A novel computer-aided detection system for pulmonary nodule identification in CT images

NASA Astrophysics Data System (ADS)

Han, Hao; Li, Lihong; Wang, Huafeng; Zhang, Hao; Moore, William; Liang, Zhengrong

2014-03-01

Computer-aided detection (CADe) of pulmonary nodules from computer tomography (CT) scans is critical for assisting radiologists to identify lung lesions at an early stage. In this paper, we propose a novel approach for CADe of lung nodules using a two-stage vector quantization (VQ) scheme. The first-stage VQ aims to extract lung from the chest volume, while the second-stage VQ is designed to extract initial nodule candidates (INCs) within the lung volume. Then rule-based expert filtering is employed to prune obvious FPs from INCs, and the commonly-used support vector machine (SVM) classifier is adopted to further reduce the FPs. The proposed system was validated on 100 CT scans randomly selected from the 262 scans that have at least one juxta-pleural nodule annotation in the publicly available database - Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). The two-stage VQ only missed 2 out of the 207 nodules at agreement level 1, and the INCs detection for each scan took about 30 seconds in average. Expert filtering reduced FPs more than 18 times, while maintaining a sensitivity of 93.24%. As it is trivial to distinguish INCs attached to pleural wall versus not on wall, we investigated the feasibility of training different SVM classifiers to further reduce FPs from these two kinds of INCs. Experiment results indicated that SVM classification over the entire set of INCs was in favor of, where the optimal operating of our CADe system achieved a sensitivity of 89.4% at a specificity of 86.8%.

Cascade of convolutional neural networks for lung texture classification: overcoming ontological overlapping

NASA Astrophysics Data System (ADS)

Tarando, Sebastian Roberto; Fetita, Catalin; Brillet, Pierre-Yves

2017-03-01

The infiltrative lung diseases are a class of irreversible, non-neoplastic lung pathologies requiring regular follow-up with CT imaging. Quantifying the evolution of the patient status imposes the development of automated classification tools for lung texture. Traditionally, such classification relies on a two-dimensional analysis of axial CT images. This paper proposes a cascade of the existing CNN based CAD system, specifically tuned-up. The advantage of using a deep learning approach is a better regularization of the classification output. In a preliminary evaluation, the combined approach was tested on a 13 patient database of various lung pathologies, showing an increase of 10% in True Positive Rate (TPR) with respect to the best suited state of the art CNN for this task.

Lung nodule malignancy classification using only radiologist-quantified image features as inputs to statistical learning algorithms: probing the Lung Image Database Consortium dataset with two statistical learning methods

PubMed Central

Hancock, Matthew C.; Magnan, Jerry F.

2016-01-01

Abstract. In the assessment of nodules in CT scans of the lungs, a number of image-derived features are diagnostically relevant. Currently, many of these features are defined only qualitatively, so they are difficult to quantify from first principles. Nevertheless, these features (through their qualitative definitions and interpretations thereof) are often quantified via a variety of mathematical methods for the purpose of computer-aided diagnosis (CAD). To determine the potential usefulness of quantified diagnostic image features as inputs to a CAD system, we investigate the predictive capability of statistical learning methods for classifying nodule malignancy. We utilize the Lung Image Database Consortium dataset and only employ the radiologist-assigned diagnostic feature values for the lung nodules therein, as well as our derived estimates of the diameter and volume of the nodules from the radiologists’ annotations. We calculate theoretical upper bounds on the classification accuracy that are achievable by an ideal classifier that only uses the radiologist-assigned feature values, and we obtain an accuracy of 85.74 (±1.14)%, which is, on average, 4.43% below the theoretical maximum of 90.17%. The corresponding area-under-the-curve (AUC) score is 0.932 (±0.012), which increases to 0.949 (±0.007) when diameter and volume features are included and has an accuracy of 88.08 (±1.11)%. Our results are comparable to those in the literature that use algorithmically derived image-based features, which supports our hypothesis that lung nodules can be classified as malignant or benign using only quantified, diagnostic image features, and indicates the competitiveness of this approach. We also analyze how the classification accuracy depends on specific features and feature subsets, and we rank the features according to their predictive power, statistically demonstrating the top four to be spiculation, lobulation, subtlety, and calcification. PMID:27990453

Lung nodule malignancy classification using only radiologist-quantified image features as inputs to statistical learning algorithms: probing the Lung Image Database Consortium dataset with two statistical learning methods.

PubMed

Hancock, Matthew C; Magnan, Jerry F

2016-10-01

In the assessment of nodules in CT scans of the lungs, a number of image-derived features are diagnostically relevant. Currently, many of these features are defined only qualitatively, so they are difficult to quantify from first principles. Nevertheless, these features (through their qualitative definitions and interpretations thereof) are often quantified via a variety of mathematical methods for the purpose of computer-aided diagnosis (CAD). To determine the potential usefulness of quantified diagnostic image features as inputs to a CAD system, we investigate the predictive capability of statistical learning methods for classifying nodule malignancy. We utilize the Lung Image Database Consortium dataset and only employ the radiologist-assigned diagnostic feature values for the lung nodules therein, as well as our derived estimates of the diameter and volume of the nodules from the radiologists' annotations. We calculate theoretical upper bounds on the classification accuracy that are achievable by an ideal classifier that only uses the radiologist-assigned feature values, and we obtain an accuracy of 85.74 [Formula: see text], which is, on average, 4.43% below the theoretical maximum of 90.17%. The corresponding area-under-the-curve (AUC) score is 0.932 ([Formula: see text]), which increases to 0.949 ([Formula: see text]) when diameter and volume features are included and has an accuracy of 88.08 [Formula: see text]. Our results are comparable to those in the literature that use algorithmically derived image-based features, which supports our hypothesis that lung nodules can be classified as malignant or benign using only quantified, diagnostic image features, and indicates the competitiveness of this approach. We also analyze how the classification accuracy depends on specific features and feature subsets, and we rank the features according to their predictive power, statistically demonstrating the top four to be spiculation, lobulation, subtlety, and calcification.

The diagnostic value of narrow-band imaging for early and invasive lung cancer: a meta-analysis.

PubMed

Zhu, Juanjuan; Li, Wei; Zhou, Jihong; Chen, Yuqing; Zhao, Chenling; Zhang, Ting; Peng, Wenjia; Wang, Xiaojing

2017-07-01

This study aimed to compare the ability of narrow-band imaging to detect early and invasive lung cancer with that of conventional pathological analysis and white-light bronchoscopy. We searched the PubMed, EMBASE, Sinomed, and China National Knowledge Infrastructure databases for relevant studies. Meta-disc software was used to perform data analysis, meta-regression analysis, sensitivity analysis, and heterogeneity testing, and STATA software was used to determine if publication bias was present, as well as to calculate the relative risks for the sensitivity and specificity of narrow-band imaging vs those of white-light bronchoscopy for the detection of early and invasive lung cancer. A random-effects model was used to assess the diagnostic efficacy of the above modalities in cases in which a high degree of between-study heterogeneity was noted with respect to their diagnostic efficacies. The database search identified six studies including 578 patients. The pooled sensitivity and specificity of narrow-band imaging were 86% (95% confidence interval: 83-88%) and 81% (95% confidence interval: 77-84%), respectively, and the pooled sensitivity and specificity of white-light bronchoscopy were 70% (95% confidence interval: 66-74%) and 66% (95% confidence interval: 62-70%), respectively. The pooled relative risks for the sensitivity and specificity of narrow-band imaging vs the sensitivity and specificity of white-light bronchoscopy for the detection of early and invasive lung cancer were 1.33 (95% confidence interval: 1.07-1.67) and 1.09 (95% confidence interval: 0.84-1.42), respectively, and sensitivity analysis showed that narrow-band imaging exhibited good diagnostic efficacy with respect to detecting early and invasive lung cancer and that the results of the study were stable. Narrow-band imaging was superior to white light bronchoscopy with respect to detecting early and invasive lung cancer; however, the specificities of the two modalities did not differ significantly.

Multiscale Rotation-Invariant Convolutional Neural Networks for Lung Texture Classification.

PubMed

Wang, Qiangchang; Zheng, Yuanjie; Yang, Gongping; Jin, Weidong; Chen, Xinjian; Yin, Yilong

2018-01-01

We propose a new multiscale rotation-invariant convolutional neural network (MRCNN) model for classifying various lung tissue types on high-resolution computed tomography. MRCNN employs Gabor-local binary pattern that introduces a good property in image analysis-invariance to image scales and rotations. In addition, we offer an approach to deal with the problems caused by imbalanced number of samples between different classes in most of the existing works, accomplished by changing the overlapping size between the adjacent patches. Experimental results on a public interstitial lung disease database show a superior performance of the proposed method to state of the art.

A resource for the assessment of lung nodule size estimation methods: database of thoracic CT scans of an anthropomorphic phantom◊

PubMed Central

Gavrielides, Marios A.; Kinnard, Lisa M.; Myers, Kyle J.; Peregoy, Jennifer; Pritchard, William F.; Zeng, Rongping; Esparza, Juan; Karanian, John; Petrick, Nicholas

2010-01-01

A number of interrelated factors can affect the precision and accuracy of lung nodule size estimation. To quantify the effect of these factors, we have been conducting phantom CT studies using an anthropomorphic thoracic phantom containing a vasculature insert to which synthetic nodules were inserted or attached. Ten repeat scans were acquired on different multi-detector scanners, using several sets of acquisition and reconstruction protocols and various nodule characteristics (size, shape, density, location). This study design enables both bias and variance analysis for the nodule size estimation task. The resulting database is in the process of becoming publicly available as a resource to facilitate the assessment of lung nodule size estimation methodologies and to enable comparisons between different methods regarding measurement error. This resource complements public databases of clinical data and will contribute towards the development of procedures that will maximize the utility of CT imaging for lung cancer screening and tumor therapy evaluation. PMID:20640011

An Automatic Detection System of Lung Nodule Based on Multi-Group Patch-Based Deep Learning Network.

PubMed

Jiang, Hongyang; Ma, He; Qian, Wei; Gao, Mengdi; Li, Yan

2017-07-14

High-efficiency lung nodule detection dramatically contributes to the risk assessment of lung cancer. It is a significant and challenging task to quickly locate the exact positions of lung nodules. Extensive work has been done by researchers around this domain for approximately two decades. However, previous computer aided detection (CADe) schemes are mostly intricate and time-consuming since they may require more image processing modules, such as the computed tomography (CT) image transformation, the lung nodule segmentation and the feature extraction, to construct a whole CADe system. It is difficult for those schemes to process and analyze enormous data when the medical images continue to increase. Besides, some state of the art deep learning schemes may be strict in the standard of database. This study proposes an effective lung nodule detection scheme based on multi-group patches cut out from the lung images, which are enhanced by the Frangi filter. Through combining two groups of images, a four-channel convolution neural networks (CNN) model is designed to learn the knowledge of radiologists for detecting nodules of four levels. This CADe scheme can acquire the sensitivity of 80.06% with 4.7 false positives per scan and the sensitivity of 94% with 15.1 false positives per scan. The results demonstrate that the multi-group patch-based learning system is efficient to improve the performance of lung nodule detection and greatly reduce the false positives under a huge amount of image data.

Computer aided lung cancer diagnosis with deep learning algorithms

NASA Astrophysics Data System (ADS)

Sun, Wenqing; Zheng, Bin; Qian, Wei

2016-03-01

Deep learning is considered as a popular and powerful method in pattern recognition and classification. However, there are not many deep structured applications used in medical imaging diagnosis area, because large dataset is not always available for medical images. In this study we tested the feasibility of using deep learning algorithms for lung cancer diagnosis with the cases from Lung Image Database Consortium (LIDC) database. The nodules on each computed tomography (CT) slice were segmented according to marks provided by the radiologists. After down sampling and rotating we acquired 174412 samples with 52 by 52 pixel each and the corresponding truth files. Three deep learning algorithms were designed and implemented, including Convolutional Neural Network (CNN), Deep Belief Networks (DBNs), Stacked Denoising Autoencoder (SDAE). To compare the performance of deep learning algorithms with traditional computer aided diagnosis (CADx) system, we designed a scheme with 28 image features and support vector machine. The accuracies of CNN, DBNs, and SDAE are 0.7976, 0.8119, and 0.7929, respectively; the accuracy of our designed traditional CADx is 0.7940, which is slightly lower than CNN and DBNs. We also noticed that the mislabeled nodules using DBNs are 4% larger than using traditional CADx, this might be resulting from down sampling process lost some size information of the nodules.

Computer-aided detection of lung nodules via 3D fast radial transform, scale space representation, and Zernike MIP classification.

PubMed

Riccardi, Alessandro; Petkov, Todor Sergueev; Ferri, Gianluca; Masotti, Matteo; Campanini, Renato

2011-04-01

The authors presented a novel system for automated nodule detection in lung CT exams. The approach is based on (1) a lung tissue segmentation preprocessing step, composed of histogram thresholding, seeded region growing, and mathematical morphology; (2) a filtering step, whose aim is the preliminary detection of candidate nodules (via 3D fast radial filtering) and estimation of their geometrical features (via scale space analysis); and (3) a false positive reduction (FPR) step, comprising a heuristic FPR, which applies thresholds based on geometrical features, and a supervised FPR, which is based on support vector machines classification, which in turn, is enhanced by a feature extraction algorithm based on maximum intensity projection processing and Zernike moments. The system was validated on 154 chest axial CT exams provided by the lung image database consortium public database. The authors obtained correct detection of 71% of nodules marked by all radiologists, with a false positive rate of 6.5 false positives per patient (FP/patient). A higher specificity of 2.5 FP/patient was reached with a sensitivity of 60%. An independent test on the ANODE09 competition database obtained an overall score of 0.310. The system shows a novel approach to the problem of lung nodule detection in CT scans: It relies on filtering techniques, image transforms, and descriptors rather than region growing and nodule segmentation, and the results are comparable to those of other recent systems in literature and show little dependency on the different types of nodules, which is a good sign of robustness.

Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis.

PubMed

Sun, Wenqing; Zheng, Bin; Qian, Wei

2017-10-01

This study aimed to analyze the ability of extracting automatically generated features using deep structured algorithms in lung nodule CT image diagnosis, and compare its performance with traditional computer aided diagnosis (CADx) systems using hand-crafted features. All of the 1018 cases were acquired from Lung Image Database Consortium (LIDC) public lung cancer database. The nodules were segmented according to four radiologists' markings, and 13,668 samples were generated by rotating every slice of nodule images. Three multichannel ROI based deep structured algorithms were designed and implemented in this study: convolutional neural network (CNN), deep belief network (DBN), and stacked denoising autoencoder (SDAE). For the comparison purpose, we also implemented a CADx system using hand-crafted features including density features, texture features and morphological features. The performance of every scheme was evaluated by using a 10-fold cross-validation method and an assessment index of the area under the receiver operating characteristic curve (AUC). The observed highest area under the curve (AUC) was 0.899±0.018 achieved by CNN, which was significantly higher than traditional CADx with the AUC=0.848±0.026. The results from DBN was also slightly higher than CADx, while SDAE was slightly lower. By visualizing the automatic generated features, we found some meaningful detectors like curvy stroke detectors from deep structured schemes. The study results showed the deep structured algorithms with automatically generated features can achieve desirable performance in lung nodule diagnosis. With well-tuned parameters and large enough dataset, the deep learning algorithms can have better performance than current popular CADx. We believe the deep learning algorithms with similar data preprocessing procedure can be used in other medical image analysis areas as well. Copyright © 2017. Published by Elsevier Ltd.

A completely automated processing pipeline for lung and lung lobe segmentation and its application to the LIDC-IDRI data base

NASA Astrophysics Data System (ADS)

Blaffert, Thomas; Wiemker, Rafael; Barschdorf, Hans; Kabus, Sven; Klinder, Tobias; Lorenz, Cristian; Schadewaldt, Nicole; Dharaiya, Ekta

2010-03-01

Automated segmentation of lung lobes in thoracic CT images has relevance for various diagnostic purposes like localization of tumors within the lung or quantification of emphysema. Since emphysema is a known risk factor for lung cancer, both purposes are even related to each other. The main steps of the segmentation pipeline described in this paper are the lung detector and the lung segmentation based on a watershed algorithm, and the lung lobe segmentation based on mesh model adaptation. The segmentation procedure was applied to data sets of the data base of the Image Database Resource Initiative (IDRI) that currently contains over 500 thoracic CT scans with delineated lung nodule annotations. We visually assessed the reliability of the single segmentation steps, with a success rate of 98% for the lung detection and 90% for lung delineation. For about 20% of the cases we found the lobe segmentation not to be anatomically plausible. A modeling confidence measure is introduced that gives a quantitative indication of the segmentation quality. For a demonstration of the segmentation method we studied the correlation between emphysema score and malignancy on a per-lobe basis.

Early detection of lung cancer from CT images: nodule segmentation and classification using deep learning

NASA Astrophysics Data System (ADS)

Sharma, Manu; Bhatt, Jignesh S.; Joshi, Manjunath V.

2018-04-01

Lung cancer is one of the most abundant causes of the cancerous deaths worldwide. It has low survival rate mainly due to the late diagnosis. With the hardware advancements in computed tomography (CT) technology, it is now possible to capture the high resolution images of lung region. However, it needs to be augmented by efficient algorithms to detect the lung cancer in the earlier stages using the acquired CT images. To this end, we propose a two-step algorithm for early detection of lung cancer. Given the CT image, we first extract the patch from the center location of the nodule and segment the lung nodule region. We propose to use Otsu method followed by morphological operations for the segmentation. This step enables accurate segmentation due to the use of data-driven threshold. Unlike other methods, we perform the segmentation without using the complete contour information of the nodule. In the second step, a deep convolutional neural network (CNN) is used for the better classification (malignant or benign) of the nodule present in the segmented patch. Accurate segmentation of even a tiny nodule followed by better classification using deep CNN enables the early detection of lung cancer. Experiments have been conducted using 6306 CT images of LIDC-IDRI database. We achieved the test accuracy of 84.13%, with the sensitivity and specificity of 91.69% and 73.16%, respectively, clearly outperforming the state-of-the-art algorithms.

Differentiation of several interstitial lung disease patterns in HRCT images using support vector machine: role of databases on performance

NASA Astrophysics Data System (ADS)

Kale, Mandar; Mukhopadhyay, Sudipta; Dash, Jatindra K.; Garg, Mandeep; Khandelwal, Niranjan

2016-03-01

Interstitial lung disease (ILD) is complicated group of pulmonary disorders. High Resolution Computed Tomography (HRCT) considered to be best imaging technique for analysis of different pulmonary disorders. HRCT findings can be categorised in several patterns viz. Consolidation, Emphysema, Ground Glass Opacity, Nodular, Normal etc. based on their texture like appearance. Clinician often find it difficult to diagnosis these pattern because of their complex nature. In such scenario computer-aided diagnosis system could help clinician to identify patterns. Several approaches had been proposed for classification of ILD patterns. This includes computation of textural feature and training /testing of classifier such as artificial neural network (ANN), support vector machine (SVM) etc. In this paper, wavelet features are calculated from two different ILD database, publically available MedGIFT ILD database and private ILD database, followed by performance evaluation of ANN and SVM classifiers in terms of average accuracy. It is found that average classification accuracy by SVM is greater than ANN where trained and tested on same database. Investigation continued further to test variation in accuracy of classifier when training and testing is performed with alternate database and training and testing of classifier with database formed by merging samples from same class from two individual databases. The average classification accuracy drops when two independent databases used for training and testing respectively. There is significant improvement in average accuracy when classifiers are trained and tested with merged database. It infers dependency of classification accuracy on training data. It is observed that SVM outperforms ANN when same database is used for training and testing.

Texture classification of lung computed tomography images

NASA Astrophysics Data System (ADS)

Pheng, Hang See; Shamsuddin, Siti M.

2013-03-01

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

3D GGO candidate extraction in lung CT images using multilevel thresholding on supervoxels

NASA Astrophysics Data System (ADS)

Huang, Shan; Liu, Xiabi; Han, Guanghui; Zhao, Xinming; Zhao, Yanfeng; Zhou, Chunwu

2018-02-01

The earlier detection of ground glass opacity (GGO) is of great importance since GGOs are more likely to be malignant than solid nodules. However, the detection of GGO is a difficult task in lung cancer screening. This paper proposes a novel GGO candidate extraction method, which performs multilevel thresholding on supervoxels in 3D lung CT images. Firstly, we segment the lung parenchyma based on Otsu algorithm. Secondly, the voxels which are adjacent in 3D discrete space and sharing similar grayscale are clustered into supervoxels. This procedure is used to enhance GGOs and reduce computational complexity. Thirdly, Hessian matrix is used to emphasize focal GGO candidates. Lastly, an improved adaptive multilevel thresholding method is applied on segmented clusters to extract GGO candidates. The proposed method was evaluated on a set of 19 lung CT scans containing 166 GGO lesions from the Lung CT Imaging Signs (LISS) database. The experimental results show that our proposed GGO candidate extraction method is effective, with a sensitivity of 100% and 26.3 of false positives per scan (665 GGO candidates, 499 non-GGO regions and 166 GGO regions). It can handle both focal GGOs and diffuse GGOs.

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

PubMed

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

2014-01-01

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

HOSVD-Based 3D Active Appearance Model: Segmentation of Lung Fields in CT Images.

PubMed

Wang, Qingzhu; Kang, Wanjun; Hu, Haihui; Wang, Bin

2016-07-01

An Active Appearance Model (AAM) is a computer vision model which can be used to effectively segment lung fields in CT images. However, the fitting result is often inadequate when the lungs are affected by high-density pathologies. To overcome this problem, we propose a Higher-order Singular Value Decomposition (HOSVD)-based Three-dimensional (3D) AAM. An evaluation was performed on 310 diseased lungs form the Lung Image Database Consortium Image Collection. Other contemporary AAMs operate directly on patterns represented by vectors, i.e., before applying the AAM to a 3D lung volume,it has to be vectorized first into a vector pattern by some technique like concatenation. However, some implicit structural or local contextual information may be lost in this transformation. According to the nature of the 3D lung volume, HOSVD is introduced to represent and process the lung in tensor space. Our method can not only directly operate on the original 3D tensor patterns, but also efficiently reduce the computer memory usage. The evaluation resulted in an average Dice coefficient of 97.0 % ± 0.59 %, a mean absolute surface distance error of 1.0403 ± 0.5716 mm, a mean border positioning errors of 0.9187 ± 0.5381 pixel, and a Hausdorff Distance of 20.4064 ± 4.3855, respectively. Experimental results showed that our methods delivered significant and better segmentation results, compared with the three other model-based lung segmentation approaches, namely 3D Snake, 3D ASM and 3D AAM.

Selecting relevant 3D image features of margin sharpness and texture for lung nodule retrieval.

PubMed

Ferreira, José Raniery; de Azevedo-Marques, Paulo Mazzoncini; Oliveira, Marcelo Costa

2017-03-01

Lung cancer is the leading cause of cancer-related deaths in the world. Its diagnosis is a challenge task to specialists due to several aspects on the classification of lung nodules. Therefore, it is important to integrate content-based image retrieval methods on the lung nodule classification process, since they are capable of retrieving similar cases from databases that were previously diagnosed. However, this mechanism depends on extracting relevant image features in order to obtain high efficiency. The goal of this paper is to perform the selection of 3D image features of margin sharpness and texture that can be relevant on the retrieval of similar cancerous and benign lung nodules. A total of 48 3D image attributes were extracted from the nodule volume. Border sharpness features were extracted from perpendicular lines drawn over the lesion boundary. Second-order texture features were extracted from a cooccurrence matrix. Relevant features were selected by a correlation-based method and a statistical significance analysis. Retrieval performance was assessed according to the nodule's potential malignancy on the 10 most similar cases and by the parameters of precision and recall. Statistical significant features reduced retrieval performance. Correlation-based method selected 2 margin sharpness attributes and 6 texture attributes and obtained higher precision compared to all 48 extracted features on similar nodule retrieval. Feature space dimensionality reduction of 83 % obtained higher retrieval performance and presented to be a computationaly low cost method of retrieving similar nodules for the diagnosis of lung cancer.

Nodule Detection in a Lung Region that's Segmented with Using Genetic Cellular Neural Networks and 3D Template Matching with Fuzzy Rule Based Thresholding

PubMed Central

Osman, Onur; Ucan, Osman N.

2008-01-01

Objective The purpose of this study was to develop a new method for automated lung nodule detection in serial section CT images with using the characteristics of the 3D appearance of the nodules that distinguish themselves from the vessels. Materials and Methods Lung nodules were detected in four steps. First, to reduce the number of region of interests (ROIs) and the computation time, the lung regions of the CTs were segmented using Genetic Cellular Neural Networks (G-CNN). Then, for each lung region, ROIs were specified with using the 8 directional search; +1 or -1 values were assigned to each voxel. The 3D ROI image was obtained by combining all the 2-Dimensional (2D) ROI images. A 3D template was created to find the nodule-like structures on the 3D ROI image. Convolution of the 3D ROI image with the proposed template strengthens the shapes that are similar to those of the template and it weakens the other ones. Finally, fuzzy rule based thresholding was applied and the ROI's were found. To test the system's efficiency, we used 16 cases with a total of 425 slices, which were taken from the Lung Image Database Consortium (LIDC) dataset. Results The computer aided diagnosis (CAD) system achieved 100% sensitivity with 13.375 FPs per case when the nodule thickness was greater than or equal to 5.625 mm. Conclusion Our results indicate that the detection performance of our algorithm is satisfactory, and this may well improve the performance of computer-aided detection of lung nodules. PMID:18253070

Segmentation of lung nodules in computed tomography images using dynamic programming and multidirection fusion techniques.

PubMed

Wang, Qian; Song, Enmin; Jin, Renchao; Han, Ping; Wang, Xiaotong; Zhou, Yanying; Zeng, Jianchao

2009-06-01

The aim of this study was to develop a novel algorithm for segmenting lung nodules on three-dimensional (3D) computed tomographic images to improve the performance of computer-aided diagnosis (CAD) systems. The database used in this study consists of two data sets obtained from the Lung Imaging Database Consortium. The first data set, containing 23 nodules (22% irregular nodules, 13% nonsolid nodules, 17% nodules attached to other structures), was used for training. The second data set, containing 64 nodules (37% irregular nodules, 40% nonsolid nodules, 62% nodules attached to other structures), was used for testing. Two key techniques were developed in the segmentation algorithm: (1) a 3D extended dynamic programming model, with a newly defined internal cost function based on the information between adjacent slices, allowing parameters to be adapted to each slice, and (2) a multidirection fusion technique, which makes use of the complementary relationships among different directions to improve the final segmentation accuracy. The performance of this approach was evaluated by the overlap criterion, complemented by the true-positive fraction and the false-positive fraction criteria. The mean values of the overlap, true-positive fraction, and false-positive fraction for the first data set achieved using the segmentation scheme were 66%, 75%, and 15%, respectively, and the corresponding values for the second data set were 58%, 71%, and 22%, respectively. The experimental results indicate that this segmentation scheme can achieve better performance for nodule segmentation than two existing algorithms reported in the literature. The proposed 3D extended dynamic programming model is an effective way to segment sequential images of lung nodules. The proposed multidirection fusion technique is capable of reducing segmentation errors especially for no-nodule and near-end slices, thus resulting in better overall performance.

Effect of segmentation algorithms on the performance of computerized detection of lung nodules in CT

PubMed Central

Guo, Wei; Li, Qiang

2014-01-01

Purpose: The purpose of this study is to reveal how the performance of lung nodule segmentation algorithm impacts the performance of lung nodule detection, and to provide guidelines for choosing an appropriate segmentation algorithm with appropriate parameters in a computer-aided detection (CAD) scheme. Methods: The database consisted of 85 CT scans with 111 nodules of 3 mm or larger in diameter from the standard CT lung nodule database created by the Lung Image Database Consortium. The initial nodule candidates were identified as those with strong response to a selective nodule enhancement filter. A uniform viewpoint reformation technique was applied to a three-dimensional nodule candidate to generate 24 two-dimensional (2D) reformatted images, which would be used to effectively distinguish between true nodules and false positives. Six different algorithms were employed to segment the initial nodule candidates in the 2D reformatted images. Finally, 2D features from the segmented areas in the 24 reformatted images were determined, selected, and classified for removal of false positives. Therefore, there were six similar CAD schemes, in which only the segmentation algorithms were different. The six segmentation algorithms included the fixed thresholding (FT), Otsu thresholding (OTSU), fuzzy C-means (FCM), Gaussian mixture model (GMM), Chan and Vese model (CV), and local binary fitting (LBF). The mean Jaccard index and the mean absolute distance (Dmean) were employed to evaluate the performance of segmentation algorithms, and the number of false positives at a fixed sensitivity was employed to evaluate the performance of the CAD schemes. Results: For the segmentation algorithms of FT, OTSU, FCM, GMM, CV, and LBF, the highest mean Jaccard index between the segmented nodule and the ground truth were 0.601, 0.586, 0.588, 0.563, 0.543, and 0.553, respectively, and the corresponding Dmean were 1.74, 1.80, 2.32, 2.80, 3.48, and 3.18 pixels, respectively. With these segmentation results of the six segmentation algorithms, the six CAD schemes reported 4.4, 8.8, 3.4, 9.2, 13.6, and 10.4 false positives per CT scan at a sensitivity of 80%. Conclusions: When multiple algorithms are available for segmenting nodule candidates in a CAD scheme, the “optimal” segmentation algorithm did not necessarily lead to the “optimal” CAD detection performance. PMID:25186393

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

NASA Astrophysics Data System (ADS)

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

2008-03-01

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

A parameterized logarithmic image processing method with Laplacian of Gaussian filtering for lung nodule enhancement in chest radiographs.

PubMed

Chen, Sheng; Yao, Liping; Chen, Bao

2016-11-01

The enhancement of lung nodules in chest radiographs (CXRs) plays an important role in the manual as well as computer-aided detection (CADe) lung cancer. In this paper, we proposed a parameterized logarithmic image processing (PLIP) method combined with the Laplacian of a Gaussian (LoG) filter to enhance lung nodules in CXRs. We first applied several LoG filters with varying parameters to an original CXR to enhance the nodule-like structures as well as the edges in the image. We then applied the PLIP model, which can enhance lung nodule images with high contrast and was beneficial in extracting effective features for nodule detection in the CADe scheme. Our method combined the advantages of both the PLIP algorithm and the LoG algorithm, which can enhance lung nodules in chest radiographs with high contrast. To test our nodule enhancement method, we tested a CADe scheme, with a relatively high performance in nodule detection, using a publically available database containing 140 nodules in 140 CXRs enhanced through our nodule enhancement method. The CADe scheme attained a sensitivity of 81 and 70 % with an average of 5.0 frame rate (FP) and 2.0 FP, respectively, in a leave-one-out cross-validation test. By contrast, the CADe scheme based on the original image recorded a sensitivity of 77 and 63 % at 5.0 FP and 2.0 FP, respectively. We introduced the measurement of enhancement by entropy evaluation to objectively assess our method. Experimental results show that the proposed method obtains an effective enhancement of lung nodules in CXRs for both radiologists and CADe schemes.

Computer-aided diagnosis workstation and database system for chest diagnosis based on multi-helical CT images

NASA Astrophysics Data System (ADS)

Satoh, Hitoshi; Niki, Noboru; Mori, Kiyoshi; Eguchi, Kenji; Kaneko, Masahiro; Kakinuma, Ryutarou; Moriyama, Noriyuki; Ohmatsu, Hironobu; Masuda, Hideo; Machida, Suguru; Sasagawa, Michizou

2006-03-01

Multi-helical CT scanner advanced remarkably at the speed at which the chest CT images were acquired for mass screening. Mass screening based on multi-helical CT images requires a considerable number of images to be read. It is this time-consuming step that makes the use of helical CT for mass screening impractical at present. To overcome this problem, we have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images and a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification. We also have developed electronic medical recording system and prototype internet system for the community health in two or more regions by using the Virtual Private Network router and Biometric fingerprint authentication system and Biometric face authentication system for safety of medical information. Based on these diagnostic assistance methods, we have now developed a new computer-aided workstation and database that can display suspected lesions three-dimensionally in a short time. This paper describes basic studies that have been conducted to evaluate this new system. The results of this study indicate that our computer-aided diagnosis workstation and network system can increase diagnostic speed, diagnostic accuracy and safety of medical information.

Segmentation of lung fields using Chan-Vese active contour model in chest radiographs

NASA Astrophysics Data System (ADS)

Sohn, Kiwon

2011-03-01

A CAD tool for chest radiographs consists of several procedures and the very first step is segmentation of lung fields. We develop a novel methodology for segmentation of lung fields in chest radiographs that can satisfy the following two requirements. First, we aim to develop a segmentation method that does not need a training stage with manual estimation of anatomical features in a large training dataset of images. Secondly, for the ease of implementation, it is desirable to apply a well established model that is widely used for various image-partitioning practices. The Chan-Vese active contour model, which is based on Mumford-Shah functional in the level set framework, is applied for segmentation of lung fields. With the use of this model, segmentation of lung fields can be carried out without detailed prior knowledge on the radiographic anatomy of the chest, yet in some chest radiographs, the trachea regions are unfavorably segmented out in addition to the lung field contours. To eliminate artifacts from the trachea, we locate the upper end of the trachea, find a vertical center line of the trachea and delineate it, and then brighten the trachea region to make it less distinctive. The segmentation process is finalized by subsequent morphological operations. We randomly select 30 images from the Japanese Society of Radiological Technology image database to test the proposed methodology and the results are shown. We hope our segmentation technique can help to promote of CAD tools, especially for emerging chest radiographic imaging techniques such as dual energy radiography and chest tomosynthesis.

[Development of a digital chest phantom for studies on energy subtraction techniques].

PubMed

Hayashi, Norio; Taniguchi, Anna; Noto, Kimiya; Shimosegawa, Masayuki; Ogura, Toshihiro; Doi, Kunio

2014-03-01

Digital chest phantoms continue to play a significant role in optimizing imaging parameters for chest X-ray examinations. The purpose of this study was to develop a digital chest phantom for studies on energy subtraction techniques under ideal conditions without image noise. Computed tomography (CT) images from the LIDC (Lung Image Database Consortium) were employed to develop a digital chest phantom. The method consisted of the following four steps: 1) segmentation of the lung and bone regions on CT images; 2) creation of simulated nodules; 3) transformation to attenuation coefficient maps from the segmented images; and 4) projection from attenuation coefficient maps. To evaluate the usefulness of digital chest phantoms, we determined the contrast of the simulated nodules in projection images of the digital chest phantom using high and low X-ray energies, soft tissue images obtained by energy subtraction, and "gold standard" images of the soft tissues. Using our method, the lung and bone regions were segmented on the original CT images. The contrast of simulated nodules in soft tissue images obtained by energy subtraction closely matched that obtained using the gold standard images. We thus conclude that it is possible to carry out simulation studies based on energy subtraction techniques using the created digital chest phantoms. Our method is potentially useful for performing simulation studies for optimizing the imaging parameters in chest X-ray examinations.

Hybrid detection of lung nodules on CT scan images

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

Lu, Lin; Tan, Yongqiang; Schwartz, Lawrence H.

Purpose: The diversity of lung nodules poses difficulty for the current computer-aided diagnostic (CAD) schemes for lung nodule detection on computed tomography (CT) scan images, especially in large-scale CT screening studies. We proposed a novel CAD scheme based on a hybrid method to address the challenges of detection in diverse lung nodules. Methods: The hybrid method proposed in this paper integrates several existing and widely used algorithms in the field of nodule detection, including morphological operation, dot-enhancement based on Hessian matrix, fuzzy connectedness segmentation, local density maximum algorithm, geodesic distance map, and regression tree classification. All of the adopted algorithmsmore » were organized into tree structures with multi-nodes. Each node in the tree structure aimed to deal with one type of lung nodule. Results: The method has been evaluated on 294 CT scans from the Lung Image Database Consortium (LIDC) dataset. The CT scans were randomly divided into two independent subsets: a training set (196 scans) and a test set (98 scans). In total, the 294 CT scans contained 631 lung nodules, which were annotated by at least two radiologists participating in the LIDC project. The sensitivity and false positive per scan for the training set were 87% and 2.61%. The sensitivity and false positive per scan for the testing set were 85.2% and 3.13%. Conclusions: The proposed hybrid method yielded high performance on the evaluation dataset and exhibits advantages over existing CAD schemes. We believe that the present method would be useful for a wide variety of CT imaging protocols used in both routine diagnosis and screening studies.« less

Automatic segmentation of tumor-laden lung volumes from the LIDC database

NASA Astrophysics Data System (ADS)

O'Dell, Walter G.

2012-03-01

The segmentation of the lung parenchyma is often a critical pre-processing step prior to application of computer-aided detection of lung nodules. Segmentation of the lung volume can dramatically decrease computation time and reduce the number of false positive detections by excluding from consideration extra-pulmonary tissue. However, while many algorithms are capable of adequately segmenting the healthy lung, none have been demonstrated to work reliably well on tumor-laden lungs. Of particular challenge is to preserve tumorous masses attached to the chest wall, mediastinum or major vessels. In this role, lung volume segmentation comprises an important computational step that can adversely affect the performance of the overall CAD algorithm. An automated lung volume segmentation algorithm has been developed with the goals to maximally exclude extra-pulmonary tissue while retaining all true nodules. The algorithm comprises a series of tasks including intensity thresholding, 2-D and 3-D morphological operations, 2-D and 3-D floodfilling, and snake-based clipping of nodules attached to the chest wall. It features the ability to (1) exclude trachea and bowels, (2) snip large attached nodules using snakes, (3) snip small attached nodules using dilation, (4) preserve large masses fully internal to lung volume, (5) account for basal aspects of the lung where in a 2-D slice the lower sections appear to be disconnected from main lung, and (6) achieve separation of the right and left hemi-lungs. The algorithm was developed and trained to on the first 100 datasets of the LIDC image database.

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

PubMed Central

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

2014-01-01

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

Segmentation of pulmonary nodules in computed tomography using a regression neural network approach and its application to the Lung Image Database Consortium and Image Database Resource Initiative dataset.

PubMed

Messay, Temesguen; Hardie, Russell C; Tuinstra, Timothy R

2015-05-01

We present new pulmonary nodule segmentation algorithms for computed tomography (CT). These include a fully-automated (FA) system, a semi-automated (SA) system, and a hybrid system. Like most traditional systems, the new FA system requires only a single user-supplied cue point. On the other hand, the SA system represents a new algorithm class requiring 8 user-supplied control points. This does increase the burden on the user, but we show that the resulting system is highly robust and can handle a variety of challenging cases. The proposed hybrid system starts with the FA system. If improved segmentation results are needed, the SA system is then deployed. The FA segmentation engine has 2 free parameters, and the SA system has 3. These parameters are adaptively determined for each nodule in a search process guided by a regression neural network (RNN). The RNN uses a number of features computed for each candidate segmentation. We train and test our systems using the new Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) data. To the best of our knowledge, this is one of the first nodule-specific performance benchmarks using the new LIDC-IDRI dataset. We also compare the performance of the proposed methods with several previously reported results on the same data used by those other methods. Our results suggest that the proposed FA system improves upon the state-of-the-art, and the SA system offers a considerable boost over the FA system. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Computer-aided diagnosis workstation and network system for chest diagnosis based on multislice CT images

NASA Astrophysics Data System (ADS)

Satoh, Hitoshi; Niki, Noboru; Mori, Kiyoshi; Eguchi, Kenji; Kaneko, Masahiro; Kakinuma, Ryutarou; Moriyama, Noriyuki; Ohmatsu, Hironobu; Masuda, Hideo; Machida, Suguru

2007-03-01

Multislice CT scanner advanced remarkably at the speed at which the chest CT images were acquired for mass screening. Mass screening based on multislice CT images requires a considerable number of images to be read. It is this time-consuming step that makes the use of helical CT for mass screening impractical at present. To overcome this problem, we have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images and a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification. Moreover, we have provided diagnostic assistance methods to medical screening specialists by using a lung cancer screening algorithm built into mobile helical CT scanner for the lung cancer mass screening done in the region without the hospital. We also have developed electronic medical recording system and prototype internet system for the community health in two or more regions by using the Virtual Private Network router and Biometric fingerprint authentication system and Biometric face authentication system for safety of medical information. Based on these diagnostic assistance methods, we have now developed a new computer-aided workstation and database that can display suspected lesions three-dimensionally in a short time. This paper describes basic studies that have been conducted to evaluate this new system.

Accurate registration of temporal CT images for pulmonary nodules detection

NASA Astrophysics Data System (ADS)

Yan, Jichao; Jiang, Luan; Li, Qiang

2017-02-01

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

Accuracy of lung cancer ICD-9-CM codes in Umbria, Napoli 3 Sud and Friuli Venezia Giulia administrative healthcare databases: a diagnostic accuracy study

PubMed Central

Montedori, Alessandro; Bidoli, Ettore; Serraino, Diego; Fusco, Mario; Giovannini, Gianni; Casucci, Paola; Franchini, David; Granata, Annalisa; Ciullo, Valerio; Vitale, Maria Francesca; Gobbato, Michele; Chiari, Rita; Cozzolino, Francesco; Orso, Massimiliano; Orlandi, Walter

2018-01-01

Objectives To assess the accuracy of International Classification of Diseases 9th Revision–Clinical Modification (ICD-9-CM) codes in identifying subjects with lung cancer. Design A cross-sectional diagnostic accuracy study comparing ICD-9-CM 162.x code (index test) in primary position with medical chart (reference standard). Case ascertainment was based on the presence of a primary nodular lesion in the lung and cytological or histological documentation of cancer from a primary or metastatic site. Setting Three operative units: administrative databases from Umbria Region (890 000 residents), ASL Napoli 3 Sud (NA) (1 170 000 residents) and Friuli Venezia Giulia (FVG) Region (1 227 000 residents). Participants Incident subjects with lung cancer (n=386) diagnosed in primary position between 2012 and 2014 and a population of non-cases (n=280). Outcome measures Sensitivity, specificity and positive predictive value (PPV) for 162.x code. Results 130 cases and 94 non-cases were randomly selected from each database and the corresponding medical charts were reviewed. Most of the diagnoses for lung cancer were performed in medical departments. True positive rates were high for all the three units. Sensitivity was 99% (95% CI 95% to 100%) for Umbria, 97% (95% CI 91% to 100%) for NA, and 99% (95% CI 95% to 100%) for FVG. The false positive rates were 24%, 37% and 23% for Umbria, NA and FVG, respectively. PPVs were 79% (73% to 83%)%) for Umbria, 58% (53% to 63%)%) for NA and 79% (73% to 84%)%) for FVG. Conclusions Case ascertainment for lung cancer based on imaging or endoscopy associated with histological examination yielded an excellent sensitivity in all the three administrative databases. PPV was moderate for Umbria and FVG but lower for NA. PMID:29773701

The Lung Image Database Consortium (LIDC): ensuring the integrity of expert-defined "truth".

PubMed

Armato, Samuel G; Roberts, Rachael Y; McNitt-Gray, Michael F; Meyer, Charles R; Reeves, Anthony P; McLennan, Geoffrey; Engelmann, Roger M; Bland, Peyton H; Aberle, Denise R; Kazerooni, Ella A; MacMahon, Heber; van Beek, Edwin J R; Yankelevitz, David; Croft, Barbara Y; Clarke, Laurence P

2007-12-01

Computer-aided diagnostic (CAD) systems fundamentally require the opinions of expert human observers to establish "truth" for algorithm development, training, and testing. The integrity of this "truth," however, must be established before investigators commit to this "gold standard" as the basis for their research. The purpose of this study was to develop a quality assurance (QA) model as an integral component of the "truth" collection process concerning the location and spatial extent of lung nodules observed on computed tomography (CT) scans to be included in the Lung Image Database Consortium (LIDC) public database. One hundred CT scans were interpreted by four radiologists through a two-phase process. For the first of these reads (the "blinded read phase"), radiologists independently identified and annotated lesions, assigning each to one of three categories: "nodule >or=3 mm," "nodule <3 mm," or "non-nodule >or=3 mm." For the second read (the "unblinded read phase"), the same radiologists independently evaluated the same CT scans, but with all of the annotations from the previously performed blinded reads presented; each radiologist could add to, edit, or delete their own marks; change the lesion category of their own marks; or leave their marks unchanged. The post-unblinded read set of marks was grouped into discrete nodules and subjected to the QA process, which consisted of identification of potential errors introduced during the complete image annotation process and correction of those errors. Seven categories of potential error were defined; any nodule with a mark that satisfied the criterion for one of these categories was referred to the radiologist who assigned that mark for either correction or confirmation that the mark was intentional. A total of 105 QA issues were identified across 45 (45.0%) of the 100 CT scans. Radiologist review resulted in modifications to 101 (96.2%) of these potential errors. Twenty-one lesions erroneously marked as lung nodules after the unblinded reads had this designation removed through the QA process. The establishment of "truth" must incorporate a QA process to guarantee the integrity of the datasets that will provide the basis for the development, training, and testing of CAD systems.

Validity of ICD-9-CM codes for breast, lung and colorectal cancers in three Italian administrative healthcare databases: a diagnostic accuracy study protocol.

PubMed

Abraha, Iosief; Serraino, Diego; Giovannini, Gianni; Stracci, Fabrizio; Casucci, Paola; Alessandrini, Giuliana; Bidoli, Ettore; Chiari, Rita; Cirocchi, Roberto; De Giorgi, Marcello; Franchini, David; Vitale, Maria Francesca; Fusco, Mario; Montedori, Alessandro

2016-03-25

Administrative healthcare databases are useful tools to study healthcare outcomes and to monitor the health status of a population. Patients with cancer can be identified through disease-specific codes, prescriptions and physician claims, but prior validation is required to achieve an accurate case definition. The objective of this protocol is to assess the accuracy of International Classification of Diseases Ninth Revision-Clinical Modification (ICD-9-CM) codes for breast, lung and colorectal cancers in identifying patients diagnosed with the relative disease in three Italian administrative databases. Data from the administrative databases of Umbria Region (910,000 residents), Local Health Unit 3 of Napoli (1,170,000 residents) and Friuli--Venezia Giulia Region (1,227,000 residents) will be considered. In each administrative database, patients with the first occurrence of diagnosis of breast, lung or colorectal cancer between 2012 and 2014 will be identified using the following groups of ICD-9-CM codes in primary position: (1) 233.0 and (2) 174.x for breast cancer; (3) 162.x for lung cancer; (4) 153.x for colon cancer and (5) 154.0-154.1 and 154.8 for rectal cancer. Only incident cases will be considered, that is, excluding cases that have the same diagnosis in the 5 years (2007-2011) before the period of interest. A random sample of cases and non-cases will be selected from each administrative database and the corresponding medical charts will be assessed for validation by pairs of trained, independent reviewers. Case ascertainment within the medical charts will be based on (1) the presence of a primary nodular lesion in the breast, lung or colon-rectum, documented with imaging or endoscopy and (2) a cytological or histological documentation of cancer from a primary or metastatic site. Sensitivity and specificity with 95% CIs will be calculated. Study results will be disseminated widely through peer-reviewed publications and presentations at national and international conferences. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

High-Throughput Histopathological Image Analysis via Robust Cell Segmentation and Hashing

PubMed Central

Zhang, Xiaofan; Xing, Fuyong; Su, Hai; Yang, Lin; Zhang, Shaoting

2015-01-01

Computer-aided diagnosis of histopathological images usually requires to examine all cells for accurate diagnosis. Traditional computational methods may have efficiency issues when performing cell-level analysis. In this paper, we propose a robust and scalable solution to enable such analysis in a real-time fashion. Specifically, a robust segmentation method is developed to delineate cells accurately using Gaussian-based hierarchical voting and repulsive balloon model. A large-scale image retrieval approach is also designed to examine and classify each cell of a testing image by comparing it with a massive database, e.g., half-million cells extracted from the training dataset. We evaluate this proposed framework on a challenging and important clinical use case, i.e., differentiation of two types of lung cancers (the adenocarcinoma and squamous carcinoma), using thousands of lung microscopic tissue images extracted from hundreds of patients. Our method has achieved promising accuracy and running time by searching among half-million cells. PMID:26599156

Increasing CAD system efficacy for lung texture analysis using a convolutional network

NASA Astrophysics Data System (ADS)

Tarando, Sebastian Roberto; Fetita, Catalin; Faccinetto, Alex; Brillet, Pierre-Yves

2016-03-01

The infiltrative lung diseases are a class of irreversible, non-neoplastic lung pathologies requiring regular follow-up with CT imaging. Quantifying the evolution of the patient status imposes the development of automated classification tools for lung texture. For the large majority of CAD systems, such classification relies on a two-dimensional analysis of axial CT images. In a previously developed CAD system, we proposed a fully-3D approach exploiting a multi-scale morphological analysis which showed good performance in detecting diseased areas, but with a major drawback consisting of sometimes overestimating the pathological areas and mixing different type of lung patterns. This paper proposes a combination of the existing CAD system with the classification outcome provided by a convolutional network, specifically tuned-up, in order to increase the specificity of the classification and the confidence to diagnosis. The advantage of using a deep learning approach is a better regularization of the classification output (because of a deeper insight into a given pathological class over a large series of samples) where the previous system is extra-sensitive due to the multi-scale response on patient-specific, localized patterns. In a preliminary evaluation, the combined approach was tested on a 10 patient database of various lung pathologies, showing a sharp increase of true detections.

Fuzzy-C-Means Clustering Based Segmentation and CNN-Classification for Accurate Segmentation of Lung Nodules

PubMed

K, Jalal Deen; R, Ganesan; A, Merline

2017-07-27

Objective: Accurate segmentation of abnormal and healthy lungs is very crucial for a steadfast computer-aided disease diagnostics. Methods: For this purpose a stack of chest CT scans are processed. In this paper, novel methods are proposed for segmentation of the multimodal grayscale lung CT scan. In the conventional methods using Markov–Gibbs Random Field (MGRF) model the required regions of interest (ROI) are identified. Result: The results of proposed FCM and CNN based process are compared with the results obtained from the conventional method using MGRF model. The results illustrate that the proposed method can able to segment the various kinds of complex multimodal medical images precisely. Conclusion: However, in this paper, to obtain an exact boundary of the regions, every empirical dispersion of the image is computed by Fuzzy C-Means Clustering segmentation. A classification process based on the Convolutional Neural Network (CNN) classifier is accomplished to distinguish the normal tissue and the abnormal tissue. The experimental evaluation is done using the Interstitial Lung Disease (ILD) database. Creative Commons Attribution License

Fuzzy-C-Means Clustering Based Segmentation and CNN-Classification for Accurate Segmentation of Lung Nodules

PubMed Central

K, Jalal Deen; R, Ganesan; A, Merline

2017-01-01

Objective: Accurate segmentation of abnormal and healthy lungs is very crucial for a steadfast computer-aided disease diagnostics. Methods: For this purpose a stack of chest CT scans are processed. In this paper, novel methods are proposed for segmentation of the multimodal grayscale lung CT scan. In the conventional methods using Markov–Gibbs Random Field (MGRF) model the required regions of interest (ROI) are identified. Result: The results of proposed FCM and CNN based process are compared with the results obtained from the conventional method using MGRF model. The results illustrate that the proposed method can able to segment the various kinds of complex multimodal medical images precisely. Conclusion: However, in this paper, to obtain an exact boundary of the regions, every empirical dispersion of the image is computed by Fuzzy C-Means Clustering segmentation. A classification process based on the Convolutional Neural Network (CNN) classifier is accomplished to distinguish the normal tissue and the abnormal tissue. The experimental evaluation is done using the Interstitial Lung Disease (ILD) database. PMID:28749127

Potential clinical impact of advanced imaging and computer-aided diagnosis in chest radiology: importance of radiologist's role and successful observer study.

PubMed

Li, Feng

2015-07-01

This review paper is based on our research experience in the past 30 years. The importance of radiologists' role is discussed in the development or evaluation of new medical images and of computer-aided detection (CAD) schemes in chest radiology. The four main topics include (1) introducing what diseases can be included in a research database for different imaging techniques or CAD systems and what imaging database can be built by radiologists, (2) understanding how radiologists' subjective judgment can be combined with technical objective features to improve CAD performance, (3) sharing our experience in the design of successful observer performance studies, and (4) finally, discussing whether the new images and CAD systems can improve radiologists' diagnostic ability in chest radiology. In conclusion, advanced imaging techniques and detection/classification of CAD systems have a potential clinical impact on improvement of radiologists' diagnostic ability, for both the detection and the differential diagnosis of various lung diseases, in chest radiology.

A new method of content based medical image retrieval and its applications to CT imaging sign retrieval.

PubMed

Ma, Ling; Liu, Xiabi; Gao, Yan; Zhao, Yanfeng; Zhao, Xinming; Zhou, Chunwu

2017-02-01

This paper proposes a new method of content based medical image retrieval through considering fused, context-sensitive similarity. Firstly, we fuse the semantic and visual similarities between the query image and each image in the database as their pairwise similarities. Then, we construct a weighted graph whose nodes represent the images and edges measure their pairwise similarities. By using the shortest path algorithm over the weighted graph, we obtain a new similarity measure, context-sensitive similarity measure, between the query image and each database image to complete the retrieval process. Actually, we use the fused pairwise similarity to narrow down the semantic gap for obtaining a more accurate pairwise similarity measure, and spread it on the intrinsic data manifold to achieve the context-sensitive similarity for a better retrieval performance. The proposed method has been evaluated on the retrieval of the Common CT Imaging Signs of Lung Diseases (CISLs) and achieved not only better retrieval results but also the satisfactory computation efficiency. Copyright © 2017 Elsevier Inc. All rights reserved.

Unsupervised sputum color image segmentation for lung cancer diagnosis based on a Hopfield neural network

NASA Astrophysics Data System (ADS)

Sammouda, Rachid; Niki, Noboru; Nishitani, Hiroshi; Nakamura, S.; Mori, Shinichiro

1997-04-01

The paper presents a method for automatic segmentation of sputum cells with color images, to develop an efficient algorithm for lung cancer diagnosis based on a Hopfield neural network. We formulate the segmentation problem as a minimization of an energy function constructed with two terms, the cost-term as a sum of squared errors, and the second term a temporary noise added to the network as an excitation to escape certain local minima with the result of being closer to the global minimum. To increase the accuracy in segmenting the regions of interest, a preclassification technique is used to extract the sputum cell regions within the color image and remove those of the debris cells. The former is then given with the raw image to the input of Hopfield neural network to make a crisp segmentation by assigning each pixel to label such as background, cytoplasm, and nucleus. The proposed technique has yielded correct segmentation of complex scene of sputum prepared by ordinary manual staining method in most of the tested images selected from our database containing thousands of sputum color images.

Network-based reading system for lung cancer screening CT

NASA Astrophysics Data System (ADS)

Fujino, Yuichi; Fujimura, Kaori; Nomura, Shin-ichiro; Kawashima, Harumi; Tsuchikawa, Megumu; Matsumoto, Toru; Nagao, Kei-ichi; Uruma, Takahiro; Yamamoto, Shinji; Takizawa, Hotaka; Kuroda, Chikazumi; Nakayama, Tomio

2006-03-01

This research aims to support chest computed tomography (CT) medical checkups to decrease the death rate by lung cancer. We have developed a remote cooperative reading system for lung cancer screening over the Internet, a secure transmission function, and a cooperative reading environment. It is called the Network-based Reading System. A telemedicine system involves many issues, such as network costs and data security if we use it over the Internet, which is an open network. In Japan, broadband access is widespread and its cost is the lowest in the world. We developed our system considering human machine interface and security. It consists of data entry terminals, a database server, a computer aided diagnosis (CAD) system, and some reading terminals. It uses a secure Digital Imaging and Communication in Medicine (DICOM) encrypting method and Public Key Infrastructure (PKI) based secure DICOM image data distribution. We carried out an experimental trial over the Japan Gigabit Network (JGN), which is the testbed for the Japanese next-generation network, and conducted verification experiments of secure screening image distribution, some kinds of data addition, and remote cooperative reading. We found that network bandwidth of about 1.5 Mbps enabled distribution of screening images and cooperative reading and that the encryption and image distribution methods we proposed were applicable to the encryption and distribution of general DICOM images via the Internet.

Neural networks for computer-aided diagnosis: detection of lung nodules in chest radiograms.

PubMed

Coppini, Giuseppe; Diciotti, Stefano; Falchini, Massimo; Villari, Natale; Valli, Guido

2003-12-01

The paper describes a neural-network-based system for the computer aided detection of lung nodules in chest radiograms. Our approach is based on multiscale processing and artificial neural networks (ANNs). The problem of nodule detection is faced by using a two-stage architecture including: 1) an attention focusing subsystem that processes whole radiographs to locate possible nodular regions ensuring high sensitivity; 2) a validation subsystem that processes regions of interest to evaluate the likelihood of the presence of a nodule, so as to reduce false alarms and increase detection specificity. Biologically inspired filters (both LoG and Gabor kernels) are used to enhance salient image features. ANNs of the feedforward type are employed, which allow an efficient use of a priori knowledge about the shape of nodules, and the background structure. The images from the public JSRT database, including 247 radiograms, were used to build and test the system. We performed a further test by using a second private database with 65 radiograms collected and annotated at the Radiology Department of the University of Florence. Both data sets include nodule and nonnodule radiographs. The use of a public data set along with independent testing with a different image set makes the comparison with other systems easier and allows a deeper understanding of system behavior. Experimental results are described by ROC/FROC analysis. For the JSRT database, we observed that by varying sensitivity from 60 to 75% the number of false alarms per image lies in the range 4-10, while accuracy is in the range 95.7-98.0%. When the second data set was used comparable results were obtained. The observed system performances support the undertaking of system validation in clinical settings.

An evaluation of consensus techniques for diagnostic interpretation

NASA Astrophysics Data System (ADS)

Sauter, Jake N.; LaBarre, Victoria M.; Furst, Jacob D.; Raicu, Daniela S.

2018-02-01

Learning diagnostic labels from image content has been the standard in computer-aided diagnosis. Most computer-aided diagnosis systems use low-level image features extracted directly from image content to train and test machine learning classifiers for diagnostic label prediction. When the ground truth for the diagnostic labels is not available, reference truth is generated from the experts diagnostic interpretations of the image/region of interest. More specifically, when the label is uncertain, e.g. when multiple experts label an image and their interpretations are different, techniques to handle the label variability are necessary. In this paper, we compare three consensus techniques that are typically used to encode the variability in the experts labeling of the medical data: mean, median and mode, and their effects on simple classifiers that can handle deterministic labels (decision trees) and probabilistic vectors of labels (belief decision trees). Given that the NIH/NCI Lung Image Database Consortium (LIDC) data provides interpretations for lung nodules by up to four radiologists, we leverage the LIDC data to evaluate and compare these consensus approaches when creating computer-aided diagnosis systems for lung nodules. First, low-level image features of nodules are extracted and paired with their radiologists semantic ratings (1= most likely benign, , 5 = most likely malignant); second, machine learning multi-class classifiers that handle deterministic labels (decision trees) and probabilistic vectors of labels (belief decision trees) are built to predict the lung nodules semantic ratings. We show that the mean-based consensus generates the most robust classi- fier overall when compared to the median- and mode-based consensus. Lastly, the results of this study show that, when building CAD systems with uncertain diagnostic interpretation, it is important to evaluate different strategies for encoding and predicting the diagnostic label.

Image segmentation evaluation for very-large datasets

NASA Astrophysics Data System (ADS)

Reeves, Anthony P.; Liu, Shuang; Xie, Yiting

2016-03-01

With the advent of modern machine learning methods and fully automated image analysis there is a need for very large image datasets having documented segmentations for both computer algorithm training and evaluation. Current approaches of visual inspection and manual markings do not scale well to big data. We present a new approach that depends on fully automated algorithm outcomes for segmentation documentation, requires no manual marking, and provides quantitative evaluation for computer algorithms. The documentation of new image segmentations and new algorithm outcomes are achieved by visual inspection. The burden of visual inspection on large datasets is minimized by (a) customized visualizations for rapid review and (b) reducing the number of cases to be reviewed through analysis of quantitative segmentation evaluation. This method has been applied to a dataset of 7,440 whole-lung CT images for 6 different segmentation algorithms designed to fully automatically facilitate the measurement of a number of very important quantitative image biomarkers. The results indicate that we could achieve 93% to 99% successful segmentation for these algorithms on this relatively large image database. The presented evaluation method may be scaled to much larger image databases.

Automated identification of the lung contours in positron emission tomography

NASA Astrophysics Data System (ADS)

Nery, F.; Silvestre Silva, J.; Ferreira, N. C.; Caramelo, F. J.; Faustino, R.

2013-03-01

Positron Emission Tomography (PET) is a nuclear medicine imaging technique that permits to analyze, in three dimensions, the physiological processes in vivo. One of the areas where PET has demonstrated its advantages is in the staging of lung cancer, where it offers better sensitivity and specificity than other techniques such as CT. On the other hand, accurate segmentation, an important procedure for Computer Aided Diagnostics (CAD) and automated image analysis, is a challenging task given the low spatial resolution and the high noise that are intrinsic characteristics of PET images. This work presents an algorithm for the segmentation of lungs in PET images, to be used in CAD and group analysis in a large patient database. The lung boundaries are automatically extracted from a PET volume through the application of a marker-driven watershed segmentation procedure which is robust to the noise. In order to test the effectiveness of the proposed method, we compared the segmentation results in several slices using our approach with the results obtained from manual delineation. The manual delineation was performed by nuclear medicine physicians that used a software routine that we developed specifically for this task. To quantify the similarity between the contours obtained from the two methods, we used figures of merit based on region and also on contour definitions. Results show that the performance of the algorithm was similar to the performance of human physicians. Additionally, we found that the algorithm-physician agreement is similar (statistically significant) to the inter-physician agreement.

Multisource Transfer Learning With Convolutional Neural Networks for Lung Pattern Analysis.

PubMed

Christodoulidis, Stergios; Anthimopoulos, Marios; Ebner, Lukas; Christe, Andreas; Mougiakakou, Stavroula

2017-01-01

Early diagnosis of interstitial lung diseases is crucial for their treatment, but even experienced physicians find it difficult, as their clinical manifestations are similar. In order to assist with the diagnosis, computer-aided diagnosis systems have been developed. These commonly rely on a fixed scale classifier that scans CT images, recognizes textural lung patterns, and generates a map of pathologies. In a previous study, we proposed a method for classifying lung tissue patterns using a deep convolutional neural network (CNN), with an architecture designed for the specific problem. In this study, we present an improved method for training the proposed network by transferring knowledge from the similar domain of general texture classification. Six publicly available texture databases are used to pretrain networks with the proposed architecture, which are then fine-tuned on the lung tissue data. The resulting CNNs are combined in an ensemble and their fused knowledge is compressed back to a network with the original architecture. The proposed approach resulted in an absolute increase of about 2% in the performance of the proposed CNN. The results demonstrate the potential of transfer learning in the field of medical image analysis, indicate the textural nature of the problem and show that the method used for training a network can be as important as designing its architecture.

A hybrid CNN feature model for pulmonary nodule malignancy risk differentiation.

PubMed

Wang, Huafeng; Zhao, Tingting; Li, Lihong Connie; Pan, Haixia; Liu, Wanquan; Gao, Haoqi; Han, Fangfang; Wang, Yuehai; Qi, Yifan; Liang, Zhengrong

2018-01-01

The malignancy risk differentiation of pulmonary nodule is one of the most challenge tasks of computer-aided diagnosis (CADx). Most recently reported CADx methods or schemes based on texture and shape estimation have shown relatively satisfactory on differentiating the risk level of malignancy among the nodules detected in lung cancer screening. However, the existing CADx schemes tend to detect and analyze characteristics of pulmonary nodules from a statistical perspective according to local features only. Enlightened by the currently prevailing learning ability of convolutional neural network (CNN), which simulates human neural network for target recognition and our previously research on texture features, we present a hybrid model that takes into consideration of both global and local features for pulmonary nodule differentiation using the largest public database founded by the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). By comparing three types of CNN models in which two of them were newly proposed by us, we observed that the multi-channel CNN model yielded the best discrimination in capacity of differentiating malignancy risk of the nodules based on the projection of distributions of extracted features. Moreover, CADx scheme using the new multi-channel CNN model outperformed our previously developed CADx scheme using the 3D texture feature analysis method, which increased the computed area under a receiver operating characteristic curve (AUC) from 0.9441 to 0.9702.

Imaging manifestations of autoimmune disease-associated lymphoproliferative disorders of the lung.

PubMed

Lee, Geewon; Lee, Ho Yun; Lee, Kyung Soo; Lee, Kyung Jong; Cha, Hoon-Suk; Han, Joungho; Chung, Man Pyo

2013-10-01

Lymphoproliferative disorders (LPDs) may involve intrathoracic organs in patients with autoimmune disease, but little is known about the radiologic manifestations of autoimmune disease-associated LPDs (ALPDs) of the lungs. The purpose of our work was to identify the radiologic characteristics of pulmonary involvement in ALPDs. A comprehensive search for PubMed database was conducted with the combination of MeSH words. All articles which had original images or description on radiologic findings were included in this analysis. Also, CT images of eight patients with biopsy-proven lymphoproliferative disorder observed from our institution were added. Overall, 44 cases of ALPD were identified, and consisted of 24 cases of bronchus-associated lymphoid tissue lymphoma (BALToma), eight cases of non-Hodgkin's lymphoma (NHL), six cases of lymphoid interstitial pneumonia (LIP), two cases of nodular lymphoid hyperplasia, two cases of unclassified lymphoproliferative disorder, and one case each of lymphomatoid granulomatosis and hyperblastic BALT. Multiple nodules (n = 14, 32 %) and single mass (n = 8, 18 %) were the predominant radiologic manifestations. The imaging findings conformed to previously described findings of BALToma, NHL, or LIP. Data suggest that BALToma, NHL, and LIP are the predominant ALPDs of the lung, and ALPD generally shared common radiologic features with sporadic LPDs. Familiarity with ALPDs and their imaging findings may enable radiologists or clinicians to include the disease as a potential differential diagnosis and thus, to prompt early biopsy followed by appropriate treatment.

Reproducibility and Prognosis of Quantitative Features Extracted from CT Images12

PubMed Central

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

2014-01-01

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

Segmentation of pulmonary nodules in three-dimensional CT images by use of a spiral-scanning technique

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

Wang Jiahui; Engelmann, Roger; Li Qiang

2007-12-15

Accurate segmentation of pulmonary nodules in computed tomography (CT) is an important and difficult task for computer-aided diagnosis of lung cancer. Therefore, the authors developed a novel automated method for accurate segmentation of nodules in three-dimensional (3D) CT. First, a volume of interest (VOI) was determined at the location of a nodule. To simplify nodule segmentation, the 3D VOI was transformed into a two-dimensional (2D) image by use of a key 'spiral-scanning' technique, in which a number of radial lines originating from the center of the VOI spirally scanned the VOI from the 'north pole' to the 'south pole'. Themore » voxels scanned by the radial lines provided a transformed 2D image. Because the surface of a nodule in the 3D image became a curve in the transformed 2D image, the spiral-scanning technique considerably simplified the segmentation method and enabled reliable segmentation results to be obtained. A dynamic programming technique was employed to delineate the 'optimal' outline of a nodule in the 2D image, which corresponded to the surface of the nodule in the 3D image. The optimal outline was then transformed back into 3D image space to provide the surface of the nodule. An overlap between nodule regions provided by computer and by the radiologists was employed as a performance metric for evaluating the segmentation method. The database included two Lung Imaging Database Consortium (LIDC) data sets that contained 23 and 86 CT scans, respectively, with 23 and 73 nodules that were 3 mm or larger in diameter. For the two data sets, six and four radiologists manually delineated the outlines of the nodules as reference standards in a performance evaluation for nodule segmentation. The segmentation method was trained on the first and was tested on the second LIDC data sets. The mean overlap values were 66% and 64% for the nodules in the first and second LIDC data sets, respectively, which represented a higher performance level than those of two existing segmentation methods that were also evaluated by use of the LIDC data sets. The segmentation method provided relatively reliable results for pulmonary nodule segmentation and would be useful for lung cancer quantification, detection, and diagnosis.« less

SU-E-J-112: Intensity-Based Pulmonary Image Registration: An Evaluation Study

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

Yang, F; Meyer, J; Sandison, G

2015-06-15

Purpose: Accurate alignment of thoracic CT images is essential for dose tracking and to safely implement adaptive radiotherapy in lung cancers. At the same time it is challenging given the highly elastic nature of lung tissue deformations. The objective of this study was to assess the performances of three state-of-art intensity-based algorithms in terms of their ability to register thoracic CT images subject to affine, barrel, and sinusoid transformation. Methods: Intensity similarity measures of the evaluated algorithms contained sum-of-squared difference (SSD), local mutual information (LMI), and residual complexity (RC). Five thoracic CT scans obtained from the EMPIRE10 challenge database weremore » included and served as reference images. Each CT dataset was distorted by realistic affine, barrel, and sinusoid transformations. Registration performances of the three algorithms were evaluated for each distortion type in terms of intensity root mean square error (IRMSE) between the reference and registered images in the lung regions. Results: For affine distortions, the three algorithms differed significantly in registration of thoracic images both visually and nominally in terms of IRMSE with a mean of 0.011 for SSD, 0.039 for RC, and 0.026 for LMI (p<0.01; Kruskal-Wallis test). For barrel distortion, the three algorithms showed nominally no significant difference in terms of IRMSE with a mean of 0.026 for SSD, 0.086 for RC, and 0.054 for LMI (p=0.16) . A significant difference was seen for sinusoid distorted thoracic CT data with mean lung IRMSE of 0.039 for SSD, 0.092 for RC, and 0.035 for LMI (p=0.02). Conclusion: Pulmonary deformations might vary to a large extent in nature in a daily clinical setting due to factors ranging from anatomy variations to respiratory motion to image quality. It can be appreciated from the results of the present study that the suitability of application of a particular algorithm for pulmonary image registration is deformation-dependent.« less

Development of an improved CAD scheme for automated detection of lung nodules in digital chest images.

PubMed

Xu, X W; Doi, K; Kobayashi, T; MacMahon, H; Giger, M L

1997-09-01

Lung cancer is the leading cause of cancer deaths in men and women in the United States, with a 5-year survival rate of only about 13%. However, this survival rate can be improved to 47% if the disease is diagnosed and treated at an early stage. In this study, we developed an improved computer-aided diagnosis (CAD) scheme for the automated detection of lung nodules in digital chest images to assist radiologists, who could miss up to 30% of the actually positive cases in their daily practice. Two hundred PA chest radiographs, 100 normals and 100 abnormals, were used as the database for our study. The presence of nodules in the 100 abnormal cases was confirmed by two experienced radiologists on the basis of CT scans or radiographic follow-up. In our CAD scheme, nodule candidates were selected initially by multiple gray-level thresholding of the difference image (which corresponds to the subtraction of a signal-enhanced image and a signal-suppressed image) and then classified into six groups. A large number of false positives were eliminated by adaptive rule-based tests and an artificial neural network (ANN). The CAD scheme achieved, on average, a sensitivity of 70% with 1.7 false positives per chest image, a performance which was substantially better as compared with other studies. The CPU time for the processing of one chest image was about 20 seconds on an IBM RISC/6000 Powerstation 590. We believe that the CAD scheme with the current performance is ready for initial clinical evaluation.

Vibration response imaging: protocol for a systematic review.

PubMed

Berry, Marc P; Camporota, Luigi; Ntoumenopoulos, George

2013-09-25

The concept of lung sounds conveying information regarding lung physiology has been used extensively in clinical practice, particularly with physical auscultation using a stethoscope. Advances in computer technology have facilitated the construction of dynamic visual images derived from recorded lung sounds. Arguably, the most significant progress in this field was the development of the commercially available vibration response imaging (VRI) (Deep Breeze Ltd, Or-Akiva, Israel). This device provides a non-invasive, dynamic image of both lungs constructed from sounds detected from the lungs using surface sensors. In the literature, VRI has been utilized in a multitude of clinical and research settings. This systematic review aims to address three study questions relating to whether VRI can be used as an evaluative device, whether the images generated can be characterized, and which tools and measures have been used to assess these images. This systematic review will involve implementing search strategies in five online journal databases in order to extract articles relating to the application of VRI. Appropriate articles will be identified against a set of pre-determined eligibility criteria and assessed for methodological quality using a standardized scale. Included articles will have data extracted by the reviewers using a standardized evidence table. A narrative synthesis based on a standardized framework will be conducted, clustering evidence into three main groups; one for each of the study questions. A meta-analysis will be conducted if two or more research articles meet pre-determined criteria that allow quantitative synthesis to take place. This systematic review aims to provide a complete overview of the scope of VRI in the clinical and research settings, as well as to discuss methods to interpret the data obtained from VRI. The systematic review intends to help clinicians to make informed decisions on the clinical applicability of the device, to allow researchers to identify further potential avenues of investigation, and to provide methods for the evaluation and interpretation of dynamic and static images. The publication and registration of this review with PROSPERO provides transparency and accountability, and facilitates the appraisal of the proposed systematic review against the original design. PROSPERO registration number: CRD42013003751.

Correlation of emphysema score with perceived malignancy of pulmonary nodules: a multi-observer study using the LIDC-IDRI CT lung database

NASA Astrophysics Data System (ADS)

Wiemker, Rafael; Bülow, Thomas; Blaffert, Thomas; Dharaiya, Ekta

2009-02-01

Presence of emphysema is recognized to be one of the single most significant risk factors in risk models for the prediction of lung cancer. Therefore, an automatically computed emphysema score would be a prime candidate as an additional numerical feature for computer aided diagnosis (CADx) for indeterminate pulmonary nodules. We have applied several histogram-based emphysema scores to 460 thoracic CT scans from the IDRI CT lung image database, and analyzed the emphysema scores in conjunction with 3000 nodule malignancy ratings of 1232 pulmonary nodules made by expert observers. Despite the emphysema being a known risk factor, we have not found any impact on the readers' malignancy rating of nodules found in a patient with higher emphysema score. We have also not found any correlation between the number of expert-detected nodules in a patient and his emphysema score, or the relative craniocaudal location of the nodules and their malignancy rating. The inter-observer agreement of the expert ratings was excellent on nodule diameter (as derived from manual delineations), good for calcification, and only modest for malignancy and shape descriptions such as spiculation, lobulation, margin, etc.

Using YOLO based deep learning network for real time detection and localization of lung nodules from low dose CT scans

NASA Astrophysics Data System (ADS)

Ramachandran S., Sindhu; George, Jose; Skaria, Shibon; V. V., Varun

2018-02-01

Lung cancer is the leading cause of cancer related deaths in the world. The survival rate can be improved if the presence of lung nodules are detected early. This has also led to more focus being given to computer aided detection (CAD) and diagnosis of lung nodules. The arbitrariness of shape, size and texture of lung nodules is a challenge to be faced when developing these detection systems. In the proposed work we use convolutional neural networks to learn the features for nodule detection, replacing the traditional method of handcrafting features like geometric shape or texture. Our network uses the DetectNet architecture based on YOLO (You Only Look Once) to detect the nodules in CT scans of lung. In this architecture, object detection is treated as a regression problem with a single convolutional network simultaneously predicting multiple bounding boxes and class probabilities for those boxes. By performing training using chest CT scans from Lung Image Database Consortium (LIDC), NVIDIA DIGITS and Caffe deep learning framework, we show that nodule detection using this single neural network can result in reasonably low false positive rates with high sensitivity and precision.

LungMAP: The Molecular Atlas of Lung Development Program

PubMed Central

Ardini-Poleske, Maryanne E.; Ansong, Charles; Carson, James P.; Corley, Richard A.; Deutsch, Gail H.; Hagood, James S.; Kaminski, Naftali; Mariani, Thomas J.; Potter, Steven S.; Pryhuber, Gloria S.; Warburton, David; Whitsett, Jeffrey A.; Palmer, Scott M.; Ambalavanan, Namasivayam

2017-01-01

The National Heart, Lung, and Blood Institute is funding an effort to create a molecular atlas of the developing lung (LungMAP) to serve as a research resource and public education tool. The lung is a complex organ with lengthy development time driven by interactive gene networks and dynamic cross talk among multiple cell types to control and coordinate lineage specification, cell proliferation, differentiation, migration, morphogenesis, and injury repair. A better understanding of the processes that regulate lung development, particularly alveologenesis, will have a significant impact on survival rates for premature infants born with incomplete lung development and will facilitate lung injury repair and regeneration in adults. A consortium of four research centers, a data coordinating center, and a human tissue repository provides high-quality molecular data of developing human and mouse lungs. LungMAP includes mouse and human data for cross correlation of developmental processes across species. LungMAP is generating foundational data and analysis, creating a web portal for presentation of results and public sharing of data sets, establishing a repository of young human lung tissues obtained through organ donor organizations, and developing a comprehensive lung ontology that incorporates the latest findings of the consortium. The LungMAP website (www.lungmap.net) currently contains more than 6,000 high-resolution lung images and transcriptomic, proteomic, and lipidomic human and mouse data and provides scientific information to stimulate interest in research careers for young audiences. This paper presents a brief description of research conducted by the consortium, database, and portal development and upcoming features that will enhance the LungMAP experience for a community of users. PMID:28798251

The Lung Image Database Consortium (LIDC): Ensuring the integrity of expert-defined “truth”

PubMed Central

Armato, Samuel G.; Roberts, Rachael Y.; McNitt-Gray, Michael F.; Meyer, Charles R.; Reeves, Anthony P.; McLennan, Geoffrey; Engelmann, Roger M.; Bland, Peyton H.; Aberle, Denise R.; Kazerooni, Ella A.; MacMahon, Heber; van Beek, Edwin J.R.; Yankelevitz, David; Croft, Barbara Y.; Clarke, Laurence P.

2007-01-01

Rationale and Objectives Computer-aided diagnostic (CAD) systems fundamentally require the opinions of expert human observers to establish “truth” for algorithm development, training, and testing. The integrity of this “truth,” however, must be established before investigators commit to this “gold standard” as the basis for their research. The purpose of this study was to develop a quality assurance (QA) model as an integral component of the “truth” collection process concerning the location and spatial extent of lung nodules observed on computed tomography (CT) scans to be included in the Lung Image Database Consortium (LIDC) public database. Materials and Methods One hundred CT scans were interpreted by four radiologists through a two-phase process. For the first of these reads (the “blinded read phase”), radiologists independently identified and annotated lesions, assigning each to one of three categories: “nodule ≥ 3mm,” “nodule < 3mm,” or “non-nodule ≥ 3mm.” For the second read (the “unblinded read phase”), the same radiologists independently evaluated the same CT scans but with all of the annotations from the previously performed blinded reads presented; each radiologist could add marks, edit or delete their own marks, change the lesion category of their own marks, or leave their marks unchanged. The post-unblinded-read set of marks was grouped into discrete nodules and subjected to the QA process, which consisted of (1) identification of potential errors introduced during the complete image annotation process (such as two marks on what appears to be a single lesion or an incomplete nodule contour) and (2) correction of those errors. Seven categories of potential error were defined; any nodule with a mark that satisfied the criterion for one of these categories was referred to the radiologist who assigned that mark for either correction or confirmation that the mark was intentional. Results A total of 105 QA issues were identified across 45 (45.0%) of the 100 CT scans. Radiologist review resulted in modifications to 101 (96.2%) of these potential errors. Twenty-one lesions erroneously marked as lung nodules after the unblinded reads had this designation removed through the QA process. Conclusion The establishment of “truth” must incorporate a QA process to guarantee the integrity of the datasets that will provide the basis for the development, training, and testing of CAD systems. PMID:18035275

Thoracic cavity definition for 3D PET/CT analysis and visualization.

PubMed

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

2015-07-01

X-ray computed tomography (CT) and positron emission tomography (PET) serve as the standard imaging modalities for lung-cancer management. CT gives anatomical details on diagnostic regions of interest (ROIs), while PET gives highly specific functional information. During the lung-cancer management process, a patient receives a co-registered whole-body PET/CT scan pair and a dedicated high-resolution chest CT scan. With these data, multimodal PET/CT ROI information can be gleaned to facilitate disease management. Effective image segmentation of the thoracic cavity, however, is needed to focus attention on the central chest. We present an automatic method for thoracic cavity segmentation from 3D CT scans. We then demonstrate how the method facilitates 3D ROI localization and visualization in patient multimodal imaging studies. Our segmentation method draws upon digital topological and morphological operations, active-contour analysis, and key organ landmarks. Using a large patient database, the method showed high agreement to ground-truth regions, with a mean coverage=99.2% and leakage=0.52%. Furthermore, it enabled extremely fast computation. For PET/CT lesion analysis, the segmentation method reduced ROI search space by 97.7% for a whole-body scan, or nearly 3 times greater than that achieved by a lung mask. Despite this reduction, we achieved 100% true-positive ROI detection, while also reducing the false-positive (FP) detection rate by >5 times over that achieved with a lung mask. Finally, the method greatly improved PET/CT visualization by eliminating false PET-avid obscurations arising from the heart, bones, and liver. In particular, PET MIP views and fused PET/CT renderings depicted unprecedented clarity of the lesions and neighboring anatomical structures truly relevant to lung-cancer assessment. Copyright © 2015 Elsevier Ltd. All rights reserved.

Thoracic Cavity Definition for 3D PET/CT Analysis and Visualization

PubMed Central

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

2015-01-01

X-ray computed tomography (CT) and positron emission tomography (PET) serve as the standard imaging modalities for lung-cancer management. CT gives anatomical detail on diagnostic regions of interest (ROIs), while PET gives highly specific functional information. During the lung-cancer management process, a patient receives a co-registered whole-body PET/CT scan pair and a dedicated high-resolution chest CT scan. With these data, multimodal PET/CT ROI information can be gleaned to facilitate disease management. Effective image segmentation of the thoracic cavity, however, is needed to focus attention on the central chest. We present an automatic method for thoracic cavity segmentation from 3D CT scans. We then demonstrate how the method facilitates 3D ROI localization and visualization in patient multimodal imaging studies. Our segmentation method draws upon digital topological and morphological operations, active-contour analysis, and key organ landmarks. Using a large patient database, the method showed high agreement to ground-truth regions, with a mean coverage = 99.2% and leakage = 0.52%. Furthermore, it enabled extremely fast computation. For PET/CT lesion analysis, the segmentation method reduced ROI search space by 97.7% for a whole-body scan, or nearly 3 times greater than that achieved by a lung mask. Despite this reduction, we achieved 100% true-positive ROI detection, while also reducing the false-positive (FP) detection rate by >5 times over that achieved with a lung mask. Finally, the method greatly improved PET/CT visualization by eliminating false PET-avid obscurations arising from the heart, bones, and liver. In particular, PET MIP views and fused PET/CT renderings depicted unprecedented clarity of the lesions and neighboring anatomical structures truly relevant to lung-cancer assessment. PMID:25957746

Interstitial lung disease induced by fluoxetine: Systematic review of literature and analysis of Vigiaccess, Eudravigilance and a national pharmacovigilance database.

PubMed

Deidda, Arianna; Pisanu, Claudia; Micheletto, Laura; Bocchetta, Alberto; Del Zompo, Maria; Stochino, Maria Erminia

2017-06-01

We investigated a pulmonary adverse drug reaction possibly induced by fluoxetine, the Interstitial Lung Disease, by performing a systematic review of published case reports on this subject, a review of the World Health Organization VigiAccess database, of the European EudraVigilance database and of a national Pharmacovigilance database (Italian Pharmacovigilance Network). The research found a total of seven cases linking fluoxetine to Interstitial Lung Disease in the literature. 36 cases of interstitial lung disease related to fluoxetine were retrieved from the VigiAccess database (updated to July 2016), and 36 reports were found in EudraVigilance database (updated to June 2016). In the Italian Pharmacovigilance database (updated to August 2016), we found only one case of Interstitial Lung Disease, codified as "pulmonary disease". Our investigation shows that fluoxetine might be considered as a possible cause of Interstitial Lung Disease. In particular, although here we do not discuss the assessment of benefits and harms of fluoxetine, since this antidepressant is widely used, our review suggests that fluoxetine-induced Interstitial Lung Disease should be considered in patients with dyspnea, associated or not with dry cough, who are treated with this drug. An early withdrawn of fluoxetine could be useful to obtain a complete remission of this adverse drug reaction and special attention should be particularly devoted to long-term therapy, and to female and elderly patients. Although the spontaneous reporting system is affected by important limitations, drug post- marketing surveillance represents an important tool to evaluate the real world effectiveness and safety of drugs. Copyright © 2017 Elsevier Ltd. All rights reserved.

A computerized scheme for lung nodule detection in multiprojection chest radiography

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

Guo Wei; Li Qiang; Boyce, Sarah J.

