dependent features like distance and orientation relative to the marker line. The object extraction is done by multi-step binarization of the region of interest. The ROI is automatically determined at the beginning of the segmentation and marker lines are removed from the images. The segmentation itself is realized by scale-invariant classification using maximum likelihood estimation and Mahalanobis distance as discriminator. The technique presented here could be successfully applied in 94% of 1835 TRUS images from 30 tissue extractions. It provides a robust method for biopsy needle localization in clinical prostate biopsy TRUS images.

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.

Pathological Gleason prediction through gland ring morphometry in immunofluorescent prostate cancer images

NASA Astrophysics Data System (ADS)

Scott, Richard; Khan, Faisal M.; Zeineh, Jack; Donovan, Michael; Fernandez, Gerardo

2016-03-01

The Gleason score is the most common architectural and morphological assessment of prostate cancer severity and prognosis. There have been numerous quantitative techniques developed to approximate and duplicate the Gleason scoring system. Most of these approaches have been developed in standard H and E brightfield microscopy. Immunofluorescence (IF) image analysis of tissue pathology has recently been proven to be extremely valuable and robust in developing prognostic assessments of disease, particularly in prostate cancer. There have been significant advances in the literature in quantitative biomarker expression as well as characterization of glandular architectures in discrete gland rings. In this work we leverage a new method of segmenting gland rings in IF images for predicting the pathological Gleason; both the clinical and the image specific grade, which may not necessarily be the same. We combine these measures with nuclear specific characteristics as assessed by the MST algorithm. Our individual features correlate well univariately with the Gleason grades, and in a multivariate setting have an accuracy of 85% in predicting the Gleason grade. Additionally, these features correlate strongly with clinical progression outcomes (CI of 0.89), significantly outperforming the clinical Gleason grades (CI of 0.78). This work presents the first assessment of morphological gland unit features from IF images for predicting the Gleason grade.

Adiposis dolorosa (Dercum's disease): MRI and ultrasound appearances.

PubMed

Tins, B J; Matthews, C; Haddaway, M; Cassar-Pullicino, V N; Lalam, R; Singh, J; Tyrrell, P N M

2013-10-01

To describe ultrasound and magnetic resonance imaging (MRI) features of adiposis dolorosa, Dercum's disease, and to evaluate the MRI features prospectively against a large number of MRI examinations. Institutional review board approval for this study was obtained. The imaging features at MRI and ultrasound of 13 cases of adiposis dolorosa (nine female, four male; age range 32-72 years) were reviewed. MRI findings typical for adiposis dolorosa were proposed and prospectively evaluated on 6247 MRI examinations performed over a period of 8 months. Adiposis dolorosa demonstrates multiple, oblong, fatty lesions in the superficial subcutaneous fatty tissue. They are mostly <2 cm in long axis diameter. They demonstrate nodular ("blush-like") increased fluid signal at unenhanced MRI and are markedly hyperechoic at ultrasound. There is no contrast medium enhancement at MRI and no increased Doppler signal at ultrasound. Most lesions were clinically asymptomatic, some were painful/tender. There was no imaging evidence of oedema or inflammation. During prospective validation of these MRI features on 6247 MRI examinations, two cases with typical imaging features were encountered; both were diagnosed as adiposis dolorosa on clinical review. All cases of adiposis dolorosa showed these imaging findings. This results in a very low likelihood that a nodular, blush-like appearance of subcutaneous fat on MRI is not due to adiposis dolorosa. Adiposis dolorosa, Dercum's disease, should be suggested in the presence of multiple (many) small, oblong, fatty lesions in the subcutaneous fatty tissue in adult patients if they are hyperechoic on ultrasound imaging or blush-like at unenhanced MRI; typically a small number of these lesions are tender/painful. Imaging does not demonstrate inflammation or oedema in relation to these lesions. These MRI features should suggest the diagnosis and are likely to be pathognomonic. The radiologist is often the first to suggest the diagnosis based on the imaging features. Copyright © 2013 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Image processing and machine learning for fully automated probabilistic evaluation of medical images.

PubMed

Sajn, Luka; Kukar, Matjaž

2011-12-01

The paper presents results of our long-term study on using image processing and data mining methods in a medical imaging. Since evaluation of modern medical images is becoming increasingly complex, advanced analytical and decision support tools are involved in integration of partial diagnostic results. Such partial results, frequently obtained from tests with substantial imperfections, are integrated into ultimate diagnostic conclusion about the probability of disease for a given patient. We study various topics such as improving the predictive power of clinical tests by utilizing pre-test and post-test probabilities, texture representation, multi-resolution feature extraction, feature construction and data mining algorithms that significantly outperform medical practice. Our long-term study reveals three significant milestones. The first improvement was achieved by significantly increasing post-test diagnostic probabilities with respect to expert physicians. The second, even more significant improvement utilizes multi-resolution image parametrization. Machine learning methods in conjunction with the feature subset selection on these parameters significantly improve diagnostic performance. However, further feature construction with the principle component analysis on these features elevates results to an even higher accuracy level that represents the third milestone. With the proposed approach clinical results are significantly improved throughout the study. The most significant result of our study is improvement in the diagnostic power of the whole diagnostic process. Our compound approach aids, but does not replace, the physician's judgment and may assist in decisions on cost effectiveness of tests. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Fast detection of vascular plaque in optical coherence tomography images using a reduced feature set

NASA Astrophysics Data System (ADS)

Prakash, Ammu; Ocana Macias, Mariano; Hewko, Mark; Sowa, Michael; Sherif, Sherif

2018-03-01

Optical coherence tomography (OCT) images are capable of detecting vascular plaque by using the full set of 26 Haralick textural features and a standard K-means clustering algorithm. However, the use of the full set of 26 textural features is computationally expensive and may not be feasible for real time implementation. In this work, we identified a reduced set of 3 textural feature which characterizes vascular plaque and used a generalized Fuzzy C-means clustering algorithm. Our work involves three steps: 1) the reduction of a full set 26 textural feature to a reduced set of 3 textural features by using genetic algorithm (GA) optimization method 2) the implementation of an unsupervised generalized clustering algorithm (Fuzzy C-means) on the reduced feature space, and 3) the validation of our results using histology and actual photographic images of vascular plaque. Our results show an excellent match with histology and actual photographic images of vascular tissue. Therefore, our results could provide an efficient pre-clinical tool for the detection of vascular plaque in real time OCT imaging.

Diagnostic imaging features of normal anal sacs in dogs and cats.

PubMed

Jung, Yechan; Jeong, Eunseok; Park, Sangjun; Jeong, Jimo; Choi, Ul Soo; Kim, Min-Su; Kim, Namsoo; Lee, Kichang

2016-09-30

This study was conducted to provide normal reference features for canine and feline anal sacs using ultrasound, low-field magnetic resonance imaging (MRI) and radiograph contrast as diagnostic imaging tools. A total of ten clinically normal beagle dogs and eight clinically normally cats were included. General radiography with contrast, ultrasonography and low-field MRI scans were performed. The visualization of anal sacs, which are located at distinct sites in dogs and cats, is possible with a contrast study on radiography. Most surfaces of the anal sacs tissue, occasionally appearing as a hyperechoic thin line, were surrounded by the hypoechoic external sphincter muscle on ultrasonography. The normal anal sac contents of dogs and cats had variable echogenicity. Signals of anal sac contents on low-field MRI varied in cats and dogs, and contrast medium using T1-weighted images enhanced the anal sac walls more obviously than that on ultrasonography. In conclusion, this study provides the normal features of anal sacs from dogs and cats on diagnostic imaging. Further studies including anal sac evaluation are expected to investigate disease conditions.

Diagnostic imaging features of normal anal sacs in dogs and cats

PubMed Central

Jung, Yechan; Jeong, Eunseok; Park, Sangjun; Jeong, Jimo; Choi, Ul Soo; Kim, Min-Su; Kim, Namsoo

2016-01-01

This study was conducted to provide normal reference features for canine and feline anal sacs using ultrasound, low-field magnetic resonance imaging (MRI) and radiograph contrast as diagnostic imaging tools. A total of ten clinically normal beagle dogs and eight clinically normally cats were included. General radiography with contrast, ultrasonography and low-field MRI scans were performed. The visualization of anal sacs, which are located at distinct sites in dogs and cats, is possible with a contrast study on radiography. Most surfaces of the anal sacs tissue, occasionally appearing as a hyperechoic thin line, were surrounded by the hypoechoic external sphincter muscle on ultrasonography. The normal anal sac contents of dogs and cats had variable echogenicity. Signals of anal sac contents on low-field MRI varied in cats and dogs, and contrast medium using T1-weighted images enhanced the anal sac walls more obviously than that on ultrasonography. In conclusion, this study provides the normal features of anal sacs from dogs and cats on diagnostic imaging. Further studies including anal sac evaluation are expected to investigate disease conditions. PMID:26645338

TU-AB-BRA-04: Quantitative Radiomics: Sensitivity of PET Textural Features to Image Acquisition and Reconstruction Parameters Implies the Need for Standards

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

Nyflot, MJ; Yang, F; Byrd, D

Purpose: Despite increased use of heterogeneity metrics for PET imaging, standards for metrics such as textural features have yet to be developed. We evaluated the quantitative variability caused by image acquisition and reconstruction parameters on PET textural features. Methods: PET images of the NEMA IQ phantom were simulated with realistic image acquisition noise. 35 features based on intensity histograms (IH), co-occurrence matrices (COM), neighborhood-difference matrices (NDM), and zone-size matrices (ZSM) were evaluated within lesions (13, 17, 22, 28, 33 mm diameter). Variability in metrics across 50 independent images was evaluated as percent difference from mean for three phantom girths (850,more » 1030, 1200 mm) and two OSEM reconstructions (2 iterations, 28 subsets, 5 mm FWHM filtration vs 6 iterations, 28 subsets, 8.6 mm FWHM filtration). Also, patient sample size to detect a clinical effect of 30% with Bonferroni-corrected α=0.001 and 95% power was estimated. Results: As a class, NDM features demonstrated greatest sensitivity in means (5–50% difference for medium girth and reconstruction comparisons and 10–100% for large girth comparisons). Some IH features (standard deviation, energy, entropy) had variability below 10% for all sensitivity studies, while others (kurtosis, skewness) had variability above 30%. COM and ZSM features had complex sensitivities; correlation, energy, entropy (COM) and zone percentage, short-zone emphasis, zone-size non-uniformity (ZSM) had variability less than 5% while other metrics had differences up to 30%. Trends were similar for sample size estimation; for example, coarseness, contrast, and strength required 12, 38, and 52 patients to detect a 30% effect for the small girth case but 38, 88, and 128 patients in the large girth case. Conclusion: The sensitivity of PET textural features to image acquisition and reconstruction parameters is large and feature-dependent. Standards are needed to ensure that prospective trials which incorporate textural features are properly designed to detect clinical endpoints. Supported by NIH grants R01 CA169072, U01 CA148131, NCI Contract (SAIC-Frederick) 24XS036-004, and a research contract from GE Healthcare.« less

Analysis of underlying causes of inter-expert disagreement in retinopathy of prematurity diagnosis. Application of machine learning principles.

PubMed

Ataer-Cansizoglu, E; Kalpathy-Cramer, J; You, S; Keck, K; Erdogmus, D; Chiang, M F

2015-01-01

Inter-expert variability in image-based clinical diagnosis has been demonstrated in many diseases including retinopathy of prematurity (ROP), which is a disease affecting low birth weight infants and is a major cause of childhood blindness. In order to better understand the underlying causes of variability among experts, we propose a method to quantify the variability of expert decisions and analyze the relationship between expert diagnoses and features computed from the images. Identification of these features is relevant for development of computer-based decision support systems and educational systems in ROP, and these methods may be applicable to other diseases where inter-expert variability is observed. The experiments were carried out on a dataset of 34 retinal images, each with diagnoses provided independently by 22 experts. Analysis was performed using concepts of Mutual Information (MI) and Kernel Density Estimation. A large set of structural features (a total of 66) were extracted from retinal images. Feature selection was utilized to identify the most important features that correlated to actual clinical decisions by the 22 study experts. The best three features for each observer were selected by an exhaustive search on all possible feature subsets and considering joint MI as a relevance criterion. We also compared our results with the results of Cohen's Kappa [36] as an inter-rater reliability measure. The results demonstrate that a group of observers (17 among 22) decide consistently with each other. Mean and second central moment of arteriolar tortuosity is among the reasons of disagreement between this group and the rest of the observers, meaning that the group of experts consider amount of tortuosity as well as the variation of tortuosity in the image. Given a set of image-based features, the proposed analysis method can identify critical image-based features that lead to expert agreement and disagreement in diagnosis of ROP. Although tree-based features and various statistics such as central moment are not popular in the literature, our results suggest that they are important for diagnosis.

Reproducibility of radiomics for deciphering tumor phenotype with imaging

NASA Astrophysics Data System (ADS)

Zhao, Binsheng; Tan, Yongqiang; Tsai, Wei-Yann; Qi, Jing; Xie, Chuanmiao; Lu, Lin; Schwartz, Lawrence H.

2016-03-01

Radiomics (radiogenomics) characterizes tumor phenotypes based on quantitative image features derived from routine radiologic imaging to improve cancer diagnosis, prognosis, prediction and response to therapy. Although radiomic features must be reproducible to qualify as biomarkers for clinical care, little is known about how routine imaging acquisition techniques/parameters affect reproducibility. To begin to fill this knowledge gap, we assessed the reproducibility of a comprehensive, commonly-used set of radiomic features using a unique, same-day repeat computed tomography data set from lung cancer patients. Each scan was reconstructed at 6 imaging settings, varying slice thicknesses (1.25 mm, 2.5 mm and 5 mm) and reconstruction algorithms (sharp, smooth). Reproducibility was assessed using the repeat scans reconstructed at identical imaging setting (6 settings in total). In separate analyses, we explored differences in radiomic features due to different imaging parameters by assessing the agreement of these radiomic features extracted from the repeat scans reconstructed at the same slice thickness but different algorithms (3 settings in total). Our data suggest that radiomic features are reproducible over a wide range of imaging settings. However, smooth and sharp reconstruction algorithms should not be used interchangeably. These findings will raise awareness of the importance of properly setting imaging acquisition parameters in radiomics/radiogenomics research.

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.

Imaging of autoimmune encephalitis--Relevance for clinical practice and hippocampal function.

PubMed

Heine, J; Prüss, H; Bartsch, T; Ploner, C J; Paul, F; Finke, C

2015-11-19

The field of autoimmune encephalitides associated with antibodies targeting cell-surface antigens is rapidly expanding and new antibodies are discovered frequently. Typical clinical presentations include cognitive deficits, psychiatric symptoms, movement disorders and seizures and the majority of patients respond well to immunotherapy. Pathophysiological mechanisms and clinical features are increasingly recognized and indicate hippocampal dysfunction in most of these syndromes. Here, we review the neuroimaging characteristics of autoimmune encephalitides, including N-methyl-d-aspartate (NMDA) receptor, leucine-rich glioma inactivated 1 (LGI1), contactin-associated protein-like 2 (CASPR2) encephalitis as well as more recently discovered and less frequent forms such as dipeptidyl-peptidase-like protein 6 (DPPX) or glycine receptor encephalitis. We summarize findings of routine magnetic resonance imaging (MRI) investigations as well as (18)F-fluoro-2-deoxy-d-glucose (FDG)-positron emission tomography (PET) and single photon emission tomography (SPECT) imaging and relate these observations to clinical features and disease outcome. We furthermore review results of advanced imaging analyses such as diffusion tensor imaging, volumetric analyses and resting-state functional MRI. Finally, we discuss contributions of these neuroimaging observations to the understanding of the pathophysiology of autoimmune encephalitides. Copyright © 2015 IBRO. Published by Elsevier Ltd. All rights reserved.

SU-E-I-01: Iterative CBCT Reconstruction with a Feature-Preserving Penalty

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

Lyu, Q; Li, B; Southern Medical University, Guangzhou

2015-06-15

Purpose: Low-dose CBCT is desired in various clinical applications. Iterative image reconstruction algorithms have shown advantages in suppressing noise in low-dose CBCT. However, due to the smoothness constraint enforced during the reconstruction process, edges may be blurred and image features may lose in the reconstructed image. In this work, we proposed a new penalty design to preserve image features in the image reconstructed by iterative algorithms. Methods: Low-dose CBCT is reconstructed by minimizing the penalized weighted least-squares (PWLS) objective function. Binary Robust Independent Elementary Features (BRIEF) of the image were integrated into the penalty of PWLS. BRIEF is a generalmore » purpose point descriptor that can be used to identify important features of an image. In this work, BRIEF distance of two neighboring pixels was used to weigh the smoothing parameter in PWLS. For pixels of large BRIEF distance, weaker smooth constraint will be enforced. Image features will be better preserved through such a design. The performance of the PWLS algorithm with BRIEF penalty was evaluated by a CatPhan 600 phantom. Results: The image quality reconstructed by the proposed PWLS-BRIEF algorithm is superior to that by the conventional PWLS method and the standard FDK method. At matched noise level, edges in PWLS-BRIEF reconstructed image are better preserved. Conclusion: This study demonstrated that the proposed PWLS-BRIEF algorithm has great potential on preserving image features in low-dose CBCT.« less

Quantification of intra-tumour cell proliferation heterogeneity using imaging descriptors of 18F fluorothymidine-positron emission tomography

NASA Astrophysics Data System (ADS)

Willaime, J. M. Y.; Turkheimer, F. E.; Kenny, L. M.; Aboagye, E. O.

2013-01-01

Intra-tumour heterogeneity is a characteristic shared by all cancers. We explored the use of texture variables derived from images of [18F]fluorothymidine-positron emission tomography (FLT-PET), thus notionally assessing the heterogeneity of proliferation in individual tumours. Our aims were to study the range of textural feature values across tissue types, verify the repeatability of these image descriptors and further, to explore associations with clinical response to chemotherapy in breast cancer patients. The repeatability of 28 textural descriptors was assessed in patients who had two FLT-PET scans prior to therapy using relative differences and the intra-class correlation coefficient (ICC). We tested associations between features at baseline and clinical response measured in 11 patients after three cycles of chemotherapy, and explored changes in FLT-PET at one week after the start of therapy. A subset of eight features was characterized by low variations at baseline (<±30%) and high repeatability (0.7 ≤ ICC ≤ 1). The intensity distribution profile suggested fewer highly proliferating cells in lesions of non-responders compared to responders at baseline. A true increase in CV and homogeneity was measured in four out of six responders one week after the start of therapy. A number of textural features derived from FLT-PET are altered following chemotherapy in breast cancer, and should be evaluated in larger clinical trials for clinical relevance.

Lesion classification using clinical and visual data fusion by multiple kernel learning

NASA Astrophysics Data System (ADS)

Kisilev, Pavel; Hashoul, Sharbell; Walach, Eugene; Tzadok, Asaf

2014-03-01

To overcome operator dependency and to increase diagnosis accuracy in breast ultrasound (US), a lot of effort has been devoted to developing computer-aided diagnosis (CAD) systems for breast cancer detection and classification. Unfortunately, the efficacy of such CAD systems is limited since they rely on correct automatic lesions detection and localization, and on robustness of features computed based on the detected areas. In this paper we propose a new approach to boost the performance of a Machine Learning based CAD system, by combining visual and clinical data from patient files. We compute a set of visual features from breast ultrasound images, and construct the textual descriptor of patients by extracting relevant keywords from patients' clinical data files. We then use the Multiple Kernel Learning (MKL) framework to train SVM based classifier to discriminate between benign and malignant cases. We investigate different types of data fusion methods, namely, early, late, and intermediate (MKL-based) fusion. Our database consists of 408 patient cases, each containing US images, textual description of complaints and symptoms filled by physicians, and confirmed diagnoses. We show experimentally that the proposed MKL-based approach is superior to other classification methods. Even though the clinical data is very sparse and noisy, its MKL-based fusion with visual features yields significant improvement of the classification accuracy, as compared to the image features only based classifier.

[Nasal NK/T cell lymphoma with outstanding performance of ocular symptoms].

PubMed

Liu, Lei; Zhao, Yulin; Wang, Jia; Ma, Fei

2012-09-01

To investigate the clinical features and misdiagnosis of nasal NK/T cell lymphoma with outstanding performance in ocular symptoms. Clinical data of 11 patients who had nasal NK/T cell lymphoma with the outstanding performances in ocular symptoms during 2009 to 2011 were retrospectively analyzed. The rate of misdiagnosis in the first diagnosis and first pathological diagnosis were 72.7% and 27.3% respectively. Nasal NK/T cell lymphoma with obvious ocular symptoms developed quickly and had almost special imaging findings. Nasal NK/T cell lymphoma with outstanding performance of ocular symptoms can be easily misdiagnosed. Comprehensive consideration of the clinical features, imaging findings and pathological examination do help to make accurate diagnosis early.

Hyperspectral Image Enhancement and Mixture Deep-Learning Classification of Corneal Epithelium Injuries.

PubMed

Noor, Siti Salwa Md; Michael, Kaleena; Marshall, Stephen; Ren, Jinchang

2017-11-16

In our preliminary study, the reflectance signatures obtained from hyperspectral imaging (HSI) of normal and abnormal corneal epithelium tissues of porcine show similar morphology with subtle differences. Here we present image enhancement algorithms that can be used to improve the interpretability of data into clinically relevant information to facilitate diagnostics. A total of 25 corneal epithelium images without the application of eye staining were used. Three image feature extraction approaches were applied for image classification: (i) image feature classification from histogram using a support vector machine with a Gaussian radial basis function (SVM-GRBF); (ii) physical image feature classification using deep-learning Convolutional Neural Networks (CNNs) only; and (iii) the combined classification of CNNs and SVM-Linear. The performance results indicate that our chosen image features from the histogram and length-scale parameter were able to classify with up to 100% accuracy; particularly, at CNNs and CNNs-SVM, by employing 80% of the data sample for training and 20% for testing. Thus, in the assessment of corneal epithelium injuries, HSI has high potential as a method that could surpass current technologies regarding speed, objectivity, and reliability.

Advanced image fusion algorithms for Gamma Knife treatment planning. Evaluation and proposal for clinical use.

PubMed

Apostolou, N; Papazoglou, Th; Koutsouris, D

2006-01-01

Image fusion is a process of combining information from multiple sensors. It is a useful tool implemented in the treatment planning programme of Gamma Knife Radiosurgery. In this paper we evaluate advanced image fusion algorithms for Matlab platform and head images. We develop nine level grayscale image fusion methods: average, principal component analysis (PCA), discrete wavelet transform (DWT) and Laplacian, filter - subtract - decimate (FSD), contrast, gradient, morphological pyramid and a shift invariant discrete wavelet transform (SIDWT) method in Matlab platform. We test these methods qualitatively and quantitatively. The quantitative criteria we use are the Root Mean Square Error (RMSE), the Mutual Information (MI), the Standard Deviation (STD), the Entropy (H), the Difference Entropy (DH) and the Cross Entropy (CEN). The qualitative are: natural appearance, brilliance contrast, presence of complementary features and enhancement of common features. Finally we make clinically useful suggestions.

An iterated Laplacian based semi-supervised dimensionality reduction for classification of breast cancer on ultrasound images.

PubMed

Liu, Xiao; Shi, Jun; Zhou, Shichong; Lu, Minhua

2014-01-01

The dimensionality reduction is an important step in ultrasound image based computer-aided diagnosis (CAD) for breast cancer. A newly proposed l2,1 regularized correntropy algorithm for robust feature selection (CRFS) has achieved good performance for noise corrupted data. Therefore, it has the potential to reduce the dimensions of ultrasound image features. However, in clinical practice, the collection of labeled instances is usually expensive and time costing, while it is relatively easy to acquire the unlabeled or undetermined instances. Therefore, the semi-supervised learning is very suitable for clinical CAD. The iterated Laplacian regularization (Iter-LR) is a new regularization method, which has been proved to outperform the traditional graph Laplacian regularization in semi-supervised classification and ranking. In this study, to augment the classification accuracy of the breast ultrasound CAD based on texture feature, we propose an Iter-LR-based semi-supervised CRFS (Iter-LR-CRFS) algorithm, and then apply it to reduce the feature dimensions of ultrasound images for breast CAD. We compared the Iter-LR-CRFS with LR-CRFS, original supervised CRFS, and principal component analysis. The experimental results indicate that the proposed Iter-LR-CRFS significantly outperforms all other algorithms.

Striatal necrosis in type 1 glutaric aciduria: Different stages in two siblings.

PubMed

Sen, Anitha; Pillay, Rajesh Subramonia

2011-07-01

Two siblings born of a consanguineous marriage with history of neurologic deterioration were imaged. Imaging features are classical of glutaric aciduria type 1 (GA-1), acute (striatal necrosis) stage in younger sibling, and chronic stage in older sibling. GA-1 is an autosomal recessive disease with typical imaging features. Greater awareness about this condition among clinicians and radiologists is essential for early diagnosis and prevention of its catastrophic consequences. Striatal necrosis with stroke-like signal intensity on imaging correlates with clinical stage of patients.

Striatal necrosis in type 1 glutaric aciduria: Different stages in two siblings

PubMed Central

Sen, Anitha; Pillay, Rajesh Subramonia

2011-01-01

Two siblings born of a consanguineous marriage with history of neurologic deterioration were imaged. Imaging features are classical of glutaric aciduria type 1 (GA-1), acute (striatal necrosis) stage in younger sibling, and chronic stage in older sibling. GA-1 is an autosomal recessive disease with typical imaging features. Greater awareness about this condition among clinicians and radiologists is essential for early diagnosis and prevention of its catastrophic consequences. Striatal necrosis with stroke-like signal intensity on imaging correlates with clinical stage of patients. PMID:22408669

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.

Computer-aided diagnostic system for detection of Hashimoto thyroiditis on ultrasound images from a Polish population.

PubMed

Acharya, U Rajendra; Sree, S Vinitha; Krishnan, M Muthu Rama; Molinari, Filippo; Zieleźnik, Witold; Bardales, Ricardo H; Witkowska, Agnieszka; Suri, Jasjit S

2014-02-01

Computer-aided diagnostic (CAD) techniques aid physicians in better diagnosis of diseases by extracting objective and accurate diagnostic information from medical data. Hashimoto thyroiditis is the most common type of inflammation of the thyroid gland. The inflammation changes the structure of the thyroid tissue, and these changes are reflected as echogenic changes on ultrasound images. In this work, we propose a novel CAD system (a class of systems called ThyroScan) that extracts textural features from a thyroid sonogram and uses them to aid in the detection of Hashimoto thyroiditis. In this paradigm, we extracted grayscale features based on stationary wavelet transform from 232 normal and 294 Hashimoto thyroiditis-affected thyroid ultrasound images obtained from a Polish population. Significant features were selected using a Student t test. The resulting feature vectors were used to build and evaluate the following 4 classifiers using a 10-fold stratified cross-validation technique: support vector machine, decision tree, fuzzy classifier, and K-nearest neighbor. Using 7 significant features that characterized the textural changes in the images, the fuzzy classifier had the highest classification accuracy of 84.6%, sensitivity of 82.8%, specificity of 87.0%, and a positive predictive value of 88.9%. The proposed ThyroScan CAD system uses novel features to noninvasively detect the presence of Hashimoto thyroiditis on ultrasound images. Compared to manual interpretations of ultrasound images, the CAD system offers a more objective interpretation of the nature of the thyroid. The preliminary results presented in this work indicate the possibility of using such a CAD system in a clinical setting after evaluating it with larger databases in multicenter clinical trials.

Optical imaging probes in oncology

PubMed Central

Martelli, Cristina; Dico, Alessia Lo; Diceglie, Cecilia; Lucignani, Giovanni; Ottobrini, Luisa

2016-01-01

Cancer is a complex disease, characterized by alteration of different physiological molecular processes and cellular features. Keeping this in mind, the possibility of early identification and detection of specific tumor biomarkers by non-invasive approaches could improve early diagnosis and patient management. Different molecular imaging procedures provide powerful tools for detection and non-invasive characterization of oncological lesions. Clinical studies are mainly based on the use of computed tomography, nuclear-based imaging techniques and magnetic resonance imaging. Preclinical imaging in small animal models entails the use of dedicated instruments, and beyond the already cited imaging techniques, it includes also optical imaging studies. Optical imaging strategies are based on the use of luminescent or fluorescent reporter genes or injectable fluorescent or luminescent probes that provide the possibility to study tumor features even by means of fluorescence and luminescence imaging. Currently, most of these probes are used only in animal models, but the possibility of applying some of them also in the clinics is under evaluation. The importance of tumor imaging, the ease of use of optical imaging instruments, the commercial availability of a wide range of probes as well as the continuous description of newly developed probes, demonstrate the significance of these applications. The aim of this review is providing a complete description of the possible optical imaging procedures available for the non-invasive assessment of tumor features in oncological murine models. In particular, the characteristics of both commercially available and newly developed probes will be outlined and discussed. PMID:27145373

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

Lower cranial nerves.

PubMed

Soldatos, Theodoros; Batra, Kiran; Blitz, Ari M; Chhabra, Avneesh

2014-02-01

Imaging evaluation of cranial neuropathies requires thorough knowledge of the anatomic, physiologic, and pathologic features of the cranial nerves, as well as detailed clinical information, which is necessary for tailoring the examinations, locating the abnormalities, and interpreting the imaging findings. This article provides clinical, anatomic, and radiological information on lower (7th to 12th) cranial nerves, along with high-resolution magnetic resonance images as a guide for optimal imaging technique, so as to improve the diagnosis of cranial neuropathy. Copyright © 2014 Elsevier Inc. All rights reserved.

Quantitative Imaging in Cancer Evolution and Ecology

PubMed Central

Grove, Olya; Gillies, Robert J.

2013-01-01

Cancer therapy, even when highly targeted, typically fails because of the remarkable capacity of malignant cells to evolve effective adaptations. These evolutionary dynamics are both a cause and a consequence of cancer system heterogeneity at many scales, ranging from genetic properties of individual cells to large-scale imaging features. Tumors of the same organ and cell type can have remarkably diverse appearances in different patients. Furthermore, even within a single tumor, marked variations in imaging features, such as necrosis or contrast enhancement, are common. Similar spatial variations recently have been reported in genetic profiles. Radiologic heterogeneity within tumors is usually governed by variations in blood flow, whereas genetic heterogeneity is typically ascribed to random mutations. However, evolution within tumors, as in all living systems, is subject to Darwinian principles; thus, it is governed by predictable and reproducible interactions between environmental selection forces and cell phenotype (not genotype). This link between regional variations in environmental properties and cellular adaptive strategies may permit clinical imaging to be used to assess and monitor intratumoral evolution in individual patients. This approach is enabled by new methods that extract, report, and analyze quantitative, reproducible, and mineable clinical imaging data. However, most current quantitative metrics lack spatialness, expressing quantitative radiologic features as a single value for a region of interest encompassing the whole tumor. In contrast, spatially explicit image analysis recognizes that tumors are heterogeneous but not well mixed and defines regionally distinct habitats, some of which appear to harbor tumor populations that are more aggressive and less treatable than others. By identifying regional variations in key environmental selection forces and evidence of cellular adaptation, clinical imaging can enable us to define intratumoral Darwinian dynamics before and during therapy. Advances in image analysis will place clinical imaging in an increasingly central role in the development of evolution-based patient-specific cancer therapy. © RSNA, 2013 PMID:24062559

Segmentation methods for breast vasculature in dual-energy contrast-enhanced digital breast tomosynthesis

NASA Astrophysics Data System (ADS)

Lau, Kristen C.; Lee, Hyo Min; Singh, Tanushriya; Maidment, Andrew D. A.

2015-03-01

Dual-energy contrast-enhanced digital breast tomosynthesis (DE CE-DBT) uses an iodinated contrast agent to image the three-dimensional breast vasculature. The University of Pennsylvania has an ongoing DE CE-DBT clinical study in patients with known breast cancers. The breast is compressed continuously and imaged at four time points (1 pre-contrast; 3 post-contrast). DE images are obtained by a weighted logarithmic subtraction of the high-energy (HE) and low-energy (LE) image pairs. Temporal subtraction of the post-contrast DE images from the pre-contrast DE image is performed to analyze iodine uptake. Our previous work investigated image registration methods to correct for patient motion, enhancing the evaluation of vascular kinetics. In this project we investigate a segmentation algorithm which identifies blood vessels in the breast from our temporal DE subtraction images. Anisotropic diffusion filtering, Gabor filtering, and morphological filtering are used for the enhancement of vessel features. Vessel labeling methods are then used to distinguish vessel and background features successfully. Statistical and clinical evaluations of segmentation accuracy in DE-CBT images are ongoing.

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.

Potential clinical applications of photoacoustics

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

Rosencwaig, A.

1982-09-01

Photoacoustic spectroscopy offers the opportunity for extending the exact science of noninvasive spectral analysis to intact medical substances such as tissues. Thermal-wave imaging offers the potential for microscopic imaging of thermal features in biological matter.

Feature-based Morphometry

PubMed Central

Toews, Matthew; Wells, William M.; Collins, Louis; Arbel, Tal

2013-01-01

This paper presents feature-based morphometry (FBM), a new, fully data-driven technique for identifying group-related differences in volumetric imagery. In contrast to most morphometry methods which assume one-to-one correspondence between all subjects, FBM models images as a collage of distinct, localized image features which may not be present in all subjects. FBM thus explicitly accounts for the case where the same anatomical tissue cannot be reliably identified in all subjects due to disease or anatomical variability. A probabilistic model describes features in terms of their appearance, geometry, and relationship to sub-groups of a population, and is automatically learned from a set of subject images and group labels. Features identified indicate group-related anatomical structure that can potentially be used as disease biomarkers or as a basis for computer-aided diagnosis. Scale-invariant image features are used, which reflect generic, salient patterns in the image. Experiments validate FBM clinically in the analysis of normal (NC) and Alzheimer’s (AD) brain images using the freely available OASIS database. FBM automatically identifies known structural differences between NC and AD subjects in a fully data-driven fashion, and obtains an equal error classification rate of 0.78 on new subjects. PMID:20426102

Sclerosing Cholangitis: Clinicopathologic Features, Imaging Spectrum, and Systemic Approach to Differential Diagnosis.

PubMed

Seo, Nieun; Kim, So Yeon; Lee, Seung Soo; Byun, Jae Ho; Kim, Jin Hee; Kim, Hyoung Jung; Lee, Moon-Gyu

2016-01-01

Sclerosing cholangitis is a spectrum of chronic progressive cholestatic liver disease characterized by inflammation, fibrosis, and stricture of the bile ducts, which can be classified as primary and secondary sclerosing cholangitis. Primary sclerosing cholangitis is a chronic progressive liver disease of unknown cause. On the other hand, secondary sclerosing cholangitis has identifiable causes that include immunoglobulin G4-related sclerosing disease, recurrent pyogenic cholangitis, ischemic cholangitis, acquired immunodeficiency syndrome-related cholangitis, and eosinophilic cholangitis. In this review, we suggest a systemic approach to the differential diagnosis of sclerosing cholangitis based on the clinical and laboratory findings, as well as the typical imaging features on computed tomography and magnetic resonance (MR) imaging with MR cholangiography. Familiarity with various etiologies of sclerosing cholangitis and awareness of their typical clinical and imaging findings are essential for an accurate diagnosis and appropriate management.

Coherent anti-Stokes Raman scattering microscopy: overcoming technical barriers for clinical translation

PubMed Central

Tu, Haohua; Boppart, Stephen A.

2015-01-01

Clinical translation of coherent anti-Stokes Raman scattering microscopy is of great interest because of the advantages of noninvasive label-free imaging, high sensitivity, and chemical specificity. For this to happen, we have identified and review the technical barriers that must be overcome. Prior investigations have developed advanced techniques (features), each of which can be used to effectively overcome one particular technical barrier. However, the implementation of one or a small number of these advanced features in previous attempts for clinical translation has often introduced more tradeoffs than benefits. In this review, we outline a strategy that would integrate multiple advanced features to overcome all the technical barriers simultaneously, effectively reduce tradeoffs, and synergistically optimize CARS microscopy for clinical translation. The operation of the envisioned system incorporates coherent Raman micro-spectroscopy for identifying vibrational biomolecular markers of disease and single-frequency (or hyperspectral) Raman imaging of these specific biomarkers for real-time in vivo diagnostics and monitoring. An optimal scheme of clinical CARS micro-spectroscopy for thin ex vivo tissues. PMID:23674234

SU-F-R-45: The Prognostic Value of Radiotherapy Based On the Changes of Texture Features Between Pre-Treatment and Post-Treatment FDG PET Image for NSCLC Patients

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

Ma, C; Yin, Y

Purpose: The purpose of this research is investigating which texture features extracted from FDG-PET images by gray-level co-occurrence matrix(GLCM) have a higher prognostic value than the other texture features. Methods: 21 non-small cell lung cancer(NSCLC) patients were approved in the study. Patients underwent 18F-FDG PET/CT scans with both pre-treatment and post-treatment. Firstly, the tumors were extracted by our house developed software. Secondly, the clinical features including the maximum SUV and tumor volume were extracted by MIM vista software, and texture features including angular second moment, contrast, inverse different moment, entropy and correlation were extracted using MATLAB.The differences can be calculatedmore » by using post-treatment features to subtract pre-treatment features. Finally, the SPSS software was used to get the Pearson correlation coefficients and Spearman rank correlation coefficients between the change ratios of texture features and change ratios of clinical features. Results: The Pearson and Spearman rank correlation coefficient between contrast and SUV maximum is 0.785 and 0.709. The P and S value between inverse difference moment and tumor volume is 0.953 and 0.942. Conclusion: This preliminary study showed that the relationships between different texture features and the same clinical feature are different. Finding the prognostic value of contrast and inverse difference moment were higher than the other three textures extracted by GLCM.« less

The effects of variations in parameters and algorithm choices on calculated radiomics feature values: initial investigations and comparisons to feature variability across CT image acquisition conditions

NASA Astrophysics Data System (ADS)

Emaminejad, Nastaran; Wahi-Anwar, Muhammad; Hoffman, John; Kim, Grace H.; Brown, Matthew S.; McNitt-Gray, Michael

2018-02-01

Translation of radiomics into clinical practice requires confidence in its interpretations. This may be obtained via understanding and overcoming the limitations in current radiomic approaches. Currently there is a lack of standardization in radiomic feature extraction. In this study we examined a few factors that are potential sources of inconsistency in characterizing lung nodules, such as 1)different choices of parameters and algorithms in feature calculation, 2)two CT image dose levels, 3)different CT reconstruction algorithms (WFBP, denoised WFBP, and Iterative). We investigated the effect of variation of these factors on entropy textural feature of lung nodules. CT images of 19 lung nodules identified from our lung cancer screening program were identified by a CAD tool and contours provided. The radiomics features were extracted by calculating 36 GLCM based and 4 histogram based entropy features in addition to 2 intensity based features. A robustness index was calculated across different image acquisition parameters to illustrate the reproducibility of features. Most GLCM based and all histogram based entropy features were robust across two CT image dose levels. Denoising of images slightly improved robustness of some entropy features at WFBP. Iterative reconstruction resulted in improvement of robustness in a fewer times and caused more variation in entropy feature values and their robustness. Within different choices of parameters and algorithms texture features showed a wide range of variation, as much as 75% for individual nodules. Results indicate the need for harmonization of feature calculations and identification of optimum parameters and algorithms in a radiomics study.

Deep learning with convolutional neural network in radiology.

PubMed

Yasaka, Koichiro; Akai, Hiroyuki; Kunimatsu, Akira; Kiryu, Shigeru; Abe, Osamu

2018-04-01

Deep learning with a convolutional neural network (CNN) is gaining attention recently for its high performance in image recognition. Images themselves can be utilized in a learning process with this technique, and feature extraction in advance of the learning process is not required. Important features can be automatically learned. Thanks to the development of hardware and software in addition to techniques regarding deep learning, application of this technique to radiological images for predicting clinically useful information, such as the detection and the evaluation of lesions, etc., are beginning to be investigated. This article illustrates basic technical knowledge regarding deep learning with CNNs along the actual course (collecting data, implementing CNNs, and training and testing phases). Pitfalls regarding this technique and how to manage them are also illustrated. We also described some advanced topics of deep learning, results of recent clinical studies, and the future directions of clinical application of deep learning techniques.

Automatic annotation of histopathological images using a latent topic model based on non-negative matrix factorization

PubMed Central

Cruz-Roa, Angel; Díaz, Gloria; Romero, Eduardo; González, Fabio A.

2011-01-01

Histopathological images are an important resource for clinical diagnosis and biomedical research. From an image understanding point of view, the automatic annotation of these images is a challenging problem. This paper presents a new method for automatic histopathological image annotation based on three complementary strategies, first, a part-based image representation, called the bag of features, which takes advantage of the natural redundancy of histopathological images for capturing the fundamental patterns of biological structures, second, a latent topic model, based on non-negative matrix factorization, which captures the high-level visual patterns hidden in the image, and, third, a probabilistic annotation model that links visual appearance of morphological and architectural features associated to 10 histopathological image annotations. The method was evaluated using 1,604 annotated images of skin tissues, which included normal and pathological architectural and morphological features, obtaining a recall of 74% and a precision of 50%, which improved a baseline annotation method based on support vector machines in a 64% and 24%, respectively. PMID:22811960

Pituitary xanthogranulomas: clinical features, radiological appearances and post-operative outcomes.

PubMed

Ved, R; Logier, N; Leach, P; Davies, J S; Hayhurst, C

2018-06-01

Xanthogranulomas are inflammatory masses most commonly found at peripheral sites such as the skin. Sellar and parasellar xanthogranulomas are rare and present a diagnostic challenge as they are difficult to differentiate from other sellar lesions such as craniopharyngiomas and Rathke's cleft cysts pre-operatively. Their radiological imaging features are yet to be clearly defined, and clinical outcomes after surgery are also uncertain. This study reviews clinical presentation, radiological appearances, and clinical outcomes in a cohort of patients with pituitary xanthogranulomas. A prospectively maintained pituitary surgery database was screened for histologically confirmed pituitary xanthogranulomas between May 2011-December 2016. Retrospective case note assessments were then performed by three independent reviewers. Patient demographics, clinical presentations, imaging, and clinical outcomes were analysed. During the study period 295 endoscopic endonasal pituitary surgeries were performed. Six patients had confirmed pituitary xanthogranulomas (2%). Patients most commonly presented with visual field deficits and/or endocrine dysfunction. Common imaging features included: a cystic consistency, hyperintensity on T1-weighted MR images, and contrast enhancement either peripherally (n = 3) or homogenously (n = 3). The most common pre-operative endocrine deficits were hyperprolactinaemia and hypoadrenalism (at least one of which was identified in 4/6 patients; 66%). Thirty-three percent (2/6) of patients presented with diabetes insipidus. The most common post-operative endocrinological deficits were adrenocortical dysfunction (66%) and gonadotropin deficiency (66%). Visual assessments normalised in all six patients post-operatively. Gross total resection was achieved in all patients, and at median follow up of 33.5 months there were no cases of tumour recurrence. The prevalence of pituitary xanthogranulomas in our series is higher than that suggested in the literature. Surgery restored normal vision to all cases, however four patients (67%) required long-term hormonal replacement post-operatively. Imaging features such peripheral rim enhancement, a suprasellar tumour epicentre, and the absence of both calcification or cavernous sinus invasion were identified as potential indicators that together should alert clinicians to the possibility of pituitary xanthogranuloma when assessing patients with cystic sellar and parasellar tumours.

Magnetic resonance imaging findings in patients presenting with (sub)acute cerebellar ataxia.

PubMed

Schneider, Tanja; Thomalla, Götz; Goebell, Einar; Piotrowski, Anna; Yousem, David Mark

2015-06-01

Acute or subacute cerebellar inflammation is mainly caused by postinfectious, toxic, neoplastic, vascular, or idiopathic processes and can result in cerebellar ataxia. Previous magnetic resonance (MR) studies in single patients who developed acute or subacute ataxia showed varying imaging features. Eighteen patients presenting with acute and subacute onset of ataxia were included in this study. Cases of chronic-progressive/hereditary and noncerebellar causes (ischemia, multiple sclerosis lesions, metastasis, bleedings) were excluded. MR imaging findings were then matched with the clinical history of the patient. An underlying etiology for ataxic symptoms were found in 14/18 patients (postinfectious/infectious, paraneoplastic, autoimmune, drug-induced). In two of five patients without MR imaging findings and three of eight patients with minimal imaging features (cerebellar atrophy, slight signal alterations, and small areas of restricted diffusion), adverse clinical outcomes were documented. Of the five patients with prominent MR findings (cerebellar swelling, contrast enhancement, or broad signal abnormalities), two were lost to follow-up and two showed long-term sequelae. No correlation was found between the presence of initial MRI findings in subacute or acute ataxia patients and their long-term clinical outcome. MR imaging was more flagrantly positive in cases due to encephalitis.

Five-class differential diagnostics of neurodegenerative diseases using random undersampling boosting.

PubMed

Tong, Tong; Ledig, Christian; Guerrero, Ricardo; Schuh, Andreas; Koikkalainen, Juha; Tolonen, Antti; Rhodius, Hanneke; Barkhof, Frederik; Tijms, Betty; Lemstra, Afina W; Soininen, Hilkka; Remes, Anne M; Waldemar, Gunhild; Hasselbalch, Steen; Mecocci, Patrizia; Baroni, Marta; Lötjönen, Jyrki; Flier, Wiesje van der; Rueckert, Daniel

2017-01-01

Differentiating between different types of neurodegenerative diseases is not only crucial in clinical practice when treatment decisions have to be made, but also has a significant potential for the enrichment of clinical trials. The purpose of this study is to develop a classification framework for distinguishing the four most common neurodegenerative diseases, including Alzheimer's disease, frontotemporal lobe degeneration, Dementia with Lewy bodies and vascular dementia, as well as patients with subjective memory complaints. Different biomarkers including features from images (volume features, region-wise grading features) and non-imaging features (CSF measures) were extracted for each subject. In clinical practice, the prevalence of different dementia types is imbalanced, posing challenges for learning an effective classification model. Therefore, we propose the use of the RUSBoost algorithm in order to train classifiers and to handle the class imbalance training problem. Furthermore, a multi-class feature selection method based on sparsity is integrated into the proposed framework to improve the classification performance. It also provides a way for investigating the importance of different features and regions. Using a dataset of 500 subjects, the proposed framework achieved a high accuracy of 75.2% with a balanced accuracy of 69.3% for the five-class classification using ten-fold cross validation, which is significantly better than the results using support vector machine or random forest, demonstrating the feasibility of the proposed framework to support clinical decision making.

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.

MO-AB-BRA-10: Cancer Therapy Outcome Prediction Based On Dempster-Shafer Theory and PET Imaging

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

Lian, C; University of Rouen, QuantIF - EA 4108 LITIS, 76000 Rouen; Li, H

2015-06-15

Purpose: In cancer therapy, utilizing FDG-18 PET image-based features for accurate outcome prediction is challenging because of 1) limited discriminative information within a small number of PET image sets, and 2) fluctuant feature characteristics caused by the inferior spatial resolution and system noise of PET imaging. In this study, we proposed a new Dempster-Shafer theory (DST) based approach, evidential low-dimensional transformation with feature selection (ELT-FS), to accurately predict cancer therapy outcome with both PET imaging features and clinical characteristics. Methods: First, a specific loss function with sparse penalty was developed to learn an adaptive low-rank distance metric for representing themore » dissimilarity between different patients’ feature vectors. By minimizing this loss function, a linear low-dimensional transformation of input features was achieved. Also, imprecise features were excluded simultaneously by applying a l2,1-norm regularization of the learnt dissimilarity metric in the loss function. Finally, the learnt dissimilarity metric was applied in an evidential K-nearest-neighbor (EK- NN) classifier to predict treatment outcome. Results: Twenty-five patients with stage II–III non-small-cell lung cancer and thirty-six patients with esophageal squamous cell carcinomas treated with chemo-radiotherapy were collected. For the two groups of patients, 52 and 29 features, respectively, were utilized. The leave-one-out cross-validation (LOOCV) protocol was used for evaluation. Compared to three existing linear transformation methods (PCA, LDA, NCA), the proposed ELT-FS leads to higher prediction accuracy for the training and testing sets both for lung-cancer patients (100+/−0.0, 88.0+/−33.17) and for esophageal-cancer patients (97.46+/−1.64, 83.33+/−37.8). The ELT-FS also provides superior class separation in both test data sets. Conclusion: A novel DST- based approach has been proposed to predict cancer treatment outcome using PET image features and clinical characteristics. A specific loss function has been designed for robust accommodation of feature set incertitude and imprecision, facilitating adaptive learning of the dissimilarity metric for the EK-NN classifier.« less

Estimation of T2* Relaxation Time of Breast Cancer: Correlation with Clinical, Imaging and Pathological Features

PubMed Central

Seo, Mirinae; Jahng, Geon-Ho; Sohn, Yu-Mee; Rhee, Sun Jung; Oh, Jang-Hoon; Won, Kyu-Yeoun

2017-01-01

Objective The purpose of this study was to estimate the T2* relaxation time in breast cancer, and to evaluate the association between the T2* value with clinical-imaging-pathological features of breast cancer. Materials and Methods Between January 2011 and July 2013, 107 consecutive women with 107 breast cancers underwent multi-echo T2*-weighted imaging on a 3T clinical magnetic resonance imaging system. The Student's t test and one-way analysis of variance were used to compare the T2* values of cancer for different groups, based on the clinical-imaging-pathological features. In addition, multiple linear regression analysis was performed to find independent predictive factors associated with the T2* values. Results Of the 107 breast cancers, 92 were invasive and 15 were ductal carcinoma in situ (DCIS). The mean T2* value of invasive cancers was significantly longer than that of DCIS (p = 0.029). Signal intensity on T2-weighted imaging (T2WI) and histologic grade of invasive breast cancers showed significant correlation with T2* relaxation time in univariate and multivariate analysis. Breast cancer groups with higher signal intensity on T2WI showed longer T2* relaxation time (p = 0.005). Cancer groups with higher histologic grade showed longer T2* relaxation time (p = 0.017). Conclusion The T2* value is significantly longer in invasive cancer than in DCIS. In invasive cancers, T2* relaxation time is significantly longer in higher histologic grades and high signal intensity on T2WI. Based on these preliminary data, quantitative T2* mapping has the potential to be useful in the characterization of breast cancer. PMID:28096732

Wide-field OCT imaging of oral lesions in vivo: quantification and classification (Conference Presentation)

NASA Astrophysics Data System (ADS)

Raizada, Rashika; Lee, Anthony M. D.; Liu, Kelly Y.; MacAulay, Calum E.; Ng, Samson; Poh, Catherine F.; Lane, Pierre M.

2017-02-01

Worldwide, there are over 450,000 new cases of oral cancer reported each year. Late-stage diagnosis remains a significant factor responsible for its high mortality rate (>50%). In-vivo non-invasive rapid imaging techniques, that can visualise clinically significant changes in the oral mucosa, may improve the management of oral cancer. We present an analysis of features extracted from oral images obtained using our hand- held wide-field Optical Coherence Tomography (OCT) instrument. The images were analyzed for epithelial scattering, overall tissue scattering, and 3D basement membrane topology. The associations between these three features and disease state (benign, pre-cancer, or cancer), as measured by clinical assessment or pathology, were determined. While scattering coefficient has previously been shown to be sensitive to cancer and dysplasia, likely due to changes in nuclear and cellular density, the addition of basement membrane topology may increase diagnostic ability- as it is known that the presence of bulbous rete pegs in the basement membrane are characteristic of dysplasia. The resolution and field-of-view of our oral OCT system allowed analysis of these features over large areas of up to 2.5mm x 90mm, in a timely fashion, which allow for application in clinical settings.

Non-rigid registration of 3D ultrasound for neurosurgery using automatic feature detection and matching.

PubMed

Machado, Inês; Toews, Matthew; Luo, Jie; Unadkat, Prashin; Essayed, Walid; George, Elizabeth; Teodoro, Pedro; Carvalho, Herculano; Martins, Jorge; Golland, Polina; Pieper, Steve; Frisken, Sarah; Golby, Alexandra; Wells, William

2018-06-04

The brain undergoes significant structural change over the course of neurosurgery, including highly nonlinear deformation and resection. It can be informative to recover the spatial mapping between structures identified in preoperative surgical planning and the intraoperative state of the brain. We present a novel feature-based method for achieving robust, fully automatic deformable registration of intraoperative neurosurgical ultrasound images. A sparse set of local image feature correspondences is first estimated between ultrasound image pairs, after which rigid, affine and thin-plate spline models are used to estimate dense mappings throughout the image. Correspondences are derived from 3D features, distinctive generic image patterns that are automatically extracted from 3D ultrasound images and characterized in terms of their geometry (i.e., location, scale, and orientation) and a descriptor of local image appearance. Feature correspondences between ultrasound images are achieved based on a nearest-neighbor descriptor matching and probabilistic voting model similar to the Hough transform. Experiments demonstrate our method on intraoperative ultrasound images acquired before and after opening of the dura mater, during resection and after resection in nine clinical cases. A total of 1620 automatically extracted 3D feature correspondences were manually validated by eleven experts and used to guide the registration. Then, using manually labeled corresponding landmarks in the pre- and post-resection ultrasound images, we show that our feature-based registration reduces the mean target registration error from an initial value of 3.3 to 1.5 mm. This result demonstrates that the 3D features promise to offer a robust and accurate solution for 3D ultrasound registration and to correct for brain shift in image-guided neurosurgery.

27 The DiPEP (Diagnosis of PE in Pregnancy) study: can clinical assessment, d-dimer or chest x-ray be used to select pregnant or postpartum women with suspected PE for diagnostic imaging?

PubMed

Goodacre, Steve; Horspool, Kimberley; Nelson-Piercy, Catherine; Knight, Marian; Shephard, Neil; Lecky, Fiona; Thomas, Steven; Hunt, Beverley; Fuller, Gordon

2017-12-01

To determine whether clinical features (in the form of a clinical decision rule) or d-dimer can be used to select pregnant or postpartum women with suspected PE for diagnostic imaging. Observational cohort study augmented with additional cases. Consultant-led maternity units participating in the UK Obstetric Surveillance System (UKOSS) and emergency departments and maternity units at eleven prospectively recruiting sites. 198 pregnant or postpartum women with diagnosed PE identified through UKOSS and 324 pregnant or postpartum women with suspected PE from prospectively recruiting sites. Data were collected relating to clinical features, elements of clinical decision rules, d-dimer measurements, diagnostic imaging, treatment for PE and adverse outcomes. Women were classified as having or not having PE on the basis of diagnostic imaging, treatment and subsequent adverse outcomes. Primary analysis was limited to women with conclusive diagnostic imaging. Secondary analyses included women with clinically diagnosed or ruled out PE. The primary analysis included 181 women with PE and 259 without. Most clinical features showed no association with PE. The only exceptions were number of previous pregnancies over 24 weeks (p=0.017), no varicose veins (p=0.045), no recent long haul travel (p=0.006), recent surgery including caesarean section (p=0.001), increased temperature (p=0.003), low oxygen saturation (p<0.001), PE-related chest x-ray abnormality (p=0.01) and other chest x-ray abnormality (p=0.001).Clinical decision rules had areas under the receiver-operator characteristic curve ranging from 0.577 to 0.732. No clinically useful threshold for decision-making was identified for any rule. The sensitivities and specificities of d-dimer were 88.4% and 8.8% using the standard laboratory threshold and 69.8% and 32.8% using a pregnancy-specific threshold. Clinical decision rules, d-dimer and chest x-ray should not be used to select pregnant or postpartum women with suspected PE for diagnostic imaging. © 2017, Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Differentiation of benign and malignant breast lesions by mechanical imaging

PubMed Central

Kearney, Thomas; Pollak, Stanley B.; Rohatgi, Chand; Sarvazyan, Noune; Airapetian, Suren; Browning, Stephanie; Sarvazyan, Armen

2009-01-01

Mechanical imaging yields tissue elasticity map and provides quantitative characterization of a detected pathology. The changes in the surface stress patterns as a function of applied load provide information about the elastic composition and geometry of the underlying tissue structures. The objective of this study is the clinical evaluation of breast mechanical imager for breast lesion characterization and differentiation between benign and malignant lesions. The breast mechanical imager includes a probe with pressure sensor array, an electronic unit providing data acquisition from the pressure sensors and communication with a touch-screen laptop computer. We have developed an examination procedure and algorithms to provide assessment of breast lesion features such as hardness related parameters, mobility, and shape. A statistical Bayesian classifier was constructed to distinguish between benign and malignant lesions by utilizing all the listed features as the input. Clinical results for 179 cases, collected at four different clinical sites, have demonstrated that the breast mechanical imager provides a reliable image formation of breast tissue abnormalities and calculation of lesion features. Malignant breast lesions (histologically confirmed) demonstrated increased hardness and strain hardening as well as decreased mobility and longer boundary length in comparison with benign lesions. Statistical analysis of differentiation capability for 147 benign and 32 malignant lesions revealed an average sensitivity of 91.4% and specificity of 86.8% with a standard deviation of ±6.1%. The area under the receiver operating characteristic curve characterizing benign and malignant lesion discrimination is 86.1% with the confidence interval ranging from 80.3 to 90.9%, with a significance level of P = 0.0001 (area = 50%). The multisite clinical study demonstrated the capability of mechanical imaging for characterization and differentiation of benign and malignant breast lesions. We hypothesize that the breast mechanical imager has the potential to be used as a cost effective device for cancer diagnostics that could reduce the benign biopsy rate, serve as an adjunct to mammography and to be utilized as a screening device for breast cancer detection. PMID:19306059

Classification of optical coherence tomography images for diagnosing different ocular diseases

NASA Astrophysics Data System (ADS)

Gholami, Peyman; Sheikh Hassani, Mohsen; Kuppuswamy Parthasarathy, Mohana; Zelek, John S.; Lakshminarayanan, Vasudevan

2018-03-01

Optical Coherence tomography (OCT) images provide several indicators, e.g., the shape and the thickness of different retinal layers, which can be used for various clinical and non-clinical purposes. We propose an automated classification method to identify different ocular diseases, based on the local binary pattern features. The database consists of normal and diseased human eye SD-OCT images. We use a multiphase approach for building our classifier, including preprocessing, Meta learning, and active learning. Pre-processing is applied to the data to handle missing features from images and replace them with the mean or median of the corresponding feature. All the features are run through a Correlation-based Feature Subset Selection algorithm to detect the most informative features and omit the less informative ones. A Meta learning approach is applied to the data, in which a SVM and random forest are combined to obtain a more robust classifier. Active learning is also applied to strengthen our classifier around the decision boundary. The primary experimental results indicate that our method is able to differentiate between the normal and non-normal retina with an area under the ROC curve (AUC) of 98.6% and also to diagnose the three common retina-related diseases, i.e., Age-related Macular Degeneration, Diabetic Retinopathy, and Macular Hole, with an AUC of 100%, 95% and 83.8% respectively. These results indicate a better performance of the proposed method compared to most of the previous works in the literature.

Quantitative description of solid breast nodules by ultrasound imaging

NASA Astrophysics Data System (ADS)

Sehgal, Chandra M.; Kangas, Sarah A.; Cary, Ted W.; Weinstein, Susan P.; Schultz, Susan M.; Arger, Peter H.; Conant, Emily F.

2004-04-01

Various features based on qualitative description of shape, contour, margin and echogenicity of solid breast nodules are used clinically to classify them as benign or malignant. However, there continues to be considerable overlap in the sonographic findings for the two types of lesions. This is related to the lack of precise definition of the various features as well as to the lack of agreement among observers, among other factors. The goal of this investigation is to define clinical features quantitatively and evaluate if they differ significantly in malignant and benign cases. Features based on margin sharpness and continuity, shadowing, and attenuation were defined and calculated from the images. These features were tested on digital phantoms. Following the evaluation, the features were measured on 116 breast sonograms of 58 biopsy-proven masses. Biopsy had been recommended for all of these breast lesions based on physical exams and conventional diagnostic imaging of ultrasound and/or mammography. Of the 58 masses, 20 were identified as malignant and 38 as benign histologically. Margin sharpness, margin echogenicity, and angular margin variation were significantly different for the two groups (p<0.03, two-tailed student t-test). Shadowing and attenuation of ultrasound did not show significant difference. The results of this preliminary study show that quantitative margin characteristics measured for the malignant and benign masses from the ultrasound images are different and could potentially be useful in identifying a subgroup of solid breast nodules that have low risk of being malignant.

Centronuclear myopathy related to dynamin 2 mutations: Clinical, morphological, muscle imaging and genetic features of an Italian cohort

PubMed Central

Catteruccia, Michela; Fattori, Fabiana; Codemo, Valentina; Ruggiero, Lucia; Maggi, Lorenzo; Tasca, Giorgio; Fiorillo, Chiara; Pane, Marika; Berardinelli, Angela; Verardo, Margherita; Bragato, Cinzia; Mora, Marina; Morandi, Lucia; Bruno, Claudio; Santoro, Lucio; Pegoraro, Elena; Mercuri, Eugenio; Bertini, Enrico; D’Amico, Adele

2013-01-01

Mutations in dynamin 2 (DNM2) gene cause autosomal dominant centronuclear myopathy and occur in around 50% of patients with centronuclear myopathy. We report clinical, morphological, muscle imaging and genetic data of 10 unrelated Italian patients with centronuclear myopathy related to DNM2 mutations. Our results confirm the clinical heterogeneity of this disease, underlining some peculiar clinical features, such as severe pulmonary impairment and jaw contracture that should be considered in the clinical follow-up of these patients. Muscle MRI showed a distinct pattern of involvement, with predominant involvement of soleus and tibialis anterior in the lower leg muscles, followed by hamstring muscles and adductor magnus at thigh level and gluteus maximus. The detection of three novel DNM2 mutations and the first case of somatic mosaicism further expand the genetic spectrum of the disease. PMID:23394783

Low-grade central osteosarcoma of distal femur, resembling fibrous dysplasia

PubMed Central

Vasiliadis, Haris S; Arnaoutoglou, Christina; Plakoutsis, Sotiris; Doukas, Michalis; Batistatou, Anna; Xenakis, Theodoros A

2013-01-01

We report a case of a 32 year-old male, admitted for a lytic lesion of the distal femur. One month after the first X-ray, clinical and imaging deterioration was evident. Open biopsy revealed fibrous dysplasia. Three months later, the lytic lesion had spread to the whole distal third of the femur reaching the articular cartilage. The malignant clinical and imaging features necessitated excision of the lesion and reconstruction with a custom-made total knee arthroplasty. Intra-operatively, no obvious soft tissue infiltration was evident. Nevertheless, an excision of the distal 15.5 cm of the femur including 3.0 cm of the surrounding muscles was finally performed. The histological examination of the excised specimen revealed central low-grade osteosarcoma. Based on the morphological features of the excised tumor, allied to the clinical findings, the diagnosis of low-grade central osteosarcoma was finally made although characters of a fibrous dysplasia were apparent. Central low-grade osteosarcoma is a rare, well-differentiated sub-type of osteosarcoma, with clinical, imaging, and histological features similar to benign tumours. Thus, initial misdiagnosis is usual with the condition commonly mistaken for fibrous dysplasia. Central low-grade osteosarcoma is usually treated with surgery alone, with rare cases of distal metastases. However, regional recurrence is quite frequent after close margin excision. PMID:24147271

Quantitative imaging biomarkers: a review of statistical methods for technical performance assessment.

PubMed

Raunig, David L; McShane, Lisa M; Pennello, Gene; Gatsonis, Constantine; Carson, Paul L; Voyvodic, James T; Wahl, Richard L; Kurland, Brenda F; Schwarz, Adam J; Gönen, Mithat; Zahlmann, Gudrun; Kondratovich, Marina V; O'Donnell, Kevin; Petrick, Nicholas; Cole, Patricia E; Garra, Brian; Sullivan, Daniel C

2015-02-01

Technological developments and greater rigor in the quantitative measurement of biological features in medical images have given rise to an increased interest in using quantitative imaging biomarkers to measure changes in these features. Critical to the performance of a quantitative imaging biomarker in preclinical or clinical settings are three primary metrology areas of interest: measurement linearity and bias, repeatability, and the ability to consistently reproduce equivalent results when conditions change, as would be expected in any clinical trial. Unfortunately, performance studies to date differ greatly in designs, analysis method, and metrics used to assess a quantitative imaging biomarker for clinical use. It is therefore difficult or not possible to integrate results from different studies or to use reported results to design studies. The Radiological Society of North America and the Quantitative Imaging Biomarker Alliance with technical, radiological, and statistical experts developed a set of technical performance analysis methods, metrics, and study designs that provide terminology, metrics, and methods consistent with widely accepted metrological standards. This document provides a consistent framework for the conduct and evaluation of quantitative imaging biomarker performance studies so that results from multiple studies can be compared, contrasted, or combined. © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

A multiscale product approach for an automatic classification of voice disorders from endoscopic high-speed videos.

PubMed

Unger, Jakob; Schuster, Maria; Hecker, Dietmar J; Schick, Bernhard; Lohscheller, Joerg

2013-01-01

Direct observation of vocal fold vibration is indispensable for a clinical diagnosis of voice disorders. Among current imaging techniques, high-speed videoendoscopy constitutes a state-of-the-art method capturing several thousand frames per second of the vocal folds during phonation. Recently, a method for extracting descriptive features from phonovibrograms, a two-dimensional image containing the spatio-temporal pattern of vocal fold dynamics, was presented. The derived features are closely related to a clinically established protocol for functional assessment of pathologic voices. The discriminative power of these features for different pathologic findings and configurations has not been assessed yet. In the current study, a collective of 220 subjects is considered for two- and multi-class problems of healthy and pathologic findings. The performance of the proposed feature set is compared to conventional feature reduction routines and was found to clearly outperform these. As such, the proposed procedure shows great potential for diagnostical issues of vocal fold disorders.

Recursive feature elimination for biomarker discovery in resting-state functional connectivity.

PubMed

Ravishankar, Hariharan; Madhavan, Radhika; Mullick, Rakesh; Shetty, Teena; Marinelli, Luca; Joel, Suresh E

2016-08-01

Biomarker discovery involves finding correlations between features and clinical symptoms to aid clinical decision. This task is especially difficult in resting state functional magnetic resonance imaging (rs-fMRI) data due to low SNR, high-dimensionality of images, inter-subject and intra-subject variability and small numbers of subjects compared to the number of derived features. Traditional univariate analysis suffers from the problem of multiple comparisons. Here, we adopt an alternative data-driven method for identifying population differences in functional connectivity. We propose a machine-learning approach to down-select functional connectivity features associated with symptom severity in mild traumatic brain injury (mTBI). Using this approach, we identified functional regions with altered connectivity in mTBI. including the executive control, visual and precuneus networks. We compared functional connections at multiple resolutions to determine which scale would be more sensitive to changes related to patient recovery. These modular network-level features can be used as diagnostic tools for predicting disease severity and recovery profiles.

