{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/122546"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/122546","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Fast learning-based registration of sparse 3D clinical images","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_html":"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_has_math":false,"creators":["Lewis, Kathleen M.(Kathleen Marie)"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","school":null,"contributors":[],"advisors":["John V. Guttag and Adrian V. Dalca."],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019","date_published":"2019","updated_at":"2026-07-22T22:21:34Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["MIT theses are protected by copyright. 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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. 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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. 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