2012-04-15

Purpose: Our previous study indicated that multiprojection chest radiography could significantly improve radiologists' performance for lung nodule detection in clinical practice. In this study, the authors further verify that multiprojection chest radiography can greatly improve the performance of a computer-aided diagnostic (CAD) scheme. Methods: Our database consisted of 59 subjects, including 43 subjects with 45 nodules and 16 subjects without nodules. The 45 nodules included 7 real and 38 simulated ones. The authors developed a conventional CAD scheme and a new fusion CAD scheme to detect lung nodules. The conventional CAD scheme consisted of four steps for (1) identification ofmore » initial nodule candidates inside lungs, (2) nodule candidate segmentation based on dynamic programming, (3) extraction of 33 features from nodule candidates, and (4) false positive reduction using a piecewise linear classifier. The conventional CAD scheme processed each of the three projection images of a subject independently and discarded the correlation information between the three images. The fusion CAD scheme included the four steps in the conventional CAD scheme and two additional steps for (5) registration of all candidates in the three images of a subject, and (6) integration of correlation information between the registered candidates in the three images. The integration step retained all candidates detected at least twice in the three images of a subject and removed those detected only once in the three images as false positives. A leave-one-subject-out testing method was used for evaluation of the performance levels of the two CAD schemes. Results: At the sensitivities of 70%, 65%, and 60%, our conventional CAD scheme reported 14.7, 11.3, and 8.6 false positives per image, respectively, whereas our fusion CAD scheme reported 3.9, 1.9, and 1.2 false positives per image, and 5.5, 2.8, and 1.7 false positives per patient, respectively. The low performance of the conventional CAD scheme may be attributed to the high noise level in chest radiography, and the small size and low contrast of most nodules. Conclusions: This study indicated that the fusion of correlation information in multiprojection chest radiography can markedly improve the performance of CAD scheme for lung nodule detection.« less

Histopathological Image Classification using Discriminative Feature-oriented Dictionary Learning

PubMed Central

Vu, Tiep Huu; Mousavi, Hojjat Seyed; Monga, Vishal; Rao, Ganesh; Rao, UK Arvind

2016-01-01

In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structures. In this paper, we propose an automatic feature discovery framework via learning class-specific dictionaries and present a low-complexity method for classification and disease grading in histopathology. Essentially, our Discriminative Feature-oriented Dictionary Learning (DFDL) method learns class-specific dictionaries such that under a sparsity constraint, the learned dictionaries allow representing a new image sample parsimoniously via the dictionary corresponding to the class identity of the sample. At the same time, the dictionary is designed to be poorly capable of representing samples from other classes. Experiments on three challenging real-world image databases: 1) histopathological images of intraductal breast lesions, 2) mammalian kidney, lung and spleen images provided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, reveal the merits of our proposal over state-of-the-art alternatives. Moreover, we demonstrate that DFDL exhibits a more graceful decay in classification accuracy against the number of training images which is highly desirable in practice where generous training is often not available. PMID:26513781

SU-E-QI-16: Reproducibility of Computed Tomography Quantitative Structural Features Using the FDA Thoracic Phantom Image Database

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

Budzevich, M; Grove, O; Balagurunathan, Y

Purpose: To assess the reproducibility of quantitative structural features using images from the computed tomography thoracic FDA phantom database under different scanning conditions. Methods: Development of quantitative image features to describe lesion shape and size, beyond conventional RECIST measures, is an evolving area of research in need of benchmarking standards. Gavrielides et al. (2010) scanned a FDA-developed thoracic phantom with nodules of various Hounsfield units (HU) values, shapes and sizes close to vascular structures using several scanners and varying scanning conditions/parameters; these images are in the public domain. We tested six structural features, namely, Convexity, Perimeter, Major Axis, Minor Axis,more » Extent Mean and Eccentricity, to characterize lung nodules. Convexity measures lesion irregularity referenced to a convex surface. Previously, we showed it to have prognostic value in lung adenocarcinoma. The above metrics and RECIST measures were evaluated on three spiculated (8mm/-300HU, 12mm/+30HU and 15mm/+30HU) and two non-spiculated (8mm/+100HU and 10mm/+100HU) nodules (from layout 2) imaged at three different mAs values: 25, 100 and 200 mAs; on a Phillips scanner (16-slice Mx8000-IDT; 3mm slice thickness). The nodules were segmented semi-automatically using a commercial software tool; the same HU range was used for all nodules. Results: Analysis showed convexity having the lowest maximum coefficient of variation (MCV): 1.1% and 0.6% for spiculated and non-spiculated nodules, respectively, much lower compared to RECIST Major and Minor axes whose MCV were 10.1% and 13.4% for spiculated, and 1.9% and 2.3% for non-spiculated nodules, respectively, across the various mAs. MCVs were consistently larger for speculated nodules. In general, the dependence of structural features on mAs (noise) was low. Conclusion: The FDA phantom CT database may be used for benchmarking of structural features for various scanners and scanning conditions; we used only a small fraction of available data. Our feature convexity outperformed other structural features including RECIST measures.« less

A new 3D texture feature based computer-aided diagnosis approach to differentiate pulmonary nodules

NASA Astrophysics Data System (ADS)

Han, Fangfang; Wang, Huafeng; Song, Bowen; Zhang, Guopeng; Lu, Hongbing; Moore, William; Zhao, Hong; Liang, Zhengrong

2013-02-01

To distinguish malignant pulmonary nodules from benign ones is of much importance in computer-aided diagnosis of lung diseases. Compared to many previous methods which are based on shape or growth assessing of nodules, this proposed three-dimensional (3D) texture feature based approach extracted fifty kinds of 3D textural features from gray level, gradient and curvature co-occurrence matrix, and more derivatives of the volume data of the nodules. To evaluate the presented approach, the Lung Image Database Consortium public database was downloaded. Each case of the database contains an annotation file, which indicates the diagnosis results from up to four radiologists. In order to relieve partial-volume effect, interpolation process was carried out to those volume data with image slice thickness more than 1mm, and thus we had categorized the downloaded datasets to five groups to validate the proposed approach, one group of thickness less than 1mm, two types of thickness range from 1mm to 1.25mm and greater than 1.25mm (each type contains two groups, one with interpolation and the other without). Since support vector machine is based on statistical learning theory and aims to learn for predicting future data, so it was chosen as the classifier to perform the differentiation task. The measure on the performance was based on the area under the curve (AUC) of Receiver Operating Characteristics. From 284 nodules (122 malignant and 162 benign ones), the validation experiments reported a mean of 0.9051 and standard deviation of 0.0397 for the AUC value on average over 100 randomizations.

LungMAP: The Molecular Atlas of Lung Development Program.

PubMed

Ardini-Poleske, Maryanne E; Clark, Robert F; Ansong, Charles; Carson, James P; Corley, Richard A; Deutsch, Gail H; Hagood, James S; Kaminski, Naftali; Mariani, Thomas J; Potter, Steven S; Pryhuber, Gloria S; Warburton, David; Whitsett, Jeffrey A; Palmer, Scott M; Ambalavanan, Namasivayam

2017-11-01

The National Heart, Lung, and Blood Institute is funding an effort to create a molecular atlas of the developing lung (LungMAP) to serve as a research resource and public education tool. The lung is a complex organ with lengthy development time driven by interactive gene networks and dynamic cross talk among multiple cell types to control and coordinate lineage specification, cell proliferation, differentiation, migration, morphogenesis, and injury repair. A better understanding of the processes that regulate lung development, particularly alveologenesis, will have a significant impact on survival rates for premature infants born with incomplete lung development and will facilitate lung injury repair and regeneration in adults. A consortium of four research centers, a data coordinating center, and a human tissue repository provides high-quality molecular data of developing human and mouse lungs. LungMAP includes mouse and human data for cross correlation of developmental processes across species. LungMAP is generating foundational data and analysis, creating a web portal for presentation of results and public sharing of data sets, establishing a repository of young human lung tissues obtained through organ donor organizations, and developing a comprehensive lung ontology that incorporates the latest findings of the consortium. The LungMAP website (www.lungmap.net) currently contains more than 6,000 high-resolution lung images and transcriptomic, proteomic, and lipidomic human and mouse data and provides scientific information to stimulate interest in research careers for young audiences. This paper presents a brief description of research conducted by the consortium, database, and portal development and upcoming features that will enhance the LungMAP experience for a community of users. Copyright © 2017 the American Physiological Society.

TH-CD-207A-08: Simulated Real-Time Image Guidance for Lung SBRT Patients Using Scatter Imaging

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

Redler, G; Cifter, G; Templeton, A

2016-06-15

Purpose: To develop a comprehensive Monte Carlo-based model for the acquisition of scatter images of patient anatomy in real-time, during lung SBRT treatment. Methods: During SBRT treatment, images of patient anatomy can be acquired from scattered radiation. To rigorously examine the utility of scatter images for image guidance, a model is developed using MCNP code to simulate scatter images of phantoms and lung cancer patients. The model is validated by comparing experimental and simulated images of phantoms of different complexity. The differentiation between tissue types is investigated by imaging objects of known compositions (water, lung, and bone equivalent). A lungmore » tumor phantom, simulating materials and geometry encountered during lung SBRT treatments, is used to investigate image noise properties for various quantities of delivered radiation (monitor units(MU)). Patient scatter images are simulated using the validated simulation model. 4DCT patient data is converted to an MCNP input geometry accounting for different tissue composition and densities. Lung tumor phantom images acquired with decreasing imaging time (decreasing MU) are used to model the expected noise amplitude in patient scatter images, producing realistic simulated patient scatter images with varying temporal resolution. Results: Image intensity in simulated and experimental scatter images of tissue equivalent objects (water, lung, bone) match within the uncertainty (∼3%). Lung tumor phantom images agree as well. Specifically, tumor-to-lung contrast matches within the uncertainty. The addition of random noise approximating quantum noise in experimental images to simulated patient images shows that scatter images of lung tumors can provide images in as fast as 0.5 seconds with CNR∼2.7. Conclusions: A scatter imaging simulation model is developed and validated using experimental phantom scatter images. Following validation, lung cancer patient scatter images are simulated. These simulated patient images demonstrate the clinical utility of scatter imaging for real-time tumor tracking during lung SBRT.« less

Correlation coefficient based supervised locally linear embedding for pulmonary nodule recognition.

PubMed

Wu, Panpan; Xia, Kewen; Yu, Hengyong

2016-11-01

Dimensionality reduction techniques are developed to suppress the negative effects of high dimensional feature space of lung CT images on classification performance in computer aided detection (CAD) systems for pulmonary nodule detection. An improved supervised locally linear embedding (SLLE) algorithm is proposed based on the concept of correlation coefficient. The Spearman's rank correlation coefficient is introduced to adjust the distance metric in the SLLE algorithm to ensure that more suitable neighborhood points could be identified, and thus to enhance the discriminating power of embedded data. The proposed Spearman's rank correlation coefficient based SLLE (SC(2)SLLE) is implemented and validated in our pilot CAD system using a clinical dataset collected from the publicly available lung image database consortium and image database resource initiative (LICD-IDRI). Particularly, a representative CAD system for solitary pulmonary nodule detection is designed and implemented. After a sequential medical image processing steps, 64 nodules and 140 non-nodules are extracted, and 34 representative features are calculated. The SC(2)SLLE, as well as SLLE and LLE algorithm, are applied to reduce the dimensionality. Several quantitative measurements are also used to evaluate and compare the performances. Using a 5-fold cross-validation methodology, the proposed algorithm achieves 87.65% accuracy, 79.23% sensitivity, 91.43% specificity, and 8.57% false positive rate, on average. Experimental results indicate that the proposed algorithm outperforms the original locally linear embedding and SLLE coupled with the support vector machine (SVM) classifier. Based on the preliminary results from a limited number of nodules in our dataset, this study demonstrates the great potential to improve the performance of a CAD system for nodule detection using the proposed SC(2)SLLE. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

The Evolving MCART Multimodal Imaging Core: Establishing a protocol for Computed Tomography and Echocardiography in the Rhesus macaque to perform longitudinal analysis of radiation-induced organ injury

PubMed Central

de Faria, Eduardo B.; Barrow, Kory R.; Ruehle, Bradley T.; Parker, Jordan T.; Swartz, Elisa; Taylor-Howell, Cheryl; Kieta, Kaitlyn M.; Lees, Cynthia J.; Sleeper, Meg M.; Dobbin, Travis; Baron, Adam D.; Mohindra, Pranshu; MacVittie, Thomas J.

2015-01-01

Computed Tomography (CT) and Echocardiography (EC) are two imaging modalities that produce critical longitudinal data that can be analyzed for radiation-induced organ-specific injury to the lung and heart. The Medical Countermeasures Against Radiological Threats (MCART) consortium has a well-established animal model research platform that includes nonhuman primate (NHP) models of the acute radiation syndrome and the delayed effects of acute radiation exposure. These models call for a definition of the latency, incidence, severity, duration, and resolution of different organ-specific radiation-induced subsyndromes. The pulmonary subsyndromes and cardiac effects are a pair of inter-dependent syndromes impacted by exposure to potentially lethal doses of radiation. Establishing a connection between these will reveal important information about their interaction and progression of injury and recovery. Herein, we demonstrate the use of CT and EC data in the rhesus macaque models to define delayed organ injury thereby establishing: a) consistent and reliable methodology to assess radiation-induced damage to the lung and heart, b) an extensive database in normal age-matched NHP for key primary and secondary endpoints, c) identified problematic variables in imaging techniques and proposed solutions to maintain data integrity and d) initiated longitudinal analysis of potentially lethal radiation-induced damage to the lung and heart. PMID:26425907

A Segmentation Method for Lung Parenchyma Image Sequences Based on Superpixels and a Self-Generating Neural Forest

PubMed Central

Liao, Xiaolei; Zhao, Juanjuan; Jiao, Cheng; Lei, Lei; Qiang, Yan; Cui, Qiang

2016-01-01

Background Lung parenchyma segmentation is often performed as an important pre-processing step in the computer-aided diagnosis of lung nodules based on CT image sequences. However, existing lung parenchyma image segmentation methods cannot fully segment all lung parenchyma images and have a slow processing speed, particularly for images in the top and bottom of the lung and the images that contain lung nodules. Method Our proposed method first uses the position of the lung parenchyma image features to obtain lung parenchyma ROI image sequences. A gradient and sequential linear iterative clustering algorithm (GSLIC) for sequence image segmentation is then proposed to segment the ROI image sequences and obtain superpixel samples. The SGNF, which is optimized by a genetic algorithm (GA), is then utilized for superpixel clustering. Finally, the grey and geometric features of the superpixel samples are used to identify and segment all of the lung parenchyma image sequences. Results Our proposed method achieves higher segmentation precision and greater accuracy in less time. It has an average processing time of 42.21 seconds for each dataset and an average volume pixel overlap ratio of 92.22 ± 4.02% for four types of lung parenchyma image sequences. PMID:27532214

Techniques for virtual lung nodule insertion: volumetric and morphometric comparison of projection-based and image-based methods for quantitative CT

NASA Astrophysics Data System (ADS)

Robins, Marthony; Solomon, Justin; Sahbaee, Pooyan; Sedlmair, Martin; Choudhury, Kingshuk Roy; Pezeshk, Aria; Sahiner, Berkman; Samei, Ehsan

2017-09-01

Virtual nodule insertion paves the way towards the development of standardized databases of hybrid CT images with known lesions. The purpose of this study was to assess three methods (an established and two newly developed techniques) for inserting virtual lung nodules into CT images. Assessment was done by comparing virtual nodule volume and shape to the CT-derived volume and shape of synthetic nodules. 24 synthetic nodules (three sizes, four morphologies, two repeats) were physically inserted into the lung cavity of an anthropomorphic chest phantom (KYOTO KAGAKU). The phantom was imaged with and without nodules on a commercial CT scanner (SOMATOM Definition Flash, Siemens) using a standard thoracic CT protocol at two dose levels (1.4 and 22 mGy CTDIvol). Raw projection data were saved and reconstructed with filtered back-projection and sinogram affirmed iterative reconstruction (SAFIRE, strength 5) at 0.6 mm slice thickness. Corresponding 3D idealized, virtual nodule models were co-registered with the CT images to determine each nodule’s location and orientation. Virtual nodules were voxelized, partial volume corrected, and inserted into nodule-free CT data (accounting for system imaging physics) using two methods: projection-based Technique A, and image-based Technique B. Also a third Technique C based on cropping a region of interest from the acquired image of the real nodule and blending it into the nodule-free image was tested. Nodule volumes were measured using a commercial segmentation tool (iNtuition, TeraRecon, Inc.) and deformation was assessed using the Hausdorff distance. Nodule volumes and deformations were compared between the idealized, CT-derived and virtual nodules using a linear mixed effects regression model which utilized the mean, standard deviation, and coefficient of variation (Mea{{n}RHD} , ST{{D}RHD} and C{{V}RHD}{) }~ of the regional Hausdorff distance. Overall, there was a close concordance between the volumes of the CT-derived and virtual nodules. Percent differences between them were less than 3% for all insertion techniques and were not statistically significant in most cases. Correlation coefficient values were greater than 0.97. The deformation according to the Hausdorff distance was also similar between the CT-derived and virtual nodules with minimal statistical significance in the (C{{V}RHD} ) for Techniques A, B, and C. This study shows that both projection-based and image-based nodule insertion techniques yield realistic nodule renderings with statistical similarity to the synthetic nodules with respect to nodule volume and deformation. These techniques could be used to create a database of hybrid CT images containing nodules of known size, location and morphology.

Techniques for virtual lung nodule insertion: volumetric and morphometric comparison of projection-based and image-based methods for quantitative CT

PubMed Central

Robins, Marthony; Solomon, Justin; Sahbaee, Pooyan; Sedlmair, Martin; Choudhury, Kingshuk Roy; Pezeshk, Aria; Sahiner, Berkman; Samei, Ehsan

2017-01-01

Virtual nodule insertion paves the way towards the development of standardized databases of hybrid CT images with known lesions. The purpose of this study was to assess three methods (an established and two newly developed techniques) for inserting virtual lung nodules into CT images. Assessment was done by comparing virtual nodule volume and shape to the CT-derived volume and shape of synthetic nodules. 24 synthetic nodules (three sizes, four morphologies, two repeats) were physically inserted into the lung cavity of an anthropomorphic chest phantom (KYOTO KAGAKU). The phantom was imaged with and without nodules on a commercial CT scanner (SOMATOM Definition Flash, Siemens) using a standard thoracic CT protocol at two dose levels (1.4 and 22 mGy CTDIvol). Raw projection data were saved and reconstructed with filtered back-projection and sinogram affirmed iterative reconstruction (SAFIRE, strength 5) at 0.6 mm slice thickness. Corresponding 3D idealized, virtual nodule models were co-registered with the CT images to determine each nodule’s location and orientation. Virtual nodules were voxelized, partial volume corrected, and inserted into nodule-free CT data (accounting for system imaging physics) using two methods: projection-based Technique A, and image-based Technique B. Also a third Technique C based on cropping a region of interest from the acquired image of the real nodule and blending it into the nodule-free image was tested. Nodule volumes were measured using a commercial segmentation tool (iNtuition, TeraRecon, Inc.) and deformation was assessed using the Hausdorff distance. Nodule volumes and deformations were compared between the idealized, CT-derived and virtual nodules using a linear mixed effects regression model which utilized the mean, standard deviation, and coefficient of variation (MeanRHD, and STDRHD CVRHD) of the regional Hausdorff distance. Overall, there was a close concordance between the volumes of the CT-derived and virtual nodules. Percent differences between them were less than 3% for all insertion techniques and were not statistically significant in most cases. Correlation coefficient values were greater than 0.97. The deformation according to the Hausdorff distance was also similar between the CT-derived and virtual nodules with minimal statistical significance in the (CVRHD) for Techniques A, B, and C. This study shows that both projection-based and image-based nodule insertion techniques yield realistic nodule renderings with statistical similarity to the synthetic nodules with respect to nodule volume and deformation. These techniques could be used to create a database of hybrid CT images containing nodules of known size, location and morphology. PMID:28786399

Large image microscope array for the compilation of multimodality whole organ image databases.

PubMed

Namati, Eman; De Ryk, Jessica; Thiesse, Jacqueline; Towfic, Zaid; Hoffman, Eric; Mclennan, Geoffrey

2007-11-01

Three-dimensional, structural and functional digital image databases have many applications in education, research, and clinical medicine. However, to date, apart from cryosectioning, there have been no reliable means to obtain whole-organ, spatially conserving histology. Our aim was to generate a system capable of acquiring high-resolution images, featuring microscopic detail that could still be spatially correlated to the whole organ. To fulfill these objectives required the construction of a system physically capable of creating very fine whole-organ sections and collecting high-magnification and resolution digital images. We therefore designed a large image microscope array (LIMA) to serially section and image entire unembedded organs while maintaining the structural integrity of the tissue. The LIMA consists of several integrated components: a novel large-blade vibrating microtome, a 1.3 megapixel peltier cooled charge-coupled device camera, a high-magnification microscope, and a three axis gantry above the microtome. A custom control program was developed to automate the entire sectioning and automated raster-scan imaging sequence. The system is capable of sectioning unembedded soft tissue down to a thickness of 40 microm at specimen dimensions of 200 x 300 mm to a total depth of 350 mm. The LIMA system has been tested on fixed lung from sheep and mice, resulting in large high-quality image data sets, with minimal distinguishable disturbance in the delicate alveolar structures. Copyright 2007 Wiley-Liss, Inc.

Classification algorithm of lung lobe for lung disease cases based on multislice CT images

NASA Astrophysics Data System (ADS)

Matsuhiro, M.; Kawata, Y.; Niki, N.; Nakano, Y.; Mishima, M.; Ohmatsu, H.; Tsuchida, T.; Eguchi, K.; Kaneko, M.; Moriyama, N.

2011-03-01

With the development of multi-slice CT technology, to obtain an accurate 3D image of lung field in a short time is possible. To support that, a lot of image processing methods need to be developed. In clinical setting for diagnosis of lung cancer, it is important to study and analyse lung structure. Therefore, classification of lung lobe provides useful information for lung cancer analysis. In this report, we describe algorithm which classify lungs into lung lobes for lung disease cases from multi-slice CT images. The classification algorithm of lung lobes is efficiently carried out using information of lung blood vessel, bronchus, and interlobar fissure. Applying the classification algorithms to multi-slice CT images of 20 normal cases and 5 lung disease cases, we demonstrate the usefulness of the proposed algorithms.

Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization.

PubMed

Nishio, Mizuho; Nishizawa, Mitsuo; Sugiyama, Osamu; Kojima, Ryosuke; Yakami, Masahiro; Kuroda, Tomohiro; Togashi, Kaori

2018-01-01

We aimed to evaluate a computer-aided diagnosis (CADx) system for lung nodule classification focussing on (i) usefulness of the conventional CADx system (hand-crafted imaging feature + machine learning algorithm), (ii) comparison between support vector machine (SVM) and gradient tree boosting (XGBoost) as machine learning algorithms, and (iii) effectiveness of parameter optimization using Bayesian optimization and random search. Data on 99 lung nodules (62 lung cancers and 37 benign lung nodules) were included from public databases of CT images. A variant of the local binary pattern was used for calculating a feature vector. SVM or XGBoost was trained using the feature vector and its corresponding label. Tree Parzen Estimator (TPE) was used as Bayesian optimization for parameters of SVM and XGBoost. Random search was done for comparison with TPE. Leave-one-out cross-validation was used for optimizing and evaluating the performance of our CADx system. Performance was evaluated using area under the curve (AUC) of receiver operating characteristic analysis. AUC was calculated 10 times, and its average was obtained. The best averaged AUC of SVM and XGBoost was 0.850 and 0.896, respectively; both were obtained using TPE. XGBoost was generally superior to SVM. Optimal parameters for achieving high AUC were obtained with fewer numbers of trials when using TPE, compared with random search. Bayesian optimization of SVM and XGBoost parameters was more efficient than random search. Based on observer study, AUC values of two board-certified radiologists were 0.898 and 0.822. The results show that diagnostic accuracy of our CADx system was comparable to that of radiologists with respect to classifying lung nodules.

A probabilistic model of emphysema based on granulometry analysis

NASA Astrophysics Data System (ADS)

Marcos, J. V.; Nava, R.; Cristobal, G.; Munoz-Barrutia, A.; Escalante-Ramírez, B.; Ortiz-de-Solórzano, C.

2013-11-01

Emphysema is associated with the destruction of lung parenchyma, resulting in abnormal enlargement of airspaces. Accurate quantification of emphysema is required for a better understanding of the disease as well as for the assessment of drugs and treatments. In the present study, a novel method for emphysema characterization from histological lung images is proposed. Elastase-induced mice were used to simulate the effect of emphysema on the lungs. A database composed of 50 normal and 50 emphysematous lung patches of size 512 x 512 pixels was used in our experiments. The purpose is to automatically identify those patches containing emphysematous tissue. The proposed approach is based on the use of granulometry analysis, which provides the pattern spectrum describing the distribution of airspaces in the lung region under evaluation. The profile of the spectrum was summarized by a set of statistical features. A logistic regression model was then used to estimate the probability for a patch to be emphysematous from this feature set. An accuracy of 87% was achieved by our method in the classification between normal and emphysematous samples. This result shows the utility of our granulometry-based method to quantify the lesions due to emphysema.

Noncalcified Lung Nodules: Volumetric Assessment with Thoracic CT

PubMed Central

Gavrielides, Marios A.; Kinnard, Lisa M.; Myers, Kyle J.; Petrick, Nicholas

2009-01-01

Lung nodule volumetry is used for nodule diagnosis, as well as for monitoring tumor response to therapy. Volume measurement precision and accuracy depend on a number of factors, including image-acquisition and reconstruction parameters, nodule characteristics, and the performance of algorithms for nodule segmentation and volume estimation. The purpose of this article is to provide a review of published studies relevant to the computed tomographic (CT) volumetric analysis of lung nodules. A number of underexamined areas of research regarding volumetric accuracy are identified, including the measurement of nonsolid nodules, the effects of pitch and section overlap, and the effect of respiratory motion. The need for public databases of phantom scans, as well as of clinical data, is discussed. The review points to the need for continued research to examine volumetric accuracy as a function of a multitude of interrelated variables involved in the assessment of lung nodules. Understanding and quantifying the sources of volumetric measurement error in the assessment of lung nodules with CT would be a first step toward the development of methods to minimize that error through system improvements and to correctly account for any remaining error. © RSNA, 2009 PMID:19332844

Validity of breast, lung and colorectal cancer diagnoses in administrative databases: a systematic review protocol.

PubMed

Abraha, Iosief; Giovannini, Gianni; Serraino, Diego; Fusco, Mario; Montedori, Alessandro

2016-03-18

Breast, lung and colorectal cancers constitute the most common cancers worldwide and their epidemiology, related health outcomes and quality indicators can be studied using administrative healthcare databases. To constitute a reliable source for research, administrative healthcare databases need to be validated. The aim of this protocol is to perform the first systematic review of studies reporting the validation of International Classification of Diseases 9th and 10th revision codes to identify breast, lung and colorectal cancer diagnoses in administrative healthcare databases. This review protocol has been developed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocol (PRISMA-P) 2015 statement. We will search the following databases: MEDLINE, EMBASE, Web of Science and the Cochrane Library, using appropriate search strategies. We will include validation studies that used administrative data to identify breast, lung and colorectal cancer diagnoses or studies that evaluated the validity of breast, lung and colorectal cancer codes in administrative data. The following inclusion criteria will be used: (1) the presence of a reference standard case definition for the disease of interest; (2) the presence of at least one test measure (eg, sensitivity, positive predictive values, etc) and (3) the use of data source from an administrative database. Pairs of reviewers will independently abstract data using standardised forms and will assess quality using a checklist based on the Standards for Reporting of Diagnostic accuracy (STARD) criteria. Ethics approval is not required. We will submit results of this study to a peer-reviewed journal for publication. The results will serve as a guide to identify appropriate case definitions and algorithms of breast, lung and colorectal cancers for researchers involved in validating administrative healthcare databases as well as for outcome research on these conditions that used administrative healthcare databases. CRD42015026881. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Java-based remote viewing and processing of nuclear medicine images: toward "the imaging department without walls".

PubMed

Slomka, P J; Elliott, E; Driedger, A A

2000-01-01

In nuclear medicine practice, images often need to be reviewed and reports prepared from locations outside the department, usually in the form of hard copy. Although hard-copy images are simple and portable, they do not offer electronic data search and image manipulation capabilities. On the other hand, picture archiving and communication systems or dedicated workstations cannot be easily deployed at numerous locations. To solve this problem, we propose a Java-based remote viewing station (JaRViS) for the reading and reporting of nuclear medicine images using Internet browser technology. JaRViS interfaces to the clinical patient database of a nuclear medicine workstation. All JaRViS software resides on a nuclear medicine department server. The contents of the clinical database can be searched by a browser interface after providing a password. Compressed images with the Java applet and color lookup tables are downloaded on the client side. This paradigm does not require nuclear medicine software to reside on remote computers, which simplifies support and deployment of such a system. To enable versatile reporting of the images, color tables and thresholds can be interactively manipulated and images can be displayed in a variety of layouts. Image filtering, frame grouping (adding frames), and movie display are available. Tomographic mode displays are supported, including gated SPECT. The time to display 14 lung perfusion images in 128 x 128 matrix together with the Java applet and color lookup tables over a V.90 modem is <1 min. SPECT and PET slice reorientation is interactive (<1 s). JaRViS could run on a Windows 95/98/NT or a Macintosh platform with Netscape Communicator or Microsoft Intemet Explorer. The performance of Java code for bilinear interpolation, cine display, and filtering approaches that of a standard imaging workstation. It is feasible to set up a remote nuclear medicine viewing station using Java and an Internet or intranet browser. Images can be made easily and cost-effectively available to referring physicians and ambulatory clinics within and outside of the hospital, providing a convenient alternative to film media. We also find this system useful in home reporting of emergency procedures such as lung ventilation-perfusion scans or dynamic studies.

A Generic Data Harmonization Process for Cross-linked Research and Network Interaction. Construction and Application for the Lung Cancer Phenotype Database of the German Center for Lung Research.

PubMed

Firnkorn, D; Ganzinger, M; Muley, T; Thomas, M; Knaup, P

2015-01-01

Joint data analysis is a key requirement in medical research networks. Data are available in heterogeneous formats at each network partner and their harmonization is often rather complex. The objective of our paper is to provide a generic approach for the harmonization process in research networks. We applied the process when harmonizing data from three sites for the Lung Cancer Phenotype Database within the German Center for Lung Research. We developed a spreadsheet-based solution as tool to support the harmonization process for lung cancer data and a data integration procedure based on Talend Open Studio. The harmonization process consists of eight steps describing a systematic approach for defining and reviewing source data elements and standardizing common data elements. The steps for defining common data elements and harmonizing them with local data definitions are repeated until consensus is reached. Application of this process for building the phenotype database led to a common basic data set on lung cancer with 285 structured parameters. The Lung Cancer Phenotype Database was realized as an i2b2 research data warehouse. Data harmonization is a challenging task requiring informatics skills as well as domain knowledge. Our approach facilitates data harmonization by providing guidance through a uniform process that can be applied in a wide range of projects.

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

NASA Astrophysics Data System (ADS)

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

2014-03-01

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

Inter-algorithm lesion volumetry comparison of real and 3D simulated lung lesions in CT

NASA Astrophysics Data System (ADS)

Robins, Marthony; Solomon, Justin; Hoye, Jocelyn; Smith, Taylor; Ebner, Lukas; Samei, Ehsan

2017-03-01

The purpose of this study was to establish volumetric exchangeability between real and computational lung lesions in CT. We compared the overall relative volume estimation performance of segmentation tools when used to measure real lesions in actual patient CT images and computational lesions virtually inserted into the same patient images (i.e., hybrid datasets). Pathologically confirmed malignancies from 30 thoracic patient cases from Reference Image Database to Evaluate Therapy Response (RIDER) were modeled and used as the basis for the comparison. Lesions included isolated nodules as well as those attached to the pleura or other lung structures. Patient images were acquired using a 16 detector row or 64 detector row CT scanner (Lightspeed 16 or VCT; GE Healthcare). Scans were acquired using standard chest protocols during a single breath-hold. Virtual 3D lesion models based on real lesions were developed in Duke Lesion Tool (Duke University), and inserted using a validated image-domain insertion program. Nodule volumes were estimated using multiple commercial segmentation tools (iNtuition, TeraRecon, Inc., Syngo.via, Siemens Healthcare, and IntelliSpace, Philips Healthcare). Consensus based volume comparison showed consistent trends in volume measurement between real and virtual lesions across all software. The average percent bias (+/- standard error) shows -9.2+/-3.2% for real lesions versus -6.7+/-1.2% for virtual lesions with tool A, 3.9+/-2.5% and 5.0+/-0.9% for tool B, and 5.3+/-2.3% and 1.8+/-0.8% for tool C, respectively. Virtual lesion volumes were statistically similar to those of real lesions (< 4% difference) with p >.05 in most cases. Results suggest that hybrid datasets had similar inter-algorithm variability compared to real datasets.

Live Imaging of the Lung

PubMed Central

Looney, Mark R.; Bhattacharya, Jahar

2015-01-01

Live lung imaging has spanned the discovery of capillaries in the frog lung by Malpighi to the current use of single and multiphoton imaging of intravital and isolated perfused lung preparations incorporating fluorescent molecular probes and transgenic reporter mice. Along the way, much has been learned about the unique microcirculation of the lung, including immune cell migration and the mechanisms by which cells at the alveolar-capillary interface communicate with each other. In this review, we highlight live lung imaging techniques as applied to the role of mitochondria in lung immunity, mechanisms of signal transduction in lung compartments, studies on the composition of alveolar wall liquid, and neutrophil and platelet trafficking in the lung under homeostatic and inflammatory conditions. New applications of live lung imaging and the limitations of current techniques are discussed. PMID:24245941

Magnetic Resonance Imaging of the Lung as an Alternative for a Pregnant Woman with Pulmonary Tuberculosis.

PubMed

Schloß, Manuel; Heckrodt, Jan; Schneider, Christian; Discher, Thomas; Krombach, Gabriele Anja

2015-05-01

We report a case of a pregnant 21-year-old woman with pulmonary tuberculosis in which magnetic resonance imaging of the lung was used to assess the extent and characteristics of the pathological changes. Although the lung has been mostly ignored in magnetic resonance imaging for many decades, today technical development enables detailed examinations of the lung. The technique is now entering the clinical arena and its indications are increasing. Magnetic resonance imaging of the lung is not only an alternative method without radiation exposure, it can provide additional information in pulmonary imaging compared to other modalities including computed tomography. We describe a successful application of magnetic resonance imaging of the lung and the imaging appearance of post-primary tuberculosis. This case report indicates that magnetic resonance imaging of the lung can potentially be the first choice imaging technique in pregnant women with suspected pulmonary tuberculosis.

Compton scatter imaging: A promising modality for image guidance in lung stereotactic body radiation therapy

PubMed Central

Redler, Gage; Jones, Kevin C.; Templeton, Alistair; Bernard, Damian; Turian, Julius; Chu, James C. H.

2018-01-01

Purpose Lung stereotactic body radiation therapy (SBRT) requires delivering large radiation doses with millimeter accuracy, making image guidance essential. An approach to forming images of patient anatomy from Compton-scattered photons during lung SBRT is presented. Methods To investigate the potential of scatter imaging, a pinhole collimator and flat-panel detector are used for spatial localization and detection of photons scattered during external beam therapy using lung SBRT treatment conditions (6 MV FFF beam). MCNP Monte Carlo software is used to develop a model to simulate scatter images. This model is validated by comparing experimental and simulated phantom images. Patient scatter images are then simulated from 4DCT data. Results Experimental lung tumor phantom images have sufficient contrast-to-noise to visualize the tumor with as few as 10 MU (0.5 s temporal resolution). The relative signal intensity from objects of different composition as well as lung tumor contrast for simulated phantom images agree quantitatively with experimental images, thus validating the Monte Carlo model. Scatter images are shown to display high contrast between different materials (lung, water, bone). Simulated patient images show superior (~double) tumor contrast compared to MV transmission images. Conclusions Compton scatter imaging is a promising modality for directly imaging patient anatomy during treatment without additional radiation, and it has the potential to complement existing technologies and aid tumor tracking and lung SBRT image guidance. PMID:29360151

Compton scatter imaging: A promising modality for image guidance in lung stereotactic body radiation therapy.

PubMed

Redler, Gage; Jones, Kevin C; Templeton, Alistair; Bernard, Damian; Turian, Julius; Chu, James C H

2018-03-01

Lung stereotactic body radiation therapy (SBRT) requires delivering large radiation doses with millimeter accuracy, making image guidance essential. An approach to forming images of patient anatomy from Compton-scattered photons during lung SBRT is presented. To investigate the potential of scatter imaging, a pinhole collimator and flat-panel detector are used for spatial localization and detection of photons scattered during external beam therapy using lung SBRT treatment conditions (6 MV FFF beam). MCNP Monte Carlo software is used to develop a model to simulate scatter images. This model is validated by comparing experimental and simulated phantom images. Patient scatter images are then simulated from 4DCT data. Experimental lung tumor phantom images have sufficient contrast-to-noise to visualize the tumor with as few as 10 MU (0.5 s temporal resolution). The relative signal intensity from objects of different composition as well as lung tumor contrast for simulated phantom images agree quantitatively with experimental images, thus validating the Monte Carlo model. Scatter images are shown to display high contrast between different materials (lung, water, bone). Simulated patient images show superior (~double) tumor contrast compared to MV transmission images. Compton scatter imaging is a promising modality for directly imaging patient anatomy during treatment without additional radiation, and it has the potential to complement existing technologies and aid tumor tracking and lung SBRT image guidance. © 2018 American Association of Physicists in Medicine.

A novel lymphoid enhancer-binding factor 1-cytoglobin axis promotes extravasation of osteosarcoma cells into the lungs.

PubMed

Pongsuchart, Mongkol; Kuchimaru, Takahiro; Yonezawa, Sakiko; Tran, Diem Thi Phuong; Kha, Nguyen The; Hoang, Ngoc Thi Hong; Kadonosono, Tetsuya; Kizaka-Kondoh, Shinae

2018-06-21

Lung metastasis is a major cause of mortality in patients with osteosarcoma (OS). A better understanding of the molecular mechanism of OS lung metastasis may facilitate development of new therapeutic strategies to prevent the metastasis. We have established high- and low-metastatic sublines (LM8-H and LM8-L respectively) from Dunn OS cell line LM8 by using in vivo image-guided screening. Among the genes whose expression was significantly increased in LM8-H compared to LM8-L, the transcription factor lymphoid enhancer-binding factor 1 (LEF1) was identified as a factor that promotes LM8-H cell extravasation into the lungs. To identify downstream effectors of LEF1 that are involved in OS lung metastasis, 13 genes were selected based on the LM8 microarray data and genome-wide meta-analysis of a public database for OS patients. Among them, the cytoglobin (Cygb) gene was identified as a key effector in promoting OS extravasation into the lungs. CYGB overexpression increased the extravasation ability of LM8-L cells, whereas knocking out the Cygb gene in LM8-H cells reduced this ability. Our results uncovered a novel LEF1-CYGB axis in OS lung metastasis and may open a new avenue for developing therapeutic strategies to prevent OS lung metastasis. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Automatic Solitary Lung Nodule Detection in Computed Tomography Images Slices

NASA Astrophysics Data System (ADS)

Sentana, I. W. B.; Jawas, N.; Asri, S. A.

2018-01-01

Lung nodule is an early indicator of some lung diseases, including lung cancer. In Computed Tomography (CT) based image, nodule is known as a shape that appears brighter than lung surrounding. This research aim to develop an application that automatically detect lung nodule in CT images. There are some steps in algorithm such as image acquisition and conversion, image binarization, lung segmentation, blob detection, and classification. Data acquisition is a step to taking image slice by slice from the original *.dicom format and then each image slices is converted into *.tif image format. Binarization that tailoring Otsu algorithm, than separated the background and foreground part of each image slices. After removing the background part, the next step is to segment part of the lung only so the nodule can localized easier. Once again Otsu algorithm is use to detect nodule blob in localized lung area. The final step is tailoring Support Vector Machine (SVM) to classify the nodule. The application has succeed detecting near round nodule with a certain threshold of size. Those detecting result shows drawback in part of thresholding size and shape of nodule that need to enhance in the next part of the research. The algorithm also cannot detect nodule that attached to wall and Lung Chanel, since it depend the searching only on colour differences.

Lung Nodule Detection via Deep Reinforcement Learning.

PubMed

Ali, Issa; Hart, Gregory R; Gunabushanam, Gowthaman; Liang, Ying; Muhammad, Wazir; Nartowt, Bradley; Kane, Michael; Ma, Xiaomei; Deng, Jun

2018-01-01

Lung cancer is the most common cause of cancer-related death globally. As a preventive measure, the United States Preventive Services Task Force (USPSTF) recommends annual screening of high risk individuals with low-dose computed tomography (CT). The resulting volume of CT scans from millions of people will pose a significant challenge for radiologists to interpret. To fill this gap, computer-aided detection (CAD) algorithms may prove to be the most promising solution. A crucial first step in the analysis of lung cancer screening results using CAD is the detection of pulmonary nodules, which may represent early-stage lung cancer. The objective of this work is to develop and validate a reinforcement learning model based on deep artificial neural networks for early detection of lung nodules in thoracic CT images. Inspired by the AlphaGo system, our deep learning algorithm takes a raw CT image as input and views it as a collection of states, and output a classification of whether a nodule is present or not. The dataset used to train our model is the LIDC/IDRI database hosted by the lung nodule analysis (LUNA) challenge. In total, there are 888 CT scans with annotations based on agreement from at least three out of four radiologists. As a result, there are 590 individuals having one or more nodules, and 298 having none. Our training results yielded an overall accuracy of 99.1% [sensitivity 99.2%, specificity 99.1%, positive predictive value (PPV) 99.1%, negative predictive value (NPV) 99.2%]. In our test, the results yielded an overall accuracy of 64.4% (sensitivity 58.9%, specificity 55.3%, PPV 54.2%, and NPV 60.0%). These early results show promise in solving the major issue of false positives in CT screening of lung nodules, and may help to save unnecessary follow-up tests and expenditures.

Lung Nodule Detection via Deep Reinforcement Learning

PubMed Central

Ali, Issa; Hart, Gregory R.; Gunabushanam, Gowthaman; Liang, Ying; Muhammad, Wazir; Nartowt, Bradley; Kane, Michael; Ma, Xiaomei; Deng, Jun

2018-01-01

Lung cancer is the most common cause of cancer-related death globally. As a preventive measure, the United States Preventive Services Task Force (USPSTF) recommends annual screening of high risk individuals with low-dose computed tomography (CT). The resulting volume of CT scans from millions of people will pose a significant challenge for radiologists to interpret. To fill this gap, computer-aided detection (CAD) algorithms may prove to be the most promising solution. A crucial first step in the analysis of lung cancer screening results using CAD is the detection of pulmonary nodules, which may represent early-stage lung cancer. The objective of this work is to develop and validate a reinforcement learning model based on deep artificial neural networks for early detection of lung nodules in thoracic CT images. Inspired by the AlphaGo system, our deep learning algorithm takes a raw CT image as input and views it as a collection of states, and output a classification of whether a nodule is present or not. The dataset used to train our model is the LIDC/IDRI database hosted by the lung nodule analysis (LUNA) challenge. In total, there are 888 CT scans with annotations based on agreement from at least three out of four radiologists. As a result, there are 590 individuals having one or more nodules, and 298 having none. Our training results yielded an overall accuracy of 99.1% [sensitivity 99.2%, specificity 99.1%, positive predictive value (PPV) 99.1%, negative predictive value (NPV) 99.2%]. In our test, the results yielded an overall accuracy of 64.4% (sensitivity 58.9%, specificity 55.3%, PPV 54.2%, and NPV 60.0%). These early results show promise in solving the major issue of false positives in CT screening of lung nodules, and may help to save unnecessary follow-up tests and expenditures. PMID:29713615

A 4DCT imaging-based breathing lung model with relative hysteresis

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

Miyawaki, Shinjiro; Choi, Sanghun; Hoffman, Eric A.

To reproduce realistic airway motion and airflow, the authors developed a deforming lung computational fluid dynamics (CFD) model based on four-dimensional (4D, space and time) dynamic computed tomography (CT) images. A total of 13 time points within controlled tidal volume respiration were used to account for realistic and irregular lung motion in human volunteers. Because of the irregular motion of 4DCT-based airways, we identified an optimal interpolation method for airway surface deformation during respiration, and implemented a computational solid mechanics-based moving mesh algorithm to produce smooth deforming airway mesh. In addition, we developed physiologically realistic airflow boundary conditions for bothmore » models based on multiple images and a single image. Furthermore, we examined simplified models based on one or two dynamic or static images. By comparing these simplified models with the model based on 13 dynamic images, we investigated the effects of relative hysteresis of lung structure with respect to lung volume, lung deformation, and imaging methods, i.e., dynamic vs. static scans, on CFD-predicted pressure drop. The effect of imaging method on pressure drop was 24 percentage points due to the differences in airflow distribution and airway geometry. - Highlights: • We developed a breathing human lung CFD model based on 4D-dynamic CT images. • The 4DCT-based breathing lung model is able to capture lung relative hysteresis. • A new boundary condition for lung model based on one static CT image was proposed. • The difference between lung models based on 4D and static CT images was quantified.« less

Clinical review: Lung imaging in acute respiratory distress syndrome patients - an update

PubMed Central

2013-01-01

Over the past 30 years lung imaging has greatly contributed to the current understanding of the pathophysiology and the management of acute respiratory distress syndrome (ARDS). In the past few years, in addition to chest X-ray and lung computed tomography, newer functional lung imaging techniques, such as lung ultrasound, positron emission tomography, electrical impedance tomography and magnetic resonance, have been gaining a role as diagnostic tools to optimize lung assessment and ventilator management in ARDS patients. Here we provide an updated clinical review of lung imaging in ARDS over the past few years to offer an overview of the literature on the available imaging techniques from a clinical perspective. PMID:24238477

Fuzzy Clustering Applied to ROI Detection in Helical Thoracic CT Scans with a New Proposal and Variants

PubMed Central

Castro, Alfonso; Boveda, Carmen; Arcay, Bernardino; Sanjurjo, Pedro

2016-01-01

The detection of pulmonary nodules is one of the most studied problems in the field of medical image analysis due to the great difficulty in the early detection of such nodules and their social impact. The traditional approach involves the development of a multistage CAD system capable of informing the radiologist of the presence or absence of nodules. One stage in such systems is the detection of ROI (regions of interest) that may be nodules in order to reduce the space of the problem. This paper evaluates fuzzy clustering algorithms that employ different classification strategies to achieve this goal. After characterising these algorithms, the authors propose a new algorithm and different variations to improve the results obtained initially. Finally it is shown as the most recent developments in fuzzy clustering are able to detect regions that may be nodules in CT studies. The algorithms were evaluated using helical thoracic CT scans obtained from the database of the LIDC (Lung Image Database Consortium). PMID:27517049

Structural basis for pulmonary functional imaging.

PubMed

Itoh, H; Nakatsu, M; Yoxtheimer, L M; Uematsu, H; Ohno, Y; Hatabu, H

2001-03-01

An understanding of fine normal lung morphology is important for effective pulmonary functional imaging. The lung specimens must be inflated. These include (a) unfixed, inflated lung specimen, (b) formaldehyde fixed lung specimen, (c) fixed, inflated dry lung specimen, and (d) histology specimen. Photography, magnified view, radiograph, computed tomography, and histology of these specimens are demonstrated. From a standpoint of diagnostic imaging, the main normal lung structures consist of airways (bronchi and bronchioles), alveoli, pulmonary vessels, secondary pulmonary lobules, and subpleural pulmonary lymphatic channels. This review summarizes fine radiologic normal lung morphology as an aid to effective pulmonary functional imaging.

Quantitative Pulmonary Imaging Using Computed Tomography and Magnetic Resonance Imaging

PubMed Central

Washko, George R.; Parraga, Grace; Coxson, Harvey O.

2011-01-01

Measurements of lung function, including spirometry and body plethesmography, are easy to perform and are the current clinical standard for assessing disease severity. However, these lung functional techniques do not adequately explain the observed variability in clinical manifestations of disease and offer little insight into the relationship of lung structure and function. Lung imaging and the image based assessment of lung disease has matured to the extent that it is common for clinical, epidemiologic, and genetic investigation to have a component dedicated to image analysis. There are several exciting imaging modalities currently being used for the non-invasive study of lung anatomy and function. In this review we will focus on two of them, x-ray computed tomography and magnetic resonance imaging. Following a brief introduction of each method we detail some of the most recent work being done to characterize smoking-related lung disease and the clinical applications of such knowledge. PMID:22142490

Axial segmentation of lungs CT scan images using canny method and morphological operation

NASA Astrophysics Data System (ADS)

Noviana, Rina; Febriani, Rasal, Isram; Lubis, Eva Utari Cintamurni

2017-08-01

Segmentation is a very important topic in digital image process. It is found simply in varied fields of image analysis, particularly within the medical imaging field. Axial segmentation of lungs CT scan is beneficial in designation of abnormalities and surgery planning. It will do to ascertain every section within the lungs. The results of the segmentation are accustomed discover the presence of nodules. The method which utilized in this analysis are image cropping, image binarization, Canny edge detection and morphological operation. Image cropping is done so as to separate the lungs areas, that is the region of interest. Binarization method generates a binary image that has 2 values with grey level, that is black and white (ROI), from another space of lungs CT scan image. Canny method used for the edge detection. Morphological operation is applied to smoothing the lungs edge. The segmentation methodology shows an honest result. It obtains an awfully smooth edge. Moreover, the image background can also be removed in order to get the main focus, the lungs.

Clinical potential for imaging in patients with asthma and other lung disorders.

PubMed

DeBoer, Emily M; Spielberg, David R; Brody, Alan S

2017-01-01

The ability of lung imaging to phenotype patients, determine prognosis, and predict response to treatment is expanding in clinical and translational research. The purpose of this perspective is to describe current imaging modalities that might be useful clinical tools in patients with asthma and other lung disorders and to explore some of the new developments in imaging modalities of the lung. These imaging modalities include chest radiography, computed tomography, lung magnetic resonance imaging, electrical impedance tomography, bronchoscopy, and others. Copyright © 2016 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

SU-C-BRA-06: Developing Clinical and Quantitative Guidelines for a 4DCT-Ventilation Functional Avoidance Clinical Trial

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

Vinogradskiy, Y; Waxweiler, T; Diot, Q

Purpose: 4DCT-ventilation is an exciting new imaging modality that uses 4DCTs to calculate lung ventilation. Because 4DCTs are acquired as part of routine care, calculating 4DCT-ventilation allows for lung function evaluation without additional cost or inconvenience to the patient. Development of a clinical trial is underway at our institution to use 4DCT-ventilation for thoracic functional avoidance with the idea that preferential sparing of functional lung regions can decrease pulmonary toxicity. The purpose of our work was to develop the practical aspects of a 4DCT-ventilation functional avoidance clinical trial including: 1.assessing patient eligibility 2.developing trial inclusion criteria and 3.developing treatment planningmore » and dose-function evaluation strategies. Methods: 96 stage III lung cancer patients from 2 institutions were retrospectively reviewed. 4DCT-ventilation maps were calculated using the patient’s 4DCTs, deformable image registrations, and a density-change-based algorithm. To assess patient eligibility and develop trial inclusion criteria we used an observer-based binary end point noting the presence or absence of a ventilation defect and developed an algorithm based on the percent ventilation in each lung third. Functional avoidance planning integrating 4DCT-ventilation was performed using rapid-arc and compared to the patient’s clinically used plan. Results: Investigator-determined clinical ventilation defects were present in 69% of patients. Our regional/lung-thirds ventilation algorithm identified that 59% of patients have lung functional profiles suitable for functional avoidance. Compared to the clinical plan, functional avoidance planning was able to reduce the mean dose to functional lung by 2 Gy while delivering comparable target coverage and cord/heart doses. Conclusions: 4DCT-ventilation functional avoidance clinical trials have great potential to reduce toxicity, and our data suggest that 59% of lung cancer patients have lung function profiles suitable for functional avoidance. Our study used a retrospective evaluation of a large lung cancer patient database to develop the practical aspects of a 4DCT-ventilation functional avoidance clinical trial. (R.C., E.C., T.G.), NIH Research Scientist Development Award K01-CA181292 (R.C.), and State of Colorado Advanced Industries Accelerator Grant (Y.V.)« less

Reproducibility of dynamically represented acoustic lung images from healthy individuals

PubMed Central

Maher, T M; Gat, M; Allen, D; Devaraj, A; Wells, A U; Geddes, D M

2008-01-01

Background and aim: Acoustic lung imaging offers a unique method for visualising the lung. This study was designed to demonstrate reproducibility of acoustic lung images recorded from healthy individuals at different time points and to assess intra- and inter-rater agreement in the assessment of dynamically represented acoustic lung images. Methods: Recordings from 29 healthy volunteers were made on three separate occasions using vibration response imaging. Reproducibility was measured using quantitative, computerised assessment of vibration energy. Dynamically represented acoustic lung images were scored by six blinded raters. Results: Quantitative measurement of acoustic recordings was highly reproducible with an intraclass correlation score of 0.86 (very good agreement). Intraclass correlations for inter-rater agreement and reproducibility were 0.61 (good agreement) and 0.86 (very good agreement), respectively. There was no significant difference found between the six raters at any time point. Raters ranged from 88% to 95% in their ability to identically evaluate the different features of the same image presented to them blinded on two separate occasions. Conclusion: Acoustic lung imaging is reproducible in healthy individuals. Graphic representation of lung images can be interpreted with a high degree of accuracy by the same and by different reviewers. PMID:18024534

WE-AB-202-04: Statistical Evaluation of Lung Function Using 4DCT Ventilation Imaging: Proton Therapy VS IMRT

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

Huang, Q; Zhang, M; Chen, T

Purpose: Variation in function of different lung regions has been ignored so far for conventional lung cancer treatment planning, which may lead to higher risk of radiation induced lung disease. 4DCT based lung ventilation imaging provides a novel yet convenient approach for lung functional imaging as 4DCT is taken as routine for lung cancer treatment. Our work aims to evaluate the impact of accounting for spatial heterogeneity in lung function using 4DCT based lung ventilation imaging for proton and IMRT plans. Methods: Six patients with advanced stage lung cancer of various tumor locations were retrospectively evaluated for the study. Protonmore » and IMRT plans were designed following identical planning objective and constrains for each patient. Ventilation images were calculated from patients’ 4DCT using deformable image registration implemented by Velocity AI software based on Jacobian-metrics. Lung was delineated into two function level regions based on ventilation (low and high functional area). High functional region was defined as lung ventilation greater than 30%. Dose distribution and statistics in different lung function area was calculated for patients. Results: Variation in dosimetric statistics of different function lung region was observed between proton and IMRT plans. In all proton plans, high function lung regions receive lower maximum dose (100.2%–108.9%), compared with IMRT plans (106.4%–119.7%). Interestingly, three out of six proton plans gave higher mean dose by up to 2.2% than IMRT to high function lung region. Lower mean dose (lower by up to 14.1%) and maximum dose (lower by up to 9%) were observed in low function lung for proton plans. Conclusion: A systematic approach was developed to generate function lung ventilation imaging and use it to evaluate plans. This method hold great promise in function analysis of lung during planning. We are currently studying more subjects to evaluate this tool.« less

A multiscale MDCT image-based breathing lung model with time-varying regional ventilation

PubMed Central

Yin, Youbing; Choi, Jiwoong; Hoffman, Eric A.; Tawhai, Merryn H.; Lin, Ching-Long

2012-01-01

A novel algorithm is presented that links local structural variables (regional ventilation and deforming central airways) to global function (total lung volume) in the lung over three imaged lung volumes, to derive a breathing lung model for computational fluid dynamics simulation. The algorithm constitutes the core of an integrative, image-based computational framework for subject-specific simulation of the breathing lung. For the first time, the algorithm is applied to three multi-detector row computed tomography (MDCT) volumetric lung images of the same individual. A key technique in linking global and local variables over multiple images is an in-house mass-preserving image registration method. Throughout breathing cycles, cubic interpolation is employed to ensure C1 continuity in constructing time-varying regional ventilation at the whole lung level, flow rate fractions exiting the terminal airways, and airway deformation. The imaged exit airway flow rate fractions are derived from regional ventilation with the aid of a three-dimensional (3D) and one-dimensional (1D) coupled airway tree that connects the airways to the alveolar tissue. An in-house parallel large-eddy simulation (LES) technique is adopted to capture turbulent-transitional-laminar flows in both normal and deep breathing conditions. The results obtained by the proposed algorithm when using three lung volume images are compared with those using only one or two volume images. The three-volume-based lung model produces physiologically-consistent time-varying pressure and ventilation distribution. The one-volume-based lung model under-predicts pressure drop and yields un-physiological lobar ventilation. The two-volume-based model can account for airway deformation and non-uniform regional ventilation to some extent, but does not capture the non-linear features of the lung. PMID:23794749

Combining deep learning and coherent anti-Stokes Raman scattering imaging for automated differential diagnosis of lung cancer

NASA Astrophysics Data System (ADS)

Weng, Sheng; Xu, Xiaoyun; Li, Jiasong; Wong, Stephen T. C.

2017-10-01

Lung cancer is the most prevalent type of cancer and the leading cause of cancer-related deaths worldwide. Coherent anti-Stokes Raman scattering (CARS) is capable of providing cellular-level images and resolving pathologically related features on human lung tissues. However, conventional means of analyzing CARS images requires extensive image processing, feature engineering, and human intervention. This study demonstrates the feasibility of applying a deep learning algorithm to automatically differentiate normal and cancerous lung tissue images acquired by CARS. We leverage the features learned by pretrained deep neural networks and retrain the model using CARS images as the input. We achieve 89.2% accuracy in classifying normal, small-cell carcinoma, adenocarcinoma, and squamous cell carcinoma lung images. This computational method is a step toward on-the-spot diagnosis of lung cancer and can be further strengthened by the efforts aimed at miniaturizing the CARS technique for fiber-based microendoscopic imaging.

Development and evaluation of a computer-aided diagnostic scheme for lung nodule detection in chest radiographs by means of two-stage nodule enhancement with support vector classification

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

Chen Sheng; Suzuki, Kenji; MacMahon, Heber

2011-04-15

Purpose: To develop a computer-aided detection (CADe) scheme for nodules in chest radiographs (CXRs) with a high sensitivity and a low false-positive (FP) rate. Methods: The authors developed a CADe scheme consisting of five major steps, which were developed for improving the overall performance of CADe schemes. First, to segment the lung fields accurately, the authors developed a multisegment active shape model. Then, a two-stage nodule-enhancement technique was developed for improving the conspicuity of nodules. Initial nodule candidates were detected and segmented by using the clustering watershed algorithm. Thirty-one shape-, gray-level-, surface-, and gradient-based features were extracted from each segmentedmore » candidate for determining the feature space, including one of the new features based on the Canny edge detector to eliminate a major FP source caused by rib crossings. Finally, a nonlinear support vector machine (SVM) with a Gaussian kernel was employed for classification of the nodule candidates. Results: To evaluate and compare the scheme to other published CADe schemes, the authors used a publicly available database containing 140 nodules in 140 CXRs and 93 normal CXRs. The CADe scheme based on the SVM classifier achieved sensitivities of 78.6% (110/140) and 71.4% (100/140) with averages of 5.0 (1165/233) FPs/image and 2.0 (466/233) FPs/image, respectively, in a leave-one-out cross-validation test, whereas the CADe scheme based on a linear discriminant analysis classifier had a sensitivity of 60.7% (85/140) at an FP rate of 5.0 FPs/image. For nodules classified as ''very subtle'' and ''extremely subtle,'' a sensitivity of 57.1% (24/42) was achieved at an FP rate of 5.0 FPs/image. When the authors used a database developed at the University of Chicago, the sensitivities was 83.3% (40/48) and 77.1% (37/48) at an FP rate of 5.0 (240/48) FPs/image and 2.0 (96/48) FPs /image, respectively. Conclusions: These results compare favorably to those described for other commercial and noncommercial CADe nodule detection systems.« less

Algorithm for lung cancer detection based on PET/CT images

NASA Astrophysics Data System (ADS)

Saita, Shinsuke; Ishimatsu, Keita; Kubo, Mitsuru; Kawata, Yoshiki; Niki, Noboru; Ohtsuka, Hideki; Nishitani, Hiromu; Ohmatsu, Hironobu; Eguchi, Kenji; Kaneko, Masahiro; Moriyama, Noriyuki

2009-02-01

The five year survival rate of the lung cancer is low with about twenty-five percent. In addition it is an obstinate lung cancer wherein three out of four people die within five years. Then, the early stage detection and treatment of the lung cancer are important. Recently, we can obtain CT and PET image at the same time because PET/CT device has been developed. PET/CT is possible for a highly accurate cancer diagnosis because it analyzes quantitative shape information from CT image and FDG distribution from PET image. However, neither benign-malignant classification nor staging intended for lung cancer have been established still enough by using PET/CT images. In this study, we detect lung nodules based on internal organs extracted from CT image, and we also develop algorithm which classifies benignmalignant and metastatic or non metastatic lung cancer using lung structure and FDG distribution(one and two hour after administering FDG). We apply the algorithm to 59 PET/CT images (malignant 43 cases [Ad:31, Sq:9, sm:3], benign 16 cases) and show the effectiveness of this algorithm.

Estimation of regional lung expansion via 3D image registration

NASA Astrophysics Data System (ADS)

Pan, Yan; Kumar, Dinesh; Hoffman, Eric A.; Christensen, Gary E.; McLennan, Geoffrey; Song, Joo Hyun; Ross, Alan; Simon, Brett A.; Reinhardt, Joseph M.

2005-04-01

A method is described to estimate regional lung expansion and related biomechanical parameters using multiple CT images of the lungs, acquired at different inflation levels. In this study, the lungs of two sheep were imaged utilizing a multi-detector row CT at different lung inflations in the prone and supine positions. Using the lung surfaces and the airway branch points for guidance, a 3D inverse consistent image registration procedure was used to match different lung volumes at each orientation. The registration was validated using a set of implanted metal markers. After registration, the Jacobian of the deformation field was computed to express regional expansion or contraction. The regional lung expansion at different pressures and different orientations are compared.

[Meta-analysis for correlation between multiple lung lobe lesions and prognostic influence on acquired pneumonia in hospitalized elderly patients].

PubMed

Huang, Wenjie; Feng, Wei; Li, Yang; Chen, Yu

2014-11-01

To explore the correlation regarding the prognostic influence between multiple lung lobe lesions and acquired pneumonia in hospitalized elderly patients by a Meta-analysis. We collected all studies which investigated the correlation regarding the prognostic effect between multiple lung lobe lesions and acquired pneumonia by searching China National Knowledge Infrastructure, Wanfang Database, Chinese Science and Technology Periodical Database, Chinese Biological Medical Literature Database, PubMed, and EMBase in accordance with the inclusion and exclusion criteria. Th e retrieval limit time of searches was from databases establishment to July 2014. Th e Meta-analysis was performed by using RevMan5.2 soft ware. We calculated the odds ratio (OR) and 95% confidence interval (95% CI) by using heterogeneous tests. Publication bias was assessed by Egger's test and funnel plot, and the sensitivity was analyzed. Ten studies involving 1 836 patients were finally included, with 487 cases (the dead group) and 1 349 controls (the survival group). The Meta-analysis demonstrated that multiple lung lobe lesions was highly correlated with the prognosis for the aged acquired pneumonia (OR=3.22, 95% CI 1.84 to 5.63). Multiple lung lobe lesions increase the risk of death in the prognosis of the aged patients with acquired pneumonia.

Lung cancer mimicking lung abscess formation on CT images.

PubMed

Taira, Naohiro; Kawabata, Tsutomu; Gabe, Atsushi; Ichi, Takaharu; Kushi, Kazuaki; Yohena, Tomofumi; Kawasaki, Hidenori; Yamashiro, Toshimitsu; Ishikawa, Kiyoshi

2014-01-01

Male, 64 FINAL DIAGNOSIS: Lung pleomorphic carcinoma Symptoms: Cough • fever - Clinical Procedure: - Specialty: Oncology. Unusual clinical course. The diagnosis of lung cancer is often made based on computed tomography (CT) image findings if it cannot be confirmed on pathological examinations, such as bronchoscopy. However, the CT image findings of cancerous lesions are similar to those of abscesses.We herein report a case of lung cancer that resembled a lung abscess on CT. We herein describe the case of 64-year-old male who was diagnosed with lung cancer using surgery. In this case, it was quite difficult to distinguish between the lung cancer and a lung abscess on CT images, and a lung abscess was initially suspected due to symptoms, such as fever and coughing, contrast-enhanced CT image findings showing a ring-enhancing mass in the right upper lobe and the patient's laboratory test results. However, a pathological diagnosis of lung cancer was confirmed according to the results of a rapid frozen section biopsy of the lesion. This case suggests that physicians should not suspect both a lung abscesses and malignancy in cases involving masses presenting as ring-enhancing lesions on contrast-enhanced CT.

MO-AB-BRA-02: A Novel Scatter Imaging Modality for Real-Time Image Guidance During Lung SBRT

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

Redler, G; Bernard, D; Templeton, A

2015-06-15

Purpose: A novel scatter imaging modality is developed and its feasibility for image-guided radiation therapy (IGRT) during stereotactic body radiation therapy (SBRT) for lung cancer patients is assessed using analytic and Monte Carlo models as well as experimental testing. Methods: During treatment, incident radiation interacts and scatters from within the patient. The presented methodology forms an image of patient anatomy from the scattered radiation for real-time localization of the treatment target. A radiographic flat panel-based pinhole camera provides spatial information regarding the origin of detected scattered radiation. An analytical model is developed, which provides a mathematical formalism for describing themore » scatter imaging system. Experimental scatter images are acquired by irradiating an object using a Varian TrueBeam accelerator. The differentiation between tissue types is investigated by imaging simple objects of known compositions (water, lung, and cortical bone equivalent). A lung tumor phantom, simulating materials and geometry encountered during lung SBRT treatments, is fabricated and imaged to investigate image quality for various quantities of delivered radiation. Monte Carlo N-Particle (MCNP) code is used for validation and testing by simulating scatter image formation using the experimental pinhole camera setup. Results: Analytical calculations, MCNP simulations, and experimental results when imaging the water, lung, and cortical bone equivalent objects show close agreement, thus validating the proposed models and demonstrating that scatter imaging differentiates these materials well. Lung tumor phantom images have sufficient contrast-to-noise ratio (CNR) to clearly distinguish tumor from surrounding lung tissue. CNR=4.1 and CNR=29.1 for 10MU and 5000MU images (equivalent to 0.5 and 250 second images), respectively. Conclusion: Lung SBRT provides favorable treatment outcomes, but depends on accurate target localization. A comprehensive approach, employing multiple simulation techniques and experiments, is taken to demonstrate the feasibility of a novel scatter imaging modality for the necessary real-time image guidance.« less

Computerized lung cancer malignancy level analysis using 3D texture features

NASA Astrophysics Data System (ADS)

Sun, Wenqing; Huang, Xia; Tseng, Tzu-Liang; Zhang, Jianying; Qian, Wei

2016-03-01

Based on the likelihood of malignancy, the nodules are classified into five different levels in Lung Image Database Consortium (LIDC) database. In this study, we tested the possibility of using threedimensional (3D) texture features to identify the malignancy level of each nodule. Five groups of features were implemented and tested on 172 nodules with confident malignancy levels from four radiologists. These five feature groups are: grey level co-occurrence matrix (GLCM) features, local binary pattern (LBP) features, scale-invariant feature transform (SIFT) features, steerable features, and wavelet features. Because of the high dimensionality of our proposed features, multidimensional scaling (MDS) was used for dimension reduction. RUSBoost was applied for our extracted features for classification, due to its advantages in handling imbalanced dataset. Each group of features and the final combined features were used to classify nodules highly suspicious for cancer (level 5) and moderately suspicious (level 4). The results showed that the area under the curve (AUC) and accuracy are 0.7659 and 0.8365 when using the finalized features. These features were also tested on differentiating benign and malignant cases, and the reported AUC and accuracy were 0.8901 and 0.9353.

Structural-functional lung imaging using a combined CT-EIT and a Discrete Cosine Transformation reconstruction method.

PubMed

Schullcke, Benjamin; Gong, Bo; Krueger-Ziolek, Sabine; Soleimani, Manuchehr; Mueller-Lisse, Ullrich; Moeller, Knut

2016-05-16

Lung EIT is a functional imaging method that utilizes electrical currents to reconstruct images of conductivity changes inside the thorax. This technique is radiation free and applicable at the bedside, but lacks of spatial resolution compared to morphological imaging methods such as X-ray computed tomography (CT). In this article we describe an approach for EIT image reconstruction using morphologic information obtained from other structural imaging modalities. This leads to recon- structed images of lung ventilation that can easily be superimposed with structural CT or MRI images, which facilitates image interpretation. The approach is based on a Discrete Cosine Transformation (DCT) of an image of the considered transversal thorax slice. The use of DCT enables reduction of the dimensionality of the reconstruction and ensures that only conductivity changes of the lungs are reconstructed and displayed. The DCT based approach is well suited to fuse morphological image information with functional lung imaging at low computational costs. Results on simulated data indicate that this approach preserves the morphological structures of the lungs and avoids blurring of the solution. Images from patient measurements reveal the capabilities of the method and demonstrate benefits in possible applications.

Structural-functional lung imaging using a combined CT-EIT and a Discrete Cosine Transformation reconstruction method

PubMed Central

Schullcke, Benjamin; Gong, Bo; Krueger-Ziolek, Sabine; Soleimani, Manuchehr; Mueller-Lisse, Ullrich; Moeller, Knut

2016-01-01

Lung EIT is a functional imaging method that utilizes electrical currents to reconstruct images of conductivity changes inside the thorax. This technique is radiation free and applicable at the bedside, but lacks of spatial resolution compared to morphological imaging methods such as X-ray computed tomography (CT). In this article we describe an approach for EIT image reconstruction using morphologic information obtained from other structural imaging modalities. This leads to recon- structed images of lung ventilation that can easily be superimposed with structural CT or MRI images, which facilitates image interpretation. The approach is based on a Discrete Cosine Transformation (DCT) of an image of the considered transversal thorax slice. The use of DCT enables reduction of the dimensionality of the reconstruction and ensures that only conductivity changes of the lungs are reconstructed and displayed. The DCT based approach is well suited to fuse morphological image information with functional lung imaging at low computational costs. Results on simulated data indicate that this approach preserves the morphological structures of the lungs and avoids blurring of the solution. Images from patient measurements reveal the capabilities of the method and demonstrate benefits in possible applications. PMID:27181695

Lung Cancer in Women with a Family History of Cancer: The Spanish Female-specific Database WORLD07.

PubMed

Isla, Dolores; Felip, Enriqueta; Viñolas, Nuria; Provencio, Mariano; Majem, Margarita; Artal, Angel; Bover, Isabel; Lianes, Pilar; DE Las Peñas, Ramón; Catot, Silvia; DE Castro, Javier; Blasco, Ana; Terrasa, Josefa; Gonzalez-Larriba, José Luis; Juan, Oscar; Dómine, Manuel; Bernabe, Reyes; Garrido, Pilar

2016-12-01

The WORLD07 project is a female-specific database to prospectively analyze the characteristics of Spanish women with lung cancer. We analyzed and compared lung cancer features in women with and without a family history of cancer/lung cancer. Two thousand and sixty women were included: 876 had a family history of cancer (lung cancer, 34%) and 886 did not, with no significant differences between groups, except for smoking status (p=0.036). We found statistically significant correlations between epidermal growth factor receptor (EGFR) mutation and smoking status in patients with a family history of cancer (r=-0.211; p<0.001) and lung cancer (r=-0.176; p<0.001). Longer median overall survival was observed in women with a family history of cancer and lung cancer. Among Spanish women with lung cancer, a greater proportion were current smokers in those with a family history of cancer/lung cancer. There was a significant correlation between the presence of EGFR mutation and smoking. Copyright© 2016 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

Combining deep learning and coherent anti-Stokes Raman scattering imaging for automated differential diagnosis of lung cancer.

PubMed

Weng, Sheng; Xu, Xiaoyun; Li, Jiasong; Wong, Stephen T C

2017-10-01

Lung cancer is the most prevalent type of cancer and the leading cause of cancer-related deaths worldwide. Coherent anti-Stokes Raman scattering (CARS) is capable of providing cellular-level images and resolving pathologically related features on human lung tissues. However, conventional means of analyzing CARS images requires extensive image processing, feature engineering, and human intervention. This study demonstrates the feasibility of applying a deep learning algorithm to automatically differentiate normal and cancerous lung tissue images acquired by CARS. We leverage the features learned by pretrained deep neural networks and retrain the model using CARS images as the input. We achieve 89.2% accuracy in classifying normal, small-cell carcinoma, adenocarcinoma, and squamous cell carcinoma lung images. This computational method is a step toward on-the-spot diagnosis of lung cancer and can be further strengthened by the efforts aimed at miniaturizing the CARS technique for fiber-based microendoscopic imaging. (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Computer aided detection system for lung cancer using computer tomography scans

NASA Astrophysics Data System (ADS)

Mahesh, Shanthi; Rakesh, Spoorthi; Patil, Vidya C.

2018-04-01

Lung Cancer is a disease can be defined as uncontrolled cell growth in tissues of the lung. If we detect the Lung Cancer in its early stage, then that could be the key of its cure. In this work the non-invasive methods are studied for assisting in nodule detection. It supplies a Computer Aided Diagnosis System (CAD) for early detection of lung cancer nodules from the Computer Tomography (CT) images. CAD system is the one which helps to improve the diagnostic performance of radiologists in their image interpretations. The main aim of this technique is to develop a CAD system for finding the lung cancer using the lung CT images and classify the nodule as Benign or Malignant. For classifying cancer cells, SVM classifier is used. Here, image processing techniques have been used to de-noise, to enhance, for segmentation and edge detection of an image is used to extract the area, perimeter and shape of nodule. The core factors of this research are Image quality and accuracy.

MRI and CT lung biomarkers: Towards an in vivo understanding of lung biomechanics.

PubMed

Young, Heather M; Eddy, Rachel L; Parraga, Grace

2017-09-29

The biomechanical properties of the lung are necessarily dependent on its structure and function, both of which are complex and change over time and space. This makes in vivo evaluation of lung biomechanics and a deep understanding of lung biomarkers, very challenging. In patients and animal models of lung disease, in vivo evaluations of lung structure and function are typically made at the mouth and include spirometry, multiple-breath gas washout tests and the forced oscillation technique. These techniques, and the biomarkers they provide, incorporate the properties of the whole organ system including the parenchyma, large and small airways, mouth, diaphragm and intercostal muscles. Unfortunately, these well-established measurements mask regional differences, limiting their ability to probe the lung's gross and micro-biomechanical properties which vary widely throughout the organ and its subcompartments. Pulmonary imaging has the advantage in providing regional, non-invasive measurements of healthy and diseased lung, in vivo. Here we summarize well-established and emerging lung imaging tools and biomarkers and how they may be used to generate lung biomechanical measurements. We review well-established and emerging lung anatomical, microstructural and functional imaging biomarkers generated using synchrotron x-ray tomographic-microscopy (SRXTM), micro-x-ray computed-tomography (micro-CT), clinical CT as well as magnetic resonance imaging (MRI). Pulmonary imaging provides measurements of lung structure, function and biomechanics with high spatial and temporal resolution. Imaging biomarkers that reflect the biomechanical properties of the lung are now being validated to provide a deeper understanding of the lung that cannot be achieved using measurements made at the mouth. Copyright © 2017 Elsevier Ltd. All rights reserved.

Multiple image x-radiography for functional lung imaging

NASA Astrophysics Data System (ADS)

Aulakh, G. K.; Mann, A.; Belev, G.; Wiebe, S.; Kuebler, W. M.; Singh, B.; Chapman, D.

2018-01-01

Detection and visualization of lung tissue structures is impaired by predominance of air. However, by using synchrotron x-rays, refraction of x-rays at the interface of tissue and air can be utilized to generate contrast which may in turn enable quantification of lung optical properties. We utilized multiple image radiography, a variant of diffraction enhanced imaging, at the Canadian light source to quantify changes in unique x-ray optical properties of lungs, namely attenuation, refraction and ultra small-angle scatter (USAXS or width) contrast ratios as a function of lung orientation in free-breathing or respiratory-gated mice before and after intra-nasal bacterial endotoxin (lipopolysaccharide) instillation. The lung ultra small-angle scatter and attenuation contrast ratios were significantly higher 9 h post lipopolysaccharide instillation compared to saline treatment whereas the refraction contrast decreased in magnitude. In ventilated mice, end-expiratory pressures result in an increase in ultra small-angle scatter contrast ratio when compared to end-inspiratory pressures. There were no detectable changes in lung attenuation or refraction contrast ratio with change in lung pressure alone. In effect, multiple image radiography can be applied towards following optical properties of lung air-tissue barrier over time during pathologies such as acute lung injury.

3D cine magnetic resonance imaging of rat lung ARDS using gradient-modulated SWIFT with retrospective respiratory gating

NASA Astrophysics Data System (ADS)

Kobayashi, Naoharu; Lei, Jianxun; Utecht, Lynn; Garwood, Michael; Ingbar, David H.; Bhargava, Maneesh

2015-03-01

SWeep Imaging with Fourier Transformation (SWIFT) with gradient modulation and DC navigator retrospective gating is introduced as a 3D cine magnetic resonance imaging (MRI) method for the lung. In anesthetized normal rats, the quasi-simultaneous excitation and acquisition in SWIFT enabled extremely high sensitivity to the fast-decaying parenchymal signals (TE=~4 μs), which are invisible with conventional MRI techniques. Respiratory motion information was extracted from DC navigator signals and the SWIFT data were reconstructed to 3D cine images with 16 respiratory phases. To test this technique's capabilities, rats exposed to > 95% O2 for 60 hours for induction of acute respiratory distress syndrome (ARDS), were imaged and compared with normal rat lungs (N=7 and 5 for ARDS and normal groups, respectively). SWIFT images showed lung tissue density differences along the gravity direction. In the cine SWIFT images, a parenchymal signal drop at the inhalation phase was consistently observed for both normal and ARDS rats due to lung inflation (i.e. decrease of the proton density), but the drop was less for ARDS rats. Depending on the respiratory phase and lung region, the lungs from the ARDS rats showed 1-24% higher parenchymal signal intensities relative to the normal rat lungs, likely due to accumulated extravascular water (EVLW). Those results demonstrate that SWIFT has high enough sensitivity for detecting the lung proton density changes due to gravity, different phases of respiration and accumulation of EVLW in the rat ARDS lungs.

Peripleural lung disease detection based on multi-slice CT images

NASA Astrophysics Data System (ADS)

Matsuhiro, M.; Suzuki, H.; Kawata, Y.; Niki, N.; Nakano, Y.; Ohmatsu, H.; Kusumoto, M.; Tsuchida, T.; Eguchi, K.; Kaneko, M.

2015-03-01

With the development of multi-slice CT technology, obtaining accurate 3D images of lung field in a short time become possible. To support that, a lot of image processing methods need to be developed. Detection peripleural lung disease is difficult due to its existence out of lung region, because lung extraction is often performed based on threshold processing. The proposed method uses thoracic inner region extracted by inner cavity of bone as well as air region, covers peripleural lung diseased cases such as lung nodule, calcification, pleural effusion and pleural plaque. We applied this method to 50 cases including 39 peripleural lung diseased cases. This method was able to detect 39 peripleural lung disease with 2.9 false positive per case.

SU-E-I-90: Characterizing Small Animal Lung Properties Using Speckle Observed with An In-Line X-Ray Phase Contrast Benchtop System

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

Garson, A; Gunsten, S; Guan, H

Purpose: We demonstrate a novel X-ray phase-contrast (XPC) method for lung imaging representing a paradigm shift in the way small animal functional imaging is performed. In our method, information regarding airway microstructure that is encoded within speckle texture of a single XPC radiograph is decoded to produce 2D parametric images that will spatially resolve changes in lung properties such as microstructure sizes and air volumes. Such information cannot be derived from conventional lung radiography or any other 2D imaging modality. By computing these images at different points within a breathing cycle, dynamic functional imaging will be readily achieved without themore » need for tomography. Methods: XPC mouse lung radiographs acquired in situ with an in-line X-ray phase contrast benchtop system. The lung air volume is varied and controlled with a small animal ventilator. XPC radiographs will be acquired for various lung air volume levels representing different phases of the respiratory cycle. Similar data will be acquired of microsphere-based lung phantoms containing hollow glass spheres with known distributions of diameters. Image texture analysis is applied to the data to investigate relationships between texture characteristics and airspace/microsphere physical properties. Results: Correlations between Fourier-based texture descriptors (FBTDs) and regional lung air volume indicate that the texture features in 2D radiographs reveal information on 3D properties of the lungs. For example, we find for a 350 × 350 πm2 lung ROI a linear relationship between injected air volume and FBTD value with slope and intercept of 8.9×10{sup 5} and 7.5, respectively. Conclusion: We demonstrate specific image texture measures related to lung speckle features are correlated with physical characteristics of refracting elements (i.e. lung air spaces). Furthermore, we present results indicating the feasibility of implementing the technique with a simple imaging system design, short exposures, and low dose which provides potential for widespread use in laboratory settings for in vivo studies. This research was supported in part by NSF Award CBET1263988.« less

High spatiotemporal resolution measurement of regional lung air volumes from 2D phase contrast x-ray images.

PubMed

Leong, Andrew F T; Fouras, Andreas; Islam, M Sirajul; Wallace, Megan J; Hooper, Stuart B; Kitchen, Marcus J

2013-04-01

Described herein is a new technique for measuring regional lung air volumes from two-dimensional propagation-based phase contrast x-ray (PBI) images at very high spatial and temporal resolution. Phase contrast dramatically increases lung visibility and the outlined volumetric reconstruction technique quantifies dynamic changes in respiratory function. These methods can be used for assessing pulmonary disease and injury and for optimizing mechanical ventilation techniques for preterm infants using animal models. The volumetric reconstruction combines the algorithms of temporal subtraction and single image phase retrieval (SIPR) to isolate the image of the lungs from the thoracic cage in order to measure regional lung air volumes. The SIPR algorithm was used to recover the change in projected thickness of the lungs on a pixel-by-pixel basis (pixel dimensions ≈ 16.2 μm). The technique has been validated using numerical simulation and compared results of measuring regional lung air volumes with and without the use of temporal subtraction for removing the thoracic cage. To test this approach, a series of PBI images of newborn rabbit pups mechanically ventilated at different frequencies was employed. Regional lung air volumes measured from PBI images of newborn rabbit pups showed on average an improvement of at least 20% in 16% of pixels within the lungs in comparison to that measured without the use of temporal subtraction. The majority of pixels that showed an improvement was found to be in regions occupied by bone. Applying the volumetric technique to sequences of PBI images of newborn rabbit pups, it is shown that lung aeration at birth can be highly heterogeneous. This paper presents an image segmentation technique based on temporal subtraction that has successfully been used to isolate the lungs from PBI chest images, allowing the change in lung air volume to be measured over regions as small as the pixel size. Using this technique, it is possible to measure changes in regional lung volume at high spatial and temporal resolution during breathing at much lower x-ray dose than would be required using computed tomography.

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

NASA Astrophysics Data System (ADS)

Zhang, Weipeng

2017-06-01

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

Radioaerosol lung imaging in chronic obstructive pulmonary disease. Comparison with pulmonary function tests and roentgenography. [/sup 113m/In, /sup 99m/Tc, /sup 133/Xe tracer techniques

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

Ramanna, L.; Tashkin, D.P.; Taplin, G.V.

1975-11-01

Seventy subjects with either no, mild, or definite evidence of pulmonary abnormality on screening studies volunteered to have detailed pulmonary function tests (PFTs), respiratory questionnaires, physical examinations, and /sup 113m/indium aerosol-inhalation lung imaging performed. Also, 22 and 52 of these subjects underwent /sup 133/xenon ventilation and lung perfusion imaging with /sup 99m/technetium-labelled macroaggregated albumin, and 56 had chest x-ray examinations performed. Results of the radionuclide lung-imaging procedures were compared with those of conventional PFTs and other clinical diagnostic procedures used to identify chronic obstructive pulmonary disease (COPD). Abnormal radioaerosol patterns were found in 32 of 33 subjects with abnormal findingsmore » on PFTs, whereas results of PFTs were abnormal in only 32 of 46 subjects with abnormal aerosol deposition. Aerosol lung images were abnormal more frequently than respiratory questionnaire responses, findings on physical examination, chest x-ray films, and perfusion lung images and with approximately the same frequency as /sup 133/xenon ventilation scintiscans. These results suggest that radioaerosol lung imaging may be a more sensitive indicator of early COPD than other diagnostic procedures, including maximal midexpiratory flow rates, single-breath nitrogen washout, and closing volume. Further studies are required to determine the physiologic and pathologic significance of isolated aerosol lung-imaging abnormalities.« less

Lung Cancers Associated with Cystic Airspaces: Underrecognized Features of Early Disease.

PubMed

Sheard, Sarah; Moser, Joanna; Sayer, Charlie; Stefanidis, Konstantinos; Devaraj, Anand; Vlahos, Ioannis

2018-01-01

Early lung cancers associated with cystic airspaces are increasingly being recognized as a cause of delayed diagnoses-owing to data gathered from screening trials and encounters in routine clinical practice as more patients undergo serial imaging. Several morphologic subtypes of cancers associated with cystic airspaces exist and can exhibit variable patterns of progression as the solid elements of the tumor grow. Current understanding of the pathogenesis of these malignancies is limited, and the numbers of cases reported in the literature are small. However, several tumor cell types are represented in these lesions, with adenocarcinoma predominating. The features of cystic airspaces differ among cases and include emphysematous bullae, congenital or fibrotic cysts, subpleural blebs, bronchiectatic airways, and distended distal airspaces. Once identified, these cystic lesions pose management challenges to radiologists in terms of distinguishing them from benign mimics of cancer that are commonly seen in patients who also are at increased risk of lung cancer. Rendering a definitive tissue-based diagnosis can be difficult when the lesions are small, and affected patients tend to be in groups that are at higher risk of requiring biopsy or resection. In addition, the decision to monitor these cases can add to patient anxiety and cause the additional burden of strained departmental resources. The authors have drawn from their experience, emerging evidence from international lung cancer screening trials, and large databases of lung cancer cases from other groups to analyze the prevalence and evolution of lung cancers associated with cystic airspaces and provide guidance for managing these lesions. Although there are insufficient data to support specific management guidelines similar to those for managing small solid and ground-glass lung nodules, these data and guidelines should be the direction for ongoing research on early detection of lung cancer. © RSNA, 2018.

