Deep-learning-based CT motion artifact recognition in coronary arteries.

The detection and subsequent, correction of motion artifacts is essential for the high diagnostic value of non-invasive coronary angiography using cardiac IT. However, motion correction algorithms have a substantial computational footprint and possible failure modes which warrants a motion artifact detection step to decide whether motion correction is required in the first place. We investigate how accurately motion artifacts in the coronary arteries can be predicted by deep learning approaches. A forward model simulating cardiac motion by creating and integrating artificial motion vector fields in the filtered back projection (FBP) algorithm allows us to generate training data from nine prospectively ECC-triggered high quality clinical cases. We train a Convolutional Neural Network (CNN) classifying 2D motion-free and motion-perturbed coronary cross-section images and achieve a classification accuracy of 94.4% +/- 2.9% by four-fold cross-validation.