Jean Kuriakose

Acute Pulmonary Embolism

Evolving MDCT technology and high accuracy for pulmonary embolism detection has led to CT pulmonary angiography (CTPA) becoming a first-line imaging test. Rapid and accurate assessment for DVT and PE can be performed with a single test. Concerns remain regarding the radiation exposure incurred with CTPA and CT venography, especially in young patients. There are concerns also regarding radiation exposure in pregnancy and search for the best diagnostic test for PE in pregnancy. The increased detection of subsegmental emboli raises the question as to which emboli are significant and should be treated and which should be left alone. We review the current role of CT in the diagnosis of pulmonary embolism.

Computerized detection of noncalcified plaques in coronary CT angiography: evaluation of topological soft gradient prescreening method and luminal analysis.

PURPOSE The buildup of noncalcified plaques (NCPs) that are vulnerable to rupture in coronary arteries is a risk for myocardial infarction. Interpretation of coronary CT angiography (cCTA) to search for NCP is a challenging task for radiologists due to the low CT number of NCP, the large number of coronary arteries, and multiple phase CT acquisition. The authors conducted a preliminary study to develop machine learning method for automated detection of NCPs in cCTA. METHODS With IRB approval, a data set of 83 ECG-gated contrast enhanced cCTA scans with 120 NCPs was collected retrospectively from patient files. A multiscale coronary artery response and rolling balloon region growing (MSCAR-RBG) method was applied to each cCTA volume to extract the coronary arterial trees. Each extracted vessel was reformatted to a straightened volume composed of cCTA slices perpendicular to the vessel centerline. A topological soft-gradient (TSG) detection method was developed to prescreen for NCP candidates by analyzing the 2D topological features of the radial gradient field surface along the vessel wall. The NCP candidates were then characterized by a luminal analysis that used 3D geometric features to quantify the shape information and gray-level features to evaluate the density of the NCP candidates. With machine learning techniques, useful features were identified and combined into an NCP score to differentiate true NCPs from false positives (FPs). To evaluate the effectiveness of the image analysis methods, the authors performed tenfold cross-validation with the available data set. Receiver operating characteristic (ROC) analysis was used to assess the classification performance of individual features and the NCP score. The overall detection performance was estimated by free response ROC (FROC) analysis. RESULTS With our TSG prescreening method, a prescreening sensitivity of 92.5% (111/120) was achieved with a total of 1181 FPs (14.2 FPs/scan). On average, six features were selected during the tenfold cross-validation training. The average area under the ROC curve (AUC) value for training was 0.87 ± 0.01 and the AUC value for validation was 0.85 ± 0.01. Using the NCP score, FROC analysis of the validation set showed that the FP rates were reduced to 3.16, 1.90, and 1.39 FPs/scan at sensitivities of 90%, 80%, and 70%, respectively. CONCLUSIONS The topological soft-gradient prescreening method in combination with the luminal analysis for FP reduction was effective for detection of NCPs in cCTA, including NCPs causing positive or negative vessel remodeling. The accuracy of vessel segmentation, tracking, and centerline identification has a strong impact on NCP detection. Studies are underway to further improve these techniques and reduce the FPs of the CADe system.

Automatic selection of best quality vessels from multiple-phase coronary CT angiography (cCTA)

We are developing an automated method to select the best-quality vessels from coronary arterial trees in multiplephase cCTA and build a best-quality tree to facilitate the detection of stenotic plaques. Using our previously developed vessel registration method, the vessels from different phases were automatically registered. Branching points on the centerline are projected onto the registered trees. The centerlines are split into branches based on the projected branching points. Each tree branch is then straightened. The registered trees and centerline branches are used to determine the correspondence of branches between phases so that each branch can be compared to its corresponding branches in the other phases. A vessel quality measure (VQM) is calculated as the average radial gradients at the vessel wall over the entire vessel branch. The quality of the corresponding branches from all phases is automatically compared using the VQM. An observer preference study was conducted with two radiologists to visually compare the quality of the vessels. For each branch, the pair that was automatically determined to be the best and worst quality by the VQM was used for the radiologists’ visual assessment. Each radiologist, blinded to the VQM, evaluated pairs of corresponding branches and provided their preference. The performance of the automatic selection using VQM was evaluated as the percentage of the total number of vessel pairs for which the automatic selection agreed with the radiologist’s selection of the higher quality branch in the pair. The agreement between the first radiologist and the automated selection was 80% and that between the second radiologist and the automated selection was 82%. In comparison, the agreement between the two radiologists was 90%. This preliminary study demonstrates the feasibility of using an automated method to select the best-quality vessels from multiple cCTA phases.

