Computerized detection of noncalcified plaques in coronary CT angiography: Evaluation of topological soft gradient prescreening method and luminal analysis. Issue 8 (7th July 2014)

• Record Type: Journal Article
• Authors: Wei, Jun; Zhou, Chuan; Chan, Heang‐Ping; Chughtai, Aamer; Agarwal, Prachi; Kuriakose, Jean; Hadjiiski, Lubomir; Patel, Smita; Kazerooni, Ella
• Is Part Of: Medical physics. Volume 41:Issue 8(2014)Part 1
• Page Start: n/a
• Page End: n/a
• Publication Date: 2014-07-07
• Subjects: Multislice -- Angiography -- Segmentation -- Algebraic topology -- Numerical approximation and analysis; blood vessels -- cardiology -- computerised tomography -- data analysis -- diagnostic radiography -- diseases -- feature extraction -- feature selection -- geometry -- gradient methods -- image classification -- image segmentation -- learning (artificial intelligence) -- medical image processing -- object tracking -- sensitivity analysis -- topology; coronary CT angiography (cCTA) -- noncalcified plaque -- computer‐aided detection; Computerised tomographs -- Biological material, e.g. blood, urine; Haemocytometers -- Methods or arrangements for processing data by operating upon the order or content of the data handled -- In which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Inference methods or devices; Vascular system -- Computer aided diagnosis -- Radiologists -- Computed tomography -- Heart disease -- Angiography -- Image reconstruction -- Medical image reconstruction -- Machine learning; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4885958
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : 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.