Long-term High-Resolution Intravital Microscopy in the Lung with a Vacuum Stabilized Imaging Window

PubMed Central

Rodriguez-Tirado, Carolina; Kitamura, Takanori; Kato, Yu; Pollard, Jeffery W.; Condeelis, John S.; Entenberg, David

2017-01-01

Metastasis to secondary sites such as the lung, liver and bone is a traumatic event with a mortality rate of approximately 90% 1. Of these sites, the lung is the most difficult to assess using intravital optical imaging due to its enclosed position within the body, delicate nature and vital role in sustaining proper physiology. While clinical modalities (positron emission tomography (PET), magnetic resonance imaging (MRI) and computed tomography (CT)) are capable of providing noninvasive images of this tissue, they lack the resolution necessary to visualize the earliest seeding events, with a single pixel consisting of nearly a thousand cells. Current models of metastatic lung seeding postulate that events just after a tumor cell's arrival are deterministic for survival and subsequent growth. This means that real-time intravital imaging tools with single cell resolution 2 are required in order to define the phenotypes of the seeding cells and test these models. While high resolution optical imaging of the lung has been performed using various ex vivo preparations, these experiments are typically single time-point assays and are susceptible to artifacts and possible erroneous conclusions due to the dramatically altered environment (temperature, profusion, cytokines, etc.) resulting from removal from the chest cavity and circulatory system 3. Recent work has shown that time-lapse intravital optical imaging of the intact lung is possible using a vacuum stabilized imaging window 2,4,5 however, typical imaging times have been limited to approximately 6 hr. Here we describe a protocol for performing long-term intravital time-lapse imaging of the lung utilizing such a window over a period of 12 hr. The time-lapse image sequences obtained using this method enable visualization and quantitation of cell-cell interactions, membrane dynamics and vascular perfusion in the lung. We further describe an image processing technique that gives an unprecedentedly clear view of the lung microvasculature. PMID:27768066

Lung dynamic MRI deblurring using low-rank decomposition and dictionary learning.

PubMed

Gou, Shuiping; Wang, Yueyue; Wu, Jiaolong; Lee, Percy; Sheng, Ke

2015-04-01

Lung dynamic MRI (dMRI) has emerged to be an appealing tool to quantify lung motion for both planning and treatment guidance purposes. However, this modality can result in blurry images due to intrinsically low signal-to-noise ratio in the lung and spatial/temporal interpolation. The image blurring could adversely affect the image processing that depends on the availability of fine landmarks. The purpose of this study is to reduce dMRI blurring using image postprocessing. To enhance the image quality and exploit the spatiotemporal continuity of dMRI sequences, a low-rank decomposition and dictionary learning (LDDL) method was employed to deblur lung dMRI and enhance the conspicuity of lung blood vessels. Fifty frames of continuous 2D coronal dMRI frames using a steady state free precession sequence were obtained from five subjects including two healthy volunteer and three lung cancer patients. In LDDL, the lung dMRI was decomposed into sparse and low-rank components. Dictionary learning was employed to estimate the blurring kernel based on the whole image, low-rank or sparse component of the first image in the lung MRI sequence. Deblurring was performed on the whole image sequences using deconvolution based on the estimated blur kernel. The deblurring results were quantified using an automated blood vessel extraction method based on the classification of Hessian matrix filtered images. Accuracy of automated extraction was calculated using manual segmentation of the blood vessels as the ground truth. In the pilot study, LDDL based on the blurring kernel estimated from the sparse component led to performance superior to the other ways of kernel estimation. LDDL consistently improved image contrast and fine feature conspicuity of the original MRI without introducing artifacts. The accuracy of automated blood vessel extraction was on average increased by 16% using manual segmentation as the ground truth. Image blurring in dMRI images can be effectively reduced using a low-rank decomposition and dictionary learning method using kernels estimated by the sparse component.

SU-E-J-86: Lobar Lung Function Quantification by PET Galligas and CT Ventilation Imaging in Lung Cancer Patients

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

Eslick, E; Kipritidis, J; Keall, P

2014-06-01

Purpose: The purpose of this study was to quantify the lobar lung function using the novel PET Galligas ([68Ga]-carbon nanoparticle) ventilation imaging and the investigational CT ventilation imaging in lung cancer patients pre-treatment. Methods: We present results on our first three lung cancer patients (2 male, mean age 78 years) as part of an ongoing ethics approved study. For each patient a PET Galligas ventilation (PET-V) image and a pair of breath hold CT images (end-exhale and end-inhale tidal volumes) were acquired using a Siemens Biograph PET CT. CT-ventilation (CT-V) images were created from the pair of CT images usingmore » deformable image registration (DIR) algorithms and the Hounsfield Unit (HU) ventilation metric. A comparison of ventilation quantification from each modality was done on the lobar level and the voxel level. A Bland-Altman plot was used to assess the difference in mean percentage contribution of each lobe to the total lung function between the two modalities. For each patient, a voxel-wise Spearmans correlation was calculated for the whole lungs between the two modalities. Results: The Bland-Altman plot demonstrated strong agreement between PET-V and CT-V for assessment of lobar function (r=0.99, p<0.001; range mean difference: −5.5 to 3.0). The correlation between PET-V and CT-V at the voxel level was moderate(r=0.60, p<0.001). Conclusion: This preliminary study on the three patients data sets demonstrated strong agreement between PET and CT ventilation imaging for the assessment of pre-treatment lung function at the lobar level. Agreement was only moderate at the level of voxel correlations. These results indicate that CT ventilation imaging has potential for assessing pre-treatment lobar lung function in lung cancer patients.« less

A hybrid approach for fusing 4D-MRI temporal information with 3D-CT for the study of lung and lung tumor motion.

PubMed

Yang, Y X; Teo, S-K; Van Reeth, E; Tan, C H; Tham, I W K; Poh, C L

2015-08-01

Accurate visualization of lung motion is important in many clinical applications, such as radiotherapy of lung cancer. Advancement in imaging modalities [e.g., computed tomography (CT) and MRI] has allowed dynamic imaging of lung and lung tumor motion. However, each imaging modality has its advantages and disadvantages. The study presented in this paper aims at generating synthetic 4D-CT dataset for lung cancer patients by combining both continuous three-dimensional (3D) motion captured by 4D-MRI and the high spatial resolution captured by CT using the authors' proposed approach. A novel hybrid approach based on deformable image registration (DIR) and finite element method simulation was developed to fuse a static 3D-CT volume (acquired under breath-hold) and the 3D motion information extracted from 4D-MRI dataset, creating a synthetic 4D-CT dataset. The study focuses on imaging of lung and lung tumor. Comparing the synthetic 4D-CT dataset with the acquired 4D-CT dataset of six lung cancer patients based on 420 landmarks, accurate results (average error <2 mm) were achieved using the authors' proposed approach. Their hybrid approach achieved a 40% error reduction (based on landmarks assessment) over using only DIR techniques. The synthetic 4D-CT dataset generated has high spatial resolution, has excellent lung details, and is able to show movement of lung and lung tumor over multiple breathing cycles.

Automatic Detection of Lung and Liver Lesions in 3-D Positron Emission Tomography Images: A Pilot Study

NASA Astrophysics Data System (ADS)

Lartizien, Carole; Marache-Francisco, Simon; Prost, Rémy

2012-02-01

Positron emission tomography (PET) using fluorine-18 deoxyglucose (18F-FDG) has become an increasingly recommended tool in clinical whole-body oncology imaging for the detection, diagnosis, and follow-up of many cancers. One way to improve the diagnostic utility of PET oncology imaging is to assist physicians facing difficult cases of residual or low-contrast lesions. This study aimed at evaluating different schemes of computer-aided detection (CADe) systems for the guided detection and localization of small and low-contrast lesions in PET. These systems are based on two supervised classifiers, linear discriminant analysis (LDA) and the nonlinear support vector machine (SVM). The image feature sets that serve as input data consisted of the coefficients of an undecimated wavelet transform. An optimization study was conducted to select the best combination of parameters for both the SVM and the LDA. Different false-positive reduction (FPR) methods were evaluated to reduce the number of false-positive detections per image (FPI). This includes the removal of small detected clusters and the combination of the LDA and SVM detection maps. The different CAD schemes were trained and evaluated based on a simulated whole-body PET image database containing 250 abnormal cases with 1230 lesions and 250 normal cases with no lesion. The detection performance was measured on a separate series of 25 testing images with 131 lesions. The combination of the LDA and SVM score maps was shown to produce very encouraging detection performance for both the lung lesions, with 91% sensitivity and 18 FPIs, and the liver lesions, with 94% sensitivity and 10 FPIs. Comparison with human performance indicated that the different CAD schemes significantly outperformed human detection sensitivities, especially regarding the low-contrast lesions.

(⁹⁹m)Tc-MAA overestimates the absorbed dose to the lungs in radioembolization: a quantitative evaluation in patients treated with ¹⁶⁶Ho-microspheres.

PubMed

Elschot, Mattijs; Nijsen, Johannes F W; Lam, Marnix G E H; Smits, Maarten L J; Prince, Jip F; Viergever, Max A; van den Bosch, Maurice A A J; Zonnenberg, Bernard A; de Jong, Hugo W A M

2014-10-01

Radiation pneumonitis is a rare but serious complication of radioembolic therapy of liver tumours. Estimation of the mean absorbed dose to the lungs based on pretreatment diagnostic (99m)Tc-macroaggregated albumin ((99m)Tc-MAA) imaging should prevent this, with administered activities adjusted accordingly. The accuracy of (99m)Tc-MAA-based lung absorbed dose estimates was evaluated and compared to absorbed dose estimates based on pretreatment diagnostic (166)Ho-microsphere imaging and to the actual lung absorbed doses after (166)Ho radioembolization. This prospective clinical study included 14 patients with chemorefractory, unresectable liver metastases treated with (166)Ho radioembolization. (99m)Tc-MAA-based and (166)Ho-microsphere-based estimation of lung absorbed doses was performed on pretreatment diagnostic planar scintigraphic and SPECT/CT images. The clinical analysis was preceded by an anthropomorphic torso phantom study with simulated lung shunt fractions of 0 to 30 % to determine the accuracy of the image-based lung absorbed dose estimates after (166)Ho radioembolization. In the phantom study, (166)Ho SPECT/CT-based lung absorbed dose estimates were more accurate (absolute error range 0.1 to -4.4 Gy) than (166)Ho planar scintigraphy-based lung absorbed dose estimates (absolute error range 9.5 to 12.1 Gy). Clinically, the actual median lung absorbed dose was 0.02 Gy (range 0.0 to 0.7 Gy) based on posttreatment (166)Ho-microsphere SPECT/CT imaging. Lung absorbed doses estimated on the basis of pretreatment diagnostic (166)Ho-microsphere SPECT/CT imaging (median 0.02 Gy, range 0.0 to 0.4 Gy) were significantly better predictors of the actual lung absorbed doses than doses estimated on the basis of (166)Ho-microsphere planar scintigraphy (median 10.4 Gy, range 4.0 to 17.3 Gy; p < 0.001), (99m)Tc-MAA SPECT/CT imaging (median 2.5 Gy, range 1.2 to 12.3 Gy; p < 0.001), and (99m)Tc-MAA planar scintigraphy (median 5.5 Gy, range 2.3 to 18.2 Gy; p < 0.001). In clinical practice, lung absorbed doses are significantly overestimated by pretreatment diagnostic (99m)Tc-MAA imaging. Pretreatment diagnostic (166)Ho-microsphere SPECT/CT imaging accurately predicts lung absorbed doses after (166)Ho radioembolization.

Registration-based assessment of regional lung function via volumetric CT images of normal subjects vs. severe asthmatics

PubMed Central

Choi, Sanghun; Hoffman, Eric A.; Wenzel, Sally E.; Tawhai, Merryn H.; Yin, Youbing; Castro, Mario

2013-01-01

The purpose of this work was to explore the use of image registration-derived variables associated with computed tomographic (CT) imaging of the lung acquired at multiple volumes. As an evaluation of the utility of such an imaging approach, we explored two groups at the extremes of population ranging from normal subjects to severe asthmatics. A mass-preserving image registration technique was employed to match CT images at total lung capacity (TLC) and functional residual capacity (FRC) for assessment of regional air volume change and lung deformation between the two states. Fourteen normal subjects and thirty severe asthmatics were analyzed via image registration-derived metrics together with their pulmonary function test (PFT) and CT-based air-trapping. Relative to the normal group, the severely asthmatic group demonstrated reduced air volume change (consistent with air trapping) and more isotropic deformation in the basal lung regions while demonstrating increased air volume change associated with increased anisotropic deformation in the apical lung regions. These differences were found despite the fact that both PFT-derived TLC and FRC in the two groups were nearly 100% of predicted values. Data suggest that reduced basal-lung air volume change in severe asthmatics was compensated by increased apical-lung air volume change and that relative increase in apical-lung air volume change in severe asthmatics was accompanied by enhanced anisotropic deformation. These data suggest that CT-based deformation, assessed via inspiration vs. expiration scans, provides a tool for distinguishing differences in lung mechanics when applied to the extreme ends of a population range. PMID:23743399

Ultrahigh resolution optical coherence tomography imaging of diseased rat lung using Gaussian shaped super continuum sources

NASA Astrophysics Data System (ADS)

Nishizawa, N.; Ishida, S.; Kitatsuji, M.; Ohshima, H.; Hasegawa, Y.; Matsushima, M.; Kawabe, T.

2012-02-01

We have been investigating ultrahigh resolution optical coherence tomography (UHR-OCT) imaging of lung tissues using fiber super continuum sources. The high power, low-noise, Gaussian shaped supercontinuum generated with ultrashort pulses and optical fibers at several wavelengths were used as the broadband light sources for UHR-OCT. For the 800 nm wavelength region, the axial resolution was 3.0 um in air and 2.0 um in tissue. Since the lung consists of tiny alveoli which are separated by thin wall, the UHR-OCT is supposed to be effective for lung imaging. The clear images of alveoli of rat were observed with and without index matching effects by saline. In this work, we investigated the UHR-OCT imaging of lung disease model. The lipopolysaccharide (LPS) induced acute lung injury / acute respiratory distress syndrome (ALI/ARDS) model of rat was prepared as the sample with disease and the UHR-OCT imaging of the disease part was demonstrated. The increment of signal intensity by pleural thickening was observed. The accumulation of exudative fluid in alveoli was also observed for two samples. By the comparison with normal lung images, we can obviously show the difference in the ALI/ARDS models. Since the lung consists of alveolar surrounded by capillary vessels, the effect of red-blood cells (RBC) is considered to be important. In this work, ex-vivo UHR-OCT imaging of RBC was demonstrated. Each RBC was able to be observed individually using UHR-OCT. The effect of RBC was estimated with the rat lung perfused with PBS.

Uniform background assumption produces misleading lung EIT images.

PubMed

Grychtol, Bartłomiej; Adler, Andy

2013-06-01

Electrical impedance tomography (EIT) estimates an image of conductivity change within a body from stimulation and measurement at body surface electrodes. There is significant interest in EIT for imaging the thorax, as a monitoring tool for lung ventilation. To be useful in this application, we require an understanding of if and when EIT images can produce inaccurate images. In this paper, we study the consequences of the homogeneous background assumption, frequently made in linear image reconstruction, which introduces a mismatch between the reference measurement and the linearization point. We show in simulation and experimental data that the resulting images may contain large and clinically significant errors. A 3D finite element model of thorax conductivity is used to simulate EIT measurements for different heart and lung conductivity, size and position, as well as different amounts of gravitational collapse and ventilation-associated conductivity change. Three common linear EIT reconstruction algorithms are studied. We find that the asymmetric position of the heart can cause EIT images of ventilation to show up to 60% undue bias towards the left lung and that the effect is particularly strong for a ventilation distribution typical of mechanically ventilated patients. The conductivity gradient associated with gravitational lung collapse causes conductivity changes in non-dependent lung to be overestimated by up to 100% with respect to the dependent lung. Eliminating the mismatch by using a realistic conductivity distribution in the forward model of the reconstruction algorithm strongly reduces these undesirable effects. We conclude that subject-specific anatomically accurate forward models should be used in lung EIT and extra care is required when analysing EIT images of subjects whose background conductivity distribution in the lungs is known to be heterogeneous or exhibiting large changes.

The Society of Thoracic Surgeons General Thoracic Surgery Database: establishing generalizability to national lung cancer resection outcomes.

PubMed

LaPar, Damien J; Bhamidipati, Castigliano M; Lau, Christine L; Jones, David R; Kozower, Benjamin D

2012-07-01

The Society of Thoracic Surgeons General Thoracic Surgery Database (GTDB) has demonstrated outstanding results for lung cancer resection. However, whether the GTDB results are generalizable nationwide is unknown. The purpose of this study was to establish the generalizability of the GTDB by comparing lung cancer resection results with those of the Nationwide Inpatient Sample (NIS), the largest all-payer inpatient database in the United States. From 2002 to 2008, primary lung cancer resection outcomes were compared between the GTDB (n = 19,903) and the NIS (n = 246,469). Primary outcomes were the proportion of procedures performed nationally that were captured in the GTDB and differences in mortality rates and hospital length of stay. Observed differences in patient characteristics, operative procedures, and postoperative events were also analyzed. Annual GTDB lung cancer resection volume has increased over time but only captures an estimated 8% of resections performed nationally. The GTDB and NIS databases had similar median patient age (67 vs 68 years) and female sex (50% vs 49%), lobectomy was the most common procedure (64.7% vs 79.7%; p < 0.001), and pneumonectomies were uncommon (6.3% vs 7.2%; p < 0.001). Compared with NIS, the GTDB had significantly lower unadjusted discharge mortality rates (1.8% vs 3.0%), median length of stay (5.0 vs 7.0 days; p < 0.001), and postoperative pulmonary complication rates (18.5% vs 23.6%, p < 0.001). The GTDB represents a small percentage of the lung cancer resections performed nationally and reports significantly lower mortality rates and shorter hospital length of stay than national results. The GTDB is not broadly generalizable. These results establish a benchmark for future GTDB comparisons and highlight the importance of increasing participation in the database. Copyright © 2012 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

Lung flooding enables efficient lung sonography and tumour imaging in human ex vivo and porcine in vivo lung cancer model

PubMed Central

2013-01-01

Background Sonography has become the imaging technique of choice for guiding intraoperative interventions in abdominal surgery. Due to artefacts from residual air content, however, videothoracoscopic and open intraoperative ultrasound-guided thermoablation of lung malignancies are impossible. Lung flooding is a new method that allows complete ultrasound imaging of lungs and their tumours. Methods Fourteen resected tumourous human lung lobes were examined transpleurally with B-mode ultrasound before (in atelectasis) and after lung flooding with isotonic saline solution. In two swine, the left lung was filled with 15 ml/kg isotonic saline solution through the left side of a double-lumen tube. Lung tumours were simulated by transthoracic ultrasound-guided injection of 5 ml of purified bovine serum albumin in glutaraldehyde, centrally into the left lower lung lobe. The rate of tumour detection, the severity of disability caused by residual gas, and sonomorphology of the lungs and tumours were assessed. Results The ex vivo tumour detection rate was 100% in flooded human lung lobes and 43% (6/14) in atelectatic lungs. In all cases of atelectasis, sonographic tumour imaging was impaired by residual gas. Tumours and atelectatic tissue were isoechoic. In 28% of flooded lungs, a little residual gas was observed that did not impair sonographic tumour imaging. In contrast to tumours, flooded lung tissue was hyperechoic, homogeneous, and of fine-grained structure. Because of the bronchial wall three-laminar structure, sonographic differentiation of vessels and bronchi was possible. In all cases, malignant tumours in the flooded lung appeared well-demarcated from the lung parenchyma. Adenocarcinoma, squamous, and large cell carcinomas were hypoechoic. Bronchioloalveolar cell carcinoma was slightly hyperechoic. Transpleural sonography identifies endobronchial tumour growth and bronchial wall destruction. With transthoracic sonography, the flooded animal lung can be completely examined in vivo. There is no residual gas, which interferes with ultrasound. Pulmonary vessels and bronchi are clearly differentiated. Simulated lung lesions can easily be detected inside the lung lobe. Conclusions Lung flooding enables complete lung sonography and tumour detection. We have developed a novel method that efficiently uses ultrasound for guiding intraoperative interventions in open and endoscopic lung surgery. PMID:23841910

TU-A-12A-02: Novel Lung Ventilation Imaging with Single Energy CT After Single Inhalation of Xenon: Comparison with SPECT Ventilation Images

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

Negahdar, M; Yamamoto, T; Shultz, D

Purpose: We propose a novel lung functional imaging method to determine the spatial distribution of xenon (Xe) gas in a single inhalation as a measure of regional ventilation. We compare Xe-CT ventilation to single-photon emission CT (SPECT) ventilation, which is the current clinical reference. Regional lung ventilation information may be useful for the diagnosis and monitoring of pulmonary diseases such as COPD, radiotherapy planning, and assessing the progression of toxicity after radiation therapy. Methods: In an IRB-approved clinical study, Xe-CT and SPECT ventilation scans were acquired for three patients including one patient with severe emphysema and two lung cancer patientsmore » treated with radiotherapy. For Xe- CT, we acquired two breath-hold single energy CT images of the entire lung with inspiration of 100% O2 and a mixture of 70% Xe and 30% O2, respectively. A video biofeedback system was used to achieve reproducible breath-holds. We used deformable image registration to align the breathhold images with each other to accurately subtract them, producing a map of the distribution of Xe as a surrogate of lung ventilation. We divided each lung into twelve parts and correlated the Hounsfield unit (HU) enhancement at each part with the SPECT ventilation count of the corresponding part of the lung. Results: The mean of the Pearson linear correlation coefficient values between the Xe-CT and ventilation SPECT count for all three patients were 0.62 (p<0.01). The Xe-CT image had a higher resolution than SPECT, and did not show central airway deposition artifacts that were present in the SPECT image. Conclusion: We developed a rapid, safe, clinically practical, and potentially widely accessible method for regional lung functional imaging. We demonstrated strong correlations between the Xe-CT ventilation image and SPECT ventilation image as the clinical reference. This ongoing study will investigate more patients to confirm this finding.« less

Racing Against Lung Cancer: Imaging Tools Help Patient in Cancer Fight

MedlinePlus

... Against Lung Cancer Follow us Racing Against Lung Cancer Imaging tools help patient in cancer fight Photo: Courtesy of Ted Simon In early ... primary care doctor. The diagnosis? Stage 4 lung cancer—advanced cancer that had already spread to some ...

Development of an exposure measurement database on five lung carcinogens (ExpoSYN) for quantitative retrospective occupational exposure assessment.

PubMed

Peters, Susan; Vermeulen, Roel; Olsson, Ann; Van Gelder, Rainer; Kendzia, Benjamin; Vincent, Raymond; Savary, Barbara; Williams, Nick; Woldbæk, Torill; Lavoué, Jérôme; Cavallo, Domenico; Cattaneo, Andrea; Mirabelli, Dario; Plato, Nils; Dahmann, Dirk; Fevotte, Joelle; Pesch, Beate; Brüning, Thomas; Straif, Kurt; Kromhout, Hans

2012-01-01

SYNERGY is a large pooled analysis of case-control studies on the joint effects of occupational carcinogens and smoking in the development of lung cancer. A quantitative job-exposure matrix (JEM) will be developed to assign exposures to five major lung carcinogens [asbestos, chromium, nickel, polycyclic aromatic hydrocarbons (PAH), and respirable crystalline silica (RCS)]. We assembled an exposure database, called ExpoSYN, to enable such a quantitative exposure assessment. Existing exposure databases were identified and European and Canadian research institutes were approached to identify pertinent exposure measurement data. Results of individual air measurements were entered anonymized according to a standardized protocol. The ExpoSYN database currently includes 356 551 measurements from 19 countries. In total, 140 666 personal and 215 885 stationary data points were available. Measurements were distributed over the five agents as follows: RCS (42%), asbestos (20%), chromium (16%), nickel (15%), and PAH (7%). The measurement data cover the time period from 1951 to present. However, only a small portion of measurements (1.4%) were performed prior to 1975. The major contributing countries for personal measurements were Germany (32%), UK (22%), France (14%), and Norway and Canada (both 11%). ExpoSYN is a unique occupational exposure database with measurements from 18 European countries and Canada covering a time period of >50 years. This database will be used to develop a country-, job-, and time period-specific quantitative JEM. This JEM will enable data-driven quantitative exposure assessment in a multinational pooled analysis of community-based lung cancer case-control studies.

[Application of medical imaging to general thoracic surgery].

PubMed

Oizumi, Hiroyuki

2014-07-01

Medical imaging technology is rapidly progressing. Positron emission tomography (PET) has played major role in the staging and choice of treatment modality in lung cancer patients. Magnetic resonance imaging (MRI) is now routinely used for mediastinal tumors and the use of diffusion-weighted images (DWI) may help in the diagnosis of malignancies including lung cancers. The benefits of medical imaging technology are not limited to diagnostics, and include simulation or navigation for complex lung resection and other procedures. Multidetector row computed tomography (MDCT) shortens imaging time to obtain detailed and precise volume data, which improves diagnosis of small-sized lung cancers. 3-dimensional reconstruction of the volume data allows the safe performance of thoracoscopic surgery. For lung lobectomy, identification of the branching structures, diameter, and length of the arteries is useful in selecting the procedure for blood vessel treatment. For lung segmentectomy, visualization of venous branches in the affected segments and intersegmental veins has facilitated the preoperative determination of the anatomical intersegmental plane. Therefore, the application of medical imaging technology is useful in general thoracic surgery.

Lung boundary detection in pediatric chest x-rays

NASA Astrophysics Data System (ADS)

Candemir, Sema; Antani, Sameer; Jaeger, Stefan; Browning, Renee; Thoma, George R.

2015-03-01

Tuberculosis (TB) is a major public health problem worldwide, and highly prevalent in developing countries. According to the World Health Organization (WHO), over 95% of TB deaths occur in low- and middle- income countries that often have under-resourced health care systems. In an effort to aid population screening in such resource challenged settings, the U.S. National Library of Medicine has developed a chest X-ray (CXR) screening system that provides a pre-decision on pulmonary abnormalities. When the system is presented with a digital CXR image from the Picture Archive and Communication Systems (PACS) or an imaging source, it automatically identifies the lung regions in the image, extracts image features, and classifies the image as normal or abnormal using trained machine-learning algorithms. The system has been trained on adult CXR images, and this article presents enhancements toward including pediatric CXR images. Our adult lung boundary detection algorithm is model-based. We note the lung shape differences during pediatric developmental stages, and adulthood, and propose building new lung models suitable for pediatric developmental stages. In this study, we quantify changes in lung shape from infancy to adulthood toward enhancing our lung segmentation algorithm. Our initial findings suggest pediatric age groupings of 0 - 23 months, 2 - 10 years, and 11 - 18 years. We present justification for our groupings. We report on the quality of boundary detection algorithm with the pediatric lung models.

A segmentation method for lung nodule image sequences based on superpixels and density-based spatial clustering of applications with noise

PubMed Central

Zhang, Wei; Zhang, Xiaolong; Qiang, Yan; Tian, Qi; Tang, Xiaoxian

2017-01-01

The fast and accurate segmentation of lung nodule image sequences is the basis of subsequent processing and diagnostic analyses. However, previous research investigating nodule segmentation algorithms cannot entirely segment cavitary nodules, and the segmentation of juxta-vascular nodules is inaccurate and inefficient. To solve these problems, we propose a new method for the segmentation of lung nodule image sequences based on superpixels and density-based spatial clustering of applications with noise (DBSCAN). First, our method uses three-dimensional computed tomography image features of the average intensity projection combined with multi-scale dot enhancement for preprocessing. Hexagonal clustering and morphological optimized sequential linear iterative clustering (HMSLIC) for sequence image oversegmentation is then proposed to obtain superpixel blocks. The adaptive weight coefficient is then constructed to calculate the distance required between superpixels to achieve precise lung nodules positioning and to obtain the subsequent clustering starting block. Moreover, by fitting the distance and detecting the change in slope, an accurate clustering threshold is obtained. Thereafter, a fast DBSCAN superpixel sequence clustering algorithm, which is optimized by the strategy of only clustering the lung nodules and adaptive threshold, is then used to obtain lung nodule mask sequences. Finally, the lung nodule image sequences are obtained. The experimental results show that our method rapidly, completely and accurately segments various types of lung nodule image sequences. PMID:28880916

Volumetric MRI of the lungs during forced expiration.

PubMed

Berman, Benjamin P; Pandey, Abhishek; Li, Zhitao; Jeffries, Lindsie; Trouard, Theodore P; Oliva, Isabel; Cortopassi, Felipe; Martin, Diego R; Altbach, Maria I; Bilgin, Ali

2016-06-01

Lung function is typically characterized by spirometer measurements, which do not offer spatially specific information. Imaging during exhalation provides spatial information but is challenging due to large movement over a short time. The purpose of this work is to provide a solution to lung imaging during forced expiration using accelerated magnetic resonance imaging. The method uses radial golden angle stack-of-stars gradient echo acquisition and compressed sensing reconstruction. A technique for dynamic three-dimensional imaging of the lungs from highly undersampled data is developed and tested on six subjects. This method takes advantage of image sparsity, both spatially and temporally, including the use of reference frames called bookends. Sparsity, with respect to total variation, and residual from the bookends, enables reconstruction from an extremely limited amount of data. Dynamic three-dimensional images can be captured at sub-150 ms temporal resolution, using only three (or less) acquired radial lines per slice per timepoint. The images have a spatial resolution of 4.6×4.6×10 mm. Lung volume calculations based on image segmentation are compared to those from simultaneously acquired spirometer measurements. Dynamic lung imaging during forced expiration is made possible by compressed sensing accelerated dynamic three-dimensional radial magnetic resonance imaging. Magn Reson Med 75:2295-2302, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Feasibility of using 'lung density' values estimated from EIT images for clinical diagnosis of lung abnormalities in mechanically ventilated ICU patients.

PubMed

Nebuya, Satoru; Koike, Tomotaka; Imai, Hiroshi; Iwashita, Yoshiaki; Brown, Brian H; Soma, Kazui

2015-06-01

This paper reports on the results of a study which compares lung density values obtained from electrical impedance tomography (EIT), clinical diagnosis and CT values (HU) within a region of interest in the lung. The purpose was to assess the clinical use of lung density estimation using EIT data. In 11 patients supported by a mechanical ventilator, the consistency of regional lung density measurements as estimated by EIT was validated to assess the feasibility of its use in intensive care medicine. There were significant differences in regional lung densities recorded in the supine position between normal lungs and diseased lungs associated with pneumonia, atelectasis and pleural effusion (normal; 240 ± 71.7 kg m(-3), pneumonia; 306 ± 38.6 kg m(-3), atelectasis; 497 ± 130 kg m(-3), pleural effusion; 467 ± 113 kg m(-3): Steel-Dwass test, p < 0.05). In addition, in order to compare lung density with CT image pixels, the image resolution of CT images, which was originally 512 × 512 pixels, was changed to 16 × 16 pixels to match that of the EIT images. The results of CT and EIT images from five patients in an intensive care unit showed a correlation coefficient of 0.66 ± 0.13 between the CT values (HU) and the lung density values (kg m(-3)) obtained from EIT. These results indicate that it may be possible to obtain a quantitative value for regional lung density using EIT.

Chest X-ray and chest CT findings in patients diagnosed with pulmonary tuberculosis following solid organ transplantation: a systematic review.

PubMed

Giacomelli, Irai Luis; Schuhmacher Neto, Roberto; Marchiori, Edson; Pereira, Marisa; Hochhegger, Bruno

2018-04-01

The objective of this systematic review was to select articles including chest X-ray or chest CT findings in patients who developed pulmonary tuberculosis following solid organ transplantation (lung, kidney, or liver). The following search terms were used: "tuberculosis"; "transplants"; "transplantation"; "mycobacterium"; and "lung". The databases used in this review were PubMed and the Brazilian Biblioteca Virtual em Saúde (Virtual Health Library). We selected articles in English, Portuguese, or Spanish, regardless of the year of publication, that met the selection criteria in their title, abstract, or body of text. Articles with no data on chest CT or chest X-ray findings were excluded, as were those not related to solid organ transplantation or pulmonary tuberculosis. We selected 29 articles involving a collective total of 219 patients. The largest samples were in studies conducted in Brazil and South Korea (78 and 35 patients, respectively). The imaging findings were subdivided into five common patterns. The imaging findings varied depending on the transplanted organ in these patients. In liver and lung transplant recipients, the most common pattern was the classic one for pulmonary tuberculosis (cavitation and "tree-in-bud" nodules), which is similar to the findings for pulmonary tuberculosis in the general population. The proportion of cases showing a miliary pattern and lymph node enlargement, which is most similar to the pattern seen in patients coinfected with tuberculosis and HIV, was highest among the kidney transplant recipients. Further studies evaluating clinical data, such as immunosuppression regimens, are needed in order to improve understanding of the distribution of these imaging patterns in this population.

Defining Tumor Cell and Immune Cell Behavior in Vivo during Pulmonary Metastasis of Breast Cancer

DTIC Science & Technology

2014-09-01

outcome of which is still to be determined. 15. SUBJECT TERMS Lung Metastasis, Intravital Imaging , Tumor Immunology, Tumor Microparticles 16. SECURITY...metastatic fitness in the lung. 2) KEYWORDS: Metastasis, Intravital Imaging , Lung, Breast Cancer, 3) ACCOMPLISHMENTS: What were the major goals of the... intravital imaging of Lung Metastasis b) Characterization of Tumor Cell Behavior During Pulmonary Seeding via 2-photon microscopy c) Characterization of

A hybrid approach for fusing 4D-MRI temporal information with 3D-CT for the study of lung and lung tumor motion

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

Yang, Y. X.; Van Reeth, E.; Poh, C. L., E-mail: clpoh@ntu.edu.sg

2015-08-15

Purpose: Accurate visualization of lung motion is important in many clinical applications, such as radiotherapy of lung cancer. Advancement in imaging modalities [e.g., computed tomography (CT) and MRI] has allowed dynamic imaging of lung and lung tumor motion. However, each imaging modality has its advantages and disadvantages. The study presented in this paper aims at generating synthetic 4D-CT dataset for lung cancer patients by combining both continuous three-dimensional (3D) motion captured by 4D-MRI and the high spatial resolution captured by CT using the authors’ proposed approach. Methods: A novel hybrid approach based on deformable image registration (DIR) and finite elementmore » method simulation was developed to fuse a static 3D-CT volume (acquired under breath-hold) and the 3D motion information extracted from 4D-MRI dataset, creating a synthetic 4D-CT dataset. Results: The study focuses on imaging of lung and lung tumor. Comparing the synthetic 4D-CT dataset with the acquired 4D-CT dataset of six lung cancer patients based on 420 landmarks, accurate results (average error <2 mm) were achieved using the authors’ proposed approach. Their hybrid approach achieved a 40% error reduction (based on landmarks assessment) over using only DIR techniques. Conclusions: The synthetic 4D-CT dataset generated has high spatial resolution, has excellent lung details, and is able to show movement of lung and lung tumor over multiple breathing cycles.« less

PET/CT Assessment of Response to Therapy: Tumor Change Measurement, Truth Data, and Error1

PubMed Central

Kinahan, Paul E; Doot, Robert K; Wanner-Roybal, Michelle; Bidaut, Luc M; Armato, Samuel G; Meyer, Charles R; McLennan, Geoffrey

2009-01-01

We describe methods and issues that are relevant to the measurement of change in tumor uptake of 18F-fluorodeoxyglucose (FDG) or other radiotracers, as measured from positron emission tomography/computed tomography (PET/CT) images, and how this would relate to the establishment of PET/CT tumor imaging as a biomarker of patient response to therapy. The primary focus is on the uptake of FDG by lung tumors, but the approach can be applied to diseases other than lung cancer and to tracers other than FDG. The first issue addressed is the sources of bias and variance in the measurement of tumor uptake of FDG, and where there are still gaps in our knowledge. These are discussed in the context of measurement variation and how these would relate to the early detection of response to therapy. Some of the research efforts currently underway to identify the magnitude of some of these sources of error are described. In addition, we describe resources for these investigations that are being made available through the Reference Image Database for the Evaluation of Response project. Measures derived from PET image data that might be predictive of patient response as well as the additional issues that each of these metrics may encounter are described briefly. The relationship between individual patient response to therapy and utility for multicenter trials is discussed. We conclude with a discussion of moving from assessing measurement variation to the steps necessary to establish the efficacy of PET/CT imaging as a biomarker for response. PMID:19956382

Three Dimensional Imaging of Paraffin Embedded Human Lung Tissue Samples by Micro-Computed Tomography

PubMed Central

Scott, Anna E.; Vasilescu, Dragos M.; Seal, Katherine A. D.; Keyes, Samuel D.; Mavrogordato, Mark N.; Hogg, James C.; Sinclair, Ian; Warner, Jane A.; Hackett, Tillie-Louise; Lackie, Peter M.

2015-01-01

Background Understanding the three-dimensional (3-D) micro-architecture of lung tissue can provide insights into the pathology of lung disease. Micro computed tomography (µCT) has previously been used to elucidate lung 3D histology and morphometry in fixed samples that have been stained with contrast agents or air inflated and dried. However, non-destructive microstructural 3D imaging of formalin-fixed paraffin embedded (FFPE) tissues would facilitate retrospective analysis of extensive tissue archives of lung FFPE lung samples with linked clinical data. Methods FFPE human lung tissue samples (n = 4) were scanned using a Nikon metrology µCT scanner. Semi-automatic techniques were used to segment the 3D structure of airways and blood vessels. Airspace size (mean linear intercept, Lm) was measured on µCT images and on matched histological sections from the same FFPE samples imaged by light microscopy to validate µCT imaging. Results The µCT imaging protocol provided contrast between tissue and paraffin in FFPE samples (15mm x 7mm). Resolution (voxel size 6.7 µm) in the reconstructed images was sufficient for semi-automatic image segmentation of airways and blood vessels as well as quantitative airspace analysis. The scans were also used to scout for regions of interest, enabling time-efficient preparation of conventional histological sections. The Lm measurements from µCT images were not significantly different to those from matched histological sections. Conclusion We demonstrated how non-destructive imaging of routinely prepared FFPE samples by laboratory µCT can be used to visualize and assess the 3D morphology of the lung including by morphometric analysis. PMID:26030902

Intensity-Based Registration for Lung Motion Estimation

NASA Astrophysics Data System (ADS)

Cao, Kunlin; Ding, Kai; Amelon, Ryan E.; Du, Kaifang; Reinhardt, Joseph M.; Raghavan, Madhavan L.; Christensen, Gary E.

Image registration plays an important role within pulmonary image analysis. The task of registration is to find the spatial mapping that brings two images into alignment. Registration algorithms designed for matching 4D lung scans or two 3D scans acquired at different inflation levels can catch the temporal changes in position and shape of the region of interest. Accurate registration is critical to post-analysis of lung mechanics and motion estimation. In this chapter, we discuss lung-specific adaptations of intensity-based registration methods for 3D/4D lung images and review approaches for assessing registration accuracy. Then we introduce methods for estimating tissue motion and studying lung mechanics. Finally, we discuss methods for assessing and quantifying specific volume change, specific ventilation, strain/ stretch information and lobar sliding.

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

NASA Astrophysics Data System (ADS)

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

2012-03-01

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

A 4DCT imaging-based breathing lung model with relative hysteresis

PubMed Central

Miyawaki, Shinjiro; Choi, Sanghun; Hoffman, Eric A.; Lin, Ching-Long

2016-01-01

To reproduce realistic airway motion and airflow, the authors developed a deforming lung computational fluid dynamics (CFD) model based on four-dimensional (4D, space and time) dynamic computed tomography (CT) images. A total of 13 time points within controlled tidal volume respiration were used to account for realistic and irregular lung motion in human volunteers. Because of the irregular motion of 4DCT-based airways, we identified an optimal interpolation method for airway surface deformation during respiration, and implemented a computational solid mechanics-based moving mesh algorithm to produce smooth deforming airway mesh. In addition, we developed physiologically realistic airflow boundary conditions for both models based on multiple images and a single image. Furthermore, we examined simplified models based on one or two dynamic or static images. By comparing these simplified models with the model based on 13 dynamic images, we investigated the effects of relative hysteresis of lung structure with respect to lung volume, lung deformation, and imaging methods, i.e., dynamic vs. static scans, on CFD-predicted pressure drop. The effect of imaging method on pressure drop was 24 percentage points due to the differences in airflow distribution and airway geometry. PMID:28260811

A 4DCT imaging-based breathing lung model with relative hysteresis

NASA Astrophysics Data System (ADS)

Miyawaki, Shinjiro; Choi, Sanghun; Hoffman, Eric A.; Lin, Ching-Long

2016-12-01

To reproduce realistic airway motion and airflow, the authors developed a deforming lung computational fluid dynamics (CFD) model based on four-dimensional (4D, space and time) dynamic computed tomography (CT) images. A total of 13 time points within controlled tidal volume respiration were used to account for realistic and irregular lung motion in human volunteers. Because of the irregular motion of 4DCT-based airways, we identified an optimal interpolation method for airway surface deformation during respiration, and implemented a computational solid mechanics-based moving mesh algorithm to produce smooth deforming airway mesh. In addition, we developed physiologically realistic airflow boundary conditions for both models based on multiple images and a single image. Furthermore, we examined simplified models based on one or two dynamic or static images. By comparing these simplified models with the model based on 13 dynamic images, we investigated the effects of relative hysteresis of lung structure with respect to lung volume, lung deformation, and imaging methods, i.e., dynamic vs. static scans, on CFD-predicted pressure drop. The effect of imaging method on pressure drop was 24 percentage points due to the differences in airflow distribution and airway geometry.

Single-energy computed tomography-based pulmonary perfusion imaging: Proof-of-principle in a canine model

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

Yamamoto, Tokihiro, E-mail: toyamamoto@ucdavis.edu

Purpose: Radiotherapy (RT) that selectively avoids irradiating highly functional lung regions may reduce pulmonary toxicity, which is substantial in lung cancer RT. Single-energy computed tomography (CT) pulmonary perfusion imaging has several advantages (e.g., higher resolution) over other modalities and has great potential for widespread clinical implementation, particularly in RT. The purpose of this study was to establish proof-of-principle for single-energy CT perfusion imaging. Methods: Single-energy CT perfusion imaging is based on the following: (1) acquisition of end-inspiratory breath-hold CT scans before and after intravenous injection of iodinated contrast agents, (2) deformable image registration (DIR) for spatial mapping of those twomore » CT image data sets, and (3) subtraction of the precontrast image data set from the postcontrast image data set, yielding a map of regional Hounsfield unit (HU) enhancement, a surrogate for regional perfusion. In a protocol approved by the institutional animal care and use committee, the authors acquired CT scans in the prone position for a total of 14 anesthetized canines (seven canines with normal lungs and seven canines with diseased lungs). The elastix algorithm was used for DIR. The accuracy of DIR was evaluated based on the target registration error (TRE) of 50 anatomic pulmonary landmarks per subject for 10 randomly selected subjects as well as on singularities (i.e., regions where the displacement vector field is not bijective). Prior to perfusion computation, HUs of the precontrast end-inspiratory image were corrected for variation in the lung inflation level between the precontrast and postcontrast end-inspiratory CT scans, using a model built from two additional precontrast CT scans at end-expiration and midinspiration. The authors also assessed spatial heterogeneity and gravitationally directed gradients of regional perfusion for normal lung subjects and diseased lung subjects using a two-sample two-tailed t-test. Results: The mean TRE (and standard deviation) was 0.6 ± 0.7 mm (smaller than the voxel dimension) for DIR between pre contrast and postcontrast end-inspiratory CT image data sets. No singularities were observed in the displacement vector fields. The mean HU enhancement (and standard deviation) was 37.3 ± 10.5 HU for normal lung subjects and 30.7 ± 13.5 HU for diseased lung subjects. Spatial heterogeneity of regional perfusion was found to be higher for diseased lung subjects than for normal lung subjects, i.e., a mean coefficient of variation of 2.06 vs 1.59 (p = 0.07). The average gravitationally directed gradient was strong and significant (R{sup 2} = 0.99, p < 0.01) for normal lung dogs, whereas it was moderate and nonsignificant (R{sup 2} = 0.61, p = 0.12) for diseased lung dogs. Conclusions: This canine study demonstrated the accuracy of DIR with subvoxel TREs on average, higher spatial heterogeneity of regional perfusion for diseased lung subjects than for normal lung subjects, and a strong gravitationally directed gradient for normal lung subjects, providing proof-of-principle for single-energy CT pulmonary perfusion imaging. Further studies such as comparison with other perfusion imaging modalities will be necessary to validate the physiological significance.« less

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

Cifter, G; Redler, G; Lee, C

Purpose: Compared to traditional radiotherapy techniques, stereotactic body radiation therapy (SBRT) provides more favorable outcomes during the treatment of certain lung tumors. Despite advancements in image guidance, accurate target localization still remains a challenge. In this work, we expand our knowledge of a novel scatter imaging modality in order to develop a real-time tumor localization method using scattered photons from the patient during treatment. Methods: Images of the QUASAR™ Respiratory Motion Phantom were taken by irradiating it on a Varian TrueBeam accelerator. The scattered radiation was detected using a flat panel-based pinhole camera detection system. Two motion settings were investigated:more » static and dynamic. In the former, the lung tumor was manually shifted between imaging. In the latter, the lung tumor was set to move at a certain frequency and amplitude while the images were acquired continuously for one minute. The accuracy of tumor localization and the irradiation time required to distinguish the lung tumor were studied. Results: The comparison of measured and expected location of the lung tumor during static motion was shown to be under standard deviation (STD) of 0.064 with a mean STD of 0.031cm. The dynamic motion was taken at a rate of 1400 MU/min for one minute and the measured location of the lung tumor was then compared with the QUASAR phantom’s sinusoidal motion pattern and the agreement found to be at an average STD of 0.275cm. The location of the lung tumor was investigated using aggregate images consisting of 1 or 2 frames/image and the change was below STD of 0.30cm. The lung tumor also appeared to be blurrier in images consisting of two frames. Conclusion: Based on our preliminary results real-time image guidance using the scatter imaging modality to localize and track tumors during lung SBRT has the potential to become clinical reality.« less

Clinical Value of a One-Stop-Shop Low-Dose Lung Screening Combined with (18)F-FDG PET/CT for the Detection of Metastatic Lung Nodules from Colorectal Cancer.

PubMed

Han, Yeon-Hee; Lim, Seok Tae; Jeong, Hwan-Jeong; Sohn, Myung-Hee

2016-06-01

The aim of this study was to evaluate the clinical usefulness of additional low-dose high-resolution lung computed tomography (LD-HRCT) combined with (18)F-fluoro-2-deoxyglucose positron emission tomography with CT ((18)F-FDG PET/CT) compared with conventional lung setting image of (18)F-FDG PET/CT for the detection of metastatic lung nodules from colorectal cancer. From January 2011 to September 2011, 649 patients with colorectal cancer underwent additional LD-HRCT at maximum inspiration combined with (18)F-FDG PET/CT. Forty-five patients were finally diagnosed to have lung metastasis based on histopathologic study or clinical follow-up. Twenty-five of the 45 patients had ≤5 metastatic lung nodules and the other 20 patients had >5 metastatic nodules. One hundred and twenty nodules in the 25 patients with ≤5 nodules were evaluated by conventional lung setting image of (18)F-FDG PET/CT and by additional LD-HRCT respectively. Sensitivities, specificities, diagnostic accuracies, positive predictive values (PPVs), and negative predictive values (NPVs) of conventional lung setting image of (18)F-FDG PET/CT and additional LD-HRCT were calculated using standard formulae. The McNemar test and receiver-operating characteristic (ROC) analysis were performed. Of the 120 nodules in the 25 patients with ≤5 metastatic lung nodules, 66 nodules were diagnosed as metastatic. Eleven of the 66 nodules were confirmed histopathologically and the others were diagnosed by clinical follow-up. Conventional lung setting image of (18)F-FDG PET/CT detected 40 of the 66 nodules and additional LD-HRCT detected 55 nodules. All 15 nodules missed by conventional lung setting imaging but detected by additional LD-HRCT were <1 cm in size. The sensitivity, specificity, and diagnostic accuracy of the modalities were 60.6 %, 85.2 %, and 71.1 % for conventional lung setting image and 83.3 %, 88.9 %, and 85.8 % for additional LD-HRCT. By ROC analysis, the area under the ROC curve (AUC) of conventional lung setting image and additional LD-HRCT were 0.712 and 0.827 respectively. Additional LD-HRCT with maximum inspiration was superior to conventional lung setting image of (18)F-FDG PET/CT for the detection of metastatic lung nodules from colorectal cancer (P < 0.05).

Optical imaging of tissue mitochondrial redox state in intact rat lungs in two models of pulmonary oxidative stress

PubMed Central

Sepehr, Reyhaneh; Staniszewski, Kevin; Maleki, Sepideh; Jacobs, Elizabeth R.; Audi, Said

2012-01-01

Abstract. Ventilation with enhanced fractions of O2 (hyperoxia) is a common and necessary treatment for hypoxemia in patients with lung failure, but prolonged exposure to hyperoxia causes lung injury. Ischemia-reperfusion (IR) injury of lung tissue is common in lung transplant or crush injury to the chest. These conditions are associated with apoptosis and decreased survival of lung tissue. The objective of this work is to use cryoimaging to evaluate the effect of exposure to hyperoxia and IR injury on lung tissue mitochondrial redox state in rats. The autofluorescent mitochondrial metabolic coenzymes nicotinamide adenine dinucleotide (NADH) and flavin adenine dinucleotide (FAD) are electron carriers in ATP generation. These intrinsic fluorophores were imaged for rat lungs using low-temperature fluorescence imaging (cryoimaging). Perfused lungs from four groups of rats were studied: normoxia (control), control perfused with an mitochondrial complex IV inhibitor (potassium cyanide, KCN), rats exposed to hyperoxia (85% O2) for seven days, and from rats subjected to lung IR in vivo 24 hours prior to study. Each lung was sectioned sequentially in the transverse direction, and the images were used to reconstruct a three-dimensional (3-D) rendering. In KCN perfused lungs the respiratory chain was more reduced, whereas hyperoxic and IR lung tissue have a more oxidized respiratory chain than control lung tissue, consistent with previously measured mitochondrial dysfunction in both hyperoxic and IR lungs. PMID:22559688

Combined Ventilation and Perfusion Imaging Correlates With the Dosimetric Parameters of Radiation Pneumonitis in Radiation Therapy Planning for Lung Cancer

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

Kimura, Tomoki, E-mail: tkkimura@hiroshima-u.ac.jp; Doi, Yoshiko; Nakashima, Takeo

2015-11-15

Purpose: The purpose of this study was to prospectively investigate clinical correlations between dosimetric parameters associated with radiation pneumonitis (RP) and functional lung imaging. Methods and Materials: Functional lung imaging was performed using four-dimensional computed tomography (4D-CT) for ventilation imaging, single-photon emission computed tomography (SPECT) for perfusion imaging, or both (V/Q-matched region). Using 4D-CT, ventilation imaging was derived from a low attenuation area according to CT numbers below different thresholds (vent-860 and -910). Perfusion imaging at the 10th, 30th, 50th, and 70th percentile perfusion levels (F10-F70) were defined as the top 10%, 30%, 50%, and 70% hyperperfused normal lung, respectively.more » All imaging data were incorporated into a 3D planning system to evaluate correlations between RP dosimetric parameters (where fV20 is the percentage of functional lung volume irradiated with >20 Gy, or fMLD, the mean dose administered to functional lung) and the percentage of functional lung volume. Radiation pneumonitis was evaluated using Common Terminology Criteria for Adverse Events version 4.0. Statistical significance was defined as a P value of <.05. Results: Sixty patients who underwent curative radiation therapy were enrolled (48 patients for non-small cell lung cancer, and 12 patients for small cell lung cancer). Grades 1, 2, and ≥3 RP were observed in 16, 44, and 6 patients, respectively. Significant correlations were observed between the percentage of functional lung volume and fV20 (r=0.4475 in vent-860 and 0.3508 in F30) or fMLD (r=0.4701 in vent-860 and 0.3128 in F30) in patients with grade ≥2 RP. F30∩vent-860 results exhibited stronger correlations with fV20 and fMLD in patients with grade ≥2 (r=0.5509 in fV20 and 0.5320 in fMLD) and grade ≥3 RP (r=0.8770 in fV20 and 0.8518 in fMLD). Conclusions: RP dosimetric parameters correlated significantly with functional lung imaging.« less

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

NASA Astrophysics Data System (ADS)

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

2016-03-01

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

Noninvasive imaging of experimental lung fibrosis.

PubMed

Zhou, Yong; Chen, Huaping; Ambalavanan, Namasivayam; Liu, Gang; Antony, Veena B; Ding, Qiang; Nath, Hrudaya; Eary, Janet F; Thannickal, Victor J

2015-07-01

Small animal models of lung fibrosis are essential for unraveling the molecular mechanisms underlying human fibrotic lung diseases; additionally, they are useful for preclinical testing of candidate antifibrotic agents. The current end-point measures of experimental lung fibrosis involve labor-intensive histological and biochemical analyses. These measures fail to account for dynamic changes in the disease process in individual animals and are limited by the need for large numbers of animals for longitudinal studies. The emergence of noninvasive imaging technologies provides exciting opportunities to image lung fibrosis in live animals as often as needed and to longitudinally track the efficacy of novel antifibrotic compounds. Data obtained by noninvasive imaging provide complementary information to histological and biochemical measurements. In addition, the use of noninvasive imaging in animal studies reduces animal usage, thus satisfying animal welfare concerns. In this article, we review these new imaging modalities with the potential for evaluation of lung fibrosis in small animal models. Such techniques include micro-computed tomography (micro-CT), magnetic resonance imaging, positron emission tomography (PET), single photon emission computed tomography (SPECT), and multimodal imaging systems including PET/CT and SPECT/CT. It is anticipated that noninvasive imaging will be increasingly used in animal models of fibrosis to gain insights into disease pathogenesis and as preclinical tools to assess drug efficacy.

Combined Electrocardiography- and Respiratory-Triggered CT of the Lung to Reduce Respiratory Misregistration Artifacts between Imaging Slabs in Free-Breathing Children: Initial Experience.

PubMed

Goo, Hyun Woo; Allmendinger, Thomas

2017-01-01

Cardiac and respiratory motion artifacts degrade the image quality of lung CT in free-breathing children. The aim of this study was to evaluate the effect of combined electrocardiography (ECG) and respiratory triggering on respiratory misregistration artifacts on lung CT in free-breathing children. In total, 15 children (median age 19 months, range 6 months-8 years; 7 boys), who underwent free-breathing ECG-triggered lung CT with and without respiratory-triggering were included. A pressure-sensing belt of a respiratory gating system was used to obtain the respiratory signal. The degree of respiratory misregistration artifacts between imaging slabs was graded on a 4-point scale (1, excellent image quality) on coronal and sagittal images and compared between ECG-triggered lung CT studies with and without respiratory triggering. A p value < 0.05 was considered significant. Lung CT with combined ECG and respiratory triggering showed significantly less respiratory misregistration artifacts than lung CT with ECG triggering only (1.1 ± 0.4 vs. 2.2 ± 1.0, p = 0.003). Additional respiratory-triggering reduces respiratory misregistration artifacts on ECG-triggered lung CT in free-breathing children.

Content-Based Image Retrieval System for Pulmonary Nodules: Assisting Radiologists in Self-Learning and Diagnosis of Lung Cancer.

PubMed

Dhara, Ashis Kumar; Mukhopadhyay, Sudipta; Dutta, Anirvan; Garg, Mandeep; Khandelwal, Niranjan

2017-02-01

Visual information of similar nodules could assist the budding radiologists in self-learning. This paper presents a content-based image retrieval (CBIR) system for pulmonary nodules, observed in lung CT images. The reported CBIR systems of pulmonary nodules cannot be put into practice as radiologists need to draw the boundary of nodules during query formation and feature database creation. In the proposed retrieval system, the pulmonary nodules are segmented using a semi-automated technique, which requires a seed point on the nodule from the end-user. The involvement of radiologists in feature database creation is also reduced, as only a seed point is expected from radiologists instead of manual delineation of the boundary of the nodules. The performance of the retrieval system depends on the accuracy of the segmentation technique. Several 3D features are explored to improve the performance of the proposed retrieval system. A set of relevant shape and texture features are considered for efficient representation of the nodules in the feature space. The proposed CBIR system is evaluated for three configurations such as configuration-1 (composite rank of malignancy "1","2" as benign and "4","5" as malignant), configuration-2 (composite rank of malignancy "1","2", "3" as benign and "4","5" as malignant), and configuration-3 (composite rank of malignancy "1","2" as benign and "3","4","5" as malignant). Considering top 5 retrieved nodules and Euclidean distance metric, the precision achieved by the proposed method for configuration-1, configuration-2, and configuration-3 are 82.14, 75.91, and 74.27 %, respectively. The performance of the proposed CBIR system is close to the most recent technique, which is dependent on radiologists for manual segmentation of nodules. A computer-aided diagnosis (CAD) system is also developed based on CBIR paradigm. Performance of the proposed CBIR-based CAD system is close to performance of the CAD system using support vector machine.

Tracking lung tissue motion and expansion/compression with inverse consistent image registration and spirometry

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

Christensen, Gary E.; Song, Joo Hyun; Lu, Wei

2007-06-15

Breathing motion is one of the major limiting factors for reducing dose and irradiation of normal tissue for conventional conformal radiotherapy. This paper describes a relationship between tracking lung motion using spirometry data and image registration of consecutive CT image volumes collected from a multislice CT scanner over multiple breathing periods. Temporal CT sequences from 5 individuals were analyzed in this study. The couch was moved from 11 to 14 different positions to image the entire lung. At each couch position, 15 image volumes were collected over approximately 3 breathing periods. It is assumed that the expansion and contraction ofmore » lung tissue can be modeled as an elastic material. Furthermore, it is assumed that the deformation of the lung is small over one-fifth of a breathing period and therefore the motion of the lung can be adequately modeled using a small deformation linear elastic model. The small deformation inverse consistent linear elastic image registration algorithm is therefore well suited for this problem and was used to register consecutive image scans. The pointwise expansion and compression of lung tissue was measured by computing the Jacobian of the transformations used to register the images. The logarithm of the Jacobian was computed so that expansion and compression of the lung were scaled equally. The log-Jacobian was computed at each voxel in the volume to produce a map of the local expansion and compression of the lung during the breathing period. These log-Jacobian images demonstrate that the lung does not expand uniformly during the breathing period, but rather expands and contracts locally at different rates during inhalation and exhalation. The log-Jacobian numbers were averaged over a cross section of the lung to produce an estimate of the average expansion or compression from one time point to the next and compared to the air flow rate measured by spirometry. In four out of five individuals, the average log-Jacobian value and the air flow rate correlated well (R{sup 2}=0.858 on average for the entire lung). The correlation for the fifth individual was not as good (R{sup 2}=0.377 on average for the entire lung) and can be explained by the small variation in tidal volume for this individual. The correlation of the average log-Jacobian value and the air flow rate for images near the diaphragm correlated well in all five individuals (R{sup 2}=0.943 on average). These preliminary results indicate a strong correlation between the expansion/compression of the lung measured by image registration and the air flow rate measured by spirometry. Predicting the location, motion, and compression/expansion of the tumor and normal tissue using image registration and spirometry could have many important benefits for radiotherapy treatment. These benefits include reducing radiation dose to normal tissue, maximizing dose to the tumor, improving patient care, reducing treatment cost, and increasing patient throughput.« less

Tracking lung tissue motion and expansion/compression with inverse consistent image registration and spirometry.

PubMed

Christensen, Gary E; Song, Joo Hyun; Lu, Wei; El Naqa, Issam; Low, Daniel A

2007-06-01

Breathing motion is one of the major limiting factors for reducing dose and irradiation of normal tissue for conventional conformal radiotherapy. This paper describes a relationship between tracking lung motion using spirometry data and image registration of consecutive CT image volumes collected from a multislice CT scanner over multiple breathing periods. Temporal CT sequences from 5 individuals were analyzed in this study. The couch was moved from 11 to 14 different positions to image the entire lung. At each couch position, 15 image volumes were collected over approximately 3 breathing periods. It is assumed that the expansion and contraction of lung tissue can be modeled as an elastic material. Furthermore, it is assumed that the deformation of the lung is small over one-fifth of a breathing period and therefore the motion of the lung can be adequately modeled using a small deformation linear elastic model. The small deformation inverse consistent linear elastic image registration algorithm is therefore well suited for this problem and was used to register consecutive image scans. The pointwise expansion and compression of lung tissue was measured by computing the Jacobian of the transformations used to register the images. The logarithm of the Jacobian was computed so that expansion and compression of the lung were scaled equally. The log-Jacobian was computed at each voxel in the volume to produce a map of the local expansion and compression of the lung during the breathing period. These log-Jacobian images demonstrate that the lung does not expand uniformly during the breathing period, but rather expands and contracts locally at different rates during inhalation and exhalation. The log-Jacobian numbers were averaged over a cross section of the lung to produce an estimate of the average expansion or compression from one time point to the next and compared to the air flow rate measured by spirometry. In four out of five individuals, the average log-Jacobian value and the air flow rate correlated well (R2 = 0.858 on average for the entire lung). The correlation for the fifth individual was not as good (R2 = 0.377 on average for the entire lung) and can be explained by the small variation in tidal volume for this individual. The correlation of the average log-Jacobian value and the air flow rate for images near the diaphragm correlated well in all five individuals (R2 = 0.943 on average). These preliminary results indicate a strong correlation between the expansion/compression of the lung measured by image registration and the air flow rate measured by spirometry. Predicting the location, motion, and compression/expansion of the tumor and normal tissue using image registration and spirometry could have many important benefits for radiotherapy treatment. These benefits include reducing radiation dose to normal tissue, maximizing dose to the tumor, improving patient care, reducing treatment cost, and increasing patient throughput.

Real-time X-ray Imaging of Lung Fluid Volumes in Neonatal Mouse Lung.

PubMed

Van Avermaete, Ashley E; Trac, Phi T; Gauthier, Theresa W; Helms, My N

2016-07-18

At birth, the lung undergoes a profound phenotypic switch from secretion to absorption, which allows for adaptation to breathing independently. Promoting and sustaining this phenotype is critically important in normal alveolar growth and gas exchange throughout life. Several in vitro studies have characterized the role of key regulatory proteins, signaling molecules, and steroid hormones that can influence the rate of lung fluid clearance. However, in vivo examinations must be performed to evaluate whether these regulatory factors play important physiological roles in regulating perinatal lung liquid absorption. As such, the utilization of real time X-ray imaging to determine perinatal lung fluid clearance, or pulmonary edema, represents a technological advancement in the field. Herein, we explain and illustrate an approach to assess the rate of alveolar lung fluid clearance and alveolar flooding in C57BL/6 mice at post natal day 10 using X-ray imaging and analysis. Successful implementation of this protocol requires prior approval from institutional animal care and use committees (IACUC), an in vivo small animal X-ray imaging system, and compatible molecular imaging software.

New Strategies and Challenges in Lung Proteomics and Metabolomics. An Official American Thoracic Society Workshop Report.

PubMed

Bowler, Russell P; Wendt, Chris H; Fessler, Michael B; Foster, Matthew W; Kelly, Rachel S; Lasky-Su, Jessica; Rogers, Angela J; Stringer, Kathleen A; Winston, Brent W

2017-12-01

This document presents the proceedings from the workshop entitled, "New Strategies and Challenges in Lung Proteomics and Metabolomics" held February 4th-5th, 2016, in Denver, Colorado. It was sponsored by the National Heart Lung Blood Institute, the American Thoracic Society, the Colorado Biological Mass Spectrometry Society, and National Jewish Health. The goal of this workshop was to convene, for the first time, relevant experts in lung proteomics and metabolomics to discuss and overcome specific challenges in these fields that are unique to the lung. The main objectives of this workshop were to identify, review, and/or understand: (1) emerging technologies in metabolomics and proteomics as applied to the study of the lung; (2) the unique composition and challenges of lung-specific biological specimens for metabolomic and proteomic analysis; (3) the diverse informatics approaches and databases unique to metabolomics and proteomics, with special emphasis on the lung; (4) integrative platforms across genetic and genomic databases that can be applied to lung-related metabolomic and proteomic studies; and (5) the clinical applications of proteomics and metabolomics. The major findings and conclusions of this workshop are summarized at the end of the report, and outline the progress and challenges that face these rapidly advancing fields.

Computed tomography of ball pythons (Python regius) in curled recumbency.

PubMed

Hedley, Joanna; Eatwell, Kevin; Schwarz, Tobias

2014-01-01

Anesthesia and tube restraint methods are often required for computed tomography (CT) of snakes due to their natural tendency to curl up. However, these restraint methods may cause animal stress. The aim of this study was to determine whether the CT appearance of the lungs differs for ball pythons in a curled position vs. tube restraint. Whole body CT was performed on ten clinically healthy ball pythons, first in curled and then in straight positions restrained in a tube. Curved multiplanar reformatted (MPR) lung images from curled position scans were compared with standard MPR lung images from straight position scans. Lung attenuation and thickness were measured at three locations for each scan. Time for positioning and scanning was 12 ± 5 min shorter for curled snakes compared to tube restraint. Lung parenchyma thickness and attenuation declined from cranial to caudal on both straight and curled position images. Mean lung parenchyma thickness was greater in curled images at locations 1 (P = 0.048) and 3 (P = 0.044). Mean lung parenchyma thickness decreased between location 1 and 2 by 86-87% (straight: curled) and between location 1 and 3 by 51-50% (straight: curled). Mean lung attenuation at location 1 was significantly greater on curled position images than tube restraint images (P = 0.043). Findings indicated that CT evaluation of the lungs is feasible for ball pythons positioned in curled recumbency if curved MPR is available. However, lung parenchyma thickness and attenuation in some locations may vary from those acquired using tube restraint. © 2014 American College of Veterinary Radiology.

Segmentation and Image Analysis of Abnormal Lungs at CT: Current Approaches, Challenges, and Future Trends

PubMed Central

Mansoor, Awais; Foster, Brent; Xu, Ziyue; Papadakis, Georgios Z.; Folio, Les R.; Udupa, Jayaram K.; Mollura, Daniel J.

2015-01-01

The computer-based process of identifying the boundaries of lung from surrounding thoracic tissue on computed tomographic (CT) images, which is called segmentation, is a vital first step in radiologic pulmonary image analysis. Many algorithms and software platforms provide image segmentation routines for quantification of lung abnormalities; however, nearly all of the current image segmentation approaches apply well only if the lungs exhibit minimal or no pathologic conditions. When moderate to high amounts of disease or abnormalities with a challenging shape or appearance exist in the lungs, computer-aided detection systems may be highly likely to fail to depict those abnormal regions because of inaccurate segmentation methods. In particular, abnormalities such as pleural effusions, consolidations, and masses often cause inaccurate lung segmentation, which greatly limits the use of image processing methods in clinical and research contexts. In this review, a critical summary of the current methods for lung segmentation on CT images is provided, with special emphasis on the accuracy and performance of the methods in cases with abnormalities and cases with exemplary pathologic findings. The currently available segmentation methods can be divided into five major classes: (a) thresholding-based, (b) region-based, (c) shape-based, (d) neighboring anatomy–guided, and (e) machine learning–based methods. The feasibility of each class and its shortcomings are explained and illustrated with the most common lung abnormalities observed on CT images. In an overview, practical applications and evolving technologies combining the presented approaches for the practicing radiologist are detailed. ©RSNA, 2015 PMID:26172351

An optimized two-photon method for in vivo lung imaging reveals intimate cell collaborations during infection

NASA Astrophysics Data System (ADS)

Fiole, Daniel; Deman, Pierre; Trescos, Yannick; Douady, Julien; Tournier, Jean-Nicolas

2013-02-01

Lung tissue motion arising from breathing and heart beating has been described as the largest annoyance of in vivo imaging. Consequently, infected lung tissue has never been imaged in vivo thus far, and little is known concerning the kinetics of the mucosal immune system at the cellular level. We have developed an optimized post-processing strategy to overcome tissue motion, based upon two-photon and second harmonic generation (SHG) microscopy. In contrast to previously published data, we have freed the lung parenchyma from any strain and depression in order to maintain the lungs under optimal physiological parameters. Excitation beams swept the sample throughout normal breathing and heart movements, allowing the collection of many images. Given that tissue motion is unpredictably, it was essential to sort images of interest. This step was enhanced by using SHG signal from collagen as a reference for sampling and realignment phases. A normalized cross-correlation criterion was used between a manually chosen reference image and rigid transformations of all others. Using CX3CR1+/gfp mice this process allowed the collection of high resolution images of pulmonary dendritic cells (DCs) interacting with Bacillus anthracis spores, a Gram-positive bacteria responsible for anthrax disease. We imaged lung tissue for up to one hour, without interrupting normal lung physiology. Interestingly, our data revealed unexpected interactions between DCs and macrophages, two specialized phagocytes. These contacts may participate in a better coordinate immune response. Our results not only demonstrate the phagocytizing task of lung DCs but also infer a cooperative role of alveolar macrophages and DCs.

Analysis of speckle patterns in phase-contrast images of lung tissue

NASA Astrophysics Data System (ADS)

Kitchen, M. J.; Paganin, D.; Lewis, R. A.; Yagi, N.; Uesugi, K.

2005-08-01

Propagation-based phase-contrast images of mice lungs have been obtained at the SPring-8 synchrotron research facility. Such images exhibit a speckled intensity pattern that bears a superficial resemblance to alveolar structures. This speckle results from focussing effects as projected air-filled alveoli form aberrated compound refractive lenses. An appropriate phase-retrieval algorithm has been utilized to reconstruct the approximate projected lung tissue thickness from single-phase-contrast mice chest radiographs. The results show projected density variations across the lung, highlighting regions of low density corresponding to air-filled regions. Potentially, this offers a better method than conventional radiography for detecting lung diseases such as fibrosis, emphysema and cancer, though this has yet to be demonstrated. As such, the approach can assist in continuing studies of lung function utilizing propagation-based phase-contrast imaging.

Evaluation of Neonatal Lung Volume Growth by Pulmonary Magnetic Resonance Imaging in Patients with Congenital Diaphragmatic Hernia.

PubMed

Schopper, Melissa A; Walkup, Laura L; Tkach, Jean A; Higano, Nara S; Lim, Foong Yen; Haberman, Beth; Woods, Jason C; Kingma, Paul S

2017-09-01

To evaluate postnatal lung volume in infants with congenital diaphragmatic hernia (CDH) and determine if a compensatory increase in lung volume occurs during the postnatal period. Using a novel pulmonary magnetic resonance imaging method for imaging neonatal lungs, the postnatal lung volumes in infants with CDH were determined and compared with prenatal lung volumes obtained via late gestation magnetic resonance imaging. Infants with left-sided CDH (2 mild, 9 moderate, and 1 severe) were evaluated. The total lung volume increased in all infants, with the contralateral lung increasing faster than the ipsilateral lung (mean ± SD: 4.9 ± 3.0 mL/week vs 3.4 ± 2.1 mL/week, P = .005). In contrast to prenatal studies, the volume of lungs of infants with more severe CDH grew faster than the lungs of infants with more mild CDH (Spearman's ρ=-0.086, P = .01). Although the contralateral lung volume grew faster in both mild and moderate groups, the majority of total lung volume growth in moderate CDH came from increased volume of the ipsilateral lung (42% of total lung volume increase in the moderate group vs 32% of total lung volume increase in the mild group, P = .09). Analysis of multiple clinical variables suggests that increased weight gain was associated with increased compensatory ipsilateral lung volume growth (ρ = 0.57, P = .05). These results suggest a potential for postnatal catch-up growth in infants with pulmonary hypoplasia and suggest that weight gain may increase the volume growth of the more severely affected lung. Copyright © 2017 Elsevier Inc. All rights reserved.

Using Dual Fluorescence Reporting Genes to Establish an In Vivo Imaging Model of Orthotopic Lung Adenocarcinoma in Mice.

PubMed

Lai, Cheng-Wei; Chen, Hsiao-Ling; Yen, Chih-Ching; Wang, Jiun-Long; Yang, Shang-Hsun; Chen, Chuan-Mu

2016-12-01

Lung adenocarcinoma is characterized by a poor prognosis and high mortality worldwide. In this study, we purposed to use the live imaging techniques and a reporter gene that generates highly penetrative near-infrared (NIR) fluorescence to establish a preclinical animal model that allows in vivo monitoring of lung cancer development and provides a non-invasive tool for the research on lung cancer pathogenesis and therapeutic efficacy. A human lung adenocarcinoma cell line (A549), which stably expressed the dual fluorescence reporting gene (pCAG-iRFP-2A-Venus), was used to generate subcutaneous or orthotopic lung cancer in nude mice. Cancer development was evaluated by live imaging via the NIR fluorescent signals from iRFP, and the signals were verified ex vivo by the green fluorescence of Venus from the gross lung. The tumor-bearing mice received miR-16 nucleic acid therapy by intranasal administration to demonstrate therapeutic efficacy in this live imaging system. For the subcutaneous xenografts, the detection of iRFP fluorescent signals revealed delicate changes occurring during tumor growth that are not distinguishable by conventional methods of tumor measurement. For the orthotopic xenografts, the positive correlation between the in vivo iRFP signal from mice chests and the ex vivo green fluorescent signal from gross lung tumors and the results of the suppressed tumorigenesis by miR-16 treatment indicated that lung tumor size can be accurately quantified by the emission of NIR fluorescence. In addition, orthotopic lung tumor localization can be accurately visualized using iRFP fluorescence tomography in vivo, thus revealing the trafficking of lung tumor cells. We introduced a novel dual fluorescence lung cancer model that provides a non-invasive option for preclinical research via the use of NIR fluorescence in live imaging of lung.

Evaluation of a deformable registration algorithm for subsequent lung computed tomography imaging during radiochemotherapy

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

Stützer, Kristin; Haase, Robert; Exner, Florian

2016-09-15

Purpose: Rating both a lung segmentation algorithm and a deformable image registration (DIR) algorithm for subsequent lung computed tomography (CT) images by different evaluation techniques. Furthermore, investigating the relative performance and the correlation of the different evaluation techniques to address their potential value in a clinical setting. Methods: Two to seven subsequent CT images (69 in total) of 15 lung cancer patients were acquired prior, during, and after radiochemotherapy. Automated lung segmentations were compared to manually adapted contours. DIR between the first and all following CT images was performed with a fast algorithm specialized for lung tissue registration, requiring themore » lung segmentation as input. DIR results were evaluated based on landmark distances, lung contour metrics, and vector field inconsistencies in different subvolumes defined by eroding the lung contour. Correlations between the results from the three methods were evaluated. Results: Automated lung contour segmentation was satisfactory in 18 cases (26%), failed in 6 cases (9%), and required manual correction in 45 cases (66%). Initial and corrected contours had large overlap but showed strong local deviations. Landmark-based DIR evaluation revealed high accuracy compared to CT resolution with an average error of 2.9 mm. Contour metrics of deformed contours were largely satisfactory. The median vector length of inconsistency vector fields was 0.9 mm in the lung volume and slightly smaller for the eroded volumes. There was no clear correlation between the three evaluation approaches. Conclusions: Automatic lung segmentation remains challenging but can assist the manual delineation process. Proven by three techniques, the inspected DIR algorithm delivers reliable results for the lung CT data sets acquired at different time points. Clinical application of DIR demands a fast DIR evaluation to identify unacceptable results, for instance, by combining different automated DIR evaluation methods.« less

Double lung transplants have significantly improved survival compared with single lung transplants in high lung allocation score patients.

PubMed

Black, Matthew C; Trivedi, Jaimin; Schumer, Erin M; Bousamra, Michael; van Berkel, Victor

2014-11-01

Historically, double lung transplantation survival rates are higher than those of single lung transplantation, but in critically ill patients a single lung transplant, with less associated operative morbidity, could afford a better outcome. This article evaluates how survival is affected in patients who have a high lung allocation score (LAS) and receive a single versus a double lung transplant. The UNOS Thoracic Transplant Database for lung transplants from January 2005 to June 2012 was used for analysis. Propensity matching was used to minimize differences between the high and low LAS groups and between single and double lung transplants in the high LAS group. Within this database, there were 8,778 patients, of whom 8,050 had an LAS less than 75 and 728 had an LAS greater than or equal to 75. Kaplan-Meier survival curves stratified by high and low LAS, and by single versus double lung transplants, showed a marked decrease in survival (p<0.001) in those with a high LAS who received a single lung transplant when compared with those with a high LAS who received a double lung transplant. This was a much greater difference in survival than was present in the low LAS patient population. Despite a higher operative morbidity, patients who had a high LAS did substantially better in terms of survival if two lungs were transplanted rather than only one, with a larger difference in survival than for patients with a lower LAS. Copyright © 2014 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

Lung Metastases in Neuroblastoma at Initial Diagnosis: A Report from the International Neuroblastoma Risk Group (INRG) Project

PubMed Central

DuBois, Steven G.; London, Wendy B.; Zhang, Yang; Matthay, Katherine K.; Monclair, Tom; Ambros, Peter F.; Cohn, Susan L.; Pearson, Andrew; Diller, Lisa

2009-01-01

Background Neuroblastoma is the most common extracranial pediatric solid cancer. Lung metastasis is rarely detected in children with newly diagnosed neuroblastoma. We aimed to describe the incidence, clinical characteristics, and outcome of patients with lung metastasis at initial diagnosis using a large international database. Procedure The subset of patients from the International Neuroblastoma Risk Group database with INSS stage 4 neuroblastoma and known data regarding lung metastasis at diagnosis was selected for analysis. Clinical and biological characteristics were compared between patients with and without lung metastasis. Survival for patients with and without lung metastasis was estimated by Kaplan-Meier methods. Cox proportional hazards methods were used to determine the independent prognostic value of lung metastasis at diagnosis. Results Of the 2,808 patients with INSS stage 4 neuroblastoma diagnosed between 1990 and 2002, 100 patients (3.6%) were reported to have lung metastasis at diagnosis. Lung metastasis was more common among patients with MYCN amplified tumors, adrenal primary tumors, or elevated lactate dehydrogenase (LDH) levels (p < 0.02 in each case). Five-year overall survival ± standard error for patients with lung metastasis was 34.5% ± 6.8% compared to 44.7% ± 1.3% for patients without lung metastasis (p=0.0002). However, in multivariable analysis, the presence of lung metastasis was not independently predictive of outcome. Conclusions Lung metastasis at initial diagnosis of neuroblastoma is associated with MYCN amplification and elevated LDH levels. Although lung metastasis at diagnosis was not independently predictive of outcome in this analysis, it remains a useful prognostic marker of unfavorable outcome. PMID:18649370

Dynamic MRI of Grid-Tagged Hyperpolarized Helium-3 for the Assessment of Lung Motion During Breathing

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

Cai Jing; Sheng Ke; Benedict, Stanley H.

2009-09-01

Purpose: To develop a dynamic magnetic resonance imaging (MRI) tagging technique using hyperpolarized helium-3 (HP He-3) to track lung motion. Methods and Materials: An accelerated non-Cartesian k-space trajectory was used to gain acquisition speed, at the cost of introducing image artifacts, providing a viable strategy for obtaining whole-lung coverage with adequate temporal resolution. Multiple-slice two-dimensional dynamic images of the lung were obtained in three healthy subjects after inhaling He-3 gas polarized to 35%-40%. Displacement, strain, and ventilation maps were computed from the observed motion of the grid peaks. Results: Both temporal and spatial variations of pulmonary mechanics were observed inmore » normal subjects, including shear motion between different lobes of the same lung. Conclusion: These initial results suggest that dynamic imaging of grid-tagged hyperpolarized magnetization may potentially be a powerful tool for observing and quantifying pulmonary biomechanics on a regional basis and for assessing, validating, and improving lung deformable image registration algorithms.« less

Intrapulmonary vascular remodeling: MSCT-based evaluation in COPD and alpha-1 antitrypsin deficient subjects

NASA Astrophysics Data System (ADS)

Crosnier, Adeline; Fetita, Catalin; Thabut, Gabriel; Brillet, Pierre-Yves

2016-03-01

Whether COPD is generally known as a small airway disease, recent investigations suggest that vascular remodeling could play a key role in disease progression. This paper develops a specific investigation framework in order to evaluate the remodeling of the intrapulmonary vascular network and its correlation with other image or clinical parameters (emphysema score or FEV1) in patients with smoking- or genetic- (alpha-1 antitrypsin deficiency - AATD) related COPD. The developed approach evaluates the vessel caliber distribution per lung or lung region (upper, lower, 10%- and 20%- periphery) in relation with the severity of the disease and computes a remodeling marker given by the area under the caliber distribution curve for radii less than 1.6mm, AUC16. It exploits a medial axis analysis in relation with local caliber information computed in the segmented vascular network, with values normalized with respect to the lung volume (for which a robust segmentation is developed). The first results obtained on a 34-patient database (13 COPD, 13 AATD and 8 controls) showed significant vascular remodeling for COPD and AATD versus controls, with a negative correlation with the emphysema degree for COPD, but not for AATD. Significant vascular remodeling at 20% lung periphery was found both for the severe COPD and AATD patients, but not for the moderate groups. Also the vascular remodeling in AATD did not correlate with the FEV1, nor with DLCO, which might suggest independent mechanisms for bronchial and vascular remodeling in the lung.

Long T2 suppression in native lung 3-D imaging using k-space reordered inversion recovery dual-echo ultrashort echo time MRI.

PubMed

Gai, Neville D; Malayeri, Ashkan A; Bluemke, David A

2017-08-01

Long T2 species can interfere with visualization of short T2 tissue imaging. For example, visualization of lung parenchyma can be hindered by breathing artifacts primarily from fat in the chest wall. The purpose of this work was to design and evaluate a scheme for long T2 species suppression in lung parenchyma imaging using 3-D inversion recovery double-echo ultrashort echo time imaging with a k-space reordering scheme for artifact suppression. A hyperbolic secant (HS) pulse was evaluated for different tissues (T1/T2). Bloch simulations were performed with the inversion pulse followed by segmented UTE acquisition. Point spread function (PSF) was simulated for a standard interleaved acquisition order and a modulo 2 forward-reverse acquisition order. Phantom and in vivo images (eight volunteers) were acquired with both acquisition orders. Contrast to noise ratio (CNR) was evaluated in in vivo images prior to and after introduction of the long T2 suppression scheme. The PSF as well as phantom and in vivo images demonstrated reduction in artifacts arising from k-space modulation after using the reordering scheme. CNR measured between lung and fat and lung and muscle increased from -114 and -148.5 to +12.5 and 2.8 after use of the IR-DUTE sequence. Paired t test between the CNRs obtained from UTE and IR-DUTE showed significant positive change (p < 0.001 for lung-fat CNR and p = 0.03 for lung-muscle CNR). Full 3-D lung parenchyma imaging with improved positive contrast between lung and other long T2 tissue types can be achieved robustly in a clinically feasible time using IR-DUTE with image subtraction when segmented radial acquisition with k-space reordering is employed.

Monotonicity-based electrical impedance tomography for lung imaging

NASA Astrophysics Data System (ADS)

Zhou, Liangdong; Harrach, Bastian; Seo, Jin Keun

2018-04-01

This paper presents a monotonicity-based spatiotemporal conductivity imaging method for continuous regional lung monitoring using electrical impedance tomography (EIT). The EIT data (i.e. the boundary current-voltage data) can be decomposed into pulmonary, cardiac and other parts using their different periodic natures. The time-differential current-voltage operator corresponding to the lung ventilation can be viewed as either semi-positive or semi-negative definite owing to monotonic conductivity changes within the lung regions. We used these monotonicity constraints to improve the quality of lung EIT imaging. We tested the proposed methods in numerical simulations, phantom experiments and human experiments.

Changes in Regional Ventilation During Treatment and Dosimetric Advantages of CT Ventilation Image Guided Radiation Therapy for Locally Advanced Lung Cancer.

PubMed

Yamamoto, Tokihiro; Kabus, Sven; Bal, Matthieu; Bzdusek, Karl; Keall, Paul J; Wright, Cari; Benedict, Stanley H; Daly, Megan E

2018-05-04

Lung functional image guided radiation therapy (RT) that avoids irradiating highly functional regions has potential to reduce pulmonary toxicity following RT. Tumor regression during RT is common, leading to recovery of lung function. We hypothesized that computed tomography (CT) ventilation image-guided treatment planning reduces the functional lung dose compared to standard anatomic image-guided planning in 2 different scenarios with or without plan adaptation. CT scans were acquired before RT and during RT at 2 time points (16-20 Gy and 30-34 Gy) for 14 patients with locally advanced lung cancer. Ventilation images were calculated by deformable image registration of four-dimensional CT image data sets and image analysis. We created 4 treatment plans at each time point for each patient: functional adapted, anatomic adapted, functional unadapted, and anatomic unadapted plans. Adaptation was performed at 2 time points. Deformable image registration was used for accumulating dose and calculating a composite of dose-weighted ventilation used to quantify the lung accumulated dose-function metrics. The functional plans were compared with the anatomic plans for each scenario separately to investigate the hypothesis at a significance level of 0.05. Tumor volume was significantly reduced by 20% after 16 to 20 Gy (P = .02) and by 32% after 30 to 34 Gy (P < .01) on average. In both scenarios, the lung accumulated dose-function metrics were significantly lower in the functional plans than in the anatomic plans without compromising target volume coverage and adherence to constraints to critical structures. For example, functional planning significantly reduced the functional mean lung dose by 5.0% (P < .01) compared to anatomic planning in the adapted scenario and by 3.6% (P = .03) in the unadapted scenario. This study demonstrated significant reductions in the accumulated dose to the functional lung with CT ventilation image-guided planning compared to anatomic image-guided planning for patients showing tumor regression and changes in regional ventilation during RT. Copyright © 2018 Elsevier Inc. All rights reserved.

Lung and Upper Aerodigestive Cancer | Division of Cancer Prevention

Cancer.gov

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Automated measurements of metabolic tumor volume and metabolic parameters in lung PET/CT imaging

NASA Astrophysics Data System (ADS)

Orologas, F.; Saitis, P.; Kallergi, M.

