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Fast learning-based registration of sparse 3D clinical images
Name
1812.06932.pdf
Description
Accepted version
Size
4.54 MB
Format
Adobe PDF
Checksum (MD5)
15ebd3be657438e960ec43cd74d3e279
Author(s)
Lewis, Kathleen M.(Kathleen Marie)
Guttag, John V
Dalca, Adrian Vasile
Date Issued
April 2020
Journal
Proceedings of the 2020 ACM Conference on Health, Inference, and Learning
Publisher
Association for Computing Machinery (ACM)
Citation
Lewis, Kathleen M. et al. “Fast learning-based registration of sparse 3D clinical images.” Proceedings of the 2020 ACM Conference on Health, Inference, and Learning, April 2020, Toronto, Canada, Association for Computing Machinery, April 2020. © 2020 The Author(s)
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
10.1145/3368555.3384462