SU-F-R-52: A Comparison of the Performance of Radiomic Features From Free Breathing and 4DCT Scans in Predicting Disease Recurrence in Lung Cancer SBRT Patients

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

Huynh, E; Coroller, T; Narayan, V

Purpose: There is a clinical need to identify patients who are at highest risk of recurrence after being treated with stereotactic body radiation therapy (SBRT). Radiomics offers a non-invasive approach by extracting quantitative features from medical images based on tumor phenotype that is predictive of an outcome. Lung cancer patients treated with SBRT routinely undergo free breathing (FB image) and 4DCT (average intensity projection (AIP) image) scans for treatment planning to account for organ motion. The aim of the current study is to evaluate and compare the prognostic performance of radiomic features extracted from FB and AIP images in lungmore » cancer patients treated with SBRT to identify which image type would generate an optimal predictive model for recurrence. Methods: FB and AIP images of 113 Stage I-II NSCLC patients treated with SBRT were analysed. The prognostic performance of radiomic features for distant metastasis (DM) was evaluated by their concordance index (CI). Radiomic features were compared with conventional imaging metrics (e.g. diameter). All p-values were corrected for multiple testing using the false discovery rate. Results: All patients received SBRT and 20.4% of patients developed DM. From each image type (FB or AIP), nineteen radiomic features were selected based on stability and variance. Both image types had five common and fourteen different radiomic features. One FB (CI=0.70) and five AIP (CI range=0.65–0.68) radiomic features were significantly prognostic for DM (p<0.05). None of the conventional features derived from FB images (range CI=0.60–0.61) were significant but all AIP conventional features were (range CI=0.64–0.66). Conclusion: Features extracted from different types of CT scans have varying prognostic performances. AIP images contain more prognostic radiomic features for DM than FB images. These methods can provide personalized medicine approaches at low cost, as FB and AIP data are readily available within a large number of radiation oncology departments. R.M. had consulting interest with Amgen (ended in 2015).« less

Hyperspectral Image Enhancement and Mixture Deep-Learning Classification of Corneal Epithelium Injuries

PubMed Central

Md Noor, Siti Salwa; Michael, Kaleena; Marshall, Stephen; Ren, Jinchang

2017-01-01

In our preliminary study, the reflectance signatures obtained from hyperspectral imaging (HSI) of normal and abnormal corneal epithelium tissues of porcine show similar morphology with subtle differences. Here we present image enhancement algorithms that can be used to improve the interpretability of data into clinically relevant information to facilitate diagnostics. A total of 25 corneal epithelium images without the application of eye staining were used. Three image feature extraction approaches were applied for image classification: (i) image feature classification from histogram using a support vector machine with a Gaussian radial basis function (SVM-GRBF); (ii) physical image feature classification using deep-learning Convolutional Neural Networks (CNNs) only; and (iii) the combined classification of CNNs and SVM-Linear. The performance results indicate that our chosen image features from the histogram and length-scale parameter were able to classify with up to 100% accuracy; particularly, at CNNs and CNNs-SVM, by employing 80% of the data sample for training and 20% for testing. Thus, in the assessment of corneal epithelium injuries, HSI has high potential as a method that could surpass current technologies regarding speed, objectivity, and reliability. PMID:29144388

Neuromuscular choristoma: characteristic magnetic resonance imaging findings and association with post-biopsy fibromatosis.

PubMed

Niederhauser, Blake D; Spinner, Robert J; Jentoft, Mark E; Everist, Brian M; Matsumoto, Jane M; Amrami, Kimberly K

2013-04-01

To describe imaging characteristics of neuromuscular choristomas (NMC) and to differentiate them from fibrolipomatous hamartomas (FLH). Clinical and imaging characteristics of six patients with biopsy-proven NMC and six patients with FLH were reviewed by musculoskeletal, a pediatric, and two in-training radiologists with a literature review to define typical magnetic resonance imaging features by consensus. Five radiology trainees blinded to cases and naive to the diagnosis of NMC and a musculoskeletal-trained radiologist rated each lesion as having more than or less than 50% intralesional fat, as well as an overall impression using axial T1 images. Sensitivity, specificity, accuracy, and interobserver agreement kappa were determined. Typical features of NMC include smoothly tapering, fusiform enlargement of the sciatic nerve or brachial plexus elements with T1 and T2 signal characteristics closely following those of muscle. Longitudinal bands of intervening low T1 and T2 signal were often present and likely corresponded to fibrous tissue by pathology. Four of five patients with long-term follow-up (80%) developed aggressive fibromatosis after percutaneous or surgical biopsy. Nerve fascicle thickening often resulted in a "coaxial cable" appearance similar to classic FLH, however, using a cutoff of <50% intralesional fat allowed for differentiation with 100% sensitivity by all reviewers and 100% specificity when all imaging features were utilized for impressions. Agreement was excellent with all differentiating methods (kappa 0.861-1.0). NMC can be confidently differentiated from FLH and malignancies using characteristic imaging and clinical features. When a diagnosis is made, biopsy should be avoided given frequent complication by aggressive fibromatosis.

Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration

PubMed Central

Wardlaw, Joanna M; Smith, Eric E; Biessels, Geert J; Cordonnier, Charlotte; Fazekas, Franz; Frayne, Richard; Lindley, Richard I; O'Brien, John T; Barkhof, Frederik; Benavente, Oscar R; Black, Sandra E; Brayne, Carol; Breteler, Monique; Chabriat, Hugues; DeCarli, Charles; de Leeuw, Frank-Erik; Doubal, Fergus; Duering, Marco; Fox, Nick C; Greenberg, Steven; Hachinski, Vladimir; Kilimann, Ingo; Mok, Vincent; Oostenbrugge, Robert van; Pantoni, Leonardo; Speck, Oliver; Stephan, Blossom C M; Teipel, Stefan; Viswanathan, Anand; Werring, David; Chen, Christopher; Smith, Colin; van Buchem, Mark; Norrving, Bo; Gorelick, Philip B; Dichgans, Martin

2013-01-01

Summary Cerebral small vessel disease (SVD) is a common accompaniment of ageing. Features seen on neuroimaging include recent small subcortical infarcts, lacunes, white matter hyperintensities, perivascular spaces, microbleeds, and brain atrophy. SVD can present as a stroke or cognitive decline, or can have few or no symptoms. SVD frequently coexists with neurodegenerative disease, and can exacerbate cognitive deficits, physical disabilities, and other symptoms of neurodegeneration. Terminology and definitions for imaging the features of SVD vary widely, which is also true for protocols for image acquisition and image analysis. This lack of consistency hampers progress in identifying the contribution of SVD to the pathophysiology and clinical features of common neurodegenerative diseases. We are an international working group from the Centres of Excellence in Neurodegeneration. We completed a structured process to develop definitions and imaging standards for markers and consequences of SVD. We aimed to achieve the following: first, to provide a common advisory about terms and definitions for features visible on MRI; second, to suggest minimum standards for image acquisition and analysis; third, to agree on standards for scientific reporting of changes related to SVD on neuroimaging; and fourth, to review emerging imaging methods for detection and quantification of preclinical manifestations of SVD. Our findings and recommendations apply to research studies, and can be used in the clinical setting to standardise image interpretation, acquisition, and reporting. This Position Paper summarises the main outcomes of this international effort to provide the STandards for ReportIng Vascular changes on nEuroimaging (STRIVE). PMID:23867200

Necrotizing fasciitis and its mimics: what radiologists need to know.

PubMed

Chaudhry, Ammar A; Baker, Kevin S; Gould, Elaine S; Gupta, Rajarsi

2015-01-01

The purpose of this article is to review the imaging features of necrotizing fasciitis and its potential mimics. Key imaging features are emphasized to enable accurate and efficient interpretation of variables that are essential in appropriate management. Necrotizing fasciitis is a medical emergency with potential lethal outcome. Dissecting gas along fascial planes in the absence of penetrating trauma (including iatrogenic) is essentially pathognomonic. However, the lack of soft-tissue emphysema does not exclude the diagnosis. Mimics of necrotizing fasciitis include nonnecrotizing fasciitis (eosinophilic, paraneoplastic, inflammatory (lupus myofasciitis, Churg-Strauss, nodular, or proliferative), myositis, neoplasm, myonecrosis, inflammatory myopathy, and compartment syndrome. Necrotizing fasciitis is a clinical diagnosis, and imaging can reveal nonspecific or negative findings (particularly during the early course of disease). One should be familiar with salient clinical and imaging findings of necrotizing fasciitis to facilitate a more rapid and accurate diagnosis and be aware that its diagnosis necessitates immediate discussion with the referring physician.

Quantitative Ultrasound Using Texture Analysis of Myofascial Pain Syndrome in the Trapezius.

PubMed

Kumbhare, Dinesh A; Ahmed, Sara; Behr, Michael G; Noseworthy, Michael D

2018-01-01

Objective-The objective of this study is to assess the discriminative ability of textural analyses to assist in the differentiation of the myofascial trigger point (MTrP) region from normal regions of skeletal muscle. Also, to measure the ability to reliably differentiate between three clinically relevant groups: healthy asymptomatic, latent MTrPs, and active MTrP. Methods-18 and 19 patients were identified with having active and latent MTrPs in the trapezius muscle, respectively. We included 24 healthy volunteers. Images were obtained by research personnel, who were blinded with respect to the clinical status of the study participant. Histograms provided first-order parameters associated with image grayscale. Haralick, Galloway, and histogram-related features were used in texture analysis. Blob analysis was conducted on the regions of interest (ROIs). Principal component analysis (PCA) was performed followed by multivariate analysis of variance (MANOVA) to determine the statistical significance of the features. Results-92 texture features were analyzed for factorability using Bartlett's test of sphericity, which was significant. The Kaiser-Meyer-Olkin measure of sampling adequacy was 0.94. PCA demonstrated rotated eigenvalues of the first eight components (each comprised of multiple texture features) explained 94.92% of the cumulative variance in the ultrasound image characteristics. The 24 features identified by PCA were included in the MANOVA as dependent variables, and the presence of a latent or active MTrP or healthy muscle were independent variables. Conclusion-Texture analysis techniques can discriminate between the three clinically relevant groups.

[Clinical value of MRI united-sequences examination in diagnosis and differentiation of morphological sub-type of hilar and extrahepatic big bile duct cholangiocarcinoma].

PubMed

Yin, Long-Lin; Song, Bin; Guan, Ying; Li, Ying-Chun; Chen, Guang-Wen; Zhao, Li-Ming; Lai, Li

2014-09-01

To investigate MRI features and associated histological and pathological changes of hilar and extrahepatic big bile duct cholangiocarcinoma with different morphological sub-types, and its value in differentiating between nodular cholangiocarcinoma (NCC) and intraductal growing cholangiocarcinoma (IDCC). Imaging data of 152 patients with pathologically confirmed hilar and extrahepatic big bile duct cholangiocarcinoma were reviewed, which included 86 periductal infiltrating cholangiocarcinoma (PDCC), 55 NCC, and 11 IDCC. Imaging features of the three morphological sub-types were compared. Each of the subtypes demonstrated its unique imaging features. Significant differences (P < 0.05) were found between NCC and IDCC in tumor shape, dynamic enhanced pattern, enhancement degree during equilibrium phase, multiplicity or singleness of tumor, changes in wall and lumen of bile duct at the tumor-bearing segment, dilatation of tumor upstream or downstream bile duct, and invasion of adjacent organs. Imaging features reveal tumor growth patterns of hilar and extrahepatic big bile duct cholangiocarcinoma. MRI united-sequences examination can accurately describe those imaging features for differentiation diagnosis.

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

The molecular mechanisms on glomangiopericytoma invasion

PubMed Central

2013-01-01

Purpose To observed the imaging and pathological features of the glomangiopericytoma. Experimental design In this paper we report a typical case of glomangiopericytoma arising in the skull base area and summarize the clinical manifestations, imaging and pathological features of such diseases. Results Immunohistochemical staining confirmed the tumor cells were strongly positive to Vim, SMA, MSA and negative to CD31, CD34. Partial cells were positive to FVIII. The imaging can’t confirm the diagnosis but indicate the the tumor has intact envelope.The cells in the tumor envelope is positive to Vim and negative SMA and FVIII. These findings were compatible with glomangiopericytoma and the cells in the tumor envelope is not glomangiopericytoma cells. Conclusion In view of the clinical and pathological features of the glomangiopericytoma, we believe that the surgery is the best treatment so far and the tumor can be resected completely. The above results can be preliminary reason to explain the low recurrence of such diseases. PMID:24074285

The effect of defect cluster size and interpolation on radiographic image quality

NASA Astrophysics Data System (ADS)

Töpfer, Karin; Yip, Kwok L.

2011-03-01

For digital X-ray detectors, the need to control factory yield and cost invariably leads to the presence of some defective pixels. Recently, a standard procedure was developed to identify such pixels for industrial applications. However, no quality standards exist in medical or industrial imaging regarding the maximum allowable number and size of detector defects. While the answer may be application specific, the minimum requirement for any defect specification is that the diagnostic quality of the images be maintained. A more stringent criterion is to keep any changes in the images due to defects below the visual threshold. Two highly sensitive image simulation and evaluation methods were employed to specify the fraction of allowable defects as a function of defect cluster size in general radiography. First, the most critical situation of the defect being located in the center of the disease feature was explored using image simulation tools and a previously verified human observer model, incorporating a channelized Hotelling observer. Detectability index d' was obtained as a function of defect cluster size for three different disease features on clinical lung and extremity backgrounds. Second, four concentrations of defects of four different sizes were added to clinical images with subtle disease features and then interpolated. Twenty observers evaluated the images against the original on a single display using a 2-AFC method, which was highly sensitive to small changes in image detail. Based on a 50% just-noticeable difference, the fraction of allowed defects was specified vs. cluster size.

TU-CD-BRB-12: Radiogenomics of MRI-Guided Prostate Cancer Biopsy Habitats

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

Stoyanova, R; Lynne, C; Abraham, S

2015-06-15

Purpose: Diagnostic prostate biopsies are subject to sampling bias. We hypothesize that quantitative imaging with multiparametric (MP)-MRI can more accurately direct targeted biopsies to index lesions associated with highest risk clinical and genomic features. Methods: Regionally distinct prostate habitats were delineated on MP-MRI (T2-weighted, perfusion and diffusion imaging). Directed biopsies were performed on 17 habitats from 6 patients using MRI-ultrasound fusion. Biopsy location was characterized with 52 radiographic features. Transcriptome-wide analysis of 1.4 million RNA probes was performed on RNA from each habitat. Genomics features with insignificant expression values (<0.25) and interquartile range <0.5 were filtered, leaving total of 212more » genes. Correlation between imaging features, genes and a 22 feature genomic classifier (GC), developed as a prognostic assay for metastasis after radical prostatectomy was investigated. Results: High quality genomic data was derived from 17 (100%) biopsies. Using the 212 ‘unbiased’ genes, the samples clustered by patient origin in unsupervised analysis. When only prostate cancer related genomic features were used, hierarchical clustering revealed samples clustered by needle-biopsy Gleason score (GS). Similarly, principal component analysis of the imaging features, found the primary source of variance segregated the samples into high (≥7) and low (6) GS. Pearson’s correlation analysis of genes with significant expression showed two main patterns of gene expression clustering prostate peripheral and transitional zone MRI features. Two-way hierarchical clustering of GC with radiomics features resulted in the expected groupings of high and low expressed genes in this metastasis signature. Conclusions: MP-MRI-targeted diagnostic biopsies can potentially improve risk stratification by directing pathological and genomic analysis to clinically significant index lesions. As determinant lesions are more reliably identified, targeting with radiotherapy should improve outcome. This is the first demonstration of a link between quantitative imaging features (radiomics) with genomic features in MRI-directed prostate biopsies. The research was supported by NIH- NCI R01 CA 189295 and R01 CA 189295; E Davicioni is partial owner of GenomeDx Biosciences, Inc. M Takhar, N Erho, L Lam, C Buerki and E Davicioni are current employees at GenomeDx Biosciences, Inc.« less

Functional mesoporous silica nanoparticles for bio-imaging applications.

PubMed

Cha, Bong Geun; Kim, Jaeyun

2018-03-22

Biomedical investigations using mesoporous silica nanoparticles (MSNs) have received significant attention because of their unique properties including controllable mesoporous structure, high specific surface area, large pore volume, and tunable particle size. These unique features make MSNs suitable for simultaneous diagnosis and therapy with unique advantages to encapsulate and load a variety of therapeutic agents, deliver these agents to the desired location, and release the drugs in a controlled manner. Among various clinical areas, nanomaterials-based bio-imaging techniques have advanced rapidly with the development of diverse functional nanoparticles. Due to the unique features of MSNs, an imaging agent supported by MSNs can be a promising system for developing targeted bio-imaging contrast agents with high structural stability and enhanced functionality that enable imaging of various modalities. Here, we review the recent achievements on the development of functional MSNs for bio-imaging applications, including optical imaging, magnetic resonance imaging (MRI), positron emission tomography (PET), computed tomography (CT), ultrasound imaging, and multimodal imaging for early diagnosis. With further improvement in noninvasive bio-imaging techniques, the MSN-supported imaging agent systems are expected to contribute to clinical applications in the future. This article is categorized under: Diagnostic Tools > In vivo Nanodiagnostics and Imaging Nanotechnology Approaches to Biology > Nanoscale Systems in Biology. © 2018 Wiley Periodicals, Inc.

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

Segmentation of optical coherence tomography images for differentiation of the cavernous nerves from the prostate gland

NASA Astrophysics Data System (ADS)

Chitchian, Shahab; Weldon, Thomas P.; Fried, Nathaniel M.

2009-07-01

The cavernous nerves course along the surface of the prostate and are responsible for erectile function. Improvements in identification, imaging, and visualization of the cavernous nerves during prostate cancer surgery may improve nerve preservation and postoperative sexual potency. Two-dimensional (2-D) optical coherence tomography (OCT) images of the rat prostate were segmented to differentiate the cavernous nerves from the prostate gland. To detect these nerves, three image features were employed: Gabor filter, Daubechies wavelet, and Laws filter. The Gabor feature was applied with different standard deviations in the x and y directions. In the Daubechies wavelet feature, an 8-tap Daubechies orthonormal wavelet was implemented, and the low-pass sub-band was chosen as the filtered image. Last, Laws feature extraction was applied to the images. The features were segmented using a nearest-neighbor classifier. N-ary morphological postprocessing was used to remove small voids. The cavernous nerves were differentiated from the prostate gland with a segmentation error rate of only 0.058+/-0.019. This algorithm may be useful for implementation in clinical endoscopic OCT systems currently being studied for potential intraoperative diagnostic use in laparoscopic and robotic nerve-sparing prostate cancer surgery.

Segmentation of optical coherence tomography images for differentiation of the cavernous nerves from the prostate gland.

PubMed

Chitchian, Shahab; Weldon, Thomas P; Fried, Nathaniel M

2009-01-01

The cavernous nerves course along the surface of the prostate and are responsible for erectile function. Improvements in identification, imaging, and visualization of the cavernous nerves during prostate cancer surgery may improve nerve preservation and postoperative sexual potency. Two-dimensional (2-D) optical coherence tomography (OCT) images of the rat prostate were segmented to differentiate the cavernous nerves from the prostate gland. To detect these nerves, three image features were employed: Gabor filter, Daubechies wavelet, and Laws filter. The Gabor feature was applied with different standard deviations in the x and y directions. In the Daubechies wavelet feature, an 8-tap Daubechies orthonormal wavelet was implemented, and the low-pass sub-band was chosen as the filtered image. Last, Laws feature extraction was applied to the images. The features were segmented using a nearest-neighbor classifier. N-ary morphological postprocessing was used to remove small voids. The cavernous nerves were differentiated from the prostate gland with a segmentation error rate of only 0.058+/-0.019. This algorithm may be useful for implementation in clinical endoscopic OCT systems currently being studied for potential intraoperative diagnostic use in laparoscopic and robotic nerve-sparing prostate cancer surgery.

What is the role of imaging in the clinical diagnosis of osteoarthritis and disease management?

PubMed

Wang, Xia; Oo, Win Min; Linklater, James M

2018-05-01

While OA is predominantly diagnosed on the basis of clinical criteria, imaging may aid with differential diagnosis in clinically suspected cases. While plain radiographs are traditionally the first choice of imaging modality, MRI and US also have a valuable role in assessing multiple pathologic features of OA, although each has particular advantages and disadvantages. Although modern imaging modalities provide the capability to detect a wide range of osseous and soft tissue (cartilage, menisci, ligaments, synovitis, effusion) OA-related structural damage, this extra information has not yet favourably influenced the clinical decision-making and management process. Imaging is recommended if there are unexpected rapid changes in clinical outcomes to determine whether it relates to disease severity or an additional diagnosis. On developing specific treatments, imaging serves as a sensitive tool to measure treatment response. This narrative review aims to describe the role of imaging modalities to aid in OA diagnosis, disease progression and management. It also provides insight into the use of these modalities in finding targeted treatment strategies in clinical research.

Mental Imagery: Functional Mechanisms and Clinical Applications

PubMed Central

Pearson, Joel; Naselaris, Thomas; Holmes, Emily A.; Kosslyn, Stephen M.

2015-01-01

Mental imagery research has weathered both disbelief of the phenomenon and inherent methodological limitations. Here we review recent behavioral, brain imaging, and clinical research that has reshaped our understanding of mental imagery. Research supports the claim that visual mental imagery is a depictive internal representation that functions like a weak form of perception. Brain imaging work has demonstrated that neural representations of mental and perceptual images resemble one another as early as the primary visual cortex (V1). Activity patterns in V1 encode mental images and perceptual images via a common set of low-level depictive visual features. Recent translational and clinical research reveals the pivotal role that imagery plays in many mental disorders and suggests how clinicians can utilize imagery in treatment. PMID:26412097

Functional Principal Component Analysis and Randomized Sparse Clustering Algorithm for Medical Image Analysis

PubMed Central

Lin, Nan; Jiang, Junhai; Guo, Shicheng; Xiong, Momiao

2015-01-01

Due to the advancement in sensor technology, the growing large medical image data have the ability to visualize the anatomical changes in biological tissues. As a consequence, the medical images have the potential to enhance the diagnosis of disease, the prediction of clinical outcomes and the characterization of disease progression. But in the meantime, the growing data dimensions pose great methodological and computational challenges for the representation and selection of features in image cluster analysis. To address these challenges, we first extend the functional principal component analysis (FPCA) from one dimension to two dimensions to fully capture the space variation of image the signals. The image signals contain a large number of redundant features which provide no additional information for clustering analysis. The widely used methods for removing the irrelevant features are sparse clustering algorithms using a lasso-type penalty to select the features. However, the accuracy of clustering using a lasso-type penalty depends on the selection of the penalty parameters and the threshold value. In practice, they are difficult to determine. Recently, randomized algorithms have received a great deal of attentions in big data analysis. This paper presents a randomized algorithm for accurate feature selection in image clustering analysis. The proposed method is applied to both the liver and kidney cancer histology image data from the TCGA database. The results demonstrate that the randomized feature selection method coupled with functional principal component analysis substantially outperforms the current sparse clustering algorithms in image cluster analysis. PMID:26196383

Sarcoidosis of the central nervous system: clinical features, imaging, and CSF results.

PubMed

Kidd, Desmond P

2018-06-19

Neurological complications of systemic sarcoidosis are uncommon and the natural history and optimal treatments under-researched. With the advent of modern biological therapies, it is important to define the clinical characteristics and immunopathology of the disease. Patients referred to and treated within the Centre for Neurosarcoidosis over a 15 year period who had biopsy-proven "highly probable" disease of the central nervous system were studied prospectively. 166 patients were studied, of whom two-thirds had involvement of the brain and spinal cord and the remainder cranial neuropathies and radiculopathy. Imaging was abnormal in all those with meningeal and parenchymal diseases, and was normal in 37% of those with cranial neuropathy. Those with leptomeningeal disease had a more severe disorder, with hydrocephalus and tissue destruction, whereas those with pachymeningeal disease had more striking imaging features but less neurological impairment. The CSF was active in 70% of cases, even when imaging was normal. Disability correlated with CSF indices in those with a leptomeningitis. Oligoclonal bands were seen in 30% of cases and correlated with disability and the presence of hydrocephalus. Unmatched bands were seen only in isolated neurological disease. This prospective study of neurosarcoidosis increases our understanding of the pathophysiology of the disease. A reclassification of the clinical and imaging features of the disease allows an understanding of its pathophysiology and correlation with CSF indices allows an early identification of those with a more destructive disease will help to define treatment and may thereby improve outcome.

Computer-aided diagnosis for classifying benign versus malignant thyroid nodules based on ultrasound images: A comparison with radiologist-based assessments.

PubMed

Chang, Yongjun; Paul, Anjan Kumar; Kim, Namkug; Baek, Jung Hwan; Choi, Young Jun; Ha, Eun Ju; Lee, Kang Dae; Lee, Hyoung Shin; Shin, DaeSeock; Kim, Nakyoung

2016-01-01

To develop a semiautomated computer-aided diagnosis (cad) system for thyroid cancer using two-dimensional ultrasound images that can be used to yield a second opinion in the clinic to differentiate malignant and benign lesions. A total of 118 ultrasound images that included axial and longitudinal images from patients with biopsy-confirmed malignant (n = 30) and benign (n = 29) nodules were collected. Thyroid cad software was developed to extract quantitative features from these images based on thyroid nodule segmentation in which adaptive diffusion flow for active contours was used. Various features, including histogram, intensity differences, elliptical fit, gray-level co-occurrence matrixes, and gray-level run-length matrixes, were evaluated for each region imaged. Based on these imaging features, a support vector machine (SVM) classifier was used to differentiate benign and malignant nodules. Leave-one-out cross-validation with sequential forward feature selection was performed to evaluate the overall accuracy of this method. Additionally, analyses with contingency tables and receiver operating characteristic (ROC) curves were performed to compare the performance of cad with visual inspection by expert radiologists based on established gold standards. Most univariate features for this proposed cad system attained accuracies that ranged from 78.0% to 83.1%. When optimal SVM parameters that were established using a grid search method with features that radiologists use for visual inspection were employed, the authors could attain rates of accuracy that ranged from 72.9% to 84.7%. Using leave-one-out cross-validation results in a multivariate analysis of various features, the highest accuracy achieved using the proposed cad system was 98.3%, whereas visual inspection by radiologists reached 94.9% accuracy. To obtain the highest accuracies, "axial ratio" and "max probability" in axial images were most frequently included in the optimal feature sets for the authors' proposed cad system, while "shape" and "calcification" in longitudinal images were most frequently included in the optimal feature sets for visual inspection by radiologists. The computed areas under curves in the ROC analysis were 0.986 and 0.979 for the proposed cad system and visual inspection by radiologists, respectively; no significant difference was detected between these groups. The use of thyroid cad to differentiate malignant from benign lesions shows accuracy similar to that obtained via visual inspection by radiologists. Thyroid cad might be considered a viable way to generate a second opinion for radiologists in clinical practice.

Computer-aided diagnosis for classifying benign versus malignant thyroid nodules based on ultrasound images: A comparison with radiologist-based assessments

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

Chang, Yongjun; Paul, Anjan Kumar; Kim, Namkug, E-mail: namkugkim@gmail.com

Purpose: To develop a semiautomated computer-aided diagnosis (CAD) system for thyroid cancer using two-dimensional ultrasound images that can be used to yield a second opinion in the clinic to differentiate malignant and benign lesions. Methods: A total of 118 ultrasound images that included axial and longitudinal images from patients with biopsy-confirmed malignant (n = 30) and benign (n = 29) nodules were collected. Thyroid CAD software was developed to extract quantitative features from these images based on thyroid nodule segmentation in which adaptive diffusion flow for active contours was used. Various features, including histogram, intensity differences, elliptical fit, gray-level co-occurrencemore » matrixes, and gray-level run-length matrixes, were evaluated for each region imaged. Based on these imaging features, a support vector machine (SVM) classifier was used to differentiate benign and malignant nodules. Leave-one-out cross-validation with sequential forward feature selection was performed to evaluate the overall accuracy of this method. Additionally, analyses with contingency tables and receiver operating characteristic (ROC) curves were performed to compare the performance of CAD with visual inspection by expert radiologists based on established gold standards. Results: Most univariate features for this proposed CAD system attained accuracies that ranged from 78.0% to 83.1%. When optimal SVM parameters that were established using a grid search method with features that radiologists use for visual inspection were employed, the authors could attain rates of accuracy that ranged from 72.9% to 84.7%. Using leave-one-out cross-validation results in a multivariate analysis of various features, the highest accuracy achieved using the proposed CAD system was 98.3%, whereas visual inspection by radiologists reached 94.9% accuracy. To obtain the highest accuracies, “axial ratio” and “max probability” in axial images were most frequently included in the optimal feature sets for the authors’ proposed CAD system, while “shape” and “calcification” in longitudinal images were most frequently included in the optimal feature sets for visual inspection by radiologists. The computed areas under curves in the ROC analysis were 0.986 and 0.979 for the proposed CAD system and visual inspection by radiologists, respectively; no significant difference was detected between these groups. Conclusions: The use of thyroid CAD to differentiate malignant from benign lesions shows accuracy similar to that obtained via visual inspection by radiologists. Thyroid CAD might be considered a viable way to generate a second opinion for radiologists in clinical practice.« less

A similarity measure method combining location feature for mammogram retrieval.

PubMed

Wang, Zhiqiong; Xin, Junchang; Huang, Yukun; Li, Chen; Xu, Ling; Li, Yang; Zhang, Hao; Gu, Huizi; Qian, Wei

2018-05-28

Breast cancer, the most common malignancy among women, has a high mortality rate in clinical practice. Early detection, diagnosis and treatment can reduce the mortalities of breast cancer greatly. The method of mammogram retrieval can help doctors to find the early breast lesions effectively and determine a reasonable feature set for image similarity measure. This will improve the accuracy effectively for mammogram retrieval. This paper proposes a similarity measure method combining location feature for mammogram retrieval. Firstly, the images are pre-processed, the regions of interest are detected and the lesions are segmented in order to get the center point and radius of the lesions. Then, the method, namely Coherent Point Drift, is used for image registration with the pre-defined standard image. The center point and radius of the lesions after registration are obtained and the standard location feature of the image is constructed. This standard location feature can help figure out the location similarity between the image pair from the query image to each dataset image in the database. Next, the content feature of the image is extracted, including the Histogram of Oriented Gradients, the Edge Direction Histogram, the Local Binary Pattern and the Gray Level Histogram, and the image pair content similarity can be calculated using the Earth Mover's Distance. Finally, the location similarity and content similarity are fused to form the image fusion similarity, and the specified number of the most similar images can be returned according to it. In the experiment, 440 mammograms, which are from Chinese women in Northeast China, are used as the database. When fusing 40% lesion location feature similarity and 60% content feature similarity, the results have obvious advantages. At this time, precision is 0.83, recall is 0.76, comprehensive indicator is 0.79, satisfaction is 96.0%, mean is 4.2 and variance is 17.7. The results show that the precision and recall of this method have obvious advantage, compared with the content-based image retrieval.

Computer-aided global breast MR image feature analysis for prediction of tumor response to chemotherapy: performance assessment

NASA Astrophysics Data System (ADS)

Aghaei, Faranak; Tan, Maxine; Hollingsworth, Alan B.; Zheng, Bin; Cheng, Samuel

2016-03-01

Dynamic contrast-enhanced breast magnetic resonance imaging (DCE-MRI) has been used increasingly in breast cancer diagnosis and assessment of cancer treatment efficacy. In this study, we applied a computer-aided detection (CAD) scheme to automatically segment breast regions depicting on MR images and used the kinetic image features computed from the global breast MR images acquired before neoadjuvant chemotherapy to build a new quantitative model to predict response of the breast cancer patients to the chemotherapy. To assess performance and robustness of this new prediction model, an image dataset involving breast MR images acquired from 151 cancer patients before undergoing neoadjuvant chemotherapy was retrospectively assembled and used. Among them, 63 patients had "complete response" (CR) to chemotherapy in which the enhanced contrast levels inside the tumor volume (pre-treatment) was reduced to the level as the normal enhanced background parenchymal tissues (post-treatment), while 88 patients had "partially response" (PR) in which the high contrast enhancement remain in the tumor regions after treatment. We performed the studies to analyze the correlation among the 22 global kinetic image features and then select a set of 4 optimal features. Applying an artificial neural network trained with the fusion of these 4 kinetic image features, the prediction model yielded an area under ROC curve (AUC) of 0.83+/-0.04. This study demonstrated that by avoiding tumor segmentation, which is often difficult and unreliable, fusion of kinetic image features computed from global breast MR images without tumor segmentation can also generate a useful clinical marker in predicting efficacy of chemotherapy.

Granulomatous mastitis: changing clinical and imaging features with image-guided biopsy correlation.

PubMed

Handa, Priyanka; Leibman, A Jill; Sun, Derek; Abadi, Maria; Goldberg, Aryeh

2014-10-01

To review clinical presentation, revisit patient demographics and imaging findings in granulomatous mastitis and determine the optimal biopsy method for diagnosis. A retrospective study was performed to review the clinical presentation, imaging findings and biopsy methods in patients with granulomatous mastitis. Twenty-seven patients with pathology-proven granulomatous mastitis were included. The average age at presentation was 38.0 years (range, 21-73 years). Seven patients were between 48 and 73 years old. Twenty-four patients presented with symptoms and three patients were asymptomatic. Nineteen patients were imaged with mammography demonstrating mammographically occult lesions as the predominant finding. Twenty-six patients were imaged with ultrasound and the most common finding was a mass lesion. Pathological diagnosis was made by image-guided biopsy in 44 % of patients. The imaging features of granulomatous mastitis on mammography are infrequently described. Our study demonstrates that granulomatous mastitis can occur in postmenopausal or asymptomatic patients, although previously reported exclusively in young women with palpable findings. Presentation on mammography as calcifications requiring mammographically guided vacuum-assisted biopsy has not been previously described. The diagnosis of granulomatous mastitis can easily be made by image-guided biopsy and surgical excision should be reserved for definitive treatment. • Characterizes radiographic appearance of granulomatous mastitis in postmenopausal or asymptomatic patients. • Granulomatous mastitis can present exclusively as calcifications on mammography. • The diagnosis of granulomatous mastitis is made by image-guided biopsy techniques.

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).

The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis

NASA Astrophysics Data System (ADS)

Leijenaar, Ralph T. H.; Nalbantov, Georgi; Carvalho, Sara; van Elmpt, Wouter J. C.; Troost, Esther G. C.; Boellaard, Ronald; Aerts, Hugo J. W. L.; Gillies, Robert J.; Lambin, Philippe

2015-08-01

FDG-PET-derived textural features describing intra-tumor heterogeneity are increasingly investigated as imaging biomarkers. As part of the process of quantifying heterogeneity, image intensities (SUVs) are typically resampled into a reduced number of discrete bins. We focused on the implications of the manner in which this discretization is implemented. Two methods were evaluated: (1) RD, dividing the SUV range into D equally spaced bins, where the intensity resolution (i.e. bin size) varies per image; and (2) RB, maintaining a constant intensity resolution B. Clinical feasibility was assessed on 35 lung cancer patients, imaged before and in the second week of radiotherapy. Forty-four textural features were determined for different D and B for both imaging time points. Feature values depended on the intensity resolution and out of both assessed methods, RB was shown to allow for a meaningful inter- and intra-patient comparison of feature values. Overall, patients ranked differently according to feature values-which was used as a surrogate for textural feature interpretation-between both discretization methods. Our study shows that the manner of SUV discretization has a crucial effect on the resulting textural features and the interpretation thereof, emphasizing the importance of standardized methodology in tumor texture analysis.

Magnetization-prepared rapid acquisition with gradient echo magnetic resonance imaging signal and texture features for the prediction of mild cognitive impairment to Alzheimer's disease progression.

PubMed

Martinez-Torteya, Antonio; Rodriguez-Rojas, Juan; Celaya-Padilla, José M; Galván-Tejada, Jorge I; Treviño, Victor; Tamez-Peña, Jose

2014-10-01

Early diagnoses of Alzheimer's disease (AD) would confer many benefits. Several biomarkers have been proposed to achieve such a task, where features extracted from magnetic resonance imaging (MRI) have played an important role. However, studies have focused exclusively on morphological characteristics. This study aims to determine whether features relating to the signal and texture of the image could predict mild cognitive impairment (MCI) to AD progression. Clinical, biological, and positron emission tomography information and MRI images of 62 subjects from the AD neuroimaging initiative were used in this study, extracting 4150 features from each MRI. Within this multimodal database, a feature selection algorithm was used to obtain an accurate and small logistic regression model, generated by a methodology that yielded a mean blind test accuracy of 0.79. This model included six features, five of them obtained from the MRI images, and one obtained from genotyping. A risk analysis divided the subjects into low-risk and high-risk groups according to a prognostic index. The groups were statistically different ([Formula: see text]). These results demonstrated that MRI features related to both signal and texture add MCI to AD predictive power, and supported the ongoing notion that multimodal biomarkers outperform single-modality ones.

Congenital portosystemic shunts: imaging findings and clinical presentations in 11 patients.

PubMed

Konstas, Angelos A; Digumarthy, Subba R; Avery, Laura L; Wallace, Karen L; Lisovsky, Mikhail; Misdraji, Joseph; Hahn, Peter F

2011-11-01

To evaluate the clinical anatomy and presentations of congenital portosystemic shunts, and determine features that promote recognition on imaging. Institutional review board approval was obtained for this HIPAA-compliant study. The requirement for written informed consent was waived. Radiology reports were retrospectively reviewed from non-cirrhotic patients who underwent imaging studies from January 1999 through February 2009. Clinical sources reviewed included electronic medical records, archived images and histopathological material. Eleven patients with congenital portosystemic shunts were identified (six male and five female; age range 20 days to 84 years). Seven patients had extrahepatic and four patients had intrahepatic shunts. All 11 patients had absent or hypoplastic intrahepatic portal veins, a feature detected by CT and MRI, but not by US. Seven patients presented with shunt complications and four with presentations unrelated to shunt pathophysiology. Three adult patients had four splenic artery aneurysms. Prospective radiological evaluation of five adult patients with cross-sectional imaging had failed prospectively to recognize the presence of congenital portosystemic shunts on one or more imaging examinations. Congenital portosystemic shunts are associated with splenic artery aneurysms, a previously unrecognized association. Portosystemic shunts were undetected during prospective radiologic evaluation in the majority of adult patients, highlighting the need to alert radiologists to this congenital anomaly. Copyright © 2010. Published by Elsevier Ireland Ltd.

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.

Comparative study between ultrasonography and optical coherence tomography in interventional cardiology

NASA Astrophysics Data System (ADS)

Fanjul-Vélez, Félix; de la Torre-Hernández, José María; Ortega-Quijano, Noé; Zueco-Gil, José Javier; Arce-Diego, José Luis

2009-07-01

In this work, we present clinical images of IVUS and OCT in the evaluation of pharmacological stent endothelization. These preliminary imaging results are analyzed and compared in order to determine the ability of these technologies to visualize relevant intravascular features of interest in interventional cardiology. The results enable to compare the performance of both techniques and to evaluate their potential for clinical purposes.

Abnormal brain magnetic resonance imaging in two patients with Smith-Magenis syndrome.

PubMed

Maya, Idit; Vinkler, Chana; Konen, Osnat; Kornreich, Liora; Steinberg, Tamar; Yeshaya, Josepha; Latarowski, Victoria; Shohat, Mordechai; Lev, Dorit; Baris, Hagit N

2014-08-01

Smith-Magenis syndrome (SMS) is a clinically recognizable contiguous gene syndrome ascribed to an interstitial deletion in chromosome 17p11.2. Seventy percent of SMS patients have a common deletion interval spanning 3.5 megabases (Mb). Clinical features of SMS include characteristic mild dysmorphic features, ocular anomalies, short stature, brachydactyly, and hypotonia. SMS patients have a unique neurobehavioral phenotype that includes intellectual disability, self-injurious behavior and severe sleep disturbance. Little has been reported in the medical literature about anatomical brain anomalies in patients with SMS. Here we describe two patients with SMS caused by the common deletion in 17p11.2 diagnosed using chromosomal microarray (CMA). Both patients had a typical clinical presentation and abnormal brain magnetic resonance imaging (MRI) findings. One patient had subependymal periventricular gray matter heterotopia, and the second had a thin corpus callosum, a thin brain stem and hypoplasia of the cerebellar vermis. This report discusses the possible abnormal MRI images in SMS and reviews the literature on brain malformations in SMS. Finally, although structural brain malformations in SMS patients are not a common feature, we suggest baseline routine brain imaging in patients with SMS in particular, and in patients with chromosomal microdeletion/microduplication syndromes in general. Structural brain malformations in these patients may affect the decision-making process regarding their management. © 2014 Wiley Periodicals, Inc.

Physician acceptance of the IRIS user interface during a clinical trial at the Ottawa Civic Hospital

NASA Astrophysics Data System (ADS)

Coristine, Marjorie; Beeton, Carolyn; Tombaugh, Jo W.; Ahuja, J.; Belanger, Garry; Dillon, Richard F.; Currie, Shawn; Hind, E.

1990-07-01

During a clinical trial, emergency physicians and radiologists at the Ottawa Civic Hospital used IRIS (Integrated Radiological Information System) to process patients' x-rays, requisitions, and reports, and to have consultations, for 319 active cases. This paper discusses IRIS user interface issues raised during the clinical trial. The IRIS workstation consists of three major system components: 1) an image screen for viewing and enhancing images; 2) a control screen for presenting patient information, selecting images, and executing commands; and 3) a hands-free telephone for reporting activities and consultations. The control screen and hands-free telephone user interface allow physicians to navigate through patient files, select images and access reports, enter new reports, and perform remote consultations. Physicians were observed using the system during the trial and responded to questions about the user interface on an extensive debriefing interview after the trial. Overall, radiologists and emergency physicians were satisfied with IRIS control screen functionality and user interface. In a number of areas radiologists and emergency physicians differed in their user interface needs. Some features were found to be acceptable to one group of physicians but required modification to meet the needs of the other physician group. The data from the interviews, along with the comments from radiologists and emergency physicians provided important information for the revision of some features, and for the evolution of new features.

Recurrent neural networks for breast lesion classification based on DCE-MRIs

NASA Astrophysics Data System (ADS)

Antropova, Natasha; Huynh, Benjamin; Giger, Maryellen

2018-02-01

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays a significant role in breast cancer screening, cancer staging, and monitoring response to therapy. Recently, deep learning methods are being rapidly incorporated in image-based breast cancer diagnosis and prognosis. However, most of the current deep learning methods make clinical decisions based on 2-dimentional (2D) or 3D images and are not well suited for temporal image data. In this study, we develop a deep learning methodology that enables integration of clinically valuable temporal components of DCE-MRIs into deep learning-based lesion classification. Our work is performed on a database of 703 DCE-MRI cases for the task of distinguishing benign and malignant lesions, and uses the area under the ROC curve (AUC) as the performance metric in conducting that task. We train a recurrent neural network, specifically a long short-term memory network (LSTM), on sequences of image features extracted from the dynamic MRI sequences. These features are extracted with VGGNet, a convolutional neural network pre-trained on a large dataset of natural images ImageNet. The features are obtained from various levels of the network, to capture low-, mid-, and high-level information about the lesion. Compared to a classification method that takes as input only images at a single time-point (yielding an AUC = 0.81 (se = 0.04)), our LSTM method improves lesion classification with an AUC of 0.85 (se = 0.03).

Endocardial left ventricle feature tracking and reconstruction from tri-plane trans-esophageal echocardiography data

NASA Astrophysics Data System (ADS)

Dangi, Shusil; Ben-Zikri, Yehuda K.; Cahill, Nathan; Schwarz, Karl Q.; Linte, Cristian A.

2015-03-01

Two-dimensional (2D) ultrasound (US) has been the clinical standard for over two decades for monitoring and assessing cardiac function and providing support via intra-operative visualization and guidance for minimally invasive cardiac interventions. Developments in three-dimensional (3D) image acquisition and transducer design and technology have revolutionized echocardiography imaging enabling both real-time 3D trans-esophageal and intra-cardiac image acquisition. However, in most cases the clinicians do not access the entire 3D image volume when analyzing the data, rather they focus on several key views that render the cardiac anatomy of interest during the US imaging exam. This approach enables image acquisition at a much higher spatial and temporal resolution. Two such common approaches are the bi-plane and tri-plane data acquisition protocols; as their name states, the former comprises two orthogonal image views, while the latter depicts the cardiac anatomy based on three co-axially intersecting views spaced at 600 to one another. Since cardiac anatomy is continuously changing, the intra-operative anatomy depicted using real-time US imaging also needs to be updated by tracking the key features of interest and endocardial left ventricle (LV) boundaries. Therefore, rapid automatic feature tracking in US images is critical for three reasons: 1) to perform cardiac function assessment; 2) to identify location of surgical targets for accurate tool to target navigation and on-target instrument positioning; and 3) to enable pre- to intra-op image registration as a means to fuse pre-op CT or MR images used during planning with intra-operative images for enhanced guidance. In this paper we utilize monogenic filtering, graph-cut based segmentation and robust spline smoothing in a combined work flow to process the acquired tri-plane TEE time series US images and demonstrate robust and accurate tracking of the LV endocardial features. We reconstruct the endocardial LV geometry using the tri-plane contours and spline interpolation, and assess the accuracy of the proposed work flow against gold-standard results from the GE Echopac PC clinical software according to quantitative clinical LV characterization parameters, such as the length, circumference, area and volume. Our proposed combined work flow leads to consistent, rapid and automated identification of the LV endocardium, suitable for intra-operative applications and "on-the-fly" computer-assisted assessment of ejection fraction for cardiac function monitoring.Two-dimensional (2D) ultrasound (US) has been the clinical standard for over two decades for monitoring and assessing cardiac function and providing support via intra-operative visualization and guidance for minimally invasive cardiac interventions. Developments in three-dimensional (3D) image acquisition and transducer design and technology have revolutionized echocardiography imaging enabling both real-time 3D trans-esophageal and intra-cardiac image acquisition. However, in most cases the clinicians do not access the entire 3D image volume when analyzing the data, rather they focus on several key views that render the cardiac anatomy of interest during the US imaging exam. This approach enables image acquisition at a much higher spatial and temporal resolution. Two such common approaches are the bi-plane and tri-plane data acquisition protocols; as their name states, the former comprises two orthogonal image views, while the latter depicts the cardiac anatomy based on three co-axially intersecting views spaced at 600 to one another. Since cardiac anatomy is continuously changing, the intra-operative anatomy depicted using real-time US imaging also needs to be updated by tracking the key features of interest and endocardial left ventricle (LV) boundaries. Therefore, rapid automatic feature tracking in US images is critical for three reasons: 1) to perform cardiac function assessment; 2) to identify location of surgical targets for accurate tool to target navigation and on-target instrument positioning; and 3) to enable pre- to intra-op image registration as a means to fuse pre-op CT or MR images used during planning with intra-operative images for enhanced guidance. In this paper we utilize monogenic filtering, graph-cut based segmentation and robust spline smoothing in a combined work flow to process the acquired tri-plane TEE time series US images and demonstrate robust and accurate tracking of the LV endocardial features. We reconstruct the endocardial LV geometry using the tri-plane contours and spline interpolation, and assess the accuracy of the proposed work flow against gold-standard results from the GE Echopac PC clinical software according to quantitative clinical LV characterization parameters, such as the length, circumference, area and volume. Our proposed combined work flow leads to consistent, rapid and automated identification of the LV endocardium, suitable for intra-operative applications and on-the- y" computer-assisted assessment of ejection fraction for cardiac function monitoring.

Partial dependence of breast tumor malignancy on ultrasound image features derived from boosted trees

NASA Astrophysics Data System (ADS)

Yang, Wei; Zhang, Su; Li, Wenying; Chen, Yaqing; Lu, Hongtao; Chen, Wufan; Chen, Yazhu

2010-04-01

Various computerized features extracted from breast ultrasound images are useful in assessing the malignancy of breast tumors. However, the underlying relationship between the computerized features and tumor malignancy may not be linear in nature. We use the decision tree ensemble trained by the cost-sensitive boosting algorithm to approximate the target function for malignancy assessment and to reflect this relationship qualitatively. Partial dependence plots are employed to explore and visualize the effect of features on the output of the decision tree ensemble. In the experiments, 31 image features are extracted to quantify the sonographic characteristics of breast tumors. Patient age is used as an external feature because of its high clinical importance. The area under the receiver-operating characteristic curve of the tree ensembles can reach 0.95 with sensitivity of 0.95 (61/64) at the associated specificity 0.74 (77/104). The partial dependence plots of the four most important features are demonstrated to show the influence of the features on malignancy, and they are in accord with the empirical observations. The results can provide visual and qualitative references on the computerized image features for physicians, and can be useful for enhancing the interpretability of computer-aided diagnosis systems for breast ultrasound.

Outcome prediction in patients with glioblastoma by using imaging, clinical, and genomic biomarkers: focus on the nonenhancing component of the tumor.

PubMed

Jain, Rajan; Poisson, Laila M; Gutman, David; Scarpace, Lisa; Hwang, Scott N; Holder, Chad A; Wintermark, Max; Rao, Arvind; Colen, Rivka R; Kirby, Justin; Freymann, John; Jaffe, C Carl; Mikkelsen, Tom; Flanders, Adam

2014-08-01

To correlate patient survival with morphologic imaging features and hemodynamic parameters obtained from the nonenhancing region (NER) of glioblastoma (GBM), along with clinical and genomic markers. An institutional review board waiver was obtained for this HIPAA-compliant retrospective study. Forty-five patients with GBM underwent baseline imaging with contrast material-enhanced magnetic resonance (MR) imaging and dynamic susceptibility contrast-enhanced T2*-weighted perfusion MR imaging. Molecular and clinical predictors of survival were obtained. Single and multivariable models of overall survival (OS) and progression-free survival (PFS) were explored with Kaplan-Meier estimates, Cox regression, and random survival forests. Worsening OS (log-rank test, P = .0103) and PFS (log-rank test, P = .0223) were associated with increasing relative cerebral blood volume of NER (rCBVNER), which was higher with deep white matter involvement (t test, P = .0482) and poor NER margin definition (t test, P = .0147). NER crossing the midline was the only morphologic feature of NER associated with poor survival (log-rank test, P = .0125). Preoperative Karnofsky performance score (KPS) and resection extent (n = 30) were clinically significant OS predictors (log-rank test, P = .0176 and P = .0038, respectively). No genomic alterations were associated with survival, except patients with high rCBVNER and wild-type epidermal growth factor receptor (EGFR) mutation had significantly poor survival (log-rank test, P = .0306; area under the receiver operating characteristic curve = 0.62). Combining resection extent with rCBVNER marginally improved prognostic ability (permutation, P = .084). Random forest models of presurgical predictors indicated rCBVNER as the top predictor; also important were KPS, age at diagnosis, and NER crossing the midline. A multivariable model containing rCBVNER, age at diagnosis, and KPS can be used to group patients with more than 1 year of difference in observed median survival (0.49-1.79 years). Patients with high rCBVNER and NER crossing the midline and those with high rCBVNER and wild-type EGFR mutation showed poor survival. In multivariable survival models, however, rCBVNER provided unique prognostic information that went above and beyond the assessment of all NER imaging features, as well as clinical and genomic features.

Outcome Prediction in Patients with Glioblastoma by Using Imaging, Clinical, and Genomic Biomarkers: Focus on the Nonenhancing Component of the Tumor

PubMed Central

Poisson, Laila M.; Gutman, David; Scarpace, Lisa; Hwang, Scott N.; Holder, Chad A.; Wintermark, Max; Rao, Arvind; Colen, Rivka R.; Kirby, Justin; Freymann, John; Jaffe, C. Carl; Mikkelsen, Tom; Flanders, Adam

2014-01-01

Purpose To correlate patient survival with morphologic imaging features and hemodynamic parameters obtained from the nonenhancing region (NER) of glioblastoma (GBM), along with clinical and genomic markers. Materials and Methods An institutional review board waiver was obtained for this HIPAA-compliant retrospective study. Forty-five patients with GBM underwent baseline imaging with contrast material–enhanced magnetic resonance (MR) imaging and dynamic susceptibility contrast-enhanced T2*-weighted perfusion MR imaging. Molecular and clinical predictors of survival were obtained. Single and multivariable models of overall survival (OS) and progression-free survival (PFS) were explored with Kaplan-Meier estimates, Cox regression, and random survival forests. Results Worsening OS (log-rank test, P = .0103) and PFS (log-rank test, P = .0223) were associated with increasing relative cerebral blood volume of NER (rCBVNER), which was higher with deep white matter involvement (t test, P = .0482) and poor NER margin definition (t test, P = .0147). NER crossing the midline was the only morphologic feature of NER associated with poor survival (log-rank test, P = .0125). Preoperative Karnofsky performance score (KPS) and resection extent (n = 30) were clinically significant OS predictors (log-rank test, P = .0176 and P = .0038, respectively). No genomic alterations were associated with survival, except patients with high rCBVNER and wild-type epidermal growth factor receptor (EGFR) mutation had significantly poor survival (log-rank test, P = .0306; area under the receiver operating characteristic curve = 0.62). Combining resection extent with rCBVNER marginally improved prognostic ability (permutation, P = .084). Random forest models of presurgical predictors indicated rCBVNER as the top predictor; also important were KPS, age at diagnosis, and NER crossing the midline. A multivariable model containing rCBVNER, age at diagnosis, and KPS can be used to group patients with more than 1 year of difference in observed median survival (0.49–1.79 years). Conclusion Patients with high rCBVNER and NER crossing the midline and those with high rCBVNER and wild-type EGFR mutation showed poor survival. In multivariable survival models, however, rCBVNER provided unique prognostic information that went above and beyond the assessment of all NER imaging features, as well as clinical and genomic features. © RSNA, 2014 Online supplemental material is available for this article. PMID:24646147

Clinical and Imaging Presentation of a Patient with Beta-Propeller Protein-Associated Neurodegeneration, a Rare and Sporadic form of Neurodegeneration with Brain Iron Accumulation (NBIA).

PubMed

Hattingen, Elke; Handke, Nikolaus; Cremer, Kirsten; Hoffjan, Sabine; Kukuk, Guido Matthias

2017-12-01

Neurodegeneration with brain iron accumulation (NBIA) is a heterogeneous group of inherited neurologic disorders with iron accumulation in the basal ganglia, which share magnetic resonance (MR) imaging characteristics, histopathologic and clinical features. According to the affected basal nuclei, clinical features include extrapyramidal movement disorders and varying degrees of intellectual disability status. The most common NBIA subtype is caused by pathogenic variants in PANK2. The hallmark of MR imaging in patients with PANK2 mutations is an eye-of-the-tiger sign in the globus pallidus. We report a 33-year-old female with a rare subtype of NBIA, called beta-propeller protein-associated neurodegeneration (BPAN) with a hitherto unknown missense variant in WDR45. She presented with BPAN's particular biphasic course of neurological symptoms and with a dominant iron accumulation in the midbrain that enclosed a spotty T2-hyperintensity.

Software for MR image overlay guided needle insertions: the clinical translation process

NASA Astrophysics Data System (ADS)

Ungi, Tamas; U-Thainual, Paweena; Fritz, Jan; Iordachita, Iulian I.; Flammang, Aaron J.; Carrino, John A.; Fichtinger, Gabor

2013-03-01

PURPOSE: Needle guidance software using augmented reality image overlay was translated from the experimental phase to support preclinical and clinical studies. Major functional and structural changes were needed to meet clinical requirements. We present the process applied to fulfill these requirements, and selected features that may be applied in the translational phase of other image-guided surgical navigation systems. METHODS: We used an agile software development process for rapid adaptation to unforeseen clinical requests. The process is based on iterations of operating room test sessions, feedback discussions, and software development sprints. The open-source application framework of 3D Slicer and the NA-MIC kit provided sufficient flexibility and stable software foundations for this work. RESULTS: All requirements were addressed in a process with 19 operating room test iterations. Most features developed in this phase were related to workflow simplification and operator feedback. CONCLUSION: Efficient and affordable modifications were facilitated by an open source application framework and frequent clinical feedback sessions. Results of cadaver experiments show that software requirements were successfully solved after a limited number of operating room tests.

Characterization of the major histopathological components of thyroid nodules using sonographic textural features for clinical diagnosis and management.

PubMed

Chen, Shao-Jer; Yu, Sung-Nien; Tzeng, Jeh-En; Chen, Yen-Ting; Chang, Ku-Yaw; Cheng, Kuo-Sheng; Hsiao, Fu-Tsung; Wei, Chang-Kuo

2009-02-01

In this study, the characteristic sonographic textural feature that represents the major histopathologic components of the thyroid nodules was objectively quantified to facilitate clinical diagnosis and management. A total of 157 regions-of-interest thyroid ultrasound image was recruited in the study. The sonographic system used was the GE LOGIQ 700), (General Electric Healthcare, Chalfant St. Giles, UK). The parameters affecting image acquisition were kept in the same condition for all lesions. Commonly used texture analysis methods were applied to characterize thyroid ultrasound images. Image features were classified according to the corresponding pathologic findings. To estimate their relevance and performance to classification, ReliefF was used as a feature selector. Among the various textural features, the sum average value derived from co-occurrence matrix can well reflect echogenicity and can effectively differentiate between follicles and fibrosis base thyroid nodules. Fibrosis shows lowest echogenicity and lowest difference sum average value. Enlarged follicles show highest echogenicity and difference sum average values. Papillary cancer or follicular tumors show the difference sum average values and echogenicity between. The rule of thumb for the echogenicity is that the more follicles are mixed in, the higher the echo of the follicular tumor and papillary cancer will be and vice versa for fibrosis mixed. Areas with intermediate and lower echo should address the possibility of follicular or papillary neoplasm mixed with either follicles or fibrosis. These areas provide more cellular information for ultrasound guided aspiration

WE-D-BRD-01: Innovation in Radiation Therapy Delivery: Advanced Digital Linac Features

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

Xing, L; Wong, J; Li, R

2014-06-15

Last few years has witnessed significant advances in linac technology and therapeutic dose delivery method. Digital linacs equipped with high dose rate FFF beams have been clinically implemented in a number of hospitals. Gated VMAT is becoming increasingly popular in treating tumors affected by respiratory motion. This session is devoted to update the audience with these technical advances and to present our experience in clinically implementing the new linacs and dose delivery methods. Topics to be covered include, technical features of new generation of linacs from different vendors, dosimetric characteristics and clinical need for FFF-beam based IMRT and VMAT, respiration-gatedmore » VMAT, the concept and implementation of station parameter optimized radiation therapy (SPORT), beam level imaging and onboard image guidance tools. Emphasis will be on providing fundamental understanding of the new treatment delivery and image guidance strategies, control systems, and the associated dosimetric characteristics. Commissioning and acceptance experience on these new treatment delivery technologies will be reported. Clinical experience and challenges encountered during the process of implementation of the new treatment techniques and future applications of the systems will also be highlighted. Learning Objectives: Present background knowledge of emerging digital linacs and summarize their key geometric and dosimetric features. SPORT as an emerging radiation therapy modality specifically designed to take advantage of digital linacs. Discuss issues related to the acceptance and commissioning of the digital linacs and FFF beams. Describe clinical utility of the new generation of digital linacs and their future applications.« less

Machine Learning Approach for Classifying Multiple Sclerosis Courses by Combining Clinical Data with Lesion Loads and Magnetic Resonance Metabolic Features.

PubMed

Ion-Mărgineanu, Adrian; Kocevar, Gabriel; Stamile, Claudio; Sima, Diana M; Durand-Dubief, Françoise; Van Huffel, Sabine; Sappey-Marinier, Dominique

2017-01-01

Purpose: The purpose of this study is classifying multiple sclerosis (MS) patients in the four clinical forms as defined by the McDonald criteria using machine learning algorithms trained on clinical data combined with lesion loads and magnetic resonance metabolic features. Materials and Methods: Eighty-seven MS patients [12 Clinically Isolated Syndrome (CIS), 30 Relapse Remitting (RR), 17 Primary Progressive (PP), and 28 Secondary Progressive (SP)] and 18 healthy controls were included in this study. Longitudinal data available for each MS patient included clinical (e.g., age, disease duration, Expanded Disability Status Scale), conventional magnetic resonance imaging and spectroscopic imaging. We extract N -acetyl-aspartate (NAA), Choline (Cho), and Creatine (Cre) concentrations, and we compute three features for each spectroscopic grid by averaging metabolite ratios (NAA/Cho, NAA/Cre, Cho/Cre) over good quality voxels. We built linear mixed-effects models to test for statistically significant differences between MS forms. We test nine binary classification tasks on clinical data, lesion loads, and metabolic features, using a leave-one-patient-out cross-validation method based on 100 random patient-based bootstrap selections. We compute F1-scores and BAR values after tuning Linear Discriminant Analysis (LDA), Support Vector Machines with gaussian kernel (SVM-rbf), and Random Forests. Results: Statistically significant differences were found between the disease starting points of each MS form using four different response variables: Lesion Load, NAA/Cre, NAA/Cho, and Cho/Cre ratios. Training SVM-rbf on clinical and lesion loads yields F1-scores of 71-72% for CIS vs. RR and CIS vs. RR+SP, respectively. For RR vs. PP we obtained good classification results (maximum F1-score of 85%) after training LDA on clinical and metabolic features, while for RR vs. SP we obtained slightly higher classification results (maximum F1-score of 87%) after training LDA and SVM-rbf on clinical, lesion loads and metabolic features. Conclusions: Our results suggest that metabolic features are better at differentiating between relapsing-remitting and primary progressive forms, while lesion loads are better at differentiating between relapsing-remitting and secondary progressive forms. Therefore, combining clinical data with magnetic resonance lesion loads and metabolic features can improve the discrimination between relapsing-remitting and progressive forms.

MRI histogram analysis enables objective and continuous classification of intervertebral disc degeneration.

PubMed

Waldenberg, Christian; Hebelka, Hanna; Brisby, Helena; Lagerstrand, Kerstin Magdalena

2018-05-01

Magnetic resonance imaging (MRI) is the best diagnostic imaging method for low back pain. However, the technique is currently not utilized in its full capacity, often failing to depict painful intervertebral discs (IVDs), potentially due to the rough degeneration classification system used clinically today. MR image histograms, which reflect the IVD heterogeneity, may offer sensitive imaging biomarkers for IVD degeneration classification. This study investigates the feasibility of using histogram analysis as means of objective and continuous grading of IVD degeneration. Forty-nine IVDs in ten low back pain patients (six males, 25-69 years) were examined with MRI (T2-weighted images and T2-maps). Each IVD was semi-automatically segmented on three mid-sagittal slices. Histogram features of the IVD were extracted from the defined regions of interest and correlated to Pfirrmann grade. Both T2-weighted images and T2-maps displayed similar histogram features. Histograms of well-hydrated IVDs displayed two separate peaks, representing annulus fibrosus and nucleus pulposus. Degenerated IVDs displayed decreased peak separation, where the separation was shown to correlate strongly with Pfirrmann grade (P < 0.05). In addition, some degenerated IVDs within the same Pfirrmann grade displayed diametrically different histogram appearances. Histogram features correlated well with IVD degeneration, suggesting that IVD histogram analysis is a suitable tool for objective and continuous IVD degeneration classification. As histogram analysis revealed IVD heterogeneity, it may be a clinical tool for characterization of regional IVD degeneration effects. To elucidate the usefulness of histogram analysis in patient management, IVD histogram features between asymptomatic and symptomatic individuals needs to be compared.

Image-based diagnostic aid for interstitial lung disease with secondary data integration

NASA Astrophysics Data System (ADS)

Depeursinge, Adrien; Müller, Henning; Hidki, Asmâa; Poletti, Pierre-Alexandre; Platon, Alexandra; Geissbuhler, Antoine

2007-03-01

Interstitial lung diseases (ILDs) are a relatively heterogeneous group of around 150 illnesses with often very unspecific symptoms. The most complete imaging method for the characterisation of ILDs is the high-resolution computed tomography (HRCT) of the chest but a correct interpretation of these images is difficult even for specialists as many diseases are rare and thus little experience exists. Moreover, interpreting HRCT images requires knowledge of the context defined by clinical data of the studied case. A computerised diagnostic aid tool based on HRCT images with associated medical data to retrieve similar cases of ILDs from a dedicated database can bring quick and precious information for example for emergency radiologists. The experience from a pilot project highlighted the need for detailed database containing high-quality annotations in addition to clinical data. The state of the art is studied to identify requirements for image-based diagnostic aid for interstitial lung disease with secondary data integration. The data acquisition steps are detailed. The selection of the most relevant clinical parameters is done in collaboration with lung specialists from current literature, along with knowledge bases of computer-based diagnostic decision support systems. In order to perform high-quality annotations of the interstitial lung tissue in the HRCT images an annotation software and its own file format is implemented for DICOM images. A multimedia database is implemented to store ILD cases with clinical data and annotated image series. Cases from the University & University Hospitals of Geneva (HUG) are retrospectively and prospectively collected to populate the database. Currently, 59 cases with certified diagnosis and their clinical parameters are stored in the database as well as 254 image series of which 26 have their regions of interest annotated. The available data was used to test primary visual features for the classification of lung tissue patterns. These features show good discriminative properties for the separation of five classes of visual observations.

WE-EF-210-08: BEST IN PHYSICS (IMAGING): 3D Prostate Segmentation in Ultrasound Images Using Patch-Based Anatomical Feature

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

Yang, X; Rossi, P; Jani, A

Purpose: Transrectal ultrasound (TRUS) is the standard imaging modality for the image-guided prostate-cancer interventions (e.g., biopsy and brachytherapy) due to its versatility and real-time capability. Accurate segmentation of the prostate plays a key role in biopsy needle placement, treatment planning, and motion monitoring. As ultrasound images have a relatively low signal-to-noise ratio (SNR), automatic segmentation of the prostate is difficult. However, manual segmentation during biopsy or radiation therapy can be time consuming. We are developing an automated method to address this technical challenge. Methods: The proposed segmentation method consists of two major stages: the training stage and the segmentation stage.more » During the training stage, patch-based anatomical features are extracted from the registered training images with patient-specific information, because these training images have been mapped to the new patient’ images, and the more informative anatomical features are selected to train the kernel support vector machine (KSVM). During the segmentation stage, the selected anatomical features are extracted from newly acquired image as the input of the well-trained KSVM and the output of this trained KSVM is the segmented prostate of this patient. Results: This segmentation technique was validated with a clinical study of 10 patients. The accuracy of our approach was assessed using the manual segmentation. The mean volume Dice Overlap Coefficient was 89.7±2.3%, and the average surface distance was 1.52 ± 0.57 mm between our and manual segmentation, which indicate that the automatic segmentation method works well and could be used for 3D ultrasound-guided prostate intervention. Conclusion: We have developed a new prostate segmentation approach based on the optimal feature learning framework, demonstrated its clinical feasibility, and validated its accuracy with manual segmentation (gold standard). This segmentation technique could be a useful tool for image-guided interventions in prostate-cancer diagnosis and treatment. This research is supported in part by DOD PCRP Award W81XWH-13-1-0269, and National Cancer Institute (NCI) Grant CA114313.« less

Quantitative assessment of dynamic PET imaging data in cancer imaging.

PubMed

Muzi, Mark; O'Sullivan, Finbarr; Mankoff, David A; Doot, Robert K; Pierce, Larry A; Kurland, Brenda F; Linden, Hannah M; Kinahan, Paul E

2012-11-01

Clinical imaging in positron emission tomography (PET) is often performed using single-time-point estimates of tracer uptake or static imaging that provides a spatial map of regional tracer concentration. However, dynamic tracer imaging can provide considerably more information about in vivo biology by delineating both the temporal and spatial pattern of tracer uptake. In addition, several potential sources of error that occur in static imaging can be mitigated. This review focuses on the application of dynamic PET imaging to measuring regional cancer biologic features and especially in using dynamic PET imaging for quantitative therapeutic response monitoring for cancer clinical trials. Dynamic PET imaging output parameters, particularly transport (flow) and overall metabolic rate, have provided imaging end points for clinical trials at single-center institutions for years. However, dynamic imaging poses many challenges for multicenter clinical trial implementations from cross-center calibration to the inadequacy of a common informatics infrastructure. Underlying principles and methodology of PET dynamic imaging are first reviewed, followed by an examination of current approaches to dynamic PET image analysis with a specific case example of dynamic fluorothymidine imaging to illustrate the approach. Copyright © 2012 Elsevier Inc. All rights reserved.

