Before revision:  This task is challenging because, in contrast to high-resolution research-quality scans, clinical images are often sparse, missing up to 85\% of the slices in comparison. Furthermore, the anatomy in the acquired slices is not consistent across scansbecause of variations in patient orientation with respect to the scanner. In this work, we introduce Sparse VoxelMorph (SparseVM), which adapts a state-of-the-art learning-based registration method to improve the registration of sparse clinical images.
After revision:  This task is challenging because, in contrast to high-resolution research-quality scans, clinical images are often sparse, missing up to 85\% of the slices available in research scans. We build on a state-of-the-art learning-based registration method to improve the registration of sparse clinical images.