Fast learning-based registration of sparse 3D clinical images
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 desig...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Association for Computing Machinery (ACM)
2021
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| Online Access: | https://hdl.handle.net/1721.1/129553 |
| _version_ | 1826188413299064832 |
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| author | Lewis, Kathleen M.(Kathleen Marie) Guttag, John V Dalca, Adrian Vasile |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Lewis, Kathleen M.(Kathleen Marie) Guttag, John V Dalca, Adrian Vasile |
| author_sort | Lewis, Kathleen M.(Kathleen Marie) |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T07:59:15Z |
| format | Article |
| id | mit-1721.1/129553 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T07:59:15Z |
| publishDate | 2021 |
| publisher | Association for Computing Machinery (ACM) |
| record_format | dspace |
| spelling | mit-1721.1/1295532022-09-30T01:31:01Z Fast learning-based registration of sparse 3D clinical images Lewis, Kathleen M.(Kathleen Marie) Guttag, John V Dalca, Adrian Vasile Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory 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. National Institutes of Health (U.S.) (Grant R21AG050122) 2021-01-25T19:39:09Z 2021-01-25T19:39:09Z 2020-04 2020-12-16T18:12:29Z Article http://purl.org/eprint/type/ConferencePaper 9781450370462 https://hdl.handle.net/1721.1/129553 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) en 10.1145/3368555.3384462 Proceedings of the 2020 ACM Conference on Health, Inference, and Learning Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Association for Computing Machinery (ACM) arXiv |
| spellingShingle | Lewis, Kathleen M.(Kathleen Marie) Guttag, John V Dalca, Adrian Vasile Fast learning-based registration of sparse 3D clinical images |
| title | Fast learning-based registration of sparse 3D clinical images |
| title_full | Fast learning-based registration of sparse 3D clinical images |
| title_fullStr | Fast learning-based registration of sparse 3D clinical images |
| title_full_unstemmed | Fast learning-based registration of sparse 3D clinical images |
| title_short | Fast learning-based registration of sparse 3D clinical images |
| title_sort | fast learning based registration of sparse 3d clinical images |
| url | https://hdl.handle.net/1721.1/129553 |
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