2017-11-01

Patients with lung tumors or inflammatory lung disease could greatly benefit in terms of treatment and follow-up by PET/CT quantitative imaging, namely measurements of metabolic tumor volume (MTV), standardized uptake values (SUVs) and total lesion glycolysis (TLG). The purpose of this study was the development of an unsupervised or partially supervised algorithm using standard image processing tools for measuring MTV, SUV, and TLG from lung PET/CT scans. Automated metabolic lesion volume and metabolic parameter measurements were achieved through a 5 step algorithm: (i) The segmentation of the lung areas on the CT slices, (ii) the registration of the CT segmented lung regions on the PET images to define the anatomical boundaries of the lungs on the functional data, (iii) the segmentation of the regions of interest (ROIs) on the PET images based on adaptive thresholding and clinical criteria, (iv) the estimation of the number of pixels and pixel intensities in the PET slices of the segmented ROIs, (v) the estimation of MTV, SUVs, and TLG from the previous step and DICOM header data. Whole body PET/CT scans of patients with sarcoidosis were used for training and testing the algorithm. Lung area segmentation on the CT slices was better achieved with semi-supervised techniques that reduced false positive detections significantly. Lung segmentation results agreed with the lung volumes published in the literature while the agreement between experts and algorithm in the segmentation of the lesions was around 88%. Segmentation results depended on the image resolution selected for processing. The clinical parameters, SUV (either mean or max or peak) and TLG estimated by the segmented ROIs and DICOM header data provided a way to correlate imaging data to clinical and demographic data. In conclusion, automated MTV, SUV, and TLG measurements offer powerful analysis tools in PET/CT imaging of the lungs. Custom-made algorithms are often a better approach than the manufacturer’s general analysis software at much lower cost. Relatively simple processing techniques could lead to customized, unsupervised or partially supervised methods that can successfully perform the desirable analysis and adapt to the specific disease requirements.

Ex vivo Live Imaging of Lung Metastasis and Their Microenvironment

PubMed Central

Maynard, Carrie; Plaks, Vicki

2016-01-01

Metastasis is a major cause for cancer-related morbidity and mortality. Metastasis is a multistep process and due to its complexity, the exact cellular and molecular processes that govern metastatic dissemination and growth are still elusive. Live imaging allows visualization of the dynamic and spatial interactions of cells and their microenvironment. Solid tumors commonly metastasize to the lungs. However, the anatomical location of the lungs poses a challenge to intravital imaging. This protocol provides a relatively simple and quick method for ex vivo live imaging of the dynamic interactions between tumor cells and their surrounding stroma within lung metastasis. Using this method, the motility of cancer cells as well as interactions between cancer cells and stromal cells in their microenvironment can be visualized in real time for several hours. By using transgenic fluorescent reporter mice, a fluorescent cell line, injectable fluorescently labeled molecules and/or antibodies, multiple components of the lung microenvironment can be visualized, such as blood vessels and immune cells. To image the different cell types, a spinning disk confocal microscope that allows long-term continuous imaging with rapid, four-color image acquisition has been used. Time-lapse movies compiled from images collected over multiple positions and focal planes show interactions between live metastatic and immune cells for at least 4 hr. This technique can be further used to test chemotherapy or targeted therapy. Moreover, this method could be adapted for the study of other lung-related pathologies that may affect the lung microenvironment. PMID:26862704

Towards lung EIT image segmentation: automatic classification of lung tissue state from analysis of EIT monitored recruitment manoeuvres.

PubMed

Grychtol, Bartłomiej; Wolf, Gerhard K; Adler, Andy; Arnold, John H

2010-08-01

There is emerging evidence that the ventilation strategy used in acute lung injury (ALI) makes a significant difference in outcome and that an inappropriate ventilation strategy may produce ventilator-associated lung injury. Most harmful during mechanical ventilation are lung overdistension and lung collapse or atelectasis. Electrical impedance tomography (EIT) as a non-invasive imaging technology may be helpful to identify lung areas at risk. Currently, no automated method is routinely available to identify lung areas that are overdistended, collapsed or ventilated appropriately. We propose a fuzzy logic-based algorithm to analyse EIT images obtained during stepwise changes of mean airway pressures during mechanical ventilation. The algorithm is tested on data from two published studies of stepwise inflation-deflation manoeuvres in an animal model of ALI using conventional and high-frequency oscillatory ventilation. The timing of lung opening and collapsing on segmented images obtained using the algorithm during an inflation-deflation manoeuvre is in agreement with well-known effects of surfactant administration and changes in shunt fraction. While the performance of the algorithm has not been verified against a gold standard, we feel that it presents an important first step in tackling this challenging and important problem.

Estimation of Lung Ventilation

NASA Astrophysics Data System (ADS)

Ding, Kai; Cao, Kunlin; Du, Kaifang; Amelon, Ryan; Christensen, Gary E.; Raghavan, Madhavan; Reinhardt, Joseph M.

Since the primary function of the lung is gas exchange, ventilation can be interpreted as an index of lung function in addition to perfusion. Injury and disease processes can alter lung function on a global and/or a local level. MDCT can be used to acquire multiple static breath-hold CT images of the lung taken at different lung volumes, or with proper respiratory control, 4DCT images of the lung reconstructed at different respiratory phases. Image registration can be applied to this data to estimate a deformation field that transforms the lung from one volume configuration to the other. This deformation field can be analyzed to estimate local lung tissue expansion, calculate voxel-by-voxel intensity change, and make biomechanical measurements. The physiologic significance of the registration-based measures of respiratory function can be established by comparing to more conventional measurements, such as nuclear medicine or contrast wash-in/wash-out studies with CT or MR. An important emerging application of these methods is the detection of pulmonary function change in subjects undergoing radiation therapy (RT) for lung cancer. During RT, treatment is commonly limited to sub-therapeutic doses due to unintended toxicity to normal lung tissue. Measurement of pulmonary function may be useful as a planning tool during RT planning, may be useful for tracking the progression of toxicity to nearby normal tissue during RT, and can be used to evaluate the effectiveness of a treatment post-therapy. This chapter reviews the basic measures to estimate regional ventilation from image registration of CT images, the comparison of them to the existing golden standard and the application in radiation therapy.

A 32-Channel Phased-Array Receive with Asymmetric Birdcage Transmit RF Coil for Hyperpolarized Xenon-129 Lung Imaging

PubMed Central

Dregely, Isabel; Ruset, Iulian C.; Wiggins, Graham; Mareyam, Azma; Mugler, John P.; Altes, Talissa A.; Meyer, Craig; Ruppert, Kai; Wald, Lawrence L.; Hersman, F. William

2012-01-01

Hyperpolarized xenon-129 (HP Xe) has the potential to become a non-invasive contrast agent for lung MRI. In addition to its utility for imaging of ventilated airspaces, the property of xenon to dissolve in lung tissue and blood upon inhalation provides the opportunity to study gas exchange. Implementations of imaging protocols for obtaining regional parameters that exploit the dissolved phase are limited by the available signal-to-noise ratio (SNR), excitation homogeneity, and length of acquisition times. To address these challenges, a 32-channel receive-array coil complemented by an asymmetric birdcage transmit coil tuned to the HP Xe resonance at 3T was developed. First results of spin-density imaging in healthy subjects and subjects with obstructive lung disease demonstrated the improvements in image quality by high resolution ventilation images with high SNR. Parallel imaging performance of the phased-array coil was demonstrated by acceleration factors up to three in 2D acquisitions and up to six in 3D acquisitions. Transmit-field maps showed a regional variation of only 8% across the whole lung. The newly developed phased-array receive coil with the birdcage transmit coil will lead to an improvement in existing imaging protocols, but moreover enable the development of new, functional lung imaging protocols based on the improvements in excitation homogeneity, SNR, and acquisition speed. PMID:23132336

Dynamic dual-energy chest radiography: a potential tool for lung tissue motion monitoring and kinetic study

PubMed Central

Xu, Tong; Ducote, Justin L.; Wong, Jerry T.; Molloi, Sabee

2011-01-01

Dual-energy chest radiography has the potential to provide better diagnosis of lung disease by removing the bone signal from the image. Dynamic dual-energy radiography is now possible with the introduction of digital flat panel detectors. The purpose of this study is to evaluate the feasibility of using dynamic dual-energy chest radiography for functional lung imaging and tumor motion assessment. The dual energy system used in this study can acquire up to 15 frame of dual-energy images per second. A swine animal model was mechanically ventilated and imaged using the dual-energy system. Sequences of soft-tissue images were obtained using dual-energy subtraction. Time subtracted soft-tissue images were shown to be able to provide information on regional ventilation. Motion tracking of a lung anatomic feature (a branch of pulmonary artery) was performed based on an image cross-correlation algorithm. The tracking precision was found to be better than 1 mm. An adaptive correlation model was established between the above tracked motion and an external surrogate signal (temperature within the tracheal tube). This model is used to predict lung feature motion using the continuous surrogate signal and low frame rate dual-energy images (0.1 to 3.0 frames /sec). The average RMS error of the prediction was (1.1 ± 0.3) mm. The dynamic dual-energy was shown to be potentially useful for lung functional imaging such as regional ventilation and kinetic studies. It can also be used for lung tumor motion assessment and prediction during radiation therapy. PMID:21285477

Dynamic dual-energy chest radiography: a potential tool for lung tissue motion monitoring and kinetic study.

PubMed

Xu, Tong; Ducote, Justin L; Wong, Jerry T; Molloi, Sabee

2011-02-21

Dual-energy chest radiography has the potential to provide better diagnosis of lung disease by removing the bone signal from the image. Dynamic dual-energy radiography is now possible with the introduction of digital flat-panel detectors. The purpose of this study is to evaluate the feasibility of using dynamic dual-energy chest radiography for functional lung imaging and tumor motion assessment. The dual-energy system used in this study can acquire up to 15 frames of dual-energy images per second. A swine animal model was mechanically ventilated and imaged using the dual-energy system. Sequences of soft-tissue images were obtained using dual-energy subtraction. Time subtracted soft-tissue images were shown to be able to provide information on regional ventilation. Motion tracking of a lung anatomic feature (a branch of pulmonary artery) was performed based on an image cross-correlation algorithm. The tracking precision was found to be better than 1 mm. An adaptive correlation model was established between the above tracked motion and an external surrogate signal (temperature within the tracheal tube). This model is used to predict lung feature motion using the continuous surrogate signal and low frame rate dual-energy images (0.1-3.0 frames per second). The average RMS error of the prediction was (1.1 ± 0.3) mm. The dynamic dual energy was shown to be potentially useful for lung functional imaging such as regional ventilation and kinetic studies. It can also be used for lung tumor motion assessment and prediction during radiation therapy.

Measurement of absolute regional lung air volumes from near-field x-ray speckles.

PubMed

Leong, Andrew F T; Paganin, David M; Hooper, Stuart B; Siew, Melissa L; Kitchen, Marcus J

2013-11-18

Propagation-based phase contrast x-ray (PBX) imaging yields high contrast images of the lung where airways that overlap in projection coherently scatter the x-rays, giving rise to a speckled intensity due to interference effects. Our previous works have shown that total and regional changes in lung air volumes can be accurately measured from two-dimensional (2D) absorption or phase contrast images when the subject is immersed in a water-filled container. In this paper we demonstrate how the phase contrast speckle patterns can be used to directly measure absolute regional lung air volumes from 2D PBX images without the need for a water-filled container. We justify this technique analytically and via simulation using the transport-of-intensity equation and calibrate the technique using our existing methods for measuring lung air volume. Finally, we show the full capabilities of this technique for measuring regional differences in lung aeration.

Combined Electrocardiography- and Respiratory-Triggered CT of the Lung to Reduce Respiratory Misregistration Artifacts between Imaging Slabs in Free-Breathing Children: Initial Experience

PubMed Central

Allmendinger, Thomas

2017-01-01

Objective Cardiac and respiratory motion artifacts degrade the image quality of lung CT in free-breathing children. The aim of this study was to evaluate the effect of combined electrocardiography (ECG) and respiratory triggering on respiratory misregistration artifacts on lung CT in free-breathing children. Materials and Methods In total, 15 children (median age 19 months, range 6 months–8 years; 7 boys), who underwent free-breathing ECG-triggered lung CT with and without respiratory-triggering were included. A pressure-sensing belt of a respiratory gating system was used to obtain the respiratory signal. The degree of respiratory misregistration artifacts between imaging slabs was graded on a 4-point scale (1, excellent image quality) on coronal and sagittal images and compared between ECG-triggered lung CT studies with and without respiratory triggering. A p value < 0.05 was considered significant. Results Lung CT with combined ECG and respiratory triggering showed significantly less respiratory misregistration artifacts than lung CT with ECG triggering only (1.1 ± 0.4 vs. 2.2 ± 1.0, p = 0.003). Conclusion Additional respiratory-triggering reduces respiratory misregistration artifacts on ECG-triggered lung CT in free-breathing children. PMID:28860904

A novel approach to analyzing lung cancer mortality disparities: Using the exposome and a graph-theoretical toolchain

PubMed Central

Juarez, Paul D; Hood, Darryl B; Rogers, Gary L; Baktash, Suzanne H; Saxton, Arnold M; Matthews-Juarez, Patricia; Im, Wansoo; Cifuentes, Myriam Patricia; Phillips, Charles A; Lichtveld, Maureen Y; Langston, Michael A

2017-01-01

Objectives The aim is to identify exposures associated with lung cancer mortality and mortality disparities by race and gender using an exposome database coupled to a graph theoretical toolchain. Methods Graph theoretical algorithms were employed to extract paracliques from correlation graphs using associations between 2162 environmental exposures and lung cancer mortality rates in 2067 counties, with clique doubling applied to compute an absolute threshold of significance. Factor analysis and multiple linear regressions then were used to analyze differences in exposures associated with lung cancer mortality and mortality disparities by race and gender. Results While cigarette consumption was highly correlated with rates of lung cancer mortality for both white men and women, previously unidentified novel exposures were more closely associated with lung cancer mortality and mortality disparities for blacks, particularly black women. Conclusions Exposures beyond smoking moderate lung cancer mortality and mortality disparities by race and gender. Policy Implications An exposome approach and database coupled with scalable combinatorial analytics provides a powerful new approach for analyzing relationships between multiple environmental exposures, pathways and health outcomes. An assessment of multiple exposures is needed to appropriately translate research findings into environmental public health practice and policy. PMID:29152601

Multi-Modal Imaging in a Mouse Model of Orthotopic Lung Cancer

PubMed Central

Patel, Priya; Kato, Tatsuya; Ujiie, Hideki; Wada, Hironobu; Lee, Daiyoon; Hu, Hsin-pei; Hirohashi, Kentaro; Ahn, Jin Young; Zheng, Jinzi; Yasufuku, Kazuhiro

2016-01-01

Background Investigation of CF800, a novel PEGylated nano-liposomal imaging agent containing indocyanine green (ICG) and iohexol, for real-time near infrared (NIR) fluorescence and computed tomography (CT) image-guided surgery in an orthotopic lung cancer model in nude mice. Methods CF800 was intravenously administered into 13 mice bearing the H460 orthotopic human lung cancer. At 48 h post-injection (peak imaging agent accumulation time point), ex vivo NIR and CT imaging was performed. A clinical NIR imaging system (SPY®, Novadaq) was used to measure fluorescence intensity of tumor and lung. Tumor-to-background-ratios (TBR) were calculated in inflated and deflated states. The mean Hounsfield unit (HU) of lung tumor was quantified using the CT data set and a semi-automated threshold-based method. Histological evaluation using H&E, the macrophage marker F4/80 and the endothelial cell marker CD31, was performed, and compared to the liposomal fluorescence signal obtained from adjacent tissue sections Results The fluorescence TBR measured when the lung is in the inflated state (2.0 ± 0.58) was significantly greater than in the deflated state (1.42 ± 0.380 (n = 7, p<0.003). Mean fluorescent signal in tumor was highly variable across samples, (49.0 ± 18.8 AU). CT image analysis revealed greater contrast enhancement in lung tumors (a mean increase of 110 ± 57 HU) when CF800 is administered compared to the no contrast enhanced tumors (p = 0.0002). Conclusion Preliminary data suggests that the high fluorescence TBR and CT tumor contrast enhancement provided by CF800 may have clinical utility in localization of lung cancer during CT and NIR image-guided surgery. PMID:27584018

Multi-Modal Imaging in a Mouse Model of Orthotopic Lung Cancer.

PubMed

Patel, Priya; Kato, Tatsuya; Ujiie, Hideki; Wada, Hironobu; Lee, Daiyoon; Hu, Hsin-Pei; Hirohashi, Kentaro; Ahn, Jin Young; Zheng, Jinzi; Yasufuku, Kazuhiro

2016-01-01

Investigation of CF800, a novel PEGylated nano-liposomal imaging agent containing indocyanine green (ICG) and iohexol, for real-time near infrared (NIR) fluorescence and computed tomography (CT) image-guided surgery in an orthotopic lung cancer model in nude mice. CF800 was intravenously administered into 13 mice bearing the H460 orthotopic human lung cancer. At 48 h post-injection (peak imaging agent accumulation time point), ex vivo NIR and CT imaging was performed. A clinical NIR imaging system (SPY®, Novadaq) was used to measure fluorescence intensity of tumor and lung. Tumor-to-background-ratios (TBR) were calculated in inflated and deflated states. The mean Hounsfield unit (HU) of lung tumor was quantified using the CT data set and a semi-automated threshold-based method. Histological evaluation using H&E, the macrophage marker F4/80 and the endothelial cell marker CD31, was performed, and compared to the liposomal fluorescence signal obtained from adjacent tissue sections. The fluorescence TBR measured when the lung is in the inflated state (2.0 ± 0.58) was significantly greater than in the deflated state (1.42 ± 0.380 (n = 7, p<0.003). Mean fluorescent signal in tumor was highly variable across samples, (49.0 ± 18.8 AU). CT image analysis revealed greater contrast enhancement in lung tumors (a mean increase of 110 ± 57 HU) when CF800 is administered compared to the no contrast enhanced tumors (p = 0.0002). Preliminary data suggests that the high fluorescence TBR and CT tumor contrast enhancement provided by CF800 may have clinical utility in localization of lung cancer during CT and NIR image-guided surgery.

Progressive massive fibrosis in patients with pneumoconiosis: utility of MRI in differentiating from lung cancer.

PubMed

Ogihara, Yukihiro; Ashizawa, Kazuto; Hayashi, Hideyuki; Nagayasu, Takeshi; Hayashi, Tomayoshi; Honda, Sumihisa; Uetani, Masataka

2018-01-01

Background It is occasionally difficult to distinguish progressive massive fibrosis (PMF) from lung cancer on computed tomography (CT) in patients with pneumoconiosis. Purpose To evaluate the magnetic resonance imaging (MRI) features of PMF and to assess its ability to differentiate PMF from lung cancer. Material and Methods Between 2000 and 2014, 40 pulmonary lesions suspected to be lung cancer on the basis of CT in 28 patients with known pneumoconiosis were evaluated. Twenty-four of the 40 lesions were pathologically or clinically diagnosed as PMF. The signal pattern on T2-weighted (T2W) images, post-contrast enhancement pattern on T1-weighted (T1W) images, and the pattern of the time intensity curve (TIC) on contrast-enhanced dynamic studies were evaluated. All images were analyzed independently by two chest radiologists. Results All 24 PMF lesions showed low signal intensity (SI) on T2W images (sensitivity, 100%), while 15 of 16 lung cancer lesions showed intermediate or high SI on T2W images (specificity, 94%) when PMF was regarded as a positive result. Six of 17 PMF lesions showed a homogeneous enhancement pattern (sensitivity, 35%), and 4/9 lung cancer lesions showed an inhomogeneous or a ring-like enhancement pattern (specificity, 44%). Six of 16 PMF lesions showed a gradually increasing enhancement pattern (sensitivity, 38%), and 7/9 lung cancer lesions showed rapid enhancement pattern (specificity, 78%). Conclusion When differentiation between PMF and lung cancer in patients with pneumoconiosis is difficult on CT, an additional MRI study, particularly the T2W imaging sequence, may help differentiate between the two.

Lung perfusion measured using magnetic resonance imaging: New tools for physiological insights into the pulmonary circulation.

PubMed

Hopkins, Susan R; Prisk, G Kim

2010-12-01

Since the lung receives the entire cardiac output, sophisticated imaging techniques are not required in order to measure total organ perfusion. However, for many years studying lung function has required physiologists to consider the lung as a single entity: in imaging terms as a single voxel. Since imaging, and in particular functional imaging, allows the acquisition of spatial information important for studying lung function, these techniques provide considerable promise and are of great interest for pulmonary physiologists. In particular, despite the challenges of low proton density and short T2* in the lung, noncontrast MRI techniques to measure pulmonary perfusion have several advantages including high reliability and the ability to make repeated measurements under a number of physiologic conditions. This brief review focuses on the application of a particular arterial spin labeling (ASL) technique, ASL-FAIRER (flow sensitive inversion recovery with an extra radiofrequency pulse), to answer physiologic questions related to pulmonary function in health and disease. The associated measurement of regional proton density to correct for gravitational-based lung deformation (the "Slinky" effect (Slinky is a registered trademark of Pauf-Slinky incorporated)) and issues related to absolute quantification are also discussed. Copyright © 2010 Wiley-Liss, Inc.

Hyperpolarized (3)He magnetic resonance imaging: comparison with four-dimensional x-ray computed tomography imaging in lung cancer.

PubMed

Mathew, Lindsay; Wheatley, Andrew; Castillo, Richard; Castillo, Edward; Rodrigues, George; Guerrero, Thomas; Parraga, Grace

2012-12-01

Pulmonary functional imaging using four-dimensional x-ray computed tomographic (4DCT) imaging and hyperpolarized (3)He magnetic resonance imaging (MRI) provides regional lung function estimates in patients with lung cancer in whom pulmonary function measurements are typically dominated by tumor burden. The aim of this study was to evaluate the quantitative spatial relationship between 4DCT and hyperpolarized (3)He MRI ventilation maps. Eleven patients with lung cancer provided written informed consent to 4DCT imaging and MRI performed within 11 ± 14 days. Hyperpolarized (3)He MRI was acquired in breath-hold after inhalation from functional residual capacity of 1 L hyperpolarized (3)He, whereas 4DCT imaging was acquired over a single tidal breath of room air. For hyperpolarized (3)He MRI, the percentage ventilated volume was generated using semiautomated segmentation; for 4DCT imaging, pulmonary function maps were generated using the correspondence between identical tissue elements at inspiratory and expiratory phases to generate percentage ventilated volume. After accounting for differences in image acquisition lung volumes ((3)He MRI: 1.9 ± 0.5 L ipsilateral, 2.3 ± 0.7 L contralateral; 4DCT imaging: 1.2 ± 0.3 L ipsilateral, 1.3 ± 0.4 L contralateral), there was no significant difference in percentage ventilated volume between hyperpolarized (3)He MRI (72 ± 11% ipsilateral, 79 ± 12% contralateral) and 4DCT imaging (74 ± 3% ipsilateral, 75 ± 4% contralateral). Spatial correspondence between 4DCT and (3)He MRI ventilation was evaluated using the Dice similarity coefficient index (ipsilateral, 86 ± 12%; contralateral, 88 ± 12%). Despite rather large differences in image acquisition breathing maneuvers, good spatial and significant quantitative agreement was observed for ventilation maps on hyperpolarized (3)He MRI and 4DCT imaging, suggesting that pulmonary regions with good lung function are similar between modalities in this small group of patients with lung cancer. Copyright © 2012 AUR. Published by Elsevier Inc. All rights reserved.

Use of diffusion-weighted magnetic resonance imaging to distinguish between lung cancer and focal inflammatory lesions: a comparison of intravoxel incoherent motion derived parameters and apparent diffusion coefficient.

PubMed

Deng, Yu; Li, Xinchun; Lei, Yongxia; Liang, Changhong; Liu, Zaiyi

2016-11-01

Background Using imaging techniques to diagnose malignant and inflammatory lesions in the lung can be challenging. Purpose To compare intravoxel incoherent motion (IVIM) and apparent diffusion coefficient (ADC) magnetic resonance imaging (MRI) analysis in their ability to discriminate lung cancer from focal inflammatory lung lesions. Material and Methods Thirty-eight patients with lung masses were included: 30 lung cancers and eight inflammatory lesions. Patients were imaged with 3.0T MRI diffusion weighted imaging (DWI) using 10 b values (range, 0-1000 s/mm 2 ). Tissue diffusivity ( D), pseudo-diffusion coefficient ( D*), and perfusion fraction ( f) were calculated using segmented biexponential analysis. ADC (total) was calculated with monoexponential fitting of the DWI data. D, D*, f, and ADC were compared between lung cancer and inflammatory lung lesions. Receiver operating characteristic analysis was performed for all DWI parameters. Results The ADC was significantly higher for inflammatory lesions than for lung cancer ([1.21 ± 0.20] × 10 -3 mm 2 /s vs. [0.97 ± 0.15] × 10 -3 mm 2 /s; P = 0.004). By IVIM, f was found to be significantly higher in inflammatory lesions than lung cancer ([46.10 ± 12.92] % vs. [29.29 ± 10.89] %; P = 0.005). There was no difference in D and D* between lung cancer and inflammatory lesions ( P = 0.747 and 0.124, respectively). f showed comparable diagnostic performance with ADC in differentiating lung cancer from inflammatory lung lesions, with areas under the curve of 0.833 and 0.826, sensitivity 80.0% and 73.3%, and specificity 75.0% and 87.5%, respectively. Conclusion The IVIM parameter f value provides comparable diagnostic performance with ADC and could be used as a surrogate marker for differentiating lung cancer from inflammatory lesions.

Computed Tomography Studies of Lung Mechanics

PubMed Central

Simon, Brett A.; Christensen, Gary E.; Low, Daniel A.; Reinhardt, Joseph M.

2005-01-01

The study of lung mechanics has progressed from global descriptions of lung pressure and volume relationships to the high-resolution, three-dimensional, quantitative measurement of dynamic regional mechanical properties and displacements. X-ray computed tomography (CT) imaging is ideally suited to the study of regional lung mechanics in intact subjects because of its high spatial and temporal resolution, correlation of functional data with anatomic detail, increasing volumetric data acquisition, and the unique relationship between CT density and lung air content. This review presents an overview of CT measurement principles and limitations for the study of regional mechanics, reviews some of the early work that set the stage for modern imaging approaches and impacted the understanding and management of patients with acute lung injury, and presents evolving novel approaches for the analysis and application of dynamic volumetric lung image data. PMID:16352757

Lung Cancer Workshop XI: Tobacco-Induced Disease: Advances in Policy, Early Detection and Management.

PubMed

Mulshine, James L; Avila, Rick; Yankelevitz, David; Baer, Thomas M; Estépar, Raul San Jose; Ambrose, Laurie Fenton; Aldigé, Carolyn R

2015-05-01

The Prevent Cancer Foundation Lung Cancer Workshop XI: Tobacco-Induced Disease: Advances in Policy, Early Detection and Management was held in New York, NY on May 16 and 17, 2014. The two goals of the Workshop were to define strategies to drive innovation in precompetitive quantitative research on the use of imaging to assess new therapies for management of early lung cancer and to discuss a process to implement a national program to provide high quality computed tomography imaging for lung cancer and other tobacco-induced disease. With the central importance of computed tomography imaging for both early detection and volumetric lung cancer assessment, strategic issues around the development of imaging and ensuring its quality are critical to ensure continued progress against this most lethal cancer.

An experimental study of interstitial lung tissue classification in HRCT images using ANN and role of cost functions

NASA Astrophysics Data System (ADS)

Dash, Jatindra K.; Kale, Mandar; Mukhopadhyay, Sudipta; Khandelwal, Niranjan; Prabhakar, Nidhi; Garg, Mandeep; Kalra, Naveen

2017-03-01

In this paper, we investigate the effect of the error criteria used during a training phase of the artificial neural network (ANN) on the accuracy of the classifier for classification of lung tissues affected with Interstitial Lung Diseases (ILD). Mean square error (MSE) and the cross-entropy (CE) criteria are chosen being most popular choice in state-of-the-art implementations. The classification experiment performed on the six interstitial lung disease (ILD) patterns viz. Consolidation, Emphysema, Ground Glass Opacity, Micronodules, Fibrosis and Healthy from MedGIFT database. The texture features from an arbitrary region of interest (AROI) are extracted using Gabor filter. Two different neural networks are trained with the scaled conjugate gradient back propagation algorithm with MSE and CE error criteria function respectively for weight updation. Performance is evaluated in terms of average accuracy of these classifiers using 4 fold cross-validation. Each network is trained for five times for each fold with randomly initialized weight vectors and accuracies are computed. Significant improvement in classification accuracy is observed when ANN is trained by using CE (67.27%) as error function compared to MSE (63.60%). Moreover, standard deviation of the classification accuracy for the network trained with CE (6.69) error criteria is found less as compared to network trained with MSE (10.32) criteria.

Automated Image Analysis of Lung Branching Morphogenesis from Microscopic Images of Fetal Rat Explants

PubMed Central

Rodrigues, Pedro L.; Rodrigues, Nuno F.; Duque, Duarte; Granja, Sara; Correia-Pinto, Jorge; Vilaça, João L.

2014-01-01

Background. Regulating mechanisms of branching morphogenesis of fetal lung rat explants have been an essential tool for molecular research. This work presents a new methodology to accurately quantify the epithelial, outer contour, and peripheral airway buds of lung explants during cellular development from microscopic images. Methods. The outer contour was defined using an adaptive and multiscale threshold algorithm whose level was automatically calculated based on an entropy maximization criterion. The inner lung epithelium was defined by a clustering procedure that groups small image regions according to the minimum description length principle and local statistical properties. Finally, the number of peripheral buds was counted as the skeleton branched ends from a skeletonized image of the lung inner epithelia. Results. The time for lung branching morphometric analysis was reduced in 98% in contrast to the manual method. Best results were obtained in the first two days of cellular development, with lesser standard deviations. Nonsignificant differences were found between the automatic and manual results in all culture days. Conclusions. The proposed method introduces a series of advantages related to its intuitive use and accuracy, making the technique suitable to images with different lighting characteristics and allowing a reliable comparison between different researchers. PMID:25250057

Multislice CT perfusion imaging of the lung in detection of pulmonary embolism

NASA Astrophysics Data System (ADS)

Hong, Helen; Lee, Jeongjin

2006-03-01

We propose a new subtraction technique for accurately imaging lung perfusion and efficiently detecting pulmonary embolism in chest MDCT angiography. Our method is composed of five stages. First, optimal segmentation technique is performed for extracting same volume of the lungs, major airway and vascular structures from pre- and post-contrast images with different lung density. Second, initial registration based on apex, hilar point and center of inertia (COI) of each unilateral lung is proposed to correct the gross translational mismatch. Third, initial alignment is refined by iterative surface registration. For fast and robust convergence of the distance measure to the optimal value, a 3D distance map is generated by the narrow-band distance propagation. Fourth, 3D nonlinear filter is applied to the lung parenchyma to compensate for residual spiral artifacts and artifacts caused by heart motion. Fifth, enhanced vessels are visualized by subtracting registered pre-contrast images from post-contrast images. To facilitate visualization of parenchyma enhancement, color-coded mapping and image fusion is used. Our method has been successfully applied to ten patients of pre- and post-contrast images in chest MDCT angiography. Experimental results show that the performance of our method is very promising compared with conventional methods with the aspects of its visual inspection, accuracy and processing time.

Welding and Lung Cancer in a Pooled Analysis of Case-Control Studies

PubMed Central

Kendzia, Benjamin; Behrens, Thomas; Jöckel, Karl-Heinz; Siemiatycki, Jack; Kromhout, Hans; Vermeulen, Roel; Peters, Susan; Van Gelder, Rainer; Olsson, Ann; Brüske, Irene; Wichmann, H.-Erich; Stücker, Isabelle; Guida, Florence; Tardón, Adonina; Merletti, Franco; Mirabelli, Dario; Richiardi, Lorenzo; Pohlabeln, Hermann; Ahrens, Wolfgang; Landi, Maria Teresa; Caporaso, Neil; Consonni, Dario; Zaridze, David; Szeszenia-Dabrowska, Neonila; Lissowska, Jolanta; Gustavsson, Per; Marcus, Michael; Fabianova, Eleonora; ‘t Mannetje, Andrea; Pearce, Neil; Tse, Lap Ah; Yu, Ignatius Tak-sun; Rudnai, Peter; Bencko, Vladimir; Janout, Vladimir; Mates, Dana; Foretova, Lenka; Forastiere, Francesco; McLaughlin, John; Demers, Paul; Bueno-de-Mesquita, Bas; Boffetta, Paolo; Schüz, Joachim; Straif, Kurt; Pesch, Beate; Brüning, Thomas

2013-01-01

Several epidemiologic studies have indicated an increased risk of lung cancer among welders. We used the SYNERGY project database to assess welding as a risk factor for developing lung cancer. The database includes data on 15,483 male lung cancer cases and 18,388 male controls from 16 studies in Europe, Canada, China, and New Zealand conducted between 1985 and 2010. Odds ratios and 95% confidence intervals between regular or occasional welding and lung cancer were estimated, with adjustment for smoking, age, study center, and employment in other occupations associated with lung cancer risk. Overall, 568 cases and 427 controls had ever worked as welders and had an odds ratio of developing lung cancer of 1.44 (95% confidence interval: 1.25, 1.67) with the odds ratio increasing for longer duration of welding. In never and light smokers, the odds ratio was 1.96 (95% confidence interval: 1.37, 2.79). The odds ratios were somewhat higher for squamous and small cell lung cancers than for adenocarcinoma. Another 1,994 cases and 1,930 controls had ever worked in occupations with occasional welding. Work in any of these occupations was associated with some elevation of risk, though not as much as observed in regular welders. Our findings lend further support to the hypothesis that welding is associated with an increased risk of lung cancer. PMID:24052544

Welding and lung cancer in a pooled analysis of case-control studies.

PubMed

Kendzia, Benjamin; Behrens, Thomas; Jöckel, Karl-Heinz; Siemiatycki, Jack; Kromhout, Hans; Vermeulen, Roel; Peters, Susan; Van Gelder, Rainer; Olsson, Ann; Brüske, Irene; Wichmann, H-Erich; Stücker, Isabelle; Guida, Florence; Tardón, Adonina; Merletti, Franco; Mirabelli, Dario; Richiardi, Lorenzo; Pohlabeln, Hermann; Ahrens, Wolfgang; Landi, Maria Teresa; Caporaso, Neil; Consonni, Dario; Zaridze, David; Szeszenia-Dabrowska, Neonila; Lissowska, Jolanta; Gustavsson, Per; Marcus, Michael; Fabianova, Eleonora; 't Mannetje, Andrea; Pearce, Neil; Tse, Lap Ah; Yu, Ignatius Tak-Sun; Rudnai, Peter; Bencko, Vladimir; Janout, Vladimir; Mates, Dana; Foretova, Lenka; Forastiere, Francesco; McLaughlin, John; Demers, Paul; Bueno-de-Mesquita, Bas; Boffetta, Paolo; Schüz, Joachim; Straif, Kurt; Pesch, Beate; Brüning, Thomas

2013-11-15

Several epidemiologic studies have indicated an increased risk of lung cancer among welders. We used the SYNERGY project database to assess welding as a risk factor for developing lung cancer. The database includes data on 15,483 male lung cancer cases and 18,388 male controls from 16 studies in Europe, Canada, China, and New Zealand conducted between 1985 and 2010. Odds ratios and 95% confidence intervals between regular or occasional welding and lung cancer were estimated, with adjustment for smoking, age, study center, and employment in other occupations associated with lung cancer risk. Overall, 568 cases and 427 controls had ever worked as welders and had an odds ratio of developing lung cancer of 1.44 (95% confidence interval: 1.25, 1.67) with the odds ratio increasing for longer duration of welding. In never and light smokers, the odds ratio was 1.96 (95% confidence interval: 1.37, 2.79). The odds ratios were somewhat higher for squamous and small cell lung cancers than for adenocarcinoma. Another 1,994 cases and 1,930 controls had ever worked in occupations with occasional welding. Work in any of these occupations was associated with some elevation of risk, though not as much as observed in regular welders. Our findings lend further support to the hypothesis that welding is associated with an increased risk of lung cancer.

Level-set-based reconstruction algorithm for EIT lung images: first clinical results.

PubMed

Rahmati, Peyman; Soleimani, Manuchehr; Pulletz, Sven; Frerichs, Inéz; Adler, Andy

2012-05-01

We show the first clinical results using the level-set-based reconstruction algorithm for electrical impedance tomography (EIT) data. The level-set-based reconstruction method (LSRM) allows the reconstruction of non-smooth interfaces between image regions, which are typically smoothed by traditional voxel-based reconstruction methods (VBRMs). We develop a time difference formulation of the LSRM for 2D images. The proposed reconstruction method is applied to reconstruct clinical EIT data of a slow flow inflation pressure-volume manoeuvre in lung-healthy and adult lung-injury patients. Images from the LSRM and the VBRM are compared. The results show comparable reconstructed images, but with an improved ability to reconstruct sharp conductivity changes in the distribution of lung ventilation using the LSRM.

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

PubMed

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

2015-01-01

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

Regional Lung Ventilation Analysis Using Temporally Resolved Magnetic Resonance Imaging.

PubMed

Kolb, Christoph; Wetscherek, Andreas; Buzan, Maria Teodora; Werner, René; Rank, Christopher M; Kachelrie, Marc; Kreuter, Michael; Dinkel, Julien; Heuel, Claus Peter; Maier-Hein, Klaus

We propose a computer-aided method for regional ventilation analysis and observation of lung diseases in temporally resolved magnetic resonance imaging (4D MRI). A shape model-based segmentation and registration workflow was used to create an atlas-derived reference system in which regional tissue motion can be quantified and multimodal image data can be compared regionally. Model-based temporal registration of the lung surfaces in 4D MRI data was compared with the registration of 4D computed tomography (CT) images. A ventilation analysis was performed on 4D MR images of patients with lung fibrosis; 4D MR ventilation maps were compared with corresponding diagnostic 3D CT images of the patients and 4D CT maps of subjects without impaired lung function (serving as reference). Comparison between the computed patient-specific 4D MR regional ventilation maps and diagnostic CT images shows good correlation in conspicuous regions. Comparison to 4D CT-derived ventilation maps supports the plausibility of the 4D MR maps. Dynamic MRI-based flow-volume loops and spirograms further visualize the free-breathing behavior. The proposed methods allow for 4D MR-based regional analysis of tissue dynamics and ventilation in spontaneous breathing and comparison of patient data. The proposed atlas-based reference coordinate system provides an automated manner of annotating and comparing multimodal lung image data.

Segmentation of ribs in digital chest radiographs

NASA Astrophysics Data System (ADS)

Cong, Lin; Guo, Wei; Li, Qiang

2016-03-01

Ribs and clavicles in posterior-anterior (PA) digital chest radiographs often overlap with lung abnormalities such as nodules, and cause missing of these abnormalities, it is therefore necessary to remove or reduce the ribs in chest radiographs. The purpose of this study was to develop a fully automated algorithm to segment ribs within lung area in digital radiography (DR) for removal of the ribs. The rib segmentation algorithm consists of three steps. Firstly, a radiograph was pre-processed for contrast adjustment and noise removal; second, generalized Hough transform was employed to localize the lower boundary of the ribs. In the third step, a novel bilateral dynamic programming algorithm was used to accurately segment the upper and lower boundaries of ribs simultaneously. The width of the ribs and the smoothness of the rib boundaries were incorporated in the cost function of the bilateral dynamic programming for obtaining consistent results for the upper and lower boundaries. Our database consisted of 93 DR images, including, respectively, 23 and 70 images acquired with a DR system from Shanghai United-Imaging Healthcare Co. and from GE Healthcare Co. The rib localization algorithm achieved a sensitivity of 98.2% with 0.1 false positives per image. The accuracy of the detected ribs was further evaluated subjectively in 3 levels: "1", good; "2", acceptable; "3", poor. The percentages of good, acceptable, and poor segmentation results were 91.1%, 7.2%, and 1.7%, respectively. Our algorithm can obtain good segmentation results for ribs in chest radiography and would be useful for rib reduction in our future study.

Quantitative Computerized Two-Point Correlation Analysis of Lung CT Scans Correlates With Pulmonary Function in Pulmonary Sarcoidosis

PubMed Central

Erdal, Barbaros Selnur; Yildiz, Vedat; King, Mark A.; Patterson, Andrew T.; Knopp, Michael V.; Clymer, Bradley D.

2012-01-01

Background: Chest CT scans are commonly used to clinically assess disease severity in patients presenting with pulmonary sarcoidosis. Despite their ability to reliably detect subtle changes in lung disease, the utility of chest CT scans for guiding therapy is limited by the fact that image interpretation by radiologists is qualitative and highly variable. We sought to create a computerized CT image analysis tool that would provide quantitative and clinically relevant information. Methods: We established that a two-point correlation analysis approach reduced the background signal attendant to normal lung structures, such as blood vessels, airways, and lymphatics while highlighting diseased tissue. This approach was applied to multiple lung fields to generate an overall lung texture score (LTS) representing the quantity of diseased lung parenchyma. Using deidentified lung CT scan and pulmonary function test (PFT) data from The Ohio State University Medical Center’s Information Warehouse, we analyzed 71 consecutive CT scans from patients with sarcoidosis for whom simultaneous matching PFTs were available to determine whether the LTS correlated with standard PFT results. Results: We found a high correlation between LTS and FVC, total lung capacity, and diffusing capacity of the lung for carbon monoxide (P < .0001 for all comparisons). Moreover, LTS was equivalent to PFTs for the detection of active lung disease. The image analysis protocol was conducted quickly (< 1 min per study) on a standard laptop computer connected to a publicly available National Institutes of Health ImageJ toolkit. Conclusions: The two-point image analysis tool is highly practical and appears to reliably assess lung disease severity. We predict that this tool will be useful for clinical and research applications. PMID:22628487

Non-contrast-enhanced perfusion and ventilation assessment of the human lung by means of fourier decomposition in proton MRI.

PubMed

Bauman, Grzegorz; Puderbach, Michael; Deimling, Michael; Jellus, Vladimir; Chefd'hotel, Christophe; Dinkel, Julien; Hintze, Christian; Kauczor, Hans-Ulrich; Schad, Lothar R

2009-09-01

Assessment of regional lung perfusion and ventilation has significant clinical value for the diagnosis and follow-up of pulmonary diseases. In this work a new method of non-contrast-enhanced functional lung MRI (not dependent on intravenous or inhalative contrast agents) is proposed. A two-dimensional (2D) true fast imaging with steady precession (TrueFISP) pulse sequence (TR/TE = 1.9 ms/0.8 ms, acquisition time [TA] = 112 ms/image) was implemented on a 1.5T whole-body MR scanner. The imaging protocol comprised sets of 198 lung images acquired with an imaging rate of 3.33 images/s in coronal and sagittal view. No electrocardiogram (ECG) or respiratory triggering was used. A nonrigid image registration algorithm was applied to compensate for respiratory motion. Rapid data acquisition allowed observing intensity changes in corresponding lung areas with respect to the cardiac and respiratory frequencies. After a Fourier analysis along the time domain, two spectral lines corresponding to both frequencies were used to calculate the perfusion- and ventilation-weighted images. The described method was applied in preliminary studies on volunteers and patients showing clinical relevance to obtain non-contrast-enhanced perfusion and ventilation data.

Islet-selectivity of G-protein coupled receptor ligands evaluated for PET imaging of pancreatic {beta}-cell mass

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

Cline, Gary W., E-mail: gary.cline@yale.edu; Zhao, Xiaojian; Jakowski, Amy B.

2011-09-02

Highlights: {yields} We screened G-protein coupled receptors for imaging pancreatic. {yields} Database mining and immunohistochemistry identified GPCRs enriched in {beta}-cells. {yields} In vitro and in vivo assays were used to determine exocrine vs endocrine specificity. {yields} GPCR candidates for imaging of {beta}-cell mass are Prokineticin-1R, mGluR5, and GLP-1R. -- Abstract: A critical unmet need exists for methods to quantitatively measure endogenous pancreatic {beta}-cell mass (BCM) for the clinical evaluation of therapies to prevent or reverse loss of BCM and diabetes progression. Our objective was to identify G-protein coupled receptors (GPCRs) that are expressed with a high degree of specificity tomore » islet {beta}-cells for receptor-targeted imaging of BCM. GPCRs enriched in pancreatic islets relative to pancreas acinar and hepatic tissue were identified using a database screen. Islet-specific expression was confirmed by human pancreas immunohistochemistry (IHC). In vitro selectivity assessment was determined from the binding and uptake of radiolabeled ligands to the rat insulinoma INS-1 832/13 cell line and isolated rat islets relative to the exocrine pancreas cell-type, PANC-1. Tail-vein injections of radioligands into rats were used to determine favorable image criteria of in vivo biodistribution to the pancreas relative to other internal organs (i.e., liver, spleen, stomach, and lungs). Database and IHC screening identified four candidate receptors for further in vitro and in vivo evaluation for PET imaging of BCM: prokineticin-1 receptor (PK-1R), metabotropic glutamate receptor type-5 (mGluR5), neuropeptide Y-2 receptor (NPY-2R), and glucagon-like peptide 1 receptor (GLP-1R). In vitro specificity ratios gave the following receptor rank order: PK-1R > GLP-1R > NPY-2R > mGluR5. The biodistribution rank order of selectivity to the pancreas was found to be PK-1R > VMAT2 {approx} GLP-1R > mGluR5. Favorable islet selectivity and biodistribution characteristics suggest several GPCRs as potential targets for PET imaging of pancreatic BCM.« less

Non-invasive microstructure and morphology investigation of the mouse lung: qualitative description and quantitative measurement.

PubMed

Zhang, Lu; Li, Dongyue; Luo, Shuqian

2011-02-25

Early detection of lung cancer is known to improve the chances of successful treatment. However, lungs are soft tissues with complex three-dimensional configuration. Conventional X-ray imaging is based purely on absorption resulting in very low contrast when imaging soft tissues without contrast agents. It is difficult to obtain adequate information of lung lesions from conventional X-ray imaging. In this study, a recently emerged imaging technique, in-line X-ray phase contrast imaging (IL-XPCI) was used. This powerful technique enabled high-resolution investigations of soft tissues without contrast agents. We applied IL-XPCI to observe the lungs in an intact mouse for the purpose of defining quantitatively the micro-structures in lung. The three-dimensional model of the lung was successfully established, which provided an excellent view of lung airways. We highlighted the use of IL-XPCI in the visualization and assessment of alveoli which had rarely been studied in three dimensions (3D). The precise view of individual alveolus was achieved. The morphological parameters, such as diameter and alveolar surface area were measured. These parameters were of great importance in the diagnosis of diseases related to alveolus and alveolar scar. Our results indicated that IL-XPCI had the ability to represent complex anatomical structures in lung. This offered a new perspective on the diagnosis of respiratory disease and may guide future work in the study of respiratory mechanism on the alveoli level.

Poster - Thur Eve - 16: Four-dimensional x-ray computed tomography and hyperpolarized 3 He magnetic resonance imaging of gas distribution in lung cancer.

PubMed

Mathew, L; Castillo, R; Castillo, E; Yaremko, B; Rodrigues, G; Etemad-Rezai, R; Guerrero, T; Parraga, G

2012-07-01

Dynamic imaging methods such as four-dimensional computed tomography (4DCT) and static imaging methods such as noble gas magnetic resonance imaging (MRI) deliver direct and regional measurements of lung function even in lung cancer patients in whom global lung function measurements are dominated by tumour burden. The purpose of this study was to directly compare quantitative measurements of gas distribution from static hyperpolarized 3 He MRI and dynamic 4DCT in a small group of lung cancer patients. MRI and 4DCT were performed in 11 subjects prior to radiation therapy. MRI was performed at 3.0T in breath-hold after inhalation 1L of hyperpolarized 3 He gas. Gas distribution in 3 He MRI was quantified using a semi-automated segmentation algorithm to generate percent-ventilated volume (PVV), reflecting the volume of gas in the lung normalized to the thoracic cavity volume. 4DCT pulmonary function maps were generated using deformable image registration of six expiratory phase images. The correspondence between identical tissue elements at inspiratory and expiratory phases was used to estimate regional gas distribution and PVV was quantified from these images. After accounting for differences in lung volumes between 3 He MRI (1.9±0.5L ipsilateral, 2.3±0.7 contralateral) and 4DCT (1.2±0.3L ipsilateral, 1.3±0.4L contralateral) during image acquisition, there was no statistically significant difference in PVV between 3 He MRI (72±11% ipsilateral, 79±12% contralateral) and 4DCT (74±3% ipsilateral, 75±4% contralateral). Our results indicate quantitative agreement in the regional distribution of inhaled gas in both static and dynamic imaging methods. PVV may be considered as a regional surrogate measurement of lung function or ventilation. © 2012 American Association of Physicists in Medicine.

WE-G-204-07: Automated Characterization of Perceptual Quality of Clinical Chest Radiographs: Improvements in Lung, Spine, and Hardware Detection

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

Wells, J; Zhang, L; Samei, E

Purpose: To develop and validate more robust methods for automated lung, spine, and hardware detection in AP/PA chest images. This work is part of a continuing effort to automatically characterize the perceptual image quality of clinical radiographs. [Y. Lin et al. Med. Phys. 39, 7019–7031 (2012)] Methods: Our previous implementation of lung/spine identification was applicable to only one vendor. A more generalized routine was devised based on three primary components: lung boundary detection, fuzzy c-means (FCM) clustering, and a clinically-derived lung pixel probability map. Boundary detection was used to constrain the lung segmentations. FCM clustering produced grayscale- and neighborhood-based pixelmore » classification probabilities which are weighted by the clinically-derived probability maps to generate a final lung segmentation. Lung centerlines were set along the left-right lung midpoints. Spine centerlines were estimated as a weighted average of body contour, lateral lung contour, and intensity-based centerline estimates. Centerline estimation was tested on 900 clinical AP/PA chest radiographs which included inpatient/outpatient, upright/bedside, men/women, and adult/pediatric images from multiple imaging systems. Our previous implementation further did not account for the presence of medical hardware (pacemakers, wires, implants, staples, stents, etc.) potentially biasing image quality analysis. A hardware detection algorithm was developed using a gradient-based thresholding method. The training and testing paradigm used a set of 48 images from which 1920 51×51 pixel{sup 2} ROIs with and 1920 ROIs without hardware were manually selected. Results: Acceptable lung centerlines were generated in 98.7% of radiographs while spine centerlines were acceptable in 99.1% of radiographs. Following threshold optimization, the hardware detection software yielded average true positive and true negative rates of 92.7% and 96.9%, respectively. Conclusion: Updated segmentation and centerline estimation methods in addition to new gradient-based hardware detection software provide improved data integrity control and error-checking for automated clinical chest image quality characterization across multiple radiography systems.« less

Penetration of Recommended Procedures for Lung Cancer Staging and Management in the United States over 10 Years: A Quality Research in Radiation Oncology Survey

PubMed Central

Komaki, Ritsuko; Khalid, Najma; Langer, Corey J.; Kong, Feng-Ming (Spring); Owen, Jean B.; Crozier, Cheryl L.; Wilson, J. Frank; Wei, Xiong; Movsas, Benjamin

2013-01-01

Purpose To document the penetration of clinical trial results, practice guidelines, and appropriateness criteria into national practice, we compared the use of components of staging and treatment for lung cancer among patients treated in 2006–2007 versus in 1998–1999. Methods Patient, staging work-up, and treatment characteristics were extracted from the process survey database of the Quality Research in Radiation Oncology (QRRO), comprising records from 340 patients with locally advanced non-small cell lung cancer (LA-NSCLC) at 44 institutions and 144 patients with limited-stage small cell lung cancer (LS-SCLC) at 39 institutions. Data were compared for patients treated in 2006–2007 versus patients treated in 1998–1999. Results Use of all recommended procedures for staging and treatment was more common in 2006–2007. Specifically, disease was staged with brain imaging (magnetic resonance imaging or computed tomography) and whole-body imaging (positron emission tomography or bone scanning) in 66% of patients with LA-NSCLC in 2006–2007 (vs. 42% in 1998–1999, p=0.0001) and in 84% of patients with LS-SCLC in 2006–2007 (vs. 58.3% in 1998–1999, p=0.0011). Concurrent chemoradiation was used for 77% of LA-NSCLC patients (vs. 45% in 1998–1999, p<0.0001) and for 90% of LS-SCLC patients (vs. 62.5% in 1998–1999, p<0.0001). Use of the recommended radiation dose (59–74 Gy for NSCLC and 60–70 Gy as once-daily therapy for SCLC) did not change appreciably, being 88% for NSCLC in both periods and 51% (2006–2007) vs. 43% (1998–1999) for SCLC. Twice-daily radiation for SCLC was used for 21% of patients in 2006–2007 vs. 8% in 1998–1999. Finally, 49% of patients with LS-SCLC received prophylactic cranial irradiation (PCI) in 2006–2007 (vs. 21% in 1998–1999). Conclusion Although adherence to all quality indicators improved over time, brain imaging and recommended radiation doses for stage III NSCLC were used in <90% of cases. Use of full thoracic doses and PCI for LS-SCLC also require improvement. PMID:23273996

Penetration of Recommended Procedures for Lung Cancer Staging and Management in the United States Over 10 Years: A Quality Research in Radiation Oncology Survey

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

Komaki, Ritsuko, E-mail: rkomaki@mdanderson.org; Khalid, Najma; Langer, Corey J.

2013-03-15

Purpose: To document the penetration of clinical trial results, practice guidelines, and appropriateness criteria into national practice, we compared the use of components of staging and treatment for lung cancer among patients treated in 2006-2007 with those used in patients treated in 1998-1999. Methods and Materials: Patient, staging work-up, and treatment characteristics were extracted from the process survey database of the Quality Research in Radiation Oncology (QRRO), consisting of records of 340 patients with locally advanced non-small cell lung cancer (LA-NSCLC) at 44 institutions and of 144 patients with limited-stage small cell lung cancer (LS-SCLC) at 39 institutions. Data weremore » compared for patients treated in 2006-2007 versus those for patients treated in 1998-1999. Results: Use of all recommended procedures for staging and treatment was more common in 2006-2007. Specifically, disease was staged with brain imaging (magnetic resonance imaging or computed tomography) and whole-body imaging (positron emission tomography or bone scanning) in 66% of patients with LA-NSCLC in 2006-2007 (vs 42% in 1998-1999, P=.0001) and in 84% of patients with LS-SCLC in 2006-2007 (vs 58.3% in 1998-1999, P=.0011). Concurrent chemoradiation was used for 77% of LA-NSCLC patients (vs 45% in 1998-1999, P<.0001) and for 90% of LS-SCLC patients (vs 62.5% in 1998-1999, P<.0001). Use of the recommended radiation dose (59-74 Gy for NSCLC and 60-70 Gy as once-daily therapy for SCLC) did not change appreciably, being 88% for NSCLC in both periods and 51% (2006-2007) versus 43% (1998-1999) for SCLC. Twice-daily radiation for SCLC was used for 21% of patients in 2006-2007 versus 8% in 1998-1999. Finally, 49% of patients with LS-SCLC received prophylactic cranial irradiation (PCI) in 2006-2007 (vs 21% in 1998-1999). Conclusions: Although adherence to all quality indicators improved over time, brain imaging and recommended radiation doses for stage III NSCLC were used in <90% of cases. Use of full thoracic doses and PCI for LS-SCLC also requires improvement.« less

Differential Lung Uptake of 99mTc-HMPAO and 99mTc-Duramycin in the Chronic Hyperoxia Rat Model

PubMed Central

Clough, Anne V.; Audi, Said H.; Haworth, Steven T.; Roerig, David L.

2015-01-01

Noninvasive radionuclide imaging has the potential to identify and assess mechanisms involved in particular stages of lung injury which occur with acute respiratory distress syndrome, for example. Lung uptake of 99mTc-hexamethylpropyleneamine oxime (HMPAO) is reported to be partially dependent on the redox status of the lung tissue while 99mTc-duramycin, a new marker of cell injury, senses cell death via apoptosis and/or necrosis. Thus, we investigated changes in lung uptake of these agents in rat exposed to hyperoxia for prolonged periods, a common model of acute lung injury. Methods Male Sprague-Dawley rats were pre-exposed to either normoxia (21% O2) or hyperoxia (85% O2) for up to 21 days. For imaging, the rats were anesthetized, injected i.v. with either 99mTc-HMPAO or 99mTc-duramycin (37-74 MBq) and planar images were acquired using a high sensitivity modular gamma camera. Subsequently, 99mTc-macroagreggated albumin (37 MBq, diam=10-40 μm) was injected i.v., imaged, and used to define a lung region-of-interest. The lung to background ratio was used as a measure of lung uptake. Results Hyperoxia exposure resulted in a 74% increase in 99mTc-HMPAO lung uptake, which peaked at 7 days and persisted for the 21 days of exposure. 99mTc-duramycin lung uptake was also maximal at 7 days of exposure but decreased to near control levels by 21 days. The sustained elevation of 99mTc-HMPAO uptake suggests ongoing changes in lung redox status whereas cell death appears to have subsided by 21 days. Conclusion These results suggest the potential use of 99mTc-HMPAO and 99mTc-duramycin as redox and cell-death imaging biomarkers, respectively, for in vivo identification and assessment of different stages of lung injury. PMID:23086010

Development of novel approach to diagnostic imaging of lung cancer with 18F-Nifene PET/CT using A/J mice treated with NNK

PubMed Central

Galitovskiy, V; Kuruvilla, SA; Sevriokov, E; Corches, A; Pan, ML; Kalantari-Dehaghi, M; Chernyavsky, AI; Mukherjee, J; Grando, SA

2017-01-01

Development of novel methods of early diagnosis of lung cancer is one of the major tasks of contemporary clinical and experimental oncology. In this study, we utilized the tobacco nitrosamine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK)-induced lung cancer in A/J mice as an animal model for development of a new imaging technique for early diagnosis of lung cancer. Lung cancer cells in A/J mice overexpress nicotinic acetylcholine receptors. Longitudinal CT scans were carried out over a period of 8 months after NNK treatment, followed by PET/CT scans with 18F-Nifene that binds to α4-made nicotinic receptors with high affinity. PET/CT scans of lungs were also obtained ex vivo. CT revealed the presence of lung nodules in 8-month NNK-treated mice, while control mice had no tumors. Imaging of live animals prior to necropsy allowed correlation of results of tumor load via PET/CT and histopathological findings. Significant amount of 18F-Nifene was seen in the lungs of NNK-treated mice, whereas lungs of control mice showed only minor uptake of 18F-Nifene. Quantitative analysis of the extent and amount of 18F-Nifene binding in lung in vivo and ex vivo demonstrated a higher tumor/nontumor ratio due to selective labeling of tumor nodules expressing abundant α4 nicotinic receptor subunits. For comparison, we performed PET/CT studies with 18F-FDG, which is used for the imaging diagnosis of lung cancer. The tumor/nontumor ratios for 18F-FDG were lower than for 18F-Nifene. Thus, we have developed a novel diagnostic imaging approach to early diagnosis of lung cancer using 18F-Nifene PET/CT. This technique allows quantitative assessment of lung tumors in live mice, which is critical for establishing tumor size and location, and also has salient clinical implications. PMID:28553544

TU-G-204-09: The Effects of Reduced- Dose Lung Cancer Screening CT On Lung Nodule Detection Using a CAD Algorithm

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

Young, S; Lo, P; Kim, G

2015-06-15

Purpose: While Lung Cancer Screening CT is being performed at low doses, the purpose of this study was to investigate the effects of further reducing dose on the performance of a CAD nodule-detection algorithm. Methods: We selected 50 cases from our local database of National Lung Screening Trial (NLST) patients for which we had both the image series and the raw CT data from the original scans. All scans were acquired with fixed mAs (25 for standard-sized patients, 40 for large patients) on a 64-slice scanner (Sensation 64, Siemens Healthcare). All images were reconstructed with 1-mm slice thickness, B50 kernel.more » 10 of the cases had at least one nodule reported on the NLST reader forms. Based on a previously-published technique, we added noise to the raw data to simulate reduced-dose versions of each case at 50% and 25% of the original NLST dose (i.e. approximately 1.0 and 0.5 mGy CTDIvol). For each case at each dose level, the CAD detection algorithm was run and nodules greater than 4 mm in diameter were reported. These CAD results were compared to “truth”, defined as the approximate nodule centroids from the NLST reports. Subject-level mean sensitivities and false-positive rates were calculated for each dose level. Results: The mean sensitivities of the CAD algorithm were 35% at the original dose, 20% at 50% dose, and 42.5% at 25% dose. The false-positive rates, in decreasing-dose order, were 3.7, 2.9, and 10 per case. In certain cases, particularly in larger patients, there were severe photon-starvation artifacts, especially in the apical region due to the high-attenuating shoulders. Conclusion: The detection task was challenging for the CAD algorithm at all dose levels, including the original NLST dose. However, the false-positive rate at 25% dose approximately tripled, suggesting a loss of CAD robustness somewhere between 0.5 and 1.0 mGy. NCI grant U01 CA181156 (Quantitative Imaging Network); Tobacco Related Disease Research Project grant 22RT-0131.« less

In vivo tomographic imaging of lung colonization of tumour in mouse with simultaneous fluorescence and X-ray CT.

PubMed

Zhang, Bin; Gao, Fuping; Wang, Mengjiao; Cao, Xu; Liu, Fei; Wang, Xin; Luo, Jianwen; Wang, Guangzhi; Bai, Jing

2014-01-01

Non-invasive in vivo imaging of diffuse and wide-spread colonization within the lungs, rather than distinct solid primary tumors, is still a challenging work. In this work, a lung colonization mouse model bearing A549 human lung tumor was simultaneously scanned by a dual-modality fluorescence molecular tomography (FMT) and X-ray computed tomography (CT) system in vivo. A two steps method which incorporates CT structural information into the FMT reconstruction procedure is employed to provide concurrent anatomical and functional information. By using the target-specific fluorescence agent, the fluorescence tomographic results show elevated fluorescence intensity deep within the lungs which is colonized with diffuse and wide-spread tumors. The results were confirmed with ex vivo fluorescence reflectance imaging and histological examination of the lung tissues. With FMT reconstruction combined with the CT information, the dual-modality FMT/micro-CT system is expected to offer sensitive and noninvasive imaging of diffuse tumor colonization within the lungs in vivo. Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Accurate segmentation of lung fields on chest radiographs using deep convolutional networks

NASA Astrophysics Data System (ADS)

Arbabshirani, Mohammad R.; Dallal, Ahmed H.; Agarwal, Chirag; Patel, Aalpan; Moore, Gregory

2017-02-01

Accurate segmentation of lung fields on chest radiographs is the primary step for computer-aided detection of various conditions such as lung cancer and tuberculosis. The size, shape and texture of lung fields are key parameters for chest X-ray (CXR) based lung disease diagnosis in which the lung field segmentation is a significant primary step. Although many methods have been proposed for this problem, lung field segmentation remains as a challenge. In recent years, deep learning has shown state of the art performance in many visual tasks such as object detection, image classification and semantic image segmentation. In this study, we propose a deep convolutional neural network (CNN) framework for segmentation of lung fields. The algorithm was developed and tested on 167 clinical posterior-anterior (PA) CXR images collected retrospectively from picture archiving and communication system (PACS) of Geisinger Health System. The proposed multi-scale network is composed of five convolutional and two fully connected layers. The framework achieved IOU (intersection over union) of 0.96 on the testing dataset as compared to manual segmentation. The suggested framework outperforms state of the art registration-based segmentation by a significant margin. To our knowledge, this is the first deep learning based study of lung field segmentation on CXR images developed on a heterogeneous clinical dataset. The results suggest that convolutional neural networks could be employed reliably for lung field segmentation.

Computer-aided pulmonary image analysis in small animal models

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

Xu, Ziyue; Mansoor, Awais; Mollura, Daniel J.

Purpose: To develop an automated pulmonary image analysis framework for infectious lung diseases in small animal models. Methods: The authors describe a novel pathological lung and airway segmentation method for small animals. The proposed framework includes identification of abnormal imaging patterns pertaining to infectious lung diseases. First, the authors’ system estimates an expected lung volume by utilizing a regression function between total lung capacity and approximated rib cage volume. A significant difference between the expected lung volume and the initial lung segmentation indicates the presence of severe pathology, and invokes a machine learning based abnormal imaging pattern detection system next.more » The final stage of the proposed framework is the automatic extraction of airway tree for which new affinity relationships within the fuzzy connectedness image segmentation framework are proposed by combining Hessian and gray-scale morphological reconstruction filters. Results: 133 CT scans were collected from four different studies encompassing a wide spectrum of pulmonary abnormalities pertaining to two commonly used small animal models (ferret and rabbit). Sensitivity and specificity were greater than 90% for pathological lung segmentation (average dice similarity coefficient > 0.9). While qualitative visual assessments of airway tree extraction were performed by the participating expert radiologists, for quantitative evaluation the authors validated the proposed airway extraction method by using publicly available EXACT’09 data set. Conclusions: The authors developed a comprehensive computer-aided pulmonary image analysis framework for preclinical research applications. The proposed framework consists of automatic pathological lung segmentation and accurate airway tree extraction. The framework has high sensitivity and specificity; therefore, it can contribute advances in preclinical research in pulmonary diseases.« less

99mTc-zolmitriptan: radiolabeling, molecular modeling, biodistribution and gamma scintigraphy as a hopeful radiopharmaceutical for lung nuclear imaging.

PubMed

Rashed, H M; Marzook, F A; Farag, H

2016-12-01

Lung imaging radiopharmaceuticals are helpful agents for measuring pulmonary blood flow and allow detection of pulmonary embolism and lung cancer. The goal of this study was to develop a novel potential radiopharmaceutical for lung imaging. Zolmitriptan (a selective serotonin receptor agonist) was successfully labeled with 99m Tc via direct labeling method under reductive conditions studying different factors affecting the labeling efficiency. 99m Tc-zolmitriptan was obtained with a maximum labeling yield of 92.5 ± 0.61 % and in vitro stability up to 24 h. Molecular modeling was done to predict the structure of 99m Tc-zolmitriptan and ensure that radiolabeling did not affect binding ability of zolmitriptan to its receptor. Biodistribution studies showed that maximum lung uptake of 99m Tc-zolmitriptan was 23.89 ± 1.2 % injected dose/g tissue at 15 min post-injection and retention in lungs remained high up to 1 h, whereas the clearance from mice appeared to proceed mainly via the renal pathway. Scintigraphic images confirmed the biodistribution results showing a high resolution lung image with low accumulation of radioactivity in other organs except kidneys and urinary bladder. 99m Tc-zolmitriptan is not a blood product and so it is more safe than the currently available 99m Tc-MAA, and its lung uptake is higher than that of the recently discovered 123 I-IPMPD, 99m Tc(CO) 5 I and 99m Tc-DHPM. So, 99m Tc-zolmitriptan could be used as a hopeful radiopharmaceutical for lung scintigraphic imaging.

The use of combined single photon emission computed tomography and X-ray computed tomography to assess the fate of inhaled aerosol.

PubMed

Fleming, John; Conway, Joy; Majoral, Caroline; Tossici-Bolt, Livia; Katz, Ira; Caillibotte, Georges; Perchet, Diane; Pichelin, Marine; Muellinger, Bernhard; Martonen, Ted; Kroneberg, Philipp; Apiou-Sbirlea, Gabriela

2011-02-01

Gamma camera imaging is widely used to assess pulmonary aerosol deposition. Conventional planar imaging provides limited information on its regional distribution. In this study, single photon emission computed tomography (SPECT) was used to describe deposition in three dimensions (3D) and combined with X-ray computed tomography (CT) to relate this to lung anatomy. Its performance was compared to planar imaging. Ten SPECT/CT studies were performed on five healthy subjects following carefully controlled inhalation of radioaerosol from a nebulizer, using a variety of inhalation regimes. The 3D spatial distribution was assessed using a central-to-peripheral ratio (C/P) normalized to lung volume and for the right lung was compared to planar C/P analysis. The deposition by airway generation was calculated for each lung and the conducting airways deposition fraction compared to 24-h clearance. The 3D normalized C/P ratio correlated more closely with 24-h clearance than the 2D ratio for the right lung [coefficient of variation (COV), 9% compared to 15% p < 0.05]. Analysis of regional distribution was possible for both lungs in 3D but not in 2D due to overlap of the stomach on the left lung. The mean conducting airways deposition fraction from SPECT for both lungs was not significantly different from 24-h clearance (COV 18%). Both spatial and generational measures of central deposition were significantly higher for the left than for the right lung. Combined SPECT/CT enabled improved analysis of aerosol deposition from gamma camera imaging compared to planar imaging. 3D radionuclide imaging combined with anatomical information from CT and computer analysis is a useful approach for applications requiring regional information on deposition.

Implementation and evaluation of a new workflow for registration and segmentation of pulmonary MRI data for regional lung perfusion assessment.

PubMed

Böttger, T; Grunewald, K; Schöbinger, M; Fink, C; Risse, F; Kauczor, H U; Meinzer, H P; Wolf, Ivo

2007-03-07

Recently it has been shown that regional lung perfusion can be assessed using time-resolved contrast-enhanced magnetic resonance (MR) imaging. Quantification of the perfusion images has been attempted, based on definition of small regions of interest (ROIs). Use of complete lung segmentations instead of ROIs could possibly increase quantification accuracy. Due to the low signal-to-noise ratio, automatic segmentation algorithms cannot be applied. On the other hand, manual segmentation of the lung tissue is very time consuming and can become inaccurate, as the borders of the lung to adjacent tissues are not always clearly visible. We propose a new workflow for semi-automatic segmentation of the lung from additionally acquired morphological HASTE MR images. First the lung is delineated semi-automatically in the HASTE image. Next the HASTE image is automatically registered with the perfusion images. Finally, the transformation resulting from the registration is used to align the lung segmentation from the morphological dataset with the perfusion images. We evaluated rigid, affine and locally elastic transformations, suitable optimizers and different implementations of mutual information (MI) metrics to determine the best possible registration algorithm. We located the shortcomings of the registration procedure and under which conditions automatic registration will succeed or fail. Segmentation results were evaluated using overlap and distance measures. Integration of the new workflow reduces the time needed for post-processing of the data, simplifies the perfusion quantification and reduces interobserver variability in the segmentation process. In addition, the matched morphological data set can be used to identify morphologic changes as the source for the perfusion abnormalities.

Retrospective evaluation of thoracic computed tomography findings in dogs naturally infected by Angiostrongylus vasorum.

PubMed

Coia, Mark E; Hammond, Gawain; Chan, Daniel; Drees, Randi; Walker, David; Murtagh, Kevin; Stone, Janine; Bexfield, Nicholas; Reeve, Lizzie; Helm, Jenny

2017-09-01

Angiostrongylus vasorum (A. vasorum) is an important emerging disease of canidae. Cardiorespiratory signs are common in affected dogs, therefore thoracic imaging is critical for diagnosing and monitoring disease. Descriptions of thoracic computed tomography (CT) findings in dogs naturally infected with A. vasorum are currently lacking. Aims of this multicenter, retrospective study were to describe thoracic CT findings in a group of dogs with confirmed disease, determine whether any changes were consistent among dogs, and propose standardized terms for describing thoracic CT findings. Nine UK-based referral centers' clinical and imaging databases were searched for dogs that had a confirmed diagnosis of A. vasorum, and had undergone thoracic CT examination. Eighteen dogs, from seven of the centers, fulfilled the inclusion criteria. The lung lobes were divided into the following three zones and the CT changes described in each: pleural (zone 1), subpleural (zone 2), and peribronchovascular (zone 3). The predominent abnormality was increased lung attenuation due to poorly defined ground-glass opacity or consolidation. There were regions of mosaic attenuation due to peripheral bronchiectasis. Nine/18 (50%) dogs showed hyperattenuating nodules of varying sizes with ill-defined margins. The distribution always affected zones 1 and 2 with varied involvement of zone 3; this resulted in clear delineation between zones 2 and 3. Tracheobronchial lymphadenomegaly was frequently noted. Findings were nonspecific and there was considerable overlap with other pulmonary conditions. However, authors recommend that A. vasorum be considered a likely differential diagnosis for dogs with a predominantly peripheral distribution of lung changes. © 2017 American College of Veterinary Radiology.

State of the Art: Response Assessment in Lung Cancer in the Era of Genomic Medicine

PubMed Central

Hatabu, Hiroto; Johnson, Bruce E.; McLoud, Theresa C.

2014-01-01

Tumor response assessment has been a foundation for advances in cancer therapy. Recent discoveries of effective targeted therapy for specific genomic abnormalities in lung cancer and their clinical application have brought revolutionary advances in lung cancer therapy and transformed the oncologist’s approach to patients with lung cancer. Because imaging is a major method of response assessment in lung cancer both in clinical trials and practice, radiologists must understand the genomic alterations in lung cancer and the rapidly evolving therapeutic approaches to effectively communicate with oncology colleagues and maintain the key role in lung cancer care. This article describes the origin and importance of tumor response assessment, presents the recent genomic discoveries in lung cancer and therapies directed against these genomic changes, and describes how these discoveries affect the radiology community. The authors then summarize the conventional Response Evaluation Criteria in Solid Tumors and World Health Organization guidelines, which continue to be the major determinants of trial endpoints, and describe their limitations particularly in an era of genomic-based therapy. More advanced imaging techniques for lung cancer response assessment are presented, including computed tomography tumor volume and perfusion, dynamic contrast material–enhanced and diffusion-weighted magnetic resonance imaging, and positron emission tomography with fluorine 18 fluorodeoxyglucose and novel tracers. State-of-art knowledge of lung cancer biology, treatment, and imaging will help the radiology community to remain effective contributors to the personalized care of lung cancer patients. © RSNA, 2014 PMID:24661292

[Exposed workers to lung cancer risk. An estimation using the ISPESL database of enterprises].

PubMed

Scarselli, Alberto; Marinaccio, Alessandro; Nesti, Massimo

2007-01-01

lung cancer is the first cause of death in the industrialized country among males and is increasing among females. In 2001 a uniform and standardised list of occupations or jobs known or suspected to be associated with lung cancer has been prepared. The aim of this study is to set up a database of Italian enterprises corresponding to activities related to this list and to assess the number of potentially exposed workers. a detailed and unique list of codes, referred to Ateco91 ISTAT classification with exclusion of the State Railways and the public administration sectors, has been developed. The list is divided into two categories: respectively for occupations or jobs definitely entailing carcinogenic risk and for those which probably/possibly entail a risk. Firms have been selected from the ISPESL database of enterprises and the number of workers has been estimated on the basis of this list. Italy. assessment of the number of workers potentially exposed to lung cancer risk and creation of a register of involved firms. the number of potentially exposed workers in the industrial and services sector related to lung cancer risk is 650,886 blue collars and the number of firms censused in Italy is 117,006 units. Corresponding figures in the agriculture sector are 163,340 and 84,839. This type of evaluation, being based on administrative sources rather then on direct measures of exposure, certainly includes an overestimation of exposed workers. the lists based on a standard classification which have been created allow for the creation of databases which can be used to control occupational exposure to carcinogens and to increase comparability between epidemiologic studies based on job-exposure matrix.

Mapping cardiogenic oscillations using synchrotron-based phase contrast CT imaging

NASA Astrophysics Data System (ADS)

Thurgood, Jordan; Dubsky, Stephen; Siu, Karen K. W.; Wallace, Megan; Siew, Melissa; Hooper, Stuart; Fouras, Andreas

2012-10-01

In many animals, including humans, the lungs encase the majority of the heart thus the motion of each organ affects the other. The effects of the motion of the heart on the lungs potentially provides information with regards to both lung and heart health. We present a novel technique that is capable of measuring the effect of the heart on the surrounding lung tissue through the use of advanced synchrotron imaging techniques and recently developed X-ray velocimetry methods. This technique generates 2D frequency response maps of the lung tissue motion at multiple projection angles from projection X-ray images. These frequency response maps are subsequently used to generate 3D reconstructions of the lung tissue exhibiting motion at the frequency of ventilation and the lung tissue exhibiting motion at the frequency of the heart. This technique has a combined spatial and temporal resolution sufficient to observe the dynamic and complex 3D nature of lung-heart interactions.

RECONSTRUCTION OF A HUMAN LUNG MORPHOLOGY MODEL FROM MAGNETIC RESONANCE IMAGES

EPA Science Inventory

RATIONALE A description of lung morphological structure is necessary for modeling the deposition and fate of inhaled therapeutic aerosols. A morphological model of the lung boundary was generated from magnetic resonance (MR) images with the goal of creating a framework for anato...

Mass preserving registration for lung CT

NASA Astrophysics Data System (ADS)

Gorbunova, Vladlena; Lo, Pechin; Loeve, Martine; Tiddens, Harm A.; Sporring, Jon; Nielsen, Mads; de Bruijne, Marleen

2009-02-01

In this paper, we evaluate a novel image registration method on a set of expiratory-inspiratory pairs of computed tomography (CT) lung scans. A free-form multi resolution image registration technique is used to match two scans of the same subject. To account for the differences in the lung intensities due to differences in inspiration level, we propose to adjust the intensity of lung tissue according to the local expansion or compression. An image registration method without intensity adjustment is compared to the proposed method. Both approaches are evaluated on a set of 10 pairs of expiration and inspiration CT scans of children with cystic fibrosis lung disease. The proposed method with mass preserving adjustment results in significantly better alignment of the vessel trees. Analysis of local volume change for regions with trapped air compared to normally ventilated regions revealed larger differences between these regions in the case of mass preserving image registration, indicating that mass preserving registration is better at capturing localized differences in lung deformation.

Comparison of distribution of lung aeration measured with EIT and CT in spontaneously breathing, awake patients1.

PubMed

Radke, Oliver C; Schneider, Thomas; Braune, Anja; Pirracchio, Romain; Fischer, Felix; Koch, Thea

2016-09-28

Both Electrical Impedance Tomography (EIT) and Computed Tomography (CT) allow the estimation of the lung area. We compared two algorithms for the detection of the lung area per quadrant from the EIT images with the lung areas derived from the CT images. 39 outpatients who were scheduled for an elective CT scan of the thorax were included in the study. For each patient we recorded EIT images immediately before the CT scan. The lung area per quadrant was estimated from both CT and EIT data using two different algorithms for the EIT data. Data showed considerable variation during spontaneous breathing of the patients. Overall correlation between EIT and CT was poor (0.58-0.77), the correlation between the two EIT algorithms was better (0.90-0.92). Bland-Altmann analysis revealed absence of bias, but wide limits of agreement. Lung area estimation from CT and EIT differs significantly, most probably because of the fundamental difference in image generation.

Clinical value of miR-452-5p expression in lung adenocarcinoma: A retrospective quantitative real-time polymerase chain reaction study and verification based on The Cancer Genome Atlas and Gene Expression Omnibus databases.

PubMed

Gan, Xiao-Ning; Luo, Jie; Tang, Rui-Xue; Wang, Han-Lin; Zhou, Hong; Qin, Hui; Gan, Ting-Qing; Chen, Gang

2017-05-01

The role and mechanism of miR-452-5p in lung adenocarcinoma remain unclear. In this study, we performed a systematic study to investigate the clinical value of miR-452-5p expression in lung adenocarcinoma. The expression of miR-452-5p in 101 lung adenocarcinoma patients was detected by quantitative real-time polymerase chain reaction. The Cancer Genome Atlas and Gene Expression Omnibus databases were joined to verify the expression level of miR-452-5p in lung adenocarcinoma. Via several online prediction databases and bioinformatics software, pathway and network analyses of miR-452-5p target genes were performed to explore its prospective molecular mechanism. The expression of miR-452-5p in lung adenocarcinoma in house was significantly lower than that in adjacent tissues (p < 0.001). Additionally, the expression level of miR-452-5p was negatively correlated with several clinicopathological parameters including the tumor size (p = 0.014), lymph node metastasis (p = 0.032), and tumor-node-metastasis stage (p = 0.036). Data from The Cancer Genome Atlas also confirmed the low expression of miR-452 in lung adenocarcinoma (p < 0.001). Furthermore, reduced expression of miR-452-5p in lung adenocarcinoma (standard mean deviations = -0.393, 95% confidence interval: -0.774 to -0.011, p = 0.044) was validated by a meta-analysis. Five hub genes targeted by miR-452-5p, including SMAD family member 4, SMAD family member 2, cyclin-dependent kinase inhibitor 1B, tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein epsilon, and tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein beta, were significantly enriched in the cell-cycle pathway. In conclusion, low expression of miR-452-5p tends to play an essential role in lung adenocarcinoma. Bioinformatics analysis might be beneficial to reveal the potential mechanism of miR-452-5p in lung adenocarcinoma.

MO-E-BRB-00: PANEL DISCUSSION: SBRT/SRS Case Studies - Lung

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

NONE

2016-06-15

In this interactive session, lung SBRT patient cases will be presented to highlight real-world considerations for ensuring safe and accurate treatment delivery. An expert panel of speakers will discuss challenges specific to lung SBRT including patient selection, patient immobilization techniques, 4D CT simulation and respiratory motion management, target delineation for treatment planning, online treatment alignment, and established prescription regimens and OAR dose limits. Practical examples of cases, including the patient flow thought the clinical process are presented and audience participation will be encouraged. This panel session is designed to provide case demonstration and review for lung SBRT in terms ofmore » (1) clinical appropriateness in patient selection, (2) strategies for simulation, including 4D and respiratory motion management, and (3) applying multi imaging modality (4D CT imaging, MRI, PET) for tumor volume delineation and motion extent, and (4) image guidance in treatment delivery. Learning Objectives: Understand the established requirements for patient selection in lung SBRT Become familiar with the various immobilization strategies for lung SBRT, including technology for respiratory motion management Understand the benefits and pitfalls of applying multi imaging modality (4D CT imaging, MRI, PET) for tumor volume delineation and motion extent determination for lung SBRT Understand established prescription regimes and OAR dose limits.« less

In Vivo Detection of Hyperoxia-Induced Pulmonary Endothelial Cell Death Using 99mTc-Duramycin

PubMed Central

Audi, Said H.; Jacobs, Elizabeth R.; Zhao, Ming; Roerig, David L.; Haworth, Steven T.; Clough, Anne V.

2014-01-01

Introduction: 99mTc-duramycin, DU, is a SPECT biomarker of tissue injury identifying cell death. The objective of this study is to investigate the potential of DU imaging to quantify capillary endothelial cell death in rat lung injury resulting from hyperoxia exposure as a model of acute lung injury. Methods: Rats were exposed to room air (normoxic) or >98% O2 for 48 or 60 hours. DU was injected i.v. in anesthetized rats, scintigraphy images were acquired at steady-state, and lung DU uptake was quantified from the images. Post-mortem, the lungs were removed for histological studies. Sequential lung sections were immunostained for caspase activation and endothelial and epithelial cells. Results: Lung DU uptake increased significantly (p < 0.001) by 39% and 146% in 48-hr and 60-hr exposed rats, respectively, compared to normoxic rats. There was strong correlation (r2 = 0.82, p = 0.005) between lung DU uptake and the number of cleaved caspase 3 (CC3) positive cells, and endothelial cells accounted for more than 50% of CC3 positive cells in the hyperoxic lungs. Histology revealed preserved lung morphology through 48 hours. By 60 hours there was evidence of edema, and modest neutrophilic infiltrate. Conclusions: Rat lung DU uptake in vivo increased after just 48 hours of >98% O2 exposure, prior to the onset of any substantial evidence of lung injury. These results suggest that apoptotic endothelial cells are the primary contributors to the enhanced DU lung uptake, and support the utility of DU imaging for detecting early endothelial cell death in vivo. PMID:25218023

Radiopharmaceutical considerations for using Tc-99m MAA in lung transplant patients.

PubMed

Ponto, James A

2010-01-01

To elucidate radiopharmaceutical considerations for using technetium Tc-99m albumin aggregated (Tc-99m MAA) in lung transplant patients and to establish an appropriate routine dose and preparation procedure. Tertiary care academic hospital during May 2007 to May 2009. Nuclear pharmacist working in nuclear medicine department. Radiopharmaceutical considerations deemed important for the use of Tc-99m MAA in lung transplant patients included radioactivity dose, particulate dose, rate of the radiolabeling reaction (preparation time), and final radiochemical purity. Evaluation of our initial 12-month experience, published literature, and professional practice guidelines provided the basis for establishing an appropriate dose and preparation procedure of Tc-99m MAA for use in lung transplant patients. Radiochemical purity at typical incubation times and image quality in subsequent lung transplant patients imaged during the next 12 months. Based on considerations of radioactivity dose, particulate dose, rate of the radiolabeling reaction (preparation time), and final radiochemical purity, a routine dose consisting of 3 mCi (111 MBq) and 100,000 particles of Tc-99m MAA for planar perfusion lung imaging of adult lung transplant patients was established as reasonable and appropriate. MAA kits were prepared with a more reasonable amount of Tc-99m and yielded high radiochemical purity values in typical incubation times. Images have continued to be of high diagnostic quality. Tc-99m MAA used for lung transplant imaging can be readily prepared with high radiochemical purity to provide a dose of 3 mCi (111 GBq)/100,000 particles, which provides images of high diagnostic quality.

Regulatory interactions between long noncoding RNA LINC00968 and miR-9-3p in non-small cell lung cancer: A bioinformatic analysis based on miRNA microarray, GEO and TCGA.

PubMed

Li, Dong-Yao; Chen, Wen-Jie; Shang, Jun; Chen, Gang; Li, Shi-Kang

2018-06-01

Long non-coding RNAs (lncRNAs) have been demonstrated to mediate carcinogenesis in various types of cancer. However, the regulatory role of lncRNA LINC00968 in lung adenocarcinoma remains unclear. The microRNA (miRNA) expression in LINC00968-overexpressing human lung adenocarcinoma A549 cells was detected using miRNA microarray analysis. miR-9-3p was selected for further analysis, and its expression was verified in the Gene Expression Omnibus (GEO) database. In addition, the regulatory axis of LINC00968 was validated using The Cancer Genome Atlas (TCGA) database. Results of the GEO database indicated miR-9-3p expression in lung adenocarcinoma was significantly higher compared with normal tissues. Functional enrichment analyses of the target genes of miR-9-3p indicated protein binding and the AMP-activated protein kinase pathway were the most enriched Gene Ontology and KEGG terms, respectively. Combining target genes with the correlated genes of LINC00968 and miR-9-3p, 120 objective genes were obtained, which were used to construct a protein-protein interaction (PPI) network. Cyclin A2 (CCNA2) was identified to have a vital role in the PPI network. Significant correlations were detected between LINC00968, miR-9-3p and CCNA2 in lung adenocarcinoma. The LINC00968/miR-9-3p/CCNA2 regulatory axis provides a new foundation for further evaluating the regulatory mechanisms of LINC00968 in lung adenocarcinoma.

