Before revision:  Clinical scans present unique challenges because, in contrast to research-quality scans, clinical scans 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 accuracy 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 a GPU, which is over 1000x faster than the most accurate clinical registration methods, without compromising accuracy.
After revision:  Clinical scans present unique challenges because, in contrast to research-quality scans, clinical scans are often sparse, missing up to 85 \% 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 to improve the accuracy 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 a GPU, which is over 1000x faster than the most accurate clinical registration methods, without compromising accuracy.