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.