Computer-Aided Assessment of Cardiac Computed Tomographic Images

摘要

The accurate interpretation of cardiac CT images is commonly hindered by the presence of motion artifacts. Since motion artifacts commonly can obscure the presence of coronary lesions, physicians must spend much effort analyzing images at multiple cardiac phases in order to determine which coronary structures are assessable for potential lesions. In this study, an artificial neural network (ANN) classifier was designed to assign assessability indices to calcified plaques in individual region-of-interest (ROI) images reconstructed at multiple cardiac phases from two cardiac scans obtained at heart rates of 66 bpm and 90 bpm. Six individual features (volume, circularity, mean intensity, margin gradient, velocity, and acceleration) were used for analyzing images. Visually-assigned assessability indices were used as a continuous truth, and jack-knife analysis with four testing sets was used to evaluate the performance of the ANN classifier. In a study in which all six features were inputted into the ANN classifier, correlation coefficients of 0.962 ± 0.006 and 0.935 ± 0.023 between true and ANN-assigned assessability indices were obtained for databases corresponding to 66 bpm and 90 bpm, respectively.