Abstract:Coronary CT angiography has become a preferred technique for the detection and diagnosis of coronary artery disease, but image artifacts due to cardiac motion frequently interfere with evaluation. Several motion compensation approaches have been developed which deal with motion estimation based on 3-D/3-D registration of multiple heart phases. The scan range required for multi-phase reconstruction is a limitation in clinical practice. In this paper, the feasibility of single-phase, image-based motion estimation by convolutional neural networks (CNNs) is investigated. First, the required data for supervised learning is generated by a forward model which introduces simulated axial motion to artifact-free CT cases. Second, regression networks are trained to estimate underlying 2D motion vectors from axial coronary cross-sections. In a phantom study with computer-simulated vessels, CNNs predict the motion direction and the motion strength with average accuracies of 1.08° and 0.06 mm, respectively. Motivated by these results, clinical performance is evaluated based on twelve prospectively ECG-triggered clinical cases and achieves average accuracies of 20.66° and 0.94 mm. Transferability and generalization capabilities are demonstrated by motion estimation and subsequent compensation on six clinical cases with real cardiac motion artifacts.