  We introduce SparseVM, a method that registers clinical-quality 3D MR scans
both faster and more accurately than previously possible. Deformable alignment,
or registration, of clinical scans is a fundamental task for many clinical
neuroscience studies. However, most registration algorithms are designed for
high-resolution research-quality scans. In contrast to research-quality scans,
clinical scans are often sparse, missing up to 86% of the slices available in
research-quality scans. Existing methods for registering these sparse images
are either inaccurate or extremely slow. We present a learning-based
registration method, SparseVM, that is more accurate and orders of magnitude
faster than the most accurate clinical registration methods. To our knowledge,
it is the first method to use deep learning specifically tailored to
registering clinical images. We demonstrate our method on a clinically-acquired
MRI dataset of stroke patients and on a simulated sparse MRI dataset. Our code
is available as part of the VoxelMorph package at http://voxelmorph.mit.edu/.
