We introduce SparseVM, a method to register clinical 3D scans faster and more accurately than previously possible. Deformable alignment, or registration, of clinical scans is a fundamental task for many medical image applications such as longitudinal population studies. Most registration algorithms are designed for high-resolution research-quality scans and under-perform when applied to clinical data. 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. Because of this, SparseVM enables clinical analyses that were not previously possible. The code is publicly available at voxelmorph.mit.edu.
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 URL
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, 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
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. 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. Because of this, SparseVM enables clinical analyses that were not previously possible. The code is publicly available at voxelmorph.mit.edu.
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