Lung Motion Model Validation Experiments, Free-Breathing Tissue Densitometry, and Ventilation Mapping using Fast Helical CT Imaging

NASA Astrophysics Data System (ADS)

Dou, Hsiang-Tai

The uncertainties due to respiratory motion present significant challenges to accurate characterization of cancerous tissues both in terms of imaging and treatment. Currently available clinical lung imaging techniques are subject to inferior image quality and incorrect motion estimation, with consequences that can systematically impact the downstream treatment delivery and outcome. The main objective of this thesis is the development of the techniques of fast helical computed tomography (CT) imaging and deformable image registration for the radiotherapy applications in accurate breathing motion modeling, lung tissue density modeling and ventilation imaging. Fast helical CT scanning was performed on 64-slice CT scanner using the shortest available gantry rotation time and largest pitch value such that scanning of the thorax region amounts to just two seconds, which is less than typical breathing cycle in humans. The scanning was conducted under free breathing condition. Any portion of the lung anatomy undergoing such scanning protocol would be irradiated for only a quarter second, effectively removing any motion induced image artifacts. The resulting CT data were pristine volumetric images that record the lung tissue position and density in a fraction of the breathing cycle. Following our developed protocol, multiple fast helical CT scans were acquired to sample the tissue positions in different breathing states. To measure the tissue displacement, deformable image registration was performed that registers the non-reference images to the reference one. In modeling breathing motion, external breathing surrogate signal was recorded synchronously with the CT image slices. This allowed for the tissue-specific displacement to be modeled as parametrization of the recorded breathing signal using the 5D lung motion model. To assess the accuracy of the motion model in describing tissue position change, the model was used to simulate the original high-pitch helical CT scan geometries, employed as ground truth data. Image similarity between the simulated and ground truth scans was evaluated. The model validation experiments were conducted in a patient cohort of seventeen patients to assess the model robustness and inter-patient variation. The model error averaged over multiple tracked positions from several breathing cycles was found to be on the order of one millimeter. In modeling the density change under free breathing condition, the determinant of Jacobian matrix from the registration-derived deformation vector field yielded volume change information of the lung tissues. Correlation of the Jacobian values to the corresponding voxel Housfield units (HU) reveals that the density variation for the majority of lung tissues can be very well described by mass conservation relationship. Different tissue types were identified and separately modeled. Large trials of validation experiments were performed. The averaged deviation between the modeled and the reference lung density was 30 HU, which was estimated to be the background CT noise level. In characterizing the lung ventilation function, a novel method was developed to determine the extent of lung tissue volume change. Information on volume change was derived from the deformable image registration of the fast helical CT images in terms of Jacobian values with respect to a reference image. Assuming the multiple volume change measurements are independently and identically distributed, statistical formulation was derived to model ventilation distribution of each lung voxels and empirical minimum and maximum probability distribution of the Jacobian values was computed. Ventilation characteristic was evaluated as the difference of the expectation value from these extremal distributions. The resulting ventilation map was compared with an independently obtained ventilation image derived directly from the lung intensities and good correlation was found using statistical test. In addition, dynamic ventilation characterization was investigated by estimating the voxel-specific ventilation distribution. Ventilation maps were generated at different percentile levels using the tissue volume expansion metrics.

High-resolution three-dimensional magnetic resonance imaging of mouse lung in situ.

PubMed

Scadeng, Miriam; Rossiter, Harry B; Dubowitz, David J; Breen, Ellen C

2007-01-01

This study establishes a method for high-resolution isotropic magnetic resonance (MR) imaging of mouse lungs using tracheal liquid-instillation to remove MR susceptibility artifacts. C57BL/6J mice were instilled sequentially with perfluorocarbon and phosphate-buffered saline to an airway pressure of 10, 20, or 30 cm H2O. Imaging was performed in a 7T MR scanner using a 2.5-cm Quadrature volume coil and a 3-dimensional (3D) FLASH imaging sequence. Liquid-instillation removed magnetic susceptibility artifacts and allowed lung structure to be viewed at an isotropic resolution of 78-90 microm. Instilled liquid and modeled lung volumes were well correlated (R = 0.92; P < 0.05) and differed by a constant tissue volume (220 +/- 92 microL). 3D image renderings allowed differences in structural dimensions (volumes and areas) to be accurately measured at each inflation pressure. These data demonstrate the efficacy of pulmonary liquid instillation for in situ high-resolution MR imaging of mouse lungs for accurate measurement of pulmonary airway, parenchymal, and vascular structures.

Image processing based detection of lung cancer on CT scan images

NASA Astrophysics Data System (ADS)

Abdillah, Bariqi; Bustamam, Alhadi; Sarwinda, Devvi

2017-10-01

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

A database for estimating organ dose for coronary angiography and brain perfusion CT scans for arbitrary spectra and angular tube current modulation

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

Rupcich, Franco; Badal, Andreu; Kyprianou, Iacovos

Purpose: The purpose of this study was to develop a database for estimating organ dose in a voxelized patient model for coronary angiography and brain perfusion CT acquisitions with any spectra and angular tube current modulation setting. The database enables organ dose estimation for existing and novel acquisition techniques without requiring Monte Carlo simulations. Methods: The study simulated transport of monoenergetic photons between 5 and 150 keV for 1000 projections over 360 Degree-Sign through anthropomorphic voxelized female chest and head (0 Degree-Sign and 30 Degree-Sign tilt) phantoms and standard head and body CTDI dosimetry cylinders. The simulations resulted in tablesmore » of normalized dose deposition for several radiosensitive organs quantifying the organ dose per emitted photon for each incident photon energy and projection angle for coronary angiography and brain perfusion acquisitions. The values in a table can be multiplied by an incident spectrum and number of photons at each projection angle and then summed across all energies and angles to estimate total organ dose. Scanner-specific organ dose may be approximated by normalizing the database-estimated organ dose by the database-estimated CTDI{sub vol} and multiplying by a physical CTDI{sub vol} measurement. Two examples are provided demonstrating how to use the tables to estimate relative organ dose. In the first, the change in breast and lung dose during coronary angiography CT scans is calculated for reduced kVp, angular tube current modulation, and partial angle scanning protocols relative to a reference protocol. In the second example, the change in dose to the eye lens is calculated for a brain perfusion CT acquisition in which the gantry is tilted 30 Degree-Sign relative to a nontilted scan. Results: Our database provides tables of normalized dose deposition for several radiosensitive organs irradiated during coronary angiography and brain perfusion CT scans. Validation results indicate total organ doses calculated using our database are within 1% of those calculated using Monte Carlo simulations with the same geometry and scan parameters for all organs except red bone marrow (within 6%), and within 23% of published estimates for different voxelized phantoms. Results from the example of using the database to estimate organ dose for coronary angiography CT acquisitions show 2.1%, 1.1%, and -32% change in breast dose and 2.1%, -0.74%, and 4.7% change in lung dose for reduced kVp, tube current modulated, and partial angle protocols, respectively, relative to the reference protocol. Results show -19.2% difference in dose to eye lens for a tilted scan relative to a nontilted scan. The reported relative changes in organ doses are presented without quantification of image quality and are for the sole purpose of demonstrating the use of the proposed database. Conclusions: The proposed database and calculation method enable the estimation of organ dose for coronary angiography and brain perfusion CT scans utilizing any spectral shape and angular tube current modulation scheme by taking advantage of the precalculated Monte Carlo simulation results. The database can be used in conjunction with image quality studies to develop optimized acquisition techniques and may be particularly beneficial for optimizing dual kVp acquisitions for which numerous kV, mA, and filtration combinations may be investigated.« less

Common-path Fourier domain optical coherence tomography of irradiated human skin and ventilated isolated rabbit lungs

NASA Astrophysics Data System (ADS)

Popp, A.; Wendel, M.; Knels, L.; Knuschke, P.; Mehner, M.; Koch, T.; Boller, D.; Koch, P.; Koch, E.

2005-08-01

A compact common path Fourier domain optical coherence tomography (FD-OCT) system based on a broadband superluminescence diode is used for biomedical imaging. The epidermal thickening of human skin after exposure to ultraviolet radiation is measured to proof the feasibility of FD-OCT for future substitution of invasive biopsies in a long term study on natural UV skin protection. The FD-OCT system is also used for imaging lung parenchyma. FD-OCT images of a formalin fixated lung show the same alveolar structure as scanning electron microscopy images. In the ventilated and blood-free perfused isolated rabbit lung FD-OCT is used for real-time cross-sectional image capture of alveolar mechanics throughout tidal ventilation. The alveolar mechanics changing from alternating recruitment-derecruitment at zero positive end-expiratory pressure (PEEP) to persistent recruitment after applying a PEEP of 5 cm H2O is observed in the OCT images.

Imaging Heterogeneity in Lung Cancer: Techniques, Applications, and Challenges.

PubMed

Bashir, Usman; Siddique, Muhammad Musib; Mclean, Emma; Goh, Vicky; Cook, Gary J

2016-09-01

Texture analysis involves the mathematic processing of medical images to derive sets of numeric quantities that measure heterogeneity. Studies on lung cancer have shown that texture analysis may have a role in characterizing tumors and predicting patient outcome. This article outlines the mathematic basis of and the most recent literature on texture analysis in lung cancer imaging. We also describe the challenges facing the clinical implementation of texture analysis. Texture analysis of lung cancer images has been applied successfully to FDG PET and CT scans. Different texture parameters have been shown to be predictive of the nature of disease and of patient outcome. In general, it appears that more heterogeneous tumors on imaging tend to be more aggressive and to be associated with poorer outcomes and that tumor heterogeneity on imaging decreases with treatment. Despite these promising results, there is a large variation in the reported data and strengths of association.

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.

Improved OCT imaging of lung tissue using a prototype for total liquid ventilation

NASA Astrophysics Data System (ADS)

Schnabel, Christian; Meissner, Sven; Koch, Edmund

2011-06-01

Optical coherence tomography (OCT) is used for imaging subpleural alveoli in animal models to gain information about dynamic and morphological changes of lung tissue during mechanical ventilation. The quality of OCT images can be increased if the refraction index inside the alveoli is matched to the one of tissue via liquid-filling. Thereby, scattering loss can be decreased and higher penetration depth and tissue contrast can be achieved. Until now, images of liquid-filled lungs were acquired in isolated and fixated lungs only, so that an in vivo measurement situation is not present. To use the advantages of liquid-filling for in vivo imaging of small rodent lungs, it was necessary to develop a liquid ventilator. Perfluorodecalin, a perfluorocarbon, was selected as breathing fluid because of its refraction index being similar to the one of water and the high transport capacity for carbon dioxide and oxygen. The setup is characterized by two independent syringe pumps to insert and withdraw the fluid into and from the lung and a custom-made control program for volume- or pressure-controlled ventilation modes. The presented results demonstrate the liquid-filling verified by optical coherence tomography and intravital microscopy (IVM) and the advantages of liquid-filling to OCT imaging of subpleural alveoli.

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

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

Bernatowicz, K., E-mail: kingab@student.ethz.ch; Knopf, A.; Lomax, A.

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

Live small-animal X-ray lung velocimetry and lung micro-tomography at the Australian Synchrotron Imaging and Medical Beamline.

PubMed

Murrie, Rhiannon P; Morgan, Kaye S; Maksimenko, Anton; Fouras, Andreas; Paganin, David M; Hall, Chris; Siu, Karen K W; Parsons, David W; Donnelley, Martin

2015-07-01

The high flux and coherence produced at long synchrotron beamlines makes them well suited to performing phase-contrast X-ray imaging of the airways and lungs of live small animals. Here, findings of the first live-animal imaging on the Imaging and Medical Beamline (IMBL) at the Australian Synchrotron are reported, demonstrating the feasibility of performing dynamic lung motion measurement and high-resolution micro-tomography. Live anaesthetized mice were imaged using 30 keV monochromatic X-rays at a range of sample-to-detector propagation distances. A frame rate of 100 frames s(-1) allowed lung motion to be determined using X-ray velocimetry. A separate group of humanely killed mice and rats were imaged by computed tomography at high resolution. Images were reconstructed and rendered to demonstrate the capacity for detailed, user-directed display of relevant respiratory anatomy. The ability to perform X-ray velocimetry on live mice at the IMBL was successfully demonstrated. High-quality renderings of the head and lungs visualized both large structures and fine details of the nasal and respiratory anatomy. The effect of sample-to-detector propagation distance on contrast and resolution was also investigated, demonstrating that soft tissue contrast increases, and resolution decreases, with increasing propagation distance. This new capability to perform live-animal imaging and high-resolution micro-tomography at the IMBL enhances the capability for investigation of respiratory diseases and the acceleration of treatment development in Australia.

Optimisation of quantitative lung SPECT applied to mild COPD: a software phantom simulation study.

PubMed

Norberg, Pernilla; Olsson, Anna; Alm Carlsson, Gudrun; Sandborg, Michael; Gustafsson, Agnetha

2015-01-01

The amount of inhomogeneities in a (99m)Tc Technegas single-photon emission computed tomography (SPECT) lung image, caused by reduced ventilation in lung regions affected by chronic obstructive pulmonary disease (COPD), is correlated to disease advancement. A quantitative analysis method, the CVT method, measuring these inhomogeneities was proposed in earlier work. To detect mild COPD, which is a difficult task, optimised parameter values are needed. In this work, the CVT method was optimised with respect to the parameter values of acquisition, reconstruction and analysis. The ordered subset expectation maximisation (OSEM) algorithm was used for reconstructing the lung SPECT images. As a first step towards clinical application of the CVT method in detecting mild COPD, this study was based on simulated SPECT images of an advanced anthropomorphic lung software phantom including respiratory and cardiac motion, where the mild COPD lung had an overall ventilation reduction of 5%. The best separation between healthy and mild COPD lung images as determined using the CVT measure of ventilation inhomogeneity and 125 MBq (99m)Tc was obtained using a low-energy high-resolution collimator (LEHR) and a power 6 Butterworth post-filter with a cutoff frequency of 0.6 to 0.7 cm(-1). Sixty-four reconstruction updates and a small kernel size should be used when the whole lung is analysed, and for the reduced lung a greater number of updates and a larger kernel size are needed. A LEHR collimator and 125 (99m)Tc MBq together with an optimal combination of cutoff frequency, number of updates and kernel size, gave the best result. Suboptimal selections of either cutoff frequency, number of updates and kernel size will reduce the imaging system's ability to detect mild COPD in the lung phantom.

Assessment of regional ventilation and deformation using 4D-CT imaging for healthy human lungs during tidal breathing

PubMed Central

Jahani, Nariman; Choi, Jiwoong; Iyer, Krishna; Hoffman, Eric A.

2015-01-01

This study aims to assess regional ventilation, nonlinearity, and hysteresis of human lungs during dynamic breathing via image registration of four-dimensional computed tomography (4D-CT) scans. Six healthy adult humans were studied by spiral multidetector-row CT during controlled tidal breathing as well as during total lung capacity and functional residual capacity breath holds. Static images were utilized to contrast static vs. dynamic (deep vs. tidal) breathing. A rolling-seal piston system was employed to maintain consistent tidal breathing during 4D-CT spiral image acquisition, providing required between-breath consistency for physiologically meaningful reconstructed respiratory motion. Registration-derived variables including local air volume and anisotropic deformation index (ADI, an indicator of preferential deformation in response to local force) were employed to assess regional ventilation and lung deformation. Lobar distributions of air volume change during tidal breathing were correlated with those of deep breathing (R2 ≈ 0.84). Small discrepancies between tidal and deep breathing were shown to be likely due to different distributions of air volume change in the left and the right lungs. We also demonstrated an asymmetric characteristic of flow rate between inhalation and exhalation. With ADI, we were able to quantify nonlinearity and hysteresis of lung deformation that can only be captured in dynamic images. Nonlinearity quantified by ADI is greater during inhalation, and it is stronger in the lower lobes (P < 0.05). Lung hysteresis estimated by the difference of ADI between inhalation and exhalation is more significant in the right lungs than that in the left lungs. PMID:26316512

Segmentation of the ovine lung in 3D CT Images

NASA Astrophysics Data System (ADS)

Shi, Lijun; Hoffman, Eric A.; Reinhardt, Joseph M.

2004-04-01

Pulmonary CT images can provide detailed information about the regional structure and function of the respiratory system. Prior to any of these analyses, however, the lungs must be identified in the CT data sets. A popular animal model for understanding lung physiology and pathophysiology is the sheep. In this paper we describe a lung segmentation algorithm for CT images of sheep. The algorithm has two main steps. The first step is lung extraction, which identifies the lung region using a technique based on optimal thresholding and connected components analysis. The second step is lung separation, which separates the left lung from the right lung by identifying the central fissure using an anatomy-based method incorporating dynamic programming and a line filter algorithm. The lung segmentation algorithm has been validated by comparing our automatic method to manual analysis for five pulmonary CT datasets. The RMS error between the computer-defined and manually-traced boundary is 0.96 mm. The segmentation requires approximately 10 minutes for a 512x512x400 dataset on a PC workstation (2.40 GHZ CPU, 2.0 GB RAM), while it takes human observer approximately two hours to accomplish the same task.

Method of lungs regional ventilation function assessment on the basis of continuous lung monitoring results using multi-angle electrical impedance tomography

NASA Astrophysics Data System (ADS)

Aleksanyan, Grayr; Shcherbakov, Ivan; Kucher, Artem; Sulyz, Andrew

2018-04-01

With continuous monitoring of the lungs using multi-angle electric impedance tomography method, a large array of images of impedance changes in the patient's chest cavity is accumulated. This article proposes a method for evaluating the regional ventilation function of lungs based on the results of continuous monitoring using the multi-angle electric impedance tomography method, which allows one image of the thoracic cavity to be formed on the basis of a large array of images of the impedance change in the patient's chest cavity. In the presence of pathologies in the lungs (neoplasms, fluid, pneumothorax, etc.) in these areas, air filling will be disrupted, which will be displayed on the generated image. When conducting continuous monitoring in several sections, a three-dimensional pattern of air filling of the thoracic cavity is possible.

Recent lung imaging studies. [Effectiveness for diagnosis of chronic obstructive pulmonary disease

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

Taplin, G.V.; Chopra, S.K.

1976-01-01

Radionuclide lung imaging procedures have been available for 11 years but only the perfusion examination has been used extensively and mainly for the diagnosis of pulmonary embolism (P.E.). Its ability to reveal localized ischemia makes it a valuable test of regional lung function as well as a useful diagnostic aid in P.E. Although it had been recognized for several years that chronic obstructive pulmonary disease (COPD) can cause lung perfusion defects which may simulate pulmonary embolism, relatively little use has been made of either the radioxenon or the radioaerosol inhalation lung imaging procedures until the last few years as amore » means of distinguishing P.E. from COPD. In this review emphasis is placed on our recent experience with both of these inhalation procedures in comparison with pulmonary function tests and roentgenography for the early detection of COPD in population studies. Equal emphasis is given to simultaneous aerosol ventilation-perfusion (V/P) imaging for a functional diagnosis of P.E. Two new developments in regional lung diffusion imaging, performed after the inhalation of radioactive gases and/or rapidly absorbed radioaerosols are described. The experimental basis for their potential clinical application in pulmonary embolism detection is presented.« less

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

Larner, J.

In this interactive session, lung SBRT patient cases will be presented to highlight real-world considerations for ensuring safe and accurate treatment delivery. An expert panel of speakers will discuss challenges specific to lung SBRT including patient selection, patient immobilization techniques, 4D CT simulation and respiratory motion management, target delineation for treatment planning, online treatment alignment, and established prescription regimens and OAR dose limits. Practical examples of cases, including the patient flow thought the clinical process are presented and audience participation will be encouraged. This panel session is designed to provide case demonstration and review for lung SBRT in terms ofmore » (1) clinical appropriateness in patient selection, (2) strategies for simulation, including 4D and respiratory motion management, and (3) applying multi imaging modality (4D CT imaging, MRI, PET) for tumor volume delineation and motion extent, and (4) image guidance in treatment delivery. Learning Objectives: Understand the established requirements for patient selection in lung SBRT Become familiar with the various immobilization strategies for lung SBRT, including technology for respiratory motion management Understand the benefits and pitfalls of applying multi imaging modality (4D CT imaging, MRI, PET) for tumor volume delineation and motion extent determination for lung SBRT Understand established prescription regimes and OAR dose limits.« less

COMPUTER RECONSTRUCTION OF A HUMAN LUNG MORPHOLOGY MODEL FROM MAGNETIC RESONANCE (MR) IMAGES

EPA Science Inventory

A mathematical description of the morphological structure of the lung is necessary for modeling and analysis of the deposition of inhaled aerosols. A morphological model of the lung boundary was generated from magnetic resonance (MR) images, with the goal of creating a frame...

What can imaging tell us about physiology? Lung growth and regional mechanical strain.

PubMed

Hsia, Connie C W; Tawhai, Merryn H

2012-09-01

The interplay of mechanical forces transduces diverse physico-biochemical processes to influence lung morphogenesis, growth, maturation, remodeling and repair. Because tissue stress is difficult to measure in vivo, mechano-sensitive responses are commonly inferred from global changes in lung volume, shape, or compliance and correlated with structural changes in tissue blocks sampled from postmortem-fixed lungs. Recent advances in noninvasive volumetric imaging technology, nonrigid image registration, and deformation analysis provide valuable tools for the quantitative analysis of in vivo regional anatomy and air and tissue-blood distributions and when combined with transpulmonary pressure measurements, allow characterization of regional mechanical function, e.g., displacement, strain, shear, within and among intact lobes, as well as between the lung and the components of its container-rib cage, diaphragm, and mediastinum-thereby yielding new insights into the inter-related metrics of mechanical stress-strain and growth/remodeling. Here, we review the state-of-the-art imaging applications for mapping asymmetric heterogeneous physical interactions within the thorax and how these interactions permit as well as constrain lung growth, remodeling, and compensation during development and following pneumonectomy to illustrate how advanced imaging could facilitate the understanding of physiology and pathophysiology. Functional imaging promises to facilitate the formulation of realistic computational models of lung growth that integrate mechano-sensitive events over multiple spatial and temporal scales to accurately describe in vivo physiology and pathophysiology. Improved computational models in turn could enhance our ability to predict regional as well as global responses to experimental and therapeutic interventions.

Refractive errors and corrections for OCT images in an inflated lung phantom

PubMed Central

Golabchi, Ali; Faust, J.; Golabchi, F. N.; Brooks, D. H.; Gouldstone, A.; DiMarzio, C. A.

2012-01-01

Visualization and correct assessment of alveolar volume via intact lung imaging is important to study and assess respiratory mechanics. Optical Coherence Tomography (OCT), a real-time imaging technique based on near-infrared interferometry, can image several layers of distal alveoli in intact, ex vivo lung tissue. However optical effects associated with heterogeneity of lung tissue, including the refraction caused by air-tissue interfaces along alveoli and duct walls, and changes in speed of light as it travels through the tissue, result in inaccurate measurement of alveolar volume. Experimentally such errors have been difficult to analyze because of lack of ’ground truth,’ as the lung has a unique microstructure of liquid-coated thin walls surrounding relatively large airspaces, which is difficult to model with cellular foams. In addition, both lung and foams contain airspaces of highly irregular shape, further complicating quantitative measurement of optical artifacts and correction. To address this we have adapted the Bragg-Nye bubble raft, a crystalline two-dimensional arrangement of elements similar in geometry to alveoli (up to several hundred μm in diameter with thin walls) as an inflated lung phantom in order to understand, analyze and correct these errors. By applying exact optical ray tracing on OCT images of the bubble raft, the errors are predicted and corrected. The results are validated by imaging the bubble raft with OCT from one edge and with a charged coupled device (CCD) camera in transillumination from top, providing ground truth for the OCT. PMID:22567599

First in vivo magnetic particle imaging of lung perfusion in rats

NASA Astrophysics Data System (ADS)

Zhou, Xinyi Y.; Jeffris, Kenneth E.; Yu, Elaine Y.; Zheng, Bo; Goodwill, Patrick W.; Nahid, Payam; Conolly, Steven M.

2017-05-01

Pulmonary embolism (PE), along with the closely related condition of deep vein thrombosis, affect an estimated 600 000 patients in the US per year. Untreated, PE carries a mortality rate of 30%. Because many patients experience mild or non-specific symptoms, imaging studies are necessary for definitive diagnosis of PE. Iodinated CT pulmonary angiography is recommended for most patients, while nuclear medicine-based ventilation/perfusion (V/Q) scans are reserved for patients in whom the use of iodine is contraindicated. Magnetic particle imaging (MPI) is an emerging tracer imaging modality with high image contrast (no tissue background signal) and sensitivity to superparamagnetic iron oxide (SPIO) tracer. Importantly, unlike CT or nuclear medicine, MPI uses no ionizing radiation. Further, MPI is not derived from magnetic resonance imaging (MRI); MPI directly images SPIO tracers via their strong electronic magnetization, enabling deep imaging of anatomy including within the lungs, which is very challenging with MRI. Here, the first high-contrast in vivo MPI lung perfusion images of rats are shown using a novel lung perfusion agent, MAA-SPIOs.

High resolution multidetector CT aided tissue analysis and quantification of lung fibrosis

NASA Astrophysics Data System (ADS)

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

2006-03-01

Idiopathic pulmonary fibrosis (IPF, also known as Idiopathic Usual Interstitial Pneumontis, pathologically) is a progressive diffuse lung disease which has a median survival rate of less than four years with a prevalence of 15-20/100,000 in the United States. Global function changes are measured by pulmonary function tests and the diagnosis and extent of pulmonary structural changes are typically assessed by acquiring two-dimensional high resolution CT (HRCT) images. The acquisition and analysis of volumetric high resolution Multi-Detector CT (MDCT) images with nearly isotropic pixels offers the potential to measure both lung function and structure. This paper presents a new approach to three dimensional lung image analysis and classification of normal and abnormal structures in lungs with IPF.

Polarized Helium to Image the Lung

NASA Astrophysics Data System (ADS)

Leduc, Michèle; Nacher, Pierre Jean

2005-05-01

The main findings of the european PHIL project (Polarised Helium to Image the Lung) are reported. State of the art optical pumping techniques for polarising 3He gas are described. MRI methodological improvements allow dynamical ventilation images with a good resolution, ultimately limited by gas diffusion. Diffusion imaging appears as a robust method of lung diagnosis. A discussion of the potential advantage of low field MRI is presented. Selected PHIL results for emphysema are given, with the perspectives that this joint work opens up for the future of respiratory medicine.

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

Kirsch, David G., E-mail: david.kirsch@duke.ed; Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA; Departments of Radiation Oncology and Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC

Purpose: To image a genetically engineered mouse model of non-small-cell lung cancer with micro-computed tomography (micro-CT) to measure tumor response to radiation therapy. Methods and Materials: The Cre-loxP system was used to generate primary lung cancers in mice with mutation in K-ras alone or in combination with p53 mutation. Mice were serially imaged by micro-CT, and tumor volumes were determined. A comparison of tumor volume by micro-CT and tumor histology was performed. Tumor response to radiation therapy (15.5 Gy) was assessed with micro-CT. Results: The tumor volume measured with free-breathing micro-CT scans was greater than the volume calculated by histology.more » Nevertheless, this imaging approach demonstrated that lung cancers with mutant p53 grew more rapidly than lung tumors with wild-type p53 and also showed that radiation therapy increased the doubling time of p53 mutant lung cancers fivefold. Conclusions: Micro-CT is an effective tool to noninvasively measure the growth of primary lung cancers in genetically engineered mice and assess tumor response to radiation therapy. This imaging approach will be useful to study the radiation biology of lung cancer.« less

The database of chromosome imbalance regions and genes resided in lung cancer from Asian and Caucasian identified by array-comparative genomic hybridization

PubMed Central

2012-01-01

Background Cancer-related genes show racial differences. Therefore, identification and characterization of DNA copy number alteration regions in different racial groups helps to dissect the mechanism of tumorigenesis. Methods Array-comparative genomic hybridization (array-CGH) was analyzed for DNA copy number profile in 40 Asian and 20 Caucasian lung cancer patients. Three methods including MetaCore analysis for disease and pathway correlations, concordance analysis between array-CGH database and the expression array database, and literature search for copy number variation genes were performed to select novel lung cancer candidate genes. Four candidate oncogenes were validated for DNA copy number and mRNA and protein expression by quantitative polymerase chain reaction (qPCR), chromogenic in situ hybridization (CISH), reverse transcriptase-qPCR (RT-qPCR), and immunohistochemistry (IHC) in more patients. Results We identified 20 chromosomal imbalance regions harboring 459 genes for Caucasian and 17 regions containing 476 genes for Asian lung cancer patients. Seven common chromosomal imbalance regions harboring 117 genes, included gain on 3p13-14, 6p22.1, 9q21.13, 13q14.1, and 17p13.3; and loss on 3p22.2-22.3 and 13q13.3 were found both in Asian and Caucasian patients. Gene validation for four genes including ARHGAP19 (10q24.1) functioning in Rho activity control, FRAT2 (10q24.1) involved in Wnt signaling, PAFAH1B1 (17p13.3) functioning in motility control, and ZNF322A (6p22.1) involved in MAPK signaling was performed using qPCR and RT-qPCR. Mean gene dosage and mRNA expression level of the four candidate genes in tumor tissues were significantly higher than the corresponding normal tissues (P<0.001~P=0.06). In addition, CISH analysis of patients indicated that copy number amplification indeed occurred for ARHGAP19 and ZNF322A genes in lung cancer patients. IHC analysis of paraffin blocks from Asian Caucasian patients demonstrated that the frequency of PAFAH1B1 protein overexpression was 68% in Asian and 70% in Caucasian. Conclusions Our study provides an invaluable database revealing common and differential imbalance regions at specific chromosomes among Asian and Caucasian lung cancer patients. Four validation methods confirmed our database, which would help in further studies on the mechanism of lung tumorigenesis. PMID:22691236

Comparison of Positron Emission Tomography Using 2-[18F]-fluoro-2-deoxy-D-glucose and 3-deoxy-3-[18F]-fluorothymidine in Lung Cancer Imaging

PubMed Central

Wang, Fu-Li; Tan, Ye-Ying; Gu, Xiang-Min; Li, Tian-Ran; Lu, Guang-Ming; Liu, Gang; Huo, Tian-Long

2016-01-01

Background: The detection of solitary pulmonary nodules (SPNs) that may potentially develop into a malignant lesion is essential for early clinical interventions. However, grading classification based on computed tomography (CT) imaging results remains a significant challenge. The 2-[18F]-fluoro-2-deoxy-D-glucose (18F-FDG) positron emission tomography (PET)/CT imaging produces both false-positive and false-negative findings for the diagnosis of SPNs. In this study, we compared 18F-FDG and 3-deoxy-3-[18F]-fluorothymidine (18F-FLT) in lung cancer PET/CT imaging. Methods: The binding ratios of the two tracers to A549 lung cancer cells were calculated. The mouse lung cancer model was established (n = 12), and micro-PET/CT analysis using the two tracers was performed. Images using the two tracers were collected from 55 lung cancer patients with SPNs. The correlation among the cell-tracer binding ratios, standardized uptake values (SUVs), and Ki-67 proliferation marker expression were investigated. Results: The cell-tracer binding ratio for the A549 cells using the 18F-FDG was greater than the ratio using 18F-FLT (P < 0.05). The Ki-67 expression showed a significant positive correlation with the 18F-FLT binding ratio (r = 0.824, P < 0.01). The tumor-to-nontumor uptake ratio of 18F-FDG imaging in xenografts was higher than that of 18F-FLT imaging. The diagnostic sensitivity, specificity, and the accuracy of 18F-FDG for lung cancer were 89%, 67%, and 73%, respectively. Moreover, the diagnostic sensitivity, specificity, and the accuracy of 18F-FLT for lung cancer were 71%, 79%, and 76%, respectively. There was an obvious positive correlation between the lung cancer Ki-67 expression and the mean maximum SUV of 18F-FDG and 18F-FLT (r = 0.658, P < 0.05 and r = 0.724, P < 0.01, respectively). Conclusions: The 18F-FDG uptake ratio is higher than that of 18F-FLT in A549 cells at the cellular level. 18F-FLT imaging might be superior for the quantitative diagnosis of lung tumor tissue and could distinguish lung cancer nodules from other SPNs. PMID:27958224

In vivo detection of hyperoxia-induced pulmonary endothelial cell death using (99m)Tc-duramycin.

PubMed

Audi, Said H; Jacobs, Elizabeth R; Zhao, Ming; Roerig, David L; Haworth, Steven T; Clough, Anne V

2015-01-01

(99m)Tc-duramycin, DU, is a SPECT biomarker of tissue injury identifying cell death. The objective of this study is to investigate the potential of DU imaging to quantify capillary endothelial cell death in rat lung injury resulting from hyperoxia exposure as a model of acute lung injury. Rats were exposed to room air (normoxic) or >98% O2 for 48 or 60 hours. DU was injected i.v. in anesthetized rats, scintigraphy images were acquired at steady-state, and lung DU uptake was quantified from the images. Post-mortem, the lungs were removed for histological studies. Sequential lung sections were immunostained for caspase activation and endothelial and epithelial cells. Lung DU uptake increased significantly (p<0.001) by 39% and 146% in 48-hr and 60-hr exposed rats, respectively, compared to normoxic rats. There was strong correlation (r(2)=0.82, p=0.005) between lung DU uptake and the number of cleaved caspase 3 (CC3) positive cells, and endothelial cells accounted for more than 50% of CC3 positive cells in the hyperoxic lungs. Histology revealed preserved lung morphology through 48 hours. By 60 hours there was evidence of edema, and modest neutrophilic infiltrate. Rat lung DU uptake in vivo increased after just 48 hours of >98% O2 exposure, prior to the onset of any substantial evidence of lung injury. These results suggest that apoptotic endothelial cells are the primary contributors to the enhanced DU lung uptake, and support the utility of DU imaging for detecting early endothelial cell death in vivo. Copyright © 2014 Elsevier Inc. All rights reserved.

Functional Image-Guided Radiotherapy Planning in Respiratory-Gated Intensity-Modulated Radiotherapy for Lung Cancer Patients With Chronic Obstructive Pulmonary Disease

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

Kimura, Tomoki, E-mail: tkkimura@hiroshima-u.ac.jp; Nishibuchi, Ikuno; Murakami, Yuji

2012-03-15

Purpose: To investigate the incorporation of functional lung image-derived low attenuation area (LAA) based on four-dimensional computed tomography (4D-CT) into respiratory-gated intensity-modulated radiotherapy (IMRT) or volumetric modulated arc therapy (VMAT) in treatment planning for lung cancer patients with chronic obstructive pulmonary disease (COPD). Methods and Materials: Eight lung cancer patients with COPD were the subjects of this study. LAA was generated from 4D-CT data sets according to CT values of less than than -860 Hounsfield units (HU) as a threshold. The functional lung image was defined as the area where LAA was excluded from the image of the total lung.more » Two respiratory-gated radiotherapy plans (70 Gy/35 fractions) were designed and compared in each patient as follows: Plan A was an anatomical IMRT or VMAT plan based on the total lung; Plan F was a functional IMRT or VMAT plan based on the functional lung. Dosimetric parameters (percentage of total lung volume irradiated with {>=}20 Gy [V20], and mean dose of total lung [MLD]) of the two plans were compared. Results: V20 was lower in Plan F than in Plan A (mean 1.5%, p = 0.025 in IMRT, mean 1.6%, p = 0.044 in VMAT) achieved by a reduction in MLD (mean 0.23 Gy, p = 0.083 in IMRT, mean 0.5 Gy, p = 0.042 in VMAT). No differences were noted in target volume coverage and organ-at-risk doses. Conclusions: Functional IGRT planning based on LAA in respiratory-guided IMRT or VMAT appears to be effective in preserving a functional lung in lung cancer patients with COPD.« less

Enhanced perfusion defect clarity and inhomogeneity in smokers' lungs with deep-inspiratory breath-hold perfusion SPECT images.

PubMed

Suga, Kazuyoshi; Yasuhiko, Kawakami; Iwanaga, Hideyuki; Hayashi, Norio; Yamashita, Tomio; Matsunaga, Naofumi

2005-09-01

Deep-inspiratory breath-hold (DIBrH) Tc-99m-macroaggregated albumin (MAA) SPECT images were developed to accurately evaluate perfusion impairment in smokers' lungs. DIBrH SPECT was performed in 28 smokers with or without low attenuation areas (LAA) on CT images, using a triple-headed SPECT system and a laser light respiratory tracking device. DIBrH SPECT images were reconstructed from every 4 degrees projection of five adequate 360 degrees projection data sets with almost the same respiratory dimension at 20 sec DIBrH. Perfusion defect clarity was assessed by the lesion (defect)-to-contralateral normal lung count ratios (L/N ratios). Perfusion inhomogeneity was assessed by the coefficient of variation (CV) values of pixel counts and correlated with the diffusing capacity of the lungs for carbon monoxide/alveolar volume (DLCO/VA) ratios. The results were compared with those on conventional images. Five DIBrH projection data sets with minimal dimension differences of 2.9+/-0.6 mm were obtained in all subjects. DIBrH images enhanced perfusion defects compared with conventional images, with significantly higher L/N ratios (P<0.0001), and detected a total of 109 (26.9%) additional detects (513 vs. 404), with excellent inter-observer agreement (kappa value of 0.816). CV values in the smokers' lungs on DIBrH images were also significantly higher compared with those on conventional images (0.31+/-0.10 vs. 0.19+/-0.06, P<0.0001). CV values in smokers on DIBrH images showed a significantly closer correlation with DLCO/VA ratios compared with conventional images (R = 0.872, P<0.0001 vs. R=0.499, P<0.01). By reducing adverse effect of respiratory motion, DIBrH SPECT images enhance perfusion defect clarity and inhomogeneity, and provide more accurate assessment of impaired perfusion in smokers' lungs compared with conventional images.

Fluorine-19 MRI Contrast Agents for Cell Tracking and Lung Imaging

PubMed Central

Fox, Matthew S.; Gaudet, Jeffrey M.; Foster, Paula J.

2015-01-01

Fluorine-19 (19F)-based contrast agents for magnetic resonance imaging stand to revolutionize imaging-based research and clinical trials in several fields of medical intervention. First, their use in characterizing in vivo cell behavior may help bring cellular therapy closer to clinical acceptance. Second, their use in lung imaging provides novel noninvasive interrogation of the ventilated airspaces without the need for complicated, hard-to-distribute hardware. This article reviews the current state of 19F-based cell tracking and lung imaging using magnetic resonance imaging and describes the link between the methods across these fields and how they may mutually benefit from solutions to mutual problems encountered when imaging 19F-containing compounds, as well as hardware and software advancements. PMID:27042089

Magnetic resonance imaging of pediatric lung parenchyma, airways, vasculature, ventilation, and perfusion: state of the art.

PubMed

Liszewski, Mark C; Hersman, F William; Altes, Talissa A; Ohno, Yoshiharu; Ciet, Pierluigi; Warfield, Simon K; Lee, Edward Y

2013-07-01

Magnetic resonance (MR) imaging is a noninvasive imaging modality, particularly attractive for pediatric patients given its lack of ionizing radiation. Despite many advantages, the physical properties of the lung (inherent low signal-to-noise ratio, magnetic susceptibility differences at lung-air interfaces, and respiratory and cardiac motion) have posed technical challenges that have limited the use of MR imaging in the evaluation of thoracic disease in the past. However, recent advances in MR imaging techniques have overcome many of these challenges. This article discusses these advances in MR imaging techniques and their potential role in the evaluation of thoracic disorders in pediatric patients. Copyright © 2013 Elsevier Inc. All rights reserved.

Relation between lung perfusion defects and intravascular clots in acute pulmonary thromboembolism: assessment with breath-hold SPECT-CT pulmonary angiography fusion images.

PubMed

Suga, Kazuyoshi; Yasuhiko, Kawakami; Iwanaga, Hideyuki; Tokuda, Osamu; Matsunaga, Naofumi

2008-09-01

The relation between lung perfusion defects and intravascular clots in acute pulmonary thromboembolism (PTE) was comprehensively assessed on deep-inspiratory breath-hold (DIBrH) perfusion SPECT-computed tomographic pulmonary angiography (CTPA) fusion images. Subjects were 34 acute PTE patients, who had successfully performed DIBrH perfusion SPECT using a dual-headed SPECT and a respiratory tracking system. Automated DIBrH SPECT-CTPA fusion images were used to assess the relation between lung perfusion defects and intravascular clots detected by CTPA. DIBrH SPECT visualized 175 lobar/segmental or subsegmental defects in 34 patients, and CTPA visualized 61 intravascular clots at variable locations in 30 (88%) patients, but no clots in four (12%) patients. In 30 patients with clots, the fusion images confirmed that 69 (41%) perfusion defects (20 segmental, 45 subsegmental and 4 lobar defects) of total 166 defects were located in lung territories without clots, although the remaining 97 (58%) defects were located in lung territories with clots. Perfusion defect was absent in lung territories with clots (one lobar branch and three segmental branches) in four (12%) of these patients. In four patients without clots, nine perfusion defects including four segmental ones were present. Because of unexpected dissociation between intravascular clots and lung perfusion defects, the present fusion images will be a useful adjunct to CTPA in the diagnosis of acute PTE.

A supervised 'lesion-enhancement' filter by use of a massive-training artificial neural network (MTANN) in computer-aided diagnosis (CAD).

PubMed

Suzuki, Kenji

2009-09-21

Computer-aided diagnosis (CAD) has been an active area of study in medical image analysis. A filter for the enhancement of lesions plays an important role for improving the sensitivity and specificity in CAD schemes. The filter enhances objects similar to a model employed in the filter; e.g. a blob-enhancement filter based on the Hessian matrix enhances sphere-like objects. Actual lesions, however, often differ from a simple model; e.g. a lung nodule is generally modeled as a solid sphere, but there are nodules of various shapes and with internal inhomogeneities such as a nodule with spiculations and ground-glass opacity. Thus, conventional filters often fail to enhance actual lesions. Our purpose in this study was to develop a supervised filter for the enhancement of actual lesions (as opposed to a lesion model) by use of a massive-training artificial neural network (MTANN) in a CAD scheme for detection of lung nodules in CT. The MTANN filter was trained with actual nodules in CT images to enhance actual patterns of nodules. By use of the MTANN filter, the sensitivity and specificity of our CAD scheme were improved substantially. With a database of 69 lung cancers, nodule candidate detection by the MTANN filter achieved a 97% sensitivity with 6.7 false positives (FPs) per section, whereas nodule candidate detection by a difference-image technique achieved a 96% sensitivity with 19.3 FPs per section. Classification-MTANNs were applied for further reduction of the FPs. The classification-MTANNs removed 60% of the FPs with a loss of one true positive; thus, it achieved a 96% sensitivity with 2.7 FPs per section. Overall, with our CAD scheme based on the MTANN filter and classification-MTANNs, an 84% sensitivity with 0.5 FPs per section was achieved.

Content based information retrieval in forensic image databases.

PubMed

Geradts, Zeno; Bijhold, Jurrien

2002-03-01

This paper gives an overview of the various available image databases and ways of searching these databases on image contents. The developments in research groups of searching in image databases is evaluated and compared with the forensic databases that exist. Forensic image databases of fingerprints, faces, shoeprints, handwriting, cartridge cases, drugs tablets, and tool marks are described. The developments in these fields appear to be valuable for forensic databases, especially that of the framework in MPEG-7, where the searching in image databases is standardized. In the future, the combination of the databases (also DNA-databases) and possibilities to combine these can result in stronger forensic evidence.

[A novel compound heterozygous mutation in ABCA3 gene in a child with diffuse parenchymal lung disease].

PubMed

Bao, Y M; Liu, X L; Liu, X L; Chen, J H; Zheng, Y J

2017-11-02

Objective: To summarize the clinical characteristics of the diffuse parenchymal lung diseases in a child caused by a novel compound heterozygous ABCA3 mutation and explore the association between the phenotype and ABCA3 mutation. Method: The clinical material of a patient diagnosed with diffuse parenchymal lung disease with ABCA3 mutation in December 2016 in Shenzhen Children's Hospital was analyzed. The information about ABCA3 gene mutation updated before April, 2017 was searched and collected from the gene databases (including 1000Genomes, HGMD, EXAC) and the literatures (including Wanfang Chinese database and Pubmed). Result: The girl was one year and nine months old. She presented with chronic cough, tachypnea, cyanosis and failure to thrive since she was one year and three months old. Her condition gradually deteriorated after she was empirically treated. Physical examination showed malnutrition, tachypnea and clubbed-fingers. Her high resolution computed tomography (HRCT) revealed diffused ground-glass opacities, thickened interlobular septum, and multiple subpleural small air-filled lung cysts. The second generation sequencing study identified a novel compound heterozygous mutation (c.1755delC+c.2890G>A) in her ABCA3 gene, which derived respectively from her parents and has not been reported in the database and the literatures mentioned above. Conclusion: c.1755delC+c.2890G>A is a new kind of compound heterozygous mutation in ABCA3, which can cause children's diffuse parenchymal lung disease. Its phenotype is related to its genotype.

A method for smoothing segmented lung boundary in chest CT images

NASA Astrophysics Data System (ADS)

Yim, Yeny; Hong, Helen

2007-03-01

To segment low density lung regions in chest CT images, most of methods use the difference in gray-level value of pixels. However, radiodense pulmonary vessels and pleural nodules that contact with the surrounding anatomy are often excluded from the segmentation result. To smooth lung boundary segmented by gray-level processing in chest CT images, we propose a new method using scan line search. Our method consists of three main steps. First, lung boundary is extracted by our automatic segmentation method. Second, segmented lung contour is smoothed in each axial CT slice. We propose a scan line search to track the points on lung contour and find rapidly changing curvature efficiently. Finally, to provide consistent appearance between lung contours in adjacent axial slices, 2D closing in coronal plane is applied within pre-defined subvolume. Our method has been applied for performance evaluation with the aspects of visual inspection, accuracy and processing time. The results of our method show that the smoothness of lung contour was considerably increased by compensating for pulmonary vessels and pleural nodules.

Causes of death and competing risk analysis of the associated factors for non-small cell lung cancer using the Surveillance, Epidemiology, and End Results database.

PubMed

Wei, Shenhai; Tian, Jintao; Song, Xiaoping; Wu, Bingqun; Liu, Limin

2018-01-01

To investigate the probability of death (POD) from any causes by time after diagnosis of non-small cell lung cancer (NSCLC) and the factors associated with survival for NSCLC patients. A total of 202,914 patients with NSCLC from 2004 to 2013 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. The overall survival (OS) and lung cancer-specific survival (LCSS) were calculated and POD from any causes at different time periods after diagnosis was explored. The predictive factors for OS, LCSS and survival from non-lung cancer deaths were investigated using multivariate analysis with Cox proportional hazards regression and competing risk regression analysis. The 5- and 10-year OS were 20.4% and 11.5%, accordingly that for LCSS were 25.5% and 18.4%, respectively. Lung cancer contributed 88.3% (n = 128,402) of the deaths. The POD from lung cancer decreased with time after diagnosis. In multivariate analysis, advanced age and advanced stage of NSCLC were associated with decreased OS and LCSS. Comparing to no surgery, any kind of resection conferred lower risk of death from lung cancer and higher risk of dying from non-lung cancer conditions except lobectomy or bilobectomy, which was associated with lower risk of death from both lung cancer and non-lung cancer conditions. Most of the patients with NSCLC died from lung cancer. Rational surveillance and treatment policies should be made for them. Early stage and lobectomy or bilobectomy were associated with improved OS and LCSS. It is reasonable to focus on early detection and optimal surgical treatment for NSCLC.

Ventilation inhomogeneity in obstructive lung diseases measured by electrical impedance tomography: a simulation study.

PubMed

Schullcke, B; Krueger-Ziolek, S; Gong, B; Jörres, R A; Mueller-Lisse, U; Moeller, K

2017-10-10

Electrical impedance tomography (EIT) has mostly been used in the Intensive Care Unit (ICU) to monitor ventilation distribution but is also promising for the diagnosis in spontaneously breathing patients with obstructive lung diseases. Beside tomographic images, several numerical measures have been proposed to quantitatively assess the lung state. In this study two common measures, the 'Global Inhomogeneity Index' and the 'Coefficient of Variation' were compared regarding their capability to reflect the severity of lung obstruction. A three-dimensional simulation model was used to simulate obstructed lungs, whereby images were reconstructed on a two-dimensional domain. Simulations revealed that minor obstructions are not adequately recognized in the reconstructed images and that obstruction above and below the electrode plane may result in misleading values of inhomogeneity measures. EIT measurements on several electrode planes are necessary to apply these measures in patients with obstructive lung diseases in a promising manner.

Automated method and system for the alignment and correlation of images from two different modalities

DOEpatents

Giger, Maryellen L.; Chen, Chin-Tu; Armato, Samuel; Doi, Kunio

1999-10-26

A method and system for the computerized registration of radionuclide images with radiographic images, including generating image data from radiographic and radionuclide images of the thorax. Techniques include contouring the lung regions in each type of chest image, scaling and registration of the contours based on location of lung apices, and superimposition after appropriate shifting of the images. Specific applications are given for the automated registration of radionuclide lungs scans with chest radiographs. The method in the example given yields a system that spatially registers and correlates digitized chest radiographs with V/Q scans in order to correlate V/Q functional information with the greater structural detail of chest radiographs. Final output could be the computer-determined contours from each type of image superimposed on any of the original images, or superimposition of the radionuclide image data, which contains high activity, onto the radiographic chest image.

Multimodal imaging of lung cancer and its microenvironment (Conference Presentation)

NASA Astrophysics Data System (ADS)

Hariri, Lida P.; Niederst, Matthew J.; Mulvey, Hillary; Adams, David C.; Hu, Haichuan; Chico Calero, Isabel; Szabari, Margit V.; Vakoc, Benjamin J.; Hasan, Tayyaba; Bouma, Brett E.; Engelman, Jeffrey A.; Suter, Melissa J.

2016-03-01

Despite significant advances in targeted therapies for lung cancer, nearly all patients develop drug resistance within 6-12 months and prognosis remains poor. Developing drug resistance is a progressive process that involves tumor cells and their microenvironment. We hypothesize that microenvironment factors alter tumor growth and response to targeted therapy. We conducted in vitro studies in human EGFR-mutant lung carcinoma cells, and demonstrated that factors secreted from lung fibroblasts results in increased tumor cell survival during targeted therapy with EGFR inhibitor, gefitinib. We also demonstrated that increased environment stiffness results in increased tumor survival during gefitinib therapy. In order to test our hypothesis in vivo, we developed a multimodal optical imaging protocol for preclinical intravital imaging in mouse models to assess tumor and its microenvironment over time. We have successfully conducted multimodal imaging of dorsal skinfold chamber (DSC) window mice implanted with GFP-labeled human EGFR mutant lung carcinoma cells and visualized changes in tumor development and microenvironment facets over time. Multimodal imaging included structural OCT to assess tumor viability and necrosis, polarization-sensitive OCT to measure tissue birefringence for collagen/fibroblast detection, and Doppler OCT to assess tumor vasculature. Confocal imaging was also performed for high-resolution visualization of EGFR-mutant lung cancer cells labeled with GFP, and was coregistered with OCT. Our results demonstrated that stromal support and vascular growth are essential to tumor progression. Multimodal imaging is a useful tool to assess tumor and its microenvironment over time.

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

NASA Astrophysics Data System (ADS)

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

2006-03-01

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

LOXL4 knockdown enhances tumor growth and lung metastasis through collagen-dependent extracellular matrix changes in triple-negative breast cancer.

PubMed

Choi, Sul Ki; Kim, Hoe Suk; Jin, Tiefeng; Moon, Woo Kyung

2017-02-14

Lysyl oxidase (LOX) family genes catalyze collagen cross-link formation. To determine the effects of lysyl oxidase-like 4 (LOXL4) expression on breast tumor formation and metastasis, we evaluated primary tumor growth and lung metastasis in mice injected with LOXL4-knockdown MDA-MB-231 triple-negative human breast cancer cells. In addition, we analyzed overall survival in breast cancer patients based on LOXL4 expression using a public online database. In the mouse xenograft model, LOXL4 knockdown increased primary tumor growth and lung colonization as well as collagen I and IV, lysine hydroxylase 1 and 2, and prolyl 4-hydroxylase subunit alpha 1 and 2 levels. Second harmonic generation imaging revealed that LOXL4 knockdown resulted in the thickening of collagen bundles within tumors. In addition, weak LOXL4 expression was associated with poor overall survival in breast cancer patients from the BreastMark dataset, and this association was strongest in triple-negative breast cancer patients. These results demonstrate that weak LOXL4 expression leads to remodeling of the extracellular matrix through induction of collagen synthesis, deposition, and structural changes. These alterations in turn promote tumor growth and metastasis and are associated with poor clinical outcomes in triple-negative breast cancer.

Outcome in fetuses with isolated congenital diaphragmatic hernia with increased nuchal translucency thickness in first trimester.

PubMed

Spaggiari, E; Stirnemann, J; Ville, Y

2012-03-01

To examine the possible association between increased nuchal translucency (NT) thickness in the first trimester and perinatal outcome in isolated congenital diaphragmatic hernia (CDH). We conducted a retrospective study between January 2004 and June 2010. The database was searched to identify all consecutive cases of CDH referred to the fetal medicine center of Necker Hospital in Paris. Enlarged NT was defined above the 95th centile. Only children born alive with an isolated CDH were selected for the analysis of prognostic factors. We also studied the correlation between NT thickness in the first trimester and lung-to-head ratio, observed to expected lung area-to-head ratio, lung volume estimated by magnetic resonance imaging, and other prenatal features of intrathoracic compression. Seventy-one cases of isolated CDH were available. The fetal NT was above the 95th centile in 9 of the 71 cases. Neonatal death occurred in 7/9 (78%) cases with enlarged NT, compared with 24/62 (38%) with normal NT (P = 0.035). Enlarged NT was significantly associated with prenatal features of intrathoracic compression. Enlarged NT thickness in CDH is associated with a poor outcome and is related to an early intrathoracic compression. © 2012 John Wiley & Sons, Ltd.

Coexistence of bronchiectasis and rheumatoid arthritis: revisited.

PubMed

Wilczynska, Maria M; Condliffe, Alison M; McKeon, Damian J

2013-04-01

The presence of bronchiectasis (BR) in patients with rheumatoid arthritis (RA) has been recognized for many decades; nevertheless, little research has been undertaken in this area. It is important to recognize that BR coexistent with RA differs from the other types of BR. The purpose of this descriptive review was to delineate the epidemiology, etiology, risk factors, pulmonary function testing, imaging, prognosis and management of concomitant BR and RA. To inform our study we searched the PubMed, EMBASE, CINAHL, and MEDLINE databases, using combinations of the following key words: computed tomography, lung function tests, rheumatoid arthritis, bronchiectasis, biological agents, and interstitial lung disease. The number of published papers covering this topic is limited, but several relevant conclusions can be drawn. Patients with concomitant RA and BR have worse obstructive airways disease, increased susceptibility to recurrent pulmonary infections, faster lung function decline, and higher mortality, compared with subjects with either RA or BR alone. The use of disease-modifying anti-rheumatic drugs (both biological and non-biological) for RA in RA-BR patients imparts a further challenge in managing these patients. Although there are not any published guidelines on the management of coexisting RA-BR, we have attempted to provide such recommendations, based on the literature review and our experience.

Small-animal dark-field radiography for pulmonary emphysema evaluation

NASA Astrophysics Data System (ADS)

Yaroshenko, Andre; Meinel, Felix G.; Hellbach, Katharina; Bech, Martin; Velroyen, Astrid; Müller, Mark; Bamberg, Fabian; Nikolaou, Konstantin; Reiser, Maximilian F.; Yildirim, Ali Ã.-.; Eickelberg, Oliver; Pfeiffer, Franz

2014-03-01

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of morbidity and mortality worldwide and emphysema is one of its main components. The disorder is characterized by irreversible destruction of the alveolar walls and enlargement of distal airspaces. Despite the severe changes in the lung tissue morphology, conventional chest radiographs have only a limited sensitivity for the detection of mild to moderate emphysema. X-ray dark-field is an imaging modality that can significantly increase the visibility of lung tissue on radiographic images. The dark-field signal is generated by coherent, small-angle scattering of x-rays on the air-tissue interfaces in the lung. Therefore, morphological changes in the lung can be clearly visualized on dark-field images. This is demonstrated by a preclinical study with a small-animal emphysema model. To generate a murine model of pulmonary emphysema, a female C57BL/6N mouse was treated with a single orotracheal application of porcine pancreatic elastase (80 U/kg body weight) dissolved in phosphate-buffered saline (PBS). Control mouse received PBS. The mice were imaged using a small-animal dark-field scanner. While conventional x-ray transmission radiography images revealed only subtle indirect signs of the pulmonary disorder, the difference between healthy and emphysematous lungs could be clearly directly visualized on the dark-field images. The dose applied to the animals is compatible with longitudinal studies. The imaging results correlate well with histology. The results of this study reveal the high potential of dark-field radiography for clinical lung imaging.

Real-time soft tissue motion estimation for lung tumors during radiotherapy delivery.

PubMed

Rottmann, Joerg; Keall, Paul; Berbeco, Ross

2013-09-01

To provide real-time lung tumor motion estimation during radiotherapy treatment delivery without the need for implanted fiducial markers or additional imaging dose to the patient. 2D radiographs from the therapy beam's-eye-view (BEV) perspective are captured at a frame rate of 12.8 Hz with a frame grabber allowing direct RAM access to the image buffer. An in-house developed real-time soft tissue localization algorithm is utilized to calculate soft tissue displacement from these images in real-time. The system is tested with a Varian TX linear accelerator and an AS-1000 amorphous silicon electronic portal imaging device operating at a resolution of 512 × 384 pixels. The accuracy of the motion estimation is verified with a dynamic motion phantom. Clinical accuracy was tested on lung SBRT images acquired at 2 fps. Real-time lung tumor motion estimation from BEV images without fiducial markers is successfully demonstrated. For the phantom study, a mean tracking error <1.0 mm [root mean square (rms) error of 0.3 mm] was observed. The tracking rms accuracy on BEV images from a lung SBRT patient (≈20 mm tumor motion range) is 1.0 mm. The authors demonstrate for the first time real-time markerless lung tumor motion estimation from BEV images alone. The described system can operate at a frame rate of 12.8 Hz and does not require prior knowledge to establish traceable landmarks for tracking on the fly. The authors show that the geometric accuracy is similar to (or better than) previously published markerless algorithms not operating in real-time.

Lung ultrasound training: curriculum implementation and learning trajectory among respiratory therapists.

PubMed

See, K C; Ong, V; Wong, S H; Leanda, R; Santos, J; Taculod, J; Phua, J; Teoh, C M

2016-01-01

Guidelines recommend teaching of lung ultrasound for critical care, though little information exists on how much training is required for independent practice, especially for non-physician trainees. We thus aimed to elucidate a threshold number of cases above which competency for independent practice may be attained for respiratory therapists (RTs). We conducted a prospective audit of lung ultrasound training between July 2014 and April 2015 in our 20-bed medical intensive care unit. Following theoretical instruction and self-learning, trainees acquired images from 12 lung zones under direct supervision and classified images into six patterns. Assistance during image acquisition and correct interpretation of ultrasound images were recorded. Eleven ultrasound-naïve RTs scanned an average of 15 patients each (170 patients in total). Among supervisor-adjudicated lung ultrasound findings, 35.5% were abnormal. Blinded verification of the adjudicated findings was done for the first 92 patients (1104 images), with an agreement of 95.4%. As RTs scanned more patients, there was a significant decrease in the proportion of images requiring supervisor assistance (Cuzick's P < 0.001), and a significant increase in the proportion of correctly identified images (Cuzick's P = 0.008). After trainees performed at least ten scans, less than 2% of images required assistance with acquisition and less than 5% were wrongly interpreted. Our training method allowed RTs to independently perform lung ultrasound after at least ten directly supervised scans. Given that RTs are likely to have less ultrasound knowledge and less clinical know-how compared to physicians, we believe that the same threshold number of scans may be also safely applied to the latter.

Carbon nanotube based respiratory gated micro-CT imaging of a murine model of lung tumors with optical imaging correlation

NASA Astrophysics Data System (ADS)

Burk, Laurel M.; Lee, Yueh Z.; Heathcote, Samuel; Wang, Ko-han; Kim, William Y.; Lu, Jianping; Zhou, Otto

2011-03-01

Current optical imaging techniques can successfully measure tumor load in murine models of lung carcinoma but lack structural detail. We demonstrate that respiratory gated micro-CT imaging of such models gives information about structure and correlates with tumor load measurements by optical methods. Four mice with multifocal, Kras-induced tumors expressing firefly luciferase were imaged against four controls using both optical imaging and respiratory gated micro-CT. CT images of anesthetized animals were acquired with a custom CNT-based system using 30 ms x-ray pulses during peak inspiration; respiration motion was tracked with a pressure sensor beneath each animal's abdomen. Optical imaging based on the Luc+ signal correlating with tumor load was performed on a Xenogen IVIS Kinetix. Micro-CT images were post-processed using Osirix, measuring lung volume with region growing. Diameters of the largest three tumors were measured. Relationships between tumor size, lung volumes, and optical signal were compared. CT images and optical signals were obtained for all animals at two time points. In all lobes of the Kras+ mice in all images, tumors were visible; the smallest to be readily identified measured approximately 300 microns diameter. CT-derived tumor volumes and optical signals related linearly, with r=0.94 for all animals. When derived for only tumor bearing animals, r=0.3. The trend of each individual animal's optical signal tracked correctly based on the CT volumes. Interestingly, lung volumes also correlated positively with optical imaging data and tumor volume burden, suggesting active remodeling.

Depiction of pneumothoraces in a large animal model using x-ray dark-field radiography.

PubMed

Hellbach, Katharina; Baehr, Andrea; De Marco, Fabio; Willer, Konstantin; Gromann, Lukas B; Herzen, Julia; Dmochewitz, Michaela; Auweter, Sigrid; Fingerle, Alexander A; Noël, Peter B; Rummeny, Ernst J; Yaroshenko, Andre; Maack, Hanns-Ingo; Pralow, Thomas; van der Heijden, Hendrik; Wieberneit, Nataly; Proksa, Roland; Koehler, Thomas; Rindt, Karsten; Schroeter, Tobias J; Mohr, Juergen; Bamberg, Fabian; Ertl-Wagner, Birgit; Pfeiffer, Franz; Reiser, Maximilian F

2018-02-08

The aim of this study was to assess the diagnostic value of x-ray dark-field radiography to detect pneumothoraces in a pig model. Eight pigs were imaged with an experimental grating-based large-animal dark-field scanner before and after induction of a unilateral pneumothorax. Image contrast-to-noise ratios between lung tissue and the air-filled pleural cavity were quantified for transmission and dark-field radiograms. The projected area in the object plane of the inflated lung was measured in dark-field images to quantify the collapse of lung parenchyma due to a pneumothorax. Means and standard deviations for lung sizes and signal intensities from dark-field and transmission images were tested for statistical significance using Student's two-tailed t-test for paired samples. The contrast-to-noise ratio between the air-filled pleural space of lateral pneumothoraces and lung tissue was significantly higher in the dark-field (3.65 ± 0.9) than in the transmission images (1.13 ± 1.1; p = 0.002). In case of dorsally located pneumothoraces, a significant decrease (-20.5%; p > 0.0001) in the projected area of inflated lung parenchyma was found after a pneumothorax was induced. Therefore, the detection of pneumothoraces in x-ray dark-field radiography was facilitated compared to transmission imaging in a large animal model.

Time Trends in Epidemiologic Characteristics and Imaging Features of Lung Adenocarcinoma: A Population Study of 21,113 Cases in China

PubMed Central

Zhang, Li; Li, Meng; Wu, Ning; Chen, Yuheng

2015-01-01

Objectives This study aims to describe time trends of epidemiologic characteristics and imaging features over 14 years among histologically confirmed lung adenocarcinoma (ADC) in China and to discuss the possible reasons for these changes. Materials and Methods Data of 21,113 pathologically confirmed lung cancer patients from January 1999 to December 2012 were analyzed retrospectively. Preoperative high-resolution computer tomography (HRCT) images were available and reviewed in 5,439 lung ADC patients since 2005. Time trends of the ADC proportion of lung cancer cases, gender distribution, age at diagnosis, the proportion of early-stage ADC and imaging features were investigated. Results The proportion of ADC increased during the 14 years (P = 0.000). The ratio of female to male ADC cases was higher than both squamous cell carcinoma (SQCC) and total lung cancer cases (P = 0.000). The median age at diagnosis of ADC patients was younger than that of both SQCC and total lung cancer during the 14 years (P = 0.000). The proportion of age group 45–59 years increased in total lung cancer cases (P = 0.000). When stratified by lung cancer histopathologic subtypes, this trend was also observed in ADC (P = 0.001) and SQCC (P = 0.007). The proportion of early-stage cases of ADC increased from 2008 to 2012 (P < 0.001). The proportion of subsolid nodules (SSN) in ADC increased (P = 0.001) from 2005 to 2012. Conclusion The data suggests that the proportion of ADC increased from 1999 to 2012 especially in middle-aged, female patients; early-stage ADC and SSN on HRCT images gradually increased, which may have been caused by a change in smoking habits and increased application of HRCT. PMID:26317971

Toward efficient biomechanical-based deformable image registration of lungs for image-guided radiotherapy

NASA Astrophysics Data System (ADS)

Al-Mayah, Adil; Moseley, Joanne; Velec, Mike; Brock, Kristy

2011-08-01

Both accuracy and efficiency are critical for the implementation of biomechanical model-based deformable registration in clinical practice. The focus of this investigation is to evaluate the potential of improving the efficiency of the deformable image registration of the human lungs without loss of accuracy. Three-dimensional finite element models have been developed using image data of 14 lung cancer patients. Each model consists of two lungs, tumor and external body. Sliding of the lungs inside the chest cavity is modeled using a frictionless surface-based contact model. The effect of the type of element, finite deformation and elasticity on the accuracy and computing time is investigated. Linear and quadrilateral tetrahedral elements are used with linear and nonlinear geometric analysis. Two types of material properties are applied namely: elastic and hyperelastic. The accuracy of each of the four models is examined using a number of anatomical landmarks representing the vessels bifurcation points distributed across the lungs. The registration error is not significantly affected by the element type or linearity of analysis, with an average vector error of around 2.8 mm. The displacement differences between linear and nonlinear analysis methods are calculated for all lungs nodes and a maximum value of 3.6 mm is found in one of the nodes near the entrance of the bronchial tree into the lungs. The 95 percentile of displacement difference ranges between 0.4 and 0.8 mm. However, the time required for the analysis is reduced from 95 min in the quadratic elements nonlinear geometry model to 3.4 min in the linear element linear geometry model. Therefore using linear tetrahedral elements with linear elastic materials and linear geometry is preferable for modeling the breathing motion of lungs for image-guided radiotherapy applications.

Single-Photon Emission Computed Tomography/Computed Tomography Imaging in a Rabbit Model of Emphysema Reveals Ongoing Apoptosis In Vivo

PubMed Central

Goldklang, Monica P.; Tekabe, Yared; Zelonina, Tina; Trischler, Jordis; Xiao, Rui; Stearns, Kyle; Romanov, Alexander; Muzio, Valeria; Shiomi, Takayuki; Johnson, Lynne L.

2016-01-01

Evaluation of lung disease is limited by the inability to visualize ongoing pathological processes. Molecular imaging that targets cellular processes related to disease pathogenesis has the potential to assess disease activity over time to allow intervention before lung destruction. Because apoptosis is a critical component of lung damage in emphysema, a functional imaging approach was taken to determine if targeting apoptosis in a smoke exposure model would allow the quantification of early lung damage in vivo. Rabbits were exposed to cigarette smoke for 4 or 16 weeks and underwent single-photon emission computed tomography/computed tomography scanning using technetium-99m–rhAnnexin V-128. Imaging results were correlated with ex vivo tissue analysis to validate the presence of lung destruction and apoptosis. Lung computed tomography scans of long-term smoke–exposed rabbits exhibit anatomical similarities to human emphysema, with increased lung volumes compared with controls. Morphometry on lung tissue confirmed increased mean linear intercept and destructive index at 16 weeks of smoke exposure and compliance measurements documented physiological changes of emphysema. Tissue and lavage analysis displayed the hallmarks of smoke exposure, including increased tissue cellularity and protease activity. Technetium-99m–rhAnnexin V-128 single-photon emission computed tomography signal was increased after smoke exposure at 4 and 16 weeks, with confirmation of increased apoptosis through terminal deoxynucleotidyl transferase dUTP nick end labeling staining and increased tissue neutral sphingomyelinase activity in the tissue. These studies not only describe a novel emphysema model for use with future therapeutic applications, but, most importantly, also characterize a promising imaging modality that identifies ongoing destructive cellular processes within the lung. PMID:27483341

TH-AB-207A-12: CT Lung Cancer Screening and the Effects of Further Dose Reduction On CAD Performance

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

Young, S; Lo, P; Hoffman, J

Purpose: CT lung screening is already performed at low doses. In this study, we investigated the effects of further dose reduction on a lung-nodule CAD detection algorithm. Methods: The original raw CT data and images from 348 patients were obtained from our local database of National Lung Screening Trial (NLST) cases. 61 patients (17.5%) had at least one nodule reported on the NLST reader forms. All scans were acquired with fixed mAs (25 for standard-sized patients, 40 for large patients) on a 64-slice scanner (Sensation 64, Siemens Healthcare). All images were reconstructed with 1-mm slice thickness, B50 kernel. Based onmore » a previously-published technique, we added noise to the raw data to simulate reduced-dose versions of each case at 50% and 25% of the original NLST dose (i.e. approximately 1.0 and 0.5 mGy CTDIvol). For each case at each dose level, a CAD detection algorithm was run and nodules greater than 4 mm in diameter were reported. These CAD results were compared to “truth”, defined as the approximate nodule centroids from the NLST forms. Sensitivities and false-positive rates (FPR) were calculated for each dose level, with a sub-analysis by nodule LungRADS category. Results: For larger category 4 nodules, median sensitivities were 100% at all three dose levels, and mean sensitivity decreased with dose. For the more challenging category 2 and 3 nodules, the dose dependence was less obvious. Overall, mean subject-level sensitivity varied from 38.5% at 100% dose to 40.4% at 50% dose, a difference of only 1.9%. However, median FPR quadrupled from 1 per case at 100% dose to 4 per case at 25% dose. Conclusions: Dose reduction affected nodule detectability differently depending on the LungRADS category, and FPR was very sensitive at sub-screening levels. Care should be taken to adapt CAD for the very challenging noise characteristics of screening. Funding support: NIH U01 CA181156; Disclosures (McNitt-Gray): Institutional research agreement, Siemens Healthcare; Past recipient, research grant support, Siemens Healthcare; Consultant, Toshiba America Medical Systems; Consultant, Samsung Electronics.« less

Fast tomosynthesis for lung cancer detection using the SBDX geometry

NASA Astrophysics Data System (ADS)

Fahrig, Rebecca; Pineda, Angel R.; Solomon, Edward G.; Leung, Ann N.; Pelc, Norbert J.

2003-06-01

Radiology-based lung-cancer detection is a high-contrast imaging task, consisting of the detection of a small mass of tissue within much lower density lung parenchyma. This imaging task requires removal of confounding image details, fast image acquisition (< 0.1 s for pericardial region), low dose (comparable to a chest x-ray), high resolution (< 0.25 mm in-plane) and patient positioning flexibility. We present an investigation of tomosynthesis, implemented using the Scanning-Beam Digital X-ray System (SBDX), to achieve these goals. We designed an image-based computer model of tomosynthesis using a high-resolution (0.15-mm isotropic voxels), low-noise CT volume image of a lung phantom, numerically added spherical lesions and convolution-based tomographic blurring. Lesion visibility was examined as a function of half-tomographic angle for 2.5 and 4.0 mm diameter lesions. Gaussian distributed noise was added to the projected images. For lesions 2.5 mm and 4.0 mm in diameter, half-tomographic angles of at least 6° and 9° respectively were necessary before visualization of the lesions improved. The addition of noise for a dose equivalent to 1/10 that used for a standard chest radiograph did not significantly impair lesion detection. The results are promising, indicating that lung-cancer detection using a modified SBDX system is possible.

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

PubMed

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

2014-01-01

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

Histogram-based quantitative evaluation of endobronchial ultrasonography images of peripheral pulmonary lesion.

PubMed

Morikawa, Kei; Kurimoto, Noriaki; Inoue, Takeo; Mineshita, Masamichi; Miyazawa, Teruomi

2015-01-01

Endobronchial ultrasonography using a guide sheath (EBUS-GS) is an increasingly common bronchoscopic technique, but currently, no methods have been established to quantitatively evaluate EBUS images of peripheral pulmonary lesions. The purpose of this study was to evaluate whether histogram data collected from EBUS-GS images can contribute to the diagnosis of lung cancer. Histogram-based analyses focusing on the brightness of EBUS images were retrospectively conducted: 60 patients (38 lung cancer; 22 inflammatory diseases), with clear EBUS images were included. For each patient, a 400-pixel region of interest was selected, typically located at a 3- to 5-mm radius from the probe, from recorded EBUS images during bronchoscopy. Histogram height, width, height/width ratio, standard deviation, kurtosis and skewness were investigated as diagnostic indicators. Median histogram height, width, height/width ratio and standard deviation were significantly different between lung cancer and benign lesions (all p < 0.01). With a cutoff value for standard deviation of 10.5, lung cancer could be diagnosed with an accuracy of 81.7%. Other characteristics investigated were inferior when compared to histogram standard deviation. Histogram standard deviation appears to be the most useful characteristic for diagnosing lung cancer using EBUS images. © 2015 S. Karger AG, Basel.

A high-resolution imaging technique using a whole-body, research photon counting detector CT system

NASA Astrophysics Data System (ADS)

Leng, S.; Yu, Z.; Halaweish, A.; Kappler, S.; Hahn, K.; Henning, A.; Li, Z.; Lane, J.; Levin, D. L.; Jorgensen, S.; Ritman, E.; McCollough, C.

2016-03-01

A high-resolution (HR) data collection mode has been introduced to a whole-body, research photon-counting-detector CT system installed in our laboratory. In this mode, 64 rows of 0.45 mm x 0.45 mm detector pixels were used, which corresponded to a pixel size of 0.25 mm x 0.25 mm at the iso-center. Spatial resolution of this HR mode was quantified by measuring the MTF from a scan of a 50 micron wire phantom. An anthropomorphic lung phantom, cadaveric swine lung, temporal bone and heart specimens were scanned using the HR mode, and image quality was subjectively assessed by two experienced radiologists. High spatial resolution of the HR mode was evidenced by the MTF measurement, with 15 lp/cm and 20 lp/cm at 10% and 2% modulation. Images from anthropomorphic phantom and cadaveric specimens showed clear delineation of small structures, such as lung vessels, lung nodules, temporal bone structures, and coronary arteries. Temporal bone images showed critical anatomy (i.e. stapes superstructure) that was clearly visible in the PCD system. These results demonstrated the potential application of this imaging mode in lung, temporal bone, and vascular imaging. Other clinical applications that require high spatial resolution, such as musculoskeletal imaging, may also benefit from this high resolution mode.

Longitudinal Assessment of Lung Cancer Progression in Mice Using the Sodium Iodide Symporter Reporter Gene and SPECT/CT Imaging.

PubMed

Price, Dominique N; McBride, Amber A; Anton, Martina; Kusewitt, Donna F; Norenberg, Jeffrey P; MacKenzie, Debra A; Thompson, Todd A; Muttil, Pavan

2016-01-01

Lung cancer has the highest mortality rate of any tissue-specific cancer in both men and women. Research continues to investigate novel drugs and therapies to mitigate poor treatment efficacy, but the lack of a good descriptive lung cancer animal model for preclinical drug evaluation remains an obstacle. Here we describe the development of an orthotopic lung cancer animal model which utilizes the human sodium iodide symporter gene (hNIS; SLC5A5) as an imaging reporter gene for the purpose of non-invasive, longitudinal tumor quantification. hNIS is a glycoprotein that naturally transports iodide (I-) into thyroid cells and has the ability to symport the radiotracer 99mTc-pertechnetate (99mTcO4-). A549 lung adenocarcinoma cells were genetically modified with plasmid or lentiviral vectors to express hNIS. Modified cells were implanted into athymic nude mice to develop two tumor models: a subcutaneous and an orthotopic xenograft tumor model. Tumor progression was longitudinally imaged using SPECT/CT and quantified by SPECT voxel analysis. hNIS expression in lung tumors was analyzed by quantitative real-time PCR. Additionally, hematoxylin and eosin staining and visual inspection of pulmonary tumors was performed. We observed that lentiviral transduction provided enhanced and stable hNIS expression in A549 cells. Furthermore, 99mTcO4- uptake and accumulation was observed within lung tumors allowing for imaging and quantification of tumor mass at two-time points. This study illustrates the development of an orthotopic lung cancer model that can be longitudinally imaged throughout the experimental timeline thus avoiding inter-animal variability and leading to a reduction in total animal numbers. Furthermore, our orthotopic lung cancer animal model is clinically relevant and the genetic modification of cells for SPECT/CT imaging can be translated to other tissue-specific tumor animal models.

Longitudinal Assessment of Lung Cancer Progression in Mice Using the Sodium Iodide Symporter Reporter Gene and SPECT/CT Imaging

PubMed Central

Anton, Martina; Kusewitt, Donna F.; Norenberg, Jeffrey P.; MacKenzie, Debra A.; Thompson, Todd A.; Muttil, Pavan

2016-01-01

Lung cancer has the highest mortality rate of any tissue-specific cancer in both men and women. Research continues to investigate novel drugs and therapies to mitigate poor treatment efficacy, but the lack of a good descriptive lung cancer animal model for preclinical drug evaluation remains an obstacle. Here we describe the development of an orthotopic lung cancer animal model which utilizes the human sodium iodide symporter gene (hNIS; SLC5A5) as an imaging reporter gene for the purpose of non-invasive, longitudinal tumor quantification. hNIS is a glycoprotein that naturally transports iodide (I-) into thyroid cells and has the ability to symport the radiotracer 99mTc-pertechnetate (99mTcO4-). A549 lung adenocarcinoma cells were genetically modified with plasmid or lentiviral vectors to express hNIS. Modified cells were implanted into athymic nude mice to develop two tumor models: a subcutaneous and an orthotopic xenograft tumor model. Tumor progression was longitudinally imaged using SPECT/CT and quantified by SPECT voxel analysis. hNIS expression in lung tumors was analyzed by quantitative real-time PCR. Additionally, hematoxylin and eosin staining and visual inspection of pulmonary tumors was performed. We observed that lentiviral transduction provided enhanced and stable hNIS expression in A549 cells. Furthermore, 99mTcO4- uptake and accumulation was observed within lung tumors allowing for imaging and quantification of tumor mass at two-time points. This study illustrates the development of an orthotopic lung cancer model that can be longitudinally imaged throughout the experimental timeline thus avoiding inter-animal variability and leading to a reduction in total animal numbers. Furthermore, our orthotopic lung cancer animal model is clinically relevant and the genetic modification of cells for SPECT/CT imaging can be translated to other tissue-specific tumor animal models. PMID:28036366

MO-FG-CAMPUS-JeP2-02: Audiovisual Biofeedback Guided Respiratory-Gated MRI: An Investigation of Tumor Definition and Scan Time for Lung Cancer

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

Lee, D; Pollock, S; Keall, P

Purpose: Breathing consistency variations can cause respiratory-related motion blurring and artifacts and increase in MRI scan time due to inadequate respiratory-gating and discarding of breathing cycles. In a previous study the concept of audiovisual biofeedback (AV) guided respiratory-gated MRI was tested with healthy volunteers and it demonstrated image quality improvement on anatomical structures and scan time reduction. This study tests the applicability of AV-guided respiratorygated MRI for lung cancer in a prospective patient study. Methods: Image quality and scan time were investigated in thirteen lung cancer patients who underwent two 3T MRI sessions. In the first MRI session (pre-treatment), respiratory-gatedmore » MR images with free breathing (FB) and AV were acquired at inhalation and exhalation. An RF navigator placed on the liver dome was employed for the respiratory-gated MRI. This was repeated in the second MRI session (mid-treatment). Lung tumors were delineated on each dataset. FB and AV were compared in terms of (1) tumor definition assessed by lung tumor contours and (2) intra-patient scan time variation using the total image acquisition time of inhalation and exhalation datasets from the first and second MRI sessions across 13 lung cancer patients. Results: Compared to FB AV-guided respiratory-gated MRI improved image quality for contouring tumors with sharper boundaries and less blurring resulted in the improvement of tumor definition. Compared to FB the variation of intra-patient scan time with AV was reduced by 48% (p<0.001) from 54 s to 28 s. Conclusion: This study demonstrated that AV-guided respiratorygated MRI improved the quality of tumor images and fixed tumor definition for lung cancer. These results suggest that audiovisual biofeedback breathing guidance has the potential to control breathing for adequate respiratory-gating for lung cancer imaging and radiotherapy.« less

Sci-Thur AM: YIS – 07: Optimizing dual-energy x-ray parameters using a single filter for both high and low-energy images to enhance soft-tissue imaging

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

Bowman, Wesley; Sattarivand, Mike

Objective: To optimize dual-energy parameters of ExacTrac stereoscopic x-ray imaging system for lung SBRT patients Methods: Simulated spectra and a lung phantom were used to optimize filter material, thickness, kVps, and weighting factors to obtain bone subtracted dual-energy images. Spektr simulations were used to identify material in the atomic number (Z) range [3–83] based on a metric defined to separate spectrums of high and low energies. Both energies used the same filter due to time constraints of image acquisition in lung SBRT imaging. A lung phantom containing bone, soft tissue, and a tumor mimicking material was imaged with filter thicknessesmore » range [0–1] mm and kVp range [60–140]. A cost function based on contrast-to-noise-ratio of bone, soft tissue, and tumor, as well as image noise content, was defined to optimize filter thickness and kVp. Using the optimized parameters, dual-energy images of anthropomorphic Rando phantom were acquired and evaluated for bone subtraction. Imaging dose was measured with dual-energy technique using tin filtering. Results: Tin was the material of choice providing the best energy separation, non-toxicity, and non-reactiveness. The best soft-tissue-only image in the lung phantom was obtained using 0.3 mm tin and [140, 80] kVp pair. Dual-energy images of the Rando phantom had noticeable bone elimination when compared to no filtration. Dose was lower with tin filtering compared to no filtration. Conclusions: Dual-energy soft-tissue imaging is feasible using ExacTrac stereoscopic imaging system utilizing a single tin filter for both high and low energies and optimized acquisition parameters.« less

Can sinogram-affirmed iterative (SAFIRE) reconstruction improve imaging quality on low-dose lung CT screening compared with traditional filtered back projection (FBP) reconstruction?

PubMed

Yang, Wen Jie; Yan, Fu Hua; Liu, Bo; Pang, Li Fang; Hou, Liang; Zhang, Huan; Pan, Zi Lai; Chen, Ke Min

2013-01-01

To evaluate the performance of sinogram-affirmed iterative (SAFIRE) reconstruction on image quality of low-dose lung computed tomographic (CT) screening compared with filtered back projection (FBP). Three hundred four patients for annual low-dose lung CT screening were examined by a dual-source CT system at 120 kilovolt (peak) with reference tube current of 40 mA·s. Six image serials were reconstructed, including one data set of FBP and 5 data sets of SAFIRE with different reconstruction strengths from 1 to 5. Image noise was recorded; and subjective scores of image noise, images artifacts, and the overall image quality were also assessed by 2 radiologists. The mean ± SD weight for all patients was 66.3 ± 12.8 kg, and the body mass index was 23.4 ± 3.2. The mean ± SD dose-length product was 95.2 ± 30.6 mGy cm, and the mean ± SD effective dose was 1.6 ± 0.5 mSv. The observation agreements for image noise grade, artifact grade, and the overall image quality were 0.785, 0.595 and 0.512, respectively. Among the overall 6 data sets, both the measured mean objective image noise and the subjective image noise of FBP was the highest, and the image noise decreased with the increasing of SAFIRE reconstruction strength. The data sets of S3 obtained the best image quality scores. Sinogram-affirmed iterative reconstruction can significantly improve image quality of low-dose lung CT screening compared with FBP, and SAFIRE with reconstruction strength 3 was a pertinent choice for low-dose lung CT.

Increasing Receipt of High-Tech/High-Cost Imaging and Its Determinants in the Last Month of Taiwanese Patients With Metastatic Cancer, 2001-2010: A Retrospective Cohort Study.

PubMed

Liu, Tsang-Wu; Hung, Yen-Ni; Soong, Thomas C; Tang, Siew Tzuh

2015-08-01

One strategy for controlling the skyrocketing costs of cancer care may be to target high-tech/high-cost imaging at the end of life (EOL). This population-based study investigated receipt of high-tech/high-cost imaging and its determinants for Taiwanese patients with metastatic cancer in their last month of life.Individual patient-level data were linked with encrypted identification numbers from computerized administrative data in Taiwan, that is, the National Register of Deaths Database, Cancer Registration System database, and National Health Insurance claims datasets, Database of Medical Care Institutions Status, and national census statistics (population/household income). We identified receipt of computerized tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and radionuclide bone scans (BSs) for 236,911 Taiwanese cancer decedents with metastatic disease, 2001 to 2010. Associations of patient, physician, hospital, and regional factors with receiving CT, MRI, and bone scan in the last month of life were evaluated by multilevel generalized linear-mixed models.Over one-third (average [range]: 36.11% [33.07%-37.31%]) of patients with metastatic cancer received at least 1 high-tech/high-cost imaging modality in their last month (usage rates for CT, MRI, PET, and BS were 31.05%, 5.81%, 0.25%, and 8.15%, respectively). In 2001 to 2010, trends of receipt increased for CT (27.96-32.22%), MRI (4.34-6.70%), and PET (0.00-0.62%), but decreased for BS (9.47-6.57%). Facilitative determinants with consistent trends for at least 2 high-tech/high-cost imaging modalities were male gender, younger age, married, rural residence, lung cancer diagnosis, dying within 1 to 2 years of diagnosis, not under medical oncology care, and receiving care at a teaching hospital with a larger volume of terminally ill cancer patients and greater EOL care intensity. Undergoing high-tech/high-cost imaging at EOL generally was not associated with regional characteristics, healthcare resources, and EOL care intensity.To more effectively use high-tech/high-cost imaging at EOL, clinical and financial interventions should target nonmedical oncologists/hematologists affiliated with teaching hospitals that tend to aggressively treat high volumes of terminally ill cancer patients, thereby avoiding unnecessary EOL care spending and transforming healthcare systems into affordable high-quality cancer care delivery systems.