Automated registration of coronary arterial trees from multiple phases in coronary CT angiography (cCTA)

We are developing an automated registration method for coronary arterial trees from multiple-phase cCTA to build a best-quality tree to facilitate detection of stenotic plaques. Cubic B-spline with fast localized optimization (CBSO) is designed to register the initially segmented left and right coronary arterial trees (LCA or RCA) separately in adjacent phase pairs where displacements are small. First, the corresponding trees in phase 1 and 2 are registered. The phase 3 tree is then registered to the combined tree. Similarly the trees in phases 4, 5, and 6 are registered. An affine transform with quadratic terms and nonlinear simplex optimization (AQSO) is designed to register the trees between phases with large displacements, namely, registering the combined tree from phases 1, 2, and 3 to that from phases 4, 5, and 6. Finally, CBSO is again applied to the AQSO registered volumes for final refinement. The costs determined by the distances between the vessel centerlines, bifurcation points and voxels of the trees are minimized to guide both CBSO and AQSO registration. The registration performance was evaluated on 22 LCA and 22 RCA trees on 22 CTA scans with 6 phases from 22 patients. The average distance between the centerlines of the registered trees was used as a registration quality index. The average distances for LCA and RCA registration for 6 phases and 22 patients were 1.49 and 1.43 pixels, respectively. This study demonstrates the feasibility of using automated method for registration of coronary arterial trees from multiple cCTA phases.

Robustness evaluation of a computer-aided detection system for pulmonary embolism (PE) in CTPA using independent test set from multiple institutions

We have developed a computer-aided detection (CAD) system for assisting radiologists in detection of pulmonary embolism (PE) in computed tomographic pulmonary angiographic (CTPA) images. The CAD system includes stages of pulmonary vessel segmentation, prescreening of PE candidates and false positive (FP) reduction to identify suspicious PEs. The system was trained with 59 CTPA PE cases collected retrospectively from our patient files (UM set) with IRB approval. Five feature groups containing 139 features that characterized the intensity texture, gradient, intensity homogeneity, shape, and topology of PE candidates were initially extracted. Stepwise feature selection guided by simplex optimization was used to select effective features for FP reduction. A linear discriminant analysis (LDA) classifier was formulated to differentiate true PEs from FPs. The purpose of this study is to evaluate the performance of our CAD system using an independent test set of CTPA cases. The test set consists of 50 PE cases from the PIOPED II data set collected by multiple institutions with access permission. A total of 537 PEs were manually marked by experienced thoracic radiologists as reference standard for the test set. The detection performance was evaluated by freeresponse receiver operating characteristic (FROC) analysis. The FP classifier obtained a test Az value of 0.847 and the FROC analysis indicated that the CAD system achieved an overall sensitivity of 80% at 8.6 FPs/case for the PIOPED test set.

Computerized detection of non-calcified plaques in coronary CT angiography: topological soft-gradient detection method for plaque prescreening

Non-calcified plaque (NCP) detection in coronary CT angiography (cCTA) is challenging due to the low CT number of NCP, the large number of coronary arteries and multiple phase CT acquisition. We are developing computervision methods for automated detection of NCPs in cCTA. A data set of 62 cCTA scans with 87 NCPs was collected retrospectively from patient files. Multiscale coronary vessel enhancement and rolling balloon tracking were first applied to each cCTA volume to extract the coronary artery trees. Each extracted vessel was reformatted to a straightened volume composed of cCTA slices perpendicular to the vessel centerline. A new topological soft-gradient (TSG) detection method was developed to prescreen for both positive and negative remodeling candidates by analyzing the 2D topological features of the radial gradient field surface along the vessel wall. Nineteen features were designed to describe the relative location along the coronary artery, shape, distribution of CT values, and radial gradients of each NCP candidate. With a machine learning algorithm and a two-loop leave-one-case-out training and testing resampling method, useful features were selected and combined into an NCP likelihood measure to differentiate TPs from FPs. The detection performance was evaluated by FROC analysis. Our TSG method achieved a sensitivity of 96.6% with 35.4 FPs/scan at prescreening. Classification with the NCP likelihood measure reduced the FP rates to 13.1, 10.0 and 6.7 FPs/scan at sensitivities of 90%, 80%, and 70%, respectively. These results demonstrated that the new TSG method is useful for computerized detection of NCPs in cCTA.

Automatic identification of origins of left and right coronary arteries in CT angiography for coronary arterial tree tracking and plaque detection

Automatic tracking and segmentation of the coronary arterial tree is the basic step for computer-aided analysis of coronary disease. The goal of this study is to develop an automated method to identify the origins of the left coronary artery (LCA) and right coronary artery (RCA) as the seed points for the tracking of the coronary arterial trees. The heart region and the contrast-filled structures in the heart region are first extracted using morphological operations and EM estimation. To identify the ascending aorta, we developed a new multiscale aorta search method (MAS) method in which the aorta is identified based on a-priori knowledge of its circular shape. Because the shape of the ascending aorta in the cCTA axial view is roughly a circle but its size can vary over a wide range for different patients, multiscale circularshape priors are used to search for the best matching circular object in each CT slice, guided by the Hausdorff distance (HD) as the matching indicator. The location of the aorta is identified by finding the minimum HD in the heart region over the set of multiscale circular priors. An adaptive region growing method is then used to extend the above initially identified aorta down to the aortic valves. The origins at the aortic sinus are finally identified by a morphological gray level top-hat operation applied to the region-grown aorta with morphological structuring element designed for coronary arteries. For the 40 test cases, the aorta was correctly identified in 38 cases (95%). The aorta can be grown to the aortic root in 36 cases, and 36 LCA origins and 34 RCA origins can be identified within 10 mm of the locations marked by radiologists.

Best-Quality Vessel Identification Using Vessel Quality Measure in Multiple-Phase Coronary CT Angiography

The detection of stenotic plaques strongly depends on the quality of the coronary arterial tree imaged with coronary CT angiography (cCTA). However, it is time consuming for the radiologist to select the best-quality vessels from the multiple-phase cCTA for interpretation in clinical practice. We are developing an automated method for selection of the best-quality vessels from coronary arterial trees in multiple-phase cCTA to facilitate radiologists reading or computerized analysis. Our automated method consists of vessel segmentation, vessel registration, corresponding vessel branch matching, vessel quality measure (VQM) estimation, and automatic selection of best branches based on VQM. For every branch, the VQM was calculated as the average radial gradient. An observer preference study was conducted to visually compare the quality of the selected vessels. 167 corresponding branch pairs were evaluated by two radiologists. The agreement between the first radiologist and the automated selection was 76% with kappa of 0.49. The agreement between the second radiologist and the automated selection was also 76% with kappa of 0.45. The agreement between the two radiologists was 81% with kappa of 0.57. The observer preference study demonstrated the feasibility of the proposed automated method for the selection of the best-quality vessels from multiple cCTA phases.

Quantitative analysis of arterial flow properties for detection of non-calcified plaques in ECG-gated coronary CT angiography

We are developing a computer-aided detection system to assist radiologists in detection of non-calcified plaques (NCPs) in coronary CT angiograms (cCTA). In this study, we performed quantitative analysis of arterial flow properties in each vessel branch and extracted flow information to differentiate the presence and absence of stenosis in a vessel segment. Under rest conditions, blood flow in a single vessel branch was assumed to follow Poiseuille’s law. For a uniform pressure distribution, two quantitative flow features, the normalized arterial compliance per unit length (Cu) and the normalized volumetric flow (Q) along the vessel centerline, were calculated based on the parabolic Poiseuille solution. The flow features were evaluated for a two-class classification task to differentiate NCP candidates obtained by prescreening as true NCPs and false positives (FPs) in cCTA. For evaluation, a data set of 83 cCTA scans was retrospectively collected from 83 patient files with IRB approval. A total of 118 NCPs were identified by experienced cardiothoracic radiologists. The correlation between the two flow features was 0.32. The discriminatory ability of the flow features evaluated as the area under the ROC curve (AUC) was 0.65 for Cu and 0.63 for Q in comparison with AUCs of 0.56-0.69 from our previous luminal features. With stepwise LDA feature selection, volumetric flow (Q) was selected in addition to three other luminal features. With FROC analysis, the test results indicated a reduction of the FP rates to 3.14, 1.98, and 1.32 FPs/scan at sensitivities of 90%, 80%, and 70%, respectively. The study indicated that quantitative blood flow analysis has the potential to provide useful features for the detection of NCPs in cCTA.

Computerized luminal analysis for detection of non-calcified plaques in coronary CT angiography

Non-calcified plaque (NCP) detection in coronary CT angiography (cCTA) is challenging due to the low CT number of NCP, the large number of coronary arteries and multiple phase CT acquisition. We are developing computer-vision methods for automated detection of NCPs in cCTA. A data set of 62 cCTA scans with 87 NCPs was collected retrospectively from patient files. Multiscale coronary vessel enhancement and rolling balloon tracking were first applied to each cCTA volume to extract the coronary artery trees. Each extracted vessel was reformatted to a straightened volume composed of cCTA slices perpendicular to the vessel centerline. A topological soft-gradient (TSG) detection method was developed to prescreen for both positive and negative remodeling candidates by analyzing the 2D topological features of the radial gradient field surface along the vessel wall. A quantitative luminal analysis was newly designed for feature extraction and false positive (FP) reduction. We extracted 9 geometric features and 6 gray-level features, to quantify the differences between NCPs and FPs. The gray-level features included 4 features to measure local statistical characteristics and 2 asymmetry features to measure the asymmetric spatial location of gray-level density along the vessel centerline. The geometric features included a radius differential feature and 8 features extracted from two transformed volumes: the volumetric shape indexing and the gradient direction mapping volumes. With a machine learning algorithm and feature selection method, useful features were selected and combined into an NCP likelihood measure to differentiate TPs from FPs. With the NCP likelihood measure as a decision variable in the receiver operating characteristic (ROC) analysis, the area under the curve achieved a value of 0.85±0.01, indicating that the luminal analysis is effective in reducing FPs for NCP detection.