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John V. Guttag and Adrian V. Dalca. Lewis, Kathleen M.(Kathleen Marie) Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2019-10-11T22:11:17Z 2019-10-11T22:11:17Z 2019 2019 https://hdl.handle.net/1721.1/122546 1122563949 Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019 Cataloged from PDF version of thesis. Includes bibliographical references (pages 37-40). We introduce SparseVM, a method to register clinical 3D MR 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. We evaluate our method on both a clinically-acquired MRI dataset of stroke patients, and on a simulated sparse clinical scan dataset. SparseVM registers MR scans in under a second on a GPU, which is over 1000 x faster than the most accurate clinical registration methods, without compromising accuracy. SparseVM has statistically significant improvements in runtimes over all baselines, and a statistically significant improvement in accuracy over the fastest baseline. Because of these contributions, SparseVM enables clinical analyses that were not previously possible. Our code is publicly available at voxelmorph. mit . edu. by Kathleen M. Lewis. S.M. S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 40 pages eng Massachusetts Institute of Technology MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Fast learning-based registration of sparse 3D clinical images Fast learning-based registration of sparse three-dimensional clinical images Thesis application/pdf 2019-10-11T22:11:16Z Publication Master EECS unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="755ee711-4d91-43c8-b187-1b64f12f7139"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Fast learning-based registration of sparse 3D clinical images</Title> <Subtitle>Fast learning-based registration of sparse three-dimensional clinical images</Subtitle> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2019</PublicationDate> <Authors> <Author> <DisplayName>Lewis, Kathleen M.(Kathleen Marie)</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>We introduce SparseVM, a method to register clinical 3D MR 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. We evaluate our method on both a clinically-acquired MRI dataset of stroke patients, and on a simulated sparse clinical scan dataset. SparseVM registers MR scans in under a second on a GPU, which is over 1000 x faster than the most accurate clinical registration methods, without compromising accuracy. SparseVM has statistically significant improvements in runtimes over all baselines, and a statistically significant improvement in accuracy over the fastest baseline. Because of these contributions, SparseVM enables clinical analyses that were not previously possible. Our code is publicly available at voxelmorph. mit . edu.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>