Ventilation/perfusion SPECT or SPECT/CT for lung function imaging in patients with pulmonary emphysema?

PubMed

Froeling, Vera; Heimann, Uwe; Huebner, Ralf-Harto; Kroencke, Thomas J; Maurer, Martin H; Doellinger, Felix; Geisel, Dominik; Hamm, Bernd; Brenner, Winfried; Schreiter, Nils F

2015-07-01

To evaluate the utility of attenuation correction (AC) of V/P SPECT images for patients with pulmonary emphysema. Twenty-one patients (mean age 67.6 years) with pulmonary emphysema who underwent V/P SPECT/CT were included. AC/non-AC V/P SPECT images were compared visually and semiquantitatively. Visual comparison of AC/non-AC images was based on a 5-point likert scale. Semiquantitative comparison assessed absolute counts per lung (aCpLu) and lung lobe (aCpLo) for AC/non-AC images using software-based analysis; percentage counts (PC = (aCpLo/aCpLu) × 100) were calculated. Correlation between AC/non-AC V/P SPECT images was analyzed using Spearman's rho correlation coefficient; differences were tested for significance with the Wilcoxon rank sum test. Visual analysis revealed high conformity for AC and non-AC V/P SPECT images. Semiquantitative analysis of PC in AC/non-AC images had an excellent correlation and showed no significant differences in perfusion (ρ = 0.986) or ventilation (ρ = 0.979, p = 0.809) SPECT/CT images. AC of V/P SPECT images for lung lobe-based function imaging in patients with pulmonary emphysema do not improve visual or semiquantitative image analysis.

Multiphoton microscopy based cryo-imaging of inflated frozen human lung sections at -60°C in healthy and COPD lungs

NASA Astrophysics Data System (ADS)

Abraham, Thomas; Kayra, Damian; Zhang, Angela; Suzuki, Masaru; McDonough, John; Elliott, W. M.; Cooper, Joel D.; Hogg, James C.

2013-02-01

Lung is a complex gas exchanger with interfacial area (where the gas exchange takes place) is about the size of a tennis court. Respiratory function is linked to the biomechanical stability of the gas exchange or alveolar regions which directly depends on the spatial distributions of the extracellular matrix fibers such fibrillar collagens and elastin fibers. It is very important to visualize and quantify these fibers at their native and inflated conditions to have correct morphometric information on differences between control and diseased states. This can be only achieved in the ex vivo states by imaging directly frozen lung specimens inflated to total lung capacity. Multiphoton microscopy, which uses ultra-short infrared laser pulses as the excitation source, produces multiphoton excitation fluorescence (MPEF) signals from endogenously fluorescent proteins (e.g. elastin) and induces specific second harmonic generation (SHG) signals from non-centrosymmetric proteins such as fibrillar collagens in fresh human lung tissues [J. Struct. Biol. (2010)171,189-196]. Here we report for the first time 3D image data obtained directly from thick frozen inflated lung specimens (~0.7- 1.0 millimeter thick) visualized at -60°C without prior fixation or staining in healthy and diseased states. Lung specimens donated for transplantation and released for research when no appropriate recipient was identified served as controls, and diseased lung specimens donated for research by patients receiving lung transplantation for very severe COPD (n=4) were prepared as previously described [N. Engl. J. Med. (2011) 201, 1567]. Lung slices evenly spaced between apex and base were examined using multiphoton microscopy while maintained at -60°C using a temperature controlled cold stage with a temperature resolution of 0.1°C. Infrared femto-second laser pulses tuned to 880nm, dry microscopic objectives, and non-de-scanned detectors/spectrophotometer located in the reflection geometry were used for generating the 3D images/spectral information. We found that this novel imaging approach can provide spatially resolved 3D images with spectral specificities from frozen inflated lungs that are sensitive enough to identity the micro-structural details of fibrillar collagens and elastin fibers in alveolar walls in both healthy and diseased tissues.

What can imaging tell us about physiology? Lung growth and regional mechanical strain

PubMed Central

Tawhai, Merryn H.

2012-01-01

The interplay of mechanical forces transduces diverse physico-biochemical processes to influence lung morphogenesis, growth, maturation, remodeling and repair. Because tissue stress is difficult to measure in vivo, mechano-sensitive responses are commonly inferred from global changes in lung volume, shape, or compliance and correlated with structural changes in tissue blocks sampled from postmortem-fixed lungs. Recent advances in noninvasive volumetric imaging technology, nonrigid image registration, and deformation analysis provide valuable tools for the quantitative analysis of in vivo regional anatomy and air and tissue-blood distributions and when combined with transpulmonary pressure measurements, allow characterization of regional mechanical function, e.g., displacement, strain, shear, within and among intact lobes, as well as between the lung and the components of its container—rib cage, diaphragm, and mediastinum—thereby yielding new insights into the inter-related metrics of mechanical stress-strain and growth/remodeling. Here, we review the state-of-the-art imaging applications for mapping asymmetric heterogeneous physical interactions within the thorax and how these interactions permit as well as constrain lung growth, remodeling, and compensation during development and following pneumonectomy to illustrate how advanced imaging could facilitate the understanding of physiology and pathophysiology. Functional imaging promises to facilitate the formulation of realistic computational models of lung growth that integrate mechano-sensitive events over multiple spatial and temporal scales to accurately describe in vivo physiology and pathophysiology. Improved computational models in turn could enhance our ability to predict regional as well as global responses to experimental and therapeutic interventions. PMID:22582216

On-the-spot lung cancer differential diagnosis by label-free, molecular vibrational imaging and knowledge-based classification

NASA Astrophysics Data System (ADS)

Gao, Liang; Li, Fuhai; Thrall, Michael J.; Yang, Yaliang; Xing, Jiong; Hammoudi, Ahmad A.; Zhao, Hong; Massoud, Yehia; Cagle, Philip T.; Fan, Yubo; Wong, Kelvin K.; Wang, Zhiyong; Wong, Stephen T. C.

2011-09-01

We report the development and application of a knowledge-based coherent anti-Stokes Raman scattering (CARS) microscopy system for label-free imaging, pattern recognition, and classification of cells and tissue structures for differentiating lung cancer from non-neoplastic lung tissues and identifying lung cancer subtypes. A total of 1014 CARS images were acquired from 92 fresh frozen lung tissue samples. The established pathological workup and diagnostic cellular were used as prior knowledge for establishment of a knowledge-based CARS system using a machine learning approach. This system functions to separate normal, non-neoplastic, and subtypes of lung cancer tissues based on extracted quantitative features describing fibrils and cell morphology. The knowledge-based CARS system showed the ability to distinguish lung cancer from normal and non-neoplastic lung tissue with 91% sensitivity and 92% specificity. Small cell carcinomas were distinguished from nonsmall cell carcinomas with 100% sensitivity and specificity. As an adjunct to submitting tissue samples to routine pathology, our novel system recognizes the patterns of fibril and cell morphology, enabling medical practitioners to perform differential diagnosis of lung lesions in mere minutes. The demonstration of the strategy is also a necessary step toward in vivo point-of-care diagnosis of precancerous and cancerous lung lesions with a fiber-based CARS microendoscope.

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.

Surveillance Practice Patterns after Curative Intent Therapy for Stage I Non-Small-Cell Lung Cancer in the Medicare Population.

PubMed

Erb, Christopher T; Su, Kevin W; Soulos, Pamela R; Tanoue, Lynn T; Gross, Cary P

2016-09-01

Recurrence after treatment for non-small cell lung cancer (NSCLC) is common, and routine imaging surveillance is recommended by evidence-based guidelines. Little is known about surveillance patterns after curative intent therapy for early stage NSCLC. We sought to understand recent practice patterns for surveillance of stage I NSCLC in the first two years after curative intent therapy in the Medicare population. Using the Surveillance, Epidemiology, and End Results (SEER)-Medicare linked database we selected patients diagnosed with stage I NSCLC between 1998 and 2008. We studied adherence to surveillance guidelines based on specialty society recommendations for chest radiography and computed tomography (CT) scanning. We also tracked the use of Positron Emission Tomography (PET) scans, which are not recommended for surveillance. We calculated the percent of patients who received guideline-adherent surveillance imaging and used logistic regression to determine associations between patient and provider factors and guideline adherence. Overall, 61.4% of patients received guideline-adherent surveillance during the initial 2 years after treatment. Use of CT scans in the first year after treatment increased from 47.4% in 1998-78.5% in 2008, and PET use increased from 5.8% to 28.9%. Adherence with surveillance imaging was associated with younger age, higher income, more comorbidities, access to primary care, and receipt of SBRT as the primary treatment. Adherence to specialty society guidelines for surveillance after treatment for stage I NSCLC was poor in this population of Medicare beneficiaries, with less than two-thirds of patients receiving recommended imaging, and almost 30% receiving non-recommended PET scans. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Stereological assessment of mouse lung parenchyma via nondestructive, multiscale micro-CT imaging validated by light microscopic histology

PubMed Central

Vasilescu, Dragoş M.; Klinge, Christine; Knudsen, Lars; Yin, Leilei; Wang, Ge; Weibel, Ewald R.; Ochs, Matthias

2013-01-01

Quantitative assessment of the lung microstructure using standard stereological methods such as volume fractions of tissue, alveolar surface area, or number of alveoli, are essential for understanding the state of normal and diseased lung. These measures are traditionally obtained from histological sections of the lung tissue, a process that ultimately destroys the three-dimensional (3-D) anatomy of the tissue. In comparison, a novel X-ray-based imaging method that allows nondestructive sectioning and imaging of fixed lungs at multiple resolutions can overcome this limitation. Scanning of the whole lung at high resolution and subsequent regional sampling at ultrahigh resolution without physically dissecting the organ allows the application of design-based stereology for assessment of the whole lung structure. Here we validate multiple stereological estimates performed on micro–computed tomography (μCT) images by comparing them with those obtained via conventional histology on the same mouse lungs. We explore and discuss the potentials and limitations of the two approaches. Histological examination offers higher resolution and the qualitative differentiation of tissues by staining, but ultimately loses 3-D tissue relationships, whereas μCT allows for the integration of morphometric data with the spatial complexity of lung structure. However, μCT has limited resolution satisfactory for the sterological estimates presented in this study but not for differentiation of tissues. We conclude that introducing stereological methods in μCT studies adds value by providing quantitative information on internal structures while not curtailing more complex approaches to the study of lung architecture in the context of physiological or pathological studies. PMID:23264542

SU-E-J-87: Ventilation Weighting Effect On Mean Doses of Both Side Lungs for Patients with Advanced Stage Lung Cancer

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

Qu, H; Xia, P; Yu, N

Purpose: To study ventilation weighting effect on radiation doses to both side lungs for patients with advanced stage lung cancer. Methods: Fourteen patients with advanced stage lung cancer were included in this retrospective study. Proprietary software was developed to calculate the lung ventilation map based on 4DCT images acquired for radiation therapy. Two phases of inhale (0%) and exhale (50%) were used for the lung ventilation calculations. For each patient, the CT images were resampled to the same dose calculation resolution of 3mmx3mmx3mm. The ventilation distribution was then normalized by the mean value of the ventilation. The ventilation weighted dosemore » was calculated by applying linearly weighted ventilation to the dose of each pixel. The lung contours were automatically delineated from patient CT image with lung window, excluding the tumor and high density tissues. For contralateral and ipsilateral lungs, the mean lung doses from the original plan and ventilation weighted mean lung doses were compared using two tail t-Test. Results: The average of mean dose was 6.1 ±3.8Gy for the contralateral lungs, and 26.2 ± 14.0Gy for the ipsilateral lungs. The average of ventilation weighted dose was 6.3± 3.8Gy for the contralateral lungs and 24.6 ± 13.1Gy for the ipsilateral lungs. The statistics analysis shows the significance of the mean dose increase (p<0.015) for the contralateral lungs and decrease (p<0.005) for the ipsilateral lungs. Conclusion: Ventilation weighted doses were greater than the un-weighted doses for contralateral lungs and smaller for ipsilateral lungs. This Result may be helpful to understand the radiation dosimetric effect on the lung function and provide planning guidance for patients with advance stage lung cancer.« less

Conditions for NIR fluorescence-guided tumor resectioning in preclinical lung cancer model (Conference Presentation)

NASA Astrophysics Data System (ADS)

Kim, Minji; Quan, Yuhua; Choi, Byeong Hyun; Choi, Yeonho; Kim, Hyun Koo; Kim, Beop-Min

2016-03-01

Pulmonary nodule could be identified by intraoperative fluorescence imaging system from systemic injection of indocyanine green (ICG) which achieves enhanced permeability and retention (EPR) effects. This study was performed to evaluate optimal injection time of ICG for detecting cancer during surgery in rabbit lung cancer model. VX2 carcinoma cell was injected in rabbit lung under fluoroscopic computed tomography-guidance. Solitary lung cancer was confirmed on positron emitting tomography with CT (PET/CT) 2 weeks after inoculation. ICG was administered intravenously and fluorescent intensity of lung tumor was measured using the custom-built intraoperative color and fluorescence merged imaging system (ICFIS) for 15 hours. Solitary lung cancer was resected through thoracoscopic version of ICFIS. ICG was observed in all animals. Because Lung has fast blood pulmonary circulation, Fluorescent signal showed maximum intensity earlier than previous studies in other organs. Fluorescent intensity showed maximum intensity within 6-9 hours in rabbit lung cancer. Overall, Fluorescent intensity decreased with increasing time, however, all tumors were detectable using fluorescent images until 12 hours. In conclusion, while there had been studies in other organs showed that optimal injection time was at least 24 hours before operation, this study showed shorter optimal injection time at lung cancer. Since fluorescent signal showed the maximum intensity within 6-9 hours, cancer resection could be performed during this time. This data informed us that optimal injection time of ICG should be evaluated in each different solid organ tumor for fluorescent image guided surgery.

Differentiation of Central Lung Cancer from Atelectasis: Comparison of Diffusion-Weighted MRI with PET/CT

PubMed Central

Yang, Rui-Meng; Li, Long; Wei, Xin-Hua; Guo, Yong-Mei; Huang, Yun-Hai; Lai, Li-Sha; Chen, A-Mei; Liu, Guo-Shun; Xiong, Wei-Feng; Luo, Liang-Ping; Jiang, Xin-Qing

2013-01-01

Objective Prospectively assess the performance of diffusion-weighted magnetic resonance imaging (DW-MRI) for differentiation of central lung cancer from atelectasis. Materials and Methods 38 consecutive lung cancer patients (26 males, 12 females; age range: 28–71 years; mean age: 49 years) who were referred for thoracic MR imaging examinations were enrolled. MR examinations were performed using a 1.5-T clinical scanner and scanning sequences of T1WI, T2WI, and DWI. Cancers and atelectasis were measured by mapping of the apparent diffusion coefficients (ADCs) obtained with a b-value of 500 s/mm2. Results PET/CT and DW-MR allowed differentiation of tumor and atelectasis in all 38 cases, but T2WI did not allow differentiation in 9 cases. Comparison of conventional T2WI and DW-MRI indicated a higher contrast noise ratio of the central lung carcinoma than the atelectasis by DW-MRI. ADC maps indicated significantly lower mean ADC in the central lung carcinoma than in the atelectasis (1.83±0.58 vs. 2.90±0.26 mm2/s, p<0.0001). ADC values of small cell lung carcinoma were significantly greater than those from squamous cell carcinoma and adenocarcinoma (p<0.0001 for both). Conclusions DW-MR imaging provides valuable information not obtained by conventional MR and may be useful for differentiation of central lung carcinoma from atelectasis. Future developments may allow DW-MR imaging to be used as an alternative to PET-CT in imaging of patients with lung cancer. PMID:23593186

The use of the Kalman filter in the automated segmentation of EIT lung images.

PubMed

Zifan, A; Liatsis, P; Chapman, B E

2013-06-01

In this paper, we present a new pipeline for the fast and accurate segmentation of impedance images of the lungs using electrical impedance tomography (EIT). EIT is an emerging, promising, non-invasive imaging modality that produces real-time, low spatial but high temporal resolution images of impedance inside a body. Recovering impedance itself constitutes a nonlinear ill-posed inverse problem, therefore the problem is usually linearized, which produces impedance-change images, rather than static impedance ones. Such images are highly blurry and fuzzy along object boundaries. We provide a mathematical reasoning behind the high suitability of the Kalman filter when it comes to segmenting and tracking conductivity changes in EIT lung images. Next, we use a two-fold approach to tackle the segmentation problem. First, we construct a global lung shape to restrict the search region of the Kalman filter. Next, we proceed with augmenting the Kalman filter by incorporating an adaptive foreground detection system to provide the boundary contours for the Kalman filter to carry out the tracking of the conductivity changes as the lungs undergo deformation in a respiratory cycle. The proposed method has been validated by using performance statistics such as misclassified area, and false positive rate, and compared to previous approaches. The results show that the proposed automated method can be a fast and reliable segmentation tool for EIT imaging.

Development of a semi-automated combined PET and CT lung lesion segmentation framework

NASA Astrophysics Data System (ADS)

Rossi, Farli; Mokri, Siti Salasiah; Rahni, Ashrani Aizzuddin Abd.

2017-03-01

Segmentation is one of the most important steps in automated medical diagnosis applications, which affects the accuracy of the overall system. In this paper, we propose a semi-automated segmentation method for extracting lung lesions from thoracic PET/CT images by combining low level processing and active contour techniques. The lesions are first segmented in PET images which are first converted to standardised uptake values (SUVs). The segmented PET images then serve as an initial contour for subsequent active contour segmentation of corresponding CT images. To evaluate its accuracy, the Jaccard Index (JI) was used as a measure of the accuracy of the segmented lesion compared to alternative segmentations from the QIN lung CT segmentation challenge, which is possible by registering the whole body PET/CT images to the corresponding thoracic CT images. The results show that our proposed technique has acceptable accuracy in lung lesion segmentation with JI values of around 0.8, especially when considering the variability of the alternative segmentations.

MO-E-BRB-03: Panel Member

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

Salter, B.

2016-06-15

In this interactive session, lung SBRT patient cases will be presented to highlight real-world considerations for ensuring safe and accurate treatment delivery. An expert panel of speakers will discuss challenges specific to lung SBRT including patient selection, patient immobilization techniques, 4D CT simulation and respiratory motion management, target delineation for treatment planning, online treatment alignment, and established prescription regimens and OAR dose limits. Practical examples of cases, including the patient flow thought the clinical process are presented and audience participation will be encouraged. This panel session is designed to provide case demonstration and review for lung SBRT in terms ofmore » (1) clinical appropriateness in patient selection, (2) strategies for simulation, including 4D and respiratory motion management, and (3) applying multi imaging modality (4D CT imaging, MRI, PET) for tumor volume delineation and motion extent, and (4) image guidance in treatment delivery. Learning Objectives: Understand the established requirements for patient selection in lung SBRT Become familiar with the various immobilization strategies for lung SBRT, including technology for respiratory motion management Understand the benefits and pitfalls of applying multi imaging modality (4D CT imaging, MRI, PET) for tumor volume delineation and motion extent determination for lung SBRT Understand established prescription regimes and OAR dose limits.« less

MO-E-BRB-01: Panel Member

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

Benedict, S.

2016-06-15

In this interactive session, lung SBRT patient cases will be presented to highlight real-world considerations for ensuring safe and accurate treatment delivery. An expert panel of speakers will discuss challenges specific to lung SBRT including patient selection, patient immobilization techniques, 4D CT simulation and respiratory motion management, target delineation for treatment planning, online treatment alignment, and established prescription regimens and OAR dose limits. Practical examples of cases, including the patient flow thought the clinical process are presented and audience participation will be encouraged. This panel session is designed to provide case demonstration and review for lung SBRT in terms ofmore » (1) clinical appropriateness in patient selection, (2) strategies for simulation, including 4D and respiratory motion management, and (3) applying multi imaging modality (4D CT imaging, MRI, PET) for tumor volume delineation and motion extent, and (4) image guidance in treatment delivery. Learning Objectives: Understand the established requirements for patient selection in lung SBRT Become familiar with the various immobilization strategies for lung SBRT, including technology for respiratory motion management Understand the benefits and pitfalls of applying multi imaging modality (4D CT imaging, MRI, PET) for tumor volume delineation and motion extent determination for lung SBRT Understand established prescription regimes and OAR dose limits.« less

EF5 PET of Tumor Hypoxia: A Predictive Imaging Biomarker of Response to Stereotactic Ablative Radiotherapy (SABR) for Early Lung Cancer

DTIC Science & Technology

2014-09-01

Predictive Imaging Biomarker of Response to Stereotactic Ablative Radiotherapy ( SABR ) for Early Lung Cancer PRINCIPAL INVESTIGATOR: Billy W...CONTRACT NUMBER Response to Stereotactic Ablative Radiotherapy ( SABR ) for Early Lung Cancer 5b. GRANT NUMBER W81XWH-12-1-0236 5c...NOTES 14. ABSTRACT Purpose and scope: Stereotactic ablative radiotherapy ( SABR ) has become a new standard of care for early stage lung

Polyp measurement with CT colonography: multiple-reader, multiple-workstation comparison.

PubMed

Young, Brett M; Fletcher, J G; Paulsen, Scott R; Booya, Fargol; Johnson, C Daniel; Johnson, Kristina T; Melton, Zackary; Rodysill, Drew; Mandrekar, Jay

2007-01-01

The risk of invasive colorectal cancer in colorectal polyps correlates with lesion size. Our purpose was to define the most accurate methods for measuring polyp size at CT colonography (CTC) using three models of workstations and multiple observers. Six reviewers measured 24 unique polyps of known size (5, 7, 10, and 12 mm), shape (sessile, flat, and pedunculated), and location (straight or curved bowel segment) using CTC data sets obtained at two doses (5 mAs and 65 mAs) and a previously described colonic phantom model. Reviewers measured the largest diameter of polyps on three proprietary workstations. Each polyp was measured with lung and soft-tissue windows on axial, 2D multiplanar reconstruction (MPR), and 3D images. There were significant differences among measurements obtained at various settings within each workstation (p < 0.0001). Measurements on 2D images were more accurate with lung window than with soft-tissue window settings (p < 0.0001). For the 65-mAs data set, the most accurate measurements were obtained in analysis of axial images with lung window, 2D MPR images with lung window, and 3D tissue cube images for Wizard, Advantage, and Vitrea workstations, respectively, without significant differences in accuracy among techniques (0.11 < p < 0.59). The mean absolute error values for these optimal settings were 0.48 mm, 0.61 mm, and 0.76 mm, respectively, for the three workstations. Within the ultralow-dose 5-mAs data set the best methods for Wizard, Advantage, and Vitrea were axial with lung window, 2D MPR with lung window, and 2D MPR with lung window, respectively. Use of nearly all measurement methods, except for the Vitrea 3D tissue cube and the Wizard 2D MPR with lung window, resulted in undermeasurement of the true size of the polyps. Use of CTC computer workstations facilitates accurate polyp measurement. For routine CTC examinations, polyps should be measured with lung window settings on 2D axial or MPR images (Wizard and Advantage) or 3D images (Vitrea). When these optimal methods are used, these three commercial workstations do not differ significantly in acquisition of accurate polyp measurements at routine dose settings.

Increased risk of lung cancer in patients with eczema: a nationwide cohort study in Taiwan.

PubMed

Juan, Chao-Kuei; Shen, Jui-Lung; Lin, Cheng-Li; Kim, Karen Wang; Chen, Wen-Chi

2016-09-01

The association between lung cancer and eczema remains controversial. Previous studies have yielded conflicting results. This retrospective population-based cohort study is aimed at clarifying the risk of lung cancer associated with eczema. By using the Taiwan National Health Insurance Research Database, we identified 43,719 patients who had been newly diagnosed with eczema in the years 2000 to 2010. The comparison cohort included 87,438 randomly selected, age-matched patients without eczema. The cases of these two cohorts were followed until 2011. The Cox proportional hazard regression model was used to calculate the risk of lung cancer in eczema patients. The database did not contain any information regarding smoking, alcohol consumption, socioeconomic status, or family history. After adjusting for age and comorbidity, the population with eczema had a 2.80-fold greater risk of developing lung cancer compared with the population in the comparison cohort (adjusted hazard ratio 2.80, 95 % confidence interval 2.59-3.03). Eczema patients with comorbid diseases including asthma, chronic obstructive -pulmonary disease, alcoholic liver damage, or diabetes were at a higher risk of lung cancer compared with the non-eczema patients without comorbidity. Eczema is associated with a greater risk for the development of lung cancer. Further studies with more comprehensive information on potential confounders are warranted. © 2016 Deutsche Dermatologische Gesellschaft (DDG). Published by John Wiley & Sons Ltd.

First experiences with in-vivo x-ray dark-field imaging of lung cancer in mice

NASA Astrophysics Data System (ADS)

Gromann, Lukas B.; Scherer, Kai; Yaroshenko, Andre; Bölükbas, Deniz A.; Hellbach, Katharina; Meinel, Felix G.; Braunagel, Margarita; Eickelberg, Oliver; Reiser, Maximilian F.; Pfeiffer, Franz; Meiners, Silke; Herzen, Julia

2017-03-01

Purpose: The purpose of the present study was to evaluate if x-ray dark-field imaging can help to visualize lung cancer in mice. Materials and Methods: The experiments were performed using mutant mice with high-grade adenocarcinomas. Eight animals with pulmonary carcinoma and eight control animals were imaged in radiography mode using a prototype small-animal x-ray dark-field scanner and three of the cancerous ones additionally in CT mode. After imaging, the lungs were harvested for histological analysis. To determine their diagnostic value, x-ray dark-field and conventional attenuation images were analyzed by three experienced readers in a blind assessment. Results radiographic imaging: The lung nodules were much clearer visualized on the dark-field radiographs compared to conventional radiographs. The loss of air-tissue interfaces in the tumor leads to a significant loss of x-ray scattering, reflected in a strong dark-field signal change. The difference between tumor and healthy tissue in terms of x-ray attenuation is significantly less pronounced. Furthermore, the signal from the overlaying structures on conventional radiographs complicates the detection of pulmonary carcinoma. Results CT imaging: The very first in-vivo CT-imaging results are quite promising as smaller tumors are often better visible in the dark-field images. However the imaging quality is still quite low, especially in the attenuation images due to un-optimized scanning parameters. Conclusion: We found a superior diagnostic performance of dark-field imaging compared to conventional attenuation based imaging, especially when it comes to the detection of small lung nodules. These results support the motivation to further develop this technique and translate it towards a clinical environment.

A Review of Clinical and Imaging Findings in Eosinophilic Lung Diseases.

PubMed

Bernheim, Adam; McLoud, Theresa

2017-05-01

The purpose of this article is to review the clinical and imaging findings associated with eosinophilic lung diseases. The spectrum of eosinophilic lung diseases comprises a diverse group of pulmonary disorders that have an association with tissue or peripheral eosinophilia. These diseases have varied clinical presentations and may be associated with several other abnormalities. Characteristic imaging findings are often detected with chest radiography, and CT best shows parenchymal abnormalities. The integration of clinical, radiologic, and pathologic findings facilitates diagnosis and directs appropriate treatment.

The impact of diagnostic imaging wait times on the prognosis of lung cancer.

PubMed

Byrne, Suzanne C; Barrett, Brendan; Bhatia, Rick

2015-02-01

This study was performed to determine whether gaps in patient flow from initial lung imaging to computed tomography (CT) guided lung biopsy in patients with non-small cell lung cancer (NSCLC) was associated with a change in tumour size, stage, and thus prognosis. All patients who had a CT-guided lung biopsy in 2009 (phase I) and in 2011 (phase II) with a pathologic diagnosis of primary lung cancer (NSCLC) at Eastern Health, Newfoundland, were identified. Dates of initial abnormal imaging, confirmatory CT (if performed), and CT-guided biopsy were recorded, along with tumour size and resulting T stage at each time point. In 2010, wait times for diagnostic imaging at Eastern Health were reduced. The stage and prognosis of NSCLC in 2009 was compared with 2011. In phase 1, there was a statistically significant increase in tumour size (mean difference, 0.67 cm; P < .0001) and stage (P < .0001) from initial image to biopsy. There was a moderate correlation between the time (in days) between the images and change in size (r = 0.33, P = .008) or stage (r = 0.26, P = .036). In phase II, the median wait time from initial imaging to confirmatory CT was reduced to 7.5 days (from 19 days). At this reduced wait time, there was no statistically significant increase in tumour size (mean difference, 0.02; P > .05) or stage (P > .05) from initial imaging to confirmatory CT. Delays in patient flow through diagnostic imaging resulted in an increase in tumour size and stage, with a negative impact on prognosis of NSCLC. This information contributed to the hiring of additional CT technologists and extended CT hours to decrease the wait time for diagnostic imaging. With reduced wait times, the prognosis of NSCLC was not adversely impacted as patients navigated through diagnostic imaging. Copyright © 2015 Canadian Association of Radiologists. All rights reserved.

Bronchiectasis

MedlinePlus

... Alternative Names Acquired bronchiectasis; Congenital bronchiectasis; Chronic lung disease - brochiectasis Patient Instructions Lung surgery - discharge Images Lungs Respiratory system References Chan ED, Iseman MD. Bronchiectasis. In: ...

LHX6, An Independent Prognostic Factor, Inhibits Lung Adenocarcinoma Progression through Transcriptional Silencing of β-catenin.

PubMed

Yang, Juntang; Han, Fei; Liu, Wenbin; Zhang, Mingqian; Huang, Yongsheng; Hao, Xianglin; Jiang, Xiao; Yin, Li; Chen, Hongqiang; Cao, Jia; Zhang, Huidong; Liu, Jinyi

2017-01-01

Introduction: Our previous study identified LIM homeobox domain 6 (LHX6) as a frequently epigenetically silenced tumor-suppressor gene in lung cancer. However, its clinical value has never been evaluated, and the in-depth anti-tumor mechanism remains unclear. Methods: Public database was used for lung cancer, lung adenocarcinoma and lung squamous carcinoma patients and tissue microarray data was used for lung adenocarcinoma patients to study prognostic outcome of LHX6 expression by Kaplan-Meier and Cox-regression analysis. In vitro proliferation, metastasis and in vivo nude mice model were used to evaluate the anti-tumor effect of LHX6 on lung adenocarcinoma cell lines. The mechanisms were explored using western blot, TOP/FOP flash assays and luciferase reporter assays. LHX6 expression and clinical stages data were collected from The Cancer Genome Atlas database (TCGA). Results: Expression of LHX6 was found to be a favorable independent prognostic factor for overall survival (OS) of total lung adenocarcinoma patients (P=0.014) and patients with negative lymph nodes status (P=0.014) but not related the prognostic outcome of lung squamous cell carcinoma patients. The expression status of LHX6 significantly correlated to histological grade (P<0.01), tumor size (P=0.026), lymph node status (P=0.039) and clinical stages (P<0.01) of lung adenocarcinoma patients. Functionally, LHX6 inhibited the proliferation and metastasis of lung adenocarcinoma cells in vitro and in vivo . Furthermore, LHX6 suppressed the Wnt/β-catenin pathway through transcriptionally silencing the expression of β-catenin, and the promoter region (-1161 bp to +27 bp) was crucial for its inhibitory activity. Conclusions: Our data indicate that the expression of LHX6 may serve as a favorable prognostic biomarker for lung adenocarcinoma patients and provide a novel mechanism of LHX6 involving in the tumorigenesis of lung adenocarcinoma.

LHX6, An Independent Prognostic Factor, Inhibits Lung Adenocarcinoma Progression through Transcriptional Silencing of β-catenin

PubMed Central

Yang, Juntang; Han, Fei; Liu, Wenbin; Zhang, Mingqian; Huang, Yongsheng; Hao, Xianglin; Jiang, Xiao; Yin, Li; Chen, Hongqiang; Cao, Jia; Zhang, Huidong; Liu, Jinyi

2017-01-01

Introduction: Our previous study identified LIM homeobox domain 6 (LHX6) as a frequently epigenetically silenced tumor-suppressor gene in lung cancer. However, its clinical value has never been evaluated, and the in-depth anti-tumor mechanism remains unclear. Methods: Public database was used for lung cancer, lung adenocarcinoma and lung squamous carcinoma patients and tissue microarray data was used for lung adenocarcinoma patients to study prognostic outcome of LHX6 expression by Kaplan-Meier and Cox-regression analysis. In vitro proliferation, metastasis and in vivo nude mice model were used to evaluate the anti-tumor effect of LHX6 on lung adenocarcinoma cell lines. The mechanisms were explored using western blot, TOP/FOP flash assays and luciferase reporter assays. LHX6 expression and clinical stages data were collected from The Cancer Genome Atlas database (TCGA). Results: Expression of LHX6 was found to be a favorable independent prognostic factor for overall survival (OS) of total lung adenocarcinoma patients (P=0.014) and patients with negative lymph nodes status (P=0.014) but not related the prognostic outcome of lung squamous cell carcinoma patients. The expression status of LHX6 significantly correlated to histological grade (P<0.01), tumor size (P=0.026), lymph node status (P=0.039) and clinical stages (P<0.01) of lung adenocarcinoma patients. Functionally, LHX6 inhibited the proliferation and metastasis of lung adenocarcinoma cells in vitro and in vivo. Furthermore, LHX6 suppressed the Wnt/β-catenin pathway through transcriptionally silencing the expression of β-catenin, and the promoter region (-1161 bp to +27 bp) was crucial for its inhibitory activity. Conclusions: Our data indicate that the expression of LHX6 may serve as a favorable prognostic biomarker for lung adenocarcinoma patients and provide a novel mechanism of LHX6 involving in the tumorigenesis of lung adenocarcinoma. PMID:28900494

ImmunoPET/MR imaging allows specific detection of Aspergillus fumigatus lung infection in vivo

PubMed Central

Rolle, Anna-Maria; Hasenberg, Mike; Thornton, Christopher R.; Solouk-Saran, Djamschid; Männ, Linda; Weski, Juliane; Maurer, Andreas; Fischer, Eliane; Spycher, Philipp R.; Schibli, Roger; Boschetti, Frederic; Stegemann-Koniszewski, Sabine; Bruder, Dunja; Severin, Gregory W.; Autenrieth, Stella E.; Krappmann, Sven; Davies, Genna; Pichler, Bernd J.; Gunzer, Matthias; Wiehr, Stefan

2016-01-01

Invasive pulmonary aspergillosis (IPA) is a life-threatening lung disease caused by the fungus Aspergillus fumigatus, and is a leading cause of invasive fungal infection-related mortality and morbidity in patients with hematological malignancies and bone marrow transplants. We developed and tested a novel probe for noninvasive detection of A. fumigatus lung infection based on antibody-guided positron emission tomography and magnetic resonance (immunoPET/MR) imaging. Administration of a [64Cu]DOTA-labeled A. fumigatus-specific monoclonal antibody (mAb), JF5, to neutrophil-depleted A. fumigatus-infected mice allowed specific localization of lung infection when combined with PET. Optical imaging with a fluorochrome-labeled version of the mAb showed colocalization with invasive hyphae. The mAb-based newly developed PET tracer [64Cu]DOTA-JF5 distinguished IPA from bacterial lung infections and, in contrast to [18F]FDG-PET, discriminated IPA from a general increase in metabolic activity associated with lung inflammation. To our knowledge, this is the first time that antibody-guided in vivo imaging has been used for noninvasive diagnosis of a fungal lung disease (IPA) of humans, an approach with enormous potential for diagnosis of infectious diseases and with potential for clinical translation. PMID:26787852

Clinical and Organizational Factors in the Initial Evaluation of Patients With Lung Cancer

PubMed Central

Jim Yeung, Sai-Ching; Tanoue, Lynn T.; Gould, Michael K.

2013-01-01

Background: This guideline is intended to provide an evidence-based approach to the initial evaluation of patients with known or suspected lung cancer. It also includes an assessment of the impact of timeliness of care and multidisciplinary teams on outcome. Methods: The applicable current medical literature was identified by a computerized search and evaluated using standardized methods. Recommendations were framed using the approach described by the Guidelines Oversight Committee of the American College of Chest Physicians. Data sources included MEDLINE and the Cochrane Database of Systematic Reviews. Results: Initial evaluation should include a thorough history and physical examination; CT imaging; pulmonary function tests; and hemoglobin, electrolyte, liver function, and calcium levels. Additional testing for distant metastases and paraneoplastic syndromes should be determined on the basis of these results. Paraneoplastic syndromes may have an adverse impact on cancer treatment, so they should be controlled rapidly with the goal of proceeding with definitive cancer treatment in a timely manner. Although the relationship between timeliness of care and survival is difficult to quantify, efforts to deliver timely care are reasonable and should be balanced with the need to attend to other dimensions of health-care quality (eg, safety, effectiveness, efficiency, equality, consistency with patient values and preferences). Quality care will require multiple disciplines. Although it is difficult to assess the impact, we suggest that a multidisciplinary team approach to care be used, particularly for patients requiring multimodality therapy. Conclusions: The initial evaluation of patients with lung cancer should include a thorough history and physical examination, pulmonary function tests, CT imaging, basic laboratory tests, and selective testing for distant metastases and paraneoplastic syndromes. PMID:23649435

Comparison of image segmentation of lungs using methods: connected threshold, neighborhood connected, and threshold level set segmentation

NASA Astrophysics Data System (ADS)

Amanda, A. R.; Widita, R.

2016-03-01

The aim of this research is to compare some image segmentation methods for lungs based on performance evaluation parameter (Mean Square Error (MSE) and Peak Signal Noise to Ratio (PSNR)). In this study, the methods compared were connected threshold, neighborhood connected, and the threshold level set segmentation on the image of the lungs. These three methods require one important parameter, i.e the threshold. The threshold interval was obtained from the histogram of the original image. The software used to segment the image here was InsightToolkit-4.7.0 (ITK). This research used 5 lung images to be analyzed. Then, the results were compared using the performance evaluation parameter determined by using MATLAB. The segmentation method is said to have a good quality if it has the smallest MSE value and the highest PSNR. The results show that four sample images match the criteria of connected threshold, while one sample refers to the threshold level set segmentation. Therefore, it can be concluded that connected threshold method is better than the other two methods for these cases.

75 FR 61765 - Agency Information Collection Activities: Submission for OMB Review; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2010-10-06

... September 17, 2010 (FR Doc. 201-023260), on page 57037, regarding the Black Lung Clinics Program Database... hours Database 15 1 15 20 300 Dated: September 28, 2010. Sahira Rafiullah, Director, Division of Policy...

Feasibility of Pathology-Correlated Lung Imaging for Accurate Target Definition of Lung Tumors

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

Stroom, Joep; Blaauwgeers, Hans; Baardwijk, Angela van

2007-09-01

Purpose: To accurately define the gross tumor volume (GTV) and clinical target volume (GTV plus microscopic disease spread) for radiotherapy, the pretreatment imaging findings should be correlated with the histopathologic findings. In this pilot study, we investigated the feasibility of pathology-correlated imaging for lung tumors, taking into account lung deformations after surgery. Methods and Materials: High-resolution multislice computed tomography (CT) and positron emission tomography (PET) scans were obtained for 5 patients who had non-small-cell lung cancer (NSCLC) before lobectomy. At the pathologic examination, the involved lung lobes were inflated with formalin, sectioned in parallel slices, and photographed, and microscopic sectionsmore » were obtained. The GTVs were delineated for CT and autocontoured at the 42% PET level, and both were compared with the histopathologic volumes. The CT data were subsequently reformatted in the direction of the macroscopic sections, and the corresponding fiducial points in both images were compared. Hence, the lung deformations were determined to correct the distances of microscopic spread. Results: In 4 of 5 patients, the GTV{sub CT} was, on average, 4 cm{sup 3} ({approx}53%) too large. In contrast, for 1 patient (with lymphangitis carcinomatosa), the GTV{sub CT} was 16 cm{sup 3} ({approx}40%) too small. The GTV{sub PET} was too small for the same patient. Regarding deformations, the volume of the well-inflated lung lobes on pathologic examination was still, on average, only 50% of the lobe volume on CT. Consequently, the observed average maximal distance of microscopic spread (5 mm) might, in vivo, be as large as 9 mm. Conclusions: Our results have shown that pathology-correlated lung imaging is feasible and can be used to improve target definition. Ignoring deformations of the lung might result in underestimation of the microscopic spread.« less

Influence of quartz exposure on lung cancer types in cases of lymph node-only silicosis and lung silicosis in German uranium miners.

PubMed

Mielke, Stefan; Taeger, Dirk; Weitmann, Kerstin; Brüning, Thomas; Hoffmann, Wolfgang

2018-05-04

Inhaled crystalline quartz is a carcinogen. Analyses show differences in the distribution of lung cancer types depending on the status of silicosis. Using 2,524 lung tumor cases from the WISMUT autopsy repository database, silicosis was differentiated into cases without silicosis in lung parenchyma and its lymph nodes, with lymph node-only silicosis, or with lung silicosis including lymph node silicosis. The proportions of adenocarcinoma, squamous cell carcinoma, and small-cell lung carcinoma mortality for increasing quartz exposures were estimated in a multinomial logistic regression model. The relative proportions of the lung cancer subtypes in lymph node-only silicosis were more similar to lung silicosis than without any silicosis. The results support the hypothesis that quartz-related carcinogenesis in case of lymph node-only silicosis is more similar to that in lung silicosis than in without silicosis.

An automated landmark-based elastic registration technique for large deformation recovery from 4-D CT lung images

NASA Astrophysics Data System (ADS)

Negahdar, Mohammadreza; Zacarias, Albert; Milam, Rebecca A.; Dunlap, Neal; Woo, Shiao Y.; Amini, Amir A.

2012-03-01

The treatment plan evaluation for lung cancer patients involves pre-treatment and post-treatment volume CT imaging of the lung. However, treatment of the tumor volume lung results in structural changes to the lung during the course of treatment. In order to register the pre-treatment volume to post-treatment volume, there is a need to find robust and homologous features which are not affected by the radiation treatment along with a smooth deformation field. Since airways are well-distributed in the entire lung, in this paper, we propose use of airway tree bifurcations for registration of the pre-treatment volume to the post-treatment volume. A dedicated and automated algorithm has been developed that finds corresponding airway bifurcations in both images. To derive the 3-D deformation field, a B-spline transformation model guided by mutual information similarity metric was used to guarantee the smoothness of the transformation while combining global information from bifurcation points. Therefore, the approach combines both global statistical intensity information with local image feature information. Since during normal breathing, the lung undergoes large nonlinear deformations, it is expected that the proposed method would also be applicable to large deformation registration between maximum inhale and maximum exhale images in the same subject. The method has been evaluated by registering 3-D CT volumes at maximum exhale data to all the other temporal volumes in the POPI-model data.

Real-time soft tissue motion estimation for lung tumors during radiotherapy delivery

PubMed Central

Rottmann, Joerg; Keall, Paul; Berbeco, Ross

2013-01-01

Purpose: To provide real-time lung tumor motion estimation during radiotherapy treatment delivery without the need for implanted fiducial markers or additional imaging dose to the patient. Methods: 2D radiographs from the therapy beam's-eye-view (BEV) perspective are captured at a frame rate of 12.8 Hz with a frame grabber allowing direct RAM access to the image buffer. An in-house developed real-time soft tissue localization algorithm is utilized to calculate soft tissue displacement from these images in real-time. The system is tested with a Varian TX linear accelerator and an AS-1000 amorphous silicon electronic portal imaging device operating at a resolution of 512 × 384 pixels. The accuracy of the motion estimation is verified with a dynamic motion phantom. Clinical accuracy was tested on lung SBRT images acquired at 2 fps. Results: Real-time lung tumor motion estimation from BEV images without fiducial markers is successfully demonstrated. For the phantom study, a mean tracking error <1.0 mm [root mean square (rms) error of 0.3 mm] was observed. The tracking rms accuracy on BEV images from a lung SBRT patient (≈20 mm tumor motion range) is 1.0 mm. Conclusions: The authors demonstrate for the first time real-time markerless lung tumor motion estimation from BEV images alone. The described system can operate at a frame rate of 12.8 Hz and does not require prior knowledge to establish traceable landmarks for tracking on the fly. The authors show that the geometric accuracy is similar to (or better than) previously published markerless algorithms not operating in real-time. PMID:24007146

The value of 99mTc-MAA SPECT/CT for lung shunt estimation in 90Y radioembolization: a phantom and patient study.

PubMed

Allred, Jonathan D; Niedbala, Jeremy; Mikell, Justin K; Owen, Dawn; Frey, Kirk A; Dewaraja, Yuni K

2018-06-15

A major toxicity concern in radioembolization therapy of hepatic malignancies is radiation-induced pneumonitis and sclerosis due to hepatopulmonary shunting of 90 Y microspheres. Currently, 99m Tc macroaggregated albumin ( 99m Tc-MAA) imaging is used to estimate the lung shunt fraction (LSF) prior to treatment. The aim of this study was to evaluate the accuracy/precision of LSF estimated from 99m Tc planar and SPECT/CT phantom imaging, and within this context, to compare the corresponding LSF and lung-absorbed dose values from 99m Tc-MAA patient studies. Additionally, LSFs from pre- and post-therapy imaging were compared. A liver/lung torso phantom filled with 99m Tc to achieve three lung shunt values was scanned by planar and SPECT/CT imaging with repeat acquisitions to assess accuracy and precision. To facilitate processing of patient data, a workflow that relies on SPECT and CT-based auto-contouring to define liver and lung volumes for the LSF calculation was implemented. Planar imaging-based LSF estimates for 40 patients, obtained from their medical records, were retrospectively compared with SPECT/CT imaging-based calculations with attenuation and scatter correction. Additionally, in a subset of 20 patients, the pre-therapy estimates were compared with 90 Y PET/CT-based measurements. In the phantom study, improved accuracy in LSF estimation was achieved using SPECT/CT with attenuation and scatter correction (within 13% of the true value) compared with planar imaging (up to 44% overestimation). The results in patients showed a similar trend with planar imaging significantly overestimating LSF compared to SPECT/CT. There was no correlation between lung shunt estimates and the delay between 99m Tc-MAA administration and scanning, but off-target extra hepatic uptake tended to be more likely in patients with a longer delay. The mean lung absorbed dose predictions for the 28 patients who underwent therapy was 9.3 Gy (range 1.3-29.4) for planar imaging and 3.2 Gy (range 0.4-13.4) for SPECT/CT. For the patients with post-therapy imaging, the mean LSF from 90 Y PET/CT was 1.0%, (range 0.3-2.8). This value was not significantly different from the mean LSF estimate from 99m Tc-MAA SPECT/CT (mean 1.0%, range 0.4-1.6; p = 0.968), but was significantly lower than the mean LSF estimate based on planar imaging (mean 4.1%, range 1.2-15.0; p = 0.0002). The improved accuracy demonstrated by the phantom study, agreement with 90 Y PET/CT in patient studies, and the practicality of using auto-contouring for liver/lung definition suggests that 99m Tc-MAA SPECT/CT with scatter and attenuation corrections should be used for lung shunt estimation prior to radioembolization.

Murine aggregation chimeras and wholemount imaging in airway stem cell biology.

PubMed

Rosewell, Ian R; Giangreco, Adam

2012-01-01

Local tissue stem cells are known to exist in mammalian lungs but their role in epithelial maintenance remains unclear. We therefore developed murine aggregation chimera and wholemount imaging techniques to assess the contribution of these cells to lung homeostasis and repair. In this chapter we provide further details regarding the generation of murine aggregation chimera mice and their subsequent use in wholemount lung imaging. We also describe methods related to the interpretation of this data that allows for quantitative assessment of airway stem cell activation versus quiescence. Using these techniques, it is possible to compare the growth and differentiation capacity of various lung epithelial cells in normal, repairing, and diseased states.

Effects of pure and hybrid iterative reconstruction algorithms on high-resolution computed tomography in the evaluation of interstitial lung disease.

PubMed

Katsura, Masaki; Sato, Jiro; Akahane, Masaaki; Mise, Yoko; Sumida, Kaoru; Abe, Osamu

2017-08-01

To compare image quality characteristics of high-resolution computed tomography (HRCT) in the evaluation of interstitial lung disease using three different reconstruction methods: model-based iterative reconstruction (MBIR), adaptive statistical iterative reconstruction (ASIR), and filtered back projection (FBP). Eighty-nine consecutive patients with interstitial lung disease underwent standard-of-care chest CT with 64-row multi-detector CT. HRCT images were reconstructed in 0.625-mm contiguous axial slices using FBP, ASIR, and MBIR. Two radiologists independently assessed the images in a blinded manner for subjective image noise, streak artifacts, and visualization of normal and pathologic structures. Objective image noise was measured in the lung parenchyma. Spatial resolution was assessed by measuring the modulation transfer function (MTF). MBIR offered significantly lower objective image noise (22.24±4.53, P<0.01 among all pairs, Student's t-test) compared with ASIR (39.76±7.41) and FBP (51.91±9.71). MTF (spatial resolution) was increased using MBIR compared with ASIR and FBP. MBIR showed improvements in visualization of normal and pathologic structures over ASIR and FBP, while ASIR was rated quite similarly to FBP. MBIR significantly improved subjective image noise (P<0.01 among all pairs, the sign test), and streak artifacts (P<0.01 each for MBIR vs. the other 2 image data sets). MBIR provides high-quality HRCT images for interstitial lung disease by reducing image noise and streak artifacts and improving spatial resolution compared with ASIR and FBP. Copyright © 2017 Elsevier B.V. All rights reserved.

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

Setio, Arnaud A. A., E-mail: arnaud.arindraadiyoso@radboudumc.nl; Jacobs, Colin; Gelderblom, Jaap

Purpose: Current computer-aided detection (CAD) systems for pulmonary nodules in computed tomography (CT) scans have a good performance for relatively small nodules, but often fail to detect the much rarer larger nodules, which are more likely to be cancerous. We present a novel CAD system specifically designed to detect solid nodules larger than 10 mm. Methods: The proposed detection pipeline is initiated by a three-dimensional lung segmentation algorithm optimized to include large nodules attached to the pleural wall via morphological processing. An additional preprocessing is used to mask out structures outside the pleural space to ensure that pleural and parenchymalmore » nodules have a similar appearance. Next, nodule candidates are obtained via a multistage process of thresholding and morphological operations, to detect both larger and smaller candidates. After segmenting each candidate, a set of 24 features based on intensity, shape, blobness, and spatial context are computed. A radial basis support vector machine (SVM) classifier was used to classify nodule candidates, and performance was evaluated using ten-fold cross-validation on the full publicly available lung image database consortium database. Results: The proposed CAD system reaches a sensitivity of 98.3% (234/238) and 94.1% (224/238) large nodules at an average of 4.0 and 1.0 false positives/scan, respectively. Conclusions: The authors conclude that the proposed dedicated CAD system for large pulmonary nodules can identify the vast majority of highly suspicious lesions in thoracic CT scans with a small number of false positives.« less

Image-Guided Hypofractionated Radiation Therapy With Stereotactic Body Radiation Therapy Boost and Combination Chemotherapy in Treating Patients With Stage II-III Non-Small Cell Lung Cancer That Cannot Be Removed By Surgery

ClinicalTrials.gov

2017-06-12

Adenocarcinoma of the Lung; Adenosquamous Cell Lung Cancer; Large Cell Lung Cancer; Recurrent Non-small Cell Lung Cancer; Squamous Cell Lung Cancer; Stage IIA Non-small Cell Lung Cancer; Stage IIB Non-small Cell Lung Cancer; Stage IIIA Non-small Cell Lung Cancer; Stage IIIB Non-small Cell Lung Cancer

Lung texture classification using bag of visual words

NASA Astrophysics Data System (ADS)

Asherov, Marina; Diamant, Idit; Greenspan, Hayit

2014-03-01

Interstitial lung diseases (ILD) refer to a group of more than 150 parenchymal lung disorders. High-Resolution Computed Tomography (HRCT) is the most essential imaging modality of ILD diagnosis. Nonetheless, classification of various lung tissue patterns caused by ILD is still regarded as a challenging task. The current study focuses on the classification of five most common categories of lung tissues of ILD in HRCT images: normal, emphysema, ground glass, fibrosis and micronodules. The objective of the research is to classify an expert-given annotated region of interest (AROI) using a bag of visual words (BoVW) framework. The images are divided into small patches and a collection of representative patches are defined as visual words. This procedure, termed dictionary construction, is performed for each individual lung texture category. The assumption is that different lung textures are represented by a different visual word distribution. The classification is performed using an SVM classifier with histogram intersection kernel. In the experiments, we use a dataset of 1018 AROIs from 95 patients. Classification using a leave-one-patient-out cross validation (LOPO CV) is used. Current classification accuracy obtained is close to 80%.

Lung parenchymal analysis on dynamic MRI in thoracic insufficiency syndrome to assess changes following surgical intervention

NASA Astrophysics Data System (ADS)

Jagadale, Basavaraj N.; Udupa, Jayaram K.; Tong, Yubing; Wu, Caiyun; McDonough, Joseph; Torigian, Drew A.; Campbell, Robert M.

2018-02-01

General surgeons, orthopedists, and pulmonologists individually treat patients with thoracic insufficiency syndrome (TIS). The benefits of growth-sparing procedures such as Vertical Expandable Prosthetic Titanium Rib (VEPTR)insertionfor treating patients with TIS have been demonstrated. However, at present there is no objective assessment metricto examine different thoracic structural components individually as to their roles in the syndrome, in contributing to dynamics and function, and in influencing treatment outcome. Using thoracic dynamic MRI (dMRI), we have been developing a methodology to overcome this problem. In this paper, we extend this methodology from our previous structural analysis approaches to examining lung tissue properties. We process the T2-weighted dMRI images through a series of steps involving 4D image construction of the acquired dMRI images, intensity non-uniformity correction and standardization of the 4D image, lung segmentation, and estimation of the parameters describing lung tissue intensity distributions in the 4D image. Based on pre- and post-operative dMRI data sets from 25 TIS patients (predominantly neuromuscular and congenital conditions), we demonstrate how lung tissue can be characterized by the estimated distribution parameters. Our results show that standardized T2-weighted image intensity values decrease from the pre- to post-operative condition, likely reflecting improved lung aeration post-operatively. In both pre- and post-operative conditions, the intensity values decrease also from end-expiration to end-inspiration, supporting the basic premise of our results.

Mutual information as a measure of image quality for 3D dynamic lung imaging with EIT

PubMed Central

Crabb, M G; Davidson, J L; Little, R; Wright, P; Morgan, A R; Miller, C A; Naish, J H; Parker, G J M; Kikinis, R; McCann, H; Lionheart, W R B

2014-01-01

We report on a pilot study of dynamic lung electrical impedance tomography (EIT) at the University of Manchester. Low-noise EIT data at 100 frames per second (fps) were obtained from healthy male subjects during controlled breathing, followed by magnetic resonance imaging (MRI) subsequently used for spatial validation of the EIT reconstruction. The torso surface in the MR image and electrode positions obtained using MRI fiducial markers informed the construction of a 3D finite element model extruded along the caudal-distal axis of the subject. Small changes in the boundary that occur during respiration were accounted for by incorporating the sensitivity with respect to boundary shape into a robust temporal difference reconstruction algorithm. EIT and MRI images were co-registered using the open source medical imaging software, 3D Slicer. A quantitative comparison of quality of different EIT reconstructions was achieved through calculation of the mutual information with a lung-segmented MR image. EIT reconstructions using a linear shape correction algorithm reduced boundary image artefacts, yielding better contrast of the lungs, and had 10% greater mutual information compared with a standard linear EIT reconstruction. PMID:24710978

Mutual information as a measure of image quality for 3D dynamic lung imaging with EIT.

PubMed

Crabb, M G; Davidson, J L; Little, R; Wright, P; Morgan, A R; Miller, C A; Naish, J H; Parker, G J M; Kikinis, R; McCann, H; Lionheart, W R B

2014-05-01

We report on a pilot study of dynamic lung electrical impedance tomography (EIT) at the University of Manchester. Low-noise EIT data at 100 frames per second were obtained from healthy male subjects during controlled breathing, followed by magnetic resonance imaging (MRI) subsequently used for spatial validation of the EIT reconstruction. The torso surface in the MR image and electrode positions obtained using MRI fiducial markers informed the construction of a 3D finite element model extruded along the caudal-distal axis of the subject. Small changes in the boundary that occur during respiration were accounted for by incorporating the sensitivity with respect to boundary shape into a robust temporal difference reconstruction algorithm. EIT and MRI images were co-registered using the open source medical imaging software, 3D Slicer. A quantitative comparison of quality of different EIT reconstructions was achieved through calculation of the mutual information with a lung-segmented MR image. EIT reconstructions using a linear shape correction algorithm reduced boundary image artefacts, yielding better contrast of the lungs, and had 10% greater mutual information compared with a standard linear EIT reconstruction.

Wolf in Sheep's Clothing: Primary Lung Cancer Mimicking Benign Entities.

PubMed

Snoeckx, Annemie; Dendooven, Amélie; Carp, Laurens; Desbuquoit, Damien; Spinhoven, Maarten J; Lauwers, Patrick; Van Schil, Paul E; van Meerbeeck, Jan P; Parizel, Paul M

2017-10-01

Lung cancer is the most common cancer worldwide. On imaging, it typically presents as mass or nodule. Recognition of these typical cases is often straightforward, whereas diagnosis of uncommon manifestations of primary lung cancer is far more challenging. Lung cancer can mimic a variety of benign entities, including pneumonia, lung abscess, postinfectious scarring, atelectasis, a mediastinal mass, emphysema and granulomatous diseases. Correlation with previous history, clinical and biochemical parameters is necessary in the assessment of these cases, but often aspecific and inconclusive. Whereas 18 F-fluorodeoxyglucose ( 18 F-FDG) Positron Emission Tomography is the cornerstone in staging of lung cancer, its role in diagnosis of these uncommon manifestations is less straightforward since benign entities can present with increased 18 F-FDG-uptake and, on the other hand, a number of these uncommon lung cancer manifestations do not exhibit increased uptake. Chest Computed Tomography (CT) is the imaging modality of choice for both lesion detection and characterization. In this pictorial review we present the wide imaging spectrum of CT-findings as well as radiologic-pathologic correlation of these uncommon lung cancer manifestations. Knowledge of the many faces of lung cancer is crucial for early diagnosis and subsequent treatment. A multidisciplinary approach in these cases is mandatory. Copyright © 2017 Elsevier B.V. All rights reserved.

EIT images of ventilation: what contributes to the resistivity changes?

PubMed

Zhang, Jie; Patterson, Robert P

2005-04-01

One promising application of electrical impedance tomography (EIT) is the monitoring of pulmonary ventilation and edema. Using three-dimensional (3D) finite difference human models as virtual phantoms, the factors that contribute to the observed lung resistivity changes in the EIT images were investigated. The results showed that the factors included not only tissue resistivity or vessel volume changes, but also chest expansion and tissue/organ movement. The chest expansion introduced artifacts in the center of the EIT images, ranging from -2% to 31% of the image magnitude. With the increase of simulated chest expansion, the percentage contribution of chest expansion relative to lung resistivity change in the EIT image remained relatively constant. The averaged resistivity changes in the lung regions caused by chest expansion ranged from 0.65% to 18.31%. Tissue/organ movement resulted in an increased resistivity in the lung region and in the center anterior region of EIT images. The increased resistivity with inspiration observed in the heart region was caused mainly by a drop in the heart position, which reduced the heart area at the electrode level and was replaced by the lung tissue with higher resistivity. This study indicates that for the analysis of EIT, data errors caused by chest expansion and tissue/organ movement need to be considered.

Western Australian cigarette smokers have fewer small lung nodules than North Americans on CT screening for lung cancer.

PubMed

Murray, C P; Wong, P M; Louw, J; Waterer, G W

2009-08-01

To determine the prevalence of small lung nodules on low-dose helical computed tomography (CT) in a Western Australian cohort of asymptomatic long-term cigarette smokers and to compare this with a large, similarly derived cohort of North Americans from the Mayo Clinic Lung Cancer Screening Trial. Forty-nine asymptomatic long-term cigarette smokers of minimum age 50 years underwent a low-dose 64-slice helical CT of the lungs. Images were viewed on a soft copy reporting station with thin section axial and coronal images, maximum intensity projection images, and advanced image manipulation tools. The prevalence of all nodules was 39%, significantly lower than the Mayo Clinic cohort prevalence of 51% (P < 0.01, Fisher's exact test), despite the use of more advanced imaging technology and image manipulation designed to increase the sensitivity for nodules. The prevalence of small nodules in asymptomatic long-term cigarette smokers in Western Australia is high, though significantly less than that found in a large study in North America. The authors postulate this is due to the relatively low rates of mycobacterium tuberculosis and soil-derived fungal pulmonary infections in Western Australia, as well as a lower degree of urban air pollution.

Multiscale multimodal fusion of histological and MRI volumes for characterization of lung inflammation

NASA Astrophysics Data System (ADS)

Rusu, Mirabela; Wang, Haibo; Golden, Thea; Gow, Andrew; Madabhushi, Anant

2013-03-01

Mouse lung models facilitate the investigation of conditions such as chronic inflammation which are associated with common lung diseases. The multi-scale manifestation of lung inflammation prompted us to use multi-scale imaging - both in vivo, ex vivo MRI along with ex vivo histology, for its study in a new quantitative way. Some imaging modalities, such as MRI, are non-invasive and capture macroscopic features of the pathology, while others, e.g. ex vivo histology, depict detailed structures. Registering such multi-modal data to the same spatial coordinates will allow the construction of a comprehensive 3D model to enable the multi-scale study of diseases. Moreover, it may facilitate the identification and definition of quantitative of in vivo imaging signatures for diseases and pathologic processes. We introduce a quantitative, image analytic framework to integrate in vivo MR images of the entire mouse with ex vivo histology of the lung alone, using lung ex vivo MRI as conduit to facilitate their co-registration. In our framework, we first align the MR images by registering the in vivo and ex vivo MRI of the lung using an interactive rigid registration approach. Then we reconstruct the 3D volume of the ex vivo histological specimen by efficient group wise registration of the 2D slices. The resulting 3D histologic volume is subsequently registered to the MRI volumes by interactive rigid registration, directly to the ex vivo MRI, and implicitly to in vivo MRI. Qualitative evaluation of the registration framework was performed by comparing airway tree structures in ex vivo MRI and ex vivo histology where airways are visible and may be annotated. We present a use case for evaluation of our co-registration framework in the context of studying chronic inammation in a diseased mouse.

3D segmentation of lung CT data with graph-cuts: analysis of parameter sensitivities

NASA Astrophysics Data System (ADS)

Cha, Jung won; Dunlap, Neal; Wang, Brian; Amini, Amir

2016-03-01

Lung boundary image segmentation is important for many tasks including for example in development of radiation treatment plans for subjects with thoracic malignancies. In this paper, we describe a method and parameter settings for accurate 3D lung boundary segmentation based on graph-cuts from X-ray CT data1. Even though previously several researchers have used graph-cuts for image segmentation, to date, no systematic studies have been performed regarding the range of parameter that give accurate results. The energy function in the graph-cuts algorithm requires 3 suitable parameter settings: K, a large constant for assigning seed points, c, the similarity coefficient for n-links, and λ, the terminal coefficient for t-links. We analyzed the parameter sensitivity with four lung data sets from subjects with lung cancer using error metrics. Large values of K created artifacts on segmented images, and relatively much larger value of c than the value of λ influenced the balance between the boundary term and the data term in the energy function, leading to unacceptable segmentation results. For a range of parameter settings, we performed 3D image segmentation, and in each case compared the results with the expert-delineated lung boundaries. We used simple 6-neighborhood systems for n-link in 3D. The 3D image segmentation took 10 minutes for a 512x512x118 ~ 512x512x190 lung CT image volume. Our results indicate that the graph-cuts algorithm was more sensitive to the K and λ parameter settings than to the C parameter and furthermore that amongst the range of parameters tested, K=5 and λ=0.5 yielded good results.

Optimizing dual-energy x-ray parameters for the ExacTrac clinical stereoscopic imaging system to enhance soft-tissue imaging.

PubMed

Bowman, Wesley A; Robar, James L; Sattarivand, Mike

2017-03-01

Stereoscopic x-ray image guided radiotherapy for lung tumors is often hindered by bone overlap and limited soft-tissue contrast. This study aims to evaluate the feasibility of dual-energy imaging techniques and to optimize parameters of the ExacTrac stereoscopic imaging system to enhance soft-tissue imaging for application to lung stereotactic body radiation therapy. Simulated spectra and a physical lung phantom were used to optimize filter material, thickness, tube potentials, and weighting factors to obtain bone subtracted dual-energy images. Spektr simulations were used to identify material in the atomic number range (3-83) based on a metric defined to separate spectra of high and low-energies. Both energies used the same filter due to time constraints of imaging in the presence of respiratory motion. The lung phantom contained bone, soft tissue, and tumor mimicking materials, and it was imaged with a filter thickness in the range of (0-0.7) mm and a kVp range of (60-80) for low energy and (120,140) for high energy. Optimal dual-energy weighting factors were obtained when the bone to soft-tissue contrast-to-noise ratio (CNR) was minimized. Optimal filter thickness and tube potential were achieved by maximizing tumor-to-background CNR. Using the optimized parameters, dual-energy images of an anthropomorphic Rando phantom with a spherical tumor mimicking material inserted in his lung were acquired and evaluated for bone subtraction and tumor contrast. Imaging dose was measured using the dual-energy technique with and without beam filtration and matched to that of a clinical conventional single energy technique. Tin was the material of choice for beam filtering providing the best energy separation, non-toxicity, and non-reactiveness. The best soft-tissue-weighted image in the lung phantom was obtained using 0.2 mm tin and (140, 60) kVp pair. Dual-energy images of the Rando phantom with the tin filter had noticeable improvement in bone elimination, tumor contrast, and noise content when compared to dual-energy imaging with no filtration. The surface dose was 0.52 mGy per each stereoscopic view for both clinical single energy technique and the dual-energy technique in both cases of with and without the tin filter. Dual-energy soft-tissue imaging is feasible without additional imaging dose using the ExacTrac stereoscopic imaging system with optimized acquisition parameters and no beam filtration. Addition of a single tin filter for both the high and low energies has noticeable improvements on dual-energy imaging with optimized parameters. Clinical implementation of a dual-energy technique on ExacTrac stereoscopic imaging could improve lung tumor visibility. © 2017 American Association of Physicists in Medicine.

Bilateral asymmetry prediction.

PubMed

Kostoff, Ronald Neil

2003-08-01

This study predicts asymmetries in lateral organ cancer incidence from text mining of the Medline database. Lung, kidney, teste, and ovary cancers were examined. For each cancer, Medline case report articles focused solely on (1) cancer of the right organ and (2) cancer of the left organ were retrieved. The ratio of right organ to left organ articles was compared to actual patient incidence data obtained from the National Cancer Institute's (NCI) SEER database for the period 1979-1998. The agreement between the Medline record ratios and the NCI's patient incidence data ratios ranged from within 3% for lung cancer to within 1% for teste and ovary cancer. This is the first known study to generate cancer lateral incidence asymmetries from the Medline database. The technique should be applicable to other diseases and other types of system asymmetries.

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

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

Tracking the engraftment and regenerative capabilities of transplanted lung stem cells using fluorescent nanodiamonds

NASA Astrophysics Data System (ADS)

Wu, Tsai-Jung; Tzeng, Yan-Kai; Chang, Wei-Wei; Cheng, Chi-An; Kuo, Yung; Chien, Chin-Hsiang; Chang, Huan-Cheng; Yu, John

2013-09-01

Lung stem/progenitor cells are potentially useful for regenerative therapy, for example in repairing damaged or lost lung tissue in patients. Several optical imaging methods and probes have been used to track how stem cells incorporate and regenerate themselves in vivo over time. However, these approaches are limited by photobleaching, toxicity and interference from background tissue autofluorescence. Here we show that fluorescent nanodiamonds, in combination with fluorescence-activated cell sorting, fluorescence lifetime imaging microscopy and immunostaining, can identify transplanted CD45-CD54+CD157+ lung stem/progenitor cells in vivo, and track their engraftment and regenerative capabilities with single-cell resolution. Fluorescent nanodiamond labelling did not eliminate the cells' properties of self-renewal and differentiation into type I and type II pneumocytes. Time-gated fluorescence imaging of tissue sections of naphthalene-injured mice indicates that the fluorescent nanodiamond-labelled lung stem/progenitor cells preferentially reside at terminal bronchioles of the lungs for 7 days after intravenous transplantation.

Development of a computer-aided detection system for lung cancer diagnosis

NASA Astrophysics Data System (ADS)

Suzuki, Hideo; Inaoka, Noriko; Takabatake, Hirotsugu; Mori, Masaki; Sasaoka, Soichi; Natori, Hiroshi; Suzuki, Akira

1992-06-01

This paper describes a modified system for automatic detection of lung nodules by means of chest x ray image processing techniques. The objective of the system is to help radiologists to improve their accuracy in cancer detection. It is known from retrospective studies of chest x- ray images that radiologists fail to detect about 30 percent of lung cancer cases. A computerized method for detecting lung nodules would be very useful for decreasing the proportion of such oversights. Our proposed system consists of five sub-systems, for image input, lung region determination, nodule detection, rule-based false-positive elimination, and statistical false-positive elimination. In an experiment with the modified system, using 30 lung cancer cases and 78 normal control cases, we obtained figures of 73.3 percent and 89.7 percent for the sensitivity and specificity of the system, respectively. The system has been developed to run on the IBM* PS/55* and IBM RISC System/6000* (RS/6000), and we give the processing time for each platform.

Algorithm of pulmonary emphysema extraction using thoracic 3-D CT images

NASA Astrophysics Data System (ADS)

Saita, Shinsuke; Kubo, Mitsuru; Kawata, Yoshiki; Niki, Noboru; Nakano, Yasutaka; Ohmatsu, Hironobu; Tominaga, Keigo; Eguchi, Kenji; Moriyama, Noriyuki

2008-03-01

Emphysema patients have the tendency to increase due to aging and smoking. Emphysematous disease destroys alveolus and to repair is impossible, thus early detection is essential. CT value of lung tissue decreases due to the destruction of lung structure. This CT value becomes lower than the normal lung- low density absorption region or referred to as Low Attenuation Area (LAA). So far, the conventional way of extracting LAA by simple thresholding has been proposed. However, the CT value of CT image fluctuates due to the measurement conditions, with various bias components such as inspiration, expiration and congestion. It is therefore necessary to consider these bias components in the extraction of LAA. We removed these bias components and we proposed LAA extraction algorithm. This algorithm has been applied to the phantom image. Then, by using the low dose CT(normal: 30 cases, obstructive lung disease: 26 cases), we extracted early stage LAA and quantitatively analyzed lung lobes using lung structure.

Real-time Non-invasive Spectral Imaging of Orthotopic Red Fluorescent Protein-expressing Lung Tumor Growth in Nude Mice.

PubMed

Zhang, Yong; Zhang, Nan; Zhao, Ming; Hoffman, Robert M

2015-07-01

Orthotopic implantation of cancer allows metastasis to occur. The most patient-like metastatic orthotopic models are developed with surgical orthotopic implantation using intact tissue in order to preserve the natural tissue structure of the tumor which contains both cancer cells and stroma. In the present study, we performed a simple thoracotomy by making an intercostal incision between the fourth and fifth ribs on the left side of the chest of nude mice. Lung tumor fragments expressing red fluorescent protein were then implanted on the left lung. It was possible to monitor tumor formation in the lung non-invasively by spectral imaging using the Maestro system with a liquid tunable filter. The model described here has high tumorigenicity in the lung (100%) and a low mortality rate (5%). This imageable nude mouse model using surgical orthotopic implantation of lung cancer will be useful for all types of longitudinal studies. Copyright© 2015 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

The seventh tumour-node-metastasis staging system for lung cancer: Sequel or prequel?

PubMed

van Meerbeeck, Jan P; Janssens, Annelies

2013-09-01

Anatomical cancer extent is an important predictor of prognosis and determines treatment choices. In non-small-cell lung cancer (NSCLC) the tumour-node-metastasis (TNM) classification developed by Pierre Denoix replaced in 1968 the Veterans Administration Lung cancer Group (VALG) classification, which was still in use for small-cell lung cancer (SCLC). Clifton Mountain suggested several improvements based on a database of mostly surgically treated United States (US) patients from a limited number of centres. This database was pivotal for a uniform reporting of lung cancer extent by the American Joint Committee of Cancer (AJCC) and the International Union against Cancer (IUCC), but it suffered increasingly from obsolete diagnostic and staging procedures and did not reflect new treatment modalities. Moreover, its findings were not externally validated in large Japanese and European databases, resulting in persisting controversies which could not be solved with the available database. The use of different mediastinal lymph-node maps in Japan, the (US) and Europe facilitated neither the exchange nor the comparison of treatment results. Peter Goldstraw, a United Kingdom (UK) thoracic surgeon, started the process of updating the sixth version in 1996 and brought it to a good end 10 years later. His goals were to improve the TNM system in lung cancer by addressing the ongoing controversies, to validate the modifications and additional descriptors, to validate the TNM for use in staging SCLC and carcinoid tumours, to propose a new uniform lymph-node map and to investigate the prognostic value of non-anatomical factors. A staging committee was formed within the International Association for the Study of Lung Cancer (IASLC) - which supervised the collection of the retrospective data from >100,000 patients with lung cancer - treated throughout the world between 1990 and 2000, analyse them with the help of solid statistics and validate externally with the Surveillance, Epidemiology and End Results (SEER) database. The ten modifications and the mediastinal lymph-node map - which were proposed in 2007 and adopted by the AJCC and IUCC in their respective seventh revision of the TNM system - were implemented as of 2010 and were rapidly adopted by the thoracic oncology community and cancer registries. As expected, not all controversies could be fully addressed, and the need for a prospective data set containing more granular information was felt early on. This data set of 25,000 consecutive incident cases will form the base for the eighth revision in 2017 and is currently being collected. Other threats are the role of stage migration and the increasing number of biological factors interfering with disease extent for prognostication. The latter issue will be addressed by the creation of a prognostic index, including several prognostic factors, of which stage will be one. For the time being, the seventh TNM classification is considered the gold standard for the description of disease extent, initial treatment allocation and the reporting of treatment results. The uniform use of the TNM descriptors and the lymph-node map by all involved in lung cancer care is to be considered a process indicator of quality.

Imaging mouse lung allograft rejection with 1H MRI

PubMed Central

Guo, Jinbang; Huang, Howard J.; Wang, Xingan; Wang, Wei; Ellison, Henry; Thomen, Robert P.; Gelman, Andrew E.; Woods, Jason C.

2014-01-01

Purpose To demonstrate that longitudinal, non-invasive monitoring via MRI can characterize acute cellular rejection (ACR) in mouse orthotopic lung allografts. Methods Nineteen Balb/c donor to C57BL/6 recipient orthotopic left lung transplants were performed, further divided into control-Ig vs anti-CD4/anti-CD8 treated groups. A two-dimensional multi-slice gradient-echo pulse sequence synchronized with ventilation was used on a small-animal MR scanner to acquire proton images of lung at post-operative days 3, 7 and 14, just before sacrifice. Lung volume and parenchymal signal were measured, and lung compliance was calculated as volume change per pressure difference between high and low pressures. Results Normalized parenchymal signal in the control-Ig allograft increased over time, with statistical significance between day 14 and day 3 post transplantation (0.046→0.789, P < 0.05), despite large inter-mouse variations; this was consistent with histopathologic evidence of rejection. Compliance of the control-Ig allograft decreased significantly over time (0.013→0.003, P < 0.05), but remained constant in mice treated with anti-CD4/anti-CD8 antibodies. Conclusion Lung allograft rejection in individual mice can be monitored by lung parenchymal signal changes and by lung compliance through MRI. Longitudinal imaging can help us better understand the time course of individual lung allograft rejection and response to treatment. PMID:24954886

Imaging mouse lung allograft rejection with (1)H MRI.

PubMed

Guo, Jinbang; Huang, Howard J; Wang, Xingan; Wang, Wei; Ellison, Henry; Thomen, Robert P; Gelman, Andrew E; Woods, Jason C

2015-05-01

To demonstrate that longitudinal, noninvasive monitoring via MRI can characterize acute cellular rejection in mouse orthotopic lung allografts. Nineteen Balb/c donor to C57BL/6 recipient orthotopic left lung transplants were performed, further divided into control-Ig versus anti-CD4/anti-CD8 treated groups. A two-dimensional multislice gradient-echo pulse sequence synchronized with ventilation was used on a small-animal MR scanner to acquire proton images of lung at postoperative days 3, 7, and 14, just before sacrifice. Lung volume and parenchymal signal were measured, and lung compliance was calculated as volume change per pressure difference between high and low pressures. Normalized parenchymal signal in the control-Ig allograft increased over time, with statistical significance between day 14 and day 3 posttransplantation (0.046→0.789; P < 0.05), despite large intermouse variations; this was consistent with histopathologic evidence of rejection. Compliance of the control-Ig allograft decreased significantly over time (0.013→0.003; P < 0.05), but remained constant in mice treated with anti-CD4/anti-CD8 antibodies. Lung allograft rejection in individual mice can be monitored by lung parenchymal signal changes and by lung compliance through MRI. Longitudinal imaging can help us better understand the time course of individual lung allograft rejection and response to treatment. © 2014 Wiley Periodicals, Inc.

Stereotactic Body Radiation Therapy Delivery in a Genetically Engineered Mouse Model of Lung Cancer.

PubMed

Du, Shisuo; Lockamy, Virginia; Zhou, Lin; Xue, Christine; LeBlanc, Justin; Glenn, Shonna; Shukla, Gaurav; Yu, Yan; Dicker, Adam P; Leeper, Dennis B; Lu, You; Lu, Bo

2016-11-01

To implement clinical stereotactic body radiation therapy (SBRT) using a small animal radiation research platform (SARRP) in a genetically engineered mouse model of lung cancer. A murine model of multinodular Kras-driven spontaneous lung tumors was used for this study. High-resolution cone beam computed tomography (CBCT) imaging was used to identify and target peripheral tumor nodules, whereas off-target lung nodules in the contralateral lung were used as a nonirradiated control. CBCT imaging helps localize tumors, facilitate high-precision irradiation, and monitor tumor growth. SBRT planning, prescription dose, and dose limits to normal tissue followed the guidelines set by RTOG protocols. Pathologic changes in the irradiated tumors were investigated using immunohistochemistry. The image guided radiation delivery using the SARRP system effectively localized and treated lung cancer with precision in a genetically engineered mouse model of lung cancer. Immunohistochemical data confirmed the precise delivery of SBRT to the targeted lung nodules. The 60 Gy delivered in 3 weekly fractions markedly reduced the proliferation index, Ki-67, and increased apoptosis per staining for cleaved caspase-3 in irradiated lung nodules. It is feasible to use the SARRP platform to perform dosimetric planning and delivery of SBRT in mice with lung cancer. This allows for preclinical studies that provide a rationale for clinical trials involving SBRT, especially when combined with immunotherapeutics. Copyright © 2016. Published by Elsevier Inc.

Volume-based response evaluation with consensual lesion selection: a pilot study by using cloud solutions and comparison to RECIST 1.1.

PubMed

Oubel, Estanislao; Bonnard, Eric; Sueoka-Aragane, Naoko; Kobayashi, Naomi; Charbonnier, Colette; Yamamichi, Junta; Mizobe, Hideaki; Kimura, Shinya

2015-02-01

Lesion volume is considered as a promising alternative to Response Evaluation Criteria in Solid Tumors (RECIST) to make tumor measurements more accurate and consistent, which would enable an earlier detection of temporal changes. In this article, we report the results of a pilot study aiming at evaluating the effects of a consensual lesion selection on volume-based response (VBR) assessments. Eleven patients with lung computed tomography scans acquired at three time points were selected from Reference Image Database to Evaluate Response to therapy in lung cancer (RIDER) and proprietary databases. Images were analyzed according to RECIST 1.1 and VBR criteria by three readers working in different geographic locations. Cloud solutions were used to connect readers and carry out a consensus process on the selection of lesions used for computing response. Because there are not currently accepted thresholds for computing VBR, we have applied a set of thresholds based on measurement variability (-35% and +55%). The benefit of this consensus was measured in terms of multiobserver agreement by using Fleiss kappa (κfleiss) and corresponding standard errors (SE). VBR after consensual selection of target lesions allowed to obtain κfleiss = 0.85 (SE = 0.091), which increases up to 0.95 (SE = 0.092), if an extra consensus on new lesions is added. As a reference, the agreement when applying RECIST without consensus was κfleiss = 0.72 (SE = 0.088). These differences were found to be statistically significant according to a z-test. An agreement on the selection of lesions allows reducing the inter-reader variability when computing VBR. Cloud solutions showed to be an interesting and feasible strategy for standardizing response evaluations, reducing variability, and increasing consistency of results in multicenter clinical trials. Copyright © 2015 AUR. Published by Elsevier Inc. All rights reserved.

MO-G-18C-03: Evaluation of Deformable Image Registration for Lung Motion Estimation Using Hyperpolarized Gas Tagging MRI

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

Huang, Q; Zhang, Y; Liu, Y

2014-06-15

Purpose: Hyperpolarized gas (HP) tagging MRI is a novel imaging technique for direct measurement of lung motion during breathing. This study aims to quantitatively evaluate the accuracy of deformable image registration (DIR) in lung motion estimation using HP tagging MRI as references. Methods: Three healthy subjects were imaged using the HP MR tagging, as well as a high-resolution 3D proton MR sequence (TrueFISP) at the end-of-inhalation (EOI) and the end-of-exhalation (EOE). Ground truth of lung motion and corresponding displacement vector field (tDVF) was derived from HP tagging MRI by manually tracking the displacement of tagging grids between EOI and EOE.more » Seven different DIR methods were applied to the high-resolution TrueFISP MR images (EOI and EOE) to generate the DIR-based DVFs (dDVF). The DIR methods include Velocity (VEL), MIM, Mirada, multi-grid B-spline from Elastix (MGB) and 3 other algorithms from DIRART toolbox (Double Force Demons (DFD), Improved Lucas-Kanade (ILK), and Iterative Optical Flow (IOF)). All registrations were performed by independent experts. Target registration error (TRE) was calculated as tDVF – dDVF. Analysis was performed for the entire lungs, and separately for the upper and lower lungs. Results: Significant differences between tDVF and dDVF were observed. Besides the DFD and IOF algorithms, all other dDVFs showed similarity in deformation magnitude distribution but away from the ground truth. The average TRE for entire lung ranged 2.5−23.7mm (mean=8.8mm), depending on the DIR method and subject's breathing amplitude. Larger TRE (13.3–23.7mm) was found in subject with larger breathing amplitude of 45.6mm. TRE was greater in lower lung (2.5−33.9 mm, mean=12.4mm) than that in upper lung (2.5−11.9 mm, mean=5.8mm). Conclusion: Significant differences were observed in lung motion estimation between the HP gas tagging MRI method and the DIR methods, especially when lung motion is large. Large variation among different DIR methods was also observed.« less

A deep learning method for early screening of lung cancer

NASA Astrophysics Data System (ADS)

Zhang, Kunpeng; Jiang, Huiqin; Ma, Ling; Gao, Jianbo; Yang, Xiaopeng

2018-04-01

Lung cancer is the leading cause of cancer-related deaths among men. In this paper, we propose a pulmonary nodule detection method for early screening of lung cancer based on the improved AlexNet model. In order to maintain the same image quality as the existing B/S architecture PACS system, we convert the original CT image into JPEG format image by analyzing the DICOM file firstly. Secondly, in view of the large size and complex background of CT chest images, we design the convolution neural network on basis of AlexNet model and sparse convolution structure. At last we train our models on the software named DIGITS which is provided by NVIDIA. The main contribution of this paper is to apply the convolutional neural network for the early screening of lung cancer and improve the screening accuracy by combining the AlexNet model with the sparse convolution structure. We make a series of experiments on the chest CT images using the proposed method, of which the sensitivity and specificity indicates that the method presented in this paper can effectively improve the accuracy of early screening of lung cancer and it has certain clinical significance at the same time.

Tracking boundary movement and exterior shape modelling in lung EIT imaging.

PubMed

Biguri, A; Grychtol, B; Adler, A; Soleimani, M

2015-06-01

Electrical impedance tomography (EIT) has shown significant promise for lung imaging. One key challenge for EIT in this application is the movement of electrodes during breathing, which introduces artefacts in reconstructed images. Various approaches have been proposed to compensate for electrode movement, but no comparison of these approaches is available. This paper analyses boundary model mismatch and electrode movement in lung EIT. The aim is to evaluate the extent to which various algorithms tolerate movement, and to determine if a patient specific model is required for EIT lung imaging. Movement data are simulated from a CT-based model, and image analysis is performed using quantitative figures of merit. The electrode movement is modelled based on expected values of chest movement and an extended Jacobian method is proposed to make use of exterior boundary tracking. Results show that a dynamical boundary tracking is the most robust method against any movement, but is computationally more expensive. Simultaneous electrode movement and conductivity reconstruction algorithms show increased robustness compared to only conductivity reconstruction. The results of this comparative study can help develop a better understanding of the impact of shape model mismatch and electrode movement in lung EIT.

Multi-frequency time-difference complex conductivity imaging of canine and human lungs using the KHU Mark1 EIT system.

