# Fast Learning-based Registration of Sparse 3D Clinical Images

## TL;DR

SparseVM is a novel deep learning-based registration method that significantly improves the speed and accuracy of aligning sparse clinical 3D MRI scans, addressing a critical need in clinical neuroscience research.

## Contribution

It introduces the first deep learning approach specifically designed for registering sparse clinical 3D MRI scans, outperforming existing methods in speed and accuracy.

## Key findings

- SparseVM is faster than existing registration methods.
- SparseVM achieves higher accuracy on clinical MRI datasets.
- The method is effective on both real and simulated sparse MRI data.

## Abstract

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/.