Mild encephalopathy/encephalitis with a reversible splenial lesion (MERS): A report of five neonatal cases.

PubMed

Sun, Dan; Chen, Wen-Hong; Baralc, Suraj; Wang, Juan; Liu, Zhi-Sheng; Xia, Yuan-Peng; Chen, Lei

2017-06-01

Mild encephalopathy/encephalitis with a reversible splenial (MERS) lesion is a clinic-radiological entity. The clinical features of MERS in neonates are still not systemically reported. This paper presents five cases of MERS, and the up-to-date reviews of previously reported cases were collected and analyzed in the literature. Here we describe five cases clinically diagnosed with MERS. All of them were neonates and the average age was about 4 days. They were admitted for the common neurological symptoms such as hyperspasmia, poor reactivity and delirium. Auxiliary examinations during hospitalization also exhibited features in common. In this report, we reached following conclusions. Firstly, magnetic resonance imaging revealed solitary or comprehensive lesions in the splenium of corpus callosum, some of them extending to almost the whole corpus callosum. The lesions showed low intensity signal on T1-weighted images, homogeneously hyperintense signal on T2-weighted images, fluid-attenuated inversion recovery and diffusion-weighted images, and exhibited an obvious reduced diffusion on apparent diffusion coefficient map. Moreover, the lesions in the magnetic resonance imaging disappeared very quickly even prior to the clinical recovery. Secondly, all the cases depicted here suffered electrolyte disturbances especially hyponatremia which could be easily corrected. Lastly, all of the cases recovered quickly over one week to one month and majority of them exhibited signs of infections and normal electroencephalography.

Pseudo CT estimation from MRI using patch-based random forest

NASA Astrophysics Data System (ADS)

Yang, Xiaofeng; Lei, Yang; Shu, Hui-Kuo; Rossi, Peter; Mao, Hui; Shim, Hyunsuk; Curran, Walter J.; Liu, Tian

2017-02-01

Recently, MR simulators gain popularity because of unnecessary radiation exposure of CT simulators being used in radiation therapy planning. We propose a method for pseudo CT estimation from MR images based on a patch-based random forest. Patient-specific anatomical features are extracted from the aligned training images and adopted as signatures for each voxel. The most robust and informative features are identified using feature selection to train the random forest. The well-trained random forest is used to predict the pseudo CT of a new patient. This prediction technique was tested with human brain images and the prediction accuracy was assessed using the original CT images. Peak signal-to-noise ratio (PSNR) and feature similarity (FSIM) indexes were used to quantify the differences between the pseudo and original CT images. The experimental results showed the proposed method could accurately generate pseudo CT images from MR images. In summary, we have developed a new pseudo CT prediction method based on patch-based random forest, demonstrated its clinical feasibility, and validated its prediction accuracy. This pseudo CT prediction technique could be a useful tool for MRI-based radiation treatment planning and attenuation correction in a PET/MRI scanner.

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.

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.

Rough-Fuzzy Clustering and Unsupervised Feature Selection for Wavelet Based MR Image Segmentation

PubMed Central

Maji, Pradipta; Roy, Shaswati

2015-01-01

Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of brain magnetic resonance (MR) images. For many human experts, manual segmentation is a difficult and time consuming task, which makes an automated brain MR image segmentation method desirable. In this regard, this paper presents a new segmentation method for brain MR images, integrating judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique. The proposed method assumes that the major brain tissues, namely, gray matter, white matter, and cerebrospinal fluid from the MR images are considered to have different textural properties. The dyadic wavelet analysis is used to extract the scale-space feature vector for each pixel, while the rough-fuzzy clustering is used to address the uncertainty problem of brain MR image segmentation. An unsupervised feature selection method is introduced, based on maximum relevance-maximum significance criterion, to select relevant and significant textural features for segmentation problem, while the mathematical morphology based skull stripping preprocessing step is proposed to remove the non-cerebral tissues like skull. The performance of the proposed method, along with a comparison with related approaches, is demonstrated on a set of synthetic and real brain MR images using standard validity indices. PMID:25848961

Magnetic resonance imaging of diabetic foot complications

PubMed Central

Low, Keynes TA; Peh, Wilfred CG

2015-01-01

This pictorial review aims to illustrate the various manifestations of the diabetic foot on magnetic resonance (MR) imaging. The utility of MR imaging and its imaging features in the diagnosis of pedal osteomyelitis are illustrated. There is often difficulty encountered in distinguishing osteomyelitis from neuroarthropathy, both clinically and on imaging. By providing an accurate diagnosis based on imaging, the radiologist plays a significant role in the management of patients with complications of diabetic foot. PMID:25640096

Feature-Based Morphometry: Discovering Group-related Anatomical Patterns

PubMed Central

Toews, Matthew; Wells, William; Collins, D. Louis; Arbel, Tal

2015-01-01

This paper presents feature-based morphometry (FBM), a new, fully data-driven technique for discovering patterns of group-related anatomical structure in volumetric imagery. In contrast to most morphometry methods which assume one-to-one correspondence between subjects, FBM explicitly aims to identify distinctive anatomical patterns that may only be present in subsets of subjects, due to disease or anatomical variability. The image is modeled as a collage of generic, localized image features that need not be present in all subjects. Scale-space theory is applied to analyze image features at the characteristic scale of underlying anatomical structures, instead of at arbitrary scales such as global or voxel-level. A probabilistic model describes features in terms of their appearance, geometry, and relationship to subject groups, and is automatically learned from a set of subject images and group labels. Features resulting from learning correspond to group-related anatomical structures that can potentially be used as image biomarkers of disease or as a basis for computer-aided diagnosis. The relationship between features and groups is quantified by the likelihood of feature occurrence within a specific group vs. the rest of the population, and feature significance is quantified in terms of the false discovery rate. Experiments validate FBM clinically in the analysis of normal (NC) and Alzheimer's (AD) brain images using the freely available OASIS database. FBM automatically identifies known structural differences between NC and AD subjects in a fully data-driven fashion, and an equal error classification rate of 0.80 is achieved for subjects aged 60-80 years exhibiting mild AD (CDR=1). PMID:19853047

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%.

Medical image classification based on multi-scale non-negative sparse coding.

PubMed

Zhang, Ruijie; Shen, Jian; Wei, Fushan; Li, Xiong; Sangaiah, Arun Kumar

2017-11-01

With the rapid development of modern medical imaging technology, medical image classification has become more and more important in medical diagnosis and clinical practice. Conventional medical image classification algorithms usually neglect the semantic gap problem between low-level features and high-level image semantic, which will largely degrade the classification performance. To solve this problem, we propose a multi-scale non-negative sparse coding based medical image classification algorithm. Firstly, Medical images are decomposed into multiple scale layers, thus diverse visual details can be extracted from different scale layers. Secondly, for each scale layer, the non-negative sparse coding model with fisher discriminative analysis is constructed to obtain the discriminative sparse representation of medical images. Then, the obtained multi-scale non-negative sparse coding features are combined to form a multi-scale feature histogram as the final representation for a medical image. Finally, SVM classifier is combined to conduct medical image classification. The experimental results demonstrate that our proposed algorithm can effectively utilize multi-scale and contextual spatial information of medical images, reduce the semantic gap in a large degree and improve medical image classification performance. Copyright © 2017 Elsevier B.V. All rights reserved.

Comparison of computed tomography and magnetic resonance imaging for the evaluation of canine intranasal neoplasia

PubMed Central

Drees, R.; Forrest, L. J.; Chappell, R.

2009-01-01

Objectives Canine intranasal neoplasia is commonly evaluated using computed tomography to indicate the diagnosis, to determine disease extent, to guide histological sampling location and to plan treatment. With the expanding use of magnetic resonance imaging in veterinary medicine, this modality has been recently applied for the same purpose. The aim of this study was to compare the features of canine intranasal neoplasia using computed tomography and magnetic resonance imaging. Methods Twenty-one dogs with confirmed intranasal neoplasia underwent both computed tomography and magnetic resonance imaging. The images were reviewed retrospectively for the bony and soft tissue features of intranasal neoplasia. Results Overall computed tomography and magnetic resonance imaging performed very similarly. However, lysis of bones bordering the nasal cavity and mucosal thickening was found on computed tomography images more often than on magnetic resonance images. Small amounts of fluid in the nasal cavity were more often seen on magnetic resonance images. However, fluid in the frontal sinuses was seen equally well with both modalities. Clinical Significance We conclude that computed tomography is satisfactory for evaluation of canine intranasal neoplasia, and no clinically relevant benefit is gained using magnetic resonance imaging for intranasal neoplasia without extent into the cranial cavity. PMID:19508490

Development of a novel imaging informatics-based system with an intelligent workflow engine (IWEIS) to support imaging-based clinical trials

PubMed Central

Wang, Ximing; Liu, Brent J; Martinez, Clarisa; Zhang, Xuejun; Winstein, Carolee J

2015-01-01

Imaging based clinical trials can benefit from a solution to efficiently collect, analyze, and distribute multimedia data at various stages within the workflow. Currently, the data management needs of these trials are typically addressed with custom-built systems. However, software development of the custom- built systems for versatile workflows can be resource-consuming. To address these challenges, we present a system with a workflow engine for imaging based clinical trials. The system enables a project coordinator to build a data collection and management system specifically related to study protocol workflow without programming. Web Access to DICOM Objects (WADO) module with novel features is integrated to further facilitate imaging related study. The system was initially evaluated by an imaging based rehabilitation clinical trial. The evaluation shows that the cost of the development of system can be much reduced compared to the custom-built system. By providing a solution to customize a system and automate the workflow, the system will save on development time and reduce errors especially for imaging clinical trials. PMID:25870169

Bladder cancer treatment response assessment with radiomic, clinical, and radiologist semantic features

NASA Astrophysics Data System (ADS)

Gordon, Marshall N.; Cha, Kenny H.; Hadjiiski, Lubomir M.; Chan, Heang-Ping; Cohan, Richard H.; Caoili, Elaine M.; Paramagul, Chintana; Alva, Ajjai; Weizer, Alon Z.

2018-02-01

We are developing a decision support system for assisting clinicians in assessment of response to neoadjuvant chemotherapy for bladder cancer. Accurate treatment response assessment is crucial for identifying responders and improving quality of life for non-responders. An objective machine learning decision support system may help reduce variability and inaccuracy in treatment response assessment. We developed a predictive model to assess the likelihood that a patient will respond based on image and clinical features. With IRB approval, we retrospectively collected a data set of pre- and post- treatment CT scans along with clinical information from surgical pathology from 98 patients. A linear discriminant analysis (LDA) classifier was used to predict the likelihood that a patient would respond to treatment based on radiomic features extracted from CT urography (CTU), a radiologist's semantic feature, and a clinical feature extracted from surgical and pathology reports. The classification accuracy was evaluated using the area under the ROC curve (AUC) with a leave-one-case-out cross validation. The classification accuracy was compared for the systems based on radiomic features, clinical feature, and radiologist's semantic feature. For the system based on only radiomic features the AUC was 0.75. With the addition of clinical information from examination under anesthesia (EUA) the AUC was improved to 0.78. Our study demonstrated the potential of designing a decision support system to assist in treatment response assessment. The combination of clinical features, radiologist semantic features and CTU radiomic features improved the performance of the classifier and the accuracy of treatment response assessment.

Pathogenesis, imaging and clinical characteristics of CF and non-CF bronchiectasis.

PubMed

Schäfer, Jürgen; Griese, Matthias; Chandrasekaran, Ravishankar; Chotirmall, Sanjay H; Hartl, Dominik

2018-05-22

Bronchiectasis is a common feature of severe inherited and acquired pulmonary disease conditions. Among inherited diseases, cystic fibrosis (CF) is the major disorder associated with bronchiectasis, while acquired conditions frequently featuring bronchiectasis include post-infective bronchiectasis and chronic obstructive pulmonary disease (COPD). Mechanistically, bronchiectasis is driven by a complex interplay of inflammation and infection with neutrophilic inflammation playing a predominant role. The clinical characterization and management of bronchiectasis should involve a precise diagnostic workup, tailored therapeutic strategies and pulmonary imaging that has become an essential tool for the diagnosis and follow-up of bronchiectasis. Prospective future studies are required to optimize the diagnostic and therapeutic management of bronchiectasis, particularly in heterogeneous non-CF bronchiectasis populations.

Congenital cystic lesions of the head and neck.

PubMed

Ibrahim, Mohannad; Hammoud, Khaled; Maheshwari, Mohit; Pandya, Amit

2011-08-01

This article presents clinical characteristics and radiologic features of congenital cervical cystic masses, among them thyroglossal duct cysts, cystic hygromas, branchial cleft cysts, and the some of the rare congenital cysts, such as thymic and cervical bronchogenic cysts. The imaging options and the value of each for particular masses, as well as present clinical and radiologic images for each, are discussed. Copyright © 2011 Elsevier Inc. All rights reserved.

Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas.

PubMed

Chang, P; Grinband, J; Weinberg, B D; Bardis, M; Khy, M; Cadena, G; Su, M-Y; Cha, S; Filippi, C G; Bota, D; Baldi, P; Poisson, L M; Jain, R; Chow, D

2018-05-10

The World Health Organization has recently placed new emphasis on the integration of genetic information for gliomas. While tissue sampling remains the criterion standard, noninvasive imaging techniques may provide complimentary insight into clinically relevant genetic mutations. Our aim was to train a convolutional neural network to independently predict underlying molecular genetic mutation status in gliomas with high accuracy and identify the most predictive imaging features for each mutation. MR imaging data and molecular information were retrospectively obtained from The Cancer Imaging Archives for 259 patients with either low- or high-grade gliomas. A convolutional neural network was trained to classify isocitrate dehydrogenase 1 ( IDH1 ) mutation status, 1p/19q codeletion, and O6-methylguanine-DNA methyltransferase ( MGMT ) promotor methylation status. Principal component analysis of the final convolutional neural network layer was used to extract the key imaging features critical for successful classification. Classification had high accuracy: IDH1 mutation status, 94%; 1p/19q codeletion, 92%; and MGMT promotor methylation status, 83%. Each genetic category was also associated with distinctive imaging features such as definition of tumor margins, T1 and FLAIR suppression, extent of edema, extent of necrosis, and textural features. Our results indicate that for The Cancer Imaging Archives dataset, machine-learning approaches allow classification of individual genetic mutations of both low- and high-grade gliomas. We show that relevant MR imaging features acquired from an added dimensionality-reduction technique demonstrate that neural networks are capable of learning key imaging components without prior feature selection or human-directed training. © 2018 by American Journal of Neuroradiology.

Predicting diagnostic error in Radiology via eye-tracking and image analytics: Application in mammography

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

Voisin, Sophie; Pinto, Frank M; Morin-Ducote, Garnetta

2013-01-01

Purpose: The primary aim of the present study was to test the feasibility of predicting diagnostic errors in mammography by merging radiologists gaze behavior and image characteristics. A secondary aim was to investigate group-based and personalized predictive models for radiologists of variable experience levels. Methods: The study was performed for the clinical task of assessing the likelihood of malignancy of mammographic masses. Eye-tracking data and diagnostic decisions for 40 cases were acquired from 4 Radiology residents and 2 breast imaging experts as part of an IRB-approved pilot study. Gaze behavior features were extracted from the eye-tracking data. Computer-generated and BIRADsmore » images features were extracted from the images. Finally, machine learning algorithms were used to merge gaze and image features for predicting human error. Feature selection was thoroughly explored to determine the relative contribution of the various features. Group-based and personalized user modeling was also investigated. Results: Diagnostic error can be predicted reliably by merging gaze behavior characteristics from the radiologist and textural characteristics from the image under review. Leveraging data collected from multiple readers produced a reasonable group model (AUC=0.79). Personalized user modeling was far more accurate for the more experienced readers (average AUC of 0.837 0.029) than for the less experienced ones (average AUC of 0.667 0.099). The best performing group-based and personalized predictive models involved combinations of both gaze and image features. Conclusions: Diagnostic errors in mammography can be predicted reliably by leveraging the radiologists gaze behavior and image content.« less

[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.

Ultrasound and MRI of Pediatric Ocular Masses with Histopathologic Correlation

PubMed Central

Brennan, Rachel C.; Wilson, Matthew W.; Kaste, Sue; Helton, Kathleen J.; McCarville, M. Beth

2012-01-01

We review our experience with unusual ocular pathologies mimicking retinoblastoma that were referred to our institution over the past two decades. After presenting the imaging anatomy of the normal eye, we discuss pertinent clinical and pathological features, and illustrate the ultrasound and magnetic resonance imaging appearance of retinoblastoma, medulloepithelioma, uveal melanoma, persistent fetal vasculature, Coats disease, corneal dermoid, retinal dysplasia and toxocara granuloma. Features useful in discriminating between these entities are emphasized. PMID:22466750

Towards noncontact skin melanoma selection by multispectral imaging analysis.

PubMed

Kuzmina, Ilona; Diebele, Ilze; Jakovels, Dainis; Spigulis, Janis; Valeine, Lauma; Kapostinsh, Janis; Berzina, Anna

2011-06-01

A clinical trial comprising 334 pigmented and vascular lesions has been performed in three Riga clinics by means of multispectral imaging analysis. The imaging system Nuance 2.4 (CRi) and self-developed software for mapping of the main skin chromophores were used. Specific features were observed and analyzed for malignant skin melanomas: notably higher absorbance (especially as the difference of optical density relative to the healthy skin), uneven chromophore distribution over the lesion area, and the possibility to select the "melanoma areas" in the correlation graphs of chromophores. The obtained results indicate clinical potential of this technology for noncontact selection of melanoma from other pigmented and vascular skin lesions.

SPECT and PET radiopharmaceuticals for molecular imaging of apoptosis: from bench to clinic

PubMed Central

Wang, Xiaobo; Feng, Han; Zhao, Shichao; Xu, Junling; Wu, Xinyu; Cui, Jing; Zhang, Ying; Qin, Yuhua; Liu, Zhiguo; Gao, Tang; Gao, Yongju; Zeng, Wenbin

2017-01-01

Owing to the central role of apoptosis in many human diseases and the wide-spread application of apoptosis-based therapeutics, molecular imaging of apoptosis in clinical practice is of great interest for clinicians, and holds great promises. Based on the well-defined biochemical changes for apoptosis, a rich assortment of probes and approaches have been developed for molecular imaging of apoptosis with various imaging modalities. Among these imaging techniques, nuclear imaging (including single photon emission computed tomography and positron emission tomography) remains the premier clinical method owing to their high specificity and sensitivity. Therefore, the corresponding radiopharmaceuticals have been a major focus, and some of them like 99mTc-Annexin V, 18F-ML-10, 18F-CP18, and 18F-ICMT-11 are currently under clinical investigations in Phase I/II or Phase II/III clinical trials on a wide scope of diseases. In this review, we summarize these radiopharmaceuticals that have been widely used in clinical trials and elaborate them in terms of radiosynthesis, pharmacokinetics and dosimetry, and their applications in different clinical stages. We also explore the unique features required to qualify a desirable radiopharmaceutical for imaging apoptosis in clinical practice. Particularly, a perspective of the impact of these clinical efforts, namely, apoptosis imaging as predictive and prognostic markers, early-response indicators and surrogate endpoints, is also the highlight of this review. PMID:28108738

Clinical studies of pigmented lesions in human skin by using a multiphoton tomograph

NASA Astrophysics Data System (ADS)

Balu, Mihaela; Kelly, Kristen M.; Zachary, Christopher B.; Harris, Ronald M.; Krasieva, Tatiana B.; König, Karsten; Tromberg, Bruce J.

2013-02-01

In vivo imaging of pigmented lesions in human skin was performed with a clinical multiphoton microscopy (MPM)-based tomograph (MPTflex, JenLab, Germany). Two-photon excited fluorescence was used for visualizing endogenous fluorophores such as NADH/FAD, keratin, melanin in the epidermal cells and elastin fibers in the dermis. Collagen fibers were imaged by second harmonic generation. Our study involved in vivo imaging of benign melanocytic nevi, atypical nevi and melanoma. The goal of this preliminary study was to identify in vivo the characteristic features and their frequency in pigmented lesions at different stages (benign, atypical and malignant) and to evaluate the ability of in vivo MPM to distinguish atypical nevi from melanoma. Comparison with histopathology was performed for the biopsied lesions. Benign melanocytic nevi were characterized by the presence of nevus cell nests at the epidermal-dermal junction. In atypical nevi, features such as lentiginous hyperplasia, acanthosis and architectural disorder were imaged. Cytological atypia was present in all the melanoma lesions imaged, showing the strongest correlation with malignancy. The MPM images demonstrated very good correlation with corresponding histological images, suggesting that MPM could be a promising tool for in vivo non-invasive pigmented lesion diagnosis, particularly distinguishing atypical nevi from melanoma.

Magnetic resonance imaging of injuries to the ankle joint: can it predict clinical outcome?

PubMed

Zanetti, M; De Simoni, C; Wetz, H H; Zollinger, H; Hodler, J

1997-02-01

To predict clinical outcome after ankle sprains on the basis of magnetic resonance (MR) findings. Twenty-nine consecutive patients (mean age 32.9 years, range 13-60 years) were examined clinically and with MR imaging both after trauma and following standardized conservative therapy. Various MR abnormalities were related to a clinical outcome score. There was a tendency for a better clinical outcome in partial, rather than complete, tears of the anterior talofibular ligament and when there was no fluid within the peroneal tendon sheath at the initial MR examination (P = 0.092 for either abnormality). A number of other MR features did not significantly influence clinical outcome, including the presence of a calcaneofibular ligament lesion and a bone bruise of the talar dome. Clinical outcome after ankle sprain cannot consistently be predicted by MR imaging, although MR imaging may be more accurate when the anterior talofibular ligament is only partially torn and there are no signs of injury to the peroneal tendon sheath.

Diagnostic analysis of liver B ultrasonic texture features based on LM neural network

NASA Astrophysics Data System (ADS)

Chi, Qingyun; Hua, Hu; Liu, Menglin; Jiang, Xiuying

2017-03-01

In this study, B ultrasound images of 124 benign and malignant patients were randomly selected as the study objects. The B ultrasound images of the liver were treated by enhanced de-noising. By constructing the gray level co-occurrence matrix which reflects the information of each angle, Principal Component Analysis of 22 texture features were extracted and combined with LM neural network for diagnosis and classification. Experimental results show that this method is a rapid and effective diagnostic method for liver imaging, which provides a quantitative basis for clinical diagnosis of liver diseases.

[Diagnostic imaging of high-grade astrocytoma: heterogeneity of clinical manifestation, image characteristics, and histopathological findings].

PubMed

Okajima, Kaoru; Ohta, Yoshio

2012-10-01

Recent developments in diagnostic radiology, which have enabled accurate differential diagnoses of brain tumors, have been well described in the last three decades. MR and PET imaging can also provide information to predict histological grades and prognoses that might influence treatment strategies. However, high-grade astrocytomas consist of many different subtypes that are associated with different imaging and histological characteristics. Hemorrhage and necrosis results in a variety of imaging features, and infiltrative tumor growth entrapping normal neurons may cause different clinical manifestations. We reviewed patients with high-grade astrocytomas that showed various imaging characteristics, with special emphasis on initial symptoms and histological features. Clinicopathological characteristics of astrocytomas were also compared with other malignant tumors. Neurological deficits were not notable in patients with grade 3-4 astrocytomas when they showed infiltrative tumor growth, while brain metastases with compact cellular proliferation caused more neurological symptoms. Infiltrative tumors did not show any enhancing masses on MR imaging, but these tumors may show intratumor heterogeneity. Seizures were reported to be more frequent in low-grade glioma and in secondary glioblastoma. Tumor heterogeneity was also reported in molecular genetic profile, and investigators identified some subsets of astrocytomas. They investigated IHD1/2 mutation, EGFR amplification, TP53 mutation, Ki-67 index, etc. In summary, high-grade astrocytomas are not homogenous groups of tumors, and this is associated with the heterogeneity of clinical manifestation, image characteristics, and histopathological findings. Molecular studies may explain the tumor heterogeneity in the near future.

Robustness of radiomic breast features of benign lesions and luminal A cancers across MR magnet strengths

NASA Astrophysics Data System (ADS)

Whitney, Heather M.; Drukker, Karen; Edwards, Alexandra; Papaioannou, John; Giger, Maryellen L.

2018-02-01

Radiomics features extracted from breast lesion images have shown potential in diagnosis and prognosis of breast cancer. As clinical institutions transition from 1.5 T to 3.0 T magnetic resonance imaging (MRI), it is helpful to identify robust features across these field strengths. In this study, dynamic contrast-enhanced MR images were acquired retrospectively under IRB/HIPAA compliance, yielding 738 cases: 241 and 124 benign lesions imaged at 1.5 T and 3.0 T and 231 and 142 luminal A cancers imaged at 1.5 T and 3.0 T, respectively. Lesions were segmented using a fuzzy C-means method. Extracted radiomic values for each group of lesions by cancer status and field strength of acquisition were compared using a Kolmogorov-Smirnov test for the null hypothesis that two groups being compared came from the same distribution, with p-values being corrected for multiple comparisons by the Holm-Bonferroni method. Two shape features, one texture feature, and three enhancement variance kinetics features were found to be potentially robust. All potentially robust features had areas under the receiver operating characteristic curve (AUC) statistically greater than 0.5 in the task of distinguishing between lesion types (range of means 0.57-0.78). The significant difference in voxel size between field strength of acquisition limits the ability to affirm more features as robust or not robust according to field strength alone, and inhomogeneities in static field strength and radiofrequency field could also have affected the assessment of kinetic curve features as robust or not. Vendor-specific image scaling could have also been a factor. These findings will contribute to the development of radiomic signatures that use features identified as robust across field strength.

Quantitative imaging as cancer biomarker

NASA Astrophysics Data System (ADS)

Mankoff, David A.

2015-03-01

The ability to assay tumor biologic features and the impact of drugs on tumor biology is fundamental to drug development. Advances in our ability to measure genomics, gene expression, protein expression, and cellular biology have led to a host of new targets for anticancer drug therapy. In translating new drugs into clinical trials and clinical practice, these same assays serve to identify patients most likely to benefit from specific anticancer treatments. As cancer therapy becomes more individualized and targeted, there is an increasing need to characterize tumors and identify therapeutic targets to select therapy most likely to be successful in treating the individual patient's cancer. Thus far assays to identify cancer therapeutic targets or anticancer drug pharmacodynamics have been based upon in vitro assay of tissue or blood samples. Advances in molecular imaging, particularly PET, have led to the ability to perform quantitative non-invasive molecular assays. Imaging has traditionally relied on structural and anatomic features to detect cancer and determine its extent. More recently, imaging has expanded to include the ability to image regional biochemistry and molecular biology, often termed molecular imaging. Molecular imaging can be considered an in vivo assay technique, capable of measuring regional tumor biology without perturbing it. This makes molecular imaging a unique tool for cancer drug development, complementary to traditional assay methods, and a potentially powerful method for guiding targeted therapy in clinical trials and clinical practice. The ability to quantify, in absolute measures, regional in vivo biologic parameters strongly supports the use of molecular imaging as a tool to guide therapy. This review summarizes current and future applications of quantitative molecular imaging as a biomarker for cancer therapy, including the use of imaging to (1) identify patients whose tumors express a specific therapeutic target; (2) determine whether the drug reaches the target; (3) identify an early response to treatment; and (4) predict the impact of therapy on long-term outcomes such as survival. The manuscript reviews basic concepts important in the application of molecular imaging to cancer drug therapy, in general, and will discuss specific examples of studies in humans, and highlight future directions, including ongoing multi-center clinical trials using molecular imaging as a cancer biomarker.

Imaging popliteal artery disease in young adults with claudication: self-assessment module.

PubMed

Chew, Felix S; Bui-Mansfield, Liem T

2007-09-01

The educational objectives of this self-assessment module on imaging popliteal artery disease in young adults with intermittent claudication are for the participant to exercise, self-assess, and improve his or her knowledge of the imaging and clinical features of popliteal artery entrapment syndrome, cystic adventitial disease,and masses associated with popliteal artery obstruction.

High-Performance Computational Analysis of Glioblastoma Pathology Images with Database Support Identifies Molecular and Survival Correlates.

PubMed

Kong, Jun; Wang, Fusheng; Teodoro, George; Cooper, Lee; Moreno, Carlos S; Kurc, Tahsin; Pan, Tony; Saltz, Joel; Brat, Daniel

2013-12-01

In this paper, we present a novel framework for microscopic image analysis of nuclei, data management, and high performance computation to support translational research involving nuclear morphometry features, molecular data, and clinical outcomes. Our image analysis pipeline consists of nuclei segmentation and feature computation facilitated by high performance computing with coordinated execution in multi-core CPUs and Graphical Processor Units (GPUs). All data derived from image analysis are managed in a spatial relational database supporting highly efficient scientific queries. We applied our image analysis workflow to 159 glioblastomas (GBM) from The Cancer Genome Atlas dataset. With integrative studies, we found statistics of four specific nuclear features were significantly associated with patient survival. Additionally, we correlated nuclear features with molecular data and found interesting results that support pathologic domain knowledge. We found that Proneural subtype GBMs had the smallest mean of nuclear Eccentricity and the largest mean of nuclear Extent, and MinorAxisLength. We also found gene expressions of stem cell marker MYC and cell proliferation maker MKI67 were correlated with nuclear features. To complement and inform pathologists of relevant diagnostic features, we queried the most representative nuclear instances from each patient population based on genetic and transcriptional classes. Our results demonstrate that specific nuclear features carry prognostic significance and associations with transcriptional and genetic classes, highlighting the potential of high throughput pathology image analysis as a complementary approach to human-based review and translational research.

Two- and three-dimensional ultrasound imaging to facilitate detection and targeting of taut bands in myofascial pain syndrome.

PubMed

Shankar, Hariharan; Reddy, Sapna

2012-07-01

Ultrasound imaging has gained acceptance in pain management interventions. Features of myofascial pain syndrome have been explored using ultrasound imaging and elastography. There is a paucity of reports showing the benefit clinically. This report provides three-dimensional features of taut bands and highlights the advantages of using two-dimensional ultrasound imaging to improve targeting of taut bands in deeper locations. Fifty-eight-year-old man with pain and decreased range of motion of the right shoulder was referred for further management of pain above the scapula after having failed conservative management for myofascial pain syndrome. Three-dimensional ultrasound images provided evidence of aberrancy in the architecture of the muscle fascicles around the taut bands compared to the adjacent normal muscle tissue during serial sectioning of the accrued image. On two-dimensional ultrasound imaging over the palpated taut band, areas of hyperechogenicity were visualized in the trapezius and supraspinatus muscles. Subsequently, the patient received ultrasound-guided real-time lidocaine injections to the trigger points with successful resolution of symptoms. This is a successful demonstration of utility of ultrasound imaging of taut bands in the management of myofascial pain syndrome. Utility of this imaging modality in myofascial pain syndrome requires further clinical validation. Wiley Periodicals, Inc.

Quantitative analysis of ex vivo colorectal epithelium using an automated feature extraction algorithm for microendoscopy image data

PubMed Central

Prieto, Sandra P.; Lai, Keith K.; Laryea, Jonathan A.; Mizell, Jason S.; Muldoon, Timothy J.

2016-01-01

Abstract. Qualitative screening for colorectal polyps via fiber bundle microendoscopy imaging has shown promising results, with studies reporting high rates of sensitivity and specificity, as well as low interobserver variability with trained clinicians. A quantitative image quality control and image feature extraction algorithm (QFEA) was designed to lessen the burden of training and provide objective data for improved clinical efficacy of this method. After a quantitative image quality control step, QFEA extracts field-of-view area, crypt area, crypt circularity, and crypt number per image. To develop and validate this QFEA, a training set of microendoscopy images was collected from freshly resected porcine colon epithelium. The algorithm was then further validated on ex vivo image data collected from eight human subjects, selected from clinically normal appearing regions distant from grossly visible tumor in surgically resected colorectal tissue. QFEA has proven flexible in application to both mosaics and individual images, and its automated crypt detection sensitivity ranges from 71 to 94% despite intensity and contrast variation within the field of view. It also demonstrates the ability to detect and quantify differences in grossly normal regions among different subjects, suggesting the potential efficacy of this approach in detecting occult regions of dysplasia. PMID:27335893

Magnetization-prepared rapid acquisition with gradient echo magnetic resonance imaging signal and texture features for the prediction of mild cognitive impairment to Alzheimer’s disease progression

PubMed Central

Martinez-Torteya, Antonio; Rodriguez-Rojas, Juan; Celaya-Padilla, José M.; Galván-Tejada, Jorge I.; Treviño, Victor; Tamez-Peña, Jose

2014-01-01

Abstract. Early diagnoses of Alzheimer’s disease (AD) would confer many benefits. Several biomarkers have been proposed to achieve such a task, where features extracted from magnetic resonance imaging (MRI) have played an important role. However, studies have focused exclusively on morphological characteristics. This study aims to determine whether features relating to the signal and texture of the image could predict mild cognitive impairment (MCI) to AD progression. Clinical, biological, and positron emission tomography information and MRI images of 62 subjects from the AD neuroimaging initiative were used in this study, extracting 4150 features from each MRI. Within this multimodal database, a feature selection algorithm was used to obtain an accurate and small logistic regression model, generated by a methodology that yielded a mean blind test accuracy of 0.79. This model included six features, five of them obtained from the MRI images, and one obtained from genotyping. A risk analysis divided the subjects into low-risk and high-risk groups according to a prognostic index. The groups were statistically different (p-value=2.04e−11). These results demonstrated that MRI features related to both signal and texture add MCI to AD predictive power, and supported the ongoing notion that multimodal biomarkers outperform single-modality ones. PMID:26158047

MRI signal and texture features for the prediction of MCI to Alzheimer's disease progression

NASA Astrophysics Data System (ADS)

Martínez-Torteya, Antonio; Rodríguez-Rojas, Juan; Celaya-Padilla, José M.; Galván-Tejada, Jorge I.; Treviño, Victor; Tamez-Peña, José G.

2014-03-01

An early diagnosis of Alzheimer's disease (AD) confers many benefits. Several biomarkers from different information modalities have been proposed for the prediction of MCI to AD progression, where features extracted from MRI have played an important role. However, studies have focused almost exclusively in the morphological characteristics of the images. This study aims to determine whether features relating to the signal and texture of the image could add predictive power. Baseline clinical, biological and PET information, and MP-RAGE images for 62 subjects from the Alzheimer's Disease Neuroimaging Initiative were used in this study. Images were divided into 83 regions and 50 features were extracted from each one of these. A multimodal database was constructed, and a feature selection algorithm was used to obtain an accurate and small logistic regression model, which achieved a cross-validation accuracy of 0.96. These model included six features, five of them obtained from the MP-RAGE image, and one obtained from genotyping. A risk analysis divided the subjects into low-risk and high-risk groups according to a prognostic index, showing that both groups are statistically different (p-value of 2.04e-11). The results demonstrate that MRI features related to both signal and texture, add MCI to AD predictive power, and support the idea that multimodal biomarkers outperform single-modality biomarkers.

Enhancement of multimodality texture-based prediction models via optimization of PET and MR image acquisition protocols: a proof of concept

NASA Astrophysics Data System (ADS)

Vallières, Martin; Laberge, Sébastien; Diamant, André; El Naqa, Issam

2017-11-01

Texture-based radiomic models constructed from medical images have the potential to support cancer treatment management via personalized assessment of tumour aggressiveness. While the identification of stable texture features under varying imaging settings is crucial for the translation of radiomics analysis into routine clinical practice, we hypothesize in this work that a complementary optimization of image acquisition parameters prior to texture feature extraction could enhance the predictive performance of texture-based radiomic models. As a proof of concept, we evaluated the possibility of enhancing a model constructed for the early prediction of lung metastases in soft-tissue sarcomas by optimizing PET and MR image acquisition protocols via computerized simulations of image acquisitions with varying parameters. Simulated PET images from 30 STS patients were acquired by varying the extent of axial data combined per slice (‘span’). Simulated T 1-weighted and T 2-weighted MR images were acquired by varying the repetition time and echo time in a spin-echo pulse sequence, respectively. We analyzed the impact of the variations of PET and MR image acquisition parameters on individual textures, and we investigated how these variations could enhance the global response and the predictive properties of a texture-based model. Our results suggest that it is feasible to identify an optimal set of image acquisition parameters to improve prediction performance. The model constructed with textures extracted from simulated images acquired with a standard clinical set of acquisition parameters reached an average AUC of 0.84 +/- 0.01 in bootstrap testing experiments. In comparison, the model performance significantly increased using an optimal set of image acquisition parameters (p = 0.04 ), with an average AUC of 0.89 +/- 0.01 . Ultimately, specific acquisition protocols optimized to generate superior radiomics measurements for a given clinical problem could be developed and standardized via dedicated computer simulations and thereafter validated using clinical scanners.

Computer aided diagnosis based on medical image processing and artificial intelligence methods

NASA Astrophysics Data System (ADS)

Stoitsis, John; Valavanis, Ioannis; Mougiakakou, Stavroula G.; Golemati, Spyretta; Nikita, Alexandra; Nikita, Konstantina S.

2006-12-01

Advances in imaging technology and computer science have greatly enhanced interpretation of medical images, and contributed to early diagnosis. The typical architecture of a Computer Aided Diagnosis (CAD) system includes image pre-processing, definition of region(s) of interest, features extraction and selection, and classification. In this paper, the principles of CAD systems design and development are demonstrated by means of two examples. The first one focuses on the differentiation between symptomatic and asymptomatic carotid atheromatous plaques. For each plaque, a vector of texture and motion features was estimated, which was then reduced to the most robust ones by means of ANalysis of VAriance (ANOVA). Using fuzzy c-means, the features were then clustered into two classes. Clustering performances of 74%, 79%, and 84% were achieved for texture only, motion only, and combinations of texture and motion features, respectively. The second CAD system presented in this paper supports the diagnosis of focal liver lesions and is able to characterize liver tissue from Computed Tomography (CT) images as normal, hepatic cyst, hemangioma, and hepatocellular carcinoma. 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 neural network classifiers. The achieved classification performance was 100%, 93.75% and 90.63% 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.

Determining degree of optic nerve edema from color fundus photography

NASA Astrophysics Data System (ADS)

Agne, Jason; Wang, Jui-Kai; Kardon, Randy H.; Garvin, Mona K.

2015-03-01

Swelling of the optic nerve head (ONH) is subjectively assessed by clinicians using the Frisén scale. It is believed that a direct measurement of the ONH volume would serve as a better representation of the swelling. However, a direct measurement requires optic nerve imaging with spectral domain optical coherence tomography (SD-OCT) and 3D segmentation of the resulting images, which is not always available during clinical evaluation. Furthermore, telemedical imaging of the eye at remote locations is more feasible with non-mydriatic fundus cameras which are less costly than OCT imagers. Therefore, there is a critical need to develop a more quantitative analysis of optic nerve swelling on a continuous scale, similar to SD-OCT. Here, we select features from more commonly available 2D fundus images and use them to predict ONH volume. Twenty-six features were extracted from each of 48 color fundus images. The features include attributes of the blood vessels, optic nerve head, and peripapillary retina areas. These features were used in a regression analysis to predict ONH volume, as computed by a segmentation of the SD-OCT image. The results of the regression analysis yielded a mean square error of 2.43 mm3 and a correlation coefficient between computed and predicted volumes of R = 0:771, which suggests that ONH volume may be predicted from fundus features alone.

Quantitative Imaging Biomarkers: A Review of Statistical Methods for Technical Performance Assessment

PubMed Central

2017-01-01

Technological developments and greater rigor in the quantitative measurement of biological features in medical images have given rise to an increased interest in using quantitative imaging biomarkers (QIBs) to measure changes in these features. Critical to the performance of a QIB in preclinical or clinical settings are three primary metrology areas of interest: measurement linearity and bias, repeatability, and the ability to consistently reproduce equivalent results when conditions change, as would be expected in any clinical trial. Unfortunately, performance studies to date differ greatly in designs, analysis method and metrics used to assess a QIB for clinical use. It is therefore, difficult or not possible to integrate results from different studies or to use reported results to design studies. The Radiological Society of North America (RSNA) and the Quantitative Imaging Biomarker Alliance (QIBA) with technical, radiological and statistical experts developed a set of technical performance analysis methods, metrics and study designs that provide terminology, metrics and methods consistent with widely accepted metrological standards. This document provides a consistent framework for the conduct and evaluation of QIB performance studies so that results from multiple studies can be compared, contrasted or combined. PMID:24919831

Body image dissatisfaction: clinical features, and psychosocial disability in inflammatory bowel disease.

PubMed

McDermott, Edel; Mullen, Georgina; Moloney, Jenny; Keegan, Denise; Byrne, Kathryn; Doherty, Glen A; Cullen, Garret; Malone, Kevin; Mulcahy, Hugh E

2015-02-01

Body image refers to a person's sense of their physical appearance and body function. A negative body image self-evaluation may result in psychosocial dysfunction. Crohn's disease and ulcerative colitis are associated with disabling features, and body image dissatisfaction is a concern for many patients with inflammatory bowel disease (IBD). However, no study has assessed body image and its comorbidities in patients with IBD using validated instruments. Our aim was to explore body image dissatisfaction in patients with IBD and assess its relationship with biological and psychosocial variables. We studied 330 patients (median age, 36 yr; range, 18-83; 169 men) using quantitative and qualitative methods. Patients completed a self-administered questionnaire that included a modified Hopwood Body Image Scale, the Cash Body Image Disturbance Questionnaire, and other validated instruments. Clinical and disease activity data were also collected. Body image dissatisfaction was associated with disease activity (P < 0.001) and steroid treatment (P = 0.03) but not with immunotherapy (P = 0.57) or biological (P = 0.55) therapy. Body image dissatisfaction was also associated with low levels of general (P < 0.001) and IBD-specific (P < 0.001) quality of life, self-esteem (P < 0.001), and sexual satisfaction (P < 0.001), and with high levels of anxiety (P < 0.001) and depression (P < 0.001). Qualitative analysis indicated that patients were concerned about both physical and psychosocial consequences of body image dissatisfaction, including steroid side effects and impaired work and social activities. Body image dissatisfaction is common in patients with IBD, relates to specific clinical variables and is associated with significant psychological dysfunction. Its measurement is warranted as part of a comprehensive patient-centered IBD assessment.

Evaluation of Imaging Methods in Tick-Borne Encephalitis.

PubMed

Zawadzki, Radosław; Garkowski, Adam; Kubas, Bożena; Zajkowska, Joanna; Hładuński, Marcin; Jurgilewicz, Dorota; Łebkowska, Urszula

2017-01-01

Tick-borne encephalitis (TBE) is caused by a virus that belongs to the Flaviviridae family and is transmitted by tick bites. The disease has a biphasic course. Diagnosis is based on laboratory examinations because of non-specific clinical features, which usually entails the detection of specific IgM antibodies in either blood or cerebrospinal fluid that appear in the second phase of the disease. Neurological symptoms, time course of the disease, and imaging findings are multifaceted. During the second phase of the disease, after the onset of neurological symptoms, magnetic resonance imaging (MRI) abnormalities are observed in a limited number of cases. However, imaging features may aid in predicting the prognosis of the disease.

Automatic detection of multi-level acetowhite regions in RGB color images of the uterine cervix

NASA Astrophysics Data System (ADS)

Lange, Holger

2005-04-01

Uterine cervical cancer is the second most common cancer among women worldwide. Colposcopy is a diagnostic method used to detect cancer precursors and cancer of the uterine cervix, whereby a physician (colposcopist) visually inspects the metaplastic epithelium on the cervix for certain distinctly abnormal morphologic features. A contrast agent, a 3-5% acetic acid solution, is used, causing abnormal and metaplastic epithelia to turn white. The colposcopist considers diagnostic features such as the acetowhite, blood vessel structure, and lesion margin to derive a clinical diagnosis. STI Medical Systems is developing a Computer-Aided-Diagnosis (CAD) system for colposcopy -- ColpoCAD, a complex image analysis system that at its core assesses the same visual features as used by colposcopists. The acetowhite feature has been identified as one of the most important individual predictors of lesion severity. Here, we present the details and preliminary results of a multi-level acetowhite region detection algorithm for RGB color images of the cervix, including the detection of the anatomic features: cervix, os and columnar region, which are used for the acetowhite region detection. The RGB images are assumed to be glare free, either obtained by cross-polarized image acquisition or glare removal pre-processing. The basic approach of the algorithm is to extract a feature image from the RGB image that provides a good acetowhite to cervix background ratio, to segment the feature image using novel pixel grouping and multi-stage region-growing algorithms that provide region segmentations with different levels of detail, to extract the acetowhite regions from the region segmentations using a novel region selection algorithm, and then finally to extract the multi-levels from the acetowhite regions using multiple thresholds. The performance of the algorithm is demonstrated using human subject data.

Giant cell angiofibroma misdiagnosed as a vascular malformation and treated with absolute alcohol for one year: a case report and review of the literature.

PubMed

He, Yue; Zhang, Chenping; Liu, Guanglong; Tian, Zhuowei; Wang, Lizhen; Kalfarentzos, Evagelos

2014-04-24

To present the clinical, imaging, pathological and immunohistochemical features of giant cell angiofibroma (GCA). In this paper we report an atypical case of a GCA extending from the parotid to the parapharyngeal space. The lesion was being treated as a vascular malformation for one year prior to surgical removal. We summarize the clinical manifestations, imaging, pathological and molecular features of this rare disease.After complete surgical removal of the tumor, immunohistochemical analysis revealed strong positivity for the mesenchymal markers vimentin, CD34, CD31 and CD99 in neoplastic cells. Tumor proliferation antigen marker Ki67 was partly positive (<5% of cells). Tumor cells were negative for muscle-specific actin, epithelial membrane antigen, smooth muscle actin, cytokeratin pan, S100, desmin, glial fibrillary acidic protein, myogenin, MyoD1 and F8. The morphological and immunohistochemical profile was consistent with the diagnosis of GCA. GCA is a rare soft tissue tumor that can easily be misdiagnosed in the clinical preoperative setting. In view of the clinical, pathological and molecular features of the tumor, complete surgical removal is the current optimal treatment option, providing accurate diagnosis and low to minimal recurrence rate.

TH-E-BRF-04: Characterizing the Response of Texture-Based CT Image Features for Quantification of Radiation-Induced Normal Lung Damage

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

Krafft, S; Court, L; Briere, T

2014-06-15

Purpose: Radiation induced lung damage (RILD) is an important dose-limiting toxicity for patients treated with radiation therapy. Scoring systems for RILD are subjective and limit our ability to find robust predictors of toxicity. We investigate the dose and time-related response for texture-based lung CT image features that serve as potential quantitative measures of RILD. Methods: Pre- and post-RT diagnostic imaging studies were collected for retrospective analysis of 21 patients treated with photon or proton radiotherapy for NSCLC. Total lung and selected isodose contours (0–5, 5–15, 15–25Gy, etc.) were deformably registered from the treatment planning scan to the pre-RT and availablemore » follow-up CT studies for each patient. A CT image analysis framework was utilized to extract 3698 unique texture-based features (including co-occurrence and run length matrices) for each region of interest defined by the isodose contours and the total lung volume. Linear mixed models were fit to determine the relationship between feature change (relative to pre-RT), planned dose and time post-RT. Results: Seventy-three follow-up CT scans from 21 patients (median: 3 scans/patient) were analyzed to describe CT image feature change. At the p=0.05 level, dose affected feature change in 2706 (73.1%) of the available features. Similarly, time affected feature change in 408 (11.0%) of the available features. Both dose and time were significant predictors of feature change in a total of 231 (6.2%) of the extracted image features. Conclusion: Characterizing the dose and time-related response of a large number of texture-based CT image features is the first step toward identifying objective measures of lung toxicity necessary for assessment and prediction of RILD. There is evidence that numerous features are sensitive to both the radiation dose and time after RT. Beyond characterizing feature response, further investigation is warranted to determine the utility of these features as surrogates of clinically significant lung injury.« less

Imaging features of orbital myxosarcoma in dogs.

PubMed

Dennis, Ruth

2008-01-01

Myxomas and myxosarcomas are infiltrative connective tissue tumors of fibroblastic origin that can be distinguished by the presence of abundant mucinous stroma. This paper describes the clinical and imaging features of orbital myxosarcoma in five dogs and suggests a predilection for the orbit. The main clinical signs were slowly progressive exophthalmos with soft swelling of the pterygopalatine fossa, and in two dogs, of the periorbital area. No pain was associated with the eye or orbit but one dog had pain on opening the mouth. The dogs were imaged using combinations of ultrasonography, radiography, and magnetic resonance imaging. In four dogs, extensive fluid-filled cavities in the orbit and fascial planes were seen and in the fifth dog, the tumor appeared more solid with small, peripheral cystic areas. In all dogs, the lesion extended along fascial planes to involve the temporomandibular joint, with osteolysis demonstrable in two dogs. Fluid aspirated from the cystic areas was viscous and sticky, mimicking that from a salivary mucocoele. Myxomas and myxosarcomas are known to be infiltrative and not readily amenable to surgical removal but their clinical course seems to be slow, with a reasonable survival time with palliative treatment. In humans, a juxta-articular form is recognized in which a prominent feature is the presence of dilated, cyst-like spaces filled with mucinous material. It is postulated that orbital myxosarcoma in dogs may be similar to the juxta-articular form in man, and may arise from the temporomandibular joint.

Molecular Imaging of Apoptosis: From Micro to Macro

PubMed Central

Zeng, Wenbin; Wang, Xiaobo; Xu, Pengfei; Liu, Gang; Eden, Henry S.; Chen, Xiaoyuan

2015-01-01

Apoptosis, or programmed cell death, is involved in numerous human conditions including neurodegenerative diseases, ischemic damage, autoimmune disorders and many types of cancer, and is often confused with other types of cell death. Therefore strategies that enable visualized detection of apoptosis would be of enormous benefit in the clinic for diagnosis, patient management, and development of new therapies. In recent years, improved understanding of the apoptotic machinery and progress in imaging modalities have provided opportunities for researchers to formulate microscopic and macroscopic imaging strategies based on well-defined molecular markers and/or physiological features. Correspondingly, a large collection of apoptosis imaging probes and approaches have been documented in preclinical and clinical studies. In this review, we mainly discuss microscopic imaging assays and macroscopic imaging probes, ranging in complexity from simple attachments of reporter moieties to proteins that interact with apoptotic biomarkers, to rationally designed probes that target biochemical changes. Their clinical translation will also be our focus. PMID:25825597

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.

Nonpuerperal mastitis and subareolar abscess of the breast.

PubMed

Kasales, Claudia J; Han, Bing; Smith, J Stanley; Chetlen, Alison L; Kaneda, Heather J; Shereef, Serene

2014-02-01

The purpose of this article is to show radiologists how to readily recognize nonpuerperal subareolar abscess and its complications in order to help reduce the time to definitive therapy and improve patient care. To achieve this purpose, the various theories of pathogenesis and the associated histopathologic features are reviewed; the typical clinical characteristics are detailed in contrast to those seen in lactational abscess and inflammatory breast cancer; the common imaging findings are described with emphasis on the sonographic features; correlative pathologic findings are presented to reinforce the imaging findings as they pertain to disease origins; and the various treatment options are reviewed. Nonpuerperal subareolar mastitis and abscess is a benign breast entity often associated with prolonged morbidity. Through better understanding of the underlying disease process the imaging, physical, and clinical findings of this rare process can be more readily recognized and treatment options expedited, improving patient care.

A proposed computer diagnostic system for malignant melanoma (CDSMM).

PubMed

Shao, S; Grams, R R

1994-04-01

This paper describes a computer diagnostic system for malignant melanoma. The diagnostic system is a rule base system based on image analyses and works under the PC windows environment. It consists of seven modules: I/O module, Patient/Clinic database, image processing module, classification module, rule base module and system control module. In the system, the image analyses are automatically carried out, and database management is efficient and fast. Both final clinic results and immediate results from various modules such as measured features, feature pictures and history records of the disease lesion can be presented on screen or printed out from each corresponding module or from the I/O module. The system can also work as a doctor's office-based tool to aid dermatologists with details not perceivable by the human eye. Since the system operates on a general purpose PC, it can be made portable if the I/O module is disconnected.

Optical Coherence Microscopy

NASA Astrophysics Data System (ADS)

Aguirre, Aaron D.; Zhou, Chao; Lee, Hsiang-Chieh; Ahsen, Osman O.; Fujimoto, James G.

Cellular imaging of human tissues remains an important advance for many clinical applications of optical coherence tomography (OCT). Imaging cells with traditional OCT systems has not been possible due to the limited transverse resolution of such techniques. Optical coherence microscopy (OCM) refers to OCT methods that achieve high transverse resolution to visualize cells and subcellular features. This chapter provides a comprehensive discussion of the rationale for cellular imaging in human tissues as well as a review of the key technological advances required to achieve it. Time domain and Fourier domain OCM approaches are described with an emphasis on state of the art system designs, including miniaturized endoscopic imaging probes. Clinical applications are discussed and multiple examples of cellular imaging in human tissues are provided.

Emerging Technology Update Intravascular Photoacoustic Imaging of Vulnerable Atherosclerotic Plaque.

PubMed

Wu, Min; Fw van der Steen, Antonius; Regar, Evelyn; van Soest, Gijs

2016-10-01

The identification of vulnerable atherosclerotic plaques in the coronary arteries is emerging as an important tool for guiding atherosclerosis diagnosis and interventions. Assessment of plaque vulnerability requires knowledge of both the structure and composition of the plaque. Intravascular photoacoustic (IVPA) imaging is able to show the morphology and composition of atherosclerotic plaque. With imminent improvements in IVPA imaging, it is becoming possible to assess human coronary artery disease in vivo . Although some challenges remain, IVPA imaging is on its way to being a powerful tool for visualising coronary atherosclerotic features that have been specifically associated with plaque vulnerability and clinical syndromes, and thus such imaging might become valuable for clinical risk assessment in the catheterisation laboratory.

Skin image retrieval using Gabor wavelet texture feature.

PubMed

Ou, X; Pan, W; Zhang, X; Xiao, P

2016-12-01

Skin imaging plays a key role in many clinical studies. We have used many skin imaging techniques, including the recently developed capacitive contact skin imaging based on fingerprint sensors. The aim of this study was to develop an effective skin image retrieval technique using Gabor wavelet transform, which can be used on different types of skin images, but with a special focus on skin capacitive contact images. Content-based image retrieval (CBIR) is a useful technology to retrieve stored images from database by supplying query images. In a typical CBIR, images are retrieved based on colour, shape, texture, etc. In this study, texture feature is used for retrieving skin images, and Gabor wavelet transform is used for texture feature description and extraction. The results show that the Gabor wavelet texture features can work efficiently on different types of skin images. Although Gabor wavelet transform is slower compared with other image retrieval techniques, such as principal component analysis (PCA) and grey-level co-occurrence matrix (GLCM), Gabor wavelet transform is the best for retrieving skin capacitive contact images and facial images with different orientations. Gabor wavelet transform can also work well on facial images with different expressions and skin cancer/disease images. We have developed an effective skin image retrieval method based on Gabor wavelet transform, that it is useful for retrieving different types of images, namely digital colour face images, digital colour skin cancer and skin disease images, and particularly greyscale skin capacitive contact images. Gabor wavelet transform can also be potentially useful for face recognition (with different orientation and expressions) and skin cancer/disease diagnosis. © 2016 Society of Cosmetic Scientists and the Société Française de Cosmétologie.

Differential diagnosis of neurodegenerative diseases using structural MRI data

PubMed Central

Koikkalainen, Juha; Rhodius-Meester, Hanneke; Tolonen, Antti; Barkhof, Frederik; Tijms, Betty; Lemstra, Afina W.; Tong, Tong; Guerrero, Ricardo; Schuh, Andreas; Ledig, Christian; Rueckert, Daniel; Soininen, Hilkka; Remes, Anne M.; Waldemar, Gunhild; Hasselbalch, Steen; Mecocci, Patrizia; van der Flier, Wiesje; Lötjönen, Jyrki

2016-01-01

Different neurodegenerative diseases can cause memory disorders and other cognitive impairments. The early detection and the stratification of patients according to the underlying disease are essential for an efficient approach to this healthcare challenge. This emphasizes the importance of differential diagnostics. Most studies compare patients and controls, or Alzheimer's disease with one other type of dementia. Such a bilateral comparison does not resemble clinical practice, where a clinician is faced with a number of different possible types of dementia. Here we studied which features in structural magnetic resonance imaging (MRI) scans could best distinguish four types of dementia, Alzheimer's disease, frontotemporal dementia, vascular dementia, and dementia with Lewy bodies, and control subjects. We extracted an extensive set of features quantifying volumetric and morphometric characteristics from T1 images, and vascular characteristics from FLAIR images. Classification was performed using a multi-class classifier based on Disease State Index methodology. The classifier provided continuous probability indices for each disease to support clinical decision making. A dataset of 504 individuals was used for evaluation. The cross-validated classification accuracy was 70.6% and balanced accuracy was 69.1% for the five disease groups using only automatically determined MRI features. Vascular dementia patients could be detected with high sensitivity (96%) using features from FLAIR images. Controls (sensitivity 82%) and Alzheimer's disease patients (sensitivity 74%) could be accurately classified using T1-based features, whereas the most difficult group was the dementia with Lewy bodies (sensitivity 32%). These results were notable better than the classification accuracies obtained with visual MRI ratings (accuracy 44.6%, balanced accuracy 51.6%). Different quantification methods provided complementary information, and consequently, the best results were obtained by utilizing several quantification methods. The results prove that automatic quantification methods and computerized decision support methods are feasible for clinical practice and provide comprehensive information that may help clinicians in the diagnosis making. PMID:27104138

Magnetic resonance neurography and diffusion tensor imaging: origins, history, and clinical impact of the first 50,000 cases with an assessment of efficacy and utility in a prospective 5000-patient study group.

PubMed

Filler, Aaron

2009-10-01

Methods were invented that made it possible to image peripheral nerves in the body and to image neural tracts in the brain. The history, physical basis, and dyadic tensor concept underlying the methods are reviewed. Over a 15-year period, these techniques-magnetic resonance neurography (MRN) and diffusion tensor imaging-were deployed in the clinical and research community in more than 2500 published research reports and applied to approximately 50,000 patients. Within this group, approximately 5000 patients having MRN were carefully tracked on a prospective basis. A uniform Neurography imaging methodology was applied in the study group, and all images were reviewed and registered by referral source, clinical indication, efficacy of imaging, and quality. Various classes of image findings were identified and subjected to a variety of small targeted prospective outcome studies. Those findings demonstrated to be clinically significant were then tracked in the larger clinical volume data set. MRN demonstrates mechanical distortion of nerves, hyperintensity consistent with nerve irritation, nerve swelling, discontinuity, relations of nerves to masses, and image features revealing distortion of nerves at entrapment points. These findings are often clinically relevant and warrant full consideration in the diagnostic process. They result in specific pathological diagnoses that are comparable to electrodiagnostic testing in clinical efficacy. A review of clinical outcome studies with diffusion tensor imaging also shows convincing utility. MRN and diffusion tensor imaging neural tract imaging have been validated as indispensable clinical diagnostic methods that provide reliable anatomic pathological information. There is no alternative diagnostic method in many situations. With the elapsing of 15 years, tens of thousands of imaging studies, and thousands of publications, these methods should no longer be considered experimental.

Two brothers with heart defects and limb shortening: case reports and review.

PubMed Central

Reardon, W; Hurst, J; Farag, T I; Hall, C; Baraitser, M

1990-01-01

Two male Arab sibs are reported with congenital heart disease and skeletal malformations. Other published case reports sharing some features in common with these brothers are considered. However, clinical and radiological features in these boys are distinct enough to represent a new cardioskeletal syndrome. Images PMID:2074559

Hepatocellular carcinoma: Advances in diagnostic imaging.

PubMed

Sun, Haoran; Song, Tianqiang

2015-10-01

Thanks to the growing knowledge on biological behaviors of hepatocellular carcinomas (HCC), as well as continuous improvement in imaging techniques and experienced interpretation of imaging features of the nodules in cirrhotic liver, the detection and characterization of HCC has improved in the past decade. A number of practice guidelines for imaging diagnosis have been developed to reduce interpretation variability and standardize management of HCC, and they are constantly updated with advances in imaging techniques and evidence based data from clinical series. In this article, we strive to review the imaging techniques and the characteristic features of hepatocellular carcinoma associated with cirrhotic liver, with emphasis on the diagnostic value of advanced magnetic resonance imaging (MRI) techniques and utilization of hepatocyte-specific MRI contrast agents. We also briefly describe the concept of liver imaging reporting and data systems and discuss the consensus and controversy of major practice guidelines.

Lateral epicondylitis and beyond: imaging of lateral elbow pain with clinical-radiologic correlation.

PubMed

Kotnis, Nikhil A; Chiavaras, Mary M; Harish, Srinivasan

2012-04-01

The diagnosis of lateral epicondylitis is often straightforward and can be made on the basis of clinical findings. However, radiological assessment is valuable where the clinical picture is less clear or where symptoms are refractory to treatment. Demographics, aspects of clinical history, or certain physical signs may suggest an alternate diagnosis. Knowledge of the typical clinical presentation and imaging findings of lateral epicondylitis, in addition to other potential causes of lateral elbow pain, is necessary. These include entrapment of the posterior interosseous and lateral antebrachial cutaneous nerves, posterolateral rotatory instability, posterolateral plica syndrome, Panner's disease, osteochondritis dissecans of the capitellum, radiocapitellar overload syndrome, occult fractures and chondral-osseous impaction injuries, and radiocapitellar arthritis. Knowledge of these potential masquerades of lateral epicondylitis and their characteristic clinical and imaging features is essential for accurate diagnosis. The goal of this review is to provide an approach to the imaging of lateral elbow pain, discussing the relevant anatomy, various causes, and discriminating factors, which will allow for an accurate diagnosis.

Applications of iQID cameras

NASA Astrophysics Data System (ADS)

Han, Ling; Miller, Brian W.; Barrett, Harrison H.; Barber, H. Bradford; Furenlid, Lars R.

2017-09-01

iQID is an intensified quantum imaging detector developed in the Center for Gamma-Ray Imaging (CGRI). Originally called BazookaSPECT, iQID was designed for high-resolution gamma-ray imaging and preclinical gamma-ray single-photon emission computed tomography (SPECT). With the use of a columnar scintillator, an image intensifier and modern CCD/CMOS sensors, iQID cameras features outstanding intrinsic spatial resolution. In recent years, many advances have been achieved that greatly boost the performance of iQID, broadening its applications to cover nuclear and particle imaging for preclinical, clinical and homeland security settings. This paper presents an overview of the recent advances of iQID technology and its applications in preclinical and clinical scintigraphy, preclinical SPECT, particle imaging (alpha, neutron, beta, and fission fragment), and digital autoradiography.

Similarity estimation for reference image retrieval in mammograms using convolutional neural network

NASA Astrophysics Data System (ADS)

Muramatsu, Chisako; Higuchi, Shunichi; Morita, Takako; Oiwa, Mikinao; Fujita, Hiroshi

2018-02-01

Periodic breast cancer screening with mammography is considered effective in decreasing breast cancer mortality. For screening programs to be successful, an intelligent image analytic system may support radiologists' efficient image interpretation. In our previous studies, we have investigated image retrieval schemes for diagnostic references of breast lesions on mammograms and ultrasound images. Using a machine learning method, reliable similarity measures that agree with radiologists' similarity were determined and relevant images could be retrieved. However, our previous method includes a feature extraction step, in which hand crafted features were determined based on manual outlines of the masses. Obtaining the manual outlines of masses is not practical in clinical practice and such data would be operator-dependent. In this study, we investigated a similarity estimation scheme using a convolutional neural network (CNN) to skip such procedure and to determine data-driven similarity scores. By using CNN as feature extractor, in which extracted features were employed in determination of similarity measures with a conventional 3-layered neural network, the determined similarity measures were correlated well with the subjective ratings and the precision of retrieving diagnostically relevant images was comparable with that of the conventional method using handcrafted features. By using CNN for determination of similarity measure directly, the result was also comparable. By optimizing the network parameters, results may be further improved. The proposed method has a potential usefulness in determination of similarity measure without precise lesion outlines for retrieval of similar mass images on mammograms.

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.

Randomized clinical trials in implant therapy: relationships among methodological, statistical, clinical, paratextual features and number of citations.

PubMed

Nieri, Michele; Clauser, Carlo; Franceschi, Debora; Pagliaro, Umberto; Saletta, Daniele; Pini-Prato, Giovanpaolo

2007-08-01

The aim of the present study was to investigate the relationships among reported methodological, statistical, clinical and paratextual variables of randomized clinical trials (RCTs) in implant therapy, and their influence on subsequent research. The material consisted of the RCTs in implant therapy published through the end of the year 2000. Methodological, statistical, clinical and paratextual features of the articles were assessed and recorded. The perceived clinical relevance was subjectively evaluated by an experienced clinician on anonymous abstracts. The impact on research was measured by the number of citations found in the Science Citation Index. A new statistical technique (Structural learning of Bayesian Networks) was used to assess the relationships among the considered variables. Descriptive statistics revealed that the reported methodology and statistics of RCTs in implant therapy were defective. Follow-up of the studies was generally short. The perceived clinical relevance appeared to be associated with the objectives of the studies and with the number of published images in the original articles. The impact on research was related to the nationality of the involved institutions and to the number of published images. RCTs in implant therapy (until 2000) show important methodological and statistical flaws and may not be appropriate for guiding clinicians in their practice. The methodological and statistical quality of the studies did not appear to affect their impact on practice and research. Bayesian Networks suggest new and unexpected relationships among the methodological, statistical, clinical and paratextual features of RCTs.

Ischemic stroke lesion segmentation in multi-spectral MR images with support vector machine classifiers

NASA Astrophysics Data System (ADS)

Maier, Oskar; Wilms, Matthias; von der Gablentz, Janina; Krämer, Ulrike; Handels, Heinz

2014-03-01

Automatic segmentation of ischemic stroke lesions in magnetic resonance (MR) images is important in clinical practice and for neuroscientific trials. The key problem is to detect largely inhomogeneous regions of varying sizes, shapes and locations. We present a stroke lesion segmentation method based on local features extracted from multi-spectral MR data that are selected to model a human observer's discrimination criteria. A support vector machine classifier is trained on expert-segmented examples and then used to classify formerly unseen images. Leave-one-out cross validation on eight datasets with lesions of varying appearances is performed, showing our method to compare favourably with other published approaches in terms of accuracy and robustness. Furthermore, we compare a number of feature selectors and closely examine each feature's and MR sequence's contribution.

Brain injury patterns in hypoglycemia in neonatal encephalopathy.

PubMed

Wong, D S T; Poskitt, K J; Chau, V; Miller, S P; Roland, E; Hill, A; Tam, E W Y

2013-07-01

Low glucose values are often seen in term infants with NE, including HIE, yet the contribution of hypoglycemia to the pattern of neurologic injury remains unclear. We hypothesized that MR features of neonatal hypoglycemia could be detected, superimposed on the predominant HIE injury pattern. Term neonates (n = 179) with NE were prospectively imaged with day-3 MR studies and had glucose data available for review. The predominant imaging pattern of HIE was recorded as watershed, basal ganglia, total, focal-multifocal, or no injury. Radiologic hypoglycemia was diagnosed on the basis of selective edema in the posterior white matter, pulvinar, and anterior medial thalamic nuclei. Clinical charts were reviewed for evidence of NE, HIE, and hypoglycemia (<46 mg/dL). The predominant pattern of HIE injury imaged included 17 watershed, 25 basal ganglia, 10 total, 42 focal-multifocal, and 85 cases of no injury. A radiologic diagnosis of hypoglycemia was made in 34 cases. Compared with laboratory-confirmed hypoglycemia, MR findings had a positive predictive value of 82% and negative predictive value of 78%. Sixty (34%) neonates had clinical hypoglycemia before MR imaging. Adjusting for 5-minute Apgar scores and umbilical artery pH with logistic regression, clinical hypoglycemia was associated with a 17.6-fold higher odds of MR imaging identification (P < .001). Selective posterior white matter and pulvinar edema were most predictive of clinical hypoglycemia, and no injury (36%) or a watershed (32%) pattern of injury was seen more often in severe hypoglycemia. In term infants with NE and hypoglycemia, specific imaging features for both hypoglycemia and hypoxia-ischemia can be identified.

