Before revision:  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. SparseVM is a fast, unsupervised method that weights voxel contributions to registration in proportion to confidence in the voxels. This leads to improved registration performance on volumes with voxels of varying reliability, such as interpolated clinical scans . SparseVM registers 3D scans in under a second on the GPU, which is orders of magnitudes faster than the best performing clinical registration methods, while still achieving comparable accuracy.
After revision:  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 image registration and demonstrate our method on a clinically-acquired MRI dataset of stroke patients . SparseVM registers 3D scans in under a second on the GPU, which is orders of magnitudes faster than the best performing clinical registration methods, while still achieving comparable accuracy.