Fast Learning-based Registration of Sparse 3D Clinical Images
This addresses the challenge of accurate and fast deformable alignment for clinical neuroscience studies, where sparse scans are common, representing an incremental improvement tailored to a specific domain.
The paper tackles the problem of registering sparse 3D clinical MRI scans, which are often missing up to 86% of slices compared to research-quality scans, and introduces SparseVM, a learning-based method that achieves higher accuracy and is orders of magnitude faster than existing clinical registration methods.
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/.