PubMed

Kuen, Jihyeon; Woo, Eung Je; Seo, Jin Keun

2009-06-01

We evaluated the performance of the lately developed electrical impedance tomography (EIT) system KHU Mark1 through time-difference imaging experiments of canine and human lungs. We derived a multi-frequency time-difference EIT (mftdEIT) image reconstruction algorithm based on the concept of the equivalent homogeneous complex conductivity. Imaging experiments were carried out at three different frequencies of 10, 50 and 100 kHz with three different postures of right lateral, sitting (or prone) and left lateral positions. For three normal canine subjects, we controlled the ventilation using a ventilator at three tidal volumes of 100, 150 and 200 ml. Three human subjects were asked to breath spontaneously at a normal tidal volume. Real- and imaginary-part images of the canine and human lungs were reconstructed at three frequencies and three postures. Images showed different stages of breathing cycles and we could interpret them based on the understanding of the proposed mftdEIT image reconstruction algorithm. Time series of images were further analyzed by using the functional EIT (fEIT) method. Images of human subjects showed the gravity effect on air distribution in two lungs. In the canine subjects, the morphological change seems to dominate the gravity effect. We could also observe that two different types of ventilation should have affected the results. The KHU Mark1 EIT system is expected to provide reliable mftdEIT images of the human lungs. In terms of the image reconstruction algorithm, it would be worthwhile including the effects of three-dimensional current flows inside the human thorax. We suggest clinical trials of the KHU Mark1 for pulmonary applications.

Validation study of an interpolation method for calculating whole lung volumes and masses from reduced numbers of CT-images in ponies.

PubMed

Reich, H; Moens, Y; Braun, C; Kneissl, S; Noreikat, K; Reske, A

2014-12-01

Quantitative computer tomographic analysis (qCTA) is an accurate but time intensive method used to quantify volume, mass and aeration of the lungs. The aim of this study was to validate a time efficient interpolation technique for application of qCTA in ponies. Forty-one thoracic computer tomographic (CT) scans obtained from eight anaesthetised ponies positioned in dorsal recumbency were included. Total lung volume and mass and their distribution into four compartments (non-aerated, poorly aerated, normally aerated and hyperaerated; defined based on the attenuation in Hounsfield Units) were determined for the entire lung from all 5 mm thick CT-images, 59 (55-66) per animal. An interpolation technique validated for use in humans was then applied to calculate qCTA results for lung volumes and masses from only 10, 12, and 14 selected CT-images per scan. The time required for both procedures was recorded. Results were compared statistically using the Bland-Altman approach. The bias ± 2 SD for total lung volume calculated from interpolation of 10, 12, and 14 CT-images was -1.2 ± 5.8%, 0.1 ± 3.5%, and 0.0 ± 2.5%, respectively. The corresponding results for total lung mass were -1.1 ± 5.9%, 0.0 ± 3.5%, and 0.0 ± 3.0%. The average time for analysis of one thoracic CT-scan using the interpolation method was 1.5-2 h compared to 8 h for analysis of all images of one complete thoracic CT-scan. The calculation of pulmonary qCTA data by interpolation from 12 CT-images was applicable for equine lung CT-scans and reduced the time required for analysis by 75%. Copyright © 2014 Elsevier Ltd. All rights reserved.

Imaging Lung Function in Mice Using SPECT/CT and Per-Voxel Analysis

PubMed Central

Jobse, Brian N.; Rhem, Rod G.; McCurry, Cory A. J. R.; Wang, Iris Q.; Labiris, N. Renée

2012-01-01

Chronic lung disease is a major worldwide health concern but better tools are required to understand the underlying pathologies. Ventilation/perfusion (V/Q) single photon emission computed tomography (SPECT) with per-voxel analysis allows for non-invasive measurement of regional lung function. A clinically adapted V/Q methodology was used in healthy mice to investigate V/Q relationships. Twelve week-old mice were imaged to describe normal lung function while 36 week-old mice were imaged to determine how age affects V/Q. Mice were ventilated with Technegas™ and injected with 99mTc-macroaggregated albumin to trace ventilation and perfusion, respectively. For both processes, SPECT and CT images were acquired, co-registered, and quantitatively analyzed. On a per-voxel basis, ventilation and perfusion were moderately correlated (R = 0.58±0.03) in 12 week old animals and a mean log(V/Q) ratio of −0.07±0.01 and standard deviation of 0.36±0.02 were found, defining the extent of V/Q matching. In contrast, 36 week old animals had significantly increased levels of V/Q mismatching throughout the periphery of the lung. Measures of V/Q were consistent across healthy animals and differences were observed with age demonstrating the capability of this technique in quantifying lung function. Per-voxel analysis and the ability to non-invasively assess lung function will aid in the investigation of chronic lung disease models and drug efficacy studies. PMID:22870297

Metachronous and Synchronous Presentation of Acute Myeloid Leukemia and Lung Cancer

PubMed Central

Varadarajan, Ramya; Ford, LaurieAnn; Sait, Sheila NJ; Block, AnneMarie W.; Barcos, Maurice; Wallace, Paul K.; Ramnath, Nithya; Wang, Eunice S.; Wetzler, Meir

2009-01-01

Smoking is associated with both acute myeloid leukemia (AML) and lung cancer. We therefore searched our database for concomitant presentation of AML and lung cancer. Among 775 AML cases and 5225 lung cancer cases presenting to Roswell Park Cancer Institute between the years January 1992 and May 2008 we found 12 (1.5% of AML cases; 0.23% of lung cancer cases) cases (seven metachronous and five synchronous) with AML and lung cancer. All but one patient were smokers. There were no unique characteristic of either AML or lung cancer in these patients. Nine patients succumbed to AML, one died from an unrelated cause while undergoing treatment for AML, one died of lung cancer and one patient is alive after allogeneic transplantation for AML. In summary, this study supports the need for effective smoking cessation programs. PMID:19181380

Qualitative and quantitative interpretation of computed tomography of the lungs in healthy neonatal foals.

PubMed

Lascola, Kara M; O'Brien, Robert T; Wilkins, Pamela A; Clark-Price, Stuart C; Hartman, Susan K; Mitchell, Mark A

2013-09-01

To qualitatively describe lung CT images obtained from sedated healthy equine neonates (≤ 14 days of age), use quantitative analysis of CT images to characterize attenuation and distribution of gas and tissue volumes within the lungs, and identify differences between lung characteristics of foals ≤ 7 days of age and foals > 7 days of age. 10 Standardbred foals between 2.5 and 13 days of age. Foals were sedated with butorphanol, midazolam, and propofol and positioned in sternal recumbency for thoracic CT. Image analysis software was used to exclude lung from nonlung structures. Lung attenuation was measured in Hounsfield units (HU) for analysis of whole lung and regional changes in attenuation and lung gas and tissue components. Degree of lung attenuation was classified as follows: hyperinflated or emphysema, -1,000 to -901 HU; well aerated, -900 to -501 HU; poorly aerated, -500 to -101 HU; and nonaerated, > -100 HU. Qualitative evidence of an increase in lung attenuation and patchy alveolar patterns in the ventral lung region were more pronounced in foals ≤ 7 days of age than in older foals. Quantitative analysis revealed that mean ± SD lung attenuation was greater in foals ≤ 7 days of age (-442 ± 28 HU) than in foals > 7 days of age (-521 ± 24 HU). Lung aeration and gas volumes were lower than in other regions ventrally and in the mid lung region caudal to the heart. CONCLUSIONS AND CLINICAL RELEVANCE-Identified radiographic patterns and changes in attenuation were most consistent with atelectasis and appeared more severe in foals ≤ 7 days of age than in older neonatal foals. Recognition of these changes may have implications for accurate CT interpretation in sedated neonatal foals with pulmonary disease.

Diagnosing lung cancer using coherent anti-Stokes Raman scattering microscopy

NASA Astrophysics Data System (ADS)

Gao, Liang; Yang, Yaliang; Xing, Jiong; Thrall, Michael J.; Wang, Zhiyong; Li, Fuhai; Luo, Pengfei; Wong, Kelvin K.; Zhao, Hong; Wong, Stephen T. C.

2011-03-01

Lung carcinoma is the most prevalent type of cancer in the world, and it is responsible for more deaths than other types of cancer. During diagnosis, a pathologist primarily aims to differentiate small cell carcinoma from non-small cell carcinoma on biopsy and cytology specimens, which is time consuming due to the time required for tissue processing and staining. To speed up the diagnostic process, we investigated the feasibility of using coherent anti-Stokes Raman scattering (CARS) microscopy as a label-free strategy to image lung lesions and differentiate subtypes of lung cancers. Different mouse lung cancer models were developed by injecting human lung cancer cell lines, including adenocarcinoma, squamous cell carcinoma, and small cell carcinoma, into lungs of the nude mice. CARS images were acquired from normal lung tissues and different subtypes of cancer lesions ex vivo using intrinsic contrasts from symmetric CH2 bonds. These images showed good correlation with the hematoxylin and eosin (H&E) stained sections from the same tissue samples with regard to cell size, density, and cell-cell distance. These features are routinely used in diagnosing lung lesions. Our results showed that the CARS technique is capable of providing a visualizable platform to differentiate different kinds of lung cancers using the same pathological features without histological staining and thus has the potential to serve as a more efficient examination tool for diagnostic pathology. In addition, incorporating with suitable fiber-optic probes would render the CARS technique as a promising approach for in vivo diagnosis of lung cancer.

A preclinical rodent model of acute radiation-induced lung injury after ablative focal irradiation reflecting clinical stereotactic body radiotherapy.

PubMed

Hong, Zhen-Yu; Lee, Hae-June; Choi, Won Hoon; Lee, Yoon-Jin; Eun, Sung Ho; Lee, Jung Il; Park, Kwangwoo; Lee, Ji Min; Cho, Jaeho

2014-07-01

In a previous study, we established an image-guided small-animal micro-irradiation system mimicking clinical stereotactic body radiotherapy (SBRT). The goal of this study was to develop a rodent model of acute phase lung injury after ablative irradiation. A radiation dose of 90 Gy was focally delivered to the left lung of C57BL/6 mice using a small animal stereotactic irradiator. At days 1, 3, 5, 7, 9, 11 and 14 after irradiation, the lungs were perfused with formalin for fixation and paraffin sections were stained with hematoxylin and eosin (H&E) and Masson's trichrome. At days 7 and 14 after irradiation, micro-computed tomography (CT) images of the lung were taken and lung functional measurements were performed with a flexiVent™ system. Gross morphological injury was evident 9 days after irradiation of normal lung tissues and dynamic sequential events occurring during the acute phase were validated by histopathological analysis. CT images of the mouse lungs indicated partial obstruction located in the peripheral area of the left lung. Significant alteration in inspiratory capacity and tissue damping were detected on day 14 after irradiation. An animal model of radiation-induced lung injury (RILI) in the acute phase reflecting clinical stereotactic body radiotherapy was established and validated with histopathological and functional analysis. This model enhances our understanding of the dynamic sequential events occurring in the acute phase of radiation-induced lung injury induced by ablative dose focal volume irradiation.

Diagnostic Imaging and Newer Modalities for Thoracic Diseases: PET/Computed Tomographic Imaging and Endobronchial Ultrasound for Staging and Its Implication for Lung Cancer.

PubMed

Counts, Sarah J; Kim, Anthony W

2017-08-01

Modalities to detect and characterize lung cancer are generally divided into those that are invasive [endobronchial ultrasound (EBUS), esophageal ultrasound (EUS), and electromagnetic navigational bronchoscopy (ENMB)] versus noninvasive [chest radiography (CXR), computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI)]. This chapter describes these modalities, the literature supporting their use, and delineates what tests to use to best evaluate the patient with lung cancer. Copyright © 2017 Elsevier Inc. All rights reserved.

Live dynamic imaging of caveolae pumping targeted antibody rapidly and specifically across endothelium in the lung.

PubMed

Oh, Phil; Borgström, Per; Witkiewicz, Halina; Li, Yan; Borgström, Bengt J; Chrastina, Adrian; Iwata, Koji; Zinn, Kurt R; Baldwin, Richard; Testa, Jacqueline E; Schnitzer, Jan E

2007-03-01

How effectively and quickly endothelial caveolae can transcytose in vivo is unknown, yet critical for understanding their function and potential clinical utility. Here we use quantitative proteomics to identify aminopeptidase P (APP) concentrated in caveolae of lung endothelium. Electron microscopy confirms this and shows that APP antibody targets nanoparticles to caveolae. Dynamic intravital fluorescence microscopy reveals that targeted caveolae operate effectively as pumps, moving antibody within seconds from blood across endothelium into lung tissue, even against a concentration gradient. This active transcytosis requires normal caveolin-1 expression. Whole body gamma-scintigraphic imaging shows rapid, specific delivery into lung well beyond that achieved by standard vascular targeting. This caveolar trafficking in vivo may underscore a key physiological mechanism for selective transvascular exchange and may provide an enhanced delivery system for imaging agents, drugs, gene-therapy vectors and nanomedicines. 'In vivo proteomic imaging' as described here integrates organellar proteomics with multiple imaging techniques to identify an accessible target space that includes the transvascular pumping space of the caveola.

Lung Cancer: Posttreatment Imaging: Radiation Therapy and Imaging Findings.

PubMed

Benveniste, Marcelo F; Welsh, James; Viswanathan, Chitra; Shroff, Girish S; Betancourt Cuellar, Sonia L; Carter, Brett W; Marom, Edith M

2018-05-01

In this review, we discuss the different radiation delivery techniques available to treat non-small cell lung cancer, typical radiologic manifestations of conventional radiotherapy, and different patterns of lung injury and temporal evolution of the newer radiotherapy techniques. More sophisticated techniques include intensity-modulated radiotherapy, stereotactic body radiotherapy, proton therapy, and respiration-correlated computed tomography or 4-dimensional computed tomography for radiotherapy planning. Knowledge of the radiation treatment plan and technique, the completion date of radiotherapy, and the temporal evolution of radiation-induced lung injury is important to identify expected manifestations of radiation-induced lung injury and differentiate them from tumor recurrence or infection. Published by Elsevier Inc.

The role of hyperpolarized 129xenon in MR imaging of pulmonary function

PubMed Central

Ebner, Lukas; Kammerman, Jeff; Driehuys, Bastiaan; Schiebler, Mark L.; Cadman, Robert V.; Fain, Sean B.

2016-01-01

In the last two decades, functional imaging of the lungs using hyperpolarized noble gases has entered the clinical stage. Both helium (3 He) and xenon (129Xe) gas have been thoroughly investigated for their ability to assess both the global and regional patterns of lung ventilation. With advances in polarizer technology and the current transition towards the widely available 129Xe gas, this method is ready for translation to the clinic. Currently, hyperpolarized (HP) noble gas lung MRI is limited to selected academic institutions; yet, the promising results from initial clinical trials have drawn the attention of the pulmonary medicine community. HP 129Xe MRI provides not only 3-dimensional ventilation imaging, but also unique capabilities for probing regional lung physiology. In this review article, we aim to (1) provide a brief overview of current ventilation MR imaging techniques, (2) emphasize the role of HP 129Xe MRI within the array of different imaging strategies, (3) discuss the unique imaging possibilities with HP 129Xe MRI, and (4) propose clinical applications. PMID:27707585

Lung lobe modeling and segmentation with individualized surface meshes

NASA Astrophysics Data System (ADS)

Blaffert, Thomas; Barschdorf, Hans; von Berg, Jens; Dries, Sebastian; Franz, Astrid; Klinder, Tobias; Lorenz, Cristian; Renisch, Steffen; Wiemker, Rafael

2008-03-01

An automated segmentation of lung lobes in thoracic CT images is of interest for various diagnostic purposes like the quantification of emphysema or the localization of tumors within the lung. Although the separating lung fissures are visible in modern multi-slice CT-scanners, their contrast in the CT-image often does not separate the lobes completely. This makes it impossible to build a reliable segmentation algorithm without additional information. Our approach uses general anatomical knowledge represented in a geometrical mesh model to construct a robust lobe segmentation, which even gives reasonable estimates of lobe volumes if fissures are not visible at all. The paper describes the generation of the lung model mesh including lobes by an average volume model, its adaptation to individual patient data using a special fissure feature image, and a performance evaluation over a test data set showing an average segmentation accuracy of 1 to 3 mm.

MMP-13 In-Vivo Molecular Imaging Reveals Early Expression in Lung Adenocarcinoma

PubMed Central

Salaün, Mathieu; Peng, Jing; Hensley, Harvey H.; Roder, Navid; Flieder, Douglas B.; Houlle-Crépin, Solène; Abramovici-Roels, Olivia; Sabourin, Jean-Christophe; Thiberville, Luc; Clapper, Margie L.

2015-01-01

Introduction Several matrix metalloproteinases (MMPs) are overexpressed in lung cancer and may serve as potential targets for the development of bioactivable probes for molecular imaging. Objective To characterize and monitor the activity of MMPs during the progression of lung adenocarcinoma. Methods K-rasLSL-G12D mice were imaged serially during the development of adenocarcinomas using fluorescence molecular tomography (FMT) and a probe specific for MMP-2, -3, -9 and -13. Lung tumors were identified using FMT and MRI co-registration, and the probe concentration in each tumor was assessed at each time-point. The expression of Mmp2, -3, -9, -13 was quantified by qRT-PCR using RNA isolated from microdissected tumor cells. Immunohistochemical staining of overexpressed MMPs in animals was assessed on human lung tumors. Results In mice, 7 adenomas and 5 adenocarcinomas showed an increase in fluorescent signal on successive FMT scans, starting between weeks 4 and 8. qRT-PCR assays revealed significant overexpression of only Mmp-13 in mice lung tumors. In human tumors, a high MMP-13 immunostaining index was found in tumor cells from invasive lesions (24/27), but in none of the non-invasive (0/4) (p=0.001). Conclusion MMP-13 is detected in early pulmonary invasive adenocarcinomas and may be a potential target for molecular imaging of lung cancer. PMID:26193700

Comparison of 4-Dimensional Computed Tomography Ventilation With Nuclear Medicine Ventilation-Perfusion Imaging: A Clinical Validation Study

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

Vinogradskiy, Yevgeniy, E-mail: yevgeniy.vinogradskiy@ucdenver.edu; Koo, Phillip J.; Castillo, Richard

Purpose: Four-dimensional computed tomography (4DCT) ventilation imaging provides lung function information for lung cancer patients undergoing radiation therapy. Before 4DCT-ventilation can be implemented clinically it needs to be validated against an established imaging modality. The purpose of this work was to compare 4DCT-ventilation to nuclear medicine ventilation, using clinically relevant global metrics and radiologist observations. Methods and Materials: Fifteen lung cancer patients with 16 sets of 4DCT and nuclear medicine ventilation-perfusion (VQ) images were used for the study. The VQ-ventilation images were acquired in planar mode using Tc-99m-labeled diethylenetriamine-pentaacetic acid aerosol inhalation. 4DCT data, spatial registration, and a density-change-based modelmore » were used to compute a 4DCT-based ventilation map for each patient. The percent ventilation was calculated in each lung and each lung third for both the 4DCT and VQ-ventilation scans. A nuclear medicine radiologist assessed the VQ and 4DCT scans for the presence of ventilation defects. The VQ and 4DCT-based images were compared using regional percent ventilation and radiologist clinical observations. Results: Individual patient examples demonstrate good qualitative agreement between the 4DCT and VQ-ventilation scans. The correlation coefficients were 0.68 and 0.45, using the percent ventilation in each individual lung and lung third, respectively. Using radiologist-noted presence of ventilation defects and receiver operating characteristic analysis, the sensitivity, specificity, and accuracy of the 4DCT-ventilation were 90%, 64%, and 81%, respectively. Conclusions: The current work compared 4DCT with VQ-based ventilation using clinically relevant global metrics and radiologist observations. We found good agreement between the radiologist's assessment of the 4DCT and VQ-ventilation images as well as the percent ventilation in each lung. The agreement lessened when the data were analyzed on a regional level. Our study presents an important step for the integration of 4DCT-ventilation into thoracic clinical practice.« less

Importance of preoperative imaging with 64-row three-dimensional multidetector computed tomography for safer video-assisted thoracic surgery in lung cancer.

PubMed

Akiba, Tadashi; Marushima, Hideki; Harada, Junta; Kobayashi, Susumu; Morikawa, Toshiaki

2009-01-01

Video-assisted thoracic surgery (VATS) has recently been adopted for complicated anatomical lung resections. During these thoracoscopic procedures, surgeons view the operative field on a two-dimensional (2-D) video monitor and cannot palpate the organ directly, thus frequently encountering anatomical difficulties. This study aimed to estimate the usefulness of preoperative three-dimensional (3-D) imaging of thoracic organs. We compared the preoperative 64-row three-dimensional multidetector computed tomography (3DMDCT) findings of lung cancer-affected thoracic organs to the operative findings. In comparison to the operative findings, the branches of pulmonary arteries, veins, and bronchi were well defined in the 3D-MDCT images of 27 patients. 3D-MDCT imaging is useful for preoperatively understanding the individual thoracic anatomy in lung cancer surgery. This modality can therefore contribute to safer anatomical pulmonary operations, especially in VATS.

Investigation of four-dimensional computed tomography-based pulmonary ventilation imaging in patients with emphysematous lung regions

NASA Astrophysics Data System (ADS)

Yamamoto, Tokihiro; Kabus, Sven; Klinder, Tobias; Lorenz, Cristian; von Berg, Jens; Blaffert, Thomas; Loo, Billy W., Jr.; Keall, Paul J.

2011-04-01

A pulmonary ventilation imaging technique based on four-dimensional (4D) computed tomography (CT) has advantages over existing techniques. However, physiologically accurate 4D-CT ventilation imaging has not been achieved in patients. The purpose of this study was to evaluate 4D-CT ventilation imaging by correlating ventilation with emphysema. Emphysematous lung regions are less ventilated and can be used as surrogates for low ventilation. We tested the hypothesis: 4D-CT ventilation in emphysematous lung regions is significantly lower than in non-emphysematous regions. Four-dimensional CT ventilation images were created for 12 patients with emphysematous lung regions as observed on CT, using a total of four combinations of two deformable image registration (DIR) algorithms: surface-based (DIRsur) and volumetric (DIRvol), and two metrics: Hounsfield unit (HU) change (VHU) and Jacobian determinant of deformation (VJac), yielding four ventilation image sets per patient. Emphysematous lung regions were detected by density masking. We tested our hypothesis using the one-tailed t-test. Visually, different DIR algorithms and metrics yielded spatially variant 4D-CT ventilation images. The mean ventilation values in emphysematous lung regions were consistently lower than in non-emphysematous regions for all the combinations of DIR algorithms and metrics. VHU resulted in statistically significant differences for both DIRsur (0.14 ± 0.14 versus 0.29 ± 0.16, p = 0.01) and DIRvol (0.13 ± 0.13 versus 0.27 ± 0.15, p < 0.01). However, VJac resulted in non-significant differences for both DIRsur (0.15 ± 0.07 versus 0.17 ± 0.08, p = 0.20) and DIRvol (0.17 ± 0.08 versus 0.19 ± 0.09, p = 0.30). This study demonstrated the strong correlation between the HU-based 4D-CT ventilation and emphysema, which indicates the potential for HU-based 4D-CT ventilation imaging to achieve high physiologic accuracy. A further study is needed to confirm these results.

Measurement of the Inter-Rater Reliability Rate Is Mandatory for Improving the Quality of a Medical Database: Experience with the Paulista Lung Cancer Registry.

PubMed

Lauricella, Leticia L; Costa, Priscila B; Salati, Michele; Pego-Fernandes, Paulo M; Terra, Ricardo M

2018-06-01

Database quality measurement should be considered a mandatory step to ensure an adequate level of confidence in data used for research and quality improvement. Several metrics have been described in the literature, but no standardized approach has been established. We aimed to describe a methodological approach applied to measure the quality and inter-rater reliability of a regional multicentric thoracic surgical database (Paulista Lung Cancer Registry). Data from the first 3 years of the Paulista Lung Cancer Registry underwent an audit process with 3 metrics: completeness, consistency, and inter-rater reliability. The first 2 methods were applied to the whole data set, and the last method was calculated using 100 cases randomized for direct auditing. Inter-rater reliability was evaluated using percentage of agreement between the data collector and auditor and through calculation of Cohen's κ and intraclass correlation. The overall completeness per section ranged from 0.88 to 1.00, and the overall consistency was 0.96. Inter-rater reliability showed many variables with high disagreement (>10%). For numerical variables, intraclass correlation was a better metric than inter-rater reliability. Cohen's κ showed that most variables had moderate to substantial agreement. The methodological approach applied to the Paulista Lung Cancer Registry showed that completeness and consistency metrics did not sufficiently reflect the real quality status of a database. The inter-rater reliability associated with κ and intraclass correlation was a better quality metric than completeness and consistency metrics because it could determine the reliability of specific variables used in research or benchmark reports. This report can be a paradigm for future studies of data quality measurement. Copyright © 2018 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

Clinical value of miR-182-5p in lung squamous cell carcinoma: a study combining data from TCGA, GEO, and RT-qPCR validation.

PubMed

Luo, Jie; Shi, Ke; Yin, Shu-Ya; Tang, Rui-Xue; Chen, Wen-Jie; Huang, Lin-Zhen; Gan, Ting-Qing; Cai, Zheng-Wen; Chen, Gang

2018-04-10

MiR-182-5p, as a member of miRNA family, can be detected in lung cancer and plays an important role in lung cancer. To explore the clinical value of miR-182-5p in lung squamous cell carcinoma (LUSC) and to unveil the molecular mechanism of LUSC. The clinical value of miR-182-5p in LUSC was investigated by collecting and calculating data from The Cancer Genome Atlas (TCGA) database, the Gene Expression Omnibus (GEO) database, and real-time quantitative polymerase chain reaction (RT-qPCR). Twelve prediction platforms were used to predict the target genes of miR-182-5p. Protein-protein interaction (PPI) networks and gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to explore the molecular mechanism of LUSC. The expression of miR-182-5p was significantly over-expressed in LUSC than in non-cancerous tissues, as evidenced by various approaches, including the TCGA database, GEO microarrays, RT-qPCR, and a comprehensive meta-analysis of 501 LUSC cases and 148 non-cancerous cases. Furthermore, a total of 81 potential target genes were chosen from the union of predicted genes and the TCGA database. GO and KEGG analyses demonstrated that the target genes are involved in pathways related to biological processes. PPIs revealed the relationships between these genes, with EPAS1, PRKCE, NR3C1, and RHOB being located in the center of the PPI network. MiR-182-5p upregulation greatly contributes to LUSC and may serve as a biomarker in LUSC.

Comparison of sensitivity of lung nodule detection between radiologists and technologists on low-dose CT lung cancer screening images

PubMed Central

Kakinuma, R; Ashizawa, K; Kobayashi, T; Fukushima, A; Hayashi, H; Kondo, T; Machida, M; Matsusako, M; Minami, K; Oikado, K; Okuda, M; Takamatsu, S; Sugawara, M; Gomi, S; Muramatsu, Y; Hanai, K; Muramatsu, Y; Kaneko, M; Tsuchiya, R; Moriyama, N

2012-01-01

Objectives The objective of this study was to compare the sensitivity of detection of lung nodules on low-dose screening CT images between radiologists and technologists. Methods 11 radiologists and 10 technologists read the low-dose screening CT images of 78 subjects. On images with a slice thickness of 5 mm, there were 60 lung nodules that were ≥5 mm in diameter: 26 nodules with pure ground-glass opacity (GGO), 7 nodules with mixed ground-glass opacity (GGO with a solid component) and 27 solid nodules. On images with a slice thickness of 2 mm, 69 lung nodules were ≥5 mm in diameter: 35 pure GGOs, 7 mixed GGOs and 27 solid nodules. The 21 observers read screening CT images of 5-mm slice thickness at first; then, 6 months later, they read screening CT images of 2-mm slice thickness from the 78 subjects. Results The differences in the mean sensitivities of detection of the pure GGOs, mixed GGOs and solid nodules between radiologists and technologists were not statistically significant, except for the case of solid nodules; the p-values of the differences for pure GGOs, mixed GGOs and solid nodules on the CT images with 5-mm slice thickness were 0.095, 0.461 and 0.005, respectively, and the corresponding p-values on CT images of 2-mm slice thickness were 0.971, 0.722 and 0.0037, respectively. Conclusion Well-trained technologists may contribute to the detection of pure and mixed GGOs ≥5 mm in diameter on low-dose screening CT images. PMID:22919013

Clinical applications of textural analysis in non-small cell lung cancer.

PubMed

Phillips, Iain; Ajaz, Mazhar; Ezhil, Veni; Prakash, Vineet; Alobaidli, Sheaka; McQuaid, Sarah J; South, Christopher; Scuffham, James; Nisbet, Andrew; Evans, Philip

2018-01-01

Lung cancer is the leading cause of cancer mortality worldwide. Treatment pathways include regular cross-sectional imaging, generating large data sets which present intriguing possibilities for exploitation beyond standard visual interpretation. This additional data mining has been termed "radiomics" and includes semantic and agnostic approaches. Textural analysis (TA) is an example of the latter, and uses a range of mathematically derived features to describe an image or region of an image. Often TA is used to describe a suspected or known tumour. TA is an attractive tool as large existing image sets can be submitted to diverse techniques for data processing, presentation, interpretation and hypothesis testing with annotated clinical outcomes. There is a growing anthology of published data using different TA techniques to differentiate between benign and malignant lung nodules, differentiate tissue subtypes of lung cancer, prognosticate and predict outcome and treatment response, as well as predict treatment side effects and potentially aid radiotherapy planning. The aim of this systematic review is to summarize the current published data and understand the potential future role of TA in managing lung cancer.

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.

Comprehensive Clinical Staging for Resectable Lung Cancer: Clinicopathological Correlations and the Role of Brain MRI.

PubMed

Vernon, Jordyn; Andruszkiewicz, Nicole; Schneider, Laura; Schieman, Colin; Finley, Christian J; Shargall, Yaron; Fahim, Christine; Farrokhyar, Forough; Hanna, Waël C

2016-11-01

In our model of comprehensive clinical staging (CCS) for lung cancer, patients with a computerized tomography scan of the chest and upper abdomen not showing distant metastases will then routinely undergo whole body positron emission tomography/computerized tomography and magnetic resonance imaging (MRI) of the brain before any therapeutic decision. Our aim was to determine the accuracy of CCS and the value of brain MRI in this population. A retrospective analysis of a prospectively entered database was performed for all patients who underwent lung cancer resection from January 2012 to June 2014. Demographics, clinical and pathological stage (seventh edition of the American Joint Committee on Cancer/Union for International Cancer Control tumor, node, and metastasis staging manual), and costs of staging were collected. Correlation between clinical and pathological stage was determined. Of 315 patients with primary lung cancer, 55.6% were female and the mean age was 70 ± 9.6 years. When correlation was analyzed without consideration for substages A and B, 49.8% of patients (158 of 315) were staged accurately, 39.7% (125 of 315) were overstaged, and 10.5% (32 of 315) were understaged. Only 4.7% of patients (15 of 315) underwent surgery without appropriate neoadjuvant treatment. Preoperative brain MRI detected asymptomatic metastases in four of 315 patients (1.3%). At a median postoperative follow-up of 19 months (range 6-43), symptomatic brain metastases developed in seven additional patients. The total cost of CCS in Canadian dollars was $367,292 over the study period, with $117,272 (31.9%) going toward brain MRI. CCS is effective for patients with resectable lung cancer, with less than 5% of patients being denied appropriate systemic treatment before surgery. Brain MRI is a low-yield and high-cost intervention in this population, and its routine use should be questioned. Copyright © 2016 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.

Time-series analysis of lung texture on bone-suppressed dynamic chest radiograph for the evaluation of pulmonary function: a preliminary study

NASA Astrophysics Data System (ADS)

Tanaka, Rie; Matsuda, Hiroaki; Sanada, Shigeru

2017-03-01

The density of lung tissue changes as demonstrated on imagery is dependent on the relative increases and decreases in the volume of air and lung vessels per unit volume of lung. Therefore, a time-series analysis of lung texture can be used to evaluate relative pulmonary function. This study was performed to assess a time-series analysis of lung texture on dynamic chest radiographs during respiration, and to demonstrate its usefulness in the diagnosis of pulmonary impairments. Sequential chest radiographs of 30 patients were obtained using a dynamic flat-panel detector (FPD; 100 kV, 0.2 mAs/pulse, 15 frames/s, SID = 2.0 m; Prototype, Konica Minolta). Imaging was performed during respiration, and 210 images were obtained over 14 seconds. Commercial bone suppression image-processing software (Clear Read Bone Suppression; Riverain Technologies, Miamisburg, Ohio, USA) was applied to the sequential chest radiographs to create corresponding bone suppression images. Average pixel values, standard deviation (SD), kurtosis, and skewness were calculated based on a density histogram analysis in lung regions. Regions of interest (ROIs) were manually located in the lungs, and the same ROIs were traced by the template matching technique during respiration. Average pixel value effectively differentiated regions with ventilatory defects and normal lung tissue. The average pixel values in normal areas changed dynamically in synchronization with the respiratory phase, whereas those in regions of ventilatory defects indicated reduced variations in pixel value. There were no significant differences between ventilatory defects and normal lung tissue in the other parameters. We confirmed that time-series analysis of lung texture was useful for the evaluation of pulmonary function in dynamic chest radiography during respiration. Pulmonary impairments were detected as reduced changes in pixel value. This technique is a simple, cost-effective diagnostic tool for the evaluation of regional pulmonary function.

Lung PET scan

MedlinePlus

... PET - chest; PET - lung; PET - tumor imaging; PET/CT - lung; Solitary pulmonary nodule - PET ... minutes. PET scans are performed along with a CT scan. This is because the combined information from ...

Quantitative evaluation of anatomical noise in chest digital tomosynthesis, digital radiography, and computed tomography

NASA Astrophysics Data System (ADS)

Lee, D.; Choi, S.; Lee, H.; Kim, D.; Choi, S.; Kim, H.-J.

2017-04-01

Lung cancer is currently the worldwide leading cause of death from cancer. Thus, detection of lung cancer at its early stages is critical for improving the survival rate of patients. Chest digital tomosynthesis (CDT) is a recently developed imaging modality, combining many advantages of digital radiography (DR) and computed tomography (CT). This method has the potential to be widely used in the clinical setting. In this study, we introduce a developed CDT R/F system and compare its image quality with those of DR and CT, especially with respect to anatomical noise and lung nodule conspicuity, for LUNGMAN phantoms. The developed CDT R/F system consists of a CsI scintillator flat panel detector, X-ray tube, and tomosynthesis data acquisition geometry. For CDT R/F imaging, 41 projections were acquired at different angles, over the ± 20° angular range, in a linear translation geometry. To evaluate the clinical effectiveness of the CDT R/F system, the acquired images were compared with CT (Philips brilliance CT 64, Philips healthcare, U.S.) and DR (ADR-M, LISTEM, Korea) phantom images in terms of the anatomical noise power spectrum (aNPS). DR images exhibited low conspicuity for a small-size lung nodule, while CDT R/F and CT exhibited relatively high sensitivity for all lung nodule sizes. The aNPS of the CDT R/F system was better than that of DR, by resolving anatomical overlapping problems. In conclusion, the developed CDT R/F system is likely to contribute to early diagnosis of lung cancer, while requiring a relatively low patient dose, compared with CT.

Cost-effectiveness of lung cancer screening and treatment methods: a systematic review of systematic reviews.

PubMed

Azar, Farbod Ebadifard; Azami-Aghdash, Saber; Pournaghi-Azar, Fatemeh; Mazdaki, Alireza; Rezapour, Aziz; Ebrahimi, Parvin; Yousefzadeh, Negar

2017-06-19

Due to extensive literature in the field of lung cancer and their heterogeneous results, the aim of this study was to systematically review of systematic reviews studies which reviewed the cost-effectiveness of various lung cancer screening and treatment methods. In this systematic review of systematic reviews study, required data were collected searching the following key words which selected from Mesh: "lung cancer", "lung oncology", "lung Carcinoma", "lung neoplasm", "lung tumors", "cost- effectiveness", "systematic review" and "Meta-analysis". The following databases were searched: PubMed, Cochrane Library electronic databases, Google Scholar, and Scopus. Two reviewers (RA and A-AS) evaluated the articles according to the checklist of "assessment of multiple systematic reviews" (AMSTAR) tool. Overall, information of 110 papers was discussed in eight systematic reviews. Authors focused on cost-effectiveness of lung cancer treatments in five systematic reviews. Targeted therapy options (bevacizumab, Erlotinib and Crizotinib) show an acceptable cost-effectiveness. Results of three studies failed to show cost-effectiveness of screening methods. None of the studies had used the meta-analysis method. The Quality of Health Economic Studies (QHES) tool and Drummond checklist were mostly used in assessing the quality of articles. Most perspective was related to the Payer (64 times) and the lowest was related to Social (11times). Most cases referred to Incremental analysis (82%) and also the lowest point of referral was related to Discounting (in 49% of the cases). The average quality score of included studies was calculated 9.2% from 11. Targeted therapy can be an option for the treatment of lung cancer. Evaluation of the cost-effectiveness of computerized tomographic colonography (CTC) in lung cancer screening is recommended. The perspective of the community should be more taken into consideration in studies of cost-effectiveness. Paying more attention to the topic of Discounting will be necessary in the studies.

Imaging for lung physiology: What do we wish we could measure?

PubMed Central

Buxton, Richard B.

2012-01-01

The role of imaging as a tool for investigating lung physiology is growing at an accelerating pace. Looking forward, we wished to identify unresolved issues in lung physiology that might realistically be addressed by imaging methods in development or imaging approaches that could be considered. The role of imaging is framed in terms of the importance of good spatial and temporal resolution and the types of questions that could be addressed as these technical capabilities improve. Recognizing that physiology is fundamentally a quantitative science, a recurring emphasis is on the need for imaging methods that provide reliable measurements of specific physiological parameters. The topics included necessarily reflect our perspective on what are interesting questions and are not meant to be a comprehensive review. Nevertheless, we hope that this essay will be a spur to physiologists to think about how imaging could usefully be applied in their research and to physical scientists developing new imaging methods to attack challenging questions imaging could potentially answer. PMID:22582217

Statistical modeling of 4D respiratory lung motion using diffeomorphic image registration.

PubMed

Ehrhardt, Jan; Werner, René; Schmidt-Richberg, Alexander; Handels, Heinz

2011-02-01

Modeling of respiratory motion has become increasingly important in various applications of medical imaging (e.g., radiation therapy of lung cancer). Current modeling approaches are usually confined to intra-patient registration of 3D image data representing the individual patient's anatomy at different breathing phases. We propose an approach to generate a mean motion model of the lung based on thoracic 4D computed tomography (CT) data of different patients to extend the motion modeling capabilities. Our modeling process consists of three steps: an intra-subject registration to generate subject-specific motion models, the generation of an average shape and intensity atlas of the lung as anatomical reference frame, and the registration of the subject-specific motion models to the atlas in order to build a statistical 4D mean motion model (4D-MMM). Furthermore, we present methods to adapt the 4D mean motion model to a patient-specific lung geometry. In all steps, a symmetric diffeomorphic nonlinear intensity-based registration method was employed. The Log-Euclidean framework was used to compute statistics on the diffeomorphic transformations. The presented methods are then used to build a mean motion model of respiratory lung motion using thoracic 4D CT data sets of 17 patients. We evaluate the model by applying it for estimating respiratory motion of ten lung cancer patients. The prediction is evaluated with respect to landmark and tumor motion, and the quantitative analysis results in a mean target registration error (TRE) of 3.3 ±1.6 mm if lung dynamics are not impaired by large lung tumors or other lung disorders (e.g., emphysema). With regard to lung tumor motion, we show that prediction accuracy is independent of tumor size and tumor motion amplitude in the considered data set. However, tumors adhering to non-lung structures degrade local lung dynamics significantly and the model-based prediction accuracy is lower in these cases. The statistical respiratory motion model is capable of providing valuable prior knowledge in many fields of applications. We present two examples of possible applications in radiation therapy and image guided diagnosis.

A feasibility study of automatic lung nodule detection in chest digital tomosynthesis with machine learning based on support vector machine

NASA Astrophysics Data System (ADS)

Lee, Donghoon; Kim, Ye-seul; Choi, Sunghoon; Lee, Haenghwa; Jo, Byungdu; Choi, Seungyeon; Shin, Jungwook; Kim, Hee-Joung

2017-03-01

The chest digital tomosynthesis(CDT) is recently developed medical device that has several advantage for diagnosing lung disease. For example, CDT provides depth information with relatively low radiation dose compared to computed tomography (CT). However, a major problem with CDT is the image artifacts associated with data incompleteness resulting from limited angle data acquisition in CDT geometry. For this reason, the sensitivity of lung disease was not clear compared to CT. In this study, to improve sensitivity of lung disease detection in CDT, we developed computer aided diagnosis (CAD) systems based on machine learning. For design CAD systems, we used 100 cases of lung nodules cropped images and 100 cases of normal lesion cropped images acquired by lung man phantoms and proto type CDT. We used machine learning techniques based on support vector machine and Gabor filter. The Gabor filter was used for extracting characteristics of lung nodules and we compared performance of feature extraction of Gabor filter with various scale and orientation parameters. We used 3, 4, 5 scales and 4, 6, 8 orientations. After extracting features, support vector machine (SVM) was used for classifying feature of lesions. The linear, polynomial and Gaussian kernels of SVM were compared to decide the best SVM conditions for CDT reconstruction images. The results of CAD system with machine learning showed the capability of automatically lung lesion detection. Furthermore detection performance was the best when Gabor filter with 5 scale and 8 orientation and SVM with Gaussian kernel were used. In conclusion, our suggested CAD system showed improving sensitivity of lung lesion detection in CDT and decide Gabor filter and SVM conditions to achieve higher detection performance of our developed CAD system for CDT.

Dosimetric feasibility of 4DCT-ventilation imaging guided proton therapy for locally advanced non-small-cell lung cancer.

PubMed

Huang, Qijie; Jabbour, Salma K; Xiao, Zhiyan; Yue, Ning; Wang, Xiao; Cao, Hongbin; Kuang, Yu; Zhang, Yin; Nie, Ke

2018-04-25

The principle aim of this study is to incorporate 4DCT ventilation imaging into functional treatment planning that preserves high-functioning lung with both double scattering and scanning beam techniques in proton therapy. Eight patients with locally advanced non-small-cell lung cancer were included in this study. Deformable image registration was performed for each patient on their planning 4DCTs and the resultant displacement vector field with Jacobian analysis was used to identify the high-, medium- and low-functional lung regions. Five plans were designed for each patient: a regular photon IMRT vs. anatomic proton plans without consideration of functional ventilation information using double scattering proton therapy (DSPT) and intensity modulated proton therapy (IMPT) vs. functional proton plans with avoidance of high-functional lung using both DSPT and IMPT. Dosimetric parameters were compared in terms of tumor coverage, plan heterogeneity, and avoidance of normal tissues. Our results showed that both DSPT and IMPT plans gave superior dose advantage to photon IMRTs in sparing low dose regions of the total lung in terms of V5 (volume receiving 5Gy). The functional DSPT only showed marginal benefit in sparing high-functioning lung in terms of V5 or V20 (volume receiving 20Gy) compared to anatomical plans. Yet, the functional planning in IMPT delivery, can further reduce the low dose in high-functioning lung without degrading the PTV dosimetric coverages, compared to anatomical proton planning. Although the doses to some critical organs might increase during functional planning, the necessary constraints were all met. Incorporating 4DCT ventilation imaging into functional proton therapy is feasible. The functional proton plans, in intensity modulated proton delivery, are effective to further preserve high-functioning lung regions without degrading the PTV coverage.

Computerized scheme for detection of diffuse lung diseases on CR chest images

NASA Astrophysics Data System (ADS)

Pereira, Roberto R., Jr.; Shiraishi, Junji; Li, Feng; Li, Qiang; Doi, Kunio

2008-03-01

We have developed a new computer-aided diagnostic (CAD) scheme for detection of diffuse lung disease in computed radiographic (CR) chest images. One hundred ninety-four chest images (56 normals and 138 abnormals with diffuse lung diseases) were used. The 138 abnormal cases were classified into three levels of severity (34 mild, 60 moderate, and 44 severe) by an experienced chest radiologist with use of five different patterns, i.e., reticular, reticulonodular, nodular, air-space opacity, and emphysema. In our computerized scheme, the first moment of the power spectrum, the root-mean-square variation, and the average pixel value were determined for each region of interest (ROI), which was selected automatically in the lung fields. The average pixel value and its dependence on the location of the ROI were employed for identifying abnormal patterns due to air-space opacity or emphysema. A rule-based method was used for determining three levels of abnormality for each ROI (0: normal, 1: mild, 2: moderate, and 3: severe). The distinction between normal lungs and abnormal lungs with diffuse lung disease was determined based on the fractional number of abnormal ROIs by taking into account the severity of abnormalities. Preliminary results indicated that the area under the ROC curve was 0.889 for the 44 severe cases, 0.825 for the 104 severe and moderate cases, and 0.794 for all cases. We have identified a number of problems and reasons causing false positives on normal cases, and also false negatives on abnormal cases. In addition, we have discussed potential approaches for improvement of our CAD scheme. In conclusion, the CAD scheme for detection of diffuse lung diseases based on texture features extracted from CR chest images has the potential to assist radiologists in their interpretation of diffuse lung diseases.

Evaluation of a pulmonary strain model by registration of dynamic CT scans

NASA Astrophysics Data System (ADS)

Pomeroy, Marc; Liang, Zhengrong; Brehm, Anthony

2017-03-01

Idiopathic pulmonary fibrosis (IPF) is a chronic fibrotic lung disease that develops in adults without any known cause. It is an interstitial lung disease in which the lung tissue becomes scarred and stiffens, ultimately leading to respiratory failure. This disease currently has no cure with limited treatment options, leading to an average survival time of 3-5 years after diagnosis. In this paper we employ a mathematical model simulating the lung parenchyma as hexagons with elastic forces applied to connecting vertices and opposing vertices. Using an image registration algorithm, we obtain trajectories of 4D-CT scans of a healthy patient, and one suffering from IPF. Converting the image trajectories into a hexagonal lattice, we fit the model parameters to match the respiratory motion seen for both patients across multiple image slices. We found the model could decently describe the healthy lung slices, with a minimum average error between corresponding vertices to be 1.66 mm. For the fibrotic lung slices the model was less accurate, maintaining a higher average error across all slices. Using the optimized parameters, we apply the forces predicted from the model using the image trajectory positions for each phase. Although the error is large, the spring constant values determined for the fibrotic patient were not as high as we expected, and more often than not determined to be lower than corresponding healthy lung slices. However, the net force distribution for some of those slices was still found to be greater than the healthy lung counterparts. Other modifications to the model, including additional directional components and which vertices were receiving with the limited sample size available, a clear distinction between the healthy and fibrotic lung cannot yet be made by this model.

SU-E-J-90: Lobar-Level Lung Ventilation Analysis Using 4DCT and Deformable Image Registration

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

Du, K; Bayouth, J; Patton, T

2015-06-15

Purpose: To assess regional changes in human lung ventilation and mechanics using four-dimensional computed tomography (4DCT) and deformable image registration. This work extends our prior analysis of the entire lung to a lobe-based analysis. Methods: 4DCT images acquired from 20 patients prior to radiation therapy (RT) were used for this analysis. Jacobian ventilation and motion maps were computed from the displacement field after deformable image registration between the end of expiration breathing phase and the end of inspiration breathing phase. The lobes were manually segmented on the reference phase by a medical physicist expert. The voxel-by-voxel ventilation and motion magnitudemore » for all subjects were grouped by lobes and plotted into cumulative voxel frequency curves respectively. In addition, to eliminate the effect of different breathing efforts across subjects, we applied the inter-subject equivalent lung volume (ELV) method on a subset of the cohort and reevaluated the lobar ventilation. Results: 95% of voxels in the lung are expanding during inspiration. However, some local regions of lung tissue show far more expansion than others. The greatest expansion with respiration occurs within the lower lobes; between exhale and inhale the median expansion in lower lobes is approximately 15%, while the median expansion in upper lobes is 10%. This appears to be driven by a subset of lung tissues within the lobe that have greater expansion; twice the number of voxels in the lower lobes (20%) expand by > 30% when compared to the upper lobes (10%). Conclusion: Lung ventilation and motion show significant difference on the lobar level. There are different lobar fractions of driving voxels that contribute to the major expansion of the lung. This work was supported by NIH grant CA166703.« less

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

Du, Shisuo; Lockamy, Virginia; Zhou, Lin

Purpose: To implement clinical stereotactic body radiation therapy (SBRT) using a small animal radiation research platform (SARRP) in a genetically engineered mouse model of lung cancer. Methods and Materials: A murine model of multinodular Kras-driven spontaneous lung tumors was used for this study. High-resolution cone beam computed tomography (CBCT) imaging was used to identify and target peripheral tumor nodules, whereas off-target lung nodules in the contralateral lung were used as a nonirradiated control. CBCT imaging helps localize tumors, facilitate high-precision irradiation, and monitor tumor growth. SBRT planning, prescription dose, and dose limits to normal tissue followed the guidelines set by RTOGmore » protocols. Pathologic changes in the irradiated tumors were investigated using immunohistochemistry. Results: The image guided radiation delivery using the SARRP system effectively localized and treated lung cancer with precision in a genetically engineered mouse model of lung cancer. Immunohistochemical data confirmed the precise delivery of SBRT to the targeted lung nodules. The 60 Gy delivered in 3 weekly fractions markedly reduced the proliferation index, Ki-67, and increased apoptosis per staining for cleaved caspase-3 in irradiated lung nodules. Conclusions: It is feasible to use the SARRP platform to perform dosimetric planning and delivery of SBRT in mice with lung cancer. This allows for preclinical studies that provide a rationale for clinical trials involving SBRT, especially when combined with immunotherapeutics.« less

Collection Fusion Using Bayesian Estimation of a Linear Regression Model in Image Databases on the Web.

ERIC Educational Resources Information Center

Kim, Deok-Hwan; Chung, Chin-Wan

2003-01-01

Discusses the collection fusion problem of image databases, concerned with retrieving relevant images by content based retrieval from image databases distributed on the Web. Focuses on a metaserver which selects image databases supporting similarity measures and proposes a new algorithm which exploits a probabilistic technique using Bayesian…

Lung Hot Spot Without Corresponding Computed Tomography Abnormality on Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography: Artifactual or Real, Iatrogenic or Pathologic?

PubMed

Liu, Yiyan

Focal lung uptake without corresponding lesions or abnormalities on computed tomography (CT) scan poses a dilemma in the interpretation of fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT). A limited number of case reports have previously suggested an artifactual or iatrogenic nature of the uptake. In the present study, 8 relevant cases were included within a retrospective search of the database. Medical records were reviewed for follow-up radiological and pathologic information. In 7 of 8 cases with focal increased FDG uptake but no corresponding lesions or abnormalities on CT scan, the lung hot spots were artifactual or iatrogenic upon follow-up diagnostic chest CT or repeated PET/CT or both the scans. Microemboli were most likely a potential cause of the pulmonary uptake, with or without partial paravenous injection. One case in the series had a real pulmonary lesion demonstrated on follow-up PET/CT scans and on surgical pathology, although the initial integrated CT and follow-up diagnostic chest CT scans revealed negative findings to demonstrate pulmonary abnormalities corresponding to the hot spot on the PET scan. In conclusion, the finding of a lung hot spot in the absence of anatomical abnormality on FDG PET/CT was most likely artifactual or iatrogenic, but it might also represent a real pulmonary lesion. Nonvisualization of anatomical abnormality could be because of its small size and position directly overlying a segmental vessel. Further image follow-up is necessary and important to clarify the nature of the uptake. Copyright © 2017 Elsevier Inc. All rights reserved.

Bias atlases for segmentation-based PET attenuation correction using PET-CT and MR.

PubMed

Ouyang, Jinsong; Chun, Se Young; Petibon, Yoann; Bonab, Ali A; Alpert, Nathaniel; Fakhri, Georges El

2013-10-01

This study was to obtain voxel-wise PET accuracy and precision using tissue-segmentation for attenuation correction. We applied multiple thresholds to the CTs of 23 patients to classify tissues. For six of the 23 patients, MR images were also acquired. The MR fat/in-phase ratio images were used for fat segmentation. Segmented tissue classes were used to create attenuation maps, which were used for attenuation correction in PET reconstruction. PET bias images were then computed using the PET reconstructed with the original CT as the reference. We registered the CTs for all the patients and transformed the corresponding bias images accordingly. We then obtained the mean and standard deviation bias atlas using all the registered bias images. Our CT-based study shows that four-class segmentation (air, lungs, fat, other tissues), which is available on most PET-MR scanners, yields 15.1%, 4.1%, 6.6%, and 12.9% RMSE bias in lungs, fat, non-fat soft-tissues, and bones, respectively. An accurate fat identification is achievable using fat/in-phase MR images. Furthermore, we have found that three-class segmentation (air, lungs, other tissues) yields less than 5% standard deviation of bias within the heart, liver, and kidneys. This implies that three-class segmentation can be sufficient to achieve small variation of bias for imaging these three organs. Finally, we have found that inter- and intra-patient lung density variations contribute almost equally to the overall standard deviation of bias within the lungs.

Small lung cancers: improved detection by use of bone suppression imaging--comparison with dual-energy subtraction chest radiography.

PubMed

Li, Feng; Engelmann, Roger; Pesce, Lorenzo L; Doi, Kunio; Metz, Charles E; Macmahon, Heber

2011-12-01

To determine whether use of bone suppression (BS) imaging, used together with a standard radiograph, could improve radiologists' performance for detection of small lung cancers compared with use of standard chest radiographs alone and whether BS imaging would provide accuracy equivalent to that of dual-energy subtraction (DES) radiography. Institutional review board approval was obtained. The requirement for informed consent was waived. The study was HIPAA compliant. Standard and DES chest radiographs of 50 patients with 55 confirmed primary nodular cancers (mean diameter, 20 mm) as well as 30 patients without cancers were included in the observer study. A new BS imaging processing system that can suppress the conspicuity of bones was applied to the standard radiographs to create corresponding BS images. Ten observers, including six experienced radiologists and four radiology residents, indicated their confidence levels regarding the presence or absence of a lung cancer for each lung, first by using a standard image, then a BS image, and finally DES soft-tissue and bone images. Receiver operating characteristic (ROC) analysis was used to evaluate observer performance. The average area under the ROC curve (AUC) for all observers was significantly improved from 0.807 to 0.867 with BS imaging and to 0.916 with DES (both P < .001). The average AUC for the six experienced radiologists was significantly improved from 0.846 with standard images to 0.894 with BS images (P < .001) and from 0.894 to 0.945 with DES images (P = .001). Use of BS imaging together with a standard radiograph can improve radiologists' accuracy for detection of small lung cancers on chest radiographs. Further improvements can be achieved by use of DES radiography but with the requirement for special equipment and a potential small increase in radiation dose. © RSNA, 2011.

Case-based lung image categorization and retrieval for interstitial lung diseases: clinical workflows.

PubMed

Depeursinge, Adrien; Vargas, Alejandro; Gaillard, Frédéric; Platon, Alexandra; Geissbuhler, Antoine; Poletti, Pierre-Alexandre; Müller, Henning

2012-01-01

Clinical workflows and user interfaces of image-based computer-aided diagnosis (CAD) for interstitial lung diseases in high-resolution computed tomography are introduced and discussed. Three use cases are implemented to assist students, radiologists, and physicians in the diagnosis workup of interstitial lung diseases. In a first step, the proposed system shows a three-dimensional map of categorized lung tissue patterns with quantification of the diseases based on texture analysis of the lung parenchyma. Then, based on the proportions of abnormal and normal lung tissue as well as clinical data of the patients, retrieval of similar cases is enabled using a multimodal distance aggregating content-based image retrieval (CBIR) and text-based information search. The global system leads to a hybrid detection-CBIR-based CAD, where detection-based and CBIR-based CAD show to be complementary both on the user's side and on the algorithmic side. The proposed approach is in accordance with the classical workflow of clinicians searching for similar cases in textbooks and personal collections. The developed system enables objective and customizable inter-case similarity assessment, and the performance measures obtained with a leave-one-patient-out cross-validation (LOPO CV) are representative of a clinical usage of the system.

Environmental Carcinogen Releases and Lung Cancer Mortality in Rural-Urban Areas of the United States

ERIC Educational Resources Information Center

Luo, Juhua; Hendryx, Michael

2011-01-01

Purpose: Environmental hazards are unevenly distributed across communities and populations; however, little is known about the distribution of environmental carcinogenic pollutants and lung cancer risk across populations defined by race, sex, and rural-urban setting. Methods: We used the Toxics Release Inventory (TRI) database to conduct an…

Lung function imaging methods in Cystic Fibrosis pulmonary disease.

PubMed

Kołodziej, Magdalena; de Veer, Michael J; Cholewa, Marian; Egan, Gary F; Thompson, Bruce R

2017-05-17

Monitoring of pulmonary physiology is fundamental to the clinical management of patients with Cystic Fibrosis. The current standard clinical practise uses spirometry to assess lung function which delivers a clinically relevant functional readout of total lung function, however does not supply any visible or localised information. High Resolution Computed Tomography (HRCT) is a well-established current 'gold standard' method for monitoring lung anatomical changes in Cystic Fibrosis patients. HRCT provides excellent morphological information, however, the X-ray radiation dose can become significant if multiple scans are required to monitor chronic diseases such as cystic fibrosis. X-ray phase-contrast imaging is another emerging X-ray based methodology for Cystic Fibrosis lung assessment which provides dynamic morphological and functional information, albeit with even higher X-ray doses than HRCT. Magnetic Resonance Imaging (MRI) is a non-ionising radiation imaging method that is garnering growing interest among researchers and clinicians working with Cystic Fibrosis patients. Recent advances in MRI have opened up the possibilities to observe lung function in real time to potentially allow sensitive and accurate assessment of disease progression. The use of hyperpolarized gas or non-contrast enhanced MRI can be tailored to clinical needs. While MRI offers significant promise it still suffers from poor spatial resolution and the development of an objective scoring system especially for ventilation assessment.

Quantification of heterogeneity in lung disease with image-based pulmonary function testing.

PubMed

Stahr, Charlene S; Samarage, Chaminda R; Donnelley, Martin; Farrow, Nigel; Morgan, Kaye S; Zosky, Graeme; Boucher, Richard C; Siu, Karen K W; Mall, Marcus A; Parsons, David W; Dubsky, Stephen; Fouras, Andreas

2016-07-27

Computed tomography (CT) and spirometry are the mainstays of clinical pulmonary assessment. Spirometry is effort dependent and only provides a single global measure that is insensitive for regional disease, and as such, poor for capturing the early onset of lung disease, especially patchy disease such as cystic fibrosis lung disease. CT sensitively measures change in structure associated with advanced lung disease. However, obstructions in the peripheral airways and early onset of lung stiffening are often difficult to detect. Furthermore, CT imaging poses a radiation risk, particularly for young children, and dose reduction tends to result in reduced resolution. Here, we apply a series of lung tissue motion analyses, to achieve regional pulmonary function assessment in β-ENaC-overexpressing mice, a well-established model of lung disease. The expiratory time constants of regional airflows in the segmented airway tree were quantified as a measure of regional lung function. Our results showed marked heterogeneous lung function in β-ENaC-Tg mice compared to wild-type littermate controls; identified locations of airway obstruction, and quantified regions of bimodal airway resistance demonstrating lung compensation. These results demonstrate the applicability of regional lung function derived from lung motion as an effective alternative respiratory diagnostic tool.

Super-resolution reconstruction for 4D computed tomography of the lung via the projections onto convex sets approach

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

Zhang, Yu, E-mail: yuzhang@smu.edu.cn, E-mail: qianjinfeng08@gmail.com; Wu, Xiuxiu; Yang, Wei

2014-11-01

Purpose: The use of 4D computed tomography (4D-CT) of the lung is important in lung cancer radiotherapy for tumor localization and treatment planning. Sometimes, dense sampling is not acquired along the superior–inferior direction. This disadvantage results in an interslice thickness that is much greater than in-plane voxel resolutions. Isotropic resolution is necessary for multiplanar display, but the commonly used interpolation operation blurs images. This paper presents a super-resolution (SR) reconstruction method to enhance 4D-CT resolution. Methods: The authors assume that the low-resolution images of different phases at the same position can be regarded as input “frames” to reconstruct high-resolution images.more » The SR technique is used to recover high-resolution images. Specifically, the Demons deformable registration algorithm is used to estimate the motion field between different “frames.” Then, the projection onto convex sets approach is implemented to reconstruct high-resolution lung images. Results: The performance of the SR algorithm is evaluated using both simulated and real datasets. Their method can generate clearer lung images and enhance image structure compared with cubic spline interpolation and back projection (BP) method. Quantitative analysis shows that the proposed algorithm decreases the root mean square error by 40.8% relative to cubic spline interpolation and 10.2% versus BP. Conclusions: A new algorithm has been developed to improve the resolution of 4D-CT. The algorithm outperforms the cubic spline interpolation and BP approaches by producing images with markedly improved structural clarity and greatly reduced artifacts.« less

Four-dimensional optical coherence tomography imaging of total liquid ventilated rats

NASA Astrophysics Data System (ADS)

Kirsten, Lars; Schnabel, Christian; Gaertner, Maria; Koch, Edmund

2013-06-01

Optical coherence tomography (OCT) can be utilized for the spatially and temporally resolved visualization of alveolar tissue and its dynamics in rodent models, which allows the investigation of lung dynamics on the microscopic scale of single alveoli. The findings could provide experimental input data for numerical simulations of lung tissue mechanics and could support the development of protective ventilation strategies. Real four-dimensional OCT imaging permits the acquisition of several OCT stacks within one single ventilation cycle. Thus, the entire four-dimensional information is directly obtained. Compared to conventional virtual four-dimensional OCT imaging, where the image acquisition is extended over many ventilation cycles and is triggered on pressure levels, real four-dimensional OCT is less vulnerable against motion artifacts and non-reproducible movement of the lung tissue over subsequent ventilation cycles, which widely reduces image artifacts. However, OCT imaging of alveolar tissue is affected by refraction and total internal reflection at air-tissue interfaces. Thus, only the first alveolar layer beneath the pleura is visible. To circumvent this effect, total liquid ventilation can be carried out to match the refractive indices of lung tissue and the breathing medium, which improves the visibility of the alveolar structure, the image quality and the penetration depth and provides the real structure of the alveolar tissue. In this study, a combination of four-dimensional OCT imaging with total liquid ventilation allowed the visualization of the alveolar structure in rat lung tissue benefiting from the improved depth range beneath the pleura and from the high spatial and temporal resolution.

[A Meta-analysis on tea drinking and the risk of lung cancer in Chinese population].

PubMed

Jin, Zi-yi; Han, Ren-qiang; Liu, Ai-min; Wang, Xu-shan; Wu, Ming; Zhang, Zuo-feng; Zhao, Jin-kou

2012-08-01

To examine the association between tea drinking and the risk of lung cancer in Chinese population. All relevant published articles in Chinese and English literature database were identified. Meta-analysis was conducted. Combined odds ratio (OR) and 95% confidence interval (CI) were calculated to estimate the associations and dose-response relationship between tea drinking and the risk of lung cancer. Twelve studies were included. An inverse association with lung cancer was observed on tea drinkers when compared to non-tea drinkers (OR = 0.66, 95%CI: 0.49 - 0.89). Tea drinking might serve as a protective factor on lung cancer in the Chinese population.

From Curves to Trees: A Tree-like Shapes Distance Using the Elastic Shape Analysis Framework.

PubMed

Mottini, A; Descombes, X; Besse, F

2015-04-01

Trees are a special type of graph that can be found in various disciplines. In the field of biomedical imaging, trees have been widely studied as they can be used to describe structures such as neurons, blood vessels and lung airways. It has been shown that the morphological characteristics of these structures can provide information on their function aiding the characterization of pathological states. Therefore, it is important to develop methods that analyze their shape and quantify differences between their structures. In this paper, we present a method for the comparison of tree-like shapes that takes into account both topological and geometrical information. This method, which is based on the Elastic Shape Analysis Framework, also computes the mean shape of a population of trees. As a first application, we have considered the comparison of axon morphology. The performance of our method has been evaluated on two sets of images. For the first set of images, we considered four different populations of neurons from different animals and brain sections from the NeuroMorpho.org open database. The second set was composed of a database of 3D confocal microscopy images of three populations of axonal trees (normal and two types of mutations) of the same type of neurons. We have calculated the inter and intra class distances between the populations and embedded the distance in a classification scheme. We have compared the performance of our method against three other state of the art algorithms, and results showed that the proposed method better distinguishes between the populations. Furthermore, we present the mean shape of each population. These shapes present a more complete picture of the morphological characteristics of each population, compared to the average value of certain predefined features.

Potential usefulness of a topic model-based categorization of lung cancers as quantitative CT biomarkers for predicting the recurrence risk after curative resection

NASA Astrophysics Data System (ADS)

Kawata, Y.; Niki, N.; Ohmatsu, H.; Satake, M.; Kusumoto, M.; Tsuchida, T.; Aokage, K.; Eguchi, K.; Kaneko, M.; Moriyama, N.

2014-03-01

In this work, we investigate a potential usefulness of a topic model-based categorization of lung cancers as quantitative CT biomarkers for predicting the recurrence risk after curative resection. The elucidation of the subcategorization of a pulmonary nodule type in CT images is an important preliminary step towards developing the nodule managements that are specific to each patient. We categorize lung cancers by analyzing volumetric distributions of CT values within lung cancers via a topic model such as latent Dirichlet allocation. Through applying our scheme to 3D CT images of nonsmall- cell lung cancer (maximum lesion size of 3 cm) , we demonstrate the potential usefulness of the topic model-based categorization of lung cancers as quantitative CT biomarkers.

A neural network approach to lung nodule segmentation

NASA Astrophysics Data System (ADS)

Hu, Yaoxiu; Menon, Prahlad G.

2016-03-01

Computed tomography (CT) imaging is a sensitive and specific lung cancer screening tool for the high-risk population and shown to be promising for detection of lung cancer. This study proposes an automatic methodology for detecting and segmenting lung nodules from CT images. The proposed methods begin with thorax segmentation, lung extraction and reconstruction of the original shape of the parenchyma using morphology operations. Next, a multi-scale hessian-based vesselness filter is applied to extract lung vasculature in lung. The lung vasculature mask is subtracted from the lung region segmentation mask to extract 3D regions representing candidate pulmonary nodules. Finally, the remaining structures are classified as nodules through shape and intensity features which are together used to train an artificial neural network. Up to 75% sensitivity and 98% specificity was achieved for detection of lung nodules in our testing dataset, with an overall accuracy of 97.62%+/-0.72% using 11 selected features as input to the neural network classifier, based on 4-fold cross-validation studies. Receiver operator characteristics for identifying nodules revealed an area under curve of 0.9476.

Estimation of gas and tissue lung volumes by MRI: functional approach of lung imaging.

PubMed

Qanadli, S D; Orvoen-Frija, E; Lacombe, P; Di Paola, R; Bittoun, J; Frija, G

1999-01-01

The purpose of this work was to assess the accuracy of MRI for the determination of lung gas and tissue volumes. Fifteen healthy subjects underwent MRI of the thorax and pulmonary function tests [vital capacity (VC) and total lung capacity (TLC)] in the supine position. MR examinations were performed at inspiration and expiration. Lung volumes were measured by a previously validated technique on phantoms. Both individual and total lung volumes and capacities were calculated. MRI total vital capacity (VC(MRI)) was compared with spirometric vital capacity (VC(SP)). Capacities were correlated to lung volumes. Tissue volume (V(T)) was estimated as the difference between the total lung volume at full inspiration and the TLC. No significant difference was seen between VC(MRI) and VC(SP). Individual capacities were well correlated (r = 0.9) to static volume at full inspiration. The V(T) was estimated to be 836+/-393 ml. This preliminary study demonstrates that MRI can accurately estimate lung gas and tissue volumes. The proposed approach appears well suited for functional imaging of the lung.

The Case for Lung Cancer Screening: What Nurses Need to Know.

PubMed

Sorrie, Kerrin; Cates, Lisa; Hill, Alethea

2016-06-01

Lung cancer screening with low-dose helical computed tomography (LDCT) can improve high-risk individuals' chances of being diagnosed at an earlier stage and increase survival. The aims of this article are to present the risk factors associated with the development of lung cancer, identify patients at high risk for lung cancer qualifying for LDCT screening, and understand the importance of early lung cancer detection through the use of LDCT screening. PubMed and CINAHL® databases were searched with key words lung cancer screening to identify full-text academic articles from 2004-2014. This resulted in 529 articles from PubMed and 195 from CINAHL. PubMed offered suggestions for additional relevant journal articles. The National Comprehensive Cancer Network guidelines also provided substantial evidence-based information. Nurses need to provide support, education, and resources for patients undergoing lung cancer screening.

Imaging in lung transplants: Checklist for the radiologist.

PubMed

Madan, Rachna; Chansakul, Thanissara; Goldberg, Hilary J

2014-10-01

Post lung transplant complications can have overlapping clinical and imaging features, and hence, the time point at which they occur is a key distinguisher. Complications of lung transplantation may occur along a continuum in the immediate or longer postoperative period, including surgical and mechanical problems due to size mismatch and vascular as well as airway anastomotic complication, injuries from ischemia and reperfusion, acute and chronic rejection, pulmonary infections, and post-transplantation lymphoproliferative disorder. Life expectancy after lung transplantation has been limited primarily by chronic rejection and infection. Multiple detector computed tomography (MDCT) is critical for evaluation and early diagnosis of complications to enable selection of effective therapy and decrease morbidity and mortality among lung transplant recipients.

Prenatal diagnosis of fetal respiratory function: evaluation of fetal lung maturity using lung-to-liver signal intensity ratio at magnetic resonance imaging.

PubMed

Oka, Yasuko; Rahman, Mosfequr; Sasakura, Chihaya; Waseda, Tomoo; Watanabe, Yukio; Fujii, Ryota; Makinoda, Satoru

2014-12-01

The purpose of this retrospective study is to determine the fetal lung-to-liver signal intensity ratio (LLSIR) on T2-weighted images for the prediction of neonatal respiratory outcome. One hundred ten fetuses who underwent magnetic resonance imaging (MRI) examination for various indications after 22 weeks of gestation participated in this study. LLSIR was measured as the ratio of signal intensities of the fetal lung and liver on T2-weighted images at MRI. We examined the changes of the ratio with advancing gestation and the relations between LLSIR and the presence of the severe respiratory disorder (SRD) after birth. The best cut-off value of the LLSIR to predict respiratory outcome after birth was calculated using receiver operating characteristic (ROC) curve analysis. Lung-to-liver signal intensity ratio correlated significantly with advancing gestational age (R = 0.35, p < 0.001). The non-SRD group had higher LLSIR compared with the SRD group (2.15 ± 0.30 vs. 1.53 ± 0.40, p < 0.001). ROC curve analysis showed that fetuses with an LLSIR < 2.00 were more likely to develop SRD [sensitivity: 100%, 95% confidence interval (CI): 52-100%; specificity: 73%, 95% CI 54-88%]. The fetal LLSIR on T2-weighted images is an accurate marker to diagnose the fetal lung maturity. © 2014 The Authors. Prenatal Diagnosis published by John Wiley & Sons, Ltd.

Novel and powerful 3D adaptive crisp active contour method applied in the segmentation of CT lung images.

PubMed

Rebouças Filho, Pedro Pedrosa; Cortez, Paulo César; da Silva Barros, Antônio C; C Albuquerque, Victor Hugo; R S Tavares, João Manuel

2017-01-01

The World Health Organization estimates that 300 million people have asthma, 210 million people have Chronic Obstructive Pulmonary Disease (COPD), and, according to WHO, COPD will become the third major cause of death worldwide in 2030. Computational Vision systems are commonly used in pulmonology to address the task of image segmentation, which is essential for accurate medical diagnoses. Segmentation defines the regions of the lungs in CT images of the thorax that must be further analyzed by the system or by a specialist physician. This work proposes a novel and powerful technique named 3D Adaptive Crisp Active Contour Method (3D ACACM) for the segmentation of CT lung images. The method starts with a sphere within the lung to be segmented that is deformed by forces acting on it towards the lung borders. This process is performed iteratively in order to minimize an energy function associated with the 3D deformable model used. In the experimental assessment, the 3D ACACM is compared against three approaches commonly used in this field: the automatic 3D Region Growing, the level-set algorithm based on coherent propagation and the semi-automatic segmentation by an expert using the 3D OsiriX toolbox. When applied to 40 CT scans of the chest the 3D ACACM had an average F-measure of 99.22%, revealing its superiority and competency to segment lungs in CT images. Copyright © 2016 Elsevier B.V. All rights reserved.

SU-E-J-189: Determination of Markerless Lung Tumor Position in Real Time: A Feasibility Study Using a Novel Tomo-Cinegraphy Imaging

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

Yi, B; Hu, E; Yu, C

2015-06-15

Purpose: A Tomo-Cinegraphy (TC) is a method to generate a series of temporal tomographic images from projection images of the on-board imager (OBI) while gantry is moving. It is to test if this technique is useful to determine a lung tumor position during treatments. Methods: Tomographic image via background subtraction, TIBS uses a priori anatomical information from a previous CT scan to isolate a SOI from a planar kV image by factoring out the attenuations by tissues outside the SOI (background). This idea was extended to a TC, which enables to generate tomographic images of same geometry from the projectionmore » of different gantry angles and different breathing phases. Projection images of a lung patient for CBCT acquisition are used to generate TC images. A region of interest (ROI) is selected around a tumor adding 2cm margins. Center of mass (COM) of the ROI is traced to determine tumor position for every projection images. Results: Tumor is visible in the TC images while the OBI projections are not. The coordinates of the COMs represent the temporal tumor positions. While, it is not possible to trace the tumor motion using the projection images. A source of time delay is the time to acquire projection images, which is always less than a second. Conclusion: TC allows tracking the tumor positions without fiducial markers in real time for some lung patients, if the projection images are acquired during treatments. Partially supported by NIH R01CA133539.« less

MRI of the lung: state of the art.

PubMed

Wielpütz, Mark; Kauczor, Hans-Ulrich

2012-01-01

Magnetic resonance imaging (MRI) of the lung is technically challenging due to the low proton density and fast signal decay of the lung parenchyma itself. Additional challenges consist of tissue loss, hyperinflation, and hypoxic hypoperfusion, e.g., in emphysema, a so-called "minus-pathology". However, pathological changes resulting in an increase of tissue ("plus-pathology"), such as atelectases, nodules, infiltrates, mucus, or pleural effusion, are easily depicted with high diagnostic accuracy. Although MRI is inferior or at best equal to multi-detector computed tomography (MDCT) for the detection of subtle morphological features, MRI now offers an increasing spectrum of functional imaging techniques such as perfusion assessment and measurement of ventilation and respiratory mechanics that are superior to what is possible with MDCT. Without putting patients at risk with ionizing radiation, repeated examinations allow for the evaluation of the course of lung disease and monitoring of the therapeutic response through quantitative imaging, providing a level of functional detail that cannot be obtained by any other single imaging modality. As such, MRI will likely be used for clinical applications beyond morphological imaging for many lung diseases. In this article, we review the technical aspects and protocol suggestions for chest MRI and discuss the role of MRI in the evaluation of nodules and masses, airway disease, respiratory mechanics, ventilation, perfusion and hemodynamics, and pulmonary vasculature.

Quantifying lung morphology with respiratory-gated micro-CT in a murine model of emphysema

NASA Astrophysics Data System (ADS)

Ford, N. L.; Martin, E. L.; Lewis, J. F.; Veldhuizen, R. A. W.; Holdsworth, D. W.; Drangova, M.

2009-04-01

Non-invasive micro-CT imaging techniques have been developed to investigate lung structure in free-breathing rodents. In this study, we investigate the utility of retrospectively respiratory-gated micro-CT imaging in an emphysema model to determine if anatomical changes could be observed in the image-derived quantitative analysis at two respiratory phases. The emphysema model chosen was a well-characterized, genetically altered model (TIMP-3 knockout mice) that exhibits a homogeneous phenotype. Micro-CT scans of the free-breathing, anaesthetized mice were obtained in 50 s and retrospectively respiratory sorted and reconstructed, providing 3D images representing peak inspiration and end expiration with 0.15 mm isotropic voxel spacing. Anatomical measurements included the volume and CT density of the lungs and the volume of the major airways, along with the diameters of the trachea, left bronchus and right bronchus. From these measurements, functional parameters such as functional residual capacity and tidal volume were calculated. Significant differences between the wild-type and TIMP-3 knockout groups were observed for measurements of CT density over the entire lung, indicating increased air content in the lungs of TIMP-3 knockout mice. These results demonstrate retrospective respiratory-gated micro-CT, providing images at multiple respiratory phases that can be analyzed quantitatively to investigate anatomical changes in murine models of emphysema.

Role of PET/CT for precision medicine in lung cancer: perspective of the Society of Nuclear Medicine and Molecular Imaging.

PubMed

Greenspan, Bennett S

2017-12-01

This article discusses the role of PET/CT in contributing to precision medicine in lung cancer, and provides the perspective of the Society of Nuclear Medicine and Molecular Imaging (SNMMI) on this process. The mission and vision of SNMMI are listed, along with the guidance provided by SNMMI to promote best practice in precision medicine. Basic principles of PET/CT are presented. An overview of the use of PET/CT imaging in lung cancer is discussed. In lung cancer patients, PET/CT is vitally important for optimal patient management. PET/CT is essential in determining staging and re-staging of disease, detecting recurrent or residual disease, evaluating response to therapy, and providing prognostic information. PET/CT is also critically important in radiation therapy planning by determining the extent of active disease, including an assessment of functional tumor volume. The current approach in tumor imaging is a significant advance over conventional imaging. However, recent advances suggest that therapeutic response criteria in the near future will be based on metabolic characteristics and will include the evaluation of biologic characteristics of tumors to further enhance the effectiveness of precision medicine in lung cancer, producing improved patient outcomes with less morbidity.

Magnetic Resonance Microscopy of the Lung

NASA Astrophysics Data System (ADS)

Johnson, G. Allan

1999-11-01

The lung presents both challenges and opportunities for study by magnetic resonance imaging (MRI). The technical challenges arise from respiratory and cardiac motion, limited signal from the tissues, and unique physical structure of the lung. These challenges are heightened in magnetic resonance microscopy (MRM) where the spatial resolution may be up to a million times higher than that of conventional MRI. The development of successful techniques for MRM of the lung present enormous opportunities for basic studies of lung structure and function, toxicology, environmental stress, and drug discovery by permitting investigators to study this most essential organ nondestructively in the live animal. Over the last 15 years, scientists at the Duke Center for In Vivo Microscopy have developed techniques for MRM in the live animal through an interdisciplinary program of biology, physics, chemistry, electrical engineering, and computer science. This talk will focus on the development of specialized radiofrequency coils for lung imaging, projection encoding methods to limit susceptibility losses, specialized support structures to control and monitor physiologic motion, and the most recent development of hyperpolarized gas imaging with ^3He and ^129Xe.

Tracking of Mesenchymal Stem Cells with Fluorescence Endomicroscopy Imaging in Radiotherapy-Induced Lung Injury

NASA Astrophysics Data System (ADS)

Perez, Jessica R.; Ybarra, Norma; Chagnon, Frederic; Serban, Monica; Lee, Sangkyu; Seuntjens, Jan; Lesur, Olivier; El Naqa, Issam

2017-01-01

Mesenchymal stem cells (MSCs) have potential for reducing inflammation and promoting organ repair. However, limitations in available techniques to track them and assess this potential for lung repair have hindered their applicability. In this work, we proposed, implemented and evaluated the use of fluorescence endomicroscopy as a novel imaging tool to track MSCs in vivo. MSCs were fluorescently labeled and injected into a rat model of radiation-induced lung injury via endotracheal (ET) or intravascular (IV) administration. Our results show that MSCs were visible in the lungs with fluorescence endomicroscopy. Moreover, we developed an automatic cell counting algorithm to quantify the number of detected cells in each condition. We observed a significantly higher number of detected cells in ET injection compared to IV and a slight increase in the mean number of detected cells in irradiated lungs compared to control, although the latter did not reach statistical significance. Fluorescence endomicroscopy imaging is a powerful new minimally invasive and translatable tool that can be used to track and quantify MSCs in the lungs and help assess their potential in organ repair.

In vivo imaging of the pathophysiological changes and neutrophil dynamics in influenza virus-infected mouse lungs.

PubMed

Ueki, Hiroshi; Wang, I-Hsuan; Fukuyama, Satoshi; Katsura, Hiroaki; da Silva Lopes, Tiago Jose; Neumann, Gabriele; Kawaoka, Yoshihiro

2018-06-25

The pathophysiological changes that occur in lungs infected with influenza viruses are poorly understood. Here we established an in vivo imaging system that combines two-photon excitation microscopy and fluorescent influenza viruses of different pathogenicity. This approach allowed us to monitor and correlate several parameters and physiological changes including the spread of infection, pulmonary permeability, pulmonary perfusion speed, number of recruited neutrophils in infected lungs, and neutrophil motion in the lungs of live mice. Several physiological changes were larger and occurred earlier in mice infected with a highly pathogenic H5N1 influenza virus compared with those infected with a mouse-adapted human strain. These findings demonstrate the potential of our in vivo imaging system to provide novel information about the pathophysiological consequences of virus infections.

An automated distinction of DICOM images for lung cancer CAD system

NASA Astrophysics Data System (ADS)

Suzuki, H.; Saita, S.; Kubo, M.; Kawata, Y.; Niki, N.; Nishitani, H.; Ohmatsu, H.; Eguchi, K.; Kaneko, M.; Moriyama, N.

2009-02-01

Automated distinction of medical images is an important preprocessing in Computer-Aided Diagnosis (CAD) systems. The CAD systems have been developed using medical image sets with specific scan conditions and body parts. However, varied examinations are performed in medical sites. The specification of the examination is contained into DICOM textual meta information. Most DICOM textual meta information can be considered reliable, however the body part information cannot always be considered reliable. In this paper, we describe an automated distinction of DICOM images as a preprocessing for lung cancer CAD system. Our approach uses DICOM textual meta information and low cost image processing. Firstly, the textual meta information such as scan conditions of DICOM image is distinguished. Secondly, the DICOM image is set to distinguish the body parts which are identified by image processing. The identification of body parts is based on anatomical structure which is represented by features of three regions, body tissue, bone, and air. The method is effective to the practical use of lung cancer CAD system in medical sites.

Assessment of Lung Function in Asthma and COPD using Hyperpolarized 129Xe Chemical Shift Saturation Recovery Spectroscopy and Dissolved-Phase MR Imaging

PubMed Central

Qing, Kun; Mugler, John P.; Altes, Talissa A.; Jiang, Yun; Mata, Jaime F.; Miller, G. Wilson; Ruset, Iulian C.; Hersman, F. William; Ruppert, Kai

2014-01-01

Magnetic-resonance spectroscopy and imaging using hyperpolarized xenon-129 show great potential for evaluation of the most important function of the human lung -- gas exchange. In particular, Chemical Shift Saturation Recovery (CSSR) xenon-129 spectroscopy provides important physiological information for the lung as a whole by characterizing the dynamic process of gas exchange, while dissolved-phase xenon-129 imaging captures the time-averaged regional distribution of gas uptake by lung tissue and blood. Herein, we present recent advances in assessing lung function using CSSR spectroscopy and dissolved-phase imaging in a total of 45 subjects (23 healthy, 13 chronic obstructive pulmonary disease (COPD) and 9 asthma). From CSSR acquisitions, the COPD subjects showed red blood cell to tissue/plasma (RBC-to-TP) ratios below the average for the healthy subjects (p<0.001), but significantly higher septal wall thicknesses, as compared with the healthy subjects (p<0.005); the RBC-to-TP ratios for the asthmatics fell outside 2 standard deviations (either higher or lower) from the mean of the healthy subjects although there was no statistically significant difference for the average ratio of the study group as a whole. Similarly, from the 3D DP imaging acquisitions, we found all the ratios (TP-to-GP, RBC-to-GP, RBC-to-TP) measured in the COPD subjects were lower than those from the healthy subjects (p<0.05 for all ratios), while these ratios in the asthmatics differed considerably between subjects. Despite having been performed at different lung inflation levels, the RBC-to-TP ratios measured by CSSR and 3D DP imaging were fairly consistent with each other, with a mean difference of 0.037 (ratios from 3D DP imaging larger). In ten subjects the RBC-to-GP ratios obtained from the 3D DP imaging acquisitions were also highly correlated with their DLCO/Va ratios measured by pulmonary function testing (R=0.91). PMID:25146558

Accuracy of lung nodule density on HRCT: analysis by PSF-based image simulation.

PubMed

Ohno, Ken; Ohkubo, Masaki; Marasinghe, Janaka C; Murao, Kohei; Matsumoto, Toru; Wada, Shinichi

2012-11-08

A computed tomography (CT) image simulation technique based on the point spread function (PSF) was applied to analyze the accuracy of CT-based clinical evaluations of lung nodule density. The PSF of the CT system was measured and used to perform the lung nodule image simulation. Then, the simulated image was resampled at intervals equal to the pixel size and the slice interval found in clinical high-resolution CT (HRCT) images. On those images, the nodule density was measured by placing a region of interest (ROI) commonly used for routine clinical practice, and comparing the measured value with the true value (a known density of object function used in the image simulation). It was quantitatively determined that the measured nodule density depended on the nodule diameter and the image reconstruction parameters (kernel and slice thickness). In addition, the measured density fluctuated, depending on the offset between the nodule center and the image voxel center. This fluctuation was reduced by decreasing the slice interval (i.e., with the use of overlapping reconstruction), leading to a stable density evaluation. Our proposed method of PSF-based image simulation accompanied with resampling enables a quantitative analysis of the accuracy of CT-based evaluations of lung nodule density. These results could potentially reveal clinical misreadings in diagnosis, and lead to more accurate and precise density evaluations. They would also be of value for determining the optimum scan and reconstruction parameters, such as image reconstruction kernels and slice thicknesses/intervals.

Utilisation of a thoracic oncology database to capture radiological and pathological images for evaluation of response to chemotherapy in patients with malignant pleural mesothelioma

PubMed Central

Carey, George B; Kazantsev, Stephanie; Surati, Mosmi; Rolle, Cleo E; Kanteti, Archana; Sadiq, Ahad; Bahroos, Neil; Raumann, Brigitte; Madduri, Ravi; Dave, Paul; Starkey, Adam; Hensing, Thomas; Husain, Aliya N; Vokes, Everett E; Vigneswaran, Wickii; Armato, Samuel G; Kindler, Hedy L; Salgia, Ravi

2012-01-01

Objective An area of need in cancer informatics is the ability to store images in a comprehensive database as part of translational cancer research. To meet this need, we have implemented a novel tandem database infrastructure that facilitates image storage and utilisation. Background We had previously implemented the Thoracic Oncology Program Database Project (TOPDP) database for our translational cancer research needs. While useful for many research endeavours, it is unable to store images, hence our need to implement an imaging database which could communicate easily with the TOPDP database. Methods The Thoracic Oncology Research Program (TORP) imaging database was designed using the Research Electronic Data Capture (REDCap) platform, which was developed by Vanderbilt University. To demonstrate proof of principle and evaluate utility, we performed a retrospective investigation into tumour response for malignant pleural mesothelioma (MPM) patients treated at the University of Chicago Medical Center with either of two analogous chemotherapy regimens and consented to at least one of two UCMC IRB protocols, 9571 and 13473A. Results A cohort of 22 MPM patients was identified using clinical data in the TOPDP database. After measurements were acquired, two representative CT images and 0–35 histological images per patient were successfully stored in the TORP database, along with clinical and demographic data. Discussion We implemented the TORP imaging database to be used in conjunction with our comprehensive TOPDP database. While it requires an additional effort to use two databases, our database infrastructure facilitates more comprehensive translational research. Conclusions The investigation described herein demonstrates the successful implementation of this novel tandem imaging database infrastructure, as well as the potential utility of investigations enabled by it. The data model presented here can be utilised as the basis for further development of other larger, more streamlined databases in the future. PMID:23103606

Automated segmentation of the lungs from high resolution CT images for quantitative study of chronic obstructive pulmonary diseases

NASA Astrophysics Data System (ADS)

Garg, Ishita; Karwoski, Ronald A.; Camp, Jon J.; Bartholmai, Brian J.; Robb, Richard A.

2005-04-01

Chronic obstructive pulmonary diseases (COPD) are debilitating conditions of the lung and are the fourth leading cause of death in the United States. Early diagnosis is critical for timely intervention and effective treatment. The ability to quantify particular imaging features of specific pathology and accurately assess progression or response to treatment with current imaging tools is relatively poor. The goal of this project was to develop automated segmentation techniques that would be clinically useful as computer assisted diagnostic tools for COPD. The lungs were segmented using an optimized segmentation threshold and the trachea was segmented using a fixed threshold characteristic of air. The segmented images were smoothed by a morphological close operation using spherical elements of different sizes. The results were compared to other segmentation approaches using an optimized threshold to segment the trachea. Comparison of the segmentation results from 10 datasets showed that the method of trachea segmentation using a fixed air threshold followed by morphological closing with spherical element of size 23x23x5 yielded the best results. Inclusion of greater number of pulmonary vessels in the lung volume is important for the development of computer assisted diagnostic tools because the physiological changes of COPD can result in quantifiable anatomic changes in pulmonary vessels. Using a fixed threshold to segment the trachea removed airways from the lungs to a better extent as compared to using an optimized threshold. Preliminary measurements gathered from patient"s CT scans suggest that segmented images can be used for accurate analysis of total lung volume and volumes of regional lung parenchyma. Additionally, reproducible segmentation allows for quantification of specific pathologic features, such as lower intensity pixels, which are characteristic of abnormal air spaces in diseases like emphysema.

Bioinformatics approach reveals systematic mechanism underlying lung adenocarcinoma.

PubMed

Wu, Xiya; Zhang, Wei; Hu, Yunhua; Yi, Xianghua

2015-01-01

The purpose of this work was to explore the systematic molecular mechanism of lung adenocarcinoma and gain a deeper insight into it. Comprehensive bioinformatics methods were applied. Initially, significant differentially expressed genes (DEGs) were analyzed from the Affymetrix microarray data (GSE27262) deposited in the Gene Expression Omnibus (GEO). Subsequently, gene ontology (GO) analysis was performed using online Database for Annotation, Visualization and Integration Discovery (DAVID) software. Finally, significant pathway crosstalk was investigated based on the information derived from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. According to our results, the N-terminal globular domain of the type X collagen (COL10A1) gene and transmembrane protein 100 (TMEM100) gene were identified to be the most significant DEGs in tumor tissue compared with the adjacent normal tissues. The main GO categories were biological process, cellular component and molecular function. In addition, the crosstalk was significantly different between non-small cell lung cancer pathways and inositol phosphate metabolism pathway, focal adhesion signal pathway, vascular smooth muscle contraction signal pathway, peroxisome proliferator-activated receptor (PPAR) signaling pathway and calcium signaling pathway in tumor. Dysfunctional genes and pathways may play key roles in the progression and development of lung adenocarcinoma. Our data provide a systematic perspective for understanding this mechanism and may be helpful in discovering an effective treatment for lung adenocarcinoma.

Reference database of lung volumes and capacities in wistar rats from 2 to 24 months.

PubMed

Filho, Wilson Jacob; Fontinele, Renata Gabriel; de Souza, Romeu Rodrigues

2014-01-01

This study determines the effects of growing and aging on lung physiological volumes and capacities and the incidence of inflammation in the small airways with age in rats. A reference database comprising of body weight gain, lung physiological volumes and capacities and an anatomopathological study of lung lesions over 240 Wistar rats from two to 24 -mo, is described. Tidal volume (TV), minute respiratory volume (MRV), and forced vital capacity (FVC) decreased during the first six months of life and then remain constant until 24 -mo of age. The respiratory frequency (Rf) and dynamical compliance (Cdyn) maintain at constant values from 2 to 24- mo of age; the functional residual capacity (FRC) increases in the first 6 -mo and then remains constant up to 24 -mo. It was verified a less intensive inflammation in the small airways with age, when compared with the median and large airways. This study showed the normal parameters for lung volumes and capacities and the incidence of infections for growing and aging male and female rats. The age-related data on these main respiratory parameters in rats would be useful in studies of aging-related disorders using this model and for safety pharmacology studies necessary for the development of drugs.

Novel algorithm to identify and differentiate specific digital signature of breath sound in patients with diffuse parenchymal lung disease.

PubMed

Bhattacharyya, Parthasarathi; Mondal, Ashok; Dey, Rana; Saha, Dipanjan; Saha, Goutam

2015-05-01

Auscultation is an important part of the clinical examination of different lung diseases. Objective analysis of lung sounds based on underlying characteristics and its subsequent automatic interpretations may help a clinical practice. We collected the breath sounds from 8 normal subjects and 20 diffuse parenchymal lung disease (DPLD) patients using a newly developed instrument and then filtered off the heart sounds using a novel technology. The collected sounds were thereafter analysed digitally on several characteristics as dynamical complexity, texture information and regularity index to find and define their unique digital signatures for differentiating normality and abnormality. For convenience of testing, these characteristic signatures of normal and DPLD lung sounds were transformed into coloured visual representations. The predictive power of these images has been validated by six independent observers that include three physicians. The proposed method gives a classification accuracy of 100% for composite features for both the normal as well as lung sound signals from DPLD patients. When tested by independent observers on the visually transformed images, the positive predictive value to diagnose the normality and DPLD remained 100%. The lung sounds from the normal and DPLD subjects could be differentiated and expressed according to their digital signatures. On visual transformation to coloured images, they retain 100% predictive power. This technique may assist physicians to diagnose DPLD from visual images bearing the digital signature of the condition. © 2015 Asian Pacific Society of Respirology.

Spirometry (image)

MedlinePlus

Spirometry is a painless study of air volume and flow rate within the lungs. Spirometry is frequently used to evaluate lung function in people with obstructive or restrictive lung diseases such as asthma or cystic fibrosis.

Distal airways in humans: dynamic hyperpolarized 3He MR imaging--feasibility

NASA Technical Reports Server (NTRS)

Tooker, Angela C.; Hong, Kwan Soo; McKinstry, Erin L.; Costello, Philip; Jolesz, Ferenc A.; Albert, Mitchell S.

2003-01-01

Dynamic hyperpolarized helium 3 (3He) magnetic resonance (MR) imaging of the human airways is achieved by using a fast gradient-echo pulse sequence during inhalation. The resulting dynamic images show differential contrast enhancement of both distal airways and the lung periphery, unlike static hyperpolarized 3He MR images on which only the lung periphery is seen. With this technique, up to seventh-generation airway branching can be visualized. Copyright RSNA, 2003.

Lung vasculature imaging using speckle variance optical coherence tomography

NASA Astrophysics Data System (ADS)

Cua, Michelle; Lee, Anthony M. D.; Lane, Pierre M.; McWilliams, Annette; Shaipanich, Tawimas; MacAulay, Calum E.; Yang, Victor X. D.; Lam, Stephen

2012-02-01

Architectural changes in and remodeling of the bronchial and pulmonary vasculature are important pathways in diseases such as asthma, chronic obstructive pulmonary disease (COPD), and lung cancer. However, there is a lack of methods that can find and examine small bronchial vasculature in vivo. Structural lung airway imaging using optical coherence tomography (OCT) has previously been shown to be of great utility in examining bronchial lesions during lung cancer screening under the guidance of autofluorescence bronchoscopy. Using a fiber optic endoscopic OCT probe, we acquire OCT images from in vivo human subjects. The side-looking, circumferentially-scanning probe is inserted down the instrument channel of a standard bronchoscope and manually guided to the imaging location. Multiple images are collected with the probe spinning proximally at 100Hz. Due to friction, the distal end of the probe does not spin perfectly synchronous with the proximal end, resulting in non-uniform rotational distortion (NURD) of the images. First, we apply a correction algorithm to remove NURD. We then use a speckle variance algorithm to identify vasculature. The initial data show a vascaulture density in small human airways similar to what would be expected.

Investigation of image components affecting the detection of lung nodules in digital chest radiography

NASA Astrophysics Data System (ADS)

Bath, Magnus; Hakansson, Markus; Borjesson, Sara; Hoeschen, Christoph; Tischenko, Oleg; Bochud, Francois O.; Verdun, Francis R.; Ullman, Gustaf; Kheddache, Susanne; Tingberg, Anders; Mansson, Lars Gunnar

2005-04-01

The aim of this work was to investigate and quantify the effects of system noise, nodule location, anatomical noise and anatomical background on the detection of lung nodules in different regions of the chest x-ray. Simulated lung nodules of diameter 10 mm but with varying detail contrast were randomly positioned in four different kinds of images: 1) clinical images collected with a 200 speed CR system, 2) images containing only system noise (including quantum noise) at the same level as the clinical images, 3) clinical images with removed anatomical noise, 4) artificial images with similar power spectrum as the clinical images but random phase spectrum. An ROC study was conducted with 5 observers. The detail contrast needed to obtain an Az of 0.80, C0.8, was used as measure of detectability. Five different regions of the chest x-ray were investigated separately. The C0.8 of the system noise images ranged from only 2% (the hilar regions) to 20% (the lateral pulmonary regions) of those of the clinical images. Compared with the original clinical images, the C0.8 was 16% lower for the de-noised clinical images and 71% higher for the random phase images, respectively, averaged over all five regions. In conclusion, regarding the detection of lung nodules with a diameter of 10 mm, the system noise is of minor importance at clinically relevant dose levels. The removal of anatomical noise and other noise sources uncorrelated from image to image leads to somewhat better detection, but the major component disturbing the detection is the overlapping of recognizable structures, which are, however, the main aspect of an x-ray image.

Extracting alveolar structure of human lung tissue specimens based on surface skeleton representation from 3D micro-CT images

NASA Astrophysics Data System (ADS)

Ishimori, Hiroyuki; Kawata, Yoshiki; Niki, Noboru; Nakaya, Yoshihiro; Ohmatsu, Hironobu; Matsui, Eisuke; Fujii, Masashi; Moriyama, Noriyuki

2007-03-01

We have developed a Micro CT system for understanding lung function at a high resolution of the micrometer order (up to 5µm in spatial resolution). Micro CT system enables the removal specimen of lungs to be observed at micro level, has expected a big contribution for micro internal organs morphology and the image diagnosis study. In this research, we develop system to visualize lung microstructures in three dimensions from micro CT images and analyze them. They characterize in that high CT value of the noise area is, and the difficulty of only using threshold processing to extract the alveolar wall of micro CT images. Thus, we are developing a method of extracting the alveolar wall with surface thinning algorithm. In this report, we propose the method which reduces the excessive degeneracy of figure which caused by surface thinning process. And, we apply this algorithm to the micro CT image of the actual pulmonary specimen. It is shown that the extraction of the alveolus wall becomes possible in the high precision.

Causes of death in long-term lung cancer survivors: a SEER database analysis.

PubMed

Abdel-Rahman, Omar

2017-07-01

Long-term (>5 years) lung cancer survivors represent a small but distinct subgroup of lung cancer patients and information about the causes of death of this subgroup is scarce. The Surveillance, Epidemiology and End Results (SEER) database (1988-2008) was utilized to determine the causes of death of long-term survivors of lung cancer. Survival analysis was conducted using Kaplan-Meier analysis and multivariate analysis was conducted using a Cox proportional hazard model. Clinicopathological characteristics and survival outcomes were assessed for the whole cohort. A total of 78,701 lung cancer patients with >5 years survival were identified. This cohort included 54,488 patients surviving 5-10 years and 24,213 patients surviving >10 years. Among patients surviving 5-10 years, 21.8% were dead because of primary lung cancer, 10.2% were dead because of other cancers, 6.8% were dead because of cardiac disease and 5.3% were dead because of non-malignant pulmonary disease. Among patients surviving >10 years, 12% were dead because of primary lung cancer, 6% were dead because of other cancers, 6.9% were dead because of cardiac disease and 5.6% were dead because of non-malignant pulmonary disease. On multivariate analysis, factors associated with longer cardiac-disease-specific survival in multivariate analysis include younger age at diagnosis (p < .0001), white race (vs. African American race) (p = .005), female gender (p < .0001), right-sided disease (p = .003), adenocarcinoma (vs. large cell or small cell carcinoma), histology and receiving local treatment by surgery rather than radiotherapy (p < .0001). The probability of death from primary lung cancer is still significant among other causes of death even 20 years after diagnosis of lung cancer. Moreover, cardiac as well as non-malignant pulmonary causes contribute a considerable proportion of deaths in long-term lung cancer survivors.

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.

Quantification of Pulmonary Fibrosis in a Bleomycin Mouse Model Using Automated Histological Image Analysis.

PubMed

Gilhodes, Jean-Claude; Julé, Yvon; Kreuz, Sebastian; Stierstorfer, Birgit; Stiller, Detlef; Wollin, Lutz

2017-01-01

Current literature on pulmonary fibrosis induced in animal models highlights the need of an accurate, reliable and reproducible histological quantitative analysis. One of the major limits of histological scoring concerns the fact that it is observer-dependent and consequently subject to variability, which may preclude comparative studies between different laboratories. To achieve a reliable and observer-independent quantification of lung fibrosis we developed an automated software histological image analysis performed from digital image of entire lung sections. This automated analysis was compared to standard evaluation methods with regard to its validation as an end-point measure of fibrosis. Lung fibrosis was induced in mice by intratracheal administration of bleomycin (BLM) at 0.25, 0.5, 0.75 and 1 mg/kg. A detailed characterization of BLM-induced fibrosis was performed 14 days after BLM administration using lung function testing, micro-computed tomography and Ashcroft scoring analysis. Quantification of fibrosis by automated analysis was assessed based on pulmonary tissue density measured from thousands of micro-tiles processed from digital images of entire lung sections. Prior to analysis, large bronchi and vessels were manually excluded from the original images. Measurement of fibrosis has been expressed by two indexes: the mean pulmonary tissue density and the high pulmonary tissue density frequency. We showed that tissue density indexes gave access to a very accurate and reliable quantification of morphological changes induced by BLM even for the lowest concentration used (0.25 mg/kg). A reconstructed 2D-image of the entire lung section at high resolution (3.6 μm/pixel) has been performed from tissue density values allowing the visualization of their distribution throughout fibrotic and non-fibrotic regions. A significant correlation (p<0.0001) was found between automated analysis and the above standard evaluation methods. This correlation establishes automated analysis as a novel end-point measure of BLM-induced lung fibrosis in mice, which will be very valuable for future preclinical drug explorations.

Quantification of Pulmonary Fibrosis in a Bleomycin Mouse Model Using Automated Histological Image Analysis

PubMed Central

Gilhodes, Jean-Claude; Kreuz, Sebastian; Stierstorfer, Birgit; Stiller, Detlef; Wollin, Lutz

2017-01-01

Current literature on pulmonary fibrosis induced in animal models highlights the need of an accurate, reliable and reproducible histological quantitative analysis. One of the major limits of histological scoring concerns the fact that it is observer-dependent and consequently subject to variability, which may preclude comparative studies between different laboratories. To achieve a reliable and observer-independent quantification of lung fibrosis we developed an automated software histological image analysis performed from digital image of entire lung sections. This automated analysis was compared to standard evaluation methods with regard to its validation as an end-point measure of fibrosis. Lung fibrosis was induced in mice by intratracheal administration of bleomycin (BLM) at 0.25, 0.5, 0.75 and 1 mg/kg. A detailed characterization of BLM-induced fibrosis was performed 14 days after BLM administration using lung function testing, micro-computed tomography and Ashcroft scoring analysis. Quantification of fibrosis by automated analysis was assessed based on pulmonary tissue density measured from thousands of micro-tiles processed from digital images of entire lung sections. Prior to analysis, large bronchi and vessels were manually excluded from the original images. Measurement of fibrosis has been expressed by two indexes: the mean pulmonary tissue density and the high pulmonary tissue density frequency. We showed that tissue density indexes gave access to a very accurate and reliable quantification of morphological changes induced by BLM even for the lowest concentration used (0.25 mg/kg). A reconstructed 2D-image of the entire lung section at high resolution (3.6 μm/pixel) has been performed from tissue density values allowing the visualization of their distribution throughout fibrotic and non-fibrotic regions. A significant correlation (p<0.0001) was found between automated analysis and the above standard evaluation methods. This correlation establishes automated analysis as a novel end-point measure of BLM-induced lung fibrosis in mice, which will be very valuable for future preclinical drug explorations. PMID:28107543

Design and deployment of a large brain-image database for clinical and nonclinical research

NASA Astrophysics Data System (ADS)

Yang, Guo Liang; Lim, Choie Cheio Tchoyoson; Banukumar, Narayanaswami; Aziz, Aamer; Hui, Francis; Nowinski, Wieslaw L.

2004-04-01

An efficient database is an essential component of organizing diverse information on image metadata and patient information for research in medical imaging. This paper describes the design, development and deployment of a large database system serving as a brain image repository that can be used across different platforms in various medical researches. It forms the infrastructure that links hospitals and institutions together and shares data among them. The database contains patient-, pathology-, image-, research- and management-specific data. The functionalities of the database system include image uploading, storage, indexing, downloading and sharing as well as database querying and management with security and data anonymization concerns well taken care of. The structure of database is multi-tier client-server architecture with Relational Database Management System, Security Layer, Application Layer and User Interface. Image source adapter has been developed to handle most of the popular image formats. The database has a user interface based on web browsers and is easy to handle. We have used Java programming language for its platform independency and vast function libraries. The brain image database can sort data according to clinically relevant information. This can be effectively used in research from the clinicians" points of view. The database is suitable for validation of algorithms on large population of cases. Medical images for processing could be identified and organized based on information in image metadata. Clinical research in various pathologies can thus be performed with greater efficiency and large image repositories can be managed more effectively. The prototype of the system has been installed in a few hospitals and is working to the satisfaction of the clinicians.

Increasing Receipt of High-Tech/High-Cost Imaging and Its Determinants in the Last Month of Taiwanese Patients With Metastatic Cancer, 2001–2010

PubMed Central

Liu, Tsang-Wu; Hung, Yen-Ni; Soong, Thomas C.; Tang, Siew Tzuh

2015-01-01

Abstract One strategy for controlling the skyrocketing costs of cancer care may be to target high-tech/high-cost imaging at the end of life (EOL). This population-based study investigated receipt of high-tech/high-cost imaging and its determinants for Taiwanese patients with metastatic cancer in their last month of life. Individual patient-level data were linked with encrypted identification numbers from computerized administrative data in Taiwan, that is, the National Register of Deaths Database, Cancer Registration System database, and National Health Insurance claims datasets, Database of Medical Care Institutions Status, and national census statistics (population/household income). We identified receipt of computerized tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and radionuclide bone scans (BSs) for 236,911 Taiwanese cancer decedents with metastatic disease, 2001 to 2010. Associations of patient, physician, hospital, and regional factors with receiving CT, MRI, and bone scan in the last month of life were evaluated by multilevel generalized linear-mixed models. Over one-third (average [range]: 36.11% [33.07%–37.31%]) of patients with metastatic cancer received at least 1 high-tech/high-cost imaging modality in their last month (usage rates for CT, MRI, PET, and BS were 31.05%, 5.81%, 0.25%, and 8.15%, respectively). In 2001 to 2010, trends of receipt increased for CT (27.96–32.22%), MRI (4.34–6.70%), and PET (0.00–0.62%), but decreased for BS (9.47–6.57%). Facilitative determinants with consistent trends for at least 2 high-tech/high-cost imaging modalities were male gender, younger age, married, rural residence, lung cancer diagnosis, dying within 1 to 2 years of diagnosis, not under medical oncology care, and receiving care at a teaching hospital with a larger volume of terminally ill cancer patients and greater EOL care intensity. Undergoing high-tech/high-cost imaging at EOL generally was not associated with regional characteristics, healthcare resources, and EOL care intensity. To more effectively use high-tech/high-cost imaging at EOL, clinical and financial interventions should target nonmedical oncologists/hematologists affiliated with teaching hospitals that tend to aggressively treat high volumes of terminally ill cancer patients, thereby avoiding unnecessary EOL care spending and transforming healthcare systems into affordable high-quality cancer care delivery systems. PMID:26266390

A System for Open-Access 3He Human Lung Imaging at Very Low Field

PubMed Central

RUSET, I.C.; TSAI, L.L.; MAIR, R.W.; PATZ, S.; HROVAT, M.I.; ROSEN, M.S.; MURADIAN, I.; NG, J.; TOPULOS, G.P.; BUTLER, J.P.; WALSWORTH, R.L.; HERSMAN, F.W.

2010-01-01

We describe a prototype system built to allow open-access very-low-field MRI of human lungs using laser-polarized 3He gas. The system employs an open four-coil electromagnet with an operational B0 field of 4 mT, and planar gradient coils that generate gradient fields up to 0.18 G/cm in the x and y direction and 0.41 G/cm in the z direction. This system was used to obtain 1H and 3He phantom images and supine and upright 3He images of human lungs. We include discussion on challenges unique to imaging at 50 –200 kHz, including noise filtering and compensation for narrow-bandwidth coils. PMID:20354575

Making sense of large data sets without annotations: analyzing age-related correlations from lung CT scans

NASA Astrophysics Data System (ADS)

Dicente Cid, Yashin; Mamonov, Artem; Beers, Andrew; Thomas, Armin; Kovalev, Vassili; Kalpathy-Cramer, Jayashree; Müller, Henning

2017-03-01

The analysis of large data sets can help to gain knowledge about specific organs or on specific diseases, just as big data analysis does in many non-medical areas. This article aims to gain information from 3D volumes, so the visual content of lung CT scans of a large number of patients. In the case of the described data set, only little annotation is available on the patients that were all part of an ongoing screening program and besides age and gender no information on the patient and the findings was available for this work. This is a scenario that can happen regularly as image data sets are produced and become available in increasingly large quantities but manual annotations are often not available and also clinical data such as text reports are often harder to share. We extracted a set of visual features from 12,414 CT scans of 9,348 patients that had CT scans of the lung taken in the context of a national lung screening program in Belarus. Lung fields were segmented by two segmentation algorithms and only cases where both algorithms were able to find left and right lung and had a Dice coefficient above 0.95 were analyzed. This assures that only segmentations of good quality were used to extract features of the lung. Patients ranged in age from 0 to 106 years. Data analysis shows that age can be predicted with a fairly high accuracy for persons under 15 years. Relatively good results were also obtained between 30 and 65 years where a steady trend is seen. For young adults and older people the results are not as good as variability is very high in these groups. Several visualizations of the data show the evolution patters of the lung texture, size and density with age. The experiments allow learning the evolution of the lung and the gained results show that even with limited metadata we can extract interesting information from large-scale visual data. These age-related changes (for example of the lung volume, the density histogram of the tissue) can also be taken into account for the interpretation of new cases. The database used includes patients that had suspicions on a chest X-ray, so it is not a group of healthy people, and only tendencies and not a model of a healthy lung at a specific age can be derived.

Lung imaging of laboratory rodents in vivo

NASA Astrophysics Data System (ADS)

Cody, Dianna D.; Cavanaugh, Dawn; Price, Roger E.; Rivera, Belinda; Gladish, Gregory; Travis, Elizabeth

2004-10-01

We have been acquiring respiratory-gated micro-CT images of live mice and rats for over a year with our General Electric (formerly Enhanced Vision Systems) hybrid scanner. This technique is especially well suited for the lung due to the inherent high tissue contrast. Our current studies focus on the assessment of lung tumors and their response to experimental agents, and the assessment of lung damage due to chemotherapy agents. We have recently installed a custom-built dual flat-panel cone-beam CT scanner with the ability to scan laboratory animals that vary in size from mice to large dogs. A breath-hold technique is used in place of respiratory gating on this scanner. The objective of this pilot study was to converge on scan acquisition parameters and optimize the visualization of lung damage in a mouse model of fibrosis. Example images from both the micro-CT scanner and the flat-panel CT scanner will be presented, as well as preliminary data describing spatial resolution, low contrast resolution, and radiation dose parameters.

DECT Ventilation Imaging

ClinicalTrials.gov

2018-03-21

For Oncologic Patients; Potentially Operable Lung Tumor; With a Recent (Less Than 1 Month) VQ Scan; For Lung Transplant Recipients; Single of Bilateral Lung Transplant; From 5 Months Onwards; With Recent (Less Than 1 Month) Respiratory Functional Explorations

Determination of a potential quantitative measure of the state of the lung using lung ultrasound spectroscopy.

PubMed

Demi, Libertario; van Hoeve, Wim; van Sloun, Ruud J G; Soldati, Gino; Demi, Marcello

2017-10-06

B-lines are ultrasound-imaging artifacts, which correlate with several lung-pathologies. However, their understanding and characterization is still largely incomplete. To further study B-lines, lung-phantoms were developed by trapping a layer of microbubbles in tissue-mimicking gel. To simulate the alveolar size reduction typical of various pathologies, 170 and 80 µm bubbles were used for phantom-type 1 and 2, respectively. A normal alveolar diameter is approximately 280 µm. A LA332 linear-array connected to the ULA-OP platform was used for imaging. Standard ultrasound (US) imaging at 4.5 MHz was performed. Subsequently, a multi-frequency approach was used where images were sequentially generated using orthogonal sub-bands centered at different frequencies (3, 4, 5, and 6 MHz). Results show that B-lines appear predominantly with phantom-type 2. Moreover, the multi-frequency approach revealed that the B-lines originate from a specific portion of the US spectrum. These results can give rise to significant clinical applications since, if further confirmed by extensive in-vivo studies, the native frequency of B-lines could provide a quantitative-measure of the state of the lung.

Lung aeration on post-mortem magnetic resonance imaging is a useful marker of live birth versus stillbirth.

PubMed

Barber, Joy L; Sebire, Neil J; Chitty, Lyn S; Taylor, Andrew M; Arthurs, Owen J

2015-05-01

Aim of this study was to investigate whether lung assessment on post-mortem magnetic resonance imaging (PMMR) can reliably differentiate between live birth and stillbirth. We retrospectively assessed PMMR imaging features of a group of late foetal terminations following fetocide and stillbirths (without witnessed breathing) and early infant deaths (breathed spontaneously before death). PMMR images were reviewed for evidence of lung aeration and other features, blinded to the clinical and autopsy data. Nineteen infant deaths (mean age 3.0 ± 6.5 post-natal weeks) and 23 foetal terminations or stillbirths (mean age 32.6 ± 10.2-week gestation) were compared. Subjective appearances of lung aeration on PMMR were the best indicator of live birth, with a sensitivity of 89.5% (95% confidence interval 68.6, 97.1%) and specificity of 95.6% (79.0, 99.2%) and positive and negative predictive values of 94.4% and 91.7%, respectively. Lung aeration on PMMR appears to have high overall accuracy for confirmation of live birth versus intrauterine foetal death but now requires validating in a larger cohort of perinatal deaths.

Adaptive Radiation for Lung Cancer

PubMed Central

Gomez, Daniel R.; Chang, Joe Y.

2011-01-01

The challenges of lung cancer radiotherapy are intra/inter-fraction tumor/organ anatomy/motion changes and the need to spare surrounding critical structures. Evolving radiotherapy technologies, such as four-dimensional (4D) image-based motion management, daily on-board imaging and adaptive radiotherapy based on volumetric images over the course of radiotherapy, have enabled us to deliver higher dose to target while minimizing normal tissue toxicities. The image-guided radiotherapy adapted to changes of motion and anatomy has made the radiotherapy more precise and allowed ablative dose delivered to the target using novel treatment approaches such as intensity-modulated radiation therapy, stereotactic body radiation therapy, and proton therapy in lung cancer, techniques used to be considered very sensitive to motion change. Future clinical trials using real time tracking and biological adaptive radiotherapy based on functional images are proposed. PMID:20814539

Applications and interpretation of krypton 81m ventilation/technetium 99m macroaggregate perfusion lung scanning in childhood

NASA Astrophysics Data System (ADS)

Davies, Hugh Trevor Frimston

Radionuclide ventilation perfusion lung scans now play an important part in the investigation of paediatric lung disease, providing a safe, noninvasive assessment of regional lung function in children with suspected pulmonary disease. In paediatric practice the most suitable radionuclides are Krypton 81m (Kr81m) and Technetium 99m (Tc99m), which are jointly used in the Kr81m ventilation/Tc99m macroaggregate perfusion lung scan (V/Q lung scan). The Kr81m ventilation scan involves a low radiation dose, requires little or no subject cooperation and because of the very short half life of Kr81m (13 seconds) the steady state image acquired during continuous inhalation of the radionuclide is considered to reflect regional distribution of ventilation. It is now the most important noninvasive method available for the investigation of the regional abnormalities of ventilation characteristic of many congenital and acquired paediatric respiratory diseases, such as diaphragmatic hernia, pulmonary sequestration, bronchopulmonary dysplasia, foreign body inhalation and bronchiectasis. It improves diagnostic accuracy, aids clinical decision making and is used to monitor the progress of disease and response to therapy. Theoretical analysis of the steady state Kr81m ventilation image suggests that it may only reflect regional ventilation when specific ventilation (ventilation per unit volume of lung) is within or below the normal adult range (1-3 L/L/min). At higher values such as those seen in neonates and infants (8-15 L/L/min) Kr81m activity may reflect regional lung volume rather than ventilation, a conclusion supported by the studies of Ciofetta et al. There is some controversy on this issue as animal studies have demonstrated that the Kr81m image reflects ventilation over a much wider range of specific ventilation (up to 13 L/L/min). A clinical study of sick infants and very young children is in agreement with this animal work and suggests that the steady state Kr81m image still reflects regional ventilation in this age group. The doubt cast on the interpretation of the Kr81m steady state image could limit the value of V/Q lung scans in following regional lung function through childhood, a period when specific ventilation is falling rapidly as the child grows. Therefore the first aim of this study was to examine the application of this theoretical model to children and determine whether the changing specific ventilation seen through childhood significantly alters the interpretation of the steady state Kr81m image. This is a necessary first step before conducting longitudinal studies of regional ventilation and perfusion in children. The effect of posture on regional ventilation and perfusion in the adult human lung has been extensively studied. Radiotracer studies have consistently shown that both ventilation and perfusion are preferentially distributed to dependent lung regions during tidal breathing regardless of posture. There is little published information concerning the pattern in children yet there are many differences in lung and chest wall mechanics of children and adults which, along with clinical observation, have led to the hypothesis that the pattern of regional ventilation observed in adults may not be seen in children. Recent reports of regional ventilation in infants and very young children have provided support for this theory. The paper of Heaf et al demonstrated that these differences may in certain circumstances be clinically important. It is not clear however at what age children adopt the "adult pattern of ventilation". In addition to the problems referred to above, attenuation of Kr81m activity as it passes through the chest wall and the changing geometry of the chest during tidal breathing have made quantitative analysis of the image difficult although fractional ventilation and perfusion to each lung can be calculated from the steady state image. In clinical practise, therefore, ventilation and perfusion are usually assessed by inspection of the steady state image. The aims of the present study were therefore: 1. To critically assess Kr81m ventilation and Tc99m MAA perfusion images in children. 2. To derive fractional ventilation and perfusion to each lung in children with normal chest radiography and homogeneous distribution of the radionuclides. 3. To conduct further studies into the effects of gravity on regional lung function. 4. To apply the technique in clinical practise. 5. To attempt to improve quantitation of the Kr81m ventilation image.

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

NASA Astrophysics Data System (ADS)

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

2015-03-01

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

Radiation dose reduction for CT lung cancer screening using ASIR and MBIR: a phantom study.

PubMed

Mathieu, Kelsey B; Ai, Hua; Fox, Patricia S; Godoy, Myrna Cobos Barco; Munden, Reginald F; de Groot, Patricia M; Pan, Tinsu

2014-03-06

The purpose of this study was to reduce the radiation dosage associated with computed tomography (CT) lung cancer screening while maintaining overall diagnostic image quality and definition of ground-glass opacities (GGOs). A lung screening phantom and a multipurpose chest phantom were used to quantitatively assess the performance of two iterative image reconstruction algorithms (adaptive statistical iterative reconstruction (ASIR) and model-based iterative reconstruction (MBIR)) used in conjunction with reduced tube currents relative to a standard clinical lung cancer screening protocol (51 effective mAs (3.9 mGy) and filtered back-projection (FBP) reconstruction). To further assess the algorithms' performances, qualitative image analysis was conducted (in the form of a reader study) using the multipurpose chest phantom, which was implanted with GGOs of two densities. Our quantitative image analysis indicated that tube current, and thus radiation dose, could be reduced by 40% or 80% from ASIR or MBIR, respectively, compared with conventional FBP, while maintaining similar image noise magnitude and contrast-to-noise ratio. The qualitative portion of our study, which assessed reader preference, yielded similar results, indicating that dose could be reduced by 60% (to 20 effective mAs (1.6 mGy)) with either ASIR or MBIR, while maintaining GGO definition. Additionally, the readers' preferences (as indicated by their ratings) regarding overall image quality were equal or better (for a given dose) when using ASIR or MBIR, compared with FBP. In conclusion, combining ASIR or MBIR with reduced tube current may allow for lower doses while maintaining overall diagnostic image quality, as well as GGO definition, during CT lung cancer screening.

CT fluoroscopy-guided robotically-assisted lung biopsy

NASA Astrophysics Data System (ADS)

Xu, Sheng; Fichtinger, Gabor; Taylor, Russell H.; Banovac, Filip; Cleary, Kevin

2006-03-01

Lung biopsy is a common interventional radiology procedure. One of the difficulties in performing the lung biopsy is that lesions move with respiration. This paper presents a new robotically assisted lung biopsy system for CT fluoroscopy that can automatically compensate for the respiratory motion during the intervention. The system consists of a needle placement robot to hold the needle on the CT scan plane, a radiolucent Z-frame for registration of the CT and robot coordinate systems, and a frame grabber to obtain the CT fluoroscopy image in real-time. The CT fluoroscopy images are used to noninvasively track the motion of a pulmonary lesion in real-time. The position of the lesion in the images is automatically determined by the image processing software and the motion of the robot is controlled to compensate for the lesion motion. The system was validated under CT fluoroscopy using a respiratory motion simulator. A swine study was also done to show the feasibility of the technique in a respiring animal.

Supercomputer description of human lung morphology for imaging analysis.

PubMed

Martonen, T B; Hwang, D; Guan, X; Fleming, J S

1998-04-01

A supercomputer code that describes the three-dimensional branching structure of the human lung has been developed. The algorithm was written for the Cray C94. In our simulations, the human lung was divided into a matrix containing discrete volumes (voxels) so as to be compatible with analyses of SPECT images. The matrix has 3840 voxels. The matrix can be segmented into transverse, sagittal and coronal layers analogous to human subject examinations. The compositions of individual voxels were identified by the type and respective number of airways present. The code provides a mapping of the spatial positions of the almost 17 million airways in human lungs and unambiguously assigns each airway to a voxel. Thus, the clinician and research scientist in the medical arena have a powerful new tool to be used in imaging analyses. The code was designed to be integrated into diverse applications, including the interpretation of SPECT images, the design of inhalation exposure experiments and the targeted delivery of inhaled pharmacologic drugs.

Mobile object retrieval in server-based image databases

NASA Astrophysics Data System (ADS)

Manger, D.; Pagel, F.; Widak, H.

2013-05-01

The increasing number of mobile phones equipped with powerful cameras leads to huge collections of user-generated images. To utilize the information of the images on site, image retrieval systems are becoming more and more popular to search for similar objects in an own image database. As the computational performance and the memory capacity of mobile devices are constantly increasing, this search can often be performed on the device itself. This is feasible, for example, if the images are represented with global image features or if the search is done using EXIF or textual metadata. However, for larger image databases, if multiple users are meant to contribute to a growing image database or if powerful content-based image retrieval methods with local features are required, a server-based image retrieval backend is needed. In this work, we present a content-based image retrieval system with a client server architecture working with local features. On the server side, the scalability to large image databases is addressed with the popular bag-of-word model with state-of-the-art extensions. The client end of the system focuses on a lightweight user interface presenting the most similar images of the database highlighting the visual information which is common with the query image. Additionally, new images can be added to the database making it a powerful and interactive tool for mobile contentbased image retrieval.

Renal cyst formation in patients treated with crizotinib for non-small cell lung cancer-Incidence, radiological features and clinical characteristics.

PubMed

Halpenny, Darragh F; McEvoy, Sinead; Li, Angela; Hayan, Sumar; Capanu, Marinela; Zheng, Junting; Riely, Gregory; Ginsberg, Michelle S

2017-04-01

Treatment with the ALK inhibitor crizotinib has been associated with complex renal cyst formation in patients with non-small cell lung cancer (NSCLC). Using patients treated with crizotinib, we aimed to evaluate the incidence of renal cyst formation, to identify risk factors for cyst formation and to provide a radiological description of cyst characteristics. Patients with ALK-positive NSCLC treated with crizotinib were retrospectively identified from an institutional database. Computed tomography (CT) imaging performed prior to and during crizotinib treatment was retrospectively reviewed to assess the size and complexity of pre-existing cysts, new cysts, and enlarging cysts. Demographic data including age, sex, ethnicity, smoking history and length of treatment were also recorded. Data from 60 patients with NSCLC treated with crizotinib at our institution between 6/5/2009 and 7/1/2015 were collected. 57 had CT imaging before and during treatment. Mean length of imaging follow-up was 18 months. 9 (16%) patients had cysts which enlarged or developed de novo during treatment. 2 (4%) patients developed complex renal cysts (1 of these patients also developed complex hepatic cysts). Female gender (p=0.008) and the presence of renal cysts on baseline scans (p=0.044) were significantly associated with cyst formation or growth. Renal cyst formation or growth occurred in 16% of crizotinib-treated patients. Women and those with pre-existing cysts were at greatest risk. Although the potential causal relationship between crizotinib use and renal cyst formation has yet to be fully defined, it is important for radiologists and clinicians to be aware of this finding. Copyright © 2017. Published by Elsevier B.V.

Multiple and solitary skeletal muscle metastases on 18F-FDG PET/CT imaging.

PubMed

Nocuń, Anna; Chrapko, Beata

2015-11-01

The aim of this study was to investigate the features and patterns of skeletal muscle metastases (SMM) detected with F-fluorodeoxyglucose (F-FDG) PET/computed tomography (PET/CT). Our database was analyzed for patients with pathologically proven malignancy, who underwent F-FDG PET/CT in our institution. The patients with SMM were included in the study group on the basis of the final diagnosis confirmed by follow-up or histopathology. Images were acquired using a PET/CT system Biograph mCT S(64)-4R. CT was performed without contrast enhancement. The selected group included 31 patients (1.7% of the database, which consisted of 1805 patients). A total of 233 lesions were found. The prevalence of SMM evaluated in specific primary malignancies was the highest in melanoma (6.9%), followed by carcinoma of unknown primary (4.4%), colorectal cancer (4.1%) and lung cancer (2.8%). Three patterns of skeletal muscle metastatic involvement were observed: multiple SMM accompanied by other metastases (64.5%), solitary lesion associated with other metastases (29%) and isolated intramuscular lesions (two cases, 6.5%). Isolated SMM represented recurrence of the malignant disease. In patients with extraskeletal metastases, solitary or multiple SMM did not affect tumor staging. Solitary SMM are less common than multiple on F-FDG PET/CT imaging. SMM are usually associated with other metastases and do not affect tumor staging. The cases of isolated SMM are very rare. Nevertheless, in patients with a diagnosis of malignant disease, a solitary, F-FDG avid intramuscular focus should be suspected to represent metastasis.

Biodistribution and Clearance of Human Mesenchymal Stem Cells by Quantitative Three-Dimensional Cryo-Imaging After Intravenous Infusion in a Rat Lung Injury Model.

PubMed

Schmuck, Eric G; Koch, Jill M; Centanni, John M; Hacker, Timothy A; Braun, Rudolf K; Eldridge, Marlowe; Hei, Derek J; Hematti, Peiman; Raval, Amish N

2016-12-01

: Cell tracking is a critical component of the safety and efficacy evaluation of therapeutic cell products. To date, cell-tracking modalities have been hampered by poor resolution, low sensitivity, and inability to track cells beyond the shortterm. Three-dimensional (3D) cryo-imaging coregisters fluorescent and bright-field microcopy images and allows for single-cell quantification within a 3D organ volume. We hypothesized that 3D cryo-imaging could be used to measure cell biodistribution and clearance after intravenous infusion in a rat lung injury model compared with normal rats. A bleomycin lung injury model was established in Sprague-Dawley rats (n = 12). Human mesenchymal stem cells (hMSCs) labeled with QTracker655 were infused via jugular vein. After 2, 4, or 8 days, a second dose of hMSCs labeled with QTracker605 was infused, and animals were euthanized after 60, 120, or 240 minutes. Lungs, liver, spleen, heart, kidney, testis, and intestine were cryopreserved, followed by 3D cryo-imaging of each organ. At 60 minutes, 82% ± 9.7% of cells were detected; detection decreased to 60% ± 17% and 66% ± 22% at 120 and 240 minutes, respectively. At day 2, 0.06% of cells were detected, and this level remained constant at days 4 and 8 postinfusion. At 60, 120, and 240 minutes, 99.7% of detected cells were found in the liver, lungs, and spleen, with cells primarily retained in the liver. This is the first study using 3D cryo-imaging to track hMSCs in a rat lung injury model. hMSCs were retained primarily in the liver, with fewer detected in lungs and spleen. Effective bench-to-bedside clinical translation of cellular therapies requires careful understanding of cell fate through tracking. Tracking cells is important to measure cell retention so that delivery methods and cell dose can be optimized and so that biodistribution and clearance can be defined to better understand potential off-target toxicity and redosing strategies. This article demonstrates, for the first time, the use of three-dimensional cryo-imaging for single-cell quantitative tracking of intravenous infused clinical-grade mesenchymal stem cells in a clinically relevant model of lung injury. The important information learned in this study will help guide future clinical and translational stem cell therapies for lung injuries. ©AlphaMed Press.

Small Nodules Localization on CT Images of Lungs

NASA Astrophysics Data System (ADS)

Snezhko, E. V.; Kharuzhyk, S. A.; Tuzikov, A. V.; Kovalev, V. A.

2017-05-01

According to the World Health Organization (WHO) lung cancer remains the leading cause of death of men among all malignant tumors [1, 2]. One of the reasons of such a statistics is the fact that the lung cancer is hardly diagnosed on the yearly stages when it is almost asymptomatic. The purpose of this paper is to present a Computer-Aided Diagnosis (CAD) software developed for assistance of early detection of nodules in CT lung images including solitary pulmonary nodules (SPN) as well as multiple nodules. The efficiency of nodule localization was intended to be as high as the level of the best practice. The software developed supports several functions including lungs segmentation, selection of nodule candidates and nodule candidates filtering.

Pediatric Lung Abscess: Immediate Diagnosis by Point-of-Care Ultrasound.

PubMed

Kraft, Clara; Lasure, Benjamin; Sharon, Melinda; Patel, Paulina; Minardi, Joseph

2018-06-01

The diagnosis of lung abscess can be difficult to make and often requires imaging beyond plain chest x-ray. The decision to further image with computed tomography should be weighed against the risks of radiation exposure, especially in pediatric patients. In addition, the cost and potential impact on length of stay from obtaining computed tomography scans should be considered. In this report, we describe a case of lung abscess made immediately using point-of-care ultrasound in the emergency department. To our knowledge, there are no previous cases describing lung abscess diagnosed by point-of-care ultrasound. This case report aims to describe a case of pediatric lung abscess, review the ultrasound findings, and discuss relevant literature on the topic.

Assessing lung deposition of inhaled medications. Consensus statement from a workshop of the British Association for Lung Research, held at the Institute of Biology, London, U.K. on 17 April 1998.

PubMed

Snell, N J; Ganderton, D

1999-02-01

In vitro measurements of aerosol fine particle fraction (FPF) using particle-sizing apparatus (e.g. the twin impinger, multi-stage liquid impingers, cascade impactors) have a key role to play in the development of new pharmaceutical products and in quality control. However, use of in vitro methodology to attempt to predict lung deposition in vivo is of limited value due, in part, to the inability of current apparatus to mimic upper and lower airway anatomy satisfactorily. Estimates of FPF based on cut-off points ranging from 5-7 microns generally overestimate lung deposition as measured in vivo by gamma scintigraphy. We recommend that: 1. multistage apparatus (minimum five stages) be used to characterize particle size distribution adequately, over the range 0.5-5.0 microns; 2. where possible, measurements should be made at a range of rates and profiles of flow reflecting those likely to be generated using the inhalation device in clinical practice (including use by young and elderly patients with varying degrees of airflow obstruction); 3. encouragement should be given to the further development, standardization, and validation of apparatus with a 'throat' which more closely resembles the human oropharynx and larynx. Pharmacokinetic methods can give a good estimate of total, but not regional, lung deposition, with drugs which are either not absorbed via the gastrointestinal tract, or whose absorption can be blocked by co-administration of charcoal, thus avoiding confounding by absorption of drug substance deposited in the oropharynx and subsequently swallowed. Techniques which rely on evaluation of a timed fractional output of drug substance in the urine are susceptible to the inherent variability of rate of absorption across the respiratory epithelium. We recommend that consideration should be given to the further refinement and validation of PK methods which would more clearly identify the fractional dose deposited in the lung. Lung-imaging methodology, e.g. gamma scintigraphy, employing formulations radiolabelled with gamma-ray-emitting radionuclides such as 99mTc, can measure total lung deposition and oropharyngeal deposition, provided that the radiolabelling process is appropriately validated and suitable corrections are made for attenuation of gamma rays by body tissues. An estimate of regional lung deposition can be made by drawing 'regions of interest' on the scintigraphic image; the precision of this measure is limited by the two-dimensional (2-D) nature of most images which mean that there is an overlay of structures of interest (alveoli, small and large airways), which is most marked centrally. Three-dimensional (3-D) imaging techniques (e.g. single photon emission computed tomography, SPECT, and positron emission tomography, PET) have the potential to give more detailed data on regional lung deposition, but are currently more expensive, employ higher radiation doses, and are less well validated than 2-D (planar) imaging. We consider that, of the available imaging modalities, planar gamma scintigraphy represents current best practice for the assessment of lung deposition from inhaler devices where regional differences may be important. The methodology should be optimized by the adoption of generally accepted standards for radiolabelling, imaging, attenuation correction, and interpretation. It is important that deposition in all sites (device, oropharynx, lungs, stomach) should be quantified. Consideration should be given to refining the concept of regions of interest to coincide more closely with anatomical lung structures. Statistical methods to compare the size distributions of drug and radiolabel in validation experiments should be developed. In the longer term it is envisaged that three-dimensional imaging may play a more important part in evaluating lung deposition; an optimal three-dimensional anatomical model of lung zones of interest needs to be developed.

Novel CT-based objective imaging biomarkers of long term radiation-induced lung damage.

PubMed

Veiga, Catarina; Landau, David; Devaraj, Anand; Doel, Tom; White, Jared; Ngai, Yenting; Hawkes, David J; McClelland, Jamie R

2018-06-14

and Purpose: Recent improvements in lung cancer survival have spurred an interest in understanding and minimizing long term radiation-induced lung damage (RILD). However, there is still no objective criteria to quantify RILD leading to variable reporting across centres and trials. We propose a set of objective imaging biomarkers to quantify common radiological findings observed 12-months after lung cancer radiotherapy (RT). Baseline and 12-month CT scans of 27 patients from a phase I/II clinical trial of isotoxic chemoradiation were included in this study. To detect and measure the severity of RILD, twelve quantitative imaging biomarkers were developed. These describe basic CT findings including parenchymal change, volume reduction and pleural change. The imaging biomarkers were implemented as semi-automated image analysis pipelines and assessed against visual assessment of the occurrence of each change. The majority of the biomarkers were measurable in each patient. Their continuous nature allows objective scoring of severity for each patient. For each imaging biomarker the cohort was split into two groups according to the presence or absence of the biomarker by visual assessment, testing the hypothesis that the imaging biomarkers were different in these two groups. All features were statistically significant except for rotation of the main bronchus and diaphragmatic curvature. The majority of the biomarkers were not strongly correlated with each other suggesting that each of the biomarkers is measuring a separate element of RILD pathology. We developed objective CT-based imaging biomarkers that quantify the severity of radiological lung damage after RT. These biomarkers are representative of typical radiological findings of RILD. Copyright © 2018. Published by Elsevier Inc.

Cell-surface marker discovery for lung cancer

PubMed Central

Cohen, Allison S.; Khalil, Farah K.; Welsh, Eric A.; Schabath, Matthew B.; Enkemann, Steven A.; Davis, Andrea; Zhou, Jun-Min; Boulware, David C.; Kim, Jongphil; Haura, Eric B.; Morse, David L.

2017-01-01

Lung cancer is the leading cause of cancer deaths in the United States. Novel lung cancer targeted therapeutic and molecular imaging agents are needed to improve outcomes and enable personalized care. Since these agents typically cannot cross the plasma membrane while carrying cytotoxic payload or imaging contrast, discovery of cell-surface targets is a necessary initial step. Herein, we report the discovery and characterization of lung cancer cell-surface markers for use in development of targeted agents. To identify putative cell-surface markers, existing microarray gene expression data from patient specimens were analyzed to select markers with differential expression in lung cancer compared to normal lung. Greater than 200 putative cell-surface markers were identified as being overexpressed in lung cancers. Ten cell-surface markers (CA9, CA12, CXorf61, DSG3, FAT2, GPR87, KISS1R, LYPD3, SLC7A11 and TMPRSS4) were selected based on differential mRNA expression in lung tumors vs. non-neoplastic lung samples and other normal tissues, and other considerations involving known biology and targeting moieties. Protein expression was confirmed by immunohistochemistry (IHC) staining and scoring of patient tumor and normal tissue samples. As further validation, marker expression was determined in lung cancer cell lines using microarray data and Kaplan–Meier survival analyses were performed for each of the markers using patient clinical data. High expression for six of the markers (CA9, CA12, CXorf61, GPR87, LYPD3, and SLC7A11) was significantly associated with worse survival. These markers should be useful for the development of novel targeted imaging probes or therapeutics for use in personalized care of lung cancer patients. PMID:29371917

Real-time imaging of inflation-induced ATP release in the ex vivo rat lung.

PubMed

Furuya, Kishio; Tan, Ju Jing; Boudreault, Francis; Sokabe, Masahiro; Berthiaume, Yves; Grygorczyk, Ryszard

2016-11-01

Extracellular ATP and other nucleotides are important autocrine/paracrine mediators that regulate diverse processes critical for lung function, including mucociliary clearance, surfactant secretion, and local blood flow. Cellular ATP release is mechanosensitive; however, the impact of physical stimuli on ATP release during breathing has never been tested in intact lungs in real time and remains elusive. In this pilot study, we investigated inflation-induced ATP release in rat lungs ex vivo by real-time luciferin-luciferase (LL) bioluminescence imaging coupled with simultaneous infrared tissue imaging to identify ATP-releasing sites. With LL solution introduced into air spaces, brief inflation of such edematous lung (1 s, ∼20 cmH 2 O) induced transient (<30 s) ATP release in a limited number of air-inflated alveolar sacs during their recruitment/opening. Released ATP reached concentrations of ∼10 -6 M, relevant for autocrine/paracrine signaling, but it remained spatially restricted to single alveolar sacs or their clusters. ATP release was stimulus dependent: prolonged (100 s) inflation evoked long-lasting ATP release that terminated upon alveoli deflation/derecruitment while cyclic inflation/suction produced cyclic ATP release. With LL introduced into blood vessels, inflation induced transient ATP release in many small patchlike areas the size of alveolar sacs. Findings suggest that inflation induces ATP release in both alveoli and the surrounding blood capillary network; the functional units of ATP release presumably consist of alveolar sacs or their clusters. Our study demonstrates the feasibility of real-time ATP release imaging in ex vivo lungs and provides the first direct evidence of inflation-induced ATP release in lung air spaces and in pulmonary blood capillaries, highlighting the importance of purinergic signaling in lung function. Copyright © 2016 the American Physiological Society.

Impact of hydrogel nanoparticle size and functionalization on in vivo behavior for lung imaging and therapeutics

PubMed Central

Liu, Yongjian; Ibricevic-Richardson, Aida; Cohen, Joel A.; Cohen, Jessica L.; Gunsten, Sean P.; Fréchet, Jean M. J.; Walter, Michael J.; Welch, Michael J.; Brody, Steven L.

2009-01-01

Polymer chemistry offers the possibility of synthesizing multifunctional nanoparticles which incorporate moieties that enhance diagnostic and therapeutic targeting of cargo delivery to the lung. However, since rules for predicting particle behavior following modification are not well defined, it is essential that probes for tracking fate in vivo are also included. Accordingly, we designed polyacrylamide-based hydrogel particles of differing sizes, functionalized with a nona-arginine cell-penetrating peptide (Arg9), and labeled with imaging components to assess lung retention and cellular uptake after intratracheal administration. Radiolabeled microparticles (1–5 µm diameter) and nanoparticles (20–40 nm diameter) without and with Arg9 showed diffuse airspace distribution by positron emission tomography imaging. Biodistribution studies revealed that particle clearance and extrapulmonary distribution was, in part, size dependent. Microparticles were rapidly cleared by mucociliary routes but unexpectedly, also through the circulation. In contrast, nanoparticles had prolonged lung retention enhanced by Arg9 and were significantly restricted to the lung. For all particle types, uptake was predominant in alveolar macrophages, and, to a lesser extent, lung epithelial cells. In general, particles did not induce local inflammatory responses, with the exception of microparticles bearing Arg9. Whereas microparticles may be advantageous for short-term applications, nano-sized particles constitute an efficient high-retention and non-inflammatory vehicle for the delivery of diagnostic imaging agents and therapeutics to lung airspaces and alveolar macrophages that can be enhanced by Arg9. Importantly, our results show that minor particle modifications may significantly impact in vivo behavior within the complex environments of the lung, underscoring the need for animal modeling. PMID:19852512

BEM-based simulation of lung respiratory deformation for CT-guided biopsy.

PubMed

Chen, Dong; Chen, Weisheng; Huang, Lipeng; Feng, Xuegang; Peters, Terry; Gu, Lixu

2017-09-01

Accurate and real-time prediction of the lung and lung tumor deformation during respiration are important considerations when performing a peripheral biopsy procedure. However, most existing work focused on offline whole lung simulation using 4D image data, which is not applicable in real-time image-guided biopsy with limited image resources. In this paper, we propose a patient-specific biomechanical model based on the boundary element method (BEM) computed from CT images to estimate the respiration motion of local target lesion region, vessel tree and lung surface for the real-time biopsy guidance. This approach applies pre-computation of various BEM parameters to facilitate the requirement for real-time lung motion simulation. The resulting boundary condition at end inspiratory phase is obtained using a nonparametric discrete registration with convex optimization, and the simulation of the internal tissue is achieved by applying a tetrahedron-based interpolation method depend on expert-determined feature points on the vessel tree model. A reference needle is tracked to update the simulated lung motion during biopsy guidance. We evaluate the model by applying it for respiratory motion estimations of ten patients. The average symmetric surface distance (ASSD) and the mean target registration error (TRE) are employed to evaluate the proposed model. Results reveal that it is possible to predict the lung motion with ASSD of [Formula: see text] mm and a mean TRE of [Formula: see text] mm at largest over the entire respiratory cycle. In the CT-/electromagnetic-guided biopsy experiment, the whole process was assisted by our BEM model and final puncture errors in two studies were 3.1 and 2.0 mm, respectively. The experiment results reveal that both the accuracy of simulation and real-time performance meet the demands of clinical biopsy guidance.

Positron emission tomography-based evidence of low-amplitude respiratory motion in patients with chronic obstructive pulmonary disease.

PubMed

Daouk, Joël; Bailly, Pascal; Kamimura, Mitsuhiro; Sacksick, David; Jounieaux, Vincent; Meyer, Marc-Etienne

2015-05-01

Chronic obstructive pulmonary disease (COPD) is characterized by low vital capacity and tidal volume, which translate into smaller respiratory motions. We sought to demonstrate the limited respiratory motion in COPD by comparing respiratory-gated and free-breathing positron emission tomography (PET) images of lung nodules ("CT-based" and "Ungated" images) in patients with and without COPD. We studied 74 lung lesions (37 malignant) in 60 patients (23 patients with COPD; 37 without). An Ungated PET examination was followed by a CT-based acquisition. Maximum standard uptake value (SUVmax) for each lesion on PET images was measured. On CT images, we checked for the presence of emphysema and pleural adhesions or indentations associated with the nodules. Lastly, we used univariate and then multivariate analyses to determine the lung function parameters possibly affecting respiratory motion in patients with and without COPD. The mean "CT-based" vs. "Ungated" difference in SUVmax was 0.3 and 0.6 for patients with and without COPD, respectively. Statistical analysis revealed that lesion site, hyperinflation and pleural indentation were independently associated with a difference in SUVmax. PET lung lesion images in patients with COPD are barely influenced by respiratory motion. Thoracic hyperinflation in patients with COPD was found to be independently associated with an effect of respiratory motion on PET images. Moreover, pleural indentation limits the respiratory motion of lung nodules, regardless of the presence or absence of COPD.

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

Ultrashort Echo-Time Magnetic Resonance Imaging Is a Sensitive Method for the Evaluation of Early Cystic Fibrosis Lung Disease

PubMed Central

Roach, David J.; Crémillieux, Yannick; Fleck, Robert J.; Brody, Alan S.; Serai, Suraj D.; Szczesniak, Rhonda D.; Kerlakian, Stephanie; Clancy, John P.

2016-01-01

Rationale: Recent advancements that have been made in magnetic resonance imaging (MRI) improve our ability to assess pulmonary structure and function in patients with cystic fibrosis (CF). A nonionizing imaging modality that can be used as a serial monitoring tool throughout life can positively affect patient care and outcomes. Objectives: To compare an ultrashort echo-time MRI method with computed tomography (CT) as a biomarker of lung structure abnormalities in young children with early CF lung disease. Methods: Eleven patients with CF (mean age, 31.8 ± 5.7 mo; median age, 33 mo; 7 male and 4 female) were imaged via CT and ultrashort echo-time MRI. Eleven healthy age-matched patients (mean age, 22.5 ± 10.2 mo; median age, 23 mo; 5 male and 6 female) were imaged via ultrashort echo-time MRI. CT scans of 13 additional patients obtained for clinical indications not affecting the heart or lungs and interpreted as normal provided a CT control group (mean age, 24.1 ± 11.7 mo; median age, 24 mo; 6 male and 7 female). Studies were scored by two experienced radiologists using a well-validated CF-specific scoring system for CF lung disease. Measurements and Main Results: Correlations between CT and ultrashort echo-time MRI scores of patients with CF were very strong, with P values ≤0.001 for bronchiectasis (r = 0.96) and overall score (r = 0.90), and moderately strong for bronchial wall thickening (r = 0.62, P = 0.043). MRI easily differentiated CF and control groups via a reader CF-specific scoring system. Conclusions: Ultrashort echo-time MRI detected structural lung disease in very young patients with CF and provided imaging data that correlated well with CT. By quantifying early CF lung disease without using ionizing radiation, ultrashort echo-time MRI appears well suited for pediatric patients requiring longitudinal imaging for clinical care or research studies. Clinical Trial registered with www.clinicaltrials.gov (NCT01832519). PMID:27551814

Micro-imaging of the Mouse Lung via MRI

NASA Astrophysics Data System (ADS)

Wang, Wei

Quantitative measurement of lung microstructure is of great significance in assessment of pulmonary disease, particularly in the earliest stages. Conventional stereological assessment of ex-vivo fixed tissue specimens under the microscope has a long and successful tradition and is regarded as a gold standard, but the invasive nature limits its applications and the practicality of use in longitudinal studies. The technique for diffusion MRI-based 3He lung morphometry was previously developed and validated for human lungs, and was recently extended to ex-vivo mouse lungs. The technique yields accurate, quantitative information about the microstructure and geometry of acinar airways. In this dissertation, the 3He lung morphometry technique is for the first time successfully implemented for in-vivo studies of mice. It can generate spatially-resolved maps of parameters that reveal the microstructure of mouse lung. Results in healthy mice indicate excellent agreement between in-vivo morphometry via 3He MRI and microscopic morphometry after sacrifice. The implementation and validation of 3He morphometry in healthy mice open up new avenues for application of the technique as a precise, noninvasive, in-vivo biomarker of changes in lung microstructure, within various mouse models of lung disease. We have applied 3He morphometry to the Sendai mouse model of lung disease. Specifically, the Sendai-virus model of chronic obstructive lung disease has demonstrated an innate immune response in mouse airways that exhibits similarities to the chronic airway inflammation in human COPD and asthma, but the effect on distal lung parenchyma had not been investigated. We imaged the time course and regional distribution of mouse lung microstructural changes in vivo after Sendai virus (SeV) infection with 1H and 3He diffusion MRI. 1H MR images detected the SeV-induced pulmonary inflammation in vivo and 3He lung morphometry showed modest increase in alveolar duct radius distal to airway inflammation, particularly in the lung periphery, indicating airspace enlargement after virus infection. Another important application of the imaging technique is the study of lung regeneration in a pneumonectomy (PNX) model. Partial resection of the lung by unilateral PNX is a robust model of compensatory lung growth. It is typically studied by postmortem morphometry in which longitudinal assessment in the same animal cannot be achieved. Here we successfully assess the microstructural changes and quantify the compensatory lung growth in vivo in the PNX mouse model via 1H and hyperpolarized 3He diffusion MRI. Our results show complete restoration in lung volume and total alveolar number with enlargement of alveolar size, which is consistent with prior histological studies conducted in different animals at various time points. This dissertation demonstrates that 3He lung morphometry has good sensitivity in quantifying small microstructural changes in the mouse lung and can be applied to a variety of mouse pulmonary models. Particularly, it has great potential to become a valuable tool in understanding the time course and the mechanism of lung growth in individual animals and may provide insight into post-natal lung growth and lung regeneration.

Initial clinical evaluation of stationary digital chest tomosynthesis

NASA Astrophysics Data System (ADS)

Hartman, Allison E.; Shan, Jing; Wu, Gongting; Lee, Yueh Z.; Zhou, Otto; Lu, Jianping; Heath, Michael; Wang, Xiaohui; Foos, David

2016-03-01

Computed Tomography (CT) is the gold standard for image evaluation of lung disease, including lung cancer and cystic fibrosis. It provides detailed information of the lung anatomy and lesions, but at a relatively high cost and high dose of radiation. Chest radiography is a low dose imaging modality but it has low sensitivity. Digital chest tomosynthesis (DCT) is an imaging modality that produces 3D images by collecting x-ray projection images over a limited angle. DCT is less expensive than CT and requires about 1/10th the dose of radiation. Commercial DCT systems acquire the projection images by mechanically scanning an x-ray tube. The movement of the tube head limits acquisition speed. We recently demonstrated the feasibility of stationary digital chest tomosynthesis (s-DCT) using a carbon nanotube (CNT) x-ray source array in benchtop phantom studies. The stationary x-ray source allows for fast image acquisition. The objective of this study is to demonstrate the feasibility of s-DCT for patient imaging. We have successfully imaged 31 patients. Preliminary evaluation by board certified radiologists suggests good depiction of thoracic anatomy and pathology.

Endoscopic high-resolution auto fluorescence imaging and optical coherence tomography of airways in vivo (Conference Presentation)

NASA Astrophysics Data System (ADS)

Pahlevaninezhad, Hamid; Lee, Anthony; Hohert, Geoffrey; Schwartz, Carley; Shaipanich, Tawimas; Ritchie, Alexander J.; Zhang, Wei; MacAulay, Calum E.; Lam, Stephen; Lane, Pierre M.

2016-03-01

In this work, we present multimodal imaging of peripheral airways in vivo using an endoscopic imaging system capable of co-registered optical coherence tomography and autofluorescence imaging (OCT-AFI). This system employs a 0.9 mm diameter double-clad fiber optic-based catheter for endoscopic imaging of small peripheral airways. Optical coherence tomography (OCT) can visualize detailed airway morphology in the lung periphery and autofluorescence imaging (AFI) can visualize fluorescent tissue components such as collagen and elastin, improving the detection of airway lesions. Results from in vivo imaging of 40 patients indicate that OCT and AFI offer complementary information that may increase the ability to identify pulmonary nodules in the lung periphery and improve the safety of biopsy collection by identifying large blood vessels. AFI can rapidly visualize in vivo vascular networks using fast scanning parameters resulting in vascular-sensitive imaging with less breathing/cardiac motion artifacts compared to Doppler OCT imaging. By providing complementary information about structure and function of tissue, OCT-AFI may improve site selection during biopsy collection in the lung periphery.

Determination of lung segments in computed tomography images using the Euclidean distance to the pulmonary artery

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

Stoecker, Christina; Moltz, Jan H.; Lassen, Bianca

Purpose: Computed tomography (CT) imaging is the modality of choice for lung cancer diagnostics. With the increasing number of lung interventions on sublobar level in recent years, determining and visualizing pulmonary segments in CT images and, in oncological cases, reliable segment-related information about the location of tumors has become increasingly desirable. Computer-assisted identification of lung segments in CT images is subject of this work.Methods: The authors present a new interactive approach for the segmentation of lung segments that uses the Euclidean distance of each point in the lung to the segmental branches of the pulmonary artery. The aim is tomore » analyze the potential of the method. Detailed manual pulmonary artery segmentations are used to achieve the best possible segment approximation results. A detailed description of the method and its evaluation on 11 CT scans from clinical routine are given.Results: An accuracy of 2–3 mm is measured for the segment boundaries computed by the pulmonary artery-based method. On average, maximum deviations of 8 mm are observed. 135 intersegmental pulmonary veins detected in the 11 test CT scans serve as reference data. Furthermore, a comparison of the presented pulmonary artery-based approach to a similar approach that uses the Euclidean distance to the segmental branches of the bronchial tree is presented. It shows a significantly higher accuracy for the pulmonary artery-based approach in lung regions at least 30 mm distal to the lung hilum.Conclusions: A pulmonary artery-based determination of lung segments in CT images is promising. In the tests, the pulmonary artery-based determination has been shown to be superior to the bronchial tree-based determination. The suitability of the segment approximation method for application in the planning of segment resections in clinical practice has already been verified in experimental cases. However, automation of the method accompanied by an evaluation on a larger number of test cases is required before application in the daily clinical routine.« less

SU-E-J-91: Biomechanical Deformable Image Registration of Longitudinal Lung CT Images

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

Cazoulat, G; Owen, D; Matuszak, M

2015-06-15

Purpose: Spatial correlation of lung tissue across longitudinal images, as the patient responds to treatment, is a critical step in adaptive radiotherapy. The goal of this work is to expand a biomechanical model-based deformable registration algorithm (Morfeus) to achieve accurate registration in the presence of significant anatomical changes. Methods: Four lung cancer patients previously treated with conventionally fractionated radiotherapy that exhibited notable tumor shrinkage during treatment were retrospectively evaluated. Exhale breathhold CT scans were obtained at treatment planning (PCT) and following three weeks (W3CT) of treatment. For each patient, the PCT was registered to the W3CT using Morfeus, a biomechanicalmore » model-based deformable registration algorithm, consisting of boundary conditions on the lungs and incorporating a sliding interface between the lung and chest wall. To model the complex response of the lung, an extension to Morfeus has been developed: (i) The vessel tree was segmented by thresholding a vesselness image based on the Hessian matrix’s eigenvalues and the centerline was extracted; (ii) A 3D shape context method was used to find correspondences between the trees of the two images; (ii) Correspondences were used as additional boundary conditions (Morfeus+vBC). An expert independently identified corresponding landmarks well distributed in the lung to compute Target Registration Errors (TRE). Results: The TRE within 15mm of the tumor boundaries (on average 11 landmarks) is: 6.1±1.8, 4.6±1.1 and 3.8±2.3 mm after rigid registration, Morfeus and Morfeus+vBC, respectively. The TRE in the rest of the lung (on average 13 landmarks) is: 6.4±3.9, 4.7±2.2 and 3.6±1.9 mm, which is on the order of the 2mm isotropic dose grid vector (3.5mm). Conclusion: The addition of boundary conditions on the vessels improved the accuracy in modeling the response of the lung and tumor over the course of radiotherapy. Minimizing and modeling these geometrical uncertainties will enable future plan adaptation strategies. This work was funded in part by NIH 2P01CA059827-16.« less

Tongue metastasis mimicking an abscess.

PubMed

Mavili, Ertuğrul; Oztürk, Mustafa; Yücel, Tuba; Yüce, Imdat; Cağli, Sedat

2010-03-01

Primary tumors metastasizing to the oral cavity are extremely rare. Lung is one of the most common primary sources of metastases to the tongue. Although the incidence of lung cancer is increasing, tongue metastasis as the initial presentation of the tumor remains uncommon. Due to the rarity of tongue metastasis, little is known about its imaging findings. Herein we report the magnetic resonance imaging and clinical findings of a lingual metastasis, mimicking an abscess, from a primary lung cancer.

The Impact of Sources of Variability on Parametric Response Mapping of Lung CT Scans

PubMed Central

Boes, Jennifer L.; Bule, Maria; Hoff, Benjamin A.; Chamberlain, Ryan; Lynch, David A.; Stojanovska, Jadranka; Martinez, Fernando J.; Han, Meilan K.; Kazerooni, Ella A.; Ross, Brian D.; Galbán, Craig J.

2015-01-01

Parametric response mapping (PRM) of inspiration and expiration computed tomography (CT) images improves the radiological phenotyping of chronic obstructive pulmonary disease (COPD). PRM classifies individual voxels of lung parenchyma as normal, emphysematous, or nonemphysematous air trapping. In this study, bias and noise characteristics of the PRM methodology to CT and clinical procedures were evaluated to determine best practices for this quantitative technique. Twenty patients of varying COPD status with paired volumetric inspiration and expiration CT scans of the lungs were identified from the baseline COPD-Gene cohort. The impact of CT scanner manufacturer and reconstruction kernels were evaluated as potential sources of variability in PRM measurements along with simulations to quantify the impact of inspiration/expiration lung volume levels, misregistration, and image spacing on PRM measurements. Negligible variation in PRM metrics was observed when CT scanner type and reconstruction were consistent and inspiration/expiration lung volume levels were near target volumes. CT scanner Hounsfield unit drift occurred but remained difficult to ameliorate. Increasing levels of image misregistration and CT slice spacing were found to have a minor effect on PRM measurements. PRM-derived values were found to be most sensitive to lung volume levels and mismatched reconstruction kernels. As with other quantitative imaging techniques, reliable PRM measurements are attainable when consistent clinical and CT protocols are implemented. PMID:26568983

Color-Coded Imaging of Breast Cancer Metastatic Niche Formation in Nude Mice.

PubMed

Suetsugu, Atsushi; Momiyama, Masashi; Hiroshima, Yukihiko; Shimizu, Masahito; Saji, Shigetoyo; Moriwaki, Hisataka; Bouvet, Michael; Hoffman, Robert M

2015-12-01

We report here a color-coded imaging model in which metastatic niches in the lung and liver of breast cancer can be identified. The transgenic green fluorescent protein (GFP)-expressing nude mouse was used as the host. The GFP nude mouse expresses GFP in all organs. However, GFP expression is dim in the liver parenchymal cells. Mouse mammary tumor cells (MMT 060562) (MMT), expressing red fluorescent protein (RFP), were injected in the tail vein of GFP nude mice to produce experimental lung metastasis and in the spleen of GFP nude mice to establish a liver metastasis model. Niche formation in the lung and liver metastasis was observed using very high resolution imaging systems. In the lung, GFP host-mouse cells accumulated around as few as a single MMT-RFP cell. In addition, GFP host cells were observed to form circle-shaped niches in the lung even without RFP cancer cells, which was possibly a niche in which future metastasis could be formed. In the liver, as with the lung, GFP host cells could form circle-shaped niches. Liver and lung metastases were removed surgically and cultured in vitro. MMT-RFP cells and GFP host cells resembling cancer-associated fibroblasts (CAFs) were observed interacting, suggesting that CAFs could serve as a metastatic niche. © 2015 Wiley Periodicals, Inc.

Flexible needle with integrated optical coherence tomography probe for imaging during transbronchial tissue aspiration

NASA Astrophysics Data System (ADS)

Li, Jiawen; Quirk, Bryden C.; Noble, Peter B.; Kirk, Rodney W.; Sampson, David D.; McLaughlin, Robert A.

2017-10-01

Transbronchial needle aspiration (TBNA) of small lesions or lymph nodes in the lung may result in nondiagnostic tissue samples. We demonstrate the integration of an optical coherence tomography (OCT) probe into a 19-gauge flexible needle for lung tissue aspiration. This probe allows simultaneous visualization and aspiration of the tissue. By eliminating the need for insertion and withdrawal of a separate imaging probe, this integrated design minimizes the risk of dislodging the needle from the lesion prior to aspiration and may facilitate more accurate placement of the needle. Results from in situ imaging in a sheep lung show clear distinction between solid tissue and two typical constituents of nondiagnostic samples (adipose and lung parenchyma). Clinical translation of this OCT-guided aspiration needle holds promise for improving the diagnostic yield of TBNA.

Super-resolution convolutional neural network for the improvement of the image quality of magnified images in chest radiographs

NASA Astrophysics Data System (ADS)

Umehara, Kensuke; Ota, Junko; Ishimaru, Naoki; Ohno, Shunsuke; Okamoto, Kentaro; Suzuki, Takanori; Shirai, Naoki; Ishida, Takayuki

2017-02-01

Single image super-resolution (SR) method can generate a high-resolution (HR) image from a low-resolution (LR) image by enhancing image resolution. In medical imaging, HR images are expected to have a potential to provide a more accurate diagnosis with the practical application of HR displays. In recent years, the super-resolution convolutional neural network (SRCNN), which is one of the state-of-the-art deep learning based SR methods, has proposed in computer vision. In this study, we applied and evaluated the SRCNN scheme to improve the image quality of magnified images in chest radiographs. For evaluation, a total of 247 chest X-rays were sampled from the JSRT database. The 247 chest X-rays were divided into 93 training cases with non-nodules and 152 test cases with lung nodules. The SRCNN was trained using the training dataset. With the trained SRCNN, the HR image was reconstructed from the LR one. We compared the image quality of the SRCNN and conventional image interpolation methods, nearest neighbor, bilinear and bicubic interpolations. For quantitative evaluation, we measured two image quality metrics, peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). In the SRCNN scheme, PSNR and SSIM were significantly higher than those of three interpolation methods (p<0.001). Visual assessment confirmed that the SRCNN produced much sharper edge than conventional interpolation methods without any obvious artifacts. These preliminary results indicate that the SRCNN scheme significantly outperforms conventional interpolation algorithms for enhancing image resolution and that the use of the SRCNN can yield substantial improvement of the image quality of magnified images in chest radiographs.

Metallic artifact mitigation and organ-constrained tissue assignment for Monte Carlo calculations of permanent implant lung brachytherapy.

PubMed

Sutherland, J G H; Miksys, N; Furutani, K M; Thomson, R M

2014-01-01

To investigate methods of generating accurate patient-specific computational phantoms for the Monte Carlo calculation of lung brachytherapy patient dose distributions. Four metallic artifact mitigation methods are applied to six lung brachytherapy patient computed tomography (CT) images: simple threshold replacement (STR) identifies high CT values in the vicinity of the seeds and replaces them with estimated true values; fan beam virtual sinogram replaces artifact-affected values in a virtual sinogram and performs a filtered back-projection to generate a corrected image; 3D median filter replaces voxel values that differ from the median value in a region of interest surrounding the voxel and then applies a second filter to reduce noise; and a combination of fan beam virtual sinogram and STR. Computational phantoms are generated from artifact-corrected and uncorrected images using several tissue assignment schemes: both lung-contour constrained and unconstrained global schemes are considered. Voxel mass densities are assigned based on voxel CT number or using the nominal tissue mass densities. Dose distributions are calculated using the EGSnrc user-code BrachyDose for (125)I, (103)Pd, and (131)Cs seeds and are compared directly as well as through dose volume histograms and dose metrics for target volumes surrounding surgical sutures. Metallic artifact mitigation techniques vary in ability to reduce artifacts while preserving tissue detail. Notably, images corrected with the fan beam virtual sinogram have reduced artifacts but residual artifacts near sources remain requiring additional use of STR; the 3D median filter removes artifacts but simultaneously removes detail in lung and bone. Doses vary considerably between computational phantoms with the largest differences arising from artifact-affected voxels assigned to bone in the vicinity of the seeds. Consequently, when metallic artifact reduction and constrained tissue assignment within lung contours are employed in generated phantoms, this erroneous assignment is reduced, generally resulting in higher doses. Lung-constrained tissue assignment also results in increased doses in regions of interest due to a reduction in the erroneous assignment of adipose to voxels within lung contours. Differences in dose metrics calculated for different computational phantoms are sensitive to radionuclide photon spectra with the largest differences for (103)Pd seeds and smallest but still considerable differences for (131)Cs seeds. Despite producing differences in CT images, dose metrics calculated using the STR, fan beam + STR, and 3D median filter techniques produce similar dose metrics. Results suggest that the accuracy of dose distributions for permanent implant lung brachytherapy is improved by applying lung-constrained tissue assignment schemes to metallic artifact corrected images.

Enhancing 18F-FDG-PET/CT analysis in lung cancer patients. Is CT-CT image fusion helpful in predicting pleural involvement? A pilot study.

PubMed

Kapfhammer, A; Winkens, T; Lesser, T; Reissig, A; Steinert, M; Freesmeyer, M

2015-01-01

To retrospectively evaluate the feasibility and value of CT-CT image fusion to assess the shift of peripheral lung cancers with/-out chest wall infiltration, comparing computed tomography acquisitions in shallow-breathing (SB-CT) and deep-inspiration breath-hold (DIBH-CT) in patients undergoing FDG-PET/CT for lung cancer staging. Image fusion of SB-CT and DIBH-CT was performed with a multimodal workstation used for nuclear medicine fusion imaging. The distance of intrathoracic landmarks and the positional shift of tumours were measured using semi-transparent overlay of both CT series. Statistical analyses were adjusted for confounders of tumour infiltration. Cutoff levels were calculated for prediction of no-/infiltration. Lateral pleural recessus and diaphragm showed the largest respiratory excursions. Infiltrating lung cancers showed more limited respiratory shifts than non-infiltrating tumours. A large respiratory tumour-motility accurately predicted non-infiltration. However, the tumour shifts were limited and variable, limiting the accuracy of prediction. This pilot fusion study proved feasible and allowed a simple analysis of the respiratory shifts of peripheral lung tumours using CT-CT image fusion in a PET/CT setting. The calculated cutoffs were useful in predicting the exclusion of chest wall infiltration but did not accurately predict tumour infiltration. This method can provide additional qualitative information in patients with lung cancers with contact to the chest wall but unclear CT evidence of infiltration undergoing PET/CT without the need of additional investigations. Considering the small sample size investigated, further studies are necessary to verify the obtained results.

Lung Parenchymal Signal Intensity in MRI: A Technical Review with Educational Aspirations Regarding Reversible Versus Irreversible Transverse Relaxation Effects in Common Pulse Sequences.

PubMed

Mulkern, Robert; Haker, Steven; Mamata, Hatsuho; Lee, Edward; Mitsouras, Dimitrios; Oshio, Koichi; Balasubramanian, Mukund; Hatabu, Hiroto

2014-03-01

Lung parenchyma is challenging to image with proton MRI. The large air space results in ~l/5th as many signal-generating protons compared to other organs. Air/tissue magnetic susceptibility differences lead to strong magnetic field gradients throughout the lungs and to broad frequency distributions, much broader than within other organs. Such distributions have been the subject of experimental and theoretical analyses which may reveal aspects of lung microarchitecture useful for diagnosis. Their most immediate relevance to current imaging practice is to cause rapid signal decays, commonly discussed in terms of short T 2 * values of 1 ms or lower at typical imaging field strengths. Herein we provide a brief review of previous studies describing and interpreting proton lung spectra. We then link these broad frequency distributions to rapid signal decays, though not necessarily the exponential decays generally used to define T 2 * values. We examine how these decays influence observed signal intensities and spatial mapping features associated with the most prominent torso imaging sequences, including spoiled gradient and spin echo sequences. Effects of imperfect refocusing pulses on the multiple echo signal decays in single shot fast spin echo (SSFSE) sequences and effects of broad frequency distributions on balanced steady state free precession (bSSFP) sequence signal intensities are also provided. The theoretical analyses are based on the concept of explicitly separating the effects of reversible and irreversible transverse relaxation processes, thus providing a somewhat novel and more general framework from which to estimate lung signal intensity behavior in modern imaging practice.

Lung Parenchymal Signal Intensity in MRI: A Technical Review with Educational Aspirations Regarding Reversible Versus Irreversible Transverse Relaxation Effects in Common Pulse Sequences

PubMed Central

MULKERN, ROBERT; HAKER, STEVEN; MAMATA, HATSUHO; LEE, EDWARD; MITSOURAS, DIMITRIOS; OSHIO, KOICHI; BALASUBRAMANIAN, MUKUND; HATABU, HIROTO

2014-01-01

Lung parenchyma is challenging to image with proton MRI. The large air space results in ~l/5th as many signal-generating protons compared to other organs. Air/tissue magnetic susceptibility differences lead to strong magnetic field gradients throughout the lungs and to broad frequency distributions, much broader than within other organs. Such distributions have been the subject of experimental and theoretical analyses which may reveal aspects of lung microarchitecture useful for diagnosis. Their most immediate relevance to current imaging practice is to cause rapid signal decays, commonly discussed in terms of short T2* values of 1 ms or lower at typical imaging field strengths. Herein we provide a brief review of previous studies describing and interpreting proton lung spectra. We then link these broad frequency distributions to rapid signal decays, though not necessarily the exponential decays generally used to define T2* values. We examine how these decays influence observed signal intensities and spatial mapping features associated with the most prominent torso imaging sequences, including spoiled gradient and spin echo sequences. Effects of imperfect refocusing pulses on the multiple echo signal decays in single shot fast spin echo (SSFSE) sequences and effects of broad frequency distributions on balanced steady state free precession (bSSFP) sequence signal intensities are also provided. The theoretical analyses are based on the concept of explicitly separating the effects of reversible and irreversible transverse relaxation processes, thus providing a somewhat novel and more general framework from which to estimate lung signal intensity behavior in modern imaging practice. PMID:25228852

A Four-Dimensional Computed Tomography Comparison of Healthy vs. Asthmatic Human Lungs

PubMed Central

Jahani, Nariman; Choi, Sanghun; Choi, Jiwoong; Haghighi, Babak; Hoffman, Eric A.; Comellas, Alejandro P.; Kline, Joel N.; Lin, Ching-Long

2017-01-01

The purpose of this study was to explore new insights in non-linearity, hysteresis and ventilation heterogeneity of asthmatic human lungs using four-dimensional computed tomography (4D-CT) image data acquired during tidal breathing. Volumetric image data were acquired for 5 non-severe and one severe asthmatic volunteers. Besides 4D-CT image data, function residual capacity and total lung capacity image data during breath-hold were acquired for comparison with dynamic scans. Quantitative results were compared with the previously reported analysis of five healthy human lungs. Using an image registration technique, local variables such as regional ventilation and anisotropic deformation index (ADI) were estimated. Regional ventilation characteristics of non-severe asthmatic subjects were similar to those of healthy subjects, but different from the severe asthmatic subject. Lobar airflow fractions were also well correlated between static and dynamic scans (R2 > 0.84). However, local ventilation heterogeneity significantly increased during tidal breathing in both healthy and asthmatic subjects relative to that of breath-hold perhaps because of airway resistance present only in dynamic breathing. ADI was used to quantify non-linearity and hysteresis of lung motion during tidal breathing. Nonlinearity was greater on inhalation than exhalation among all subjects. However, exhalation nonlinearity among asthmatic subjects was greater than healthy subjects and the difference diminished during inhalation. An increase of non-linearity during exhalation in asthmatic subjects accounted for lower hysteresis relative to that of healthy ones. Thus, assessment of nonlinearity differences between healthy and asthmatic lungs during exhalation may provide quantitative metrics for subject identification and outcome assessment of new interventions. PMID:28372795