Fluorescence guided evaluation of photodynamic therapy as acne treatment

NASA Astrophysics Data System (ADS)

Ericson, Marica B.; Horfelt, Camilla; Cheng, Elaine; Larsson, Frida; Larko, Olle; Wennberg, Ann-Marie

2005-08-01

Photodynamic therapy (PDT) is an attractive alternative treatment for patients with acne because of its efficiency and few side effects. Propionibacterium acnes (P.acnes) are bacteria present in the skin, which produce endogenous porphyrins that act as photosensitisers. In addition, application of aminolaevulinic acid or its methyl ester (mALA) results in increased accumulation of porphyrins in the pilosebaceous units. This makes it possible to treat acne with PDT. This initial study investigates the possibility of fluorescence imaging as assessment tool in adjunct to PDT of patients with acne. Twenty-four patients with acne on the cheeks have been treated with PDT with and without mALA. Fluorescence images have been obtained before and after treatment. The clinical acne score was assessed as base line before PDT, and at every follow up visit. Additionally the amount of P.acnes was determined. The clinical evaluation showed a general improvement of acne, even though no difference between treatment with and without mALA was observed. By performing texture analysis and multivariate data analsysis on the fluorescence images, the extracted texture features were found to correlate with the corresponding clinical assessment (67%) and amount of P.acnes (72%). The analysis showed that features describing the highly fluorescent pores could be related to the clinical assessment. This result suggests that fluorescence imaging can be used as an objective assessment of acne, but further improvement of the technique is possible, for example by including colour images.

Automatic classification for mammogram backgrounds based on bi-rads complexity definition and on a multi content analysis framework

NASA Astrophysics Data System (ADS)

Wu, Jie; Besnehard, Quentin; Marchessoux, Cédric

2011-03-01

Clinical studies for the validation of new medical imaging devices require hundreds of images. An important step in creating and tuning the study protocol is the classification of images into "difficult" and "easy" cases. This consists of classifying the image based on features like the complexity of the background, the visibility of the disease (lesions). Therefore, an automatic medical background classification tool for mammograms would help for such clinical studies. This classification tool is based on a multi-content analysis framework (MCA) which was firstly developed to recognize image content of computer screen shots. With the implementation of new texture features and a defined breast density scale, the MCA framework is able to automatically classify digital mammograms with a satisfying accuracy. BI-RADS (Breast Imaging Reporting Data System) density scale is used for grouping the mammograms, which standardizes the mammography reporting terminology and assessment and recommendation categories. Selected features are input into a decision tree classification scheme in MCA framework, which is the so called "weak classifier" (any classifier with a global error rate below 50%). With the AdaBoost iteration algorithm, these "weak classifiers" are combined into a "strong classifier" (a classifier with a low global error rate) for classifying one category. The results of classification for one "strong classifier" show the good accuracy with the high true positive rates. For the four categories the results are: TP=90.38%, TN=67.88%, FP=32.12% and FN =9.62%.

Review of fluorescence guided surgery systems: identification of key performance capabilities beyond indocyanine green imaging

PubMed Central

DSouza, Alisha V.; Lin, Huiyun; Henderson, Eric R.; Samkoe, Kimberley S.; Pogue, Brian W.

2016-01-01

Abstract. There is growing interest in using fluorescence imaging instruments to guide surgery, and the leading options for open-field imaging are reviewed here. While the clinical fluorescence-guided surgery (FGS) field has been focused predominantly on indocyanine green (ICG) imaging, there is accelerated development of more specific molecular tracers. These agents should help advance new indications for which FGS presents a paradigm shift in how molecular information is provided for resection decisions. There has been a steady growth in commercially marketed FGS systems, each with their own differentiated performance characteristics and specifications. A set of desirable criteria is presented to guide the evaluation of instruments, including: (i) real-time overlay of white-light and fluorescence images, (ii) operation within ambient room lighting, (iii) nanomolar-level sensitivity, (iv) quantitative capabilities, (v) simultaneous multiple fluorophore imaging, and (vi) ergonomic utility for open surgery. In this review, United States Food and Drug Administration 510(k) cleared commercial systems and some leading premarket FGS research systems were evaluated to illustrate the continual increase in this performance feature base. Generally, the systems designed for ICG-only imaging have sufficient sensitivity to ICG, but a fraction of the other desired features listed above, with both lower sensitivity and dynamic range. In comparison, the emerging research systems targeted for use with molecular agents have unique capabilities that will be essential for successful clinical imaging studies with low-concentration agents or where superior rejection of ambient light is needed. There is no perfect imaging system, but the feature differences among them are important differentiators in their utility, as outlined in the data and tables here. PMID:27533438

Review of fluorescence guided surgery systems: identification of key performance capabilities beyond indocyanine green imaging

NASA Astrophysics Data System (ADS)

DSouza, Alisha V.; Lin, Huiyun; Henderson, Eric R.; Samkoe, Kimberley S.; Pogue, Brian W.

2016-08-01

There is growing interest in using fluorescence imaging instruments to guide surgery, and the leading options for open-field imaging are reviewed here. While the clinical fluorescence-guided surgery (FGS) field has been focused predominantly on indocyanine green (ICG) imaging, there is accelerated development of more specific molecular tracers. These agents should help advance new indications for which FGS presents a paradigm shift in how molecular information is provided for resection decisions. There has been a steady growth in commercially marketed FGS systems, each with their own differentiated performance characteristics and specifications. A set of desirable criteria is presented to guide the evaluation of instruments, including: (i) real-time overlay of white-light and fluorescence images, (ii) operation within ambient room lighting, (iii) nanomolar-level sensitivity, (iv) quantitative capabilities, (v) simultaneous multiple fluorophore imaging, and (vi) ergonomic utility for open surgery. In this review, United States Food and Drug Administration 510(k) cleared commercial systems and some leading premarket FGS research systems were evaluated to illustrate the continual increase in this performance feature base. Generally, the systems designed for ICG-only imaging have sufficient sensitivity to ICG, but a fraction of the other desired features listed above, with both lower sensitivity and dynamic range. In comparison, the emerging research systems targeted for use with molecular agents have unique capabilities that will be essential for successful clinical imaging studies with low-concentration agents or where superior rejection of ambient light is needed. There is no perfect imaging system, but the feature differences among them are important differentiators in their utility, as outlined in the data and tables here.

Serial diffusion-weighted imaging in subacute sclerosing panencephalitis.

PubMed

Kanemura, Hideaki; Aihara, Masao

2008-06-01

Subacute sclerosing panencephalitis may be associated with clinical features of frontal lobe dysfunction. We previously reported that frontal lobe volume falls significantly as clinical stage progresses, using three-dimensional magnetic resonance imaging-based brain volumetry. The hypothesis that frontal volume increases correlate with clinical improvement, however, was not tested in our previous study. Therefore, we reevaluated our patient with subacute sclerosing panencephalitis, to determine whether apparent diffusion coefficient maps can characterize the clinical course of subacute sclerosing panencephalitis. We studied an 8-year-old boy with subacute sclerosing panencephalitis, using serial diffusion-weighted imaging magnetic resonance imaging, and measured the regional apparent diffusion coefficient. The regional apparent diffusion coefficient of the frontal lobe decreased significantly with clinical progression, whereas it increased to within normal range during clinical improvements. The apparent diffusion coefficient of the other regions did not change. These results suggest that the clinical signs of patients with subacute sclerosing panencephalitis are attributable to frontal lobe dysfunction, and that apparent diffusion coefficient measurements may be useful in predicting the clinical course of subacute sclerosing panencephalitis.

Radiomics analysis of DWI data to identify the rectal cancer patients qualified for local excision after neoadjuvant chemoradiotherapy

NASA Astrophysics Data System (ADS)

Tang, Zhenchao; Liu, Zhenyu; Zhang, Xiaoyan; Shi, Yanjie; Wang, Shou; Fang, Mengjie; Sun, Yingshi; Dong, Enqing; Tian, Jie

2018-02-01

The Locally advanced rectal cancer (LARC) patients were routinely treated with neoadjuvant chemoradiotherapy (CRT) firstly and received total excision afterwards. While, the LARC patients might relieve to T1N0M0/T0N0M0 stage after the CRT, which would enable the patients be qualified for local excision. However, accurate pathological TNM stage could only be obtained by the pathological examination after surgery. We aimed to conduct a Radiomics analysis of Diffusion weighted Imaging (DWI) data to identify the patients in T1N0M0/T0N0M0 stages before surgery, in hope of providing clinical surgery decision support. 223 routinely treated LARC patients in Beijing Cancer Hospital were enrolled in current study. DWI data and clinical characteristics were collected after CRT. According to the pathological TNM stage, the patients of T1N0M0 and T0N0M0 stages were labelled as 1 and the other patients were labelled as 0. The first 123 patients in chronological order were used as training set, and the rest patients as validation set. 563 image features extracted from the DWI data and clinical characteristics were used as features. Two-sample T test was conducted to pre-select the top 50% discriminating features. Least absolute shrinkage and selection operator (Lasso)-Logistic regression model was conducted to further select features and construct the classification model. Based on the 14 selected image features, the area under the Receiver Operating Characteristic (ROC) curve (AUC) of 0.8781, classification Accuracy (ACC) of 0.8432 were achieved in the training set. In the validation set, AUC of 0.8707, ACC (ACC) of 0.84 were observed.

Imaging of musculoskeletal manifestations in sickle cell disease patients.

PubMed

Kosaraju, Vijaya; Harwani, Alok; Partovi, Sasan; Bhojwani, Nicholas; Garg, Vasant; Ayyappan, Sabarish; Kosmas, Christos; Robbin, Mark

2017-05-01

Sickle cell disease (SCD) is a hereditary red cell disorder with clinical manifestations secondary to sickling or crescent-shaped distortion of the red blood cells. Major clinical manifestations of SCD include haemolytic anaemia and vaso-occlusive phenomena resulting in ischaemic tissue injury and organ damage. Chronic sequelae of the anaemia and vaso-occlusive processes involving the musculoskeletal system include complications related to extramedullary haematopoiesis, osteonecrosis, myonecrosis and osteomyelitis. Sickle cell bone disease is one of the commonest clinical presentations. Awareness and knowledge of the imaging features related to these complications are essential for early diagnosis and prompt management. In this article, the pathophysiology and key imaging findings related to these complications are reviewed.

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.

[Clinical, pathological and imaging features of primary pelvic Ewing's sarcoma].

PubMed

Liu, J; Chen, Y; Ling, X L; Gong, Y; Ding, J P; Zhang, Z K; Wang, Y J

2016-07-19

To explore the clinical, pathological and imaging features of Ewing's sarcoma in pelvis and to improve knowledge and diagnosis of the disease. A retrospective analysis of the clinical, pathological and imaging data of pathologically confirmed 13 cases of Ewing's sarcoma in pelvis was carried out between May 2008 and March 2016 in the Affiliated Hospital of Hangzhou Normal University, the Third Hospital of Hebei Medical University and the Second Hospital of Hebei Medical University. The median age 13 cases of pelvic primary Ewing's sarcoma was 17 years old.The X-ray and CT imagings showed osteolytic and mixed bone destruction, CT showed mixed type in 10 cases, 8 cases of bone tumors as a flocculent, 10 cases of bone expansion failure, 10 cases of periosteal reaction, the layered 5 cases, radial in 5 cases.Thirteen cases showed soft tissue mass, soft tissue mass was equal or slightly lower density.Four cases showed heterogeneous contrast enhancement.The lesions showed low signal in T1WI and mixed high signal in T2WI of magnetic resonance imaging(MRI). The boundary of the lesions were obscure, and 5 cases had patchy necrosis area, and 9 cases had incomplete false capsule, surrounding soft tissue was violated.Four cases showed heterogeneous contrast enhancement after MRI enhancement scan. The age of onset of Ewing's sarcoma of the pelvis is more concentrated in about 15 years.The imaging feaures are mixed bone destruction and more bone is swelling and permeability damage, soft tissue mass is larger, bone tumor is cloudy or acicular, periosteal reaction in a layered and radial, most cases show that the false envelope is not complete.Combined with clinical and imaging examination, the diagnosis of the disease can be made.

Real-time clinically oriented array-based in vivo combined photoacoustic and power Doppler imaging

NASA Astrophysics Data System (ADS)

Harrison, Tyler; Jeffery, Dean; Wiebe, Edward; Zemp, Roger J.

2014-03-01

Photoacoustic imaging has great potential for identifying vascular regions for clinical imaging. In addition to assessing angiogenesis in cancers, there are many other disease processes that result in increased vascularity that present novel targets for photoacoustic imaging. Doppler imaging can provide good localization of large vessels, but poor imaging of small or low flow speed vessels and is susceptible to motion artifacts. Photoacoustic imaging can provide visualization of small vessels, but due to the filtering effects of ultrasound transducers, only shows the edges of large vessels. Thus, we have combined photoacoustic imaging with ultrasound power Doppler to provide contrast agent- free vascular imaging. We use a research-oriented ultrasound array system to provide interlaced ultrasound, Doppler, and photoacoustic imaging. This system features realtime display of all three modalities with adjustable persistence, rejection, and compression. For ease of use in a clinical setting, display of each mode can be disabled. We verify the ability of this system to identify vessels with varying flow speeds using receiver operating characteristic curves, and find that as flow speed falls, photoacoustic imaging becomes a much better method for identifying blood vessels. We also present several in vivo images of the thyroid and several synovial joints to assess the practicality of this imaging for clinical applications.

Tracking scanning laser ophthalmoscope (TSLO)

NASA Astrophysics Data System (ADS)

Hammer, Daniel X.; Ferguson, R. Daniel; Magill, John C.; White, Michael A.; Elsner, Ann E.; Webb, Robert H.

2003-07-01

The effectiveness of image stabilization with a retinal tracker in a multi-function, compact scanning laser ophthalmoscope (TSLO) was demonstrated in initial human subject tests. The retinal tracking system uses a confocal reflectometer with a closed loop optical servo system to lock onto features in the fundus. The system is modular to allow configuration for many research and clinical applications, including hyperspectral imaging, multifocal electroretinography (MFERG), perimetry, quantification of macular and photo-pigmentation, imaging of neovascularization and other subretinal structures (drusen, hyper-, and hypo-pigmentation), and endogenous fluorescence imaging. Optical hardware features include dual wavelength imaging and detection, integrated monochromator, higher-order motion control, and a stimulus source. The system software consists of a real-time feedback control algorithm and a user interface. Software enhancements include automatic bias correction, asymmetric feature tracking, image averaging, automatic track re-lock, and acquisition and logging of uncompressed images and video files. Normal adult subjects were tested without mydriasis to optimize the tracking instrumentation and to characterize imaging performance. The retinal tracking system achieves a bandwidth of greater than 1 kHz, which permits tracking at rates that greatly exceed the maximum rate of motion of the human eye. The TSLO stabilized images in all test subjects during ordinary saccades up to 500 deg/sec with an inter-frame accuracy better than 0.05 deg. Feature lock was maintained for minutes despite subject eye blinking. Successful frame averaging allowed image acquisition with decreased noise in low-light applications. The retinal tracking system significantly enhances the imaging capabilities of the scanning laser ophthalmoscope.

The effects of TIS and MI on the texture features in ultrasonic fatty liver images

NASA Astrophysics Data System (ADS)

Zhao, Yuan; Cheng, Xinyao; Ding, Mingyue

2017-03-01

Nonalcoholic fatty liver disease (NAFLD) is prevalent and has a worldwide distribution now. Although ultrasound imaging technology has been deemed as the common method to diagnose fatty liver, it is not able to detect NAFLD in its early stage and limited by the diagnostic instruments and some other factors. B-scan image feature extraction of fatty liver can assist doctor to analyze the patient's situation and enhance the efficiency and accuracy of clinical diagnoses. However, some uncertain factors in ultrasonic diagnoses are often been ignored during feature extraction. In this study, the nonalcoholic fatty liver rabbit model was made and its liver ultrasound images were collected by setting different Thermal index of soft tissue (TIS) and mechanical index (MI). Then, texture features were calculated based on gray level co-occurrence matrix (GLCM) and the impacts of TIS and MI on these features were analyzed and discussed. Furthermore, the receiver operating characteristic (ROC) curve was used to evaluate whether each feature was effective or not when TIS and MI were given. The results showed that TIS and MI do affect the features extracted from the healthy liver, while the texture features of fatty liver are relatively stable. In addition, TIS set to 0.3 and MI equal to 0.9 might be a better choice when using a computer aided diagnosis (CAD) method for fatty liver recognition.

A data model and database for high-resolution pathology analytical image informatics.

PubMed

Wang, Fusheng; Kong, Jun; Cooper, Lee; Pan, Tony; Kurc, Tahsin; Chen, Wenjin; Sharma, Ashish; Niedermayr, Cristobal; Oh, Tae W; Brat, Daniel; Farris, Alton B; Foran, David J; Saltz, Joel

2011-01-01

The systematic analysis of imaged pathology specimens often results in a vast amount of morphological information at both the cellular and sub-cellular scales. While microscopy scanners and computerized analysis are capable of capturing and analyzing data rapidly, microscopy image data remain underutilized in research and clinical settings. One major obstacle which tends to reduce wider adoption of these new technologies throughout the clinical and scientific communities is the challenge of managing, querying, and integrating the vast amounts of data resulting from the analysis of large digital pathology datasets. This paper presents a data model, which addresses these challenges, and demonstrates its implementation in a relational database system. This paper describes a data model, referred to as Pathology Analytic Imaging Standards (PAIS), and a database implementation, which are designed to support the data management and query requirements of detailed characterization of micro-anatomic morphology through many interrelated analysis pipelines on whole-slide images and tissue microarrays (TMAs). (1) Development of a data model capable of efficiently representing and storing virtual slide related image, annotation, markup, and feature information. (2) Development of a database, based on the data model, capable of supporting queries for data retrieval based on analysis and image metadata, queries for comparison of results from different analyses, and spatial queries on segmented regions, features, and classified objects. The work described in this paper is motivated by the challenges associated with characterization of micro-scale features for comparative and correlative analyses involving whole-slides tissue images and TMAs. Technologies for digitizing tissues have advanced significantly in the past decade. Slide scanners are capable of producing high-magnification, high-resolution images from whole slides and TMAs within several minutes. Hence, it is becoming increasingly feasible for basic, clinical, and translational research studies to produce thousands of whole-slide images. Systematic analysis of these large datasets requires efficient data management support for representing and indexing results from hundreds of interrelated analyses generating very large volumes of quantifications such as shape and texture and of classifications of the quantified features. We have designed a data model and a database to address the data management requirements of detailed characterization of micro-anatomic morphology through many interrelated analysis pipelines. The data model represents virtual slide related image, annotation, markup and feature information. The database supports a wide range of metadata and spatial queries on images, annotations, markups, and features. We currently have three databases running on a Dell PowerEdge T410 server with CentOS 5.5 Linux operating system. The database server is IBM DB2 Enterprise Edition 9.7.2. The set of databases consists of 1) a TMA database containing image analysis results from 4740 cases of breast cancer, with 641 MB storage size; 2) an algorithm validation database, which stores markups and annotations from two segmentation algorithms and two parameter sets on 18 selected slides, with 66 GB storage size; and 3) an in silico brain tumor study database comprising results from 307 TCGA slides, with 365 GB storage size. The latter two databases also contain human-generated annotations and markups for regions and nuclei. Modeling and managing pathology image analysis results in a database provide immediate benefits on the value and usability of data in a research study. The database provides powerful query capabilities, which are otherwise difficult or cumbersome to support by other approaches such as programming languages. Standardized, semantic annotated data representation and interfaces also make it possible to more efficiently share image data and analysis results.

Sonographic Findings in Necrotizing Fasciitis: Two Ends of the Spectrum.

PubMed

Shyy, William; Knight, Roneesha S; Goldstein, Ruth; Isaacs, Eric D; Teismann, Nathan A

2016-10-01

Necrotizing fasciitis is a rare but serious disease, and early diagnosis is essential to reducing its substantial morbidity and mortality. The 2 cases presented show that the key clinical and radiographic features of necrotizing fasciitis exist along a continuum of severity at initial presentation; thus, this diagnosis should not be prematurely ruled out in cases that do not show the dramatic features familiar to most clinicians. Although computed tomography and magnetic resonance imaging are considered the most effective imaging modalities, the cases described here illustrate how sonography should be recommended as an initial imaging test to make a rapid diagnosis and initiate therapy.

A Novel Clinically Translatable Fluorescent Nanoparticle for Targeted Molecular Imaging of Tumors in Living Subjects

PubMed Central

Gao, Jinhao; Chen, Kai; Luong, Richard; Bouley, Donna M.; Mao, Hua; Qiao, Tiecheng; Gambhir, Sanjiv S.; Cheng, Zhen

2011-01-01

The use of quantum dots (QDs) in biomedical research has grown tremendously, yet successful examples of clinical applications are absent due to many clinical concerns. Here, we report on a new type of stable and biocompatible dendron-coated InP/ZnS core/shell QDs as a clinically translatable nanoprobe for molecular imaging applications. The QDs (QD710-Dendron) were demonstrated to hold several significant features: near-infrared (NIR) emission, high stability in biological media, suitable size with possible renal clearance and ability of extravasation. More importantly, a pilot mouse toxicity study confirmed that QD710-Dendron lacks significant toxicity at the doses tested. The acute tumor uptake of QD710-Dendron resulted in good contrast from the surrounding non-tumorous tissues, indicating the possibility of passive targeting of the QDs. The highly specific targeting of QD710-Dendron-RGD2 to integrin αvβ3–positive tumor cells resulted in high tumor uptake and long retention of the nanoprobe at tumor sites. In summary, QD710-Dendron and RGD modified nanoparticles demonstrate small size, high stability, biocompatibility, favorable in vivo pharmacokinetics, and successful tumor imaging properties. These features satisfy the requirements for clinical translation and should promote efforts to further investigate the possibility of using QD710-Dendron based nanoprobes in the clinical setting in the near future. PMID:22172022

A novel clinically translatable fluorescent nanoparticle for targeted molecular imaging of tumors in living subjects.

PubMed

Gao, Jinhao; Chen, Kai; Luong, Richard; Bouley, Donna M; Mao, Hua; Qiao, Tiecheng; Gambhir, Sanjiv S; Cheng, Zhen

2012-01-11

The use of quantum dots (QDs) in biomedical research has grown tremendously, yet successful examples of clinical applications are absent due to many clinical concerns. Here, we report on a new type of stable and biocompatible dendron-coated InP/ZnS core/shell QD as a clinically translatable nanoprobe for molecular imaging applications. The QDs (QD710-Dendron) were demonstrated to hold several significant features: near-infrared (NIR) emission, high stability in biological media, suitable size with possible renal clearance, and ability of extravasation. More importantly, a pilot mouse toxicity study confirmed that QD710-Dendron lacks significant toxicity at the doses tested. The acute tumor uptake of QD710-Dendron resulted in good contrast from the surrounding nontumorous tissues, indicating the possibility of passive targeting of the QDs. The highly specific targeting of QD710-Dendron-RGD(2) to integrin α(v)β(3)-positive tumor cells resulted in high tumor uptake and long retention of the nanoprobe at tumor sites. In summary, QD710-Dendron and RGD-modified nanoparticles demonstrate small size, high stability, biocompatibility, favorable in vivo pharmacokinetics, and successful tumor imaging properties. These features satisfy the requirements for clinical translation and should promote efforts to further investigate the possibility of using QD710-Dendron-based nanoprobes in the clinical setting in the near future. © 2011 American Chemical Society

Characterization of coronary plaque regions in intravascular ultrasound images using a hybrid ensemble classifier.

PubMed

Hwang, Yoo Na; Lee, Ju Hwan; Kim, Ga Young; Shin, Eun Seok; Kim, Sung Min

2018-01-01

The purpose of this study was to propose a hybrid ensemble classifier to characterize coronary plaque regions in intravascular ultrasound (IVUS) images. Pixels were allocated to one of four tissues (fibrous tissue (FT), fibro-fatty tissue (FFT), necrotic core (NC), and dense calcium (DC)) through processes of border segmentation, feature extraction, feature selection, and classification. Grayscale IVUS images and their corresponding virtual histology images were acquired from 11 patients with known or suspected coronary artery disease using 20 MHz catheter. A total of 102 hybrid textural features including first order statistics (FOS), gray level co-occurrence matrix (GLCM), extended gray level run-length matrix (GLRLM), Laws, local binary pattern (LBP), intensity, and discrete wavelet features (DWF) were extracted from IVUS images. To select optimal feature sets, genetic algorithm was implemented. A hybrid ensemble classifier based on histogram and texture information was then used for plaque characterization in this study. The optimal feature set was used as input of this ensemble classifier. After tissue characterization, parameters including sensitivity, specificity, and accuracy were calculated to validate the proposed approach. A ten-fold cross validation approach was used to determine the statistical significance of the proposed method. Our experimental results showed that the proposed method had reliable performance for tissue characterization in IVUS images. The hybrid ensemble classification method outperformed other existing methods by achieving characterization accuracy of 81% for FFT and 75% for NC. In addition, this study showed that Laws features (SSV and SAV) were key indicators for coronary tissue characterization. The proposed method had high clinical applicability for image-based tissue characterization. Copyright © 2017 Elsevier B.V. All rights reserved.

Classification of brain tumors using texture based analysis of T1-post contrast MR scans in a preclinical model

NASA Astrophysics Data System (ADS)

Tang, Tien T.; Zawaski, Janice A.; Francis, Kathleen N.; Qutub, Amina A.; Gaber, M. Waleed

2018-02-01

Accurate diagnosis of tumor type is vital for effective treatment planning. Diagnosis relies heavily on tumor biopsies and other clinical factors. However, biopsies do not fully capture the tumor's heterogeneity due to sampling bias and are only performed if the tumor is accessible. An alternative approach is to use features derived from routine diagnostic imaging such as magnetic resonance (MR) imaging. In this study we aim to establish the use of quantitative image features to classify brain tumors and extend the use of MR images beyond tumor detection and localization. To control for interscanner, acquisition and reconstruction protocol variations, the established workflow was performed in a preclinical model. Using glioma (U87 and GL261) and medulloblastoma (Daoy) models, T1-weighted post contrast scans were acquired at different time points post-implant. The tumor regions at the center, middle, and peripheral were analyzed using in-house software to extract 32 different image features consisting of first and second order features. The extracted features were used to construct a decision tree, which could predict tumor type with 10-fold cross-validation. Results from the final classification model demonstrated that middle tumor region had the highest overall accuracy at 79%, while the AUC accuracy was over 90% for GL261 and U87 tumors. Our analysis further identified image features that were unique to certain tumor region, although GL261 tumors were more homogenous with no significant differences between the central and peripheral tumor regions. In conclusion our study shows that texture features derived from MR scans can be used to classify tumor type with high success rates. Furthermore, the algorithm we have developed can be implemented with any imaging datasets and may be applicable to multiple tumor types to determine diagnosis.

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.

A Feature-based Approach to Big Data Analysis of Medical Images

PubMed Central

Toews, Matthew; Wachinger, Christian; Estepar, Raul San Jose; Wells, William M.

2015-01-01

This paper proposes an inference method well-suited to large sets of medical images. The method is based upon a framework where distinctive 3D scale-invariant features are indexed efficiently to identify approximate nearest-neighbor (NN) feature matches in O(log N) computational complexity in the number of images N. It thus scales well to large data sets, in contrast to methods based on pair-wise image registration or feature matching requiring O(N) complexity. Our theoretical contribution is a density estimator based on a generative model that generalizes kernel density estimation and K-nearest neighbor (KNN) methods. The estimator can be used for on-the-fly queries, without requiring explicit parametric models or an off-line training phase. The method is validated on a large multi-site data set of 95,000,000 features extracted from 19,000 lung CT scans. Subject-level classification identifies all images of the same subjects across the entire data set despite deformation due to breathing state, including unintentional duplicate scans. State-of-the-art performance is achieved in predicting chronic pulmonary obstructive disorder (COPD) severity across the 5-category GOLD clinical rating, with an accuracy of 89% if both exact and one-off predictions are considered correct. PMID:26221685

A Feature-Based Approach to Big Data Analysis of Medical Images.

PubMed

Toews, Matthew; Wachinger, Christian; Estepar, Raul San Jose; Wells, William M

2015-01-01

This paper proposes an inference method well-suited to large sets of medical images. The method is based upon a framework where distinctive 3D scale-invariant features are indexed efficiently to identify approximate nearest-neighbor (NN) feature matches-in O (log N) computational complexity in the number of images N. It thus scales well to large data sets, in contrast to methods based on pair-wise image registration or feature matching requiring O(N) complexity. Our theoretical contribution is a density estimator based on a generative model that generalizes kernel density estimation and K-nearest neighbor (KNN) methods.. The estimator can be used for on-the-fly queries, without requiring explicit parametric models or an off-line training phase. The method is validated on a large multi-site data set of 95,000,000 features extracted from 19,000 lung CT scans. Subject-level classification identifies all images of the same subjects across the entire data set despite deformation due to breathing state, including unintentional duplicate scans. State-of-the-art performance is achieved in predicting chronic pulmonary obstructive disorder (COPD) severity across the 5-category GOLD clinical rating, with an accuracy of 89% if both exact and one-off predictions are considered correct.

Identification of lesion images from gastrointestinal endoscope based on feature extraction of combinational methods with and without learning process.

PubMed

Liu, Ding-Yun; Gan, Tao; Rao, Ni-Ni; Xing, Yao-Wen; Zheng, Jie; Li, Sang; Luo, Cheng-Si; Zhou, Zhong-Jun; Wan, Yong-Li

2016-08-01

The gastrointestinal endoscopy in this study refers to conventional gastroscopy and wireless capsule endoscopy (WCE). Both of these techniques produce a large number of images in each diagnosis. The lesion detection done by hand from the images above is time consuming and inaccurate. This study designed a new computer-aided method to detect lesion images. We initially designed an algorithm named joint diagonalisation principal component analysis (JDPCA), in which there are no approximation, iteration or inverting procedures. Thus, JDPCA has a low computational complexity and is suitable for dimension reduction of the gastrointestinal endoscopic images. Then, a novel image feature extraction method was established through combining the algorithm of machine learning based on JDPCA and conventional feature extraction algorithm without learning. Finally, a new computer-aided method is proposed to identify the gastrointestinal endoscopic images containing lesions. The clinical data of gastroscopic images and WCE images containing the lesions of early upper digestive tract cancer and small intestinal bleeding, which consist of 1330 images from 291 patients totally, were used to confirm the validation of the proposed method. The experimental results shows that, for the detection of early oesophageal cancer images, early gastric cancer images and small intestinal bleeding images, the mean values of accuracy of the proposed method were 90.75%, 90.75% and 94.34%, with the standard deviations (SDs) of 0.0426, 0.0334 and 0.0235, respectively. The areas under the curves (AUCs) were 0.9471, 0.9532 and 0.9776, with the SDs of 0.0296, 0.0285 and 0.0172, respectively. Compared with the traditional related methods, our method showed a better performance. It may therefore provide worthwhile guidance for improving the efficiency and accuracy of gastrointestinal disease diagnosis and is a good prospect for clinical application. Copyright © 2016 Elsevier B.V. All rights reserved.

Evaluation of web-based annotation of ophthalmic images for multicentric clinical trials.

PubMed

Chalam, K V; Jain, P; Shah, V A; Shah, Gaurav Y

2006-06-01

An Internet browser-based annotation system can be used to identify and describe features in digitalized retinal images, in multicentric clinical trials, in real time. In this web-based annotation system, the user employs a mouse to draw and create annotations on a transparent layer, that encapsulates the observations and interpretations of a specific image. Multiple annotation layers may be overlaid on a single image. These layers may correspond to annotations by different users on the same image or annotations of a temporal sequence of images of a disease process, over a period of time. In addition, geometrical properties of annotated figures may be computed and measured. The annotations are stored in a central repository database on a server, which can be retrieved by multiple users in real time. This system facilitates objective evaluation of digital images and comparison of double-blind readings of digital photographs, with an identifiable audit trail. Annotation of ophthalmic images allowed clinically feasible and useful interpretation to track properties of an area of fundus pathology. This provided an objective method to monitor properties of pathologies over time, an essential component of multicentric clinical trials. The annotation system also allowed users to view stereoscopic images that are stereo pairs. This web-based annotation system is useful and valuable in monitoring patient care, in multicentric clinical trials, telemedicine, teaching and routine clinical settings.

Optimization of breast mass classification using sequential forward floating selection (SFFS) and a support vector machine (SVM) model

PubMed Central

Tan, Maxine; Pu, Jiantao; Zheng, Bin

2014-01-01

Purpose: Improving radiologists’ performance in classification between malignant and benign breast lesions is important to increase cancer detection sensitivity and reduce false-positive recalls. For this purpose, developing computer-aided diagnosis (CAD) schemes has been attracting research interest in recent years. In this study, we investigated a new feature selection method for the task of breast mass classification. Methods: We initially computed 181 image features based on mass shape, spiculation, contrast, presence of fat or calcifications, texture, isodensity, and other morphological features. From this large image feature pool, we used a sequential forward floating selection (SFFS)-based feature selection method to select relevant features, and analyzed their performance using a support vector machine (SVM) model trained for the classification task. On a database of 600 benign and 600 malignant mass regions of interest (ROIs), we performed the study using a ten-fold cross-validation method. Feature selection and optimization of the SVM parameters were conducted on the training subsets only. Results: The area under the receiver operating characteristic curve (AUC) = 0.805±0.012 was obtained for the classification task. The results also showed that the most frequently-selected features by the SFFS-based algorithm in 10-fold iterations were those related to mass shape, isodensity and presence of fat, which are consistent with the image features frequently used by radiologists in the clinical environment for mass classification. The study also indicated that accurately computing mass spiculation features from the projection mammograms was difficult, and failed to perform well for the mass classification task due to tissue overlap within the benign mass regions. Conclusions: In conclusion, this comprehensive feature analysis study provided new and valuable information for optimizing computerized mass classification schemes that may have potential to be useful as a “second reader” in future clinical practice. PMID:24664267

TU-CD-BRB-09: Prediction of Chemo-Radiation Outcome for Rectal Cancer Based On Radiomics of Tumor Clinical Characteristics and Multi-Parametric MRI

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

Nie, K; Yue, N; Shi, L

2015-06-15

Purpose: To evaluate the tumor clinical characteristics and quantitative multi-parametric MR imaging features for prediction of response to chemo-radiation treatment (CRT) in locally advanced rectal cancer (LARC). Methods: Forty-three consecutive patients (59.7±6.9 years, from 09/2013 – 06/2014) receiving neoadjuvant CRT followed by surgery were enrolled. All underwent MRI including anatomical T1/T2, Dynamic Contrast Enhanced (DCE)-MRI and Diffusion-Weighted MRI (DWI) prior to the treatment. A total of 151 quantitative features, including morphology/Gray Level Co-occurrence Matrix (GLCM) texture from T1/T2, enhancement kinetics and the voxelized distribution from DCE-MRI, apparent diffusion coefficient (ADC) from DWI, along with clinical information (carcinoembryonic antigen CEA level,more » TNM staging etc.), were extracted for each patient. Response groups were separated based on down-staging, good response and pathological complete response (pCR) status. Logistic regression analysis (LRA) was used to select the best predictors to classify different groups and the predictive performance were calculated using receiver operating characteristic (ROC) analysis. Results: Individual imaging category or clinical charateristics might yield certain level of power in assessing the response. However, the combined model outperformed than any category alone in prediction. With selected features as Volume, GLCM AutoCorrelation (T2), MaxEnhancementProbability (DCE-MRI), and MeanADC (DWI), the down-staging prediciton accuracy (area under the ROC curve, AUC) could be 0.95, better than individual tumor metrics with AUC from 0.53–0.85. While for the pCR prediction, the best set included CEA (clinical charateristics), Homogeneity (DCE-MRI) and MeanADC (DWI) with an AUC of 0.89, more favorable compared to conventional tumor metrics with an AUC ranging from 0.511–0.79. Conclusion: Through a systematic analysis of multi-parametric MR imaging features, we are able to build models with improved predictive value over conventional imaging or clinical metrics. This is encouraging, suggesting the wealth of imaging radiomics should be further explored to help tailor the treatment into the era of personalized medicine. This work is supported by the National Science Foundation of China (NSFC Grant No. 81201091), National High Technology Research and Development Program of China (863 program, Grant No. 2015AA020917), and Fund Project for Excellent Abroad Scholar Personnel in Science and Technology.« less

Urinary bladder cancer T-staging from T2-weighted MR images using an optimal biomarker approach

NASA Astrophysics Data System (ADS)

Wang, Chuang; Udupa, Jayaram K.; Tong, Yubing; Chen, Jerry; Venigalla, Sriram; Odhner, Dewey; Guzzo, Thomas J.; Christodouleas, John; Torigian, Drew A.

2018-02-01

Magnetic resonance imaging (MRI) is often used in clinical practice to stage patients with bladder cancer to help plan treatment. However, qualitative assessment of MR images is prone to inaccuracies, adversely affecting patient outcomes. In this paper, T2-weighted MR image-based quantitative features were extracted from the bladder wall in 65 patients with bladder cancer to classify them into two primary tumor (T) stage groups: group 1 - T stage < T2, with primary tumor locally confined to the bladder, and group 2 - T stage < T2, with primary tumor locally extending beyond the bladder. The bladder was divided into 8 sectors in the axial plane, where each sector has a corresponding reference standard T stage that is based on expert radiology qualitative MR image review and histopathologic results. The performance of the classification for correct assignment of T stage grouping was then evaluated at both the patient level and the sector level. Each bladder sector was divided into 3 shells (inner, middle, and outer), and 15,834 features including intensity features and texture features from local binary pattern and gray-level co-occurrence matrix were extracted from the 3 shells of each sector. An optimal feature set was selected from all features using an optimal biomarker approach. Nine optimal biomarker features were derived based on texture properties from the middle shell, with an area under the ROC curve of AUC value at the sector and patient level of 0.813 and 0.806, respectively.

A practical salient region feature based 3D multi-modality registration method for medical images

NASA Astrophysics Data System (ADS)

Hahn, Dieter A.; Wolz, Gabriele; Sun, Yiyong; Hornegger, Joachim; Sauer, Frank; Kuwert, Torsten; Xu, Chenyang

2006-03-01

We present a novel representation of 3D salient region features and its integration into a hybrid rigid-body registration framework. We adopt scale, translation and rotation invariance properties of those intrinsic 3D features to estimate a transform between underlying mono- or multi-modal 3D medical images. Our method combines advantageous aspects of both feature- and intensity-based approaches and consists of three steps: an automatic extraction of a set of 3D salient region features on each image, a robust estimation of correspondences and their sub-pixel accurate refinement with outliers elimination. We propose a region-growing based approach for the extraction of 3D salient region features, a solution to the problem of feature clustering and a reduction of the correspondence search space complexity. Results of the developed algorithm are presented for both mono- and multi-modal intra-patient 3D image pairs (CT, PET and SPECT) that have been acquired for change detection, tumor localization, and time based intra-person studies. The accuracy of the method is clinically evaluated by a medical expert with an approach that measures the distance between a set of selected corresponding points consisting of both anatomical and functional structures or lesion sites. This demonstrates the robustness of the proposed method to image overlap, missing information and artefacts. We conclude by discussing potential medical applications and possibilities for integration into a non-rigid registration framework.

A computer-aided diagnosis system of nuclear cataract.

PubMed

Li, Huiqi; Lim, Joo Hwee; Liu, Jiang; Mitchell, Paul; Tan, Ava Grace; Wang, Jie Jin; Wong, Tien Yin

2010-07-01

Cataracts are the leading cause of blindness worldwide, and nuclear cataract is the most common form of cataract. An algorithm for automatic diagnosis of nuclear cataract is investigated in this paper. Nuclear cataract is graded according to the severity of opacity using slit lamp lens images. Anatomical structure in the lens image is detected using a modified active shape model. On the basis of the anatomical landmark, local features are extracted according to clinical grading protocol. Support vector machine regression is employed for grade prediction. This is the first time that the nucleus region can be detected automatically in slit lamp images. The system is validated using clinical images and clinical ground truth on >5000 images. The success rate of structure detection is 95% and the average grading difference is 0.36 on a 5.0 scale. The automatic diagnosis system can improve the grading objectivity and potentially be used in clinics and population studies to save the workload of ophthalmologists.

Machine Learning methods for Quantitative Radiomic Biomarkers.

PubMed

Parmar, Chintan; Grossmann, Patrick; Bussink, Johan; Lambin, Philippe; Aerts, Hugo J W L

2015-08-17

Radiomics extracts and mines large number of medical imaging features quantifying tumor phenotypic characteristics. Highly accurate and reliable machine-learning approaches can drive the success of radiomic applications in clinical care. In this radiomic study, fourteen feature selection methods and twelve classification methods were examined in terms of their performance and stability for predicting overall survival. A total of 440 radiomic features were extracted from pre-treatment computed tomography (CT) images of 464 lung cancer patients. To ensure the unbiased evaluation of different machine-learning methods, publicly available implementations along with reported parameter configurations were used. Furthermore, we used two independent radiomic cohorts for training (n = 310 patients) and validation (n = 154 patients). We identified that Wilcoxon test based feature selection method WLCX (stability = 0.84 ± 0.05, AUC = 0.65 ± 0.02) and a classification method random forest RF (RSD = 3.52%, AUC = 0.66 ± 0.03) had highest prognostic performance with high stability against data perturbation. Our variability analysis indicated that the choice of classification method is the most dominant source of performance variation (34.21% of total variance). Identification of optimal machine-learning methods for radiomic applications is a crucial step towards stable and clinically relevant radiomic biomarkers, providing a non-invasive way of quantifying and monitoring tumor-phenotypic characteristics in clinical practice.

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.

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.

Detection of protruding lesion in wireless capsule endoscopy videos of small intestine

NASA Astrophysics Data System (ADS)

Wang, Chengliang; Luo, Zhuo; Liu, Xiaoqi; Bai, Jianying; Liao, Guobin

2018-02-01

Wireless capsule endoscopy (WCE) is a developed revolutionary technology with important clinical benefits. But the huge image data brings a heavy burden to the doctors for locating and diagnosing the lesion images. In this paper, a novel and efficient approach is proposed to help clinicians to detect protruding lesion images in small intestine. First, since there are many possible disturbances such as air bubbles and so on in WCE video frames, which add the difficulty of efficient feature extraction, the color-saliency region detection (CSD) method is developed for extracting the potentially saliency region of interest (SROI). Second, a novel color channels modelling of local binary pattern operator (CCLBP) is proposed to describe WCE images, which combines grayscale and color angle. The CCLBP feature is more robust to variation of illumination and more discriminative for classification. Moreover, support vector machine (SVM) classifier with CCLBP feature is utilized to detect protruding lesion images. Experimental results on real WCE images demonstrate that proposed method has higher accuracy on protruding lesion detection than some art-of-state methods.

Imaging mass spectrometry data reduction: automated feature identification and extraction.

PubMed

McDonnell, Liam A; van Remoortere, Alexandra; de Velde, Nico; van Zeijl, René J M; Deelder, André M

2010-12-01

Imaging MS now enables the parallel analysis of hundreds of biomolecules, spanning multiple molecular classes, which allows tissues to be described by their molecular content and distribution. When combined with advanced data analysis routines, tissues can be analyzed and classified based solely on their molecular content. Such molecular histology techniques have been used to distinguish regions with differential molecular signatures that could not be distinguished using established histologic tools. However, its potential to provide an independent, complementary analysis of clinical tissues has been limited by the very large file sizes and large number of discrete variables associated with imaging MS experiments. Here we demonstrate data reduction tools, based on automated feature identification and extraction, for peptide, protein, and lipid imaging MS, using multiple imaging MS technologies, that reduce data loads and the number of variables by >100×, and that highlight highly-localized features that can be missed using standard data analysis strategies. It is then demonstrated how these capabilities enable multivariate analysis on large imaging MS datasets spanning multiple tissues. Copyright © 2010 American Society for Mass Spectrometry. Published by Elsevier Inc. All rights reserved.

Predicting diagnostic error in radiology via eye-tracking and image analytics: Preliminary investigation in mammography

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

Voisin, Sophie; Tourassi, Georgia D.; Pinto, Frank

2013-10-15

Purpose: The primary aim of the present study was to test the feasibility of predicting diagnostic errors in mammography by merging radiologists’ gaze behavior and image characteristics. A secondary aim was to investigate group-based and personalized predictive models for radiologists of variable experience levels.Methods: The study was performed for the clinical task of assessing the likelihood of malignancy of mammographic masses. Eye-tracking data and diagnostic decisions for 40 cases were acquired from four Radiology residents and two breast imaging experts as part of an IRB-approved pilot study. Gaze behavior features were extracted from the eye-tracking data. Computer-generated and BIRADS imagesmore » features were extracted from the images. Finally, machine learning algorithms were used to merge gaze and image features for predicting human error. Feature selection was thoroughly explored to determine the relative contribution of the various features. Group-based and personalized user modeling was also investigated.Results: Machine learning can be used to predict diagnostic error by merging gaze behavior characteristics from the radiologist and textural characteristics from the image under review. Leveraging data collected from multiple readers produced a reasonable group model [area under the ROC curve (AUC) = 0.792 ± 0.030]. Personalized user modeling was far more accurate for the more experienced readers (AUC = 0.837 ± 0.029) than for the less experienced ones (AUC = 0.667 ± 0.099). The best performing group-based and personalized predictive models involved combinations of both gaze and image features.Conclusions: Diagnostic errors in mammography can be predicted to a good extent by leveraging the radiologists’ gaze behavior and image content.« less

A hierarchical SVG image abstraction layer for medical imaging

NASA Astrophysics Data System (ADS)

Kim, Edward; Huang, Xiaolei; Tan, Gang; Long, L. Rodney; Antani, Sameer

2010-03-01

As medical imaging rapidly expands, there is an increasing need to structure and organize image data for efficient analysis, storage and retrieval. In response, a large fraction of research in the areas of content-based image retrieval (CBIR) and picture archiving and communication systems (PACS) has focused on structuring information to bridge the "semantic gap", a disparity between machine and human image understanding. An additional consideration in medical images is the organization and integration of clinical diagnostic information. As a step towards bridging the semantic gap, we design and implement a hierarchical image abstraction layer using an XML based language, Scalable Vector Graphics (SVG). Our method encodes features from the raw image and clinical information into an extensible "layer" that can be stored in a SVG document and efficiently searched. Any feature extracted from the raw image including, color, texture, orientation, size, neighbor information, etc., can be combined in our abstraction with high level descriptions or classifications. And our representation can natively characterize an image in a hierarchical tree structure to support multiple levels of segmentation. Furthermore, being a world wide web consortium (W3C) standard, SVG is able to be displayed by most web browsers, interacted with by ECMAScript (standardized scripting language, e.g. JavaScript, JScript), and indexed and retrieved by XML databases and XQuery. Using these open source technologies enables straightforward integration into existing systems. From our results, we show that the flexibility and extensibility of our abstraction facilitates effective storage and retrieval of medical images.

Ultrahigh-resolution endoscopic optical coherence tomography

NASA Astrophysics Data System (ADS)

Chen, Yu; Herz, Paul R.; Hsiung, Pei-Lin; Aguirre, Aaron D.; Mashimo, Hiroshi; Desai, Saleem; Pedrosa, Macos; Koski, Amanda; Schmitt, Joseph M.; Fujimoto, James G.

2005-01-01

Early detection of gastrointestinal cancer is essential for the patient treatment and medical care. Endoscopically guided biopsy is currently the gold standard for the diagnosis of early esophageal cancer, but can suffer from high false negative rates due to sampling errors. Optical coherence tomography (OCT) is an emerging medical imaging technology which can generate high resolution, cross-sectional images of tissue in situ and in real time, without the removal of tissue specimen. Although endoscopic OCT has been used successfully to identify certain pathologies in the gastrointestinal tract, the resolution of current endoscopic OCT systems has been limited to 10 - 15 m for clinical procedures. In this study, in vivo imaging of the gastrointestinal tract is demonstrated at a three-fold higher resolution (< 5 m), using a portable, broadband, Cr4+:Forsterite laser as the optical light source. Images acquired from the esophagus, gastro-esophageal junction and colon on animal model display tissue microstructures and architectural details at high resolution, and the features observed in the OCT images are well-matched with histology. The clinical feasibility study is conducted through delivering OCT imaging catheter using standard endoscope. OCT images of normal esophagus, Barrett's esophagus, and esophageal cancers are demonstrated with distinct features. The ability of high resolution endoscopic OCT to image tissue morphology at an unprecedented resolution in vivo would facilitate the development of OCT as a potential imaging modality for early detection of neoplastic changes.

Clinics in diagnostic imaging (153). Severe hypoxic ischaemic brain injury.

PubMed Central

Chua, Wynne; Lim, Boon Keat; Lim, Tchoyoson Choie Cheio

2014-01-01

A 58-year-old Indian woman presented with asystole after an episode of haemetemesis, with a patient downtime of 20 mins. After initial resuscitation efforts, computed tomography of the brain, obtained to evaluate neurological injury, demonstrated evidence of severe hypoxic ischaemic brain injury. The imaging features of hypoxic ischaemic brain injury and the potential pitfalls with regard to image interpretation are herein discussed. PMID:25091891

High-level intuitive features (HLIFs) for intuitive skin lesion description.

PubMed

Amelard, Robert; Glaister, Jeffrey; Wong, Alexander; Clausi, David A

2015-03-01

A set of high-level intuitive features (HLIFs) is proposed to quantitatively describe melanoma in standard camera images. Melanoma is the deadliest form of skin cancer. With rising incidence rates and subjectivity in current clinical detection methods, there is a need for melanoma decision support systems. Feature extraction is a critical step in melanoma decision support systems. Existing feature sets for analyzing standard camera images are comprised of low-level features, which exist in high-dimensional feature spaces and limit the system's ability to convey intuitive diagnostic rationale. The proposed HLIFs were designed to model the ABCD criteria commonly used by dermatologists such that each HLIF represents a human-observable characteristic. As such, intuitive diagnostic rationale can be conveyed to the user. Experimental results show that concatenating the proposed HLIFs with a full low-level feature set increased classification accuracy, and that HLIFs were able to separate the data better than low-level features with statistical significance. An example of a graphical interface for providing intuitive rationale is given.

Progress in atherosclerotic plaque imaging

PubMed Central

Soloperto, Giulia; Casciaro, Sergio

2012-01-01

Cardiovascular diseases are the primary cause of mortality in the industrialized world, and arterial obstruction, triggered by rupture-prone atherosclerotic plaques, lead to myocardial infarction and cerebral stroke. Vulnerable plaques do not necessarily occur with flow-limiting stenosis, thus conventional luminographic assessment of the pathology fails to identify unstable lesions. In this review we discuss the currently available imaging modalities used to investigate morphological features and biological characteristics of the atherosclerotic plaque. The different imaging modalities such as ultrasound, magnetic resonance imaging, computed tomography, nuclear imaging and their intravascular applications are illustrated, highlighting their specific diagnostic potential. Clinically available and upcoming methodologies are also reviewed along with the related challenges in their clinical translation, concerning the specific invasiveness, accuracy and cost-effectiveness of these methods. PMID:22937215

Deep learning based classification of breast tumors with shear-wave elastography.

PubMed

Zhang, Qi; Xiao, Yang; Dai, Wei; Suo, Jingfeng; Wang, Congzhi; Shi, Jun; Zheng, Hairong

2016-12-01

This study aims to build a deep learning (DL) architecture for automated extraction of learned-from-data image features from the shear-wave elastography (SWE), and to evaluate the DL architecture in differentiation between benign and malignant breast tumors. We construct a two-layer DL architecture for SWE feature extraction, comprised of the point-wise gated Boltzmann machine (PGBM) and the restricted Boltzmann machine (RBM). The PGBM contains task-relevant and task-irrelevant hidden units, and the task-relevant units are connected to the RBM. Experimental evaluation was performed with five-fold cross validation on a set of 227 SWE images, 135 of benign tumors and 92 of malignant tumors, from 121 patients. The features learned with our DL architecture were compared with the statistical features quantifying image intensity and texture. Results showed that the DL features achieved better classification performance with an accuracy of 93.4%, a sensitivity of 88.6%, a specificity of 97.1%, and an area under the receiver operating characteristic curve of 0.947. The DL-based method integrates feature learning with feature selection on SWE. It may be potentially used in clinical computer-aided diagnosis of breast cancer. Copyright © 2016 Elsevier B.V. All rights reserved.

Potential application of a handheld confocal endomicroscope imaging system using a variety of fluorophores in experimental gliomas and normal brain.

PubMed

Martirosyan, Nikolay L; Georges, Joseph; Eschbacher, Jennifer M; Cavalcanti, Daniel D; Elhadi, Ali M; Abdelwahab, Mohammed G; Scheck, Adrienne C; Nakaji, Peter; Spetzler, Robert F; Preul, Mark C

2014-02-01

The authors sought to assess the feasibility of a handheld visible-wavelength confocal endomicroscope imaging system (Optiscan 5.1, Optiscan Pty., Ltd.) using a variety of rapid-acting fluorophores to provide histological information on gliomas, tumor margins, and normal brain in animal models. Mice (n = 25) implanted with GL261 cells were used to image fluorescein sodium (FNa), 5-aminolevulinic acid (5-ALA), acridine orange (AO), acriflavine (AF), and cresyl violet (CV). A U251 glioma xenograft model in rats (n = 5) was used to image sulforhodamine 101 (SR101). A swine (n = 3) model with AO was used to identify confocal features of normal brain. Images of normal brain, obvious tumor, and peritumoral zones were collected using the handheld confocal endomicroscope. Histological samples were acquired through biopsies from matched imaging areas. Samples were visualized with a benchtop confocal microscope. Histopathological features in corresponding confocal images and photomicrographs of H & E-stained tissues were reviewed. Fluorescence induced by FNa, 5-ALA, AO, AF, CV, and SR101 and detected with the confocal endomicroscope allowed interpretation of histological features. Confocal endomicroscopy revealed satellite tumor cells within peritumoral tissue, a definitive tumor border, and striking fluorescent cellular and subcellular structures. Fluorescence in various tumor regions correlated with standard histology and known tissue architecture. Characteristic features of different areas of normal brain were identified as well. Confocal endomicroscopy provided rapid histological information precisely related to the site of microscopic imaging with imaging characteristics of cells related to the unique labeling features of the fluorophores. Although experimental with further clinical trial validation required, these data suggest that intraoperative confocal imaging can help to distinguish normal brain from tumor and tumor margin and may have application in improving intraoperative decisions during resection of brain tumors.

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.

Quantitative Analysis of the Cervical Texture by Ultrasound and Correlation with Gestational Age.

PubMed

Baños, Núria; Perez-Moreno, Alvaro; Migliorelli, Federico; Triginer, Laura; Cobo, Teresa; Bonet-Carne, Elisenda; Gratacos, Eduard; Palacio, Montse

2017-01-01

Quantitative texture analysis has been proposed to extract robust features from the ultrasound image to detect subtle changes in the textures of the images. The aim of this study was to evaluate the feasibility of quantitative cervical texture analysis to assess cervical tissue changes throughout pregnancy. This was a cross-sectional study including singleton pregnancies between 20.0 and 41.6 weeks of gestation from women who delivered at term. Cervical length was measured, and a selected region of interest in the cervix was delineated. A model to predict gestational age based on features extracted from cervical images was developed following three steps: data splitting, feature transformation, and regression model computation. Seven hundred images, 30 per gestational week, were included for analysis. There was a strong correlation between the gestational age at which the images were obtained and the estimated gestational age by quantitative analysis of the cervical texture (R = 0.88). This study provides evidence that quantitative analysis of cervical texture can extract features from cervical ultrasound images which correlate with gestational age. Further research is needed to evaluate its applicability as a biomarker of the risk of spontaneous preterm birth, as well as its role in cervical assessment in other clinical situations in which cervical evaluation might be relevant. © 2016 S. Karger AG, Basel.

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.

Feature-Motivated Simplified Adaptive PCNN-Based Medical Image Fusion Algorithm in NSST Domain.

PubMed

Ganasala, Padma; Kumar, Vinod

2016-02-01

Multimodality medical image fusion plays a vital role in diagnosis, treatment planning, and follow-up studies of various diseases. It provides a composite image containing critical information of source images required for better localization and definition of different organs and lesions. In the state-of-the-art image fusion methods based on nonsubsampled shearlet transform (NSST) and pulse-coupled neural network (PCNN), authors have used normalized coefficient value to motivate the PCNN-processing both low-frequency (LF) and high-frequency (HF) sub-bands. This makes the fused image blurred and decreases its contrast. The main objective of this work is to design an image fusion method that gives the fused image with better contrast, more detail information, and suitable for clinical use. We propose a novel image fusion method utilizing feature-motivated adaptive PCNN in NSST domain for fusion of anatomical images. The basic PCNN model is simplified, and adaptive-linking strength is used. Different features are used to motivate the PCNN-processing LF and HF sub-bands. The proposed method is extended for fusion of functional image with an anatomical image in improved nonlinear intensity hue and saturation (INIHS) color model. Extensive fusion experiments have been performed on CT-MRI and SPECT-MRI datasets. Visual and quantitative analysis of experimental results proved that the proposed method provides satisfactory fusion outcome compared to other image fusion methods.

Diagnosis of response and non-response to dry eye treatment using infrared thermography images

NASA Astrophysics Data System (ADS)

Acharya, U. Rajendra; Tan, Jen Hong; Vidya, S.; Yeo, Sharon; Too, Cheah Loon; Lim, Wei Jie Eugene; Chua, Kuang Chua; Tong, Louis

2014-11-01

The dry eye treatment outcome depends on the assessment of clinical relevance of the treatment effect. The potential approach to assess the clinical relevance of the treatment is to identify the symptoms responders and non-responders to the given treatments using the responder analysis. In our work, we have performed the responder analysis to assess the clinical relevance effect of the dry eye treatments namely, hot towel, EyeGiene®, and Blephasteam® twice daily and 12 min session of Lipiflow®. Thermography is performed at week 0 (baseline), at weeks 4 and 12 after treatment. The clinical parameters such as, change in the clinical irritations scores, tear break up time (TBUT), corneal staining and Schirmer's symptoms tests values are used to obtain the responders and non-responders groups. We have obtained the infrared thermography images of dry eye symptoms responders and non-responders to the three types of warming treatments. The energy, kurtosis, skewness, mean, standard deviation, and various entropies namely Shannon, Renyi and Kapoor are extracted from responders and non-responders thermograms. The extracted features are ranked based on t-values. These ranked features are fed to the various classifiers to get the highest performance using minimum features. We have used decision tree (DT), K nearest neighbour (KNN), Naves Bayesian (NB) and support vector machine (SVM) to classify the features into responder and non-responder classes. We have obtained an average accuracy of 99.88%, sensitivity of 99.7% and specificity of 100% using KNN classifier using ten-fold cross validation.

[Clinical and endoscopic features of a selected population with serrated colorectal adenomas in a private clinic in Lima - Peru].

PubMed

Castillo, Ofelia; Barreda, Carlos; Recavarren, Sixto; Barriga, José A; Salazar M, Fernando; Yriberry, Simón; Barriga, Eduardo; Salazar C, Fernando

2013-01-01

To describe the clinical and endoscopic caracteristics of a population that has only serrated polyps of colon (mainly sessile serrated adenomas) in a private clinic in Lima, Perú, from 2009-2011. Retrospective study conducted at the endoscopy center of Clinic Ricardo Palma, Lima, Peru. Olympus colonoscope was used with high definition, including NBI (narrow band imaging) and electronic magnification. Patients had pathologic diagnosis of “polyps and / or colorectal serrated adenomas” and excluded those with synchronous tubular or villous adenomas. Images were evaluated by two endoscopists and then by a third gastroenterologist. We found 201 serrated polyps in 108 patients. Women were 60.2% and overweight predominated. Eighty (74.1%) had only one serrated adenoma and 23 (21.3%) with at least one synchronous hyperplastic polyp. The average size of sessile serrated adenomas was 5.12 mm (± 3.87 DS) and the flat type was 91 (58.7%). There were significant differences in the diameter of sessile serrated adenomas between the distal and proximal colon (4.47 mm ± 2.23 vs. 6.90 mm ± 6.25; p<0.000). The common features of sessile serrated adenomas were: White (31/36, 86.1%), smooth (28/36, 77.8%) and regular margins (26/36, 72.2%). There was a relationship between vascular pattern according NBI and serrated polyp histology (p=0.024). The endoscopic features of sessile serrated adenomas can evade detection to white light. NBI is a useful tool to define some features of these lesions.

Sino-Orbital Osteoma With Osteoblastoma-Like Features.

PubMed

McCann, James M; Tyler, Donald; Foss, Robert D

2015-12-01

An 18 year old male presented with worsening headaches, pain with ocular movement and swelling that involved the left anterior periorbital and frontal sinus region. Radiographic images revealed a polypoid bony mass of mixed radiodensity extending into the left and right frontal sinuses. Histologic examination of the resection material resulted in the diagnosis of an osteoma with osteoblastoma-like features, an osteoma variant that has zones indistinguishable from an osteoblastoma. The clinical, radiographic, and morphologic features of sino-orbital osteoma with osteoblastoma-like features are discussed.

Giant cell angiofibroma misdiagnosed as a vascular malformation and treated with absolute alcohol for one year: a case report and review of the literature

PubMed Central

2014-01-01

Purpose To present the clinical, imaging, pathological and immunohistochemical features of giant cell angiofibroma (GCA). Case presentation In this paper we report an atypical case of a GCA extending from the parotid to the parapharyngeal space. The lesion was being treated as a vascular malformation for one year prior to surgical removal. We summarize the clinical manifestations, imaging, pathological and molecular features of this rare disease. After complete surgical removal of the tumor, immunohistochemical analysis revealed strong positivity for the mesenchymal markers vimentin, CD34, CD31 and CD99 in neoplastic cells. Tumor proliferation antigen marker Ki67 was partly positive (<5% of cells). Tumor cells were negative for muscle-specific actin, epithelial membrane antigen, smooth muscle actin, cytokeratin pan, S100, desmin, glial fibrillary acidic protein, myogenin, MyoD1 and F8. The morphological and immunohistochemical profile was consistent with the diagnosis of GCA. Conclusion GCA is a rare soft tissue tumor that can easily be misdiagnosed in the clinical preoperative setting. In view of the clinical, pathological and molecular features of the tumor, complete surgical removal is the current optimal treatment option, providing accurate diagnosis and low to minimal recurrence rate. PMID:24758544

Oncogenous osteomalacia and myopericytoma of the thoracic spine: a case report.

PubMed

Brunschweiler, Benoit; Guedj, Nathalie; Lenoir, Thibault; Faillot, Thierry; Rillardon, Ludovic; Guigui, Pierre

2009-11-01

A case report. To illustrate a rare case of oncogenous osteomalacia caused by a spinal thoracic myopericytoma. Osteomalacia related to a tumor is well known. The cause of the disorder is usually a highly vascularized, benign tumor of mesenchymal origin. Location of the tumor in the spine is very rare. Removal of the tumor is followed by resolution of osteomalacia. Diagnosis of oseomalacia was established on the presence of cardinal clinical, biologic, and radiologic features of osteomalacia. Localization of the tumor at T5 and T6 levels was obtained by magnetic resonance imaging. Surgical treatment consisted in a circumferential correction-fusion with hemivertebrectomy of T5 and T6 and tumor removal. Tumor removal was rapidly followed by disappearance of the clinical symptoms of osteomalacia, and by correction of hypophosphatemia. At 2-years follow-up, no recurrence of the tumor was detectable on imaging studies-the correction fusion remained stable. Histologically, the tumor was classified as a myopericytoma. There was no relapse of the clinical features of osteomalacia. However, secondary recurrence of the biologic markers due to an incomplete tumor removal was disclosed. Removal of the tumor was followed by healing of the clinical features of osteomalacia, demonstrating the causal connection between the myopericytoma and the osteopathy.

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.

Longitudinal strain bull's eye plot patterns in patients with cardiomyopathy and concentric left ventricular hypertrophy.

PubMed

Liu, Dan; Hu, Kai; Nordbeck, Peter; Ertl, Georg; Störk, Stefan; Weidemann, Frank

2016-05-10

Despite substantial advances in the imaging techniques and pathophysiological understanding over the last decades, identification of the underlying causes of left ventricular hypertrophy by means of echocardiographic examination remains a challenge in current clinical practice. The longitudinal strain bull's eye plot derived from 2D speckle tracking imaging offers an intuitive visual overview of the global and regional left ventricular myocardial function in a single diagram. The bull's eye mapping is clinically feasible and the plot patterns could provide clues to the etiology of cardiomyopathies. The present review summarizes the longitudinal strain, bull's eye plot features in patients with various cardiomyopathies and concentric left ventricular hypertrophy and the bull's eye plot features might serve as one of the cardiac workup steps on evaluating patients with left ventricular hypertrophy.

Association of mammographic image feature change and an increasing risk trend of developing breast cancer: an assessment

NASA Astrophysics Data System (ADS)

Tan, Maxine; Leader, Joseph K.; Liu, Hong; Zheng, Bin

2015-03-01

We recently investigated a new mammographic image feature based risk factor to predict near-term breast cancer risk after a woman has a negative mammographic screening. We hypothesized that unlike the conventional epidemiology-based long-term (or lifetime) risk factors, the mammographic image feature based risk factor value will increase as the time lag between the negative and positive mammography screening decreases. The purpose of this study is to test this hypothesis. From a large and diverse full-field digital mammography (FFDM) image database with 1278 cases, we collected all available sequential FFDM examinations for each case including the "current" and 1 to 3 most recently "prior" examinations. All "prior" examinations were interpreted negative, and "current" ones were either malignant or recalled negative/benign. We computed 92 global mammographic texture and density based features, and included three clinical risk factors (woman's age, family history and subjective breast density BIRADS ratings). On this initial feature set, we applied a fast and accurate Sequential Forward Floating Selection (SFFS) feature selection algorithm to reduce feature dimensionality. The features computed on both mammographic views were individually/ separately trained using two artificial neural network (ANN) classifiers. The classification scores of the two ANNs were then merged with a sequential ANN. The results show that the maximum adjusted odds ratios were 5.59, 7.98, and 15.77 for using the 3rd, 2nd, and 1st "prior" FFDM examinations, respectively, which demonstrates a higher association of mammographic image feature change and an increasing risk trend of developing breast cancer in the near-term after a negative screening.

Integration of basic sciences and clinical sciences in oral radiology education for dental students.

