Patch-Based Discrete Registration of Clinical Brain Images
© Springer International Publishing AG 2016. We introduce a method for registration of brain images acquired in clinical settings. The algorithm relies on three-dimensional patches in a discrete registration framework to estimate correspondences. Clinical images present significant challenges for co...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Springer Nature America, Inc
2021
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| Online Access: | https://hdl.handle.net/1721.1/137570 |
| _version_ | 1826210699029774336 |
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| author | Dalca, Adrian V. Bobu, Andreea Rost, Natalia S. Golland, Polina |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Dalca, Adrian V. Bobu, Andreea Rost, Natalia S. Golland, Polina |
| author_sort | Dalca, Adrian V. |
| collection | MIT |
| description | © Springer International Publishing AG 2016. We introduce a method for registration of brain images acquired in clinical settings. The algorithm relies on three-dimensional patches in a discrete registration framework to estimate correspondences. Clinical images present significant challenges for computational analysis. Fast acquisition often results in images with sparse slices, severe artifacts, and variable fields of view. Yet, large clinical datasets hold a wealth of clinically relevant information. Despite significant progress in image registration, most algorithms make strong assumptions about the continuity of image data, failing when presented with clinical images that violate these assumptions. In this paper, we demonstrate a non-rigid registration method for aligning such images. The method explicitly models the sparsely available image information to achieve robust registration. We demonstrate the algorithm on clinical images of stroke patients. The proposed method outperforms state of the art registration algorithms and avoids catastrophic failures often caused by these images. We provide a freely available open source implementation of the algorithm. |
| first_indexed | 2024-09-23T14:54:03Z |
| format | Article |
| id | mit-1721.1/137570 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T14:54:03Z |
| publishDate | 2021 |
| publisher | Springer Nature America, Inc |
| record_format | dspace |
| spelling | mit-1721.1/1375702021-11-06T03:10:25Z Patch-Based Discrete Registration of Clinical Brain Images Dalca, Adrian V. Bobu, Andreea Rost, Natalia S. Golland, Polina Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science © Springer International Publishing AG 2016. We introduce a method for registration of brain images acquired in clinical settings. The algorithm relies on three-dimensional patches in a discrete registration framework to estimate correspondences. Clinical images present significant challenges for computational analysis. Fast acquisition often results in images with sparse slices, severe artifacts, and variable fields of view. Yet, large clinical datasets hold a wealth of clinically relevant information. Despite significant progress in image registration, most algorithms make strong assumptions about the continuity of image data, failing when presented with clinical images that violate these assumptions. In this paper, we demonstrate a non-rigid registration method for aligning such images. The method explicitly models the sparsely available image information to achieve robust registration. We demonstrate the algorithm on clinical images of stroke patients. The proposed method outperforms state of the art registration algorithms and avoids catastrophic failures often caused by these images. We provide a freely available open source implementation of the algorithm. 2021-11-05T18:38:52Z 2021-11-05T18:38:52Z 2016 2019-05-29T17:56:50Z Article http://purl.org/eprint/type/ConferencePaper 0302-9743 1611-3349 https://hdl.handle.net/1721.1/137570 Dalca, Adrian V., Bobu, Andreea, Rost, Natalia S. and Golland, Polina. 2016. "Patch-Based Discrete Registration of Clinical Brain Images." en 10.1007/978-3-319-47118-1_8 Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Springer Nature America, Inc PMC |
| spellingShingle | Dalca, Adrian V. Bobu, Andreea Rost, Natalia S. Golland, Polina Patch-Based Discrete Registration of Clinical Brain Images |
| title | Patch-Based Discrete Registration of Clinical Brain Images |
| title_full | Patch-Based Discrete Registration of Clinical Brain Images |
| title_fullStr | Patch-Based Discrete Registration of Clinical Brain Images |
| title_full_unstemmed | Patch-Based