Motion compensated cardiac reconstruction in computed tomography (CT) has traditionally been focused on coronary arteries. However, with the increasing number of cardiac CT scans being performed for the diagnosis and treatment planning of valvular diseases, there is a clear need for motion correction of the aortic valve region to assist with the reproducibility of aortic annulus measurements. A second pass approach for aortic valve motion compensation on retrospective ECG-gated CT scans is introduced here. The processing chain is comprised of four steps. A gated multi-phase cardiac reconstruction is first performed, followed by a gradient based filter to enhance the edges in the resulting time series of volume images. Subsequently these normalized filtered results are made to undergo an elastic registration and finally followed by a motion compensated reconstruction that includes the estimated motion vector fields. The method was applied to twelve clinical cases and tested for systolic (30% R-R interval) and diastolic (70% R-R interval) imaging of the aortic valve. This second pass approach leads to a significant reduction of motion artifacts especially in late systole.
The detection and subsequent correction of motion artifacts is essential for the high diagnostic value of non- invasive coronary angiography using cardiac CT. 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 ECG-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.
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