PubMed

Baghdady, Mariam T; Carnahan, Heather; Lam, Ernest W N; Woods, Nicole N

2013-06-01

Educational research suggests that cognitive processing in diagnostic radiology requires a solid foundation in the basic sciences and knowledge of the radiological changes associated with disease. Although it is generally assumed that dental students must acquire both sets of knowledge, little is known about the most effective way to teach them. Currently, the basic and clinical sciences are taught separately. This study was conducted to compare the diagnostic accuracy of students when taught basic sciences segregated or integrated with clinical features. Predoctoral dental students (n=51) were taught four confusable intrabony abnormalities using basic science descriptions integrated with the radiographic features or taught segregated from the radiographic features. The students were tested with diagnostic images, and memory tests were performed immediately after learning and one week later. On immediate and delayed testing, participants in the integrated basic science group outperformed those from the segregated group. A main effect of learning condition was found to be significant (p<0.05). The results of this study support the critical role of integrating biomedical knowledge in diagnostic radiology and shows that teaching basic sciences integrated with clinical features produces higher diagnostic accuracy in novices than teaching basic sciences segregated from clinical features.

Adrenal cortical oncocytoma mimicking pheochromocytoma.

PubMed

Kiriakopoulos, Andreas; Papaioannou, Dimitrios; Linos, Dimitrios

2011-01-01

Adrenal tumors present with clinical features and signs unique to their specific hormonal hypersecretion. However, there have been cases in which the clinical expression has been in conflict with the histologic features of the tumor. In this communication we report an unusual clinical presentation of an adrenal cortical tumor with histologic features of an oncocytoma that clinically mimicked a pheochromocytoma. A 49-year old man was referred to our Unit due to type B aortic dissection and a mass of the left adrenal gland. Computed tomography and magnetic resonance imaging confirmed the presence of aortic dissection extending from the left subclavian artery to both iliac arteries and also revealed a 6 cm tumor on the left adrenal gland. Preoperative endocrine evaluation showed a near tenfold increase of urinary vanillylmandelic acid (VMA) and metanephrine values. Transperitoneal laparoscopic adrenalectomy was successfully performed. The adrenal tumor proved to be an adrenal cortical neoplasm with histologic features of oncocytoma. Although the case of an adrenal cortical adenoma clinically mimicking a pheochromocytoma has been described in the literature, to the best of our knowledge, there has been no previous report of an adrenal cortical neoplasm with predominant features of oncocytoma.

Application of dermoscopy image analysis technique in diagnosing urethral condylomata acuminata.

PubMed

Zhang, Yunjie; Jiang, Shuang; Lin, Hui; Guo, Xiaojuan; Zou, Xianbiao

2018-01-01

In this study, cases with suspected urethral condylomata acuminata were examined by dermoscopy, in order to explore an effective method for clinical. To study the application of dermoscopy image analysis technique in clinical diagnosis of urethral condylomata acuminata. A total of 220 suspected urethral condylomata acuminata were clinically diagnosed first with the naked eyes, and then by using dermoscopy image analysis technique. Afterwards, a comparative analysis was made for the two diagnostic methods. Among the 220 suspected urethral condylomata acuminata, there was a higher positive rate by dermoscopy examination than visual observation. Dermoscopy examination technique is still restricted by its inapplicability in deep urethral orifice and skin wrinkles, and concordance between different clinicians may also vary. Dermoscopy image analysis technique features a high sensitivity, quick and accurate diagnosis and is non-invasive, and we recommend its use.

Hepatitis Diagnosis Using Facial Color Image

NASA Astrophysics Data System (ADS)

Liu, Mingjia; Guo, Zhenhua

Facial color diagnosis is an important diagnostic method in traditional Chinese medicine (TCM). However, due to its qualitative, subjective and experi-ence-based nature, traditional facial color diagnosis has a very limited application in clinical medicine. To circumvent the subjective and qualitative problems of facial color diagnosis of Traditional Chinese Medicine, in this paper, we present a novel computer aided facial color diagnosis method (CAFCDM). The method has three parts: face Image Database, Image Preprocessing Module and Diagnosis Engine. Face Image Database is carried out on a group of 116 patients affected by 2 kinds of liver diseases and 29 healthy volunteers. The quantitative color feature is extracted from facial images by using popular digital image processing techni-ques. Then, KNN classifier is employed to model the relationship between the quantitative color feature and diseases. The results show that the method can properly identify three groups: healthy, severe hepatitis with jaundice and severe hepatitis without jaundice with accuracy higher than 73%.

Lumbar spinal stenosis CAD from clinical MRM and MRI based on inter- and intra-context features with a two-level classifier

NASA Astrophysics Data System (ADS)

Koh, Jaehan; Alomari, Raja S.; Chaudhary, Vipin; Dhillon, Gurmeet

2011-03-01

An imaging test has an important role in the diagnosis of lumbar abnormalities since it allows to examine the internal structure of soft tissues and bony elements without the need of an unnecessary surgery and recovery time. For the past decade, among various imaging modalities, magnetic resonance imaging (MRI) has taken the significant part of the clinical evaluation of the lumbar spine. This is mainly due to technological advancements that lead to the improvement of imaging devices in spatial resolution, contrast resolution, and multi-planar capabilities. In addition, noninvasive nature of MRI makes it easy to diagnose many common causes of low back pain such as disc herniation, spinal stenosis, and degenerative disc diseases. In this paper, we propose a method to diagnose lumbar spinal stenosis (LSS), a narrowing of the spinal canal, from magnetic resonance myelography (MRM) images. Our method segments the thecal sac in the preprocessing stage, generates the features based on inter- and intra-context information, and diagnoses lumbar disc stenosis. Experiments with 55 subjects show that our method achieves 91.3% diagnostic accuracy. In the future, we plan to test our method on more subjects.

A Study on Clinical Characteristics and Magnetic Resonance Imaging Manifestations on Systemic Rosai-Dorfman Disease

PubMed Central

Cheng, Xiao; Cheng, Jing-Liang; Gao, An-Kang

2018-01-01

Background: Rosai-Dorfman disease (RDD) is typically characterized by painless bilateral and symmetrical cervical lymphadenopathy, with associated fever and leukocytosis. The aim of the current study was to summarize the clinical features and imaging characteristics of RDD, in an effort to improve its diagnostic accuracy. Methods: The study was analyzed from 32 patients between January 2011 and December 2017; of these, 16 patients had pathologically diagnosed RDD, eight had pathologically diagnosed meningioma, and eight pathologically diagnosed lymphoma. All patients underwent computed tomography and magnetic resonance imaging (MRI). Clinical features and imaging characteristics of RDD were analyzed retrospectively. The mean apparent diffusion coefficient (ADC) values of lesions at different sites were measured, and one-way analysis of variance and the least significant difference t-test were used to compare the differences between groups and draw receiver operating characteristic curves. The tumors were excised for biopsy and analyzed using immunohistochemistry. Results: The mean ADCs were (0.81 ± 0.10) × 10−3 mm2/s for intercranial RDD, (0.73 ± 0.05) × 10−3 mm2/s for nasopharyngeal RDD, (0.74 ± 0.11) × 10−3 mm2/s for bone RDD, and (0.71 ± 0.04) × 10−3 mm2/s for soft-tissue RDD. The optimum ADC to distinguish intracranial RDD from lymphoma was 0.79 × 10−3 mm2/s (62.5% sensitivity and 100% specificity) and to distinguish meningioma from intracranial RDD was 0.92 × 10−3 mm2/s (62.5% sensitivity and 100% specificity). Levels of C-reactive protein, erythrocyte sediment rate and D-dimer were significantly elevated (81%, 87%, and 75%, respectively). On immunohistochemistry, RDD was positive for both S-100 and CD68 proteins but negative for CD1a. Conclusions: Conventional MRI, combined with diffusion-weighted imaging and ADC mapping, is an important diagnostic tool in evaluating RDD patients. An accurate diagnosis of RDD should consider the clinical features, imaging characteristics, and the pathological findings. PMID:29451149

Using Functional or Structural Magnetic Resonance Images and Personal Characteristic Data to Identify ADHD and Autism

PubMed Central

Ghiassian, Sina; Greiner, Russell; Jin, Ping; Brown, Matthew R. G.

2016-01-01

A clinical tool that can diagnose psychiatric illness using functional or structural magnetic resonance (MR) brain images has the potential to greatly assist physicians and improve treatment efficacy. Working toward the goal of automated diagnosis, we propose an approach for automated classification of ADHD and autism based on histogram of oriented gradients (HOG) features extracted from MR brain images, as well as personal characteristic data features. We describe a learning algorithm that can produce effective classifiers for ADHD and autism when run on two large public datasets. The algorithm is able to distinguish ADHD from control with hold-out accuracy of 69.6% (over baseline 55.0%) using personal characteristics and structural brain scan features when trained on the ADHD-200 dataset (769 participants in training set, 171 in test set). It is able to distinguish autism from control with hold-out accuracy of 65.0% (over baseline 51.6%) using functional images with personal characteristic data when trained on the Autism Brain Imaging Data Exchange (ABIDE) dataset (889 participants in training set, 222 in test set). These results outperform all previously presented methods on both datasets. To our knowledge, this is the first demonstration of a single automated learning process that can produce classifiers for distinguishing patients vs. controls from brain imaging data with above-chance accuracy on large datasets for two different psychiatric illnesses (ADHD and autism). Working toward clinical applications requires robustness against real-world conditions, including the substantial variability that often exists among data collected at different institutions. It is therefore important that our algorithm was successful with the large ADHD-200 and ABIDE datasets, which include data from hundreds of participants collected at multiple institutions. While the resulting classifiers are not yet clinically relevant, this work shows that there is a signal in the (f)MRI data that a learning algorithm is able to find. We anticipate this will lead to yet more accurate classifiers, over these and other psychiatric disorders, working toward the goal of a clinical tool for high accuracy differential diagnosis. PMID:28030565

Automatic segmentation of the prostate on CT images using deep learning and multi-atlas fusion

NASA Astrophysics Data System (ADS)

Ma, Ling; Guo, Rongrong; Zhang, Guoyi; Tade, Funmilayo; Schuster, David M.; Nieh, Peter; Master, Viraj; Fei, Baowei

2017-02-01

Automatic segmentation of the prostate on CT images has many applications in prostate cancer diagnosis and therapy. However, prostate CT image segmentation is challenging because of the low contrast of soft tissue on CT images. In this paper, we propose an automatic segmentation method by combining a deep learning method and multi-atlas refinement. First, instead of segmenting the whole image, we extract the region of interesting (ROI) to delete irrelevant regions. Then, we use the convolutional neural networks (CNN) to learn the deep features for distinguishing the prostate pixels from the non-prostate pixels in order to obtain the preliminary segmentation results. CNN can automatically learn the deep features adapting to the data, which are different from some handcrafted features. Finally, we select some similar atlases to refine the initial segmentation results. The proposed method has been evaluated on a dataset of 92 prostate CT images. Experimental results show that our method achieved a Dice similarity coefficient of 86.80% as compared to the manual segmentation. The deep learning based method can provide a useful tool for automatic segmentation of the prostate on CT images and thus can have a variety of clinical applications.

Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image.

PubMed

Xu, Kele; Feng, Dawei; Mi, Haibo

2017-11-23

The automatic detection of diabetic retinopathy is of vital importance, as it is the main cause of irreversible vision loss in the working-age population in the developed world. The early detection of diabetic retinopathy occurrence can be very helpful for clinical treatment; although several different feature extraction approaches have been proposed, the classification task for retinal images is still tedious even for those trained clinicians. Recently, deep convolutional neural networks have manifested superior performance in image classification compared to previous handcrafted feature-based image classification methods. Thus, in this paper, we explored the use of deep convolutional neural network methodology for the automatic classification of diabetic retinopathy using color fundus image, and obtained an accuracy of 94.5% on our dataset, outperforming the results obtained by using classical approaches.

Imaging findings in craniofacial childhood rhabdomyosarcoma

PubMed Central

Merks, Johannes H. M.; Saeed, Peerooz; Balm, Alfons J. M.; Bras, Johannes; Pieters, Bradley R.; Adam, Judit A.; van Rijn, Rick R.

2010-01-01

Rhabdomyosarcoma (RMS) is the commonest paediatric soft-tissue sarcoma constituting 3–5% of all malignancies in childhood. RMS has a predilection for the head and neck area and tumours in this location account for 40% of all childhood RMS cases. In this review we address the clinical and imaging presentations of craniofacial RMS, discuss the most appropriate imaging techniques, present characteristic imaging features and offer an overview of differential diagnostic considerations. Post-treatment changes will be briefly addressed. PMID:20725831

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 diagnosis of bladder cancer by morphometric analysis of HE images

NASA Astrophysics Data System (ADS)

Wu, Binlin; Nebylitsa, Samantha V.; Mukherjee, Sushmita; Jain, Manu

2015-02-01

In clinical practice, histopathological analysis of biopsied tissue is the main method for bladder cancer diagnosis and prognosis. The diagnosis is performed by a pathologist based on the morphological features in the image of a hematoxylin and eosin (HE) stained tissue sample. This manuscript proposes algorithms to perform morphometric analysis on the HE images, quantify the features in the images, and discriminate bladder cancers with different grades, i.e. high grade and low grade. The nuclei are separated from the background and other types of cells such as red blood cells (RBCs) and immune cells using manual outlining, color deconvolution and image segmentation. A mask of nuclei is generated for each image for quantitative morphometric analysis. The features of the nuclei in the mask image including size, shape, orientation, and their spatial distributions are measured. To quantify local clustering and alignment of nuclei, we propose a 1-nearest-neighbor (1-NN) algorithm which measures nearest neighbor distance and nearest neighbor parallelism. The global distributions of the features are measured using statistics of the proposed parameters. A linear support vector machine (SVM) algorithm is used to classify the high grade and low grade bladder cancers. The results show using a particular group of nuclei such as large ones, and combining multiple parameters can achieve better discrimination. This study shows the proposed approach can potentially help expedite pathological diagnosis by triaging potentially suspicious biopsies.

An advanced design of non-radioactive image capturing and management system for applications in non-invasive skin disorder diagnosis

NASA Astrophysics Data System (ADS)

Liu, Carol Y. B.; Luk, David C. K.; Zhou, Kany S. Y.; So, Bryan M. K.; Louie, Derek C. H.

2015-03-01

Due to the increasing incidences of malignant melanoma, there is a rising demand for assistive technologies for its early diagnosis and improving the survival rate. The commonly used visual screening method is with limited accuracy as the early phase of melanoma shares many clinical features with an atypical nevus, while conventional dermoscopes are not user-friendly in terms of setup time and operations. Therefore, the development of an intelligent and handy system to assist the accurate screening and long-term monitoring of melanocytic skin lesions is crucial for early diagnosis and prevention of melanoma. In this paper, an advanced design of non-invasive and non-radioactive dermoscopy system was reported. Computer-aided simulations were conducted for optimizing the optical design and uniform illumination distribution. Functional prototype and the software system were further developed, which could enable image capturing at 10x amplified and general modes, convenient data transmission, analysis of dermoscopic features (e.g., asymmetry, border irregularity, color, diameter and dermoscopic structure) for assisting the early detection of melanoma, extract patient information (e.g. code, lesion location) and integrate with dermoscopic images, thus further support long term monitoring of diagnostic analysis results. A clinical trial study was further conducted on 185 Chinese children (0-18 years old). The results showed that for all subjects, skin conditions diagnosed based on the developed system accurately confirmed the diagnoses by conventional clinical procedures. Besides, clinical analysis on dermoscopic features and a potential standard approach by the developed system to support identifying specific melanocytic patterns for dermoscopic examination in Chinese children were also reported.

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.

Imaging Diagnosis of Splanchnic Venous Thrombosis

PubMed Central

Rajesh, S.; Mukund, Amar

2015-01-01

Splanchnic vein thrombosis (SVT) is a broad term that includes Budd-Chiari syndrome and occlusion of veins that constitute the portal venous system. Due to the common risk factors involved in the pathogenesis of these clinically distinct disorders, concurrent involvement of two different regions is quite common. In acute and subacute SVT, the symptoms may overlap with a variety of other abdominal emergencies while in chronic SVT, the extent of portal hypertension and its attendant complications determine the clinical course. As a result, clinical diagnosis is often difficult and is frequently reliant on imaging. Tremendous improvements in vascular imaging in recent years have ensured that this once rare entity is being increasingly detected. Treatment of acute SVT requires immediate anticoagulation. Transcatheter thrombolysis or transjugular intrahepatic portosystemic shunt is used in the event of clinical deterioration. In cases with peritonitis, immediate laparotomy and bowel resection may be required for irreversible bowel ischemia. In chronic SVT, the underlying cause should be identified and treated. The imaging manifestations of the clinical syndromes resulting from SVT are comprehensively discussed here along with a brief review of the relevant clinical features and therapeutic approach. PMID:26600801

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

Do Orthopaedic Oncologists Agree on the Diagnosis and Treatment of Cartilage Tumors of the Appendicular Skeleton?

PubMed

Zamora, Tomas; Urrutia, Julio; Schweitzer, Daniel; Amenabar, Pedro Pablo; Botello, Eduardo

2017-09-01

Distinguishing a benign enchondroma from a low-grade chondrosarcoma is a common diagnostic challenge for orthopaedic oncologists. Low interrater agreement has been observed for the diagnosis of cartilaginous neoplasms among radiologists and pathologists, but, to our knowledge, no study has evaluated inter- and intraobserver agreement among orthopaedic oncologists grading these lesions using initial clinical and imaging information. Determining such agreement is important since it reflects the certainty in the diagnosis by orthopaedic oncologists. Agreement also is important as it will guide future treatment and prognosis, considering that there is no gold standard for diagnosis of these lesions. (1) to determine inter- and intraobserver agreement among a multinational panel of expert orthopaedic oncologists in diagnosing cartilaginous neoplasms based on their assessment of clinical symptoms and imaging at diagnosis. (2) To describe the most important clinical and imaging features that experts use during the initial diagnostic process. (3) To determine interobserver agreement for proposed initial treatment strategies for cartilaginous neoplasms by this panel of evaluators. Thirty-nine patients with intramedullary cartilaginous neoplasms of the appendicular skeleton of various histopathologic grades were selected and classified as having benign, low-grade malignant, or intermediate- or high-grade malignant neoplasms by 10 experienced orthopaedic oncologists based on clinical and imaging information. Additionally, they chose the three most important clinical or imaging features for the diagnosis of these neoplasms, and they proposed a treatment strategy for each patient. The Kappa coefficient (κ) was used to determine inter- and intraobserver agreement. Inter- and intraobserver agreements were only fair to good, κ = 0.44(95% CI, 0.41-0.48) and κ = 0.62 (95% CI, 0.52-0.72), respectively. The three factors most frequently identified as helpful in making the diagnosis by our panel were cortical involvement in 65% of evaluations (253/390), neoplasm size in 51% (198/390), and pain in 50% (194/390). The interobserver agreement for the proposed initial treatment strategy after diagnosis was poor (κ = 0.21; 95% CI, 0.18-0.24). This study showed barely fair interobserver and fair to good intraobserver agreement for grading of intramedullary cartilaginous neoplasms by orthopaedic oncologists using initial clinical and imaging findings. These results reflect the insufficient guidance interpreting clinical and imaging features, and the limitations of the systems we use today when making these diagnoses. In the same way, they generate concern for the implications that this may have on different treatment strategies and the future prognosis of our patients. Future studies should build on these observations and focus on clarifying our criteria of diagnosis so that treatment recommendations are standardized regardless of the treating institution or oncologist. Level III, diagnostic study.

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

Imaging features of macrodystrophia lipomatosa: an unusual cause of a brawny arm

PubMed Central

Dhanasekaran, Jagadeesan; Reddy, Ajit Kumar; Sarawagi, Radha; Lakshmanan, Prakash Manikka

2014-01-01

Macrodystrophia lipomatosa (MDL), a rare non-hereditary congenital disorder of localised gigantism, is characterised by progressive proliferation of all mesenchymal elements, with a disproportionate increase in fibroadipose tissue. We report a case of a 19-year-old man who presented with a history of painless enlargement of the left upper limb since childhood, which was gradually increasing in size and predominantly involving the radial aspect of the upper limb with relative sparing of the ulnar aspect. The patient was imaged with X-ray and MRI. Imaging and clinical features were classical of MDL. The patient underwent stage 1 reduction plasty of the left forearm; preoperative and histopathological findings confirmed the preoperative diagnosis. PMID:25406225

Clinical and Imaging Signs of Spondyloarthritis in First-Degree Relatives of HLA-B27-Positive Ankylosing Spondylitis Patients: The Pre-Spondyloarthritis (Pre-SpA) Cohort Study.

PubMed

Turina, Maureen C; de Winter, Janneke J; Paramarta, Jacky E; Gamala, Mihaela; Yeremenko, Nataliya; Nabibux, Marita N; Landewé, Robert; Baeten, Dominique L

2016-10-01

To investigate whether seemingly healthy first-degree relatives of patients with ankylosing spondylitis (AS) have clinical, laboratory, or imaging features of spondyloarthritis (SpA). First-degree relatives (ages 18-40 years) of HLA-B27-positive AS patients were included in the pre-spondyloarthritis (Pre-SpA) cohort, a prospective inception cohort study. Clinical, biologic, and imaging features were recorded. First-degree relatives were classified according to several sets of SpA classification criteria. We report baseline features of 51 first-degree relatives included in this study. Twenty-nine (57%) had back pain, 2 (4%) had psoriasis, 1 (2%) had inflammatory bowel disease, and 1 (2%) had uveitis. Three (6%) had low-grade sacroiliitis, 1 (2%) had cervical syndesmophytes on radiography, and 10 (20%) had bone marrow edema on magnetic resonance imaging of the sacroiliiac joints. Seventeen of 51 first-degree relatives (33%) fulfilled SpA classification criteria: 7 (14%) fulfilled both Assessment of SpondyloArthritis international Society (ASAS) axial SpA and European Spondylarthropathy Study Group (ESSG) classification criteria, 6 (12%) fulfilled only ASAS axial SpA classification criteria, and 4 (8%) fulfilled only ESSG classification criteria; 3 (6%) also fulfilled the Amor criteria. None fulfilled other SpA classification criteria. First-degree relatives fulfilling the ASAS axial SpA and/or ESSG classification criteria had more frequent inflammatory back pain, had a higher level of disease activity, and had more psoriasis. No differences were found in parameters of inflammation, peripheral and extraarticular disease other than psoriasis, and HLA-B27 positivity between those who did and those who did not fulfill the ASAS axial SpA and/or ESSG classification criteria. Four first-degree relatives (12%) who did not fulfill the ASAS axial SpA and/or ESSG classification criteria had imaging abnormalities suggestive of SpA. A substantial proportion of seemingly healthy first-degree relatives of HLA-B27-positive AS patients have clinical and/or imaging abnormalities suggestive of SpA. Thirty-three percent could be classified as having SpA. Further follow-up will show which first-degree relatives will develop clinically manifest SpA. © 2016, American College of Rheumatology.

Improved Image-Guided Laparoscopic Prostatectomy

DTIC Science & Technology

2012-08-01

prevalent technique used in widening the field of view (FOV) of medical ultrasound images. Also referred to as stitching or panorama , the ultra- sound mosaic...tissue which can add valu- able features to the B-mode panorama . Many clinical applications deal with large cancerous lesions which expand beyond the...1999) 203–233 2. Varghese, T., Zagzebski, J., Lee Jr., F.: Elastographic imaging of thermal lesions in the liver in vivo following radiofrequency

Rotator Cuff Tear Arthropathy: Pathophysiology, Imaging Characteristics, and Treatment Options.

PubMed

Eajazi, Alireza; Kussman, Steve; LeBedis, Christina; Guermazi, Ali; Kompel, Andrew; Jawa, Andrew; Murakami, Akira M

2015-11-01

The purpose of this article is to review the biomechanical properties of the rotator cuff and glenohumeral joint and the pathophysiology, imaging characteristics, and treatment options of rotator cuff tear arthropathy (RCTA). Although multiple pathways have been proposed as causes of RCTA, the exact cause remains unclear. Increasing knowledge about the clinical diagnosis, imaging features, and indicators of severity improves recognition and treatment of this pathologic condition.

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.

Automated image processing and analysis of cartilage MRI: enabling technology for data mining applied to osteoarthritis

PubMed Central

Tameem, Hussain Z.; Sinha, Usha S.

2011-01-01

Osteoarthritis (OA) is a heterogeneous and multi-factorial disease characterized by the progressive loss of articular cartilage. Magnetic Resonance Imaging has been established as an accurate technique to assess cartilage damage through both cartilage morphology (volume and thickness) and cartilage water mobility (Spin-lattice relaxation, T2). The Osteoarthritis Initiative, OAI, is a large scale serial assessment of subjects at different stages of OA including those with pre-clinical symptoms. The electronic availability of the comprehensive data collected as part of the initiative provides an unprecedented opportunity to discover new relationships in complex diseases such as OA. However, imaging data, which provides the most accurate non-invasive assessment of OA, is not directly amenable for data mining. Changes in morphometry and relaxivity with OA disease are both complex and subtle, making manual methods extremely difficult. This chapter focuses on the image analysis techniques to automatically localize the differences in morphometry and relaxivity changes in different population sub-groups (normal and OA subjects segregated by age, gender, and race). The image analysis infrastructure will enable automatic extraction of cartilage features at the voxel level; the ultimate goal is to integrate this infrastructure to discover relationships between the image findings and other clinical features. PMID:21785520

Automated image processing and analysis of cartilage MRI: enabling technology for data mining applied to osteoarthritis

NASA Astrophysics Data System (ADS)

Tameem, Hussain Z.; Sinha, Usha S.

2007-11-01

Osteoarthritis (OA) is a heterogeneous and multi-factorial disease characterized by the progressive loss of articular cartilage. Magnetic Resonance Imaging has been established as an accurate technique to assess cartilage damage through both cartilage morphology (volume and thickness) and cartilage water mobility (Spin-lattice relaxation, T2). The Osteoarthritis Initiative, OAI, is a large scale serial assessment of subjects at different stages of OA including those with pre-clinical symptoms. The electronic availability of the comprehensive data collected as part of the initiative provides an unprecedented opportunity to discover new relationships in complex diseases such as OA. However, imaging data, which provides the most accurate non-invasive assessment of OA, is not directly amenable for data mining. Changes in morphometry and relaxivity with OA disease are both complex and subtle, making manual methods extremely difficult. This chapter focuses on the image analysis techniques to automatically localize the differences in morphometry and relaxivity changes in different population sub-groups (normal and OA subjects segregated by age, gender, and race). The image analysis infrastructure will enable automatic extraction of cartilage features at the voxel level; the ultimate goal is to integrate this infrastructure to discover relationships between the image findings and other clinical features.

TDC-based readout electronics for real-time acquisition of high resolution PET bio-images

NASA Astrophysics Data System (ADS)

Marino, N.; Saponara, S.; Ambrosi, G.; Baronti, F.; Bisogni, M. G.; Cerello, P.,; Ciciriello, F.; Corsi, F.; Fanucci, L.; Ionica, M.; Licciulli, F.; Marzocca, C.; Morrocchi, M.; Pennazio, F.; Roncella, R.; Santoni, C.; Wheadon, R.; Del Guerra, A.

2013-02-01

Positron emission tomography (PET) is a clinical and research tool for in vivo metabolic imaging. The demand for better image quality entails continuous research to improve PET instrumentation. In clinical applications, PET image quality benefits from the time of flight (TOF) feature. Indeed, by measuring the photons arrival time on the detectors with a resolution less than 100 ps, the annihilation point can be estimated with centimeter resolution. This leads to better noise level, contrast and clarity of detail in the images either using analytical or iterative reconstruction algorithms. This work discusses a silicon photomultiplier (SiPM)-based magnetic-field compatible TOF-PET module with depth of interaction (DOI) correction. The detector features a 3D architecture with two tiles of SiPMs coupled to a single LYSO scintillator on both its faces. The real-time front-end electronics is based on a current-mode ASIC where a low input impedance, fast current buffer allows achieving the required time resolution. A pipelined time to digital converter (TDC) measures and digitizes the arrival time and the energy of the events with a timestamp of 100 ps and 400 ps, respectively. An FPGA clusters the data and evaluates the DOI, with a simulated z resolution of the PET image of 1.4 mm FWHM.

Effects of image compression and degradation on an automatic diabetic retinopathy screening algorithm

NASA Astrophysics Data System (ADS)

Agurto, C.; Barriga, S.; Murray, V.; Pattichis, M.; Soliz, P.

2010-03-01

Diabetic retinopathy (DR) is one of the leading causes of blindness among adult Americans. Automatic methods for detection of the disease have been developed in recent years, most of them addressing the segmentation of bright and red lesions. In this paper we present an automatic DR screening system that does approach the problem through the segmentation of features. The algorithm determines non-diseased retinal images from those with pathology based on textural features obtained using multiscale Amplitude Modulation-Frequency Modulation (AM-FM) decompositions. The decomposition is represented as features that are the inputs to a classifier. The algorithm achieves 0.88 area under the ROC curve (AROC) for a set of 280 images from the MESSIDOR database. The algorithm is then used to analyze the effects of image compression and degradation, which will be present in most actual clinical or screening environments. Results show that the algorithm is insensitive to illumination variations, but high rates of compression and large blurring effects degrade its performance.

Computed tomography in hypersensitivity pneumonitis: main findings, differential diagnosis and pitfalls.

PubMed

Dias, Olívia Meira; Baldi, Bruno Guedes; Pennati, Francesca; Aliverti, Andrea; Chate, Rodrigo Caruso; Sawamura, Márcio Valente Yamada; Carvalho, Carlos Roberto Ribeiro de; Albuquerque, André Luis Pereira de

2018-01-01

Hypersensitivity pneumonitis (HP) is a disease with variable clinical presentation in which inflammation in the lung parenchyma is caused by the inhalation of specific organic antigens or low molecular weight substances in genetically susceptible individuals. Alterations of the acute, subacute and chronic forms may eventually overlap, and the diagnosis based on temporality and presence of fibrosis (acute/inflammatory HP vs. chronic HP) seems to be more feasible and useful in clinical practice. Differential diagnosis of chronic HP with other interstitial fibrotic diseases is challenging due to the overlap of the clinical history, and the functional and imaging findings of these pathologies in the terminal stages. Areas covered: This article reviews the essential features of HP with emphasis on imaging features. Moreover, the main methodological limitations of high-resolution computed tomography (HRCT) interpretation are discussed, as well as new perspectives with volumetric quantitative CT analysis as a useful tool for retrieving detailed and accurate information from the lung parenchyma. Expert commentary: Mosaic attenuation is a prominent feature of this disease, but air trapping in chronic HP seems overestimated. Quantitative analysis has the potential to estimate the involvement of the pulmonary parenchyma more accurately and could correlate better with pulmonary function results.

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.

The syndrome of perisylvian polymicrogyria with congenital arthrogryposis.

PubMed

Poduri, Annapurna; Chitsazzadeh, Vida; D'Arrigo, Stefano; Fedrizzi, Ermellina; Pantaleoni, Chiara; Riva, Daria; Busse, Claudia; Küster, Helmut; Duplessis, Adre; Gaitanis, John; Sahin, Mustafa; Garganta, Cheryl; Topcu, Meral; Dies, Kira A; Barry, Brenda J; Partlow, Jennifer; Barkovich, A James; Walsh, Christopher A; Chang, Bernard S

2010-08-01

Bilateral perisylvian polymicrogyria (BPP) is a well-recognized malformation of cortical development commonly associated with epilepsy, cognitive impairment, and oromotor apraxia. Reports have suggested the association of BPP with arthrogryposis multiplex congenita. We sought to investigate the clinical, electrophysiological, and neuroradiological features of this combined syndrome to determine if there are unique features that distinguish BPP with arthrogryposis from BPP alone. Cases of BPP with congenital arthrogryposis were identified from a large research database of individuals with polymicrogyria. Clinical features (including oromotor function, seizures, and joint contractures), MR brain imaging, and results of neuromuscular testing were reviewed. Ten cases of BPP with congenital arthrogryposis were identified. Most cases had some degree of oromotor apraxia. Only a few had seizures, but a majority of cases were still young children. Electrophysiological studies provided evidence for lower motor neuron or peripheral nervous system involvement. On brain imaging, bilateral polymicrogyria (PMG) centered along the Sylvian fissures was seen, with variable extension frontally or parietally; no other cortical malformations were present. We did not identify obvious neuroimaging features that distinguish this syndrome from that of BPP without arthrogryposis. The clinical and neuroimaging features of the syndrome of BPP with congenital arthrogryposis appear similar to those seen in cases of isolated BPP without joint contractures, but electrophysiological studies often demonstrate coexistent lower motor neuron or peripheral nervous system pathology. These findings suggest that BPP with arthrogryposis may have a genetic etiology with effects at two levels of the neuraxis. Copyright 2009 Elsevier B.V. All rights reserved.

Technical Note: Rod phantom analysis for comparison of PET detector sampling and reconstruction methods.

PubMed

Wollenweber, Scott D; Kemp, Brad J

2016-11-01

This investigation aimed to develop a scanner quantification performance methodology and compare multiple metrics between two scanners under different imaging conditions. Most PET scanners are designed to work over a wide dynamic range of patient imaging conditions. Clinical constraints, however, often impact the realization of the entitlement performance for a particular scanner design. Using less injected dose and imaging for a shorter time are often key considerations, all while maintaining "acceptable" image quality and quantitative capability. A dual phantom measurement including resolution inserts was used to measure the effects of in-plane (x, y) and axial (z) system resolution between two PET/CT systems with different block detector crystal dimensions. One of the scanners had significantly thinner slices. Several quantitative measures, including feature contrast recovery, max/min value, and feature profile accuracy were derived from the resulting data and compared between the two scanners and multiple phantoms and alignments. At the clinically relevant count levels used, the scanner with thinner slices had improved performance of approximately 2%, averaged over phantom alignments, measures, and reconstruction methods, for the head-sized phantom, mainly demonstrated with the rods aligned perpendicular to the scanner axis. That same scanner had a slightly decreased performance of -1% for the larger body-size phantom, mostly due to an apparent noise increase in the images. Most of the differences in the metrics between the two scanners were less than 10%. Using the proposed scanner performance methodology, it was shown that smaller detector elements and a larger number of image voxels require higher count density in order to demonstrate improved image quality and quantitation. In a body imaging scenario under typical clinical conditions, the potential advantages of the design must overcome increases in noise due to lower count density.

Infiltrative cervical lesions causing symptomatic occipital neuralgia.

PubMed

Sierra-Hidalgo, F; Ruíz, J; Morales-Cartagena, A; Martínez-Salio, A; Serna, J de la; Hernández-Gallego, J

2011-10-01

Occipital neuralgia is a well-recognized cause of posterior head and neck pain that may associate mild sensory changes in the cutaneous distribution of the occipital nerves, lacking a recognizable local structural aetiology in most cases. Atypical clinical features or an abnormal neurological examination are alerts for a potential underlying cause of pain, although cases of clinically typical occipital neuralgia as isolated manifestation of lesions of the cervical spinal cord, cervical roots, or occipital nerves have been increasingly reported. We describe two cases (one with typical and another one with atypical clinical features) of occipital neuralgia secondary to paravertebral pyomyositis and vertebral relapse of multiple myeloma in patients with relevant medical history that aroused the possibility of an underlying structural lesion. We discuss the need for cranio-cervical magnetic resonance imaging in all patients with occipital neuralgia, even when typical clinical features are present and neurological examination is completely normal.

Telangiectatic adenoma – computed tomography and magnetic resonance findings: a case report and review of the literature

PubMed Central

2009-01-01

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. PMID:19128493

SU-F-R-42: Association of Radiomic and Metabolic Tumor Volumes in Radiation Treatment of Glioblastoma Multiforme

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

Lopez, C; Nagornaya, N; Parra, N

Purpose: High-throughput extraction of imaging and metabolomic quantitative features from MRI and MR Spectroscopy Imaging (MRSI) of Glioblastoma Multiforme (GBM) results in tens of variables per patient. In radiotherapy (RT) of GBM, the relevant metabolic tumor volumes (MTVs) are related to aberrant levels of N-acetyl Aspartate (NAA) and Choline (Cho). Corresponding Clinical Target Volumes (CTVs) for RT planning are based on Contrast Enhancing T1-weighted MRI (CE-T1w) and T2-weighted/Fluid Attenuated Inversion Recovery (FLAIR) MRI. The objective is to build a framework for investigation of associations between imaging, CTV, and MTV features better understanding of the underlying information in the CTVs andmore » dependencies between these volumes. Methods: Necrotic portions, enhancing lesion and edema were manually contoured on T1w/T2w images for 17 GBM patients. CTVs and MTVs for NAA (MTV{sub NAA}) and Cho (MTV{sub Cho}) were constructed. Tumors were scored categorically for ten semantic imaging traits by neuroradiologist. All features were investigated for redundancy. Two-way correlations between imaging and RT/MTV features were visualized as heat maps. Associations between MTV{sub NAA}, MTV{sub Cho} and imaging features were studied using Spearman correlation. Results: 39 imaging features were computed per patient. Half of the imaging traits were replaced with automatically extracted continuous variables. 21 features were extracted from MTVs/CTVs. There were a high number (43) of significant correlations of imaging with CTVs/MTV{sub NAA} while very few (10) significant correlations were with CTVs/MTV{sub Cho}. MTV{sub NAA} was found to be closely associated with MRI volumes, MTV{sub Cho} remains elusive for characterization with imaging. Conclusion: A framework for investigation of co-dependency between MRI and RT/metabolic features is established. A series of semantic imaging traits were replaced with automatically extracted continuous variables. The approach will allow for exploration of relationships between sizes and intersection of imaging features of tumors, RT volumes, metabolite concentrations and comparing those to therapy outcome, quality of life evaluation and overall survival rate. This publication was supported by Grant 10BN03 from Bankhead Coley Cancer Research Program, R01EB000822, R01EB016064, and R01CA172210 from the National Institutes of Health, and Indo-US Science & Technology Forum award #20-2009. Bhaswati Roy received financial assistance from University Grant Commission, New Delhi, India.« less

Comparing features sets for content-based image retrieval in a medical-case database

NASA Astrophysics Data System (ADS)

Muller, Henning; Rosset, Antoine; Vallee, Jean-Paul; Geissbuhler, Antoine

2004-04-01

Content-based image retrieval systems (CBIRSs) have frequently been proposed for the use in medical image databases and PACS. Still, only few systems were developed and used in a real clinical environment. It rather seems that medical professionals define their needs and computer scientists develop systems based on data sets they receive with little or no interaction between the two groups. A first study on the diagnostic use of medical image retrieval also shows an improvement in diagnostics when using CBIRSs which underlines the potential importance of this technique. This article explains the use of an open source image retrieval system (GIFT - GNU Image Finding Tool) for the retrieval of medical images in the medical case database system CasImage that is used in daily, clinical routine in the university hospitals of Geneva. Although the base system of GIFT shows an unsatisfactory performance, already little changes in the feature space show to significantly improve the retrieval results. The performance of variations in feature space with respect to color (gray level) quantizations and changes in texture analysis (Gabor filters) is compared. Whereas stock photography relies mainly on colors for retrieval, medical images need a large number of gray levels for successful retrieval, especially when executing feedback queries. The results also show that a too fine granularity in the gray levels lowers the retrieval quality, especially with single-image queries. For the evaluation of the retrieval peformance, a subset of the entire case database of more than 40,000 images is taken with a total of 3752 images. Ground truth was generated by a user who defined the expected query result of a perfect system by selecting images relevant to a given query image. The results show that a smaller number of gray levels (32 - 64) leads to a better retrieval performance, especially when using relevance feedback. The use of more scales and directions for the Gabor filters in the texture analysis also leads to improved results but response time is going up equally due to the larger feature space. CBIRSs can be of great use in managing large medical image databases. They allow to find images that might otherwise be lost for research and publications. They also give students students the possibility to navigate within large image repositories. In the future, CBIR might also become more important in case-based reasoning and evidence-based medicine to support the diagnostics because first studies show good results.

Noninvasive characterisation of foot reflexology areas by swept source-optical coherence tomography in patients with low back pain.

PubMed

Dalal, Krishna; Elanchezhiyan, D; Das, Raunak; Dalal, Devjyoti; Pandey, Ravindra Mohan; Chatterjee, Subhamoy; Upadhyay, Ashish Datt; Maran, V Bharathi; Chatterjee, Jyotirmoy

2013-01-01

Objective. When exploring the scientific basis of reflexology techniques, elucidation of the surface and subsurface features of reflexology areas (RAs) is crucial. In this study, the subcutaneous features of RAs related to the lumbar vertebrae were evaluated by swept source-optical coherence tomography (SS-OCT) in subjects with and without low back pain (LBP). Methods. Volunteers without LBP (n = 6 (male : female = 1 : 1)) and subjects with LBP (n = 15 (male : female = 2 : 3)) were clinically examined in terms of skin colour (visual perception), localised tenderness (visual analogue scale) and structural as well as optical attributes as per SS-OCT. From each subject, 6 optical tomograms were recorded from equidistant transverse planes along the longitudinal axis of the RAs, and from each tomogram, 25 different spatial locations were considered for recording SS-OCT image attributes. The images were analysed with respect to the optical intensity distributions and thicknesses of different skin layers by using AxioVision Rel. 4.8.2 software. The SS-OCT images could be categorised into 4 pathological grades (i.e., 0, 1, 2, and 3) according to distinctness in the visible skin layers. Results. Three specific grades for abnormalities in SS-OCT images were identified considering gradual loss of distinctness and increase in luminosity of skin layers. Almost 90.05% subjects were of mixed type having predominance in certain grades. Conclusion. The skin SS-OCT system demonstrated a definite association of the surface features of healthy/unhealthy RAs with cutaneous features and the clinical status of the lumbar vertebrae.

Noninvasive Characterisation of Foot Reflexology Areas by Swept Source-Optical Coherence Tomography in Patients with Low Back Pain

PubMed Central

Dalal, Krishna; Elanchezhiyan, D.; Das, Raunak; Dalal, Devjyoti; Pandey, Ravindra Mohan; Chatterjee, Subhamoy; Upadhyay, Ashish Datt; Maran, V. Bharathi; Chatterjee, Jyotirmoy

2013-01-01

Objective. When exploring the scientific basis of reflexology techniques, elucidation of the surface and subsurface features of reflexology areas (RAs) is crucial. In this study, the subcutaneous features of RAs related to the lumbar vertebrae were evaluated by swept source-optical coherence tomography (SS-OCT) in subjects with and without low back pain (LBP). Methods. Volunteers without LBP (n = 6 (male : female = 1 : 1)) and subjects with LBP (n = 15 (male : female = 2 : 3)) were clinically examined in terms of skin colour (visual perception), localised tenderness (visual analogue scale) and structural as well as optical attributes as per SS-OCT. From each subject, 6 optical tomograms were recorded from equidistant transverse planes along the longitudinal axis of the RAs, and from each tomogram, 25 different spatial locations were considered for recording SS-OCT image attributes. The images were analysed with respect to the optical intensity distributions and thicknesses of different skin layers by using AxioVision Rel. 4.8.2 software. The SS-OCT images could be categorised into 4 pathological grades (i.e., 0, 1, 2, and 3) according to distinctness in the visible skin layers. Results. Three specific grades for abnormalities in SS-OCT images were identified considering gradual loss of distinctness and increase in luminosity of skin layers. Almost 90.05% subjects were of mixed type having predominance in certain grades. Conclusion. The skin SS-OCT system demonstrated a definite association of the surface features of healthy/unhealthy RAs with cutaneous features and the clinical status of the lumbar vertebrae. PMID:23662156

Generation of 3D synthetic breast tissue

NASA Astrophysics Data System (ADS)

Elangovan, Premkumar; Dance, David R.; Young, Kenneth C.; Wells, Kevin

2016-03-01

Virtual clinical trials are an emergent approach for the rapid evaluation and comparison of various breast imaging technologies and techniques using computer-based modeling tools. A fundamental requirement of this approach for mammography is the use of realistic looking breast anatomy in the studies to produce clinically relevant results. In this work, a biologically inspired approach has been used to simulate realistic synthetic breast phantom blocks for use in virtual clinical trials. A variety of high and low frequency features (including Cooper's ligaments, blood vessels and glandular tissue) have been extracted from clinical digital breast tomosynthesis images and used to simulate synthetic breast blocks. The appearance of the phantom blocks was validated by presenting a selection of simulated 2D and DBT images interleaved with real images to a team of experienced readers for rating using an ROC paradigm. The average areas under the curve for 2D and DBT images were 0.53+/-.04 and 0.55+/-.07 respectively; errors are the standard errors of the mean. The values indicate that the observers had difficulty in differentiating the real images from simulated images. The statistical properties of simulated images of the phantom blocks were evaluated by means of power spectrum analysis. The power spectrum curves for real and simulated images closely match and overlap indicating good agreement.

User-guided segmentation for volumetric retinal optical coherence tomography images

PubMed Central

Yin, Xin; Chao, Jennifer R.; Wang, Ruikang K.

2014-01-01

Abstract. Despite the existence of automatic segmentation techniques, trained graders still rely on manual segmentation to provide retinal layers and features from clinical optical coherence tomography (OCT) images for accurate measurements. To bridge the gap between this time-consuming need of manual segmentation and currently available automatic segmentation techniques, this paper proposes a user-guided segmentation method to perform the segmentation of retinal layers and features in OCT images. With this method, by interactively navigating three-dimensional (3-D) OCT images, the user first manually defines user-defined (or sketched) lines at regions where the retinal layers appear very irregular for which the automatic segmentation method often fails to provide satisfactory results. The algorithm is then guided by these sketched lines to trace the entire 3-D retinal layer and anatomical features by the use of novel layer and edge detectors that are based on robust likelihood estimation. The layer and edge boundaries are finally obtained to achieve segmentation. Segmentation of retinal layers in mouse and human OCT images demonstrates the reliability and efficiency of the proposed user-guided segmentation method. PMID:25147962

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.

User-guided segmentation for volumetric retinal optical coherence tomography images.

PubMed

Yin, Xin; Chao, Jennifer R; Wang, Ruikang K

2014-08-01

Despite the existence of automatic segmentation techniques, trained graders still rely on manual segmentation to provide retinal layers and features from clinical optical coherence tomography (OCT) images for accurate measurements. To bridge the gap between this time-consuming need of manual segmentation and currently available automatic segmentation techniques, this paper proposes a user-guided segmentation method to perform the segmentation of retinal layers and features in OCT images. With this method, by interactively navigating three-dimensional (3-D) OCT images, the user first manually defines user-defined (or sketched) lines at regions where the retinal layers appear very irregular for which the automatic segmentation method often fails to provide satisfactory results. The algorithm is then guided by these sketched lines to trace the entire 3-D retinal layer and anatomical features by the use of novel layer and edge detectors that are based on robust likelihood estimation. The layer and edge boundaries are finally obtained to achieve segmentation. Segmentation of retinal layers in mouse and human OCT images demonstrates the reliability and efficiency of the proposed user-guided segmentation method.

Comprehensive Computational Pathological Image Analysis Predicts Lung Cancer Prognosis.

PubMed

Luo, Xin; Zang, Xiao; Yang, Lin; Huang, Junzhou; Liang, Faming; Rodriguez-Canales, Jaime; Wistuba, Ignacio I; Gazdar, Adi; Xie, Yang; Xiao, Guanghua

2017-03-01

Pathological examination of histopathological slides is a routine clinical procedure for lung cancer diagnosis and prognosis. Although the classification of lung cancer has been updated to become more specific, only a small subset of the total morphological features are taken into consideration. The vast majority of the detailed morphological features of tumor tissues, particularly tumor cells' surrounding microenvironment, are not fully analyzed. The heterogeneity of tumor cells and close interactions between tumor cells and their microenvironments are closely related to tumor development and progression. The goal of this study is to develop morphological feature-based prediction models for the prognosis of patients with lung cancer. We developed objective and quantitative computational approaches to analyze the morphological features of pathological images for patients with NSCLC. Tissue pathological images were analyzed for 523 patients with adenocarcinoma (ADC) and 511 patients with squamous cell carcinoma (SCC) from The Cancer Genome Atlas lung cancer cohorts. The features extracted from the pathological images were used to develop statistical models that predict patients' survival outcomes in ADC and SCC, respectively. We extracted 943 morphological features from pathological images of hematoxylin and eosin-stained tissue and identified morphological features that are significantly associated with prognosis in ADC and SCC, respectively. Statistical models based on these extracted features stratified NSCLC patients into high-risk and low-risk groups. The models were developed from training sets and validated in independent testing sets: a predicted high-risk group versus a predicted low-risk group (for patients with ADC: hazard ratio = 2.34, 95% confidence interval: 1.12-4.91, p = 0.024; for patients with SCC: hazard ratio = 2.22, 95% confidence interval: 1.15-4.27, p = 0.017) after adjustment for age, sex, smoking status, and pathologic tumor stage. The results suggest that the quantitative morphological features of tumor pathological images predict prognosis in patients with lung cancer. Copyright © 2016 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.

TU-CD-BRB-10: 18F-FDG PET Image-Derived Tumor Features Highlight Altered Pathways Identified by Trancriptomic Analysis in Head and Neck Cancer

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

Tixier, F; INSERM UMR1101 LaTIM, Brest; Cheze-Le-Rest, C

2015-06-15

Purpose: Several quantitative features can be extracted from 18F-FDG PET images, such as standardized uptake values (SUVs), metabolic tumor volume (MTV), shape characterization (SC) or intra-tumor radiotracer heterogeneity quantification (HQ). Some of these features calculated from baseline 18F-FDG PET images have shown a prognostic and predictive clinical value. It has been hypothesized that these features highlight underlying tumor patho-physiological processes at smaller scales. The objective of this study was to investigate the ability of recovering alterations of signaling pathways from FDG PET image-derived features. Methods: 52 patients were prospectively recruited from two medical centers (Brest and Poitiers). All patients underwentmore » an FDG PET scan for staging and biopsies of both healthy and primary tumor tissues. Biopsies went through a transcriptomic analysis performed in four spates on 4×44k chips (Agilent™). Primary tumors were delineated in the PET images using the Fuzzy Locally Adaptive Bayesian algorithm and characterized using 10 features including SUVs, SC and HQ. A module network algorithm followed by functional annotation was exploited in order to link PET features with signaling pathways alterations. Results: Several PET-derived features were found to discriminate differentially expressed genes between tumor and healthy tissue (fold-change >2, p<0.01) into 30 co-regulated groups (p<0.05). Functional annotations applied to these groups of genes highlighted associations with well-known pathways involved in cancer processes, such as cell proliferation and apoptosis, as well as with more specific ones such as unsaturated fatty acids. Conclusion: Quantitative features extracted from baseline 18F-FDG PET images usually exploited only for diagnosis and staging, were identified in this work as being related to specific altered pathways and may show promise as tools for personalizing treatment decisions.« less

Stargardt disease: clinical features, molecular genetics, animal models and therapeutic options

PubMed Central

Tanna, Preena; Strauss, Rupert W; Fujinami, Kaoru; Michaelides, Michel

2017-01-01

Stargardt disease (STGD1; MIM 248200) is the most prevalent inherited macular dystrophy and is associated with disease-causing sequence variants in the gene ABCA4. Significant advances have been made over the last 10 years in our understanding of both the clinical and molecular features of STGD1, and also the underlying pathophysiology, which has culminated in ongoing and planned human clinical trials of novel therapies. The aims of this review are to describe the detailed phenotypic and genotypic characteristics of the disease, conventional and novel imaging findings, current knowledge of animal models and pathogenesis, and the multiple avenues of intervention being explored. PMID:27491360

Preliminary clinical results: an analyzing tool for 2D optical imaging in detection of active inflammation in rheumatoid arthritis

NASA Astrophysics Data System (ADS)

Adi Aizudin Bin Radin Nasirudin, Radin; Meier, Reinhard; Ahari, Carmen; Sievert, Matti; Fiebich, Martin; Rummeny, Ernst J.; No"l, Peter B.

2011-03-01

Optical imaging (OI) is a relatively new method in detecting active inflammation of hand joints of patients suffering from rheumatoid arthritis (RA). With the high number of people affected by this disease especially in western countries, the availability of OI as an early diagnostic imaging method is clinically highly relevant. In this paper, we present a newly in-house developed OI analyzing tool and a clinical evaluation study. Our analyzing tool extends the capability of existing OI tools. We include many features in the tool, such as region-based image analysis, hyper perfusion curve analysis, and multi-modality image fusion to aid clinicians in localizing and determining the intensity of inflammation in joints. Additionally, image data management options, such as the full integration of PACS/RIS, are included. In our clinical study we demonstrate how OI facilitates the detection of active inflammation in rheumatoid arthritis. The preliminary clinical results indicate a sensitivity of 43.5%, a specificity of 80.3%, an accuracy of 65.7%, a positive predictive value of 76.6%, and a negative predictive value of 64.9% in relation to clinical results from MRI. The accuracy of inflammation detection serves as evidence to the potential of OI as a useful imaging modality for early detection of active inflammation in patients with rheumatoid arthritis. With our in-house developed tool we extend the usefulness of OI imaging in the clinical arena. Overall, we show that OI is a fast, inexpensive, non-invasive and nonionizing yet highly sensitive and accurate imaging modality.-

A Japanese Encephalitis Patient Presenting with Parkinsonism with Corresponding Laterality of Magnetic Resonance and Dopamine Transporter Imaging Findings.

PubMed

Tadokoro, Koh; Ohta, Yasuyuki; Sato, Kota; Maeki, Takahiro; Sasaki, Ryo; Takahashi, Yoshiaki; Shang, Jingwei; Takemoto, Mami; Hishikawa, Nozomi; Yamashita, Toru; Lim, Chang Kweng; Tajima, Shigeru; Abe, Koji

2018-03-09

Japanese encephalitis (JE) survivors often present with nigrostriatal aftereffects with parkinsonian features. A 67-year-old woman with JE showed right-dominant clinical parkinsonism and left-dominant substantia nigra lesions after magnetic resonance imaging (MRI). Dopamine transporter (DAT) imaging using 123 I-labeled 2β-carbomethoxy-3β-(4-iodophenyl)-N-(3-fluoropropyl)-nortropane ( 123 I-FP-CIT) revealed a corresponding left-dominant decrease. The present case is the first to reveal a clear match of laterality between clinical parkinsonism, MRI-based substantia nigra lesions, and impaired DAT in presynaptic dopaminergic neurons in JE.

IDH mutation assessment of glioma using texture features of multimodal MR images

NASA Astrophysics Data System (ADS)

Zhang, Xi; Tian, Qiang; Wu, Yu-Xia; Xu, Xiao-Pan; Li, Bao-Juan; Liu, Yi-Xiong; Liu, Yang; Lu, Hong-Bing

2017-03-01

Purpose: To 1) find effective texture features from multimodal MRI that can distinguish IDH mutant and wild status, and 2) propose a radiomic strategy for preoperatively detecting IDH mutation patients with glioma. Materials and Methods: 152 patients with glioma were retrospectively included from the Cancer Genome Atlas. Corresponding T1-weighted image before- and post-contrast, T2-weighted image and fluid-attenuation inversion recovery image from the Cancer Imaging Archive were analyzed. Specific statistical tests were applied to analyze the different kind of baseline information of LrGG patients. Finally, 168 texture features were derived from multimodal MRI per patient. Then the support vector machine-based recursive feature elimination (SVM-RFE) and classification strategy was adopted to find the optimal feature subset and build the identification models for detecting the IDH mutation. Results: Among 152 patients, 92 and 60 were confirmed to be IDH-wild and mutant, respectively. Statistical analysis showed that the patients without IDH mutation was significant older than patients with IDH mutation (p<0.01), and the distribution of some histological subtypes was significant different between IDH wild and mutant groups (p<0.01). After SVM-RFE, 15 optimal features were determined for IDH mutation detection. The accuracy, sensitivity, specificity, and AUC after SVM-RFE and parameter optimization were 82.2%, 85.0%, 78.3%, and 0.841, respectively. Conclusion: This study presented a radiomic strategy for noninvasively discriminating IDH mutation of patients with glioma. It effectively incorporated kinds of texture features from multimodal MRI, and SVM-based classification strategy. Results suggested that features selected from SVM-RFE were more potential to identifying IDH mutation. The proposed radiomics strategy could facilitate the clinical decision making in patients with glioma.

A novel method for morphological pleomorphism and heterogeneity quantitative measurement: Named cell feature level co-occurrence matrix.

PubMed

Saito, Akira; Numata, Yasushi; Hamada, Takuya; Horisawa, Tomoyoshi; Cosatto, Eric; Graf, Hans-Peter; Kuroda, Masahiko; Yamamoto, Yoichiro

2016-01-01

Recent developments in molecular pathology and genetic/epigenetic analysis of cancer tissue have resulted in a marked increase in objective and measurable data. In comparison, the traditional morphological analysis approach to pathology diagnosis, which can connect these molecular data and clinical diagnosis, is still mostly subjective. Even though the advent and popularization of digital pathology has provided a boost to computer-aided diagnosis, some important pathological concepts still remain largely non-quantitative and their associated data measurements depend on the pathologist's sense and experience. Such features include pleomorphism and heterogeneity. In this paper, we propose a method for the objective measurement of pleomorphism and heterogeneity, using the cell-level co-occurrence matrix. Our method is based on the widely used Gray-level co-occurrence matrix (GLCM), where relations between neighboring pixel intensity levels are captured into a co-occurrence matrix, followed by the application of analysis functions such as Haralick features. In the pathological tissue image, through image processing techniques, each nucleus can be measured and each nucleus has its own measureable features like nucleus size, roundness, contour length, intra-nucleus texture data (GLCM is one of the methods). In GLCM each nucleus in the tissue image corresponds to one pixel. In this approach the most important point is how to define the neighborhood of each nucleus. We define three types of neighborhoods of a nucleus, then create the co-occurrence matrix and apply Haralick feature functions. In each image pleomorphism and heterogeneity are then determined quantitatively. For our method, one pixel corresponds to one nucleus feature, and we therefore named our method Cell Feature Level Co-occurrence Matrix (CFLCM). We tested this method for several nucleus features. CFLCM is showed as a useful quantitative method for pleomorphism and heterogeneity on histopathological image analysis.

Imaging Review of Skeletal Tumors of the Pelvis Malignant Tumors and Tumor Mimics

PubMed Central

Girish, Gandikota; Finlay, Karen; Fessell, David; Pai, Deepa; Dong, Qian; Jamadar, David

2012-01-01

Malignant lesions of the pelvis are not uncommon and need to be differentiated from benign lesions and tumor mimics. Appearances are sometimes nonspecific leading to consideration of a broad differential diagnosis. Clinical history, anatomic location, and imaging characterization can help narrow the differential diagnosis. The focus of this paper is to demonstrate the imaging features and the role of plain films, computed tomography, and magnetic resonance imaging for detecting and characterizing malignant osseous pelvic lesions and their common mimics. PMID:22593667

Incorporating Colour Information for Computer-Aided Diagnosis of Melanoma from Dermoscopy Images: A Retrospective Survey and Critical Analysis

PubMed Central

Drew, Mark S.

2016-01-01

Cutaneous melanoma is the most life-threatening form of skin cancer. Although advanced melanoma is often considered as incurable, if detected and excised early, the prognosis is promising. Today, clinicians use computer vision in an increasing number of applications to aid early detection of melanoma through dermatological image analysis (dermoscopy images, in particular). Colour assessment is essential for the clinical diagnosis of skin cancers. Due to this diagnostic importance, many studies have either focused on or employed colour features as a constituent part of their skin lesion analysis systems. These studies range from using low-level colour features, such as simple statistical measures of colours occurring in the lesion, to availing themselves of high-level semantic features such as the presence of blue-white veil, globules, or colour variegation in the lesion. This paper provides a retrospective survey and critical analysis of contributions in this research direction. PMID:28096807

Iterative feature refinement for accurate undersampled MR image reconstruction

NASA Astrophysics Data System (ADS)

Wang, Shanshan; Liu, Jianbo; Liu, Qiegen; Ying, Leslie; Liu, Xin; Zheng, Hairong; Liang, Dong

2016-05-01

Accelerating MR scan is of great significance for clinical, research and advanced applications, and one main effort to achieve this is the utilization of compressed sensing (CS) theory. Nevertheless, the existing CSMRI approaches still have limitations such as fine structure loss or high computational complexity. This paper proposes a novel iterative feature refinement (IFR) module for accurate MR image reconstruction from undersampled K-space data. Integrating IFR with CSMRI which is equipped with fixed transforms, we develop an IFR-CS method to restore meaningful structures and details that are originally discarded without introducing too much additional complexity. Specifically, the proposed IFR-CS is realized with three iterative steps, namely sparsity-promoting denoising, feature refinement and Tikhonov regularization. Experimental results on both simulated and in vivo MR datasets have shown that the proposed module has a strong capability to capture image details, and that IFR-CS is comparable and even superior to other state-of-the-art reconstruction approaches.

High resolution propagation-based imaging system for in vivo dynamic computed tomography of lungs in small animals

NASA Astrophysics Data System (ADS)

Preissner, M.; Murrie, R. P.; Pinar, I.; Werdiger, F.; Carnibella, R. P.; Zosky, G. R.; Fouras, A.; Dubsky, S.

2018-04-01

We have developed an x-ray imaging system for in vivo four-dimensional computed tomography (4DCT) of small animals for pre-clinical lung investigations. Our customized laboratory facility is capable of high resolution in vivo imaging at high frame rates. Characterization using phantoms demonstrate a spatial resolution of slightly below 50 μm at imaging rates of 30 Hz, and the ability to quantify material density differences of at least 3%. We benchmark our system against existing small animal pre-clinical CT scanners using a quality factor that combines spatial resolution, image noise, dose and scan time. In vivo 4DCT images obtained on our system demonstrate resolution of important features such as blood vessels and small airways, of which the smallest discernible were measured as 55–60 μm in cross section. Quantitative analysis of the images demonstrate regional differences in ventilation between injured and healthy lungs.

Dedifferentiated chondrosarcoma of the larynx: Radiological, gross, microscopic and clinical features.

PubMed

Magliocca, Kelly R; Edgar, Mark A; Corey, Amanda; Villari, Craig R

2017-10-01

Laryngeal chondrosarcoma is an uncommon malignancy with a predilection for the cricoid cartilage of adult male patients. Although rare, identification of aggressive chondrosarcoma variants, such as dedifferentiated chondrosarcoma (DDCS) may influence preoperative patient counseling, definitive surgical management, potential implementation of post-operative adjuvant therapy and prognosis. Herein we describe clinical and imaging features of laryngeal DDCS, the unique perspective of fresh and formalin fixed macroscopic examination, a spectrum of histopathologic findings, and detail the full course of the patient's disease. Copyright © 2017 Elsevier Inc. All rights reserved.

Guanidinoacetate Methyltransferase (GAMT) Deficiency: Late Onset of Movement Disorder and Preserved Expressive Language

ERIC Educational Resources Information Center

O'Rourke, Declan J.; Ryan, Stephanie; Salomons, Gajja; Jakobs, Cornelis; Monavari, Ahmad; King, Mary D.

2009-01-01

Guanidinoacetate methyltransferase (GAMT) deficiency is a disorder of creatine biosynthesis, characterized by early-onset learning disability and epilepsy in most affected children. Severe expressive language delay is a constant feature even in the mildest clinical phenotypes. We report the clinical, biochemical, imaging, and treatment data of two…

tranSMART-XNAT Connector tranSMART-XNAT connector-image selection based on clinical phenotypes and genetic profiles.

PubMed

He, Sijin; Yong, May; Matthews, Paul M; Guo, Yike

2017-03-01

TranSMART has a wide range of functionalities for translational research and a large user community, but it does not support imaging data. In this context, imaging data typically includes 2D or 3D sets of magnitude data and metadata information. Imaging data may summarise complex feature descriptions in a less biased fashion than user defined plain texts and numeric numbers. Imaging data also is contextualised by other data sets and may be analysed jointly with other data that can explain features or their variation. Here we describe the tranSMART-XNAT Connector we have developed. This connector consists of components for data capture, organisation and analysis. Data capture is responsible for imaging capture either from PACS system or directly from an MRI scanner, or from raw data files. Data are organised in a similar fashion as tranSMART and are stored in a format that allows direct analysis within tranSMART. The connector enables selection and download of DICOM images and associated resources using subjects' clinical phenotypic and genotypic criteria. tranSMART-XNAT connector is written in Java/Groovy/Grails. It is maintained and available for download at https://github.com/sh107/transmart-xnat-connector.git. sijin@ebi.ac.uk. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Swiss cheese striatum: clinical implications.

PubMed

Burnett, Melinda S; Witte, Robert J; Ahlskog, J Eric

2014-06-01

Markedly enlarged Virchow-Robin spaces throughout the striatum appear occasionally on magnetic resonance imaging (MRI) scans of the elderly, and this type of striatum is known as the Swiss cheese striatum (SCS); however, its clinical impact is unknown. To determine the clinical features associated with SCS detected on MRI scans. A blinded, retrospective case-control study using medical records from 2000 to 2007 obtained from an MRI database at the Mayo Clinic in Rochester, Minnesota, of residents 40 years of age or older of Olmsted County, Minnesota, who had extensive Mayo Clinic medical records and MRI reports suggestive of SCS. Cases with a severe form of SCS (n = 27) were randomly selected for comparison with age-, sex-, and examination year-matched controls (n = 52) with a minimal form of SCS or no SCS. Magnetic resonance imaging. Associations of clinical and imaging features with the presence of a severe form of SCS. Medical records were reviewed for clinical features such as parkinsonism, dementia, and vascular risk factors. The MRI scans were visually scored for degree of leukoaraiosis, central atrophy, and cortical atrophy. No significant differences were found between those with a severe form of SCS and controls in rates of parkinsonism (19% vs 17%; odds ratio, 1.09 [95% CI, 0.28-4.16]) or dementia of any type (30% vs 21%; odds ratio, 1.57 [95% CI, 0.48-5.13]). Vascular risk factors were not significantly different between groups. Swiss cheese striatum correlated with degree of leukoaraiosis (P < .001). Potential associations with visualized cortical atrophy (P = .01), nonobstructive urinary incontinence (18.5% vs 3.9%; P = .04), and syncope (37% vs 9.6%; P = .01) did not hold up after correction for the false discovery rate. Our study suggests that marked cribriform change in the striatum was not associated with the development of extrapyramidal clinical disorders, including parkinsonism. The association of SCS with leukoaraiosis suggests that it is part of a more generalized cerebrovascular process. Skepticism is called for when attributing clinical symptoms to this MRI finding.

Imaging features of non-traumatic vascular liver emergencies.

PubMed

Onur, Mehmet Ruhi; Karaosmanoglu, Ali Devrim; Akca, Onur; Ocal, Osman; Akpinar, Erhan; Karcaaltincaba, Musturay

2017-05-01

Acute non-traumatic liver disorders can originate from abnormalities of the hepatic artery, portal vein and hepatic veins. Ultrasonography and computed tomography can be used in non-traumatic acute vascular liver disorders according to patient status, indication and appropriateness of imaging modality. Awareness of the imaging findings, in the appropriate clinical context, is crucial for prompt and correct diagnosis, as delay may cause severe consequences with significant morbidity and mortality. This review article will discuss imaging algorithms, and multimodality imaging findings for suspected acute vascular disorders of the liver.

Ultra-Widefield Steering-Based SD-OCT Imaging of the Retinal Periphery

PubMed Central

Choudhry, Netan; Golding, John; Manry, Matthew W.; Rao, Rajesh C.

2016-01-01

Objective To describe the spectral-domain optical coherence tomography (SD-OCT) features of peripheral retinal findings using an ultra-widefield (UWF) steering technique to image the retinal periphery. Design Observational study. Participants 68 patients (68 eyes) with 19 peripheral retinal features. Main Outcome Measures SD-OCT-based structural features. Methods Nineteen peripheral retinal features including: vortex vein, congenital hypertrophy of the retinal pigment epithelium (CHRPE), pars plana, ora serrata pearl, typical cystoid degeneration (TCD), cystic retinal tuft, meridional fold, lattice and cobblestone degeneration, retinal hole, retinal tear, rhegmatogenous retinal detachment (RRD), typical degenerative senile retinoschisis, peripheral laser coagulation scars, ora tooth, cryopexy scars (retinal tear and treated retinoblastoma scar), bone spicules, white without pressure, and peripheral drusen were identified by peripheral clinical examination. Near infrared (NIR) scanning laser ophthalmoscopy (SLO) images and SD-OCT of these entities were registered to UWF color photographs. Results SD-OCT resolved structural features of all peripheral findings. Dilated hyporeflective tubular structures within the choroid were observed in the vortex vein. Loss of retinal lamination, neural retinal attenuation, RPE loss or hypertrophy were seen in several entities including CHRPE, ora serrata pearl, TCD, cystic retinal tuft, meridional fold, lattice and cobblestone degenerations. Hyporeflective intraretinal spaces, indicating cystoid or schitic fluid, were seen in ora serrata pearl, ora tooth, TCD, cystic retinal tuft, meridional fold, retinal hole, and typical degenerative senile retinoschisis. The vitreoretinal interface, which often consisted of lamellae-like structures of the condensed cortical vitreous near or adherent to the neural retina, appeared clearly in most peripheral findings, confirming its association with many low-risk and vision-threatening pathologies such as lattice degeneration, meridional folds, retinal breaks, and RRDs. Conclusions UWF steering technique-based SD-OCT imaging of the retinal periphery is feasible with current commercially available devices, and provides detailed anatomical information of the peripheral retina, including benign and pathological entities, not previously imaged. This imaging technique may deepen our structural understanding of these entities, their potentially associated macular and systemic pathologies, and may influence decision-making in clinical practice, particularly in areas with teleretinal capabilities but poor access to retinal specialists. PMID:26992837

Multimodality Imaging Approach towards Primary Aortic Sarcomas Arising after Endovascular Abdominal Aortic Aneurysm Repair: Case Series Report.

PubMed

Kamran, Mudassar; Fowler, Kathryn J; Mellnick, Vincent M; Sicard, Gregorio A; Narra, Vamsi R

2016-06-01

Primary aortic neoplasms are rare. Aortic sarcoma arising after endovascular aneurysm repair (EVAR) is a scarce subset of primary aortic malignancies, reports of which are infrequent in the published literature. The diagnosis of aortic sarcoma is challenging due to its non-specific clinical presentation, and the prognosis is poor due to delayed diagnosis, rapid proliferation, and propensity for metastasis. Post-EVAR, aortic sarcomas may mimic other more common aortic processes on surveillance imaging. Radiologists are rarely knowledgeable about this rare entity for which multimodality imaging and awareness are invaluable in early diagnosis. A series of three pathologically confirmed cases are presented to display the multimodality imaging features and clinical presentations of aortic sarcoma arising after EVAR.

Clinical applications of optical coherence tomography in the posterior pole: the 2011 José Manuel Espino Lecture – Part II

PubMed Central

Arevalo, J Fernando; Lasave, Andres F; Arias, Juan D; Serrano, Martin A; Arevalo, Fernando A

2013-01-01

Optical coherence tomography (OCT) is a high-resolution, cross-sectional imaging technique that allows detailed assessment of retinal thickness and morphologic evaluation of the retinal layers. This technology has developed quickly over the past two decades. OCT imaging has rapidly been integrated into routine ophthalmic clinical practice and trials. It has complemented fluorescein angiography in many instances, especially in the diagnosis and management of retinal disorders, including diabetic macular edema and age-related macular degeneration. With OCT, the exact localization of pathologic features can be visualized in segmentation maps of the retina, and this has allowed OCT to be used to evaluate specific features that may serve as predictive factors in the prognosis and follow up of these pathologies. Therefore, it has become an important clinical and research tool for the diagnosis, follow up, treatment, and assessment of new treatment modalities for all diseases that affect the posterior pole of the eye. PMID:24235811

Multiple Schwannomas of the Spine: Review of the Schwannomatosis or Congenital Neurilemmomatosis: A Case Report.

PubMed

Lee, Sang-Hoon; Kim, Se-Hoon; Kim, Bum-Joon; Lim, Dong-Jun

2015-06-01

Schwannomas are the most common benign nerve sheath tumors originating in Schwann cells. With special conditions like neurofibromatosis type 2 or entity called schwannomatosis, patients develop multiple schwannomas. But in clinical setting, distinguishing schwannomatosis from neurofibromatosis type 2 is challengeable. We describe 58-year-old male who presented with severe neuropathic pain, from schwannomatosis featuring multiple schwannomas of spine and trunk, and underwent surgical treatment. We demonstrate his radiologic and clinical findings, and discuss about important clinical features of this condition. To confirm schwannomatosis, we performed brain magnetic resonance imaging, and took his familial history. Staged surgery was done for pathological confirmation and relief of the pain. Schwannomatosis and neurofibromatosis type 2 are similar but different disease. There are diagnostic hallmarks of these conditions, including familial history, pathology, and brain imaging. Because of different prognosis, the two diseases must be distinguished, so diagnostic tests that are mentioned above should be performed in caution.

Multiple Schwannomas of the Spine: Review of the Schwannomatosis or Congenital Neurilemmomatosis: A Case Report

PubMed Central

Lee, Sang-Hoon; Kim, Bum-Joon; Lim, Dong-Jun

2015-01-01

Schwannomas are the most common benign nerve sheath tumors originating in Schwann cells. With special conditions like neurofibromatosis type 2 or entity called schwannomatosis, patients develop multiple schwannomas. But in clinical setting, distinguishing schwannomatosis from neurofibromatosis type 2 is challengeable. We describe 58-year-old male who presented with severe neuropathic pain, from schwannomatosis featuring multiple schwannomas of spine and trunk, and underwent surgical treatment. We demonstrate his radiologic and clinical findings, and discuss about important clinical features of this condition. To confirm schwannomatosis, we performed brain magnetic resonance imaging, and took his familial history. Staged surgery was done for pathological confirmation and relief of the pain. Schwannomatosis and neurofibromatosis type 2 are similar but different disease. There are diagnostic hallmarks of these conditions, including familial history, pathology, and brain imaging. Because of different prognosis, the two diseases must be distinguished, so diagnostic tests that are mentioned above should be performed in caution. PMID:26217390

[Magnetic Resonance Imaging Conversion Predictors of Clinically Isolated Syndrome to Multiple Sclerosis].

PubMed

Peixoto, Sara; Abreu, Pedro

2016-11-01

Clinically isolated syndrome may be the first manifestation of multiple sclerosis, a chronic demyelinating disease of the central nervous system, and it is defined by a single clinical episode suggestive of demyelination. However, patients with this syndrome, even with long term follow up, may not develop new symptoms or demyelinating lesions that fulfils multiple sclerosis diagnostic criteria. We reviewed, in clinically isolated syndrome, what are the best magnetic resonance imaging findings that may predict its conversion to multiple sclerosis. A search was made in the PubMed database for papers published between January 2010 and June 2015 using the following terms: 'clinically isolated syndrome', 'cis', 'multiple sclerosis', 'magnetic resonance imaging', 'magnetic resonance' and 'mri'. In this review, the following conventional magnetic resonance imaging abnormalities found in literature were included: lesion load, lesion location, Barkhof's criteria and brain atrophy related features. The non conventional magnetic resonance imaging techniques studied were double inversion recovery, magnetization transfer imaging, spectroscopy and diffusion tensor imaging. The number and location of demyelinating lesions have a clear role in predicting clinically isolated syndrome conversion to multiple sclerosis. On the other hand, more data are needed to confirm the ability to predict this disease development of non conventional techniques and remaining neuroimaging abnormalities. In forthcoming years, in addition to the established predictive value of the above mentioned neuroimaging abnormalities, different clinically isolated syndrome neuroradiological findings may be considered in multiple sclerosis diagnostic criteria and/or change its treatment recommendations.

[Clinical features and management of acute myositis in idiopathic orbital inflammation].

PubMed

Halimi, E; Rosenberg, R; Wavreille, O; Bouckehove, S; Franquet, N; Labalette, P

2013-09-01

Acute myositis is the second most common component of non-specific orbital inflammation. We will describe its clinical features and natural history. This is a retrospective study of 10 cases. The diagnosis of acute myositis was based on clinical and imaging criteria. Our study includes five men and five women. The average age was 35.8 years (17-59 years). Clinical symptoms were: pain increased on eye movement (10/10), diplopia (4/10), proptosis (6/10), visual loss (3/10), lid edema (6/10), conjunctival hyperemia (7/10), anterior scleritis (2/10), episcleritis (2/10), chemosis (4/10), upper lid retraction (1/10), limitation of eye movement (3/10), fundus abnormalities (2/10). Imaging showed thickening of one or more extraocular muscles (10/10). Recovery was complete with anti-inflammatory therapy in six patients. Three patients experienced recurrence, and one patient had a clinical rebound upon tapering the treatment. Acute myositis can be defined by pain on eye movement, signs of inflammation, and extraocular muscle thickening on imaging. If the clinical presentation is typical, histopathological analysis can be deferred but remains necessary in cases of poor response to treatment, chronic duration or suspicion of tumor infiltration. The diagnosis of acute myositis may be suspected in the presence of consistent, well-defined clinical signs. Contiguous inflammation is often associated. Treatment is based on steroids or non-steroidal treatment anti-inflammatory therapy, administered alone or consecutively. Recurrences are frequent but do not alter the final prognosis. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

Textural analysis of optical coherence tomography skin images: quantitative differentiation between healthy and cancerous tissues

NASA Astrophysics Data System (ADS)

Adabi, Saba; Conforto, Silvia; Hosseinzadeh, Matin; Noe, Shahryar; Daveluy, Steven; Mehregan, Darius; Nasiriavanaki, Mohammadreza

2017-02-01

Optical Coherence Tomography (OCT) offers real-time high-resolution three-dimensional images of tissue microstructures. In this study, we used OCT skin images acquired from ten volunteers, neither of whom had any skin conditions addressing the features of their anatomic location. OCT segmented images are analyzed based on their optical properties (attenuation coefficient) and textural image features e.g., contrast, correlation, homogeneity, energy, entropy, etc. Utilizing the information and referring to their clinical insight, we aim to make a comprehensive computational model for the healthy skin. The derived parameters represent the OCT microstructural morphology and might provide biological information for generating an atlas of normal skin from different anatomic sites of human skin and may allow for identification of cell microstructural changes in cancer patients. We then compared the parameters of healthy samples with those of abnormal skin and classified them using a linear Support Vector Machines (SVM) with 82% accuracy.

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

Connor, D.M.; Miller, L.; Benveniste, H.

Our understanding of early development in Alzheimer's disease (AD) is clouded by the scale at which the disease progresses; amyloid beta (A{beta}) plaques, a hallmark feature of AD, are small ({approx} 50 {micro}m) and low contrast in diagnostic clinical imaging techniques. Diffraction enhanced imaging (DEI), a phase contrast x-ray imaging technique, has greater soft tissue contrast than conventional radiography and generates higher resolution images than magnetic resonance microimaging. Thus, in this proof of principle study, DEI in micro-CT mode was performed on the brains of AD-model mice to determine if DEI can visualize A{beta} plaques. Results revealed small nodules inmore » the cortex and hippocampus of the brain. Histology confirmed that the features seen in the DEI images of the brain were A{beta} plaques. Several anatomical structures, including hippocampal subregions and white matter tracks, were also observed. Thus, DEI has strong promise in early diagnosis of AD, as well as general studies of the mouse brain.« less

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.

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.

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.

Computer-assisted diagnosis of melanoma.

PubMed

Fuller, Collin; Cellura, A Paul; Hibler, Brian P; Burris, Katy

2016-03-01

The computer-assisted diagnosis of melanoma is an exciting area of research where imaging techniques are combined with diagnostic algorithms in an attempt to improve detection and outcomes for patients with skin lesions suspicious for malignancy. Once an image has been acquired, it undergoes a processing pathway which includes preprocessing, enhancement, segmentation, feature extraction, feature selection, change detection, and ultimately classification. Practicality for everyday clinical use remains a vital question. A successful model must obtain results that are on par or outperform experienced dermatologists, keep costs at a minimum, be user-friendly, and be time efficient with high sensitivity and specificity. ©2015 Frontline Medical Communications.

The value of specific MRI features in the evaluation of suspected placental invasion.

PubMed

Lax, Allison; Prince, Martin R; Mennitt, Kevin W; Schwebach, J Reid; Budorick, Nancy E

2007-01-01

The objective of this study was to determine imaging features that may help predict the presence of placenta accreta, placenta increta or placenta percreta on prenatal MRI scanning. A retrospective review of the prenatal MR scans of 10 patients with a diagnosis of placenta accreta, placenta increta or placenta percreta made by pathologic and clinical reports and of 10 patients without placental invasion was performed. Two expert MRI readers were blinded to the patients' true diagnosis and were asked to score a total of 17 MRI features of the placenta and adjacent structures. The interrater reliability was assessed using kappa statistics. The features with a moderate kappa statistic or better (kappa > .40) were then compared with the true diagnosis for each observer. Seven of the scored features had an interobserver reliability of kappa > .40: placenta previa (kappa = .83); abnormal uterine bulging (kappa = .48); intraplacental hemorrhage (kappa = .51); heterogeneity of signal intensity on T2-weighted (T2W) imaging (kappa = .61); the presence of dark intraplacental bands on T2W imaging (kappa = .53); increased placental thickness (kappa = .69); and visualization of the myometrium beneath the placenta on T2W imaging (kappa = .44). Using Fisher's two-sided exact test, there was a statistically significant difference between the proportion of patients with placental invasion and those without placental invasion for three of the features: abnormal uterine bulging (Rater 1, P = .005; Rater 2, P = .011); heterogeneity of T2W imaging signal intensity (Rater 1, P = .006; Rater 2, P = .010); and presence of dark intraplacental bands on T2W imaging (Rater 1, P = .003; Rater 2, P = .033). MRI can be a useful adjunct to ultrasound in diagnosing placenta accreta prenatally. Three features that are seen on MRI in patients with placental invasion appear to be useful for diagnosis: uterine bulging; heterogeneous signal intensity within the placenta; and the presence of dark intraplacental bands on T2W imaging.

Estimation of cardiac motion in cine-MRI sequences by correlation transform optical flow of monogenic features distance

NASA Astrophysics Data System (ADS)

Gao, Bin; Liu, Wanyu; Wang, Liang; Liu, Zhengjun; Croisille, Pierre; Delachartre, Philippe; Clarysse, Patrick

2016-12-01

Cine-MRI is widely used for the analysis of cardiac function in clinical routine, because of its high soft tissue contrast and relatively short acquisition time in comparison with other cardiac MRI techniques. The gray level distribution in cardiac cine-MRI is relatively homogenous within the myocardium, and can therefore make motion quantification difficult. To ensure that the motion estimation problem is well posed, more image features have to be considered. This work is inspired by a method previously developed for color image processing. The monogenic signal provides a framework to estimate the local phase, orientation, and amplitude, of an image, three features which locally characterize the 2D intensity profile. The independent monogenic features are combined into a 3D matrix for motion estimation. To improve motion estimation accuracy, we chose the zero-mean normalized cross-correlation as a matching measure, and implemented a bilateral filter for denoising and edge-preservation. The monogenic features distance is used in lieu of the color space distance in the bilateral filter. Results obtained from four realistic simulated sequences outperformed two other state of the art methods even in the presence of noise. The motion estimation errors (end point error) using our proposed method were reduced by about 20% in comparison with those obtained by the other tested methods. The new methodology was evaluated on four clinical sequences from patients presenting with cardiac motion dysfunctions and one healthy volunteer. The derived strain fields were analyzed favorably in their ability to identify myocardial regions with impaired motion.

A Dynamic Graph Cuts Method with Integrated Multiple Feature Maps for Segmenting Kidneys in 2D Ultrasound Images.

PubMed

Zheng, Qiang; Warner, Steven; Tasian, Gregory; Fan, Yong

2018-02-12

Automatic segmentation of kidneys in ultrasound (US) images remains a challenging task because of high speckle noise, low contrast, and large appearance variations of kidneys in US images. Because texture features may improve the US image segmentation performance, we propose a novel graph cuts method to segment kidney in US images by integrating image intensity information and texture feature maps. We develop a new graph cuts-based method to segment kidney US images by integrating original image intensity information and texture feature maps extracted using Gabor filters. To handle large appearance variation within kidney images and improve computational efficiency, we build a graph of image pixels close to kidney boundary instead of building a graph of the whole image. To make the kidney segmentation robust to weak boundaries, we adopt localized regional information to measure similarity between image pixels for computing edge weights to build the graph of image pixels. The localized graph is dynamically updated and the graph cuts-based segmentation iteratively progresses until convergence. Our method has been evaluated based on kidney US images of 85 subjects. The imaging data of 20 randomly selected subjects were used as training data to tune parameters of the image segmentation method, and the remaining data were used as testing data for validation. Experiment results demonstrated that the proposed method obtained promising segmentation results for bilateral kidneys (average Dice index = 0.9446, average mean distance = 2.2551, average specificity = 0.9971, average accuracy = 0.9919), better than other methods under comparison (P < .05, paired Wilcoxon rank sum tests). The proposed method achieved promising performance for segmenting kidneys in two-dimensional US images, better than segmentation methods built on any single channel of image information. This method will facilitate extraction of kidney characteristics that may predict important clinical outcomes such as progression of chronic kidney disease. Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

A New Feature-Enhanced Speckle Reduction Method Based on Multiscale Analysis for Ultrasound B-Mode Imaging.

PubMed

Kang, Jinbum; Lee, Jae Young; Yoo, Yangmo

2016-06-01

Effective speckle reduction in ultrasound B-mode imaging is important for enhancing the image quality and improving the accuracy in image analysis and interpretation. In this paper, a new feature-enhanced speckle reduction (FESR) method based on multiscale analysis and feature enhancement filtering is proposed for ultrasound B-mode imaging. In FESR, clinical features (e.g., boundaries and borders of lesions) are selectively emphasized by edge, coherence, and contrast enhancement filtering from fine to coarse scales while simultaneously suppressing speckle development via robust diffusion filtering. In the simulation study, the proposed FESR method showed statistically significant improvements in edge preservation, mean structure similarity, speckle signal-to-noise ratio, and contrast-to-noise ratio (CNR) compared with other speckle reduction methods, e.g., oriented speckle reducing anisotropic diffusion (OSRAD), nonlinear multiscale wavelet diffusion (NMWD), the Laplacian pyramid-based nonlinear diffusion and shock filter (LPNDSF), and the Bayesian nonlocal means filter (OBNLM). Similarly, the FESR method outperformed the OSRAD, NMWD, LPNDSF, and OBNLM methods in terms of CNR, i.e., 10.70 ± 0.06 versus 9.00 ± 0.06, 9.78 ± 0.06, 8.67 ± 0.04, and 9.22 ± 0.06 in the phantom study, respectively. Reconstructed B-mode images that were developed using the five speckle reduction methods were reviewed by three radiologists for evaluation based on each radiologist's diagnostic preferences. All three radiologists showed a significant preference for the abdominal liver images obtained using the FESR methods in terms of conspicuity, margin sharpness, artificiality, and contrast, p<0.0001. For the kidney and thyroid images, the FESR method showed similar improvement over other methods. However, the FESR method did not show statistically significant improvement compared with the OBNLM method in margin sharpness for the kidney and thyroid images. These results demonstrate that the proposed FESR method can improve the image quality of ultrasound B-mode imaging by enhancing the visualization of lesion features while effectively suppressing speckle noise.

Recent advances in oral oncology 2008; squamous cell carcinoma imaging, treatment, prognostication and treatment outcomes.

PubMed

Scully, Crispian; Bagan, Jose V

2009-06-01

This paper provides a synopsis of the main papers on diagnosis, imaging, treatment, prognostication and treatment outcomes in patients with oral and oropharyngeal squamous cell carcinoma (OSCC) and head and neck SCC (HNSCC) published in 2008 in Oral Oncology - an international interdisciplinary journal which publishes high quality original research, clinical trials and review articles, and all other scientific articles relating to the aetiopathogenesis, epidemiology, prevention, clinical features, diagnosis, treatment and management of patients with neoplasms in the head and neck, and orofacial disease in patients with malignant disease.

A novel end-to-end classifier using domain transferred deep convolutional neural networks for biomedical images.

PubMed

Pang, Shuchao; Yu, Zhezhou; Orgun, Mehmet A

2017-03-01

Highly accurate classification of biomedical images is an essential task in the clinical diagnosis of numerous medical diseases identified from those images. Traditional image classification methods combined with hand-crafted image feature descriptors and various classifiers are not able to effectively improve the accuracy rate and meet the high requirements of classification of biomedical images. The same also holds true for artificial neural network models directly trained with limited biomedical images used as training data or directly used as a black box to extract the deep features based on another distant dataset. In this study, we propose a highly reliable and accurate end-to-end classifier for all kinds of biomedical images via deep learning and transfer learning. We first apply domain transferred deep convolutional neural network for building a deep model; and then develop an overall deep learning architecture based on the raw pixels of original biomedical images using supervised training. In our model, we do not need the manual design of the feature space, seek an effective feature vector classifier or segment specific detection object and image patches, which are the main technological difficulties in the adoption of traditional image classification methods. Moreover, we do not need to be concerned with whether there are large training sets of annotated biomedical images, affordable parallel computing resources featuring GPUs or long times to wait for training a perfect deep model, which are the main problems to train deep neural networks for biomedical image classification as observed in recent works. With the utilization of a simple data augmentation method and fast convergence speed, our algorithm can achieve the best accuracy rate and outstanding classification ability for biomedical images. We have evaluated our classifier on several well-known public biomedical datasets and compared it with several state-of-the-art approaches. We propose a robust automated end-to-end classifier for biomedical images based on a domain transferred deep convolutional neural network model that shows a highly reliable and accurate performance which has been confirmed on several public biomedical image datasets. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Computer-aided-diagnosis (CAD) for colposcopy

NASA Astrophysics Data System (ADS)

Lange, Holger; Ferris, Daron G.

2005-04-01

Uterine cervical cancer is the second most common cancer among women worldwide. Colposcopy is a diagnostic method, whereby a physician (colposcopist) visually inspects the lower genital tract (cervix, vulva and vagina), with special emphasis on the subjective appearance of metaplastic epithelium comprising the transformation zone on the cervix. Cervical cancer precursor lesions and invasive cancer exhibit certain distinctly abnormal morphologic features. Lesion characteristics such as margin; color or opacity; blood vessel caliber, intercapillary spacing and distribution; and contour are considered by colposcopists to derive a clinical diagnosis. Clinicians and academia have suggested and shown proof of concept that automated image analysis of cervical imagery can be used for cervical cancer screening and diagnosis, having the potential to have a direct impact on improving women"s health care and reducing associated costs. STI Medical Systems is developing a Computer-Aided-Diagnosis (CAD) system for colposcopy -- ColpoCAD. At the heart of ColpoCAD is a complex multi-sensor, multi-data and multi-feature image analysis system. A functional description is presented of the envisioned ColpoCAD system, broken down into: Modality Data Management System, Image Enhancement, Feature Extraction, Reference Database, and Diagnosis and directed Biopsies. The system design and development process of the image analysis system is outlined. The system design provides a modular and open architecture built on feature based processing. The core feature set includes the visual features used by colposcopists. This feature set can be extended to include new features introduced by new instrument technologies, like fluorescence and impedance, and any other plausible feature that can be extracted from the cervical data. Preliminary results of our research on detecting the three most important features: blood vessel structures, acetowhite regions and lesion margins are shown. As this is a new and very complex field in medical image processing, the hope is that this paper can provide a framework and basis to encourage and facilitate collaboration and discussion between industry, academia, and medical practitioners.

Prediction models for solitary pulmonary nodules based on curvelet textural features and clinical parameters.

PubMed

Wang, Jing-Jing; Wu, Hai-Feng; Sun, Tao; Li, Xia; Wang, Wei; Tao, Li-Xin; Huo, Da; Lv, Ping-Xin; He, Wen; Guo, Xiu-Hua

2013-01-01

Lung cancer, one of the leading causes of cancer-related deaths, usually appears as solitary pulmonary nodules (SPNs) which are hard to diagnose using the naked eye. In this paper, curvelet-based textural features and clinical parameters are used with three prediction models [a multilevel model, a least absolute shrinkage and selection operator (LASSO) regression method, and a support vector machine (SVM)] to improve the diagnosis of benign and malignant SPNs. Dimensionality reduction of the original curvelet-based textural features was achieved using principal component analysis. In addition, non-conditional logistical regression was used to find clinical predictors among demographic parameters and morphological features. The results showed that, combined with 11 clinical predictors, the accuracy rates using 12 principal components were higher than those using the original curvelet-based textural features. To evaluate the models, 10-fold cross validation and back substitution were applied. The results obtained, respectively, were 0.8549 and 0.9221 for the LASSO method, 0.9443 and 0.9831 for SVM, and 0.8722 and 0.9722 for the multilevel model. All in all, it was found that using curvelet-based textural features after dimensionality reduction and using clinical predictors, the highest accuracy rate was achieved with SVM. The method may be used as an auxiliary tool to differentiate between benign and malignant SPNs in CT images.

Imaging of Muscle Injuries in Sports Medicine: Sports Imaging Series.

PubMed

Guermazi, Ali; Roemer, Frank W; Robinson, Philip; Tol, Johannes L; Regatte, Ravindar R; Crema, Michel D

2017-03-01

In sports-related muscle injuries, the main goal of the sports medicine physician is to return the athlete to competition-balanced against the need to prevent the injury from worsening or recurring. Prognosis based on the available clinical and imaging information is crucial. Imaging is crucial to confirm and assess the extent of sports-related muscle injuries and may help to guide management, which directly affects the prognosis. This is especially important when the diagnosis or grade of injury is unclear, when recovery is taking longer than expected, and when interventional or surgical management may be necessary. Several imaging techniques are widely available, with ultrasonography and magnetic resonance imaging currently the most frequently applied in sports medicine. This state of the art review will discuss the main imaging modalities for the assessment of sports-related muscle injuries, including advanced imaging techniques, with the focus on the clinical relevance of imaging features of muscle injuries. © RSNA, 2017 Online supplemental material is available for this article.

SU-E-J-237: Image Feature Based DRR and Portal Image Registration

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

Wang, X; Chang, J

Purpose: Two-dimensional (2D) matching of the kV X-ray and digitally reconstructed radiography (DRR) images is an important setup technique for image-guided radiotherapy (IGRT). In our clinics, mutual information based methods are used for this purpose on commercial linear accelerators, but with often needs for manual corrections. This work proved the feasibility that feature based image transform can be used to register kV and DRR images. Methods: The scale invariant feature transform (SIFT) method was implemented to detect the matching image details (or key points) between the kV and DRR images. These key points represent high image intensity gradients, and thusmore » the scale invariant features. Due to the poor image contrast from our kV image, direct application of the SIFT method yielded many detection errors. To assist the finding of key points, the center coordinates of the kV and DRR images were read from the DICOM header, and the two groups of key points with similar relative positions to their corresponding centers were paired up. Using these points, a rigid transform (with scaling, horizontal and vertical shifts) was estimated. We also artificially introduced vertical and horizontal shifts to test the accuracy of our registration method on anterior-posterior (AP) and lateral pelvic images. Results: The results provided a satisfactory overlay of the transformed kV onto the DRR image. The introduced vs. detected shifts were fit into a linear regression. In the AP image experiments, linear regression analysis showed a slope of 1.15 and 0.98 with an R2 of 0.89 and 0.99 for the horizontal and vertical shifts, respectively. The results are 1.2 and 1.3 with R2 of 0.72 and 0.82 for the lateral image shifts. Conclusion: This work provided an alternative technique for kV to DRR alignment. Further improvements in the estimation accuracy and image contrast tolerance are underway.« less

A new CAD approach for improving efficacy of cancer screening

NASA Astrophysics Data System (ADS)

Zheng, Bin; Qian, Wei; Li, Lihua; Pu, Jiantao; Kang, Yan; Lure, Fleming; Tan, Maxine; Qiu, Yuchen

2015-03-01

Since performance and clinical utility of current computer-aided detection (CAD) schemes of detecting and classifying soft tissue lesions (e.g., breast masses and lung nodules) is not satisfactory, many researchers in CAD field call for new CAD research ideas and approaches. The purpose of presenting this opinion paper is to share our vision and stimulate more discussions of how to overcome or compensate the limitation of current lesion-detection based CAD schemes in the CAD research community. Since based on our observation that analyzing global image information plays an important role in radiologists' decision making, we hypothesized that using the targeted quantitative image features computed from global images could also provide highly discriminatory power, which are supplementary to the lesion-based information. To test our hypothesis, we recently performed a number of independent studies. Based on our published preliminary study results, we demonstrated that global mammographic image features and background parenchymal enhancement of breast MR images carried useful information to (1) predict near-term breast cancer risk based on negative screening mammograms, (2) distinguish between true- and false-positive recalls in mammography screening examinations, and (3) classify between malignant and benign breast MR examinations. The global case-based CAD scheme only warns a risk level of the cases without cueing a large number of false-positive lesions. It can also be applied to guide lesion-based CAD cueing to reduce false-positives but enhance clinically relevant true-positive cueing. However, before such a new CAD approach is clinically acceptable, more work is needed to optimize not only the scheme performance but also how to integrate with lesion-based CAD schemes in the clinical practice.

Preoperative easily misdiagnosed telangiectatic osteosarcoma: clinical-radiologic-pathologic correlations.

PubMed

Gao, Zhen-Hua; Yin, Jun-Qiang; Liu, Da-Wei; Meng, Quan-Fei; Li, Jia-Ping

2013-12-11

To describe the clinical, imaging, and pathologic characteristics and diagnostic methods of telangiectatic osteosarcoma (TOS) for improving the diagnostic level. The authors retrospectively reviewed patient demographics, serum alkaline phosphatase (AKP) levels, preoperative biopsy pathologic reports, pathologic materials, imaging findings, and treatment outcomes from 26 patients with TOS. Patient images from radiography (26 cases) and magnetic resonance (MR) imaging (22 cases) were evaluated by 3 authors in consensus for intrinsic characteristics. There were 15 male and 11 female patients in the study, with an age of 9-32 years (mean age 15.9 years). Eighteen of 26 patients died of lung metastases within 5 years of follow-up. The distal femur was affected more commonly (14 cases, 53.8%). Regarding serum AKP, normal (8 cases) or mildly elevated (18 cases) levels were found before preoperative chemotherapy. Radiographs showed geographic bone lysis without sclerotic margin (26 cases), cortical destruction (26 cases), periosteal new bone formation (24 cases), soft-tissue mass (23 cases), and matrix mineralization (4 cases). The aggressive radiographic features of TOS simulated the appearance of conventional high-grade intramedullary osteosarcoma, though different from aneurysmal bone cyst. MR images demonstrated multiple big (16 cases) or small (6 cases) cystic spaces, fluid-fluid levels (14 cases), soft-tissue mass (22 cases), and thick peripheral and septal enhancement (22 cases). Nine of 26 cases were misdiagnosed as aneurysmal bone cysts by preoperative core-needle biopsy, owing to the absence of viable high-grade sarcomatous cells in the small tissue samples. The aggressive growth pattern with occasional matrix mineralization, and multiple big or small fluid-filled cavities with thick peripheral, septal, and nodular tissue surrounding the fluid-filled cavities are characteristic imaging features of TOS, and these features are helpful in making the correct preoperative diagnosis of TOS.

Imaging features of macrodystrophia lipomatosa: an unusual cause of a brawny arm.

PubMed

Dhanasekaran, Jagadeesan; Reddy, Ajit Kumar; Sarawagi, Radha; Lakshmanan, Prakash Manikka

2014-11-18

Macrodystrophia lipomatosa (MDL), a rare non-hereditary congenital disorder of localised gigantism, is characterised by progressive proliferation of all mesenchymal elements, with a disproportionate increase in fibroadipose tissue. We report a case of a 19-year-old man who presented with a history of painless enlargement of the left upper limb since childhood, which was gradually increasing in size and predominantly involving the radial aspect of the upper limb with relative sparing of the ulnar aspect. The patient was imaged with X-ray and MRI. Imaging and clinical features were classical of MDL. The patient underwent stage 1 reduction plasty of the left forearm; preoperative and histopathological findings confirmed the preoperative diagnosis. 2014 BMJ Publishing Group Ltd.

Clinicopathological features of pulmonary cryptococcosis with cryptococcal titan cells: a comparative analysis of 27 cases.

PubMed

Wang, Jing-Mei; Zhou, Qiang; Cai, Hou-Rong; Zhuang, Yi; Zhang, Yi-Fen; Xin, Xiao-Yan; Meng, Fan-Qing; Wang, Ya-Ping

2014-01-01

In addition to the typical size, Cryptococcus neoformans can enlarge its size to form titan cells during infection, and its diameter can reach up to 100 μm. Clinical reports about cryptococcal titan cells are rare. Most studies focus on aspects of animal models of infection with titan cells. Herein, we report the clinical and imaging characteristics and histopathologic features of 3 patients with titan cells and 27 patients with pathogens of typical size, and describe the morphological characteristics of titan cells in details. Histologically, 3 patients with titan cells show necrosis, fibrosis and macrophage accumulation. The titan cells appear in necrotic tissue and between macrophages, and have thick wall with unstained halo around them and diameters range from 20 to 80 μm with characteristic of narrow-necked single budding. There are also organisms with typical size. All 27 patients with normal pathogens show epithelioid granulomatous lesions. There is no significantly difference in clinical and imaging feature between the two groups. Cryptococcus neoformans exhibits a striking morphological change for the formation of titan cells during pulmonary infection, which will result in misdiagnosis and under diagnosis. The histopathological changes may be new manifestation, which need to be further confirmed by the study with animal models of infection and the observation of more clinical cases. Careful observation of the tissue sections is necessary.

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

A New Method for Automated Identification and Morphometry of Myelinated Fibers Through Light Microscopy Image Analysis.

PubMed

Novas, Romulo Bourget; Fazan, Valeria Paula Sassoli; Felipe, Joaquim Cezar

2016-02-01

Nerve morphometry is known to produce relevant information for the evaluation of several phenomena, such as nerve repair, regeneration, implant, transplant, aging, and different human neuropathies. Manual morphometry is laborious, tedious, time consuming, and subject to many sources of error. Therefore, in this paper, we propose a new method for the automated morphometry of myelinated fibers in cross-section light microscopy images. Images from the recurrent laryngeal nerve of adult rats and the vestibulocochlear nerve of adult guinea pigs were used herein. The proposed pipeline for fiber segmentation is based on the techniques of competitive clustering and concavity analysis. The evaluation of the proposed method for segmentation of images was done by comparing the automatic segmentation with the manual segmentation. To further evaluate the proposed method considering morphometric features extracted from the segmented images, the distributions of these features were tested for statistical significant difference. The method achieved a high overall sensitivity and very low false-positive rates per image. We detect no statistical difference between the distribution of the features extracted from the manual and the pipeline segmentations. The method presented a good overall performance, showing widespread potential in experimental and clinical settings allowing large-scale image analysis and, thus, leading to more reliable results.

Quantification of the heterogeneity of prognostic cellular biomarkers in ewing sarcoma using automated image and random survival forest analysis.

PubMed

Bühnemann, Claudia; Li, Simon; Yu, Haiyue; Branford White, Harriet; Schäfer, Karl L; Llombart-Bosch, Antonio; Machado, Isidro; Picci, Piero; Hogendoorn, Pancras C W; Athanasou, Nicholas A; Noble, J Alison; Hassan, A Bassim

2014-01-01

Driven by genomic somatic variation, tumour tissues are typically heterogeneous, yet unbiased quantitative methods are rarely used to analyse heterogeneity at the protein level. Motivated by this problem, we developed automated image segmentation of images of multiple biomarkers in Ewing sarcoma to generate distributions of biomarkers between and within tumour cells. We further integrate high dimensional data with patient clinical outcomes utilising random survival forest (RSF) machine learning. Using material from cohorts of genetically diagnosed Ewing sarcoma with EWSR1 chromosomal translocations, confocal images of tissue microarrays were segmented with level sets and watershed algorithms. Each cell nucleus and cytoplasm were identified in relation to DAPI and CD99, respectively, and protein biomarkers (e.g. Ki67, pS6, Foxo3a, EGR1, MAPK) localised relative to nuclear and cytoplasmic regions of each cell in order to generate image feature distributions. The image distribution features were analysed with RSF in relation to known overall patient survival from three separate cohorts (185 informative cases). Variation in pre-analytical processing resulted in elimination of a high number of non-informative images that had poor DAPI localisation or biomarker preservation (67 cases, 36%). The distribution of image features for biomarkers in the remaining high quality material (118 cases, 104 features per case) were analysed by RSF with feature selection, and performance assessed using internal cross-validation, rather than a separate validation cohort. A prognostic classifier for Ewing sarcoma with low cross-validation error rates (0.36) was comprised of multiple features, including the Ki67 proliferative marker and a sub-population of cells with low cytoplasmic/nuclear ratio of CD99. Through elimination of bias, the evaluation of high-dimensionality biomarker distribution within cell populations of a tumour using random forest analysis in quality controlled tumour material could be achieved. Such an automated and integrated methodology has potential application in the identification of prognostic classifiers based on tumour cell heterogeneity.

No significant difference between chiari malformation type 1.5 and type I.

PubMed

Liu, Wei; Wu, Hongxing; Aikebaier, Yalikun; Wulabieke, Maoliti; Paerhati, Rexiti; Yang, Xiaopeng

2017-06-01

Chiari malformation Type 1.5 (CM 1.5) was defined as the association of Chiari malformation Type I (CM I) and brainstem herniation. The objective was to demonstrate the difference of clinical features and surgical outcomes between CM 1.5 and CM I. All CM 1.5 and CM I adult patients who underwent posterior fossa decompression with duraplasty at our institution between 2006 and 2010 were retrospectively reviewed. Clinical characteristics, imaging features, and long-term outcomes were compared between CM 1.5 and CM I patients. A total of 142 adult patients were enrolled, including 27 CM 1.5 and 115 CM I patients. The average follow-up period was 102 months. Age at diagnosis was significantly younger in CM 1.5 group than CM I group (p=0.039). And the degree of tonsillar herniation was significantly more severe in CM 1.5 group than CM I group (p<0.001). There was no significant difference in other clinical and imaging characteristics. Moreover, improvement of symptoms was observed in 21 CM 1.5 patients (77.8%) and 94 CM I patients (81.7%), and no significant difference was detected (p=0.637). There was no significant difference in the resolution of syringomyelia between CM 1.5 (72.7%) and CM I (76.5%) patients, either (p=0. 710). Although CM 1.5 patients presented with brainstem herniation and more severe tonsillar herniation, other clinical and imaging features and surgical outcomes were similar with CM I patients. We think CM 1.5 is just a subtype of CM I, rather than a unique type of Chiari malformations. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

A preliminary study for fully automated quantification of psoriasis severity using image mapping

NASA Astrophysics Data System (ADS)

Mukai, Kazuhiro; Iyatomi, Hitoshi

2014-03-01

Psoriasis is a common chronic skin disease and it detracts patients' QoL seriously. Since there is no known permanent cure so far, controlling appropriate disease condition is necessary and therefore quantification of its severity is important. In clinical, psoriasis area and severity index (PASI) is commonly used for abovementioned purpose, however it is often subjective and troublesome. A fully automatic computer-assisted area and severity index (CASI) was proposed to make an objective quantification of skin disease. It investigates the size and density of erythema based on digital image analysis, however it does not consider various inadequate effects caused by different geometrical conditions under clinical follow-up (i.e. variability in direction and distance between camera and patient). In this study, we proposed an image alignment method for clinical images and investigated to quantify the severity of psoriasis under clinical follow-up combined with the idea of CASI. The proposed method finds geometrical same points in patient's body (ROI) between images with Scale Invariant Feature Transform (SIFT) and performs the Affine transform to map the pixel value to the other. In this study, clinical images from 7 patients with psoriasis lesions on their trunk under clinical follow-up were used. In each series, our image alignment algorithm align images to the geometry of their first image. Our proposed method aligned images appropriately on visual assessment and confirmed that psoriasis areas were properly extracted using the approach of CASI. Although we cannot evaluate PASI and CASI directly due to their different definition of ROI, we confirmed that there is a large correlation between those scores with our image quantification method.

GPU accelerated optical coherence tomography angiography using strip-based registration (Conference Presentation)

NASA Astrophysics Data System (ADS)

Heisler, Morgan; Lee, Sieun; Mammo, Zaid; Jian, Yifan; Ju, Myeong Jin; Miao, Dongkai; Raposo, Eric; Wahl, Daniel J.; Merkur, Andrew; Navajas, Eduardo; Balaratnasingam, Chandrakumar; Beg, Mirza Faisal; Sarunic, Marinko V.

2017-02-01

High quality visualization of the retinal microvasculature can improve our understanding of the onset and development of retinal vascular diseases, which are a major cause of visual morbidity and are increasing in prevalence. Optical Coherence Tomography Angiography (OCT-A) images are acquired over multiple seconds and are particularly susceptible to motion artifacts, which are more prevalent when imaging patients with pathology whose ability to fixate is limited. The acquisition of multiple OCT-A images sequentially can be performed for the purpose of removing motion artifact and increasing the contrast of the vascular network through averaging. Due to the motion artifacts, a robust registration pipeline is needed before feature preserving image averaging can be performed. In this report, we present a novel method for a GPU-accelerated pipeline for acquisition, processing, segmentation, and registration of multiple, sequentially acquired OCT-A images to correct for the motion artifacts in individual images for the purpose of averaging. High performance computing, blending CPU and GPU, was introduced to accelerate processing in order to provide high quality visualization of the retinal microvasculature and to enable a more accurate quantitative analysis in a clinically useful time frame. Specifically, image discontinuities caused by rapid micro-saccadic movements and image warping due to smoother reflex movements were corrected by strip-wise affine registration estimated using Scale Invariant Feature Transform (SIFT) keypoints and subsequent local similarity-based non-rigid registration. These techniques improve the image quality, increasing the value for clinical diagnosis and increasing the range of patients for whom high quality OCT-A images can be acquired.

Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques

PubMed Central

Macyszyn, Luke; Akbari, Hamed; Pisapia, Jared M.; Da, Xiao; Attiah, Mark; Pigrish, Vadim; Bi, Yingtao; Pal, Sharmistha; Davuluri, Ramana V.; Roccograndi, Laura; Dahmane, Nadia; Martinez-Lage, Maria; Biros, George; Wolf, Ronald L.; Bilello, Michel; O'Rourke, Donald M.; Davatzikos, Christos

2016-01-01

Background MRI characteristics of brain gliomas have been used to predict clinical outcome and molecular tumor characteristics. However, previously reported imaging biomarkers have not been sufficiently accurate or reproducible to enter routine clinical practice and often rely on relatively simple MRI measures. The current study leverages advanced image analysis and machine learning algorithms to identify complex and reproducible imaging patterns predictive of overall survival and molecular subtype in glioblastoma (GB). Methods One hundred five patients with GB were first used to extract approximately 60 diverse features from preoperative multiparametric MRIs. These imaging features were used by a machine learning algorithm to derive imaging predictors of patient survival and molecular subtype. Cross-validation ensured generalizability of these predictors to new patients. Subsequently, the predictors were evaluated in a prospective cohort of 29 new patients. Results Survival curves yielded a hazard ratio of 10.64 for predicted long versus short survivors. The overall, 3-way (long/medium/short survival) accuracy in the prospective cohort approached 80%. Classification of patients into the 4 molecular subtypes of GB achieved 76% accuracy. Conclusions By employing machine learning techniques, we were able to demonstrate that imaging patterns are highly predictive of patient survival. Additionally, we found that GB subtypes have distinctive imaging phenotypes. These results reveal that when imaging markers related to infiltration, cell density, microvascularity, and blood–brain barrier compromise are integrated via advanced pattern analysis methods, they form very accurate predictive biomarkers. These predictive markers used solely preoperative images, hence they can significantly augment diagnosis and treatment of GB patients. PMID:26188015

Temporal assessment of radiomic features on clinical mammography in a high-risk population

NASA Astrophysics Data System (ADS)

Mendel, Kayla R.; Li, Hui; Lan, Li; Chan, Chun-Wai; King, Lauren M.; Tayob, Nabihah; Whitman, Gary; El-Zein, Randa; Bedrosian, Isabelle; Giger, Maryellen L.

2018-02-01

Extraction of high-dimensional quantitative data from medical images has become necessary in disease risk assessment, diagnostics and prognostics. Radiomic workflows for mammography typically involve a single medical image for each patient although medical images may exist for multiple imaging exams, especially in screening protocols. Our study takes advantage of the availability of mammograms acquired over multiple years for the prediction of cancer onset. This study included 841 images from 328 patients who developed subsequent mammographic abnormalities, which were confirmed as either cancer (n=173) or non-cancer (n=155) through diagnostic core needle biopsy. Quantitative radiomic analysis was conducted on antecedent FFDMs acquired a year or more prior to diagnostic biopsy. Analysis was limited to the breast contralateral to that in which the abnormality arose. Novel metrics were used to identify robust radiomic features. The most robust features were evaluated in the task of predicting future malignancies on a subset of 72 subjects (23 cancer cases and 49 non-cancer controls) with mammograms over multiple years. Using linear discriminant analysis, the robust radiomic features were merged into predictive signatures by: (i) using features from only the most recent contralateral mammogram, (ii) change in feature values between mammograms, and (iii) ratio of feature values over time, yielding AUCs of 0.57 (SE=0.07), 0.63 (SE=0.06), and 0.66 (SE=0.06), respectively. The AUCs for temporal radiomics (ratio) statistically differed from chance, suggesting that changes in radiomics over time may be critical for risk assessment. Overall, we found that our two-stage process of robustness assessment followed by performance evaluation served well in our investigation on the role of temporal radiomics in risk assessment.

Magnetic resonance imaging features of dogs with incomplete recovery after acute, severe spinal cord injury

PubMed Central

Lewis, Melissa J.; Cohen, Eli B.; Olby, Natasha J.

2017-01-01

Study Design Retrospective case series Objectives Describe the magnetic resonance imaging (MRI) features of dogs chronically impaired after severe spinal cord injury (SCI) and investigate associations between imaging variables and residual motor function. Setting United States of America Methods Thoracolumbar MRI from dogs with incomplete recovery months to years after clinically complete (paralysis with loss of pain perception) thoracolumbar SCI were reviewed. Lesion features were described and quantified. Gait was quantified using an ordinal, open field scale (OFS). Associations between imaging features and gait scores, duration of injury (DOI) or SCI treatment were determined. Results 35 dogs were included. Median OFS was 2 (0–6), median DOI was 13 months (3–83) and intervertebral disc herniation was the most common diagnosis (n=27). Myelomalacia was the most common qualitative feature followed by cystic change; syringomyelia and fibrosis were uncommon. Lesion length corrected to L2 length (LL:L2) was variable (median LL:L2=3.5 (1.34–11.54)). Twenty-nine dogs had 100% maximum cross-sectional spinal cord compromise (MSCC) at the lesion epicenter and the length of 100% compromised area varied widely (median length 100% MSCC:L2=1.29 (0.39–7.64). Length 100% MSCC:L2 was associated with OFS (p=0.012). OFS was not associated with any qualitative features. DOI or treatment type were not associated with imaging features or lesion quantification. Conclusions Lesion characteristics on MRI in dogs with incomplete recovery after severe SCI were established. Length of 100% MSCC was associated with hind limb motor function. Findings demonstrate a spectrum of injury severity on MRI amongst severely affected dogs which is related to functional status. PMID:29057987

Quantitative Image Feature Engine (QIFE): an Open-Source, Modular Engine for 3D Quantitative Feature Extraction from Volumetric Medical Images.

PubMed

Echegaray, Sebastian; Bakr, Shaimaa; Rubin, Daniel L; Napel, Sandy

2017-10-06

The aim of this study was to develop an open-source, modular, locally run or server-based system for 3D radiomics feature computation that can be used on any computer system and included in existing workflows for understanding associations and building predictive models between image features and clinical data, such as survival. The QIFE exploits various levels of parallelization for use on multiprocessor systems. It consists of a managing framework and four stages: input, pre-processing, feature computation, and output. Each stage contains one or more swappable components, allowing run-time customization. We benchmarked the engine using various levels of parallelization on a cohort of CT scans presenting 108 lung tumors. Two versions of the QIFE have been released: (1) the open-source MATLAB code posted to Github, (2) a compiled version loaded in a Docker container, posted to DockerHub, which can be easily deployed on any computer. The QIFE processed 108 objects (tumors) in 2:12 (h/mm) using 1 core, and 1:04 (h/mm) hours using four cores with object-level parallelization. We developed the Quantitative Image Feature Engine (QIFE), an open-source feature-extraction framework that focuses on modularity, standards, parallelism, provenance, and integration. Researchers can easily integrate it with their existing segmentation and imaging workflows by creating input and output components that implement their existing interfaces. Computational efficiency can be improved by parallelizing execution at the cost of memory usage. Different parallelization levels provide different trade-offs, and the optimal setting will depend on the size and composition of the dataset to be processed.

Imaging pediatric magnet ingestion with surgical-pathological correlation.

PubMed

Otjen, Jeffrey P; Rohrmann, Charles A; Iyer, Ramesh S

2013-07-01

Foreign body ingestion is a common problem in the pediatric population and a frequent cause for emergency room visits. Magnets are common household objects that when ingested can bring about severe, possibly fatal gastrointestinal complications. Radiography is an integral component of the management of these children. Pediatric and emergency radiologists alike must be aware of imaging manifestations of magnet ingestion, as their identification drives decision-making for consulting surgeons and gastroenterologists. Radiology can thus substantially augment the clinical history and physical exam, facilitating appropriate management. This manuscript sequentially presents cases of magnet ingestion featuring imaging findings coupled with surgical and pathological correlation. Each case is presented to highlight ways in which the radiologist can make impactful contributions to diagnosis and management. Clinical overview with pitfalls of magnet ingestion imaging and an imaging decision tree will also be presented.

Automated segmentation of oral mucosa from wide-field OCT images (Conference Presentation)

NASA Astrophysics Data System (ADS)

Goldan, Ryan N.; Lee, Anthony M. D.; Cahill, Lucas; Liu, Kelly; MacAulay, Calum; Poh, Catherine F.; Lane, Pierre

2016-03-01

Optical Coherence Tomography (OCT) can discriminate morphological tissue features important for oral cancer detection such as the presence or absence of basement membrane and epithelial thickness. We previously reported an OCT system employing a rotary-pullback catheter capable of in vivo, rapid, wide-field (up to 90 x 2.5mm2) imaging in the oral cavity. Due to the size and complexity of these OCT data sets, rapid automated image processing software that immediately displays important tissue features is required to facilitate prompt bed-side clinical decisions. We present an automated segmentation algorithm capable of detecting the epithelial surface and basement membrane in 3D OCT images of the oral cavity. The algorithm was trained using volumetric OCT data acquired in vivo from a variety of tissue types and histology-confirmed pathologies spanning normal through cancer (8 sites, 21 patients). The algorithm was validated using a second dataset of similar size and tissue diversity. We demonstrate application of the algorithm to an entire OCT volume to map epithelial thickness, and detection of the basement membrane, over the tissue surface. These maps may be clinically useful for delineating pre-surgical tumor margins, or for biopsy site guidance.

Magnetic Resonance Imaging Assessment of Squamous Cell Carcinoma of the Anal Canal Before and After Chemoradiation: Can MRI Predict for Eventual Clinical Outcome?

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

Goh, Vicky, E-mail: vicky.goh@stricklandscanner.org.u; Gollub, Frank K.; Liaw, Jonathan

2010-11-01

Purpose: To describe the MRI appearances of squamous cell carcinoma of the anal canal before and after chemoradiation and to assess whether MRI features predict for clinical outcome. Methods and Materials: Thirty-five patients (15 male, 20 female; mean age 60.8 years) with histologically proven squamous cell cancer of the anal canal underwent MRI before and 6-8 weeks after definitive chemoradiation. Images were reviewed retrospectively by two radiologists in consensus blinded to clinical outcome: tumor size, signal intensity, extent, and TNM stage were recorded. Following treatment, patients were defined as responders by T and N downstaging and Response Evaluation Criteria inmore » Solid Tumors (RECIST). Final clinical outcome was determined by imaging and case note review: patients were divided into (1) disease-free and (2) with relapse and compared using appropriate univariate methods to identify imaging predictors; statistical significance was at 5%. Results: The majority of tumors were {<=}T2 (23/35; 65.7%) and N0 (21/35; 60%), mean size 3.75cm, and hyperintense (++ to +++, 24/35 patients; 68%). Following chemoradiation, there was a size reduction in all cases (mean 73.3%) and a reduction in signal intensity in 26/35 patients (74.2%). The majority of patients were classified as responders (26/35 (74.2%) patients by T and N downstaging; and 30/35 (85.7%) patients by RECIST). At a median follow-up of 33.5 months, 25 patients (71.4%) remained disease-free; 10 patients (28.6%) had locoregional or metastatic disease. Univariate analysis showed that no individual MRI features were predictive of eventual outcome. Conclusion: Early assessment of response by MRI at 6-8 weeks is unhelpful in predicting future clinical outcome.« less

Paediatric acquired demyelinating syndromes: incidence, clinical and magnetic resonance imaging features.

PubMed

Absoud, Michael; Lim, Ming J; Chong, Wui K; De Goede, Christian G; Foster, Katharine; Gunny, Roxana; Hemingway, Cheryl; Jardine, Philip E; Kneen, Rachel; Likeman, Marcus; Nischal, Ken K; Pike, Michael G; Sibtain, Naomi A; Whitehouse, William P; Cummins, Carole; Wassmer, Evangeline

2013-01-01

Changing trends in multiple sclerosis (MS) epidemiology may first be apparent in the childhood population affected with first onset acquired demyelinating syndromes (ADSs). We aimed to determine the incidence, clinical, investigative and magnetic resonance imaging (MRI) features of childhood central nervous system ADSs in the British Isles for the first time. We conducted a population active surveillance study. All paediatricians, and ophthalmologists (n = 4095) were sent monthly reporting cards (September 2009-September 2010). International Paediatric MS Study Group 2007 definitions and McDonald 2010 MS imaging criteria were used for acute disseminated encephalomyelitis (ADEM), clinically isolated syndrome (CIS) and neuromyelitis optica (NMO). Clinicians completed a standard questionnaire and provided an MRI copy for review. Card return rates were 90%, with information available for 200/222 positive notifications (90%). After exclusion of cases, 125 remained (age range 1.3-15.9), with CIS in 66.4%, ADEM in 32.0% and NMO in 1.6%. The female-to-male ratio in children older than 10 years (n = 63) was 1.52:1 (p = 0.045). The incidence of first onset ADS in children aged 1-15 years old was 9.83 per million children per year (95% confidence interval [CI] 8.18-11.71). A trend towards higher incidence rates of ADS in children of South Asian and Black ethnicity was observed compared with White children. Importantly, a number of MRI characteristics distinguished ADEM from CIS cases. Of CIS cases with contrast imaging, 26% fulfilled McDonald 2010 MS diagnostic criteria. We report the highest surveillance incidence rates of childhood ADS. Paediatric MS diagnosis at first ADS presentation has implications for clinical practice and clinical trial design.

Computer-aided diagnostic method for classification of Alzheimer's disease with atrophic image features on MR images

NASA Astrophysics Data System (ADS)

Arimura, Hidetaka; Yoshiura, Takashi; Kumazawa, Seiji; Tanaka, Kazuhiro; Koga, Hiroshi; Mihara, Futoshi; Honda, Hiroshi; Sakai, Shuji; Toyofuku, Fukai; Higashida, Yoshiharu

2008-03-01

Our goal for this study was to attempt to develop a computer-aided diagnostic (CAD) method for classification of Alzheimer's disease (AD) with atrophic image features derived from specific anatomical regions in three-dimensional (3-D) T1-weighted magnetic resonance (MR) images. Specific regions related to the cerebral atrophy of AD were white matter and gray matter regions, and CSF regions in this study. Cerebral cortical gray matter regions were determined by extracting a brain and white matter regions based on a level set based method, whose speed function depended on gradient vectors in an original image and pixel values in grown regions. The CSF regions in cerebral sulci and lateral ventricles were extracted by wrapping the brain tightly with a zero level set determined from a level set function. Volumes of the specific regions and the cortical thickness were determined as atrophic image features. Average cortical thickness was calculated in 32 subregions, which were obtained by dividing each brain region. Finally, AD patients were classified by using a support vector machine, which was trained by the image features of AD and non-AD cases. We applied our CAD method to MR images of whole brains obtained from 29 clinically diagnosed AD cases and 25 non-AD cases. As a result, the area under a receiver operating characteristic (ROC) curve obtained by our computerized method was 0.901 based on a leave-one-out test in identification of AD cases among 54 cases including 8 AD patients at early stages. The accuracy for discrimination between 29 AD patients and 25 non-AD subjects was 0.840, which was determined at the point where the sensitivity was the same as the specificity on the ROC curve. This result showed that our CAD method based on atrophic image features may be promising for detecting AD patients by using 3-D MR images.

Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome.

PubMed

Davatzikos, Christos; Rathore, Saima; Bakas, Spyridon; Pati, Sarthak; Bergman, Mark; Kalarot, Ratheesh; Sridharan, Patmaa; Gastounioti, Aimilia; Jahani, Nariman; Cohen, Eric; Akbari, Hamed; Tunc, Birkan; Doshi, Jimit; Parker, Drew; Hsieh, Michael; Sotiras, Aristeidis; Li, Hongming; Ou, Yangming; Doot, Robert K; Bilello, Michel; Fan, Yong; Shinohara, Russell T; Yushkevich, Paul; Verma, Ragini; Kontos, Despina

2018-01-01

The growth of multiparametric imaging protocols has paved the way for quantitative imaging phenotypes that predict treatment response and clinical outcome, reflect underlying cancer molecular characteristics and spatiotemporal heterogeneity, and can guide personalized treatment planning. This growth has underlined the need for efficient quantitative analytics to derive high-dimensional imaging signatures of diagnostic and predictive value in this emerging era of integrated precision diagnostics. This paper presents cancer imaging phenomics toolkit (CaPTk), a new and dynamically growing software platform for analysis of radiographic images of cancer, currently focusing on brain, breast, and lung cancer. CaPTk leverages the value of quantitative imaging analytics along with machine learning to derive phenotypic imaging signatures, based on two-level functionality. First, image analysis algorithms are used to extract comprehensive panels of diverse and complementary features, such as multiparametric intensity histogram distributions, texture, shape, kinetics, connectomics, and spatial patterns. At the second level, these quantitative imaging signatures are fed into multivariate machine learning models to produce diagnostic, prognostic, and predictive biomarkers. Results from clinical studies in three areas are shown: (i) computational neuro-oncology of brain gliomas for precision diagnostics, prediction of outcome, and treatment planning; (ii) prediction of treatment response for breast and lung cancer, and (iii) risk assessment for breast cancer.

Obstructive Sleep Apnea in Women: Study of Speech and Craniofacial Characteristics

PubMed Central

Tyan, Marina; Fernández Pozo, Rubén; Toledano, Doroteo; Lopez Gonzalo, Eduardo; Alcazar Ramirez, Jose Daniel; Hernandez Gomez, Luis Alfonso

2017-01-01

Background Obstructive sleep apnea (OSA) is a common sleep disorder characterized by frequent cessation of breathing lasting 10 seconds or longer. The diagnosis of OSA is performed through an expensive procedure, which requires an overnight stay at the hospital. This has led to several proposals based on the analysis of patients’ facial images and speech recordings as an attempt to develop simpler and cheaper methods to diagnose OSA. Objective The objective of this study was to analyze possible relationships between OSA and speech and facial features on a female population and whether these possible connections may be affected by the specific clinical characteristics in OSA population and, more specifically, to explore how the connection between OSA and speech and facial features can be affected by gender. Methods All the subjects are Spanish subjects suspected to suffer from OSA and referred to a sleep disorders unit. Voice recordings and photographs were collected in a supervised but not highly controlled way, trying to test a scenario close to a realistic clinical practice scenario where OSA is assessed using an app running on a mobile device. Furthermore, clinical variables such as weight, height, age, and cervical perimeter, which are usually reported as predictors of OSA, were also gathered. Acoustic analysis is centered in sustained vowels. Facial analysis consists of a set of local craniofacial features related to OSA, which were extracted from images after detecting facial landmarks by using the active appearance models. To study the probable OSA connection with speech and craniofacial features, correlations among apnea-hypopnea index (AHI), clinical variables, and acoustic and facial measurements were analyzed. Results The results obtained for female population indicate mainly weak correlations (r values between .20 and .39). Correlations between AHI, clinical variables, and speech features show the prevalence of formant frequencies over bandwidths, with F2/i/ being the most appropriate formant frequency for OSA prediction in women. Results obtained for male population indicate mainly very weak correlations (r values between .01 and .19). In this case, bandwidths prevail over formant frequencies. Correlations between AHI, clinical variables, and craniofacial measurements are very weak. Conclusions In accordance with previous studies, some clinical variables are found to be good predictors of OSA. Besides, strong correlations are found between AHI and some clinical variables with speech and facial features. Regarding speech feature, the results show the prevalence of formant frequency F2/i/ over the rest of features for the female population as OSA predictive feature. Although the correlation reported is weak, this study aims to find some traces that could explain the possible connection between OSA and speech in women. In the case of craniofacial measurements, results evidence that some features that can be used for predicting OSA in male patients are not suitable for testing female population. PMID:29109068

Preliminary clinical evaluation of semi-automated nailfold capillaroscopy in the assessment of patients with Raynaud's phenomenon.

PubMed

Murray, Andrea K; Feng, Kaiyan; Moore, Tonia L; Allen, Phillip D; Taylor, Christopher J; Herrick, Ariane L

2011-08-01

  Nailfold capillaroscopy is well established in screening patients with Raynaud's phenomenon for underlying SSc-spectrum disorders, by identifying abnormal capillaries. Our aim was to compare semi-automatic feature measurement from newly developed software with manual measurements, and determine the degree to which semi-automated data allows disease group classification.   Images from 46 healthy controls, 21 patients with PRP and 49 with SSc were preprocessed, and semi-automated measurements of intercapillary distance and capillary width, tortuosity, and derangement were performed. These were compared with manual measurements. Features were used to classify images into the three subject groups.   Comparison of automatic and manual measures for distance, width, tortuosity, and derangement had correlations of r=0.583, 0.624, 0.495 (p<0.001), and 0.195 (p=0.040). For automatic measures, correlations were found between width and intercapillary distance, r=0.374, and width and tortuosity, r=0.573 (p<0.001). Significant differences between subject groups were found for all features (p<0.002). Overall, 75% of images correctly matched clinical classification using semi-automated features, compared with 71% for manual measurements.   Semi-automatic and manual measurements of distance, width, and tortuosity showed moderate (but statistically significant) correlations. Correlation for derangement was weaker. Semi-automatic measurements are faster than manual measurements. Semi-automatic parameters identify differences between groups, and are as good as manual measurements for between-group classification. © 2011 John Wiley & Sons Ltd.

[Study on computed tomography features of nasal septum cellule and its clinical significance].

PubMed

Huang, Dingqiang; Li, Wanrong; Gao, Liming; Xu, Guanqiang; Ou, Xiaoyi; Tang, Guangcai

2008-03-01

To investigate the features of nasal septum cellule in computed tomographic (CT) images and its clinical significance. CT scans data of nasal septum in 173 patients were randomly obtained from January 2001 to June 2005. Prevalence and clinical features were summarized in the data of 19 patients with nasal septum cellule retrospectively. (1) Nineteen cases with nasal septum cellule were found in 173 patients. (2) All nasal septum cellule of 19 cases located in perpendicular plate of the ethmoid bone, in which 8 cases located in upper part of nasal septum and 11 located in middle. (3) There were totally seven patients with nasal diseases related to nasal septum cellule, in which 3 cases with inflammation, 2 cases with bone fracture, 1 case with cholesterol granuloma, 1 case with mucocele. Nasal septum cellule is an anatomic variation of nasal septum bone, and its features can provide further understanding of some diseases related to nasal septum cellule.

Neuroradiologic correlation with aphasias. Cortico-subcortical map of language.

PubMed

Jiménez de la Peña, M M; Gómez Vicente, L; García Cobos, R; Martínez de Vega, V

Aphasia is an acquired language disorder due to a cerebral lesion; it is characterized by errors in production, denomination, or comprehension of language. Although most aphasias are mixed, from a practical point of view they are classified into different types according to their main clinical features: Broca's aphasia, Wernicke's aphasia, conduction aphasia, transcortical aphasia, and alexia with or without agraphia. We present the clinical findings for the main subtypes of aphasia, illustrating them with imaging cases, and we provide an up-to-date review of the language network with images from functional magnetic resonance imaging and tractography. Copyright © 2018 SERAM. Publicado por Elsevier España, S.L.U. All rights reserved.

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

Kamran, Mudassar, E-mail: kamranm@mir.wustl.edu; Fowler, Kathryn J., E-mail: fowlerk@mir.wustl.edu; Mellnick, Vincent M., E-mail: mellnickv@mir.wustl.edu

Primary aortic neoplasms are rare. Aortic sarcoma arising after endovascular aneurysm repair (EVAR) is a scarce subset of primary aortic malignancies, reports of which are infrequent in the published literature. The diagnosis of aortic sarcoma is challenging due to its non-specific clinical presentation, and the prognosis is poor due to delayed diagnosis, rapid proliferation, and propensity for metastasis. Post-EVAR, aortic sarcomas may mimic other more common aortic processes on surveillance imaging. Radiologists are rarely knowledgeable about this rare entity for which multimodality imaging and awareness are invaluable in early diagnosis. A series of three pathologically confirmed cases are presented tomore » display the multimodality imaging features and clinical presentations of aortic sarcoma arising after EVAR.« less

Targeting Strategies for Multifunctional Nanoparticles in Cancer Imaging and Therapy

PubMed Central

Yu, Mi Kyung; Park, Jinho; Jon, Sangyong

2012-01-01

Nanomaterials offer new opportunities for cancer diagnosis and treatment. Multifunctional nanoparticles harboring various functions including targeting, imaging, therapy, and etc have been intensively studied aiming to overcome limitations associated with conventional cancer diagnosis and therapy. Of various nanoparticles, magnetic iron oxide nanoparticles with superparamagnetic property have shown potential as multifunctional nanoparticles for clinical translation because they have been used asmagnetic resonance imaging (MRI) constrast agents in clinic and their features could be easily tailored by including targeting moieties, fluorescence dyes, or therapeutic agents. This review summarizes targeting strategies for construction of multifunctional nanoparticles including magnetic nanoparticles-based theranostic systems, and the various surface engineering strategies of nanoparticles for in vivo applications. PMID:22272217

3D-2D registration in endovascular image-guided surgery: evaluation of state-of-the-art methods on cerebral angiograms.

PubMed

Mitrović, Uroš; Likar, Boštjan; Pernuš, Franjo; Špiclin, Žiga

2018-02-01

Image guidance for minimally invasive surgery is based on spatial co-registration and fusion of 3D pre-interventional images and treatment plans with the 2D live intra-interventional images. The spatial co-registration or 3D-2D registration is the key enabling technology; however, the performance of state-of-the-art automated methods is rather unclear as they have not been assessed under the same test conditions. Herein we perform a quantitative and comparative evaluation of ten state-of-the-art methods for 3D-2D registration on a public dataset of clinical angiograms. Image database consisted of 3D and 2D angiograms of 25 patients undergoing treatment for cerebral aneurysms or arteriovenous malformations. On each of the datasets, highly accurate "gold-standard" registrations of 3D and 2D images were established based on patient-attached fiducial markers. The database was used to rigorously evaluate ten state-of-the-art 3D-2D registration methods, namely two intensity-, two gradient-, three feature-based and three hybrid methods, both for registration of 3D pre-interventional image to monoplane or biplane 2D images. Intensity-based methods were most accurate in all tests (0.3 mm). One of the hybrid methods was most robust with 98.75% of successful registrations (SR) and capture range of 18 mm for registrations of 3D to biplane 2D angiograms. In general, registration accuracy was similar whether registration of 3D image was performed onto mono- or biplanar 2D images; however, the SR was substantially lower in case of 3D to monoplane 2D registration. Two feature-based and two hybrid methods had clinically feasible execution times in the order of a second. Performance of methods seems to fall below expectations in terms of robustness in case of registration of 3D to monoplane 2D images, while translation into clinical image guidance systems seems readily feasible for methods that perform registration of the 3D pre-interventional image onto biplanar intra-interventional 2D images.

Classification of focal liver lesions on ultrasound images by extracting hybrid textural features and using an artificial neural network.

PubMed

Hwang, Yoo Na; Lee, Ju Hwan; Kim, Ga Young; Jiang, Yuan Yuan; Kim, Sung Min

2015-01-01

This paper focuses on the improvement of the diagnostic accuracy of focal liver lesions by quantifying the key features of cysts, hemangiomas, and malignant lesions on ultrasound images. The focal liver lesions were divided into 29 cysts, 37 hemangiomas, and 33 malignancies. A total of 42 hybrid textural features that composed of 5 first order statistics, 18 gray level co-occurrence matrices, 18 Law's, and echogenicity were extracted. A total of 29 key features that were selected by principal component analysis were used as a set of inputs for a feed-forward neural network. For each lesion, the performance of the diagnosis was evaluated by using the positive predictive value, negative predictive value, sensitivity, specificity, and accuracy. The results of the experiment indicate that the proposed method exhibits great performance, a high diagnosis accuracy of over 96% among all focal liver lesion groups (cyst vs. hemangioma, cyst vs. malignant, and hemangioma vs. malignant) on ultrasound images. The accuracy was slightly increased when echogenicity was included in the optimal feature set. These results indicate that it is possible for the proposed method to be applied clinically.

Comparative study of protoporphyrin IX fluorescence image enhancement methods to improve an optical imaging system for oral cancer detection

NASA Astrophysics Data System (ADS)

Jiang, Ching-Fen; Wang, Chih-Yu; Chiang, Chun-Ping

2011-07-01

Optoelectronics techniques to induce protoporphyrin IX fluorescence with topically applied 5-aminolevulinic acid on the oral mucosa have been developed to noninvasively detect oral cancer. Fluorescence imaging enables wide-area screening for oral premalignancy, but the lack of an adequate fluorescence enhancement method restricts the clinical imaging application of these techniques. This study aimed to develop a reliable fluorescence enhancement method to improve PpIX fluorescence imaging systems for oral cancer detection. Three contrast features, red-green-blue reflectance difference, R/B ratio, and R/G ratio, were developed first based on the optical properties of the fluorescence images. A comparative study was then carried out with one negative control and four biopsy confirmed clinical cases to validate the optimal image processing method for the detection of the distribution of malignancy. The results showed the superiority of the R/G ratio in terms of yielding a better contrast between normal and neoplastic tissue, and this method was less prone to errors in detection. Quantitative comparison with the clinical diagnoses in the four neoplastic cases showed that the regions of premalignancy obtained using the proposed method accorded with the expert's determination, suggesting the potential clinical application of this method for the detection of oral cancer.

Nonsubsampled rotated complex wavelet transform (NSRCxWT) for medical image fusion related to clinical aspects in neurocysticercosis.

PubMed

Chavan, Satishkumar S; Mahajan, Abhishek; Talbar, Sanjay N; Desai, Subhash; Thakur, Meenakshi; D'cruz, Anil

2017-02-01

Neurocysticercosis (NCC) is a parasite infection caused by the tapeworm Taenia solium in its larvae stage which affects the central nervous system of the human body (a definite host). It results in the formation of multiple lesions in the brain at different locations during its various stages. During diagnosis of such symptomatic patients, these lesions can be better visualized using a feature based fusion of Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). This paper presents a novel approach to Multimodality Medical Image Fusion (MMIF) used for the analysis of the lesions for the diagnostic purpose and post treatment review of NCC. The MMIF presented here is a technique of combining CT and MRI data of the same patient into a new slice using a Nonsubsampled Rotated Complex Wavelet Transform (NSRCxWT). The forward NSRCxWT is applied on both the source modalities separately to extract the complementary and the edge related features. These features are then combined to form a composite spectral plane using average and maximum value selection fusion rules. The inverse transformation on this composite plane results into a new, visually better, and enriched fused image. The proposed technique is tested on the pilot study data sets of patients infected with NCC. The quality of these fused images is measured using objective and subjective evaluation metrics. Objective evaluation is performed by estimating the fusion parameters like entropy, fusion factor, image quality index, edge quality measure, mean structural similarity index measure, etc. The fused images are also evaluated for their visual quality using subjective analysis with the help of three expert radiologists. The experimental results on 43 image data sets of 17 patients are promising and superior when compared with the state of the art wavelet based fusion algorithms. The proposed algorithm can be a part of computer-aided detection and diagnosis (CADD) system which assists the radiologists in clinical practices. Copyright © 2016 Elsevier Ltd. All rights reserved.

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.

Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching

PubMed Central

Guo, Yanrong; Gao, Yaozong

2016-01-01

Automatic and reliable segmentation of the prostate is an important but difficult task for various clinical applications such as prostate cancer radiotherapy. The main challenges for accurate MR prostate localization lie in two aspects: (1) inhomogeneous and inconsistent appearance around prostate boundary, and (2) the large shape variation across different patients. To tackle these two problems, we propose a new deformable MR prostate segmentation method by unifying deep feature learning with the sparse patch matching. First, instead of directly using handcrafted features, we propose to learn the latent feature representation from prostate MR images by the stacked sparse auto-encoder (SSAE). Since the deep learning algorithm learns the feature hierarchy from the data, the learned features are often more concise and effective than the handcrafted features in describing the underlying data. To improve the discriminability of learned features, we further refine the feature representation in a supervised fashion. Second, based on the learned features, a sparse patch matching method is proposed to infer a prostate likelihood map by transferring the prostate labels from multiple atlases to the new prostate MR image. Finally, a deformable segmentation is used to integrate a sparse shape model with the prostate likelihood map for achieving the final segmentation. The proposed method has been extensively evaluated on the dataset that contains 66 T2-wighted prostate MR images. Experimental results show that the deep-learned features are more effective than the handcrafted features in guiding MR prostate segmentation. Moreover, our method shows superior performance than other state-of-the-art segmentation methods. PMID:26685226

Multi-channel metabolic imaging, with SENSE reconstruction, of hyperpolarized [1- 13C] pyruvate in a live rat at 3.0 tesla on a clinical MR scanner

NASA Astrophysics Data System (ADS)

Tropp, James; Lupo, Janine M.; Chen, Albert; Calderon, Paul; McCune, Don; Grafendorfer, Thomas; Ozturk-Isik, Esin; Larson, Peder E. Z.; Hu, Simon; Yen, Yi-Fen; Robb, Fraser; Bok, Robert; Schulte, Rolf; Xu, Duan; Hurd, Ralph; Vigneron, Daniel; Nelson, Sarah

2011-01-01

We report metabolic images of 13C, following injection of a bolus of hyperpolarized [1-13C] pyruvate in a live rat. The data were acquired on a clinical scanner, using custom coils for volume transmission and array reception. Proton blocking of all carbon resonators enabled proton anatomic imaging with the system body coil, to allow for registration of anatomic and metabolic images, for which good correlation was achieved, with some anatomic features (kidney and heart) clearly visible in a carbon image, without reference to the corresponding proton image. Parallel imaging with sensitivity encoding was used to increase the spatial resolution in the SI direction of the rat. The signal to noise ratio in was in some instances unexpectedly high in the parallel images; variability of the polarization among different trials, plus partial volume effects, are noted as a possible cause of this.

Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer’s disease

PubMed Central

Gray, Katherine R.; Wolz, Robin; Heckemann, Rolf A.; Aljabar, Paul; Hammers, Alexander; Rueckert, Daniel

2012-01-01

Imaging biomarkers for Alzheimer’s disease are desirable for improved diagnosis and monitoring, as well as drug discovery. Automated image-based classification of individual patients could provide valuable diagnostic support for clinicians, when considered alongside cognitive assessment scores. We investigate the value of combining cross-sectional and longitudinal multi-region FDG-PET information for classification, using clinical and imaging data from the Alzheimer’s Disease Neuroimaging Initiative. Whole-brain segmentations into 83 anatomically defined regions were automatically generated for baseline and 12-month FDG-PET images. Regional signal intensities were extracted at each timepoint, as well as changes in signal intensity over the follow-up period. Features were provided to a support vector machine classifier. By combining 12-month signal intensities and changes over 12 months, we achieve significantly increased classification performance compared with using any of the three feature sets independently. Based on this combined feature set, we report classification accuracies of 88% between patients with Alzheimer’s disease and elderly healthy controls, and 65% between patients with stable mild cognitive impairment and those who subsequently progressed to Alzheimer’s disease. We demonstrate that information extracted from serial FDG-PET through regional analysis can be used to achieve state-of-the-art classification of diagnostic groups in a realistic multi-centre setting. This finding may be usefully applied in the diagnosis of Alzheimer’s disease, predicting disease course in individuals with mild cognitive impairment, and in the selection of participants for clinical trials. PMID:22236449

Soft-Tissue Sarcomas of the Abdomen and Pelvis: Radiologic-Pathologic Features, Part 2-Uncommon Sarcomas.

PubMed

Levy, Angela D; Manning, Maria A; Miettinen, Markku M

2017-01-01

Soft-tissue sarcomas occurring in the abdomen and pelvis are an uncommon but important group of malignancies. Recent changes to the World Health Organization classification of soft-tissue tumors include the movement of gastrointestinal stromal tumors (GISTs) into the soft-tissue tumor classification. GIST is the most common intraperitoneal sarcoma. Liposarcoma is the most common retroperitoneal sarcoma, and leiomyosarcoma is the second most common. GIST, liposarcoma, and leiomyosarcoma account for the majority of sarcomas encountered in the abdomen and pelvis and are discussed in part 1 of this article. Undifferentiated pleomorphic sarcoma (previously called malignant fibrous histiocytoma), dermatofibrosarcoma protuberans, solitary fibrous tumor, malignant peripheral nerve sheath tumor, rhabdomyosarcoma, extraskeletal chondro-osseous sarcomas, vascular sarcomas, and sarcomas of uncertain differentiation uncommonly arise in the abdomen and pelvis and the abdominal wall. Although these lesions are rare sarcomas and their imaging features overlap, familiarity with the locations where they occur and their imaging features is important so they can be diagnosed accurately. The anatomic location and clinical history are important factors in the differential diagnosis of these lesions because metastasis, more-common sarcomas, borderline fibroblastic proliferations (such as desmoid tumors), and endometriosis have imaging findings that overlap with those of these uncommon sarcomas. In this article, the clinical, pathologic, and imaging findings of uncommon soft-tissue sarcomas of the abdomen and pelvis and the abdominal wall are reviewed, with an emphasis on their differential diagnosis.

Soft-Tissue Sarcomas of the Abdomen and Pelvis: Radiologic-Pathologic Features, Part 2—Uncommon Sarcomas

PubMed Central

Manning, Maria A.; Miettinen, Markku M.

2017-01-01

Soft-tissue sarcomas occurring in the abdomen and pelvis are an uncommon but important group of malignancies. Recent changes to the World Health Organization classification of soft-tissue tumors include the movement of gastrointestinal stromal tumors (GISTs) into the soft-tissue tumor classification. GIST is the most common intraperitoneal sarcoma. Liposarcoma is the most common retroperitoneal sarcoma, and leiomyosarcoma is the second most common. GIST, liposarcoma, and leiomyosarcoma account for the majority of sarcomas encountered in the abdomen and pelvis and are discussed in part 1 of this article. Undifferentiated pleomorphic sarcoma (previously called malignant fibrous histiocytoma), dermatofibrosarcoma protuberans, solitary fibrous tumor, malignant peripheral nerve sheath tumor, rhabdomyosarcoma, extraskeletal chondro-osseous sarcomas, vascular sarcomas, and sarcomas of uncertain differentiation uncommonly arise in the abdomen and pelvis and the abdominal wall. Although these lesions are rare sarcomas and their imaging features overlap, familiarity with the locations where they occur and their imaging features is important so they can be diagnosed accurately. The anatomic location and clinical history are important factors in the differential diagnosis of these lesions because metastasis, more-common sarcomas, borderline fibroblastic proliferations (such as desmoid tumors), and endometriosis have imaging findings that overlap with those of these uncommon sarcomas. In this article, the clinical, pathologic, and imaging findings of uncommon soft-tissue sarcomas of the abdomen and pelvis and the abdominal wall are reviewed, with an emphasis on their differential diagnosis. PMID:28493803

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.

Primary central nervous system vasculitis and its mimicking diseases - clinical features, outcome, comorbidities and diagnostic results - A case control study.

PubMed

Becker, J; Horn, P A; Keyvani, K; Metz, I; Wegner, C; Brück, W; Heinemann, F M; Schwitalla, J C; Berlit, P; Kraemer, M

2017-05-01

To compare clinical features and outcome, imaging characteristics, biopsy results and laboratory findings in a cohort of 69 patients with suspected or diagnosed primary central nervous system vasculitis (PCNSV) in adults; to identify risk factors and predictive features for PCNSV. We performed a case-control-study including 69 patients referred with suspected PCNSV from whom 25 were confirmed by predetermined diagnostic criteria based on biopsy (72%) or angiography (28%). Forty-four patients turned out to have 15 distinct other diagnoses. Clinical and diagnostic data were compared between PCNSV and Non-PCNSV cohorts. Clinical presentation was not able to discriminate between PCNSV and its differential diagnoses. However, a worse clinical outcome was associated with PCNSV (p=0.005). Biopsy (p=0.004), contrast enhancement (p=0.000) or tumour-like mass lesion (p=0.008) in magnetic resonance imaging (MRI), intrathecal IgG increase (p=0.020), normal Duplex findings of cerebral arteries (p=0.022) and conventional angiography (p 0.010) were able to distinguish between the two cohorts. In a cohort of 69 patients with suspected PCNSV, a large number (64%) was misdiagnosed and partly received treatment, since mimicking diseases are very difficult to discriminate. Clinical presentation at manifestation does not help to differentiate PCNSV from its mimicking diseases. MRI and cerebrospinal fluid analysis are unlikely to be normal in PCNSV, though unspecific if pathological. Cerebral angiography and biopsy must complement other diagnostics when establishing the diagnosis in order to avoid misdiagnosis and mistreatment. German clinical trials register: http://drks-neu.uniklinik-freiburg.de/drks_web/, Unique identifier: DRKS00005347. Copyright © 2017 Elsevier B.V. All rights reserved.

Effect of Quantitative Nuclear Image Features on Recurrence of Ductal Carcinoma In Situ (DCIS) of the Breast

PubMed Central

Axelrod, David E.; Miller, Naomi A.; Lickley, H. Lavina; Qian, Jin; Christens-Barry, William A.; Yuan, Yan; Fu, Yuejiao; Chapman, Judith-Anne W.

2008-01-01

Background Nuclear grade has been associated with breast DCIS recurrence and progression to invasive carcinoma; however, our previous study of a cohort of patients with breast DCIS did not find such an association with outcome. Fifty percent of patients had heterogeneous DCIS with more than one nuclear grade. The aim of the current study was to investigate the effect of quantitative nuclear features assessed with digital image analysis on ipsilateral DCIS recurrence. Methods Hematoxylin and eosin stained slides for a cohort of 80 patients with primary breast DCIS were reviewed and two fields with representative grade (or grades) were identified by a Pathologist and simultaneously used for acquisition of digital images for each field. Van Nuys worst nuclear grade was assigned, as was predominant grade, and heterogeneous grading when present. Patients were grouped by heterogeneity of their nuclear grade: Group A: nuclear grade 1 only, nuclear grades 1 and 2, or nuclear grade 2 only (32 patients), Group B: nuclear grades 1, 2 and 3, or nuclear grades 2 and 3 (31 patients), Group 3: nuclear grade 3 only (17 patients). Nuclear fine structure was assessed by software which captured thirty-nine nuclear feature values describing nuclear morphometry, densitometry, and texture. Step-wise forward Cox regressions were performed with previous clinical and pathologic factors, and the new image analysis features. Results Duplicate measurements were similar for 89.7% to 97.4% of assessed image features. The rate of correct classification of nuclear grading with digital image analysis features was similar in the two fields, and pooled assessment across both fields. In the pooled assessment, a discriminant function with one nuclear morphometric and one texture feature was significantly (p = 0.001) associated with nuclear grading, and provided correct jackknifed classification of a patient’s nuclear grade for Group A (78.1%), Group B (48.4%), and Group C (70.6%). The factors significantly associated with DCIS recurrence were those previously found, type of initial presentation (p = 0.03) and amount of parenchymal involvement (p = 0.05), along with the morphometry image feature of ellipticity (p = 0.04). Conclusion Analysis of nuclear features measured by image cytometry may contribute to the classification and prognosis of breast DCIS patients with more than one nuclear grade. PMID:18779878

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.

SU-D-207B-02: Early Grade Classification in Meningioma Patients Combining Radiomics and Semantics Data

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

Coroller, T; Bi, W; Abedalthagafi, M

Purpose: The clinical management of meningioma is guided by its grade and biologic behavior. Currently, diagnosis of tumor grade follows surgical resection and histopathologic review. Reliable techniques for pre-operative determination of tumor behavior are needed. We investigated the association between imaging features extracted from preoperative gadolinium-enhanced T1-weighted MRI and meningioma grade. Methods: We retrospectively examined the pre-operative MRI for 139 patients with de novo WHO grade I (63%) and grade II (37%) meningiomas. We investigated the predictive power of ten semantic radiologic features as determined by a neuroradiologist, fifteen radiomic features, and tumor location. Conventional (volume and diameter) imaging featuresmore » were added for comparison. AUC was computed for continuous and χ{sup 2} for discrete variables. Classification was done using random forest. Performance was evaluated using cross validation (1000 iterations, 75% training and 25% validation). All p-values were adjusted for multiple testing. Results: Significant association was observed between meningioma grade and tumor location (p<0.001) and two semantic features including intra-tumoral heterogeneity (p<0.001) and overt hemorrhage (p=0.01). Conventional (AUC 0.61–0.67) and eleven radiomic (AUC 0.60–0.70) features were significant from random (p<0.05, Noether test). Median AUC values for classification of tumor grade were 0.57, 0.71, 0.72 and 0.77 respectively for conventional, radiomic, location, and semantic features after using random forest. By combining all imaging data (semantic, radiomic, and location), the median AUC was 0.81, which offers superior predicting power to that of conventional imaging descriptors for meningioma as well as radiomic features alone (p<0.05, permutation test). Conclusion: We demonstrate a strong association between radiologic features and meningioma grade. Pre-operative prediction of tumor behavior based on imaging features offers promise for guiding personalized medicine and improving patient management.« less

Automated diagnosis of prostate cancer in multi-parametric MRI based on multimodal convolutional neural networks

NASA Astrophysics Data System (ADS)

Le, Minh Hung; Chen, Jingyu; Wang, Liang; Wang, Zhiwei; Liu, Wenyu; (Tim Cheng, Kwang-Ting; Yang, Xin

2017-08-01

Automated methods for prostate cancer (PCa) diagnosis in multi-parametric magnetic resonance imaging (MP-MRIs) are critical for alleviating requirements for interpretation of radiographs while helping to improve diagnostic accuracy (Artan et al 2010 IEEE Trans. Image Process. 19 2444-55, Litjens et al 2014 IEEE Trans. Med. Imaging 33 1083-92, Liu et al 2013 SPIE Medical Imaging (International Society for Optics and Photonics) p 86701G, Moradi et al 2012 J. Magn. Reson. Imaging 35 1403-13, Niaf et al 2014 IEEE Trans. Image Process. 23 979-91, Niaf et al 2012 Phys. Med. Biol. 57 3833, Peng et al 2013a SPIE Medical Imaging (International Society for Optics and Photonics) p 86701H, Peng et al 2013b Radiology 267 787-96, Wang et al 2014 BioMed. Res. Int. 2014). This paper presents an automated method based on multimodal convolutional neural networks (CNNs) for two PCa diagnostic tasks: (1) distinguishing between cancerous and noncancerous tissues and (2) distinguishing between clinically significant (CS) and indolent PCa. Specifically, our multimodal CNNs effectively fuse apparent diffusion coefficients (ADCs) and T2-weighted MP-MRI images (T2WIs). To effectively fuse ADCs and T2WIs we design a new similarity loss function to enforce consistent features being extracted from both ADCs and T2WIs. The similarity loss is combined with the conventional classification loss functions and integrated into the back-propagation procedure of CNN training. The similarity loss enables better fusion results than existing methods as the feature learning processes of both modalities are mutually guided, jointly facilitating CNN to ‘see’ the true visual patterns of PCa. The classification results of multimodal CNNs are further combined with the results based on handcrafted features using a support vector machine classifier. To achieve a satisfactory accuracy for clinical use, we comprehensively investigate three critical factors which could greatly affect the performance of our multimodal CNNs but have not been carefully studied previously. (1) Given limited training data, how can these be augmented in sufficient numbers and variety for fine-tuning deep CNN networks for PCa diagnosis? (2) How can multimodal MP-MRI information be effectively combined in CNNs? (3) What is the impact of different CNN architectures on the accuracy of PCa diagnosis? Experimental results on extensive clinical data from 364 patients with a total of 463 PCa lesions and 450 identified noncancerous image patches demonstrate that our system can achieve a sensitivity of 89.85% and a specificity of 95.83% for distinguishing cancer from noncancerous tissues and a sensitivity of 100% and a specificity of 76.92% for distinguishing indolent PCa from CS PCa. This result is significantly superior to the state-of-the-art method relying on handcrafted features.

Optical Coherence Tomography of the Tympanic Membrane and Middle Ear: A Review.

PubMed

Tan, Hsern Ern Ivan; Santa Maria, Peter Luke; Wijesinghe, Philip; Francis Kennedy, Brendan; Allardyce, Benjamin James; Eikelboom, Robert Henry; Atlas, Marcus David; Dilley, Rodney James

2018-05-01

Objective To evaluate the recent developments in optical coherence tomography (OCT) for tympanic membrane (TM) and middle ear (ME) imaging and to identify what further development is required for the technology to be integrated into common clinical use. Data Sources PubMed, Embase, Google Scholar, Scopus, and Web of Science. Review Methods A comprehensive literature search was performed for English language articles published from January 1966 to January 2018 with the keywords "tympanic membrane or middle ear,"optical coherence tomography," and "imaging." Conclusion Conventional imaging techniques cannot adequately resolve the microscale features of TM and ME, sometimes necessitating diagnostic exploratory surgery in challenging otologic pathology. As a high-resolution noninvasive imaging technique, OCT offers promise as a diagnostic aid for otologic conditions, such as otitis media, cholesteatoma, and conductive hearing loss. Using OCT vibrometry to image the nanoscale vibrations of the TM and ME as they conduct acoustic waves may detect the location of ossicular chain dysfunction and differentiate between stapes fixation and incus-stapes discontinuity. The capacity of OCT to image depth and thickness at high resolution allows 3-dimensional volumetric reconstruction of the ME and has potential use for reconstructive tympanoplasty planning and the follow-up of ossicular prostheses. Implications for Practice To achieve common clinical use beyond these initial discoveries, future in vivo imaging devices must feature low-cost probe or endoscopic designs and faster imaging speeds and demonstrate superior diagnostic utility to computed tomography and magnetic resonance imaging. While such technology has been available for OCT, its translation requires focused development through a close collaboration between engineers and clinicians.

Breed-Specific Magnetic Resonance Imaging Characteristics of Necrotizing Encephalitis in Dogs

PubMed Central

Flegel, Thomas

2017-01-01

Diagnosing necrotizing encephalitis, with its subcategories of necrotizing leukoencephalitis and necrotizing meningoencephalitis, based on magnetic resonance imaging alone can be challenging. However, there are breed-specific imaging characteristics in both subcategories that allow establishing a clinical diagnosis with a relatively high degree of certainty. Typical breed specific imaging features, such as lesion distribution, signal intensity, contrast enhancement, and gross changes of brain structure (midline shift, ventriculomegaly, and brain herniation) are summarized here, using current literature, for the most commonly affected canine breeds: Yorkshire Terrier, French Bulldog, Pug, and Chihuahua. PMID:29255715

Iterative variational mode decomposition based automated detection of glaucoma using fundus images.

PubMed

Maheshwari, Shishir; Pachori, Ram Bilas; Kanhangad, Vivek; Bhandary, Sulatha V; Acharya, U Rajendra

2017-09-01

Glaucoma is one of the leading causes of permanent vision loss. It is an ocular disorder caused by increased fluid pressure within the eye. The clinical methods available for the diagnosis of glaucoma require skilled supervision. They are manual, time consuming, and out of reach of common people. Hence, there is a need for an automated glaucoma diagnosis system for mass screening. In this paper, we present a novel method for an automated diagnosis of glaucoma using digital fundus images. Variational mode decomposition (VMD) method is used in an iterative manner for image decomposition. Various features namely, Kapoor entropy, Renyi entropy, Yager entropy, and fractal dimensions are extracted from VMD components. ReliefF algorithm is used to select the discriminatory features and these features are then fed to the least squares support vector machine (LS-SVM) for classification. Our proposed method achieved classification accuracies of 95.19% and 94.79% using three-fold and ten-fold cross-validation strategies, respectively. This system can aid the ophthalmologists in confirming their manual reading of classes (glaucoma or normal) using fundus images. Copyright © 2017 Elsevier Ltd. All rights reserved.

Integration of co-localized glandular morphometry and protein biomarker expression in immunofluorescent images for prostate cancer prognosis

NASA Astrophysics Data System (ADS)

Scott, Richard; Khan, Faisal M.; Zeineh, Jack; Donovan, Michael; Fernandez, Gerardo

2015-03-01

Immunofluorescent (IF) image analysis of tissue pathology has proven to be extremely valuable and robust in developing prognostic assessments of disease, particularly in prostate cancer. There have been significant advances in the literature in quantitative biomarker expression as well as characterization of glandular architectures in discrete gland rings. However, while biomarker and glandular morphometric features have been combined as separate predictors in multivariate models, there is a lack of integrative features for biomarkers co-localized within specific morphological sub-types; for example the evaluation of androgen receptor (AR) expression within Gleason 3 glands only. In this work we propose a novel framework employing multiple techniques to generate integrated metrics of morphology and biomarker expression. We demonstrate the utility of the approaches in predicting clinical disease progression in images from 326 prostate biopsies and 373 prostatectomies. Our proposed integrative approaches yield significant improvements over existing IF image feature metrics. This work presents some of the first algorithms for generating innovative characteristics in tissue diagnostics that integrate co-localized morphometry and protein biomarker expression.

IBM Application System/400 as the foundation of the Mayo Clinic/IBM PACS project

NASA Astrophysics Data System (ADS)

Rothman, Melvyn L.; Morin, Richard L.; Persons, Kenneth R.; Gibbons, Patricia S.

1990-08-01

An IBM Application System/400 (AS/400) anchors the Mayo Clinic/IBM joint development PACS project. This paper highlights some of the AS/400's features and the resulting benefits which make it a strong foundation for a medical image archival and review system. Among the AS/400's key features are: 1. A high-level machine architecture 2. Object orientation 3. Relational data base and other functions integrated into the system's architecture 4. High-function interfaces to IBM Personal Computers and IBM Personal System/2s' (pS/2TM).

Compensation for the signal processing characteristics of ultrasound B-mode scanners in adaptive speckle reduction.

PubMed

Crawford, D C; Bell, D S; Bamber, J C

1993-01-01

A systematic method to compensate for nonlinear amplification of individual ultrasound B-scanners has been investigated in order to optimise performance of an adaptive speckle reduction (ASR) filter for a wide range of clinical ultrasonic imaging equipment. Three potential methods have been investigated: (1) a method involving an appropriate selection of the speckle recognition feature was successful when the scanner signal processing executes simple logarithmic compressions; (2) an inverse transform (decompression) of the B-mode image was effective in correcting for the measured characteristics of image data compression when the algorithm was implemented in full floating point arithmetic; (3) characterising the behaviour of the statistical speckle recognition feature under conditions of speckle noise was found to be the method of choice for implementation of the adaptive speckle reduction algorithm in limited precision integer arithmetic. In this example, the statistical features of variance and mean were investigated. The third method may be implemented on commercially available fast image processing hardware and is also better suited for transfer into dedicated hardware to facilitate real-time adaptive speckle reduction. A systematic method is described for obtaining ASR calibration data from B-mode images of a speckle producing phantom.

Automated discovery of structural features of the optic nerve head on the basis of image and genetic data

NASA Astrophysics Data System (ADS)

Christopher, Mark; Tang, Li; Fingert, John H.; Scheetz, Todd E.; Abramoff, Michael D.

2014-03-01

Evaluation of optic nerve head (ONH) structure is a commonly used clinical technique for both diagnosis and monitoring of glaucoma. Glaucoma is associated with characteristic changes in the structure of the ONH. We present a method for computationally identifying ONH structural features using both imaging and genetic data from a large cohort of participants at risk for primary open angle glaucoma (POAG). Using 1054 participants from the Ocular Hypertension Treatment Study, ONH structure was measured by application of a stereo correspondence algorithm to stereo fundus images. In addition, the genotypes of several known POAG genetic risk factors were considered for each participant. ONH structural features were discovered using both a principal component analysis approach to identify the major modes of variance within structural measurements and a linear discriminant analysis approach to capture the relationship between genetic risk factors and ONH structure. The identified ONH structural features were evaluated based on the strength of their associations with genotype and development of POAG by the end of the OHTS study. ONH structural features with strong associations with genotype were identified for each of the genetic loci considered. Several identified ONH structural features were significantly associated (p < 0.05) with the development of POAG after Bonferroni correction. Further, incorporation of genetic risk status was found to substantially increase performance of early POAG prediction. These results suggest incorporating both imaging and genetic data into ONH structural modeling significantly improves the ability to explain POAG-related changes to ONH structure.

Breast tissue classification in digital tomosynthesis images based on global gradient minimization and texture features

NASA Astrophysics Data System (ADS)

Qin, Xulei; Lu, Guolan; Sechopoulos, Ioannis; Fei, Baowei

2014-03-01

Digital breast tomosynthesis (DBT) is a pseudo-three-dimensional x-ray imaging modality proposed to decrease the effect of tissue superposition present in mammography, potentially resulting in an increase in clinical performance for the detection and diagnosis of breast cancer. Tissue classification in DBT images can be useful in risk assessment, computer-aided detection and radiation dosimetry, among other aspects. However, classifying breast tissue in DBT is a challenging problem because DBT images include complicated structures, image noise, and out-of-plane artifacts due to limited angular tomographic sampling. In this project, we propose an automatic method to classify fatty and glandular tissue in DBT images. First, the DBT images are pre-processed to enhance the tissue structures and to decrease image noise and artifacts. Second, a global smooth filter based on L0 gradient minimization is applied to eliminate detailed structures and enhance large-scale ones. Third, the similar structure regions are extracted and labeled by fuzzy C-means (FCM) classification. At the same time, the texture features are also calculated. Finally, each region is classified into different tissue types based on both intensity and texture features. The proposed method is validated using five patient DBT images using manual segmentation as the gold standard. The Dice scores and the confusion matrix are utilized to evaluate the classified results. The evaluation results demonstrated the feasibility of the proposed method for classifying breast glandular and fat tissue on DBT images.

Limited utility of routine surveillance imaging for classical Hodgkin lymphoma patients in first complete remission.

PubMed

Pingali, Sai Ravi; Jewell, Sarah W; Havlat, Luiza; Bast, Martin A; Thompson, Jonathan R; Eastwood, Daniel C; Bartlett, Nancy L; Armitage, James O; Wagner-Johnston, Nina D; Vose, Julie M; Fenske, Timothy S

2014-07-15

The objective of this study was to compare the outcomes of patients with classical Hodgkin lymphoma (cHL) who achieved complete remission with frontline therapy and then underwent either clinical surveillance or routine surveillance imaging. In total, 241 patients who were newly diagnosed with cHL between January 2000 and December 2010 at 3 participating tertiary care centers and achieved complete remission after first-line therapy were retrospectively analyzed. Of these, there were 174 patients in the routine surveillance imaging group and 67 patients in the clinical surveillance group, based on the intended mode of surveillance. In the routine surveillance imaging group, the intended plan of surveillance included computed tomography and/or positron emission tomography scans; whereas, in the clinical surveillance group, the intended plan of surveillance was clinical examination and laboratory studies, and scans were obtained only to evaluate concerning signs or symptoms. Baseline patient characteristics, prognostic features, treatment records, and outcomes were collected. The primary objective was to compare overall survival for patients in both groups. For secondary objectives, we compared the success of second-line therapy and estimated the costs of imaging for each group. After 5 years of follow-up, the overall survival rate was 97% (95% confidence interval, 92%-99%) in the routine surveillance imaging group and 96% (95% confidence interval, 87%-99%) in the clinical surveillance group (P = .41). There were few relapses in each group, and all patients who relapsed in both groups achieved complete remission with second-line therapy. The charges associated with routine surveillance imaging were significantly higher than those for the clinical surveillance strategy, with no apparent clinical benefit. Clinical surveillance was not inferior to routine surveillance imaging in patients with cHL who achieved complete remission with frontline therapy. Routine surveillance imaging was associated with significantly increased estimated imaging charges. © 2014 American Cancer Society.

Clinical features of olfactory disorders in patients seeking medical consultation

PubMed Central

Chen, Guowei; Wei, Yongxiang; Miao, Xutao; Li, Kunyan; Ren, Yuanyuan; Liu, Jia

2013-01-01

Background Olfactory disorders are common complaints in ENT clinics. We investigated causes and relevant features of olfactory disorders and the need for gustatory testing in patients with olfactory dysfunction. Material/Methods A total of 140 patients seeking medical consultations were enrolled. All patients were asked about their olfactory disorders in a structured interview of medical history and underwent thorough otolaryngologic examinations and imaging of the head. Results Causes of olfactory disorders were classified as: upper respiratory tract infection (URTI), sinonasal diseases (NSD), head trauma, idiopathic, endoscopic sinus surgery, congenital anosmia, and other causes. Each of the various causes of olfactory dysfunction had its own distinct clinical features. Nineteen of 54 patients whose gustation was assessed had gustatory disorders. Conclusions The leading causes of olfactory dysfunction were URTI, NSD, head trauma, and idiopathic causes. Gustatory disorders were fairly common in patients with olfactory dysfunction. High priority should be given to complaints of olfactory disorders. PMID:23748259

Computer-assisted liver graft steatosis assessment via learning-based texture analysis.

PubMed

Moccia, Sara; Mattos, Leonardo S; Patrini, Ilaria; Ruperti, Michela; Poté, Nicolas; Dondero, Federica; Cauchy, François; Sepulveda, Ailton; Soubrane, Olivier; De Momi, Elena; Diaspro, Alberto; Cesaretti, Manuela

2018-05-23

Fast and accurate graft hepatic steatosis (HS) assessment is of primary importance for lowering liver dysfunction risks after transplantation. Histopathological analysis of biopsied liver is the gold standard for assessing HS, despite being invasive and time consuming. Due to the short time availability between liver procurement and transplantation, surgeons perform HS assessment through clinical evaluation (medical history, blood tests) and liver texture visual analysis. Despite visual analysis being recognized as challenging in the clinical literature, few efforts have been invested to develop computer-assisted solutions for HS assessment. The objective of this paper is to investigate the automatic analysis of liver texture with machine learning algorithms to automate the HS assessment process and offer support for the surgeon decision process. Forty RGB images of forty different donors were analyzed. The images were captured with an RGB smartphone camera in the operating room (OR). Twenty images refer to livers that were accepted and 20 to discarded livers. Fifteen randomly selected liver patches were extracted from each image. Patch size was [Formula: see text]. This way, a balanced dataset of 600 patches was obtained. Intensity-based features (INT), histogram of local binary pattern ([Formula: see text]), and gray-level co-occurrence matrix ([Formula: see text]) were investigated. Blood-sample features (Blo) were included in the analysis, too. Supervised and semisupervised learning approaches were investigated for feature classification. The leave-one-patient-out cross-validation was performed to estimate the classification performance. With the best-performing feature set ([Formula: see text]) and semisupervised learning, the achieved classification sensitivity, specificity, and accuracy were 95, 81, and 88%, respectively. This research represents the first attempt to use machine learning and automatic texture analysis of RGB images from ubiquitous smartphone cameras for the task of graft HS assessment. The results suggest that is a promising strategy to develop a fully automatic solution to assist surgeons in HS assessment inside the OR.

Pseudo progression identification of glioblastoma with dictionary learning.

PubMed

Zhang, Jian; Yu, Hengyong; Qian, Xiaohua; Liu, Keqin; Tan, Hua; Yang, Tielin; Wang, Maode; Li, King Chuen; Chan, Michael D; Debinski, Waldemar; Paulsson, Anna; Wang, Ge; Zhou, Xiaobo

2016-06-01

Although the use of temozolomide in chemoradiotherapy is effective, the challenging clinical problem of pseudo progression has been raised in brain tumor treatment. This study aims to distinguish pseudo progression from true progression. Between 2000 and 2012, a total of 161 patients with glioblastoma multiforme (GBM) were treated with chemoradiotherapy at our hospital. Among the patients, 79 had their diffusion tensor imaging (DTI) data acquired at the earliest diagnosed date of pseudo progression or true progression, and 23 had both DTI data and genomic data. Clinical records of all patients were kept in good condition. Volumetric fractional anisotropy (FA) images obtained from the DTI data were decomposed into a sequence of sparse representations. Then, a feature selection algorithm was applied to extract the critical features from the feature matrix to reduce the size of the feature matrix and to improve the classification accuracy. The proposed approach was validated using the 79 samples with clinical DTI data. Satisfactory results were obtained under different experimental conditions. The area under the receiver operating characteristic (ROC) curve (AUC) was 0.87 for a given dictionary with 1024 atoms. For the subgroup of 23 samples, genomics data analysis was also performed. Results implied further perspective on pseudo progression classification. The proposed method can determine pseudo progression and true progression with improved accuracy. Laboring segmentation is no longer necessary because this skillfully designed method is not sensitive to tumor location. Copyright © 2016 Elsevier Ltd. All rights reserved.

Kernel Regression Estimation of Fiber Orientation Mixtures in Diffusion MRI

PubMed Central

Cabeen, Ryan P.; Bastin, Mark E.; Laidlaw, David H.

2016-01-01

We present and evaluate a method for kernel regression estimation of fiber orientations and associated volume fractions for diffusion MR tractography and population-based atlas construction in clinical imaging studies of brain white matter. This is a model-based image processing technique in which representative fiber models are estimated from collections of component fiber models in model-valued image data. This extends prior work in nonparametric image processing and multi-compartment processing to provide computational tools for image interpolation, smoothing, and fusion with fiber orientation mixtures. In contrast to related work on multi-compartment processing, this approach is based on directional measures of divergence and includes data-adaptive extensions for model selection and bilateral filtering. This is useful for reconstructing complex anatomical features in clinical datasets analyzed with the ball-and-sticks model, and our framework’s data-adaptive extensions are potentially useful for general multi-compartment image processing. We experimentally evaluate our approach with both synthetic data from computational phantoms and in vivo clinical data from human subjects. With synthetic data experiments, we evaluate performance based on errors in fiber orientation, volume fraction, compartment count, and tractography-based connectivity. With in vivo data experiments, we first show improved scan-rescan reproducibility and reliability of quantitative fiber bundle metrics, including mean length, volume, streamline count, and mean volume fraction. We then demonstrate the creation of a multi-fiber tractography atlas from a population of 80 human subjects. In comparison to single tensor atlasing, our multi-fiber atlas shows more complete features of known fiber bundles and includes reconstructions of the lateral projections of the corpus callosum and complex fronto-parietal connections of the superior longitudinal fasciculus I, II, and III. PMID:26691524

Photo acoustic imaging: technology, systems and market trends

NASA Astrophysics Data System (ADS)

Faucheux, Marc; d'Humières, Benoît; Cochard, Jacques

2017-03-01

Although the Photo Acoustic effect was observed by Graham Bell in 1880, the first applications (gas analysis) occurred in 1970's using the required energetic light pulses from lasers. During mid 1990's medical imaging research begun to use Photo Acoustic effect and in vivo images were obtained in mid-2000. Since 2009, the number of patent related to Photo Acoustic Imaging (PAI) has dramatically increased. PAI machines for pre-clinical and small animal imaging have been being used in a routine way for several years. Based on its very interesting features (non-ionizing radiation, noninvasive, high depth resolution ratio, scalability, moderate price) and because it is able to deliver not only anatomical, but functional and molecular information, PAI is a very promising clinical imaging modality. It penetrates deeper into tissue than OCT (Optical Coherence Tomography) and provides a higher resolution than ultrasounds. The PAI is one of the most growing imaging modality and some innovative clinical systems are planned to be on market in 2017. Our study analyzes the different approaches such as photoacoustic computed tomography, 3D photoacoustic microscopy, multispectral photoacoustic tomography and endoscopy with the recent and tremendous technological progress over the past decade: advances in image reconstruction algorithms, laser technology, ultrasound detectors and miniaturization. We analyze which medical domains and applications are the most concerned and explain what should be the forthcoming medical system in the near future. We segment the market in four parts: Components and R&D, pre-clinical, analytics, clinical. We analyzed what should be, quantitatively and qualitatively, the PAI medical markets in each segment and its main trends. We point out the market accessibility (patents, regulations, clinical evaluations, clinical acceptance, funding). In conclusion, we explain the main market drivers and challenges to overcome and give a road map for medical approved PAI products.

[Medical imaging in tumor precision medicine: opportunities and challenges].

PubMed

Xu, Jingjing; Tan, Yanbin; Zhang, Minming

2017-05-25

Tumor precision medicine is an emerging approach for tumor diagnosis, treatment and prevention, which takes account of individual variability of environment, lifestyle and genetic information. Tumor precision medicine is built up on the medical imaging innovations developed during the past decades, including the new hardware, new imaging agents, standardized protocols, image analysis and multimodal imaging fusion technology. Also the development of automated and reproducible analysis algorithm has extracted large amount of information from image-based features. With the continuous development and mining of tumor clinical and imaging databases, the radiogenomics, radiomics and artificial intelligence have been flourishing. Therefore, these new technological advances bring new opportunities and challenges to the application of imaging in tumor precision medicine.

Characterizing stroke lesions using digital templates and lesion quantification tools in a web-based imaging informatics system for a large-scale stroke rehabilitation clinical trial

NASA Astrophysics Data System (ADS)

Wang, Ximing; Edwardson, Matthew; Dromerick, Alexander; Winstein, Carolee; Wang, Jing; Liu, Brent

2015-03-01

Previously, we presented an Interdisciplinary Comprehensive Arm Rehabilitation Evaluation (ICARE) imaging informatics system that supports a large-scale phase III stroke rehabilitation trial. The ePR system is capable of displaying anonymized patient imaging studies and reports, and the system is accessible to multiple clinical trial sites and users across the United States via the web. However, the prior multicenter stroke rehabilitation trials lack any significant neuroimaging analysis infrastructure. In stroke related clinical trials, identification of the stroke lesion characteristics can be meaningful as recent research shows that lesion characteristics are related to stroke scale and functional recovery after stroke. To facilitate the stroke clinical trials, we hope to gain insight into specific lesion characteristics, such as vascular territory, for patients enrolled into large stroke rehabilitation trials. To enhance the system's capability for data analysis and data reporting, we have integrated new features with the system: a digital brain template display, a lesion quantification tool and a digital case report form. The digital brain templates are compiled from published vascular territory templates at each of 5 angles of incidence. These templates were updated to include territories in the brainstem using a vascular territory atlas and the Medical Image Processing, Analysis and Visualization (MIPAV) tool. The digital templates are displayed for side-by-side comparisons and transparent template overlay onto patients' images in the image viewer. The lesion quantification tool quantifies planimetric lesion area from user-defined contour. The digital case report form stores user input into a database, then displays contents in the interface to allow for reviewing, editing, and new inputs. In sum, the newly integrated system features provide the user with readily-accessible web-based tools to identify the vascular territory involved, estimate lesion area, and store these results in a web-based digital format.

Learning-based prediction of gestational age from ultrasound images of the fetal brain.

PubMed

Namburete, Ana I L; Stebbing, Richard V; Kemp, Bryn; Yaqub, Mohammad; Papageorghiou, Aris T; Alison Noble, J

2015-04-01

We propose an automated framework for predicting gestational age (GA) and neurodevelopmental maturation of a fetus based on 3D ultrasound (US) brain image appearance. Our method capitalizes on age-related sonographic image patterns in conjunction with clinical measurements to develop, for the first time, a predictive age model which improves on the GA-prediction potential of US images. The framework benefits from a manifold surface representation of the fetal head which delineates the inner skull boundary and serves as a common coordinate system based on cranial position. This allows for fast and efficient sampling of anatomically-corresponding brain regions to achieve like-for-like structural comparison of different developmental stages. We develop bespoke features which capture neurosonographic patterns in 3D images, and using a regression forest classifier, we characterize structural brain development both spatially and temporally to capture the natural variation existing in a healthy population (N=447) over an age range of active brain maturation (18-34weeks). On a routine clinical dataset (N=187) our age prediction results strongly correlate with true GA (r=0.98,accurate within±6.10days), confirming the link between maturational progression and neurosonographic activity observable across gestation. Our model also outperforms current clinical methods by ±4.57 days in the third trimester-a period complicated by biological variations in the fetal population. Through feature selection, the model successfully identified the most age-discriminating anatomies over this age range as being the Sylvian fissure, cingulate, and callosal sulci. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Parahippocampal epilepsy with subtle dysplasia: A cause of "imaging negative" partial epilepsy.

PubMed

Pillay, Neelan; Fabinyi, Gavin C A; Myles, Terry S; Fitt, Gregory J; Berkovic, Samuel F; Jackson, Graeme D

2009-12-01

Lesion-negative refractory partial epilepsy is a major challenge in the assessment of patients for potential surgery. Finding a potential epileptogenic lesion simplifies assessment and is associated with good outcome. Here we describe imaging features of subtle parahippocampal dysplasia in five cases that were initially assessed as having imaging-negative frontal or temporal lobe epilepsy. We analyzed the clinical and imaging features of five patients with seizures from the parahippocampal region. Five patients had subtle but distinctive magnetic resonance imaging (MRI) abnormalities in the parahippocampal gyrus. This was a unilateral signal abnormality in the parahippocampal white matter extending into gray matter on heavily T(1)- and T(2)-weighted images with relative preservation of the gray-white matter boundary on T(1)-weighted volume sequences. Only one of these patients had typical electroclinical unilateral temporal lobe epilepsy (TLE); one mimicked frontal lobe epilepsy, two showed bitemporal seizures, and one had unlocalized partial seizures. All have had surgery; four are seizure-free (one has occasional auras only, follow-up 6 months to 10 years), and one has a >50% seizure reduction. Histopathologic evaluation suggested dysplastic features in the surgical specimens in all. In patients with lesion-negative partial epilepsy with frontal or temporal semiology, or in cases with apparent bitemporal seizures, subtle parahippocampal abnormalities should be carefully excluded. Recognizing the MRI findings of an abnormal parahippocampal gyrus can lead to successful surgery without invasive monitoring, despite apparently incongruent electroclinical features.

MR imaging features and staging of neuroendocrine carcinomas of the uterine cervix with pathological correlations.

PubMed

Duan, Xiaohui; Ban, Xiaohua; Zhang, Xiang; Hu, Huijun; Li, Guozhao; Wang, Dongye; Wang, Charles Qian; Zhang, Fang; Shen, Jun

2016-12-01

To determine MR imaging features and staging accuracy of neuroendocrine carcinomas (NECs) of the uterine cervix with pathological correlations. Twenty-six patients with histologically proven NECs, 60 patients with squamous cell carcinomas (SCCs), and 30 patients with adenocarcinomas of the uterine cervix were included. The clinical data, pathological findings, and MRI findings were reviewed retrospectively. MRI features of cervical NECs, SCCs, and adenocarcinomas were compared, and MRI staging of cervical NECs was compared with the pathological staging. Cervical NECs showed a higher tendency toward a homogeneous signal intensity on T2-weighted imaging and a homogeneous enhancement pattern, as well as a lower ADC value of tumour and a higher incidence of lymphadenopathy, compared with SCCs and adenocarcinomas (P < 0.05). An ADC value cutoff of 0.90 × 10 -3  mm 2 /s was robust for differentiation between cervical NECs and other cervical cancers, with a sensitivity of 63.3 % and a specificity of 95 %. In 21 patients who underwent radical hysterectomy and lymphadenectomy, the overall accuracy of tumour staging by MR imaging was 85.7 % with reference to pathology staging. Homogeneous lesion texture and low ADC value are likely suggestive features of cervical NECs and MR imaging is reliable for the staging of cervical NECs. • Cervical NECs show a tendency of lesion homogeneity and lymphadenopathy • Low ADC values are found in cervical NECs • MRI is an accurate imaging modality for the cervical NEC staging.

Differentiation between benign and malignant palatal tumors using conventional MRI: a retrospective analysis of 130 cases.

PubMed

Zheng, Yingyan; Xiao, Zebin; Zhang, Hua; She, Dejun; Lin, Xuehua; Lin, Yu; Cao, Dairong

2018-04-01

To evaluate the discriminative value of conventional magnetic resonance imaging between benign and malignant palatal tumors. Conventional magnetic resonance imaging features of 130 patients with palatal tumors confirmed by histopathologic examination were retrospectively reviewed. Clinical data and imaging findings were assessed between benign and malignant tumors and between benign and low-grade malignant salivary gland tumors. The variables that were significant in differentiating benign from malignant lesions were further identified using logistic regression analysis. Moreover, imaging features of each common palatal histologic entity were statistically analyzed with the rest of the tumors to define their typical imaging features. Older age, partially defined and ill-defined margins, and absence of a capsule were highly suggestive of malignant palatal tumors, especially ill-defined margins (β = 6.400). The precision in determining malignant palatal tumors achieved a sensitivity of 92.8% and a specificity of 85.6%. In addition, irregular shape, ill-defined margins, lack of a capsule, perineural spread, and invasion of surrounding structures were more often associated with low-grade malignant salivary gland tumors. Conventional magnetic resonance imaging is useful for differentiating benign from malignant palatal tumors as well as benign salivary gland tumors from low-grade salivary gland malignancies. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

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 role of magnetic resonance imaging and ultrasound in patients with adnexal masses.

PubMed

Sohaib, S A; Mills, T D; Sahdev, A; Webb, J A W; Vantrappen, P O; Jacobs, I J; Reznek, R H

2005-03-01

To evaluate the accuracy of ultrasonography (US) and magnetic resonance imaging (MRI) in characterizing adnexal masses, and to determine which patients may benefit from MRI. We prospectively studied 72 women (mean age 53 years, range 19 to 86 years) with clinically suspected adnexal masses. A single experienced sonographer performed transabdominal and transvaginal greyscale spectral and colour Doppler examinations. MRI was carried out on a 1.5T system using T1, T2 and fat-suppressed T1-weighted sequences before and after intravenous injection of gadolinium. The adnexal masses were categorized as benign or malignant without knowledge of clinical details, according to the imaging features which were compared with the surgical and pathological findings. For characterizing lesions as malignant, the sensitivity, specificity and accuracy of MRI were 96.6%, 83.7% and 88.9%, respectively, and of US were 100%, 39.5% and 63.9%, respectively. MRI was more specific (p<0.05) than US. Both MRI and US correctly diagnosed 17 (24%) cases with benign and 28 (39%) cases with malignant masses. MRI correctly diagnosed 19 (26%) cases with benign lesion(s), which on US were thought to be malignant. The age, menopausal status and CA-125 levels in these women made benign disease likely, but US features were suggestive of malignancy (large masses and solid-cystic lesions with nodules). MRI is more specific and accurate than US and Doppler assessment for characterizing adnexal masses. Women who clinically have a relatively low risk of malignancy but who have complex sonographic features may benefit from MRI.

Radiogenomics analysis identifies correlations of digital mammography with clinical molecular signatures in breast cancer.

PubMed

Tamez-Peña, Jose-Gerardo; Rodriguez-Rojas, Juan-Andrés; Gomez-Rueda, Hugo; Celaya-Padilla, Jose-Maria; Rivera-Prieto, Roxana-Alicia; Palacios-Corona, Rebeca; Garza-Montemayor, Margarita; Cardona-Huerta, Servando; Treviño, Victor

2018-01-01

In breast cancer, well-known gene expression subtypes have been related to a specific clinical outcome. However, their impact on the breast tissue phenotype has been poorly studied. Here, we investigate the association of imaging data of tumors to gene expression signatures from 71 patients with breast cancer that underwent pre-treatment digital mammograms and tumor biopsies. From digital mammograms, a semi-automated radiogenomics analysis generated 1,078 features describing the shape, signal distribution, and texture of tumors along their contralateral image used as control. From tumor biopsy, we estimated the OncotypeDX and PAM50 recurrence scores using gene expression microarrays. Then, we used multivariate analysis under stringent cross-validation to train models predicting recurrence scores. Few univariate features reached Spearman correlation coefficients above 0.4. Nevertheless, multivariate analysis yielded significantly correlated models for both signatures (correlation of OncotypeDX = 0.49 ± 0.07 and PAM50 = 0.32 ± 0.10 in stringent cross-validation and OncotypeDX = 0.83 and PAM50 = 0.78 for a unique model). Equivalent models trained from the unaffected contralateral breast were not correlated suggesting that the image signatures were tumor-specific and that overfitting was not a considerable issue. We also noted that models were improved by combining clinical information (triple negative status and progesterone receptor). The models used mostly wavelets and fractal features suggesting their importance to capture tumor information. Our results suggest that molecular-based recurrence risk and breast cancer subtypes have observable radiographic phenotypes. To our knowledge, this is the first study associating mammographic information to gene expression recurrence signatures.

Radiogenomics analysis identifies correlations of digital mammography with clinical molecular signatures in breast cancer

PubMed Central

Tamez-Peña, Jose-Gerardo; Rodriguez-Rojas, Juan-Andrés; Gomez-Rueda, Hugo; Celaya-Padilla, Jose-Maria; Rivera-Prieto, Roxana-Alicia; Palacios-Corona, Rebeca; Garza-Montemayor, Margarita; Cardona-Huerta, Servando

2018-01-01

In breast cancer, well-known gene expression subtypes have been related to a specific clinical outcome. However, their impact on the breast tissue phenotype has been poorly studied. Here, we investigate the association of imaging data of tumors to gene expression signatures from 71 patients with breast cancer that underwent pre-treatment digital mammograms and tumor biopsies. From digital mammograms, a semi-automated radiogenomics analysis generated 1,078 features describing the shape, signal distribution, and texture of tumors along their contralateral image used as control. From tumor biopsy, we estimated the OncotypeDX and PAM50 recurrence scores using gene expression microarrays. Then, we used multivariate analysis under stringent cross-validation to train models predicting recurrence scores. Few univariate features reached Spearman correlation coefficients above 0.4. Nevertheless, multivariate analysis yielded significantly correlated models for both signatures (correlation of OncotypeDX = 0.49 ± 0.07 and PAM50 = 0.32 ± 0.10 in stringent cross-validation and OncotypeDX = 0.83 and PAM50 = 0.78 for a unique model). Equivalent models trained from the unaffected contralateral breast were not correlated suggesting that the image signatures were tumor-specific and that overfitting was not a considerable issue. We also noted that models were improved by combining clinical information (triple negative status and progesterone receptor). The models used mostly wavelets and fractal features suggesting their importance to capture tumor information. Our results suggest that molecular-based recurrence risk and breast cancer subtypes have observable radiographic phenotypes. To our knowledge, this is the first study associating mammographic information to gene expression recurrence signatures. PMID:29596496

Clinical and ultrasonographic features of male breast tumors: A retrospective analysis.

PubMed

Yuan, Wei-Hsin; Li, Anna Fen-Yau; Chou, Yi-Hong; Hsu, Hui-Chen; Chen, Ying-Yuan

2018-01-01

The purpose of this study was to determine clinical and ultrasonographic characteristics of male breast tumors. The medical records of male patients with breast lesions were retrieved from an electronic medical record database and a pathology database and retrospectively reviewed. A total of 112 men (125 breast masses) with preoperative breast ultrasonography (US) were included (median age, 59.50 years; age range, 15-96 years). Data extracted included patient age, if the lesions were bilateral, palpable, and tender, and the presence of nipple discharge. Breast lesion features on static US images were reviewed by three experienced radiologists without knowledge of physical examination or pathology results, original breast US image interpretations, or surgical outcomes. The US features were documented according to the BI-RADS (Breast Imaging-Reporting and Data System) US lexicons. A forth radiologist compiled the data for analysis. Of the 125 breast masses, palpable tender lumps and bilateral synchronous masses were more likely to be benign than malignant (both, 100% vs 0%, P < 0.05). Advanced age and bloody discharge from nipples were common in malignant lesions (P <0.05). A mass eccentric to a nipple, irregular shape, the presence of an echogenic halo, predominantly internal vascularity, and rich color flow signal on color Doppler ultrasound were significantly related to malignancy (all, P < 0.05). An echogenic halo and the presence of rich color flow signal were independent predictors of malignancy. Specific clinical and US characteristics of male breast tumors may help guide treatment, and determine if surgery or conservative treatment is preferable.

Clinical and ultrasonographic features of male breast tumors: A retrospective analysis

PubMed Central

Li, Anna Fen-Yau; Chou, Yi-Hong; Hsu, Hui-Chen; Chen, Ying-Yuan

2018-01-01

Objective The purpose of this study was to determine clinical and ultrasonographic characteristics of male breast tumors. Methods The medical records of male patients with breast lesions were retrieved from an electronic medical record database and a pathology database and retrospectively reviewed. A total of 112 men (125 breast masses) with preoperative breast ultrasonography (US) were included (median age, 59.50 years; age range, 15–96 years). Data extracted included patient age, if the lesions were bilateral, palpable, and tender, and the presence of nipple discharge. Breast lesion features on static US images were reviewed by three experienced radiologists without knowledge of physical examination or pathology results, original breast US image interpretations, or surgical outcomes. The US features were documented according to the BI-RADS (Breast Imaging-Reporting and Data System) US lexicons. A forth radiologist compiled the data for analysis. Results Of the 125 breast masses, palpable tender lumps and bilateral synchronous masses were more likely to be benign than malignant (both, 100% vs 0%, P < 0.05). Advanced age and bloody discharge from nipples were common in malignant lesions (P <0.05). A mass eccentric to a nipple, irregular shape, the presence of an echogenic halo, predominantly internal vascularity, and rich color flow signal on color Doppler ultrasound were significantly related to malignancy (all, P < 0.05). An echogenic halo and the presence of rich color flow signal were independent predictors of malignancy. Conclusion Specific clinical and US characteristics of male breast tumors may help guide treatment, and determine if surgery or conservative treatment is preferable. PMID:29558507

Imaging of Pancreatic and Duodenal Trauma.

PubMed

Melamud, Kira; LeBedis, Christina A; Soto, Jorge A

2015-07-01

Pancreatic and duodenal injuries are rare but life-threatening occurrences, often occurring in association with other solid organ injuries. Findings of pancreatic and duodenal trauma on computed tomography and MR imaging are often nonspecific, and high levels of clinical suspicion and understanding of mechanism of injury are imperative. Familiarity with the grading schemes of pancreatic and duodenal injury is important because they help in assessing for key imaging findings that directly influence management. This article presents an overview of imaging of blunt and penetrating pancreatic and duodenal injuries, including pathophysiology, available imaging techniques, and variety of imaging features. Copyright © 2015 Elsevier Inc. All rights reserved.

Tumour heterogeneity in glioblastoma assessed by MRI texture analysis: a potential marker of survival

PubMed Central

Pérez-Beteta, Julián; Luque, Belén; Arregui, Elena; Calvo, Manuel; Borrás, José M; López, Carlos; Martino, Juan; Velasquez, Carlos; Asenjo, Beatriz; Benavides, Manuel; Herruzo, Ismael; Martínez-González, Alicia; Pérez-Romasanta, Luis; Arana, Estanislao; Pérez-García, Víctor M

2016-01-01

Objective: The main objective of this retrospective work was the study of three-dimensional (3D) heterogeneity measures of post-contrast pre-operative MR images acquired with T1 weighted sequences of patients with glioblastoma (GBM) as predictors of clinical outcome. Methods: 79 patients from 3 hospitals were included in the study. 16 3D textural heterogeneity measures were computed including run-length matrix (RLM) features (regional heterogeneity) and co-occurrence matrix (CM) features (local heterogeneity). The significance of the results was studied using Kaplan–Meier curves and Cox proportional hazards analysis. Correlation between the variables of the study was assessed using the Spearman's correlation coefficient. Results: Kaplan–Meyer survival analysis showed that 4 of the 11 RLM features and 4 of the 5 CM features considered were robust predictors of survival. The median survival differences in the most significant cases were of over 6 months. Conclusion: Heterogeneity measures computed on the post-contrast pre-operative T1 weighted MR images of patients with GBM are predictors of survival. Advances in knowledge: Texture analysis to assess tumour heterogeneity has been widely studied. However, most works develop a two-dimensional analysis, focusing only on one MRI slice to state tumour heterogeneity. The study of fully 3D heterogeneity textural features as predictors of clinical outcome is more robust and is not dependent on the selected slice of the tumour. PMID:27319577

Tumour heterogeneity in glioblastoma assessed by MRI texture analysis: a potential marker of survival.

PubMed

Molina, David; Pérez-Beteta, Julián; Luque, Belén; Arregui, Elena; Calvo, Manuel; Borrás, José M; López, Carlos; Martino, Juan; Velasquez, Carlos; Asenjo, Beatriz; Benavides, Manuel; Herruzo, Ismael; Martínez-González, Alicia; Pérez-Romasanta, Luis; Arana, Estanislao; Pérez-García, Víctor M

2016-07-04

The main objective of this retrospective work was the study of three-dimensional (3D) heterogeneity measures of post-contrast pre-operative MR images acquired with T 1 weighted sequences of patients with glioblastoma (GBM) as predictors of clinical outcome. 79 patients from 3 hospitals were included in the study. 16 3D textural heterogeneity measures were computed including run-length matrix (RLM) features (regional heterogeneity) and co-occurrence matrix (CM) features (local heterogeneity). The significance of the results was studied using Kaplan-Meier curves and Cox proportional hazards analysis. Correlation between the variables of the study was assessed using the Spearman's correlation coefficient. Kaplan-Meyer survival analysis showed that 4 of the 11 RLM features and 4 of the 5 CM features considered were robust predictors of survival. The median survival differences in the most significant cases were of over 6 months. Heterogeneity measures computed on the post-contrast pre-operative T 1 weighted MR images of patients with GBM are predictors of survival. Texture analysis to assess tumour heterogeneity has been widely studied. However, most works develop a two-dimensional analysis, focusing only on one MRI slice to state tumour heterogeneity. The study of fully 3D heterogeneity textural features as predictors of clinical outcome is more robust and is not dependent on the selected slice of the tumour.

Stratification of pseudoprogression and true progression of glioblastoma multiform based on longitudinal diffusion tensor imaging without segmentation

PubMed Central

Qian, Xiaohua; Tan, Hua; Zhang, Jian; Zhao, Weilin; Chan, Michael D.; Zhou, Xiaobo

2016-01-01

Purpose: Pseudoprogression (PsP) can mimic true tumor progression (TTP) on magnetic resonance imaging in patients with glioblastoma multiform (GBM). The phenotypical similarity between PsP and TTP makes it a challenging task for physicians to distinguish these entities. So far, no approved biomarkers or computer-aided diagnosis systems have been used clinically for this purpose. Methods: To address this challenge, the authors developed an objective classification system for PsP and TTP based on longitudinal diffusion tensor imaging. A novel spatio-temporal discriminative dictionary learning scheme was proposed to differentiate PsP and TTP, thereby avoiding segmentation of the region of interest. The authors constructed a novel discriminative sparse matrix with the classification-oriented dictionary learning approach by excluding the shared features of two categories, so that the pooled features captured the subtle difference between PsP and TTP. The most discriminating features were then identified from the pooled features by their feature scoring system. Finally, the authors stratified patients with GBM into PsP and TTP by a support vector machine approach. Tenfold cross-validation (CV) and the area under the receiver operating characteristic (AUC) were used to assess the robustness of the developed system. Results: The average accuracy and AUC values after ten rounds of tenfold CV were 0.867 and 0.92, respectively. The authors also assessed the effects of different methods and factors (such as data types, pooling techniques, and dimensionality reduction approaches) on the performance of their classification system which obtained the best performance. Conclusions: The proposed objective classification system without segmentation achieved a desirable and reliable performance in differentiating PsP from TTP. Thus, the developed approach is expected to advance the clinical research and diagnosis of PsP and TTP. PMID:27806598

Comparison of computed tomography and magnetic resonance imaging for the evaluation of canine intranasal neoplasia.

PubMed

Drees, R; Forrest, L J; Chappell, R

2009-07-01

Canine intranasal neoplasia is commonly evaluated using computed tomography to indicate the diagnosis, to determine disease extent, to guide histological sampling location and to plan treatment. With the expanding use of magnetic resonance imaging in veterinary medicine, this modality has been recently applied for the same purpose. The aim of this study was to compare the features of canine intranasal neoplasia using computed tomography and magnetic resonance imaging. Twenty-one dogs with confirmed intranasal neoplasia underwent both computed tomography and magnetic resonance imaging. The images were reviewed retrospectively for the bony and soft tissue features of intranasal neoplasia. Overall computed tomography and magnetic resonance imaging performed very similarly. However, lysis of bones bordering the nasal cavity and mucosal thickening was found on computed tomography images more often than on magnetic resonance images. Small amounts of fluid in the nasal cavity were more often seen on magnetic resonance images. However, fluid in the frontal sinuses was seen equally well with both modalities. We conclude that computed tomography is satisfactory for evaluation of canine intranasal neoplasia, and no clinically relevant benefit is gained using magnetic resonance imaging for intranasal neoplasia without extent into the cranial cavity.

Usefulness of tissue autofluorescence imaging in actinic cheilitis diagnosis.

PubMed

Takahama Junior, Ademar; Kurachi, Cristina; Cosci, Alessandro; Pereira Faustino, Isabel Schausltz; Camisasca, Danielle Resende; da Costa Fontes, Karla Bianca Fernandes; Pires, Fábio Ramôa; Azevedo, Rebeca Souza

2013-07-01

Actinic cheilitis (AC) is a potentially malignant disorder of the lips. Because of its heterogeneous clinical aspect, it is difficult to indicate representative biopsy area. The purpose of this study was to evaluate the usefulness of tissue autofluorescence in AC diagnosis. The system was composed of a 405-nm light-emitting diode, sent to the sample by a dichroic, that allows the fluorescence signal to reach a camera directly plugged in the system. Fifty-seven patients with clinical diagnosis of AC and 45 normal volunteers were selected. According to clinical and fluorescence features, one or more areas were selected for biopsies in the AC group and epithelial dysplasia (ED) grades were established. The autofluorescence images were processed by a clustering algorithm for AC automated diagnosis. The tissue autofluorescence image revealed a heterogeneous pattern of loss and increase of fluorescence in patients with AC. ED was found in 93% of the cases, and most of the areas graded as moderate or severe ED were chosen with the aid of autofluorescence. The processed autofluorescence images from AC patients showed a higher number of spots in an irregular pattern. Tissue autofluorescence image system is a useful technique in association with clinical examination for AC diagnosis.

Photoacoustic microscopy and computed tomography: from bench to bedside

PubMed Central

Wang, Lihong V.; Gao, Liang

2014-01-01

Photoacoustic imaging (PAI) of biological tissue has seen immense growth in the past decade, providing unprecedented spatial resolution and functional information at depths in the optical diffusive regime. PAI uniquely combines the advantages of optical excitation and acoustic detection. The hybrid imaging modality features high sensitivity to optical absorption and wide scalability of spatial resolution with the desired imaging depth. Here we first summarize the fundamental principles underpinning the technology, then highlight its practical implementation, and finally discuss recent advances towards clinical translation. PMID:24905877

Thoracic Imaging Features of Legionnaire's Disease.

PubMed

Mittal, Sameer; Singh, Ayushi P; Gold, Menachem; Leung, Ann N; Haramati, Linda B; Katz, Douglas S

2017-03-01

Imaging examinations are often performed in patients with Legionnaires' disease. The literature to date has documented that the imaging findings in this disorder are relatively nonspecific, and it is therefore difficult to prospectively differentiate legionella pneumonia from other forms of pneumonia, and from other noninfectious thoracic processes. Through a review of clinical cases and the literature, our objective is for the reader to gain a better understanding of the spectrum of radiographic manifestations of Legionnaires' disease. Copyright © 2016 Elsevier Inc. All rights reserved.

Optic Nerve Assessment Using 7-Tesla Magnetic Resonance Imaging.

PubMed

Singh, Arun D; Platt, Sean M; Lystad, Lisa; Lowe, Mark; Oh, Sehong; Jones, Stephen E; Alzahrani, Yahya; Plesec, Thomas

2016-04-01

The purpose of this study was to correlate high-resolution magnetic resonance imaging (MRI) and histologic findings in a case of juxtapapillary choroidal melanoma with clinical evidence of optic nerve invasion. With institutional review board approval, an enucleated globe with choroidal melanoma and optic nerve invasion was imaged using a 7-tesla MRI followed by histopathologic evaluation. Optical coherence tomography, B-scan ultrasonography, and 1.5-tesla MRI of the orbit (1-mm sections) could not detect optic disc invasion. Ex vivo, 7-tesla MRI detected optic nerve invasion, which correlated with histopathologic features. Our case demonstrates the potential to document the existence of optic nerve invasion in the presence of an intraocular tumor, a feature that has a major bearing on decision making, particularly for consideration of enucleation.

Image management within a PACS

NASA Astrophysics Data System (ADS)

Glicksman, Robert A.; Prior, Fred W.; Wilson, Dennis L.

1993-09-01

The full benefits of a PACS system cannot be achieved by a departmental system, as films must still be made to service referring physicians and clinics. Therefore, a full hospital PACS must provide workstations throughout the hospital which are connected to the central file server and database, but which present `clinical' views of radiological data. In contrast to the radiologist, the clinician needs to select examinations from a `patient list' which presents the results of his/her radiology referrals. The most important data for the clinician is the radiology report, which must be immediately available upon selection of the examination. The images themselves, perhaps with annotations provided by the reading radiologist, must also be available in a few seconds from selection. Furthermore, the ability to display radiologist selected relevant historical images along with the new examination is necessary in those instances where the radiologist felt that certain historical images were important in the interpretation and diagnosis of the patient. Therefore, views of the new and historical data along clinical lines, conference preparation features, and modality and body part specific selections are also required to successfully implement a full hospital PACS. This paper describes the concepts for image selection and presentation at PACS workstations, both `diagnostic' workstations within the radiology department and `clinical' workstations which support the rest of the hospital and outpatient clinics.

An overview of PET/MR, focused on clinical applications.

PubMed

Catalano, Onofrio Antonio; Masch, William Roger; Catana, Ciprian; Mahmood, Umar; Sahani, Dushyant Vasudeo; Gee, Michael Stanley; Menezes, Leon; Soricelli, Andrea; Salvatore, Marco; Gervais, Debra; Rosen, Bruce Robert

2017-02-01

Hybrid PET/MR scanners are innovative imaging devices that simultaneously or sequentially acquire and fuse anatomical and functional data from magnetic resonance (MR) with metabolic information from positron emission tomography (PET) (Delso et al. in J Nucl Med 52:1914-1922, 2011; Zaidi et al. in Phys Med Biol 56:3091-3106, 2011). Hybrid PET/MR scanners have the potential to greatly impact not only on medical research but also, and more importantly, on patient management. Although their clinical applications are still under investigation, the increased worldwide availability of PET/MR scanners, and the growing published literature are important determinants in their rising utilization for primarily clinical applications. In this manuscript, we provide a summary of the physical features of PET/MR, including its limitations, which are most relevant to clinical PET/MR implementation and to interpretation. Thereafter, we discuss the most important current and emergent clinical applications of such hybrid technology in the abdomen and pelvis, both in the field of oncologic and non-oncologic imaging, and we provide, when possible, a comparison with clinically consolidated imaging techniques, like for example PET/CT.

Enhancing interpretability of automatically extracted machine learning features: application to a RBM-Random Forest system on brain lesion segmentation.

PubMed

Pereira, Sérgio; Meier, Raphael; McKinley, Richard; Wiest, Roland; Alves, Victor; Silva, Carlos A; Reyes, Mauricio

2018-02-01

Machine learning systems are achieving better performances at the cost of becoming increasingly complex. However, because of that, they become less interpretable, which may cause some distrust by the end-user of the system. This is especially important as these systems are pervasively being introduced to critical domains, such as the medical field. Representation Learning techniques are general methods for automatic feature computation. Nevertheless, these techniques are regarded as uninterpretable "black boxes". In this paper, we propose a methodology to enhance the interpretability of automatically extracted machine learning features. The proposed system is composed of a Restricted Boltzmann Machine for unsupervised feature learning, and a Random Forest classifier, which are combined to jointly consider existing correlations between imaging data, features, and target variables. We define two levels of interpretation: global and local. The former is devoted to understanding if the system learned the relevant relations in the data correctly, while the later is focused on predictions performed on a voxel- and patient-level. In addition, we propose a novel feature importance strategy that considers both imaging data and target variables, and we demonstrate the ability of the approach to leverage the interpretability of the obtained representation for the task at hand. We evaluated the proposed methodology in brain tumor segmentation and penumbra estimation in ischemic stroke lesions. We show the ability of the proposed methodology to unveil information regarding relationships between imaging modalities and extracted features and their usefulness for the task at hand. In both clinical scenarios, we demonstrate that the proposed methodology enhances the interpretability of automatically learned features, highlighting specific learning patterns that resemble how an expert extracts relevant data from medical images. Copyright © 2017 Elsevier B.V. All rights reserved.

Quantitative Analysis of {sup 18}F-Fluorodeoxyglucose Positron Emission Tomography Identifies Novel Prognostic Imaging Biomarkers in Locally Advanced Pancreatic Cancer Patients Treated With Stereotactic Body Radiation Therapy

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

Cui, Yi; Global Institution for Collaborative Research and Education, Hokkaido University, Sapporo; Song, Jie

Purpose: To identify prognostic biomarkers in pancreatic cancer using high-throughput quantitative image analysis. Methods and Materials: In this institutional review board–approved study, we retrospectively analyzed images and outcomes for 139 locally advanced pancreatic cancer patients treated with stereotactic body radiation therapy (SBRT). The overall population was split into a training cohort (n=90) and a validation cohort (n=49) according to the time of treatment. We extracted quantitative imaging characteristics from pre-SBRT {sup 18}F-fluorodeoxyglucose positron emission tomography, including statistical, morphologic, and texture features. A Cox proportional hazard regression model was built to predict overall survival (OS) in the training cohort using 162more » robust image features. To avoid over-fitting, we applied the elastic net to obtain a sparse set of image features, whose linear combination constitutes a prognostic imaging signature. Univariate and multivariate Cox regression analyses were used to evaluate the association with OS, and concordance index (CI) was used to evaluate the survival prediction accuracy. Results: The prognostic imaging signature included 7 features characterizing different tumor phenotypes, including shape, intensity, and texture. On the validation cohort, univariate analysis showed that this prognostic signature was significantly associated with OS (P=.002, hazard ratio 2.74), which improved upon conventional imaging predictors including tumor volume, maximum standardized uptake value, and total legion glycolysis (P=.018-.028, hazard ratio 1.51-1.57). On multivariate analysis, the proposed signature was the only significant prognostic index (P=.037, hazard ratio 3.72) when adjusted for conventional imaging and clinical factors (P=.123-.870, hazard ratio 0.53-1.30). In terms of CI, the proposed signature scored 0.66 and was significantly better than competing prognostic indices (CI 0.48-0.64, Wilcoxon rank sum test P<1e-6). Conclusion: Quantitative analysis identified novel {sup 18}F-fluorodeoxyglucose positron emission tomography image features that showed improved prognostic value over conventional imaging metrics. If validated in large, prospective cohorts, the new prognostic signature might be used to identify patients for individualized risk-adaptive therapy.« less

Intelligent estimation of noise and blur variances using ANN for the restoration of ultrasound images.

PubMed

Uddin, Muhammad Shahin; Halder, Kalyan Kumar; Tahtali, Murat; Lambert, Andrew J; Pickering, Mark R; Marchese, Margaret; Stuart, Iain

2016-11-01

Ultrasound (US) imaging is a widely used clinical diagnostic tool in medical imaging techniques. It is a comparatively safe, economical, painless, portable, and noninvasive real-time tool compared to the other imaging modalities. However, the image quality of US imaging is severely affected by the presence of speckle noise and blur during the acquisition process. In order to ensure a high-quality clinical diagnosis, US images must be restored by reducing their speckle noise and blur. In general, speckle noise is modeled as a multiplicative noise following a Rayleigh distribution and blur as a Gaussian function. Hereto, we propose an intelligent estimator based on artificial neural networks (ANNs) to estimate the variances of noise and blur, which, in turn, are used to obtain an image without discernible distortions. A set of statistical features computed from the image and its complex wavelet sub-bands are used as input to the ANN. In the proposed method, we solve the inverse Rayleigh function numerically for speckle reduction and use the Richardson-Lucy algorithm for de-blurring. The performance of this method is compared with that of the traditional methods by applying them to a synthetic, physical phantom and clinical data, which confirms better restoration results by the proposed method.

Twists and turns in the body: an imaging spectrum.

PubMed

Luk, Shiobhon Y; Fung, K H

2010-10-01

Life is full of twists and turns. These surprises can sometimes be wonderfully invigorating. Twists and turns can also occur in the body, however, sometimes with dangerous consequences. Torsion and volvulus are important causes of acute abdominal pain. The clinical symptoms and signs associated with torsion and volvulus are often non-specific and are difficult to diagnose clinically. Clinicians frequently rely on imaging methods to make the diagnosis. Prompt and accurate diagnosis is important to avoid the life-threatening complications of torsion and volvulus. Therefore, it is helpful to be familiar with the features of torsion and volvulus.

Automatic slice-alignment method in cardiac magnetic resonance imaging for evaluation of the right ventricle in patients with pulmonary hypertension

NASA Astrophysics Data System (ADS)

Yokoyama, Kenichi; Nitta, Shuhei; Kuhara, Shigehide; Ishimura, Rieko; Kariyasu, Toshiya; Imai, Masamichi; Nitatori, Toshiaki; Takeguchi, Tomoyuki; Shiodera, Taichiro

2015-09-01

We propose a new automatic slice-alignment method, which enables right ventricular scan planning in addition to the left ventricular scan planning developed in our previous work, to simplify right ventricular cardiac scan planning and assess its accuracy and the clinical acceptability of the acquired imaging planes in the evaluation of patients with pulmonary hypertension. Steady-state free precession (SSFP) sequences covering the whole heart in the end-diastolic phase with ECG gating were used to acquire 2D axial multislice images. To realize right ventricular scan planning, two morphological feature points are added to be detected and a total of eight morphological features of the heart were extracted from these series of images, and six left ventricular planes and four right ventricular planes were calculated simultaneously based on the extracted features. The subjects were 33 patients (25 with chronic thromboembolic pulmonary hypertension and 8 with idiopathic pulmonary arterial hypertension). The four right ventricular reference planes including right ventricular short-axis, 4-chamber, 2-chamber, and 3-chamber images were evaluated. The acceptability of the acquired imaging planes was visually evaluated using a 4-point scale, and the angular differences between the results obtained by this method and by conventional manual annotation were measured for each view. The average visual scores were 3.9±0.4 for short-axis images, 3.8±0.4 for 4-chamber images, 3.8±0.4 for 2-chamber images, and 3.5±0.6 for 3-chamber images. The average angular differences were 8.7±5.3, 8.3±4.9, 8.1±4.8, and 7.9±5.3 degrees, respectively. The processing time was less than 2.5 seconds in all subjects. The proposed method, which enables right ventricular scan planning in addition to the left ventricular scan planning developed in our previous work, can provide clinically acceptable planes in a short time and is useful because special proficiency in performing cardiac MR for patients with right ventricles of various sizes and shapes is not required.

Preferred Visuographic Images to Support Reading by People with Chronic Aphasia.

PubMed

Knollman-Porter, Kelly; Brown, Jessica; Hux, Karen; Wallace, Sarah E; Uchtman, Elizabeth

2016-08-01

Written materials used both clinically and in everyday reading tasks can contain visuographic images that vary in content and attributes. People with aphasia may benefit from visuographic images to support reading comprehension. Understanding the image type and feature preferences of individuals with aphasia is an important first step when developing guidelines for selecting reading materials that motivate and support reading comprehension. The study purposes were to determine the preferences and explore the perceptions of and opinions provided by adults with chronic aphasia regarding various image features and types on facilitating the reading process. Six adults with chronic aphasia ranked visuographic materials varying in context, engagement, and content regarding their perceived degree of helpfulness in comprehending written materials. Then, they participated in semi-structured interviews that allowed them to elaborate on their choices and convey opinions about potential benefits and detriments associated with preferred and non-preferred materials. All participants preferred high-context photographs rather than iconic images or portraits as potential supports to facilitate reading activities. Differences in opinions emerged across participants regarding the amount of preferred content included in high context images.

Hybrid ANN optimized artificial fish swarm algorithm based classifier for classification of suspicious lesions in breast DCE-MRI

NASA Astrophysics Data System (ADS)

Janaki Sathya, D.; Geetha, K.

2017-12-01

Automatic mass or lesion classification systems are developed to aid in distinguishing between malignant and benign lesions present in the breast DCE-MR images, the systems need to improve both the sensitivity and specificity of DCE-MR image interpretation in order to be successful for clinical use. A new classifier (a set of features together with a classification method) based on artificial neural networks trained using artificial fish swarm optimization (AFSO) algorithm is proposed in this paper. The basic idea behind the proposed classifier is to use AFSO algorithm for searching the best combination of synaptic weights for the neural network. An optimal set of features based on the statistical textural features is presented. The investigational outcomes of the proposed suspicious lesion classifier algorithm therefore confirm that the resulting classifier performs better than other such classifiers reported in the literature. Therefore this classifier demonstrates that the improvement in both the sensitivity and specificity are possible through automated image analysis.

Portal vascular anomalies in Down syndrome: spectrum of clinical presentation and management approach.

PubMed

Golewale, Nazar; Paltiel, Harriet J; Fishman, Steven J; Alomari, Ahmad I

2010-08-01

The occurrence of portal vascular anomalies in Down syndrome has been sporadically reported in the literature. These rare disorders have a wide spectrum of anatomical and clinical presentations. The aim of this communication was to describe the clinical course, imaging features, and management approaches in patients with this association. We conducted a comprehensive search of the databases of the Vascular Anomalies Center and the Department of Radiology at Children's Hospital Boston for patients with Down syndrome and portal vascular anomalies. Medical records and imaging studies of varying modalities were reviewed. Three children with Down syndrome and portal anomalies (portosystemic shunt, simple arterioportal shunt, complex arterioportal shunt) were managed at our institution. The portosystemic shunt was clinically insignificant and resolved without any intervention. The simple arterioportal shunt was successfully treated with embolization. The complex arterioportal shunt was associated with major congenital cardiac defects and the child ultimately expired despite a decrease in the arterioportal shunting after embolization. Three is a wide spectrum of clinical and anatomical features of portal vascular shunts in Down syndrome. The management approach should be tailored based on the severity of symptoms. Percutaneous embolization can offer a safe, effective, and minimally invasive alternative to the surgical approach in selective cases. Copyright 2010 Elsevier Inc. All rights reserved.

Clinical and vascular features of Takayasu arteritis at the time of ischemic stroke.

PubMed

de Paula, Luiz Eduardo; Alverne, Andrea Rocha; Shinjo, Samuel K

2013-01-01

Takayasus arteritis (TA) is a systemic vasculitis whose clinical presentation varies from asymptomatic to serious neurovascular events, including stroke. However, few studies are currently available assessing stroke in TA. Thus, we described the clinical and laboratory characteristics and vascular imaging features in patients with TA at the time of stroke. This is a single center retrospective cohort study investigating the clinical and demographic data of 18 (15.0%) patients with a history of stroke confirmed by imaging methods, among 120 patients with TA, assessed in the 1985-2012 period. The mean age of the 18 patients at the time of stroke was 29.4+/-10.9 years, with 94.4% female and 88.9% Caucasian. Of these patients, 14 (77.8%) had previous stroke at diagnosis of TA, while in four cases the stroke occurred after confirmed TA diagnosis. Regarding the clinical course, 12 (66.7%) had peripheral neurological sequelae and one patient died as a result of cerebral hyperperfusion syndrome after carotid revascularization. Our results showed a high prevalence of stroke in TA and revealed most of these events occurred concomitantly with diagnosed TA. Moreover, although four patients had strokes after diagnosis of TA, these occurred at a young age, demonstrating they were most likely the result of vascular changes secondary to TA.

Deep erosions of the palmar aspect of the navicular bone diagnosed by standing magnetic resonance imaging.

PubMed

Sherlock, C; Mair, T; Blunden, T

2008-11-01

Erosion of the palmar (flexor) aspect of the navicular bone is difficult to diagnose with conventional imaging techniques. To review the clinical, magnetic resonance (MR) and pathological features of deep erosions of the palmar aspect of the navicular bone. Cases of deep erosions of the palmar aspect of the navicular bone, diagnosed by standing low field MR imaging, were selected. Clinical details, results of diagnostic procedures, MR features and pathological findings were reviewed. Deep erosions of the palmar aspect of the navicular bone were diagnosed in 16 mature horses, 6 of which were bilaterally lame. Sudden onset of lameness was recorded in 63%. Radiography prior to MR imaging showed equivocal changes in 7 horses. The MR features consisted of focal areas of intermediate or high signal intensity on T1-, T2*- and T2-weighted images and STIR images affecting the dorsal aspect of the deep digital flexor tendon, the fibrocartilage of the palmar aspect, subchondral compact bone and medulla of the navicular bone. On follow-up, 7/16 horses (44%) had been subjected to euthanasia and only one was being worked at its previous level. Erosions of the palmar aspect of the navicular bone were confirmed post mortem in 2 horses. Histologically, the lesions were characterised by localised degeneration of fibrocartilage with underlying focal osteonecrosis and fibroplasia. The adjacent deep digital flexor tendon showed fibril formation and fibrocartilaginous metaplasia. Deep erosions of the palmar aspect of the navicular bone are more easily diagnosed by standing low field MR imaging than by conventional radiography. The lesions involve degeneration of the palmar fibrocartilage with underlying osteonecrosis and fibroplasia affecting the subchondral compact bone and medulla, and carry a poor prognosis for return to performance. Diagnosis of shallow erosive lesions of the palmar fibrocartilage may allow therapeutic intervention earlier in the disease process, thereby preventing progression to deep erosive lesions.

Association of high proliferation marker Ki-67 expression with DCEMR imaging features of breast: a large scale evaluation

NASA Astrophysics Data System (ADS)

Saha, Ashirbani; Harowicz, Michael R.; Grimm, Lars J.; Kim, Connie E.; Ghate, Sujata V.; Walsh, Ruth; Mazurowski, Maciej A.

2018-02-01

One of the methods widely used to measure the proliferative activity of cells in breast cancer patients is the immunohistochemical (IHC) measurement of the percentage of cells stained for nuclear antigen Ki-67. Use of Ki-67 expression as a prognostic marker is still under investigation. However, numerous clinical studies have reported an association between a high Ki-67 and overall survival (OS) and disease free survival (DFS). On the other hand, to offer non-invasive alternative in determining Ki-67 expression, researchers have made recent attempts to study the association of Ki-67 expression with magnetic resonance (MR) imaging features of breast cancer in small cohorts (<30). Here, we present a large scale evaluation of the relationship between imaging features and Ki-67 score as: (a) we used a set of 450 invasive breast cancer patients, (b) we extracted a set of 529 imaging features of shape and enhancement from breast, tumor and fibroglandular tissue of the patients, (c) used a subset of patients as the training set to select features and trained a multivariate logistic regression model to predict high versus low Ki-67 values, and (d) we validated the performance of the trained model in an independent test set using the area-under the receiver operating characteristics (ROC) curve (AUC) of the values predicted. Our model was able to predict high versus low Ki-67 in the test set with an AUC of 0.67 (95% CI: 0.58-0.75, p<1.1e-04). Thus, a moderate strength of association of Ki-67 values and MRextracted imaging features was demonstrated in our experiments.

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.

Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques.

PubMed

Macyszyn, Luke; Akbari, Hamed; Pisapia, Jared M; Da, Xiao; Attiah, Mark; Pigrish, Vadim; Bi, Yingtao; Pal, Sharmistha; Davuluri, Ramana V; Roccograndi, Laura; Dahmane, Nadia; Martinez-Lage, Maria; Biros, George; Wolf, Ronald L; Bilello, Michel; O'Rourke, Donald M; Davatzikos, Christos

2016-03-01

MRI characteristics of brain gliomas have been used to predict clinical outcome and molecular tumor characteristics. However, previously reported imaging biomarkers have not been sufficiently accurate or reproducible to enter routine clinical practice and often rely on relatively simple MRI measures. The current study leverages advanced image analysis and machine learning algorithms to identify complex and reproducible imaging patterns predictive of overall survival and molecular subtype in glioblastoma (GB). One hundred five patients with GB were first used to extract approximately 60 diverse features from preoperative multiparametric MRIs. These imaging features were used by a machine learning algorithm to derive imaging predictors of patient survival and molecular subtype. Cross-validation ensured generalizability of these predictors to new patients. Subsequently, the predictors were evaluated in a prospective cohort of 29 new patients. Survival curves yielded a hazard ratio of 10.64 for predicted long versus short survivors. The overall, 3-way (long/medium/short survival) accuracy in the prospective cohort approached 80%. Classification of patients into the 4 molecular subtypes of GB achieved 76% accuracy. By employing machine learning techniques, we were able to demonstrate that imaging patterns are highly predictive of patient survival. Additionally, we found that GB subtypes have distinctive imaging phenotypes. These results reveal that when imaging markers related to infiltration, cell density, microvascularity, and blood-brain barrier compromise are integrated via advanced pattern analysis methods, they form very accurate predictive biomarkers. These predictive markers used solely preoperative images, hence they can significantly augment diagnosis and treatment of GB patients. © The Author(s) 2015. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Differentiation of Glioblastoma and Lymphoma Using Feature Extraction and Support Vector Machine.

PubMed

Yang, Zhangjing; Feng, Piaopiao; Wen, Tian; Wan, Minghua; Hong, Xunning

2017-01-01

Differentiation of glioblastoma multiformes (GBMs) and lymphomas using multi-sequence magnetic resonance imaging (MRI) is an important task that is valuable for treatment planning. However, this task is a challenge because GBMs and lymphomas may have a similar appearance in MRI images. This similarity may lead to misclassification and could affect the treatment results. In this paper, we propose a semi-automatic method based on multi-sequence MRI to differentiate these two types of brain tumors. Our method consists of three steps: 1) the key slice is selected from 3D MRIs and region of interests (ROIs) are drawn around the tumor region; 2) different features are extracted based on prior clinical knowledge and validated using a t-test; and 3) features that are helpful for classification are used to build an original feature vector and a support vector machine is applied to perform classification. In total, 58 GBM cases and 37 lymphoma cases are used to validate our method. A leave-one-out crossvalidation strategy is adopted in our experiments. The global accuracy of our method was determined as 96.84%, which indicates that our method is effective for the differentiation of GBM and lymphoma and can be applied in clinical diagnosis. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Soft-Tissue Sarcomas of the Abdomen and Pelvis: Radiologic-Pathologic Features, Part 1-Common Sarcomas: From the Radiologic Pathology Archives.

PubMed

Levy, Angela D; Manning, Maria A; Al-Refaie, Waddah B; Miettinen, Markku M

2017-01-01

Soft-tissue sarcomas are a diverse group of rare mesenchymal malignancies that can arise at any location in the body and affect all age groups. These sarcomas are most common in the extremities, trunk wall, retroperitoneum, and head and neck. In the adult population, soft-tissue sarcomas arising in the abdomen and pelvis are often large masses at the time of diagnosis because they are usually clinically silent or cause vague or mild symptoms until they invade or compress vital organs. In contrast, soft-tissue sarcomas arising from the abdominal wall come to clinical attention earlier in the course of disease because they cause a palpable mass, abdominal wall deformity, or pain that is more clinically apparent. The imaging features of abdominal and pelvic sarcomas and abdominal wall sarcomas can be nonspecific and overlap with more common pathologic conditions, making diagnosis difficult or, in some cases, delaying diagnosis. Liposarcoma (well-differentiated and dedifferentiated liposarcomas), leiomyosarcoma, and gastrointestinal stromal tumor (GIST) are the most common intra-abdominal primary sarcomas. Any soft-tissue sarcoma can arise in the abdominal wall. Knowledge of the classification and pathologic features of soft-tissue sarcomas, the anatomic locations where they occur, and their cross-sectional imaging features helps the radiologist establish the diagnosis or differential diagnosis so that patients with soft-tissue sarcomas can receive optimal treatment and management. In part 1 of this article, the most common soft-tissue sarcomas (liposarcoma, leiomyosarcoma, and GIST) are reviewed, with a discussion on anatomic locations, classification, clinical considerations, and differential diagnosis. Part 2 will focus on the remainder of the soft-tissue sarcomas occurring in the abdomen and pelvis.

Soft-Tissue Sarcomas of the Abdomen and Pelvis: Radiologic-Pathologic Features, Part 1—Common Sarcomas: From the Radiologic Pathology Archives

PubMed Central

Manning, Maria A.; Al-Refaie, Waddah B.; Miettinen, Markku M.

2017-01-01

Soft-tissue sarcomas are a diverse group of rare mesenchymal malignancies that can arise at any location in the body and affect all age groups. These sarcomas are most common in the extremities, trunk wall, retroperitoneum, and head and neck. In the adult population, soft-tissue sarcomas arising in the abdomen and pelvis are often large masses at the time of diagnosis because they are usually clinically silent or cause vague or mild symptoms until they invade or compress vital organs. In contrast, soft-tissue sarcomas arising from the abdominal wall come to clinical attention earlier in the course of disease because they cause a palpable mass, abdominal wall deformity, or pain that is more clinically apparent. The imaging features of abdominal and pelvic sarcomas and abdominal wall sarcomas can be nonspecific and overlap with more common pathologic conditions, making diagnosis difficult or, in some cases, delaying diagnosis. Liposarcoma (well-differentiated and dedifferentiated liposarcomas), leiomyosarcoma, and gastrointestinal stromal tumor (GIST) are the most common intra-abdominal primary sarcomas. Any soft-tissue sarcoma can arise in the abdominal wall. Knowledge of the classification and pathologic features of soft-tissue sarcomas, the anatomic locations where they occur, and their cross-sectional imaging features helps the radiologist establish the diagnosis or differential diagnosis so that patients with soft-tissue sarcomas can receive optimal treatment and management. In part 1 of this article, the most common soft-tissue sarcomas (liposarcoma, leiomyosarcoma, and GIST) are reviewed, with a discussion on anatomic locations, classification, clinical considerations, and differential diagnosis. Part 2 will focus on the remainder of the soft-tissue sarcomas occurring in the abdomen and pelvis. PMID:28287938

SU-F-R-04: Radiomics for Survival Prediction in Glioblastoma (GBM)

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

Zhang, H; Molitoris, J; Bhooshan, N

Purpose: To develop a quantitative radiomics approach for survival prediction of glioblastoma (GBM) patients treated with chemoradiotherapy (CRT). Methods: 28 GBM patients who received CRT at our institution were retrospectively studied. 255 radiomic features were extracted from 3 gadolinium-enhanced T1 weighted MRIs for 2 regions of interest (ROIs) (the surgical cavity and its surrounding enhancement rim). The 3 MRIs were at pre-treatment, 1-month and 3-month post-CRT. The imaging features comprehensively quantified the intensity, spatial variation (texture), geometric property and their spatial-temporal changes for the 2 ROIs. 3 demographics features (age, race, gender) and 12 clinical parameters (KPS, extent of resection,more » whether concurrent temozolomide was adjusted/stopped and radiotherapy related information) were also included. 4 Machine learning models (logistic regression (LR), support vector machine (SVM), decision tree (DT), neural network (NN)) were applied to predict overall survival (OS) and progression-free survival (PFS). The number of cases and percentage of cases predicted correctly were collected and AUC (area under the receiver operating characteristic (ROC) curve) were determined after leave-one-out cross-validation. Results: From univariate analysis, 27 features (1 demographic, 1 clinical and 25 imaging) were statistically significant (p<0.05) for both OS and PFS. Two sets of features (each contained 24 features) were algorithmically selected from all features to predict OS and PFS. High prediction accuracy of OS was achieved by using NN (96%, 27 of 28 cases were correctly predicted, AUC = 0.99), LR (93%, 26 of 28 cases were correctly predicted, AUC = 0.95) and SVM (93%, 26 of 28 cases were correctly predicted, AUC = 0.90). When predicting PFS, NN obtained the highest prediction accuracy (89%, 25 of 28 cases were correctly predicted, AUC = 0.92). Conclusion: Radiomics approach combined with patients’ demographics and clinical parameters can accurately predict survival in GBM patients treated with CRT.« less

Identification of design features to enhance utilization and acceptance of systems for Internet-based decision support at the point of care.

PubMed Central

Gadd, C. S.; Baskaran, P.; Lobach, D. F.

1998-01-01

Extensive utilization of point-of-care decision support systems will be largely dependent on the development of user interaction capabilities that make them effective clinical tools in patient care settings. This research identified critical design features of point-of-care decision support systems that are preferred by physicians, through a multi-method formative evaluation of an evolving prototype of an Internet-based clinical decision support system. Clinicians used four versions of the system--each highlighting a different functionality. Surveys and qualitative evaluation methodologies assessed clinicians' perceptions regarding system usability and usefulness. Our analyses identified features that improve perceived usability, such as telegraphic representations of guideline-related information, facile navigation, and a forgiving, flexible interface. Users also preferred features that enhance usefulness and motivate use, such as an encounter documentation tool and the availability of physician instruction and patient education materials. In addition to identifying design features that are relevant to efforts to develop clinical systems for point-of-care decision support, this study demonstrates the value of combining quantitative and qualitative methods of formative evaluation with an iterative system development strategy to implement new information technology in complex clinical settings. Images Figure 1 PMID:9929188

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.

Veterinary software application for comparison of thermograms for pathology evaluation

NASA Astrophysics Data System (ADS)

Pant, Gita; Umbaugh, Scott E.; Dahal, Rohini; Lama, Norsang; Marino, Dominic J.; Sackman, Joseph

2017-09-01

The bilateral symmetry property in mammals allows for the detection of pathology by comparison of opposing sides. For any pathological disorder, thermal patterns differ compared to the normal body part. A software application for veterinary clinics has been under development to input two thermograms of body parts on both sides, one normal and the other unknown, and the application compares them based on extracted features and appropriate similarity and difference measures and outputs the likelihood of pathology. Here thermographic image data from 19° C to 40° C was linearly remapped to create images with 256 gray level values. Features were extracted from these images, including histogram, texture and spectral features. The comparison metrics used are the vector inner product, Tanimoto, Euclidean, city block, Minkowski and maximum value metric. Previous research with the anterior cruciate ligament (ACL) pathology in dogs suggested any thermogram variation below a threshold of 40% of Euclidean distance is normal and above 40% is abnormal. Here the 40% threshold was applied to a new ACL image set and achieved a sensitivity of 75%, an improvement from the 55% sensitivity of the previous work. With the new data set it was determined that using a threshold of 20% provided a much improved 92% sensitivity metric. However, this will require further research to determine the corresponding specificity success rate. Additionally, it was found that the anterior view provided better results than the lateral view. It was also determined that better results were obtained with all three feature sets than with just the histogram and texture sets. Further experiments are ongoing with larger image datasets, and pathologies, new features and comparison metric evaluation for determination of more accurate threshold values to separate normal and abnormal images.

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

Krishnan, Kalpagam; Liu, Jeff; Kohli, Kirpal

Purpose: Fusion of electrical impedance tomography (EIT) with computed tomography (CT) can be useful as a clinical tool for providing additional physiological information about tissues, but requires suitable fusion algorithms and validation procedures. This work explores the feasibility of fusing EIT and CT images using an algorithm for coregistration. The imaging performance is validated through feature space assessment on phantom contrast targets. Methods: EIT data were acquired by scanning a phantom using a circuit, configured for injecting current through 16 electrodes, placed around the phantom. A conductivity image of the phantom was obtained from the data using electrical impedance andmore » diffuse optical tomography reconstruction software (EIDORS). A CT image of the phantom was also acquired. The EIT and CT images were fused using a region of interest (ROI) coregistration fusion algorithm. Phantom imaging experiments were carried out on objects of different contrasts, sizes, and positions. The conductive medium of the phantoms was made of a tissue-mimicking bolus material that is routinely used in clinical radiation therapy settings. To validate the imaging performance in detecting different contrasts, the ROI of the phantom was filled with distilled water and normal saline. Spatially separated cylindrical objects of different sizes were used for validating the imaging performance in multiple target detection. Analyses of the CT, EIT and the EIT/CT phantom images were carried out based on the variations of contrast, correlation, energy, and homogeneity, using a gray level co-occurrence matrix (GLCM). A reference image of the phantom was simulated using EIDORS, and the performances of the CT and EIT imaging systems were evaluated and compared against the performance of the EIT/CT system using various feature metrics, detectability, and structural similarity index measures. Results: In detecting distilled and normal saline water in bolus medium, EIT as a stand-alone imaging system showed contrast discrimination of 47%, while the CT imaging system showed a discrimination of only 1.5%. The structural similarity index measure showed a drop of 24% with EIT imaging compared to CT imaging. The average detectability measure for CT imaging was found to be 2.375 ± 0.19 before fusion. After complementing with EIT information, the detectability measure increased to 11.06 ± 2.04. Based on the feature metrics, the functional imaging quality of CT and EIT were found to be 2.29% and 86%, respectively, before fusion. Structural imaging quality was found to be 66% for CT and 16% for EIT. After fusion, functional imaging quality improved in CT imaging from 2.29% to 42% and the structural imaging quality of EIT imaging changed from 16% to 66%. The improvement in image quality was also observed in detecting objects of different sizes. Conclusions: The authors found a significant improvement in the contrast detectability performance of CT imaging when complemented with functional imaging information from EIT. Along with the feature assessment metrics, the concept of complementing CT with EIT imaging can lead to an EIT/CT imaging modality which might fully utilize the functional imaging abilities of EIT imaging, thereby enhancing the quality of care in the areas of cancer diagnosis and radiotherapy treatment planning.« less

Design of a decision support system, trained on GPU, for assisting melanoma diagnosis in dermatoscopy images

NASA Astrophysics Data System (ADS)

Glotsos, Dimitris; Kostopoulos, Spiros; Lalissidou, Stella; Sidiropoulos, Konstantinos; Asvestas, Pantelis; Konstandinou, Christos; Xenogiannopoulos, George; Konstantina Nikolatou, Eirini; Perakis, Konstantinos; Bouras, Thanassis; Cavouras, Dionisis

2015-09-01

The purpose of this study was to design a decision support system for assisting the diagnosis of melanoma in dermatoscopy images. Clinical material comprised images of 44 dysplastic (clark's nevi) and 44 malignant melanoma lesions, obtained from the dermatology database Dermnet. Initially, images were processed for hair removal and background correction using the Dull Razor algorithm. Processed images were segmented to isolate moles from surrounding background, using a combination of level sets and an automated thresholding approach. Morphological (area, size, shape) and textural features (first and second order) were calculated from each one of the segmented moles. Extracted features were fed to a pattern recognition system assembled with the Probabilistic Neural Network Classifier, which was trained to distinguish between benign and malignant cases, using the exhaustive search and the leave one out method. The system was designed on the GPU card (GeForce 580GTX) using CUDA programming framework and C++ programming language. Results showed that the designed system discriminated benign from malignant moles with 88.6% accuracy employing morphological and textural features. The proposed system could be used for analysing moles depicted on smart phone images after appropriate training with smartphone images cases. This could assist towards early detection of melanoma cases, if suspicious moles were to be captured on smartphone by patients and be transferred to the physician together with an assessment of the mole's nature.

Clinical and Imaging Findings in Childhood Posterior Reversible Encephalopathy Syndrome

PubMed Central

GUNGOR, Serdal; KILIC, Betul; TABEL, Yilmaz; SELIMOGLU, Ayse; OZGEN, Unsal; YILMAZ, Sezai

2018-01-01

Objective Posterior reversible encephalopathy syndrome (PRES) is characterized by typical radiologic findings in the posterior regions of the cerebral hemispheres and cerebellum. The symptoms include headache, nausea, vomiting, visual disturbances, focal neurologic deficits, and seizures. The aim of this study is to evaluate the clinical and radiological features of PRES in children and to emphasize the recognition of atypical features. Materials & Methods We retrospectively examined 23 children with PRES from Mar 2010-Apr 2015 in Inonu University Turgut Ozal Medical Center in Turkey. We compared the clinical features and cranial MRI findings between underlying diseases of PRES. Results The most common precipitating factors were hypertension (78.2%) and medications, namely immunosuppressive and antineoplastic agents (60.8%). Manifestations included mental changes (100%), seizures (95.6%), headache (60.8%), and visual disturbances (21.7%) of mean 3.6 (range 1-10) days' duration. Cranial magnetic resonance imaging (MRI) showed bilateral occipital lesions in all patients, associated in 82.6% with less typical distribution of lesions in frontal, temporal or parietal lobes, cerebellum, corpus callosum, basal ganglia, thalamus, and brain stem. Frontal involvement was predominant, observed in 56.5% of patients. Clinical recovery was followed by radiologic resolution in all patients. Conclusion PRES is often unsuspected by the clinician, thus radiologists may be the first to suggest this diagnosis on an MRI obtained for seizures or encephalopathy. Atypical MRI finding is seen quite often. Rapid diagnosis and treatment are required to avoid a devastating outcome. PMID:29379559

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.

Ultra-Widefield Steering-Based Spectral-Domain Optical Coherence Tomography Imaging of the Retinal Periphery.

PubMed

Choudhry, Netan; Golding, John; Manry, Matthew W; Rao, Rajesh C

2016-06-01

To describe the spectral-domain optical coherence tomography (SD OCT) features of peripheral retinal findings using an ultra-widefield (UWF) steering technique to image the retinal periphery. Observational study. A total of 68 patients (68 eyes) with 19 peripheral retinal features. Spectral-domain OCT-based structural features. Nineteen peripheral retinal features, including vortex vein, congenital hypertrophy of the retinal pigment epithelium, pars plana, ora serrata pearl, typical cystoid degeneration (TCD), cystic retinal tuft, meridional fold, lattice and cobblestone degeneration, retinal hole, retinal tear, rhegmatogenous retinal detachment, typical degenerative senile retinoschisis, peripheral laser coagulation scars, ora tooth, cryopexy scars (retinal tear and treated retinoblastoma scar), bone spicules, white without pressure, and peripheral drusen, were identified by peripheral clinical examination. Near-infrared scanning laser ophthalmoscopy images and SD OCT of these entities were registered to UWF color photographs. Spectral-domain OCT resolved structural features of all peripheral findings. Dilated hyporeflective tubular structures within the choroid were observed in the vortex vein. Loss of retinal lamination, neural retinal attenuation, retinal pigment epithelium loss, or hypertrophy was seen in several entities, including congenital hypertrophy of the retinal pigment epithelium, ora serrata pearl, TCD, cystic retinal tuft, meridional fold, lattice, and cobblestone degenerations. Hyporeflective intraretinal spaces, indicating cystoid or schitic fluid, were seen in ora serrata pearl, ora tooth, TCD, cystic retinal tuft, meridional fold, retinal hole, and typical degenerative senile retinoschisis. The vitreoretinal interface, which often consisted of lamellae-like structures of the condensed cortical vitreous near or adherent to the neural retina, appeared clearly in most peripheral findings, confirming its association with many low-risk and vision-threatening pathologies, such as lattice degeneration, meridional folds, retinal breaks, and rhegmatogenous retinal detachments. Ultra-widefield steering-based SD OCT imaging of the retinal periphery is feasible with current commercially available devices and provides detailed anatomic information of the peripheral retina, including benign and pathologic entities, not previously imaged. This imaging technique may deepen our structural understanding of these entities and their potentially associated macular and systemic pathologies, and may influence decision-making in clinical practice, particularly in areas with teleretinal capabilities but poor access to retinal specialists. Copyright © 2016 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

Experimental Investigation on Minimum Frame Rate Requirements of High-Speed Videoendoscopy for Clinical Voice Assessment

PubMed Central

Deliyski, Dimitar D; Powell, Maria EG; Zacharias, Stephanie RC; Gerlach, Terri Treman; de Alarcon, Alessandro

2015-01-01

This study investigated the impact of high-speed videoendoscopy (HSV) frame rates on the assessment of nine clinically-relevant vocal-fold vibratory features. Fourteen adult patients with voice disorder and 14 adult normal controls were recorded using monochromatic rigid HSV at a rate of 16000 frames per second (fps) and spatial resolution of 639×639 pixels. The 16000-fps data were downsampled to 16 other rate denominations. Using paired comparisons design, nine common clinical vibratory features were visually compared between the downsampled and the original images. Three raters reported the thresholds at which: (1) a detectable difference between the two videos was first noticed, and (2) differences between the two videos would result in a change of clinical rating. Results indicated that glottal edge, mucosal wave magnitude and extent, aperiodicity, contact and loss of contact of the vocal folds were the vibratory features most sensitive to frame rate. Of these vibratory features, the glottal edge was selected for further analysis, due to its higher rating reliability, universal prevalence and consistent definition. Rates of 8000 fps were found to be free from visually-perceivable feature degradation, and for rates of 5333 fps, degradation was minimal. For rates of 4000 fps and higher, clinical assessments of glottal edge were not affected. Rates of 2000 fps changed the clinical ratings in over 16% of the samples, which could lead to inaccurate functional assessment. PMID:28989342

Quantification of confocal fluorescence microscopy for the detection of cervical intraepithelial neoplasia.

PubMed

Sheikhzadeh, Fahime; Ward, Rabab K; Carraro, Anita; Chen, Zhao Yang; van Niekerk, Dirk; Miller, Dianne; Ehlen, Tom; MacAulay, Calum E; Follen, Michele; Lane, Pierre M; Guillaud, Martial

2015-10-24

Cervical cancer remains a major health problem, especially in developing countries. Colposcopic examination is used to detect high-grade lesions in patients with a history of abnormal pap smears. New technologies are needed to improve the sensitivity and specificity of this technique. We propose to test the potential of fluorescence confocal microscopy to identify high-grade lesions. We examined the quantification of ex vivo confocal fluorescence microscopy to differentiate among normal cervical tissue, low-grade Cervical Intraepithelial Neoplasia (CIN), and high-grade CIN. We sought to (1) quantify nuclear morphology and tissue architecture features by analyzing images of cervical biopsies; and (2) determine the accuracy of high-grade CIN detection via confocal microscopy relative to the accuracy of detection by colposcopic impression. Forty-six biopsies obtained from colposcopically normal and abnormal cervical sites were evaluated. Confocal images were acquired at different depths from the epithelial surface and histological images were analyzed using in-house software. The features calculated from the confocal images compared well with those features obtained from the histological images and histopathological reviews of the specimens (obtained by a gynecologic pathologist). The correlations between two of these features (the nuclear-cytoplasmic ratio and the average of three nearest Delaunay-neighbors distance) and the grade of dysplasia were higher than that of colposcopic impression. The sensitivity of detecting high-grade dysplasia by analysing images collected at the surface of the epithelium, and at 15 and 30 μm below the epithelial surface were respectively 100, 100, and 92 %. Quantitative analysis of confocal fluorescence images showed its capacity for discriminating high-grade CIN lesions vs. low-grade CIN lesions and normal tissues, at different depth of imaging. This approach could be used to help clinicians identify high-grade CIN in clinical settings.

Diagnostic features of Alzheimer's disease extracted from PET sinograms

NASA Astrophysics Data System (ADS)

Sayeed, A.; Petrou, M.; Spyrou, N.; Kadyrov, A.; Spinks, T.

2002-01-01

Texture analysis of positron emission tomography (PET) images of the brain is a very difficult task, due to the poor signal to noise ratio. As a consequence, very few techniques can be implemented successfully. We use a new global analysis technique known as the Trace transform triple features. This technique can be applied directly to the raw sinograms to distinguish patients with Alzheimer's disease (AD) from normal volunteers. FDG-PET images of 18 AD and 10 normal controls obtained from the same CTI ECAT-953 scanner were used in this study. The Trace transform triple feature technique was used to extract features that were invariant to scaling, translation and rotation, referred to as invariant features, as well as features that were sensitive to rotation but invariant to scaling and translation, referred to as sensitive features in this study. The features were used to classify the groups using discriminant function analysis. Cross-validation tests using stepwise discriminant function analysis showed that combining both sensitive and invariant features produced the best results, when compared with the clinical diagnosis. Selecting the five best features produces an overall accuracy of 93% with sensitivity of 94% and specificity of 90%. This is comparable with the classification accuracy achieved by Kippenhan et al (1992), using regional metabolic activity.

Utility of Clinical Parameters and Multiparametric MRI as Predictive Factors for Differentiating Uterine Sarcoma From Atypical Leiomyoma.

PubMed

Bi, Qiu; Xiao, Zhibo; Lv, Fajin; Liu, Yao; Zou, Chunxia; Shen, Yiqing

2018-02-05

The objective of this study was to find clinical parameters and qualitative and quantitative magnetic resonance imaging (MRI) features for differentiating uterine sarcoma from atypical leiomyoma (ALM) preoperatively and to calculate predictive values for uterine sarcoma. Data from 60 patients with uterine sarcoma and 88 patients with ALM confirmed by surgery and pathology were collected. Clinical parameters, qualitative MRI features, diffusion-weighted imaging with apparent diffusion coefficient values, and quantitative parameters of dynamic contrast-enhanced MRI of these two tumor types were compared. Predictive values for uterine sarcoma were calculated using multivariable logistic regression. Patient clinical manifestations, tumor locations, margins, T2-weighted imaging signals, mean apparent diffusion coefficient values, minimum apparent diffusion coefficient values, and time-signal intensity curves of solid tumor components were obvious significant parameters for distinguishing between uterine sarcoma and ALM (all P <.001). Abnormal vaginal bleeding, tumors located mainly in the uterine cavity, ill-defined tumor margins, and mean apparent diffusion coefficient values of <1.272 × 10 -3  mm 2 /s were significant preoperative predictors of uterine sarcoma. When the overall scores of these four predictors were greater than or equal to 7 points, the sensitivity, the specificity, the accuracy, and the positive and negative predictive values were 88.9%, 99.9%, 95.7%, 97.0%, and 95.1%, respectively. The use of clinical parameters and multiparametric MRI as predictive factors was beneficial for diagnosing uterine sarcoma preoperatively. These findings could be helpful for guiding treatment decisions. Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Spinal cord infarction: Clinical and imaging insights from the periprocedural setting.

PubMed

Zalewski, Nicholas L; Rabinstein, Alejandro A; Krecke, Karl N; Brown, Robert D; Wijdicks, Eelco F M; Weinshenker, Brian G; Doolittle, Derrick A; Flanagan, Eoin P

2018-05-15

Describe the range of procedures associated with spinal cord infarction (SCI) as a complication of a medical/surgical procedure and define clinical and imaging characteristics that could be applied to help diagnose spontaneous SCI, where the diagnosis is often less secure. We used an institution-based search tool to identify patients evaluated at Mayo Clinic, Rochester, MN from 1997 to 2016 with a periprocedural SCI. We performed a descriptive analysis of clinical features, MRI and other laboratory findings, and outcome. Seventy-five patients were identified with SCI related to an invasive or non-invasive surgery including: aortic aneurysm repair (49%); other aortic surgery (15%); and a variety of other procedures (e.g., cardiac surgery, spinal decompression, epidural injection, angiography, nerve block, embolization, other vascular surgery, thoracic surgery) (36%). Deficits were severe (66% para/quadriplegia) and maximal at first post-procedural evaluation in 61 patients (81%). Impaired dorsal column function was common on initial examination. Imaging features included classic findings of owl eyes or anterior pencil sign on MRI (70%), but several other T2-hyperintensity patterns were also seen. Gadolinium enhancement of the SCI and/or cauda equina was also common when assessed. Six patients (10%) had an initial normal MRI despite a severe deficit. Procedures associated with SCI are many, and this complication does not exclusively occur following aortic surgery. The clinical and radiologic findings that we describe with periprocedural SCI may be used in future studies to help distinguish spontaneous SCI from alternate causes of acute myelopathy. Copyright © 2018 Elsevier B.V. All rights reserved.

Cranial dystonia, blepharospasm and hemifacial spasm: clinical features and treatment, including the use of botulinum toxin.

PubMed Central

Kraft, S P; Lang, A E

1988-01-01

Blepharospasm, the most frequent feature of cranial dystonia, and hemifacial spasm are two involuntary movement disorders that affect facial muscles. The cause of blepharospasm and other forms of cranial dystonia is not known. Hemifacial spasm is usually due to compression of the seventh cranial nerve at its exit from the brain stem. Cranial dystonia may result in severe disability. Hemifacial spasm tends to be much less disabling but may cause considerable distress and embarrassment. Patients affected with these disorders are often mistakenly considered to have psychiatric problems. Although the two disorders are quite distinct pathophysiologically, therapy with botulinum toxin has proven very effective in both. We review the clinical features, proposed pathophysiologic features, differential diagnosis and treatment, including the use of botulinum toxin, of cranial dystonia and hemifacial spasm. Images Fig. 2 Fig. 3 PMID:3052771

Automated tissue characterization of in vivo atherosclerotic plaques by intravascular optical coherence tomography images

PubMed Central

Ughi, Giovanni Jacopo; Adriaenssens, Tom; Sinnaeve, Peter; Desmet, Walter; D’hooge, Jan

2013-01-01

Intravascular optical coherence tomography (IVOCT) is rapidly becoming the method of choice for the in vivo investigation of coronary artery disease. While IVOCT visualizes atherosclerotic plaques with a resolution <20µm, image analysis in terms of tissue composition is currently performed by a time-consuming manual procedure based on the qualitative interpretation of image features. We illustrate an algorithm for the automated and systematic characterization of IVOCT atherosclerotic tissue. The proposed method consists in a supervised classification of image pixels according to textural features combined with the estimated value of the optical attenuation coefficient. IVOCT images of 64 plaques, from 49 in vivo IVOCT data sets, constituted the algorithm’s training and testing data sets. Validation was obtained by comparing automated analysis results to the manual assessment of atherosclerotic plaques. An overall pixel-wise accuracy of 81.5% with a classification feasibility of 76.5% and per-class accuracy of 89.5%, 72.1% and 79.5% for fibrotic, calcified and lipid-rich tissue respectively, was found. Moreover, measured optical properties were in agreement with previous results reported in literature. As such, an algorithm for automated tissue characterization was developed and validated using in vivo human data, suggesting that it can be applied to clinical IVOCT data. This might be an important step towards the integration of IVOCT in cardiovascular research and routine clinical practice. PMID:23847728

Somatomedin C deficiency in Asian sisters.

PubMed Central

McGraw, M E; Price, D A; Hill, D J

1986-01-01

Two sisters of Asian origin showed typical clinical and biochemical features of primary somatomedin C (SM-C) deficiency (Laron dwarfism). Abnormalities of SM-C binding proteins were observed, one sister lacking the high molecular weight (150 Kd) protein. Images Figure PMID:2434036

Towards the use of OCT angiography in clinical dermatology

NASA Astrophysics Data System (ADS)

Baran, Utku; Choi, Woo June; Wang, Ruikang K.

2016-02-01

Optical coherence tomography (OCT) is a popular imaging technique used in ophthalmology, and on the way to become clinically viable alternative in dermatology due to its capability of acquiring histopathology level images of in vivo tissue, noninvasively. In this study, we demonstrate the capabilities of OCT-based angiography (OMAG) in detecting high-resolution, volumetric structural and microvascular features of in vivo human skin with various conditions using a swept source OCT system that operates on a central wavelength of 1310 nm with an A-line rate of 100 kHz. OMAG images provide detailed in vivo visualization of microvasculature of abnormal human skin conditions from face, chest and belly. Moreover, the progress of wound healing on human skin from arm is monitored during longitudinal wound healing process. The presented results promise the clinical use of OCT angiography in treatment of prevalent cutaneous diseases within human skin, in vivo.

Clinical Applications of Near-infrared Diffuse Correlation Spectroscopy and Tomography for Tissue Blood Flow Monitoring and Imaging

PubMed Central

Shang, Yu; Li, Ting; Yu, Guoqiang

2017-01-01

Blood flow is one such available observable promoting a wealth of physiological insight both individually and in combination with other metrics. Near-infrared diffuse correlation spectroscopy (DCS) and, to a lesser extent, diffuse correlation tomography (DCT), have increasingly received interest over the past decade as noninvasive methods for tissue blood flow measurements and imaging. DCS/DCT offers several attractive features for tissue blood flow measurements/imaging such as noninvasiveness, portability, high temporal resolution, and relatively large penetration depth (up to several centimeters). This review first introduces the basic principle and instrumentation of DCS/DCT, followed by presenting clinical application examples of DCS/DCT for the diagnosis and therapeutic monitoring of diseases in a variety of organs/tissues including brain, skeletal muscle, and tumor. Clinical study results demonstrate technical versatility of DCS/DCT in providing important information for disease diagnosis and intervention monitoring. PMID:28199219

Ultrasound of skeletal muscle injury.

PubMed

Koh, Eamon Su Chun; McNally, Eugene G

2007-06-01

The professional and recreational demands of modern society make the treatment of muscle injury an increasingly important clinical problem, particularly in the athletic population. In the elite athlete, significant financial and professional pressures may also exist that emphasize the need for accurate diagnosis and treatment. With new advances in ultrasound technology, images of exquisite detail allow diagnosis of muscle injury that matches the accuracy of magnetic resonance imaging (MRI). Furthermore, the benefits of real-time and Doppler imaging, ability to perform interventional procedures, and relative cost benefits compared with MRI place ultrasound at the forefront for investigation for these injuries in many circumstances. Muscle injury may be divided into acute and chronic pathology, with muscle strain injury the most common clinical problem presenting to sports physicians. This article reviews the spectrum of acute and chronic muscle injuries, with particular attention to clinical features and some common or important muscle strain injuries.

Clinically oriented three-year medical physics curriculum: a new design for the future.

PubMed

Nachiappan, Arun C; Lee, Stephen R; Willis, Marc H; Galfione, Matthew R; Chinnappan, Raj R; Diaz-Marchan, Pedro J; Bushong, Stewart C

2012-09-01

Medical physics instruction for diagnostic radiology residency at our institution has been redesigned with an interactive and image-based approach that encourages clinical application. The new medical physics curriculum spans the first 3 years of radiology residency and is integrated with the core didactic curriculum. Salient features include clinical medical physics conferences, fundamentals of medical physics lectures, practicums, online modules, journal club, and a final review before the American Board of Radiology core examination.

Fluorescence lifetime technique for surgical imaging, guidance and augmented reality (Conference Presentation)

NASA Astrophysics Data System (ADS)

Marcu, Laura

2017-02-01

The surgeon's limited ability to accurately delineate the tumor margin during surgical interventions is one key challenge in clinical management of cancer. New methods for guiding tumor resection decisions are needed. Numerous studies have shown that tissue autofluorescence properties have the potential to asses biochemical features associates with distinct pathologies in tissue and to distinguish various cancers from normal tissues. However, despite these promising reports, autofluorescence techniques were sparsely adopted in clinical settings. Moreover, when adopted they were primarily used for pre-operative diagnosis rather than guiding interventions. To address this need, we have researched and engineered instrumentation that utilizes label-free fluorescence lifetime contrast to characterize tissue biochemical features in vivo in patients and methodologies conducive to real-time (few seconds) diagnosis of tissue pathologies during surgical procedures. This presentation overviews clinically-compatible multispectral fluorescence lifetime imaging techniques developed in our laboratory and their ability to operate as stand-alone tools, integrated in a biopsy needle and in conjunction with the da Vinci surgical robot. We present pre-clinical and clinical studies in patients that demonstrate the potential of these techniques for intraoperative assessment of brain tumors and head and neck cancer. Current results demonstrate that intrinsic fluorescence signals can provide useful contrast for delineation distinct types of tissues including tumors intraoperatively. Challenges and solutions in the clinical implementation of these techniques are discussed.

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

Obstructive Sleep Apnea in Women: Study of Speech and Craniofacial Characteristics.

PubMed

Tyan, Marina; Espinoza-Cuadros, Fernando; Fernández Pozo, Rubén; Toledano, Doroteo; Lopez Gonzalo, Eduardo; Alcazar Ramirez, Jose Daniel; Hernandez Gomez, Luis Alfonso

2017-11-06

Obstructive sleep apnea (OSA) is a common sleep disorder characterized by frequent cessation of breathing lasting 10 seconds or longer. The diagnosis of OSA is performed through an expensive procedure, which requires an overnight stay at the hospital. This has led to several proposals based on the analysis of patients' facial images and speech recordings as an attempt to develop simpler and cheaper methods to diagnose OSA. The objective of this study was to analyze possible relationships between OSA and speech and facial features on a female population and whether these possible connections may be affected by the specific clinical characteristics in OSA population and, more specifically, to explore how the connection between OSA and speech and facial features can be affected by gender. All the subjects are Spanish subjects suspected to suffer from OSA and referred to a sleep disorders unit. Voice recordings and photographs were collected in a supervised but not highly controlled way, trying to test a scenario close to a realistic clinical practice scenario where OSA is assessed using an app running on a mobile device. Furthermore, clinical variables such as weight, height, age, and cervical perimeter, which are usually reported as predictors of OSA, were also gathered. Acoustic analysis is centered in sustained vowels. Facial analysis consists of a set of local craniofacial features related to OSA, which were extracted from images after detecting facial landmarks by using the active appearance models. To study the probable OSA connection with speech and craniofacial features, correlations among apnea-hypopnea index (AHI), clinical variables, and acoustic and facial measurements were analyzed. The results obtained for female population indicate mainly weak correlations (r values between .20 and .39). Correlations between AHI, clinical variables, and speech features show the prevalence of formant frequencies over bandwidths, with F2/i/ being the most appropriate formant frequency for OSA prediction in women. Results obtained for male population indicate mainly very weak correlations (r values between .01 and .19). In this case, bandwidths prevail over formant frequencies. Correlations between AHI, clinical variables, and craniofacial measurements are very weak. In accordance with previous studies, some clinical variables are found to be good predictors of OSA. Besides, strong correlations are found between AHI and some clinical variables with speech and facial features. Regarding speech feature, the results show the prevalence of formant frequency F2/i/ over the rest of features for the female population as OSA predictive feature. Although the correlation reported is weak, this study aims to find some traces that could explain the possible connection between OSA and speech in women. In the case of craniofacial measurements, results evidence that some features that can be used for predicting OSA in male patients are not suitable for testing female population. ©Marina Tyan, Fernando Espinoza-Cuadros, Rubén Fernández Pozo, Doroteo Toledano, Eduardo Lopez Gonzalo, Jose Daniel Alcazar Ramirez, Luis Alfonso Hernandez Gomez. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 06.11.2017.

Magnetic resonance imaging criteria for thrombolysis in hyperacute cerebral infarction.

PubMed

Ahmetgjekaj, Ilir; Kabashi-Muçaj, Serbeze; Lascu, Luana Corina; Kabashi, Antigona; Bondari, A; Bondari, Simona; Dedushi-Hoti, Kreshnike; Biçaku, Ardian; Shatri, Jeton

2014-01-01

Selection of patients with cerebral infarction for MRI that is suitable for thrombolytic therapy as an emerging application. Although the efficiency of the therapy with i.v. tissue plasminogen activator (tPA) within 3 hours after onset of symptoms has been proven in selected patients with CT, now these criteria are determined by MRI, as the data we gather are fast and accurate in the first hours. MRI screening in patients with acute cerebral infarction before application of thrombolytic therapy was done in a UCC Mannheim in Germany. Unlike trials with CT, MRI studies demonstrated the benefits of therapy up to 6 hours after the onset of symptoms. We studied 21 patients hospitalized in Clinic of Neuroradiology at University Clinical Centre in Mannheim-Germany. They all undergo brain MRI evaluation for stroke. This article reviews literature that has followed application of thrombolysis in patients with cerebral infarction based on MRI. We have analyzed the MRI criteria for i.v. application of tPA at this University Centre. Alongside the personal viewpoints of clinicians, survey reveals a variety of clinical aspects and MRI features that are opened for further more exploration: therapeutic effects, the use of the MRI angiography, dynamics, and other. MRI is a tested imaging method for rapid evaluation of patients with hyperacute cerebral infarction, replacing the use of CT imaging and clinical features. MRI criteria for thrombolytic therapy are being applied in some cerebral vascular centres. In Kosovo, the application of thrombolytic therapy has not started yet.

The association between sleep-disordered breathing and magnetic resonance imaging findings in a pediatric cohort with Chiari 1 malformation

PubMed Central

Amin, Reshma; Sayal, Priya; Sayal, Aarti; Massicote, Colin; Pham, Robin; Al-Saleh, Suhail; Drake, James; Narang, Indra

2015-01-01

BACKGROUND: The prevalence of sleep-disordered breathing (SDB) reported in the literature for Chiari malformation type 1 (CM1) is uniformly high (24% to 70%). In Canada, there is limited access to pediatric polysomnography (PSG). Therefore, the identification of clinical features would be invaluable for triaging these children. OBJECTIVE: To identify demographic features, clinical symptoms/signs and radiological findings associated with SDB in a large pediatric cohort with CM1. METHODS: A retrospective review was conducted on children with CM1 who underwent baseline PSG. Data were collected on patient demographics (age, sex, weight, height, body mass index), clinical symptoms (chart review and clinical questionnaires), diagnostic imaging of the brain and cervicothoracic spine, and medical history at the time of referral. RESULTS: A total of 68 children were included in the review. The mean (± SD) age of the children at the time of PSG was 7.33±4.01 years; 56% (n=38) were male. There was a 49% prevalence of SDB in this cohort based on the overall apnea-hypopnea index. Obstructive sleep apnea was the predominant type of SDB. Tonsillar herniation was significantly correlated with obstructive apnea-hypopnea index (r=0.24; P=0.036). CONCLUSIONS: A direct relationship between the degree of cerebellar tonsillar herniation and obstructive sleep apnea was demonstrated. However, further prospective studies that include neurophysiological assessment are needed to further translate the central nervous system imaging findings to predict the presence of SDB. PMID:25379655

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.

An exudate detection method for diagnosis risk of diabetic macular edema in retinal images using feature-based and supervised classification.

PubMed

Marin, D; Gegundez-Arias, M E; Ponte, B; Alvarez, F; Garrido, J; Ortega, C; Vasallo, M J; Bravo, J M

2018-01-10

The present paper aims at presenting the methodology and first results of a detection system of risk of diabetic macular edema (DME) in fundus images. The system is based on the detection of retinal exudates (Ex), whose presence in the image is clinically used for an early diagnosis of the disease. To do so, the system applies digital image processing algorithms to the retinal image in order to obtain a set of candidate regions to be Ex, which are validated by means of feature extraction and supervised classification techniques. The diagnoses provided by the system on 1058 retinographies of 529 diabetic patients at risk of having DME show that the system can operate at a level of sensitivity comparable to that of ophthalmological specialists: it achieved 0.9000 sensitivity per patient against 0.7733, 0.9133 and 0.9000 of several specialists, where the false negatives were mild clinical cases of the disease. In addition, the level of specificity reached by the system was 0.6939, high enough to screen about 70% of the patients with no evidence of DME. These values show that the system fulfils the requirements for its possible integration into a complete diabetic retinopathy pre-screening tool for the automated management of patients within a screening programme. Graphical Abstract Diagnosis system of risk of diabetic macular edema (DME) based on exudate (Ex) detection in fundus images.

A descriptive study of Lewy body dementia with functional imaging support in a Chinese population: a preliminary study.

PubMed

Shea, Y F; Chu, L W; Lee, S C

2017-06-01

Lewy body dementia includes dementia with Lewy bodies and Parkinson's disease dementia. There have been limited clinical studies among Chinese patients with Lewy body dementia. This study aimed to review the presenting clinical features and identify risk factors for complications including falls, dysphagia, aspiration pneumonia, pressure sores, and mortality in Chinese patients with Lewy body dementia. We also wished to identify any difference in clinical features of patients with Lewy body dementia with and without an Alzheimer's disease pattern of functional imaging. We retrospectively reviewed 23 patients with Lewy body dementia supported by functional imaging. Baseline demographics, presenting clinical and behavioural and psychological symptoms of dementia, functional and cognitive assessment scores, and complications during follow-up were reviewed. Patients with Lewy body dementia were further classified as having an Alzheimer's disease imaging pattern if functional imaging demonstrated bilateral temporoparietal hypometabolism or hypoperfusion with or without precuneus and posterior cingulate gyrus hypometabolism or hypoperfusion. The pre-imaging accuracy of clinical diagnosis was 52%. In 83% of patients, behavioural and psychological symptoms of dementia were evident. Falls, dysphagia, aspiration pneumonia, pressure sores, and death occurred in 70%, 52%, 26%, 26%, and 30% of patients, respectively with corresponding event rates per person-years of 0.32, 0.17, 0.18, 0.08, and 0.10. Patients with aspiration pneumonia compared with those without were more likely to have dysphagia (100% vs 35%; P=0.01). Deceased patients with Lewy body dementia, compared with alive patients, had a higher (median [interquartile range]) presenting Clinical Dementia Rating score (1 [1-2] vs 0.5 [0.5-1.0]; P=0.01), lower mean (± standard deviation) baseline Barthel index (13 ± 7 vs 18 ± 4; P=0.04), and were more likely to be prescribed levodopa (86% vs 31%; P=0.03). Patients with Lewy body dementia with an Alzheimer's disease pattern of functional imaging, compared with those without the pattern, were younger at presentation (mean ± standard deviation, 73 ± 6 vs 80 ± 6 years; P=0.02) and had a lower Mini-Mental State Examination score at 1 year (15 ± 8 vs 22 ± 6; P=0.05). Falls, dysphagia, aspiration pneumonia, and pressure sores were common among patients with Lewy body dementia. Those with an Alzheimer's disease pattern of functional imaging had a younger age of onset and lower 1-year Mini-Mental State Examination score.