Combination of CT motion simulation and deep convolutional neural networks with transfer learning to recover Agatston scores

Motion of the coronary arteries during the cardiac cycle can distort the reconstructed CT image and negatively affect the evaluation of calcified plaques. These movements are manifested as motion artifacts. These artifacts and their corresponding stationary calcifications were used to train a Deep Convolutional Neural Network (DCNN). We used reported ranges of motions for coronary arteries to create a computer moving phantom of calcified plaques. We created a computer model of a CT scanner and created CT projections and reconstructions of stationary and moving plaques. CT images with artifacts and stationary images were used as input and targets of the DCNN, respectively. To control the progression of the DCNN, transfer learning was implemented to slowly introduce increasingly complicated images. The results of the regression plots generated before and after from a representative data set show a slope of 1.85 (r²=0.72) vs 1.08 (r²=0.90) before the network recovery and after DCNN, respectively. DCNNs demonstrate a promising approach to the complicated problem of CT motion correction in computer simulations. Further evaluation with actual motion artifacts is needed.