title: Computerized detection of noncalcified plaques in coronary CT angiography: evaluation of topological soft gradient prescreening method and luminal analysis.
abstract: OBJECTIVE
  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.
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109.
authors.forename: Jun
authors.initials: J
authors.lastname: Wei
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109.
authors.forename: Chuan
authors.initials: C
authors.lastname: Zhou
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109.
authors.forename: Heang-Ping
authors.initials: HP
authors.lastname: Chan
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109.
authors.forename: Aamer
authors.initials: A
authors.lastname: Chughtai
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109.
authors.forename: Prachi
authors.initials: P
authors.lastname: Agarwal
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109.
authors.forename: Jean
authors.initials: J
authors.lastname: Kuriakose
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109.
authors.forename: Lubomir
authors.initials: L
authors.lastname: Hadjiiski
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109.
authors.forename: Smita
authors.initials: S
authors.lastname: Patel
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109.
authors.forename: Ella
authors.initials: E
authors.lastname: Kazerooni
mesh_terms: D019540:Area Under Curve; D001185:Artificial Intelligence; D017023:Coronary Angiography / Q000379:methods*; D003324:Coronary Artery Disease / Q000000981:diagnostic imaging*; D003331:Coronary Vessels / Q000000981:diagnostic imaging; D005240:Feasibility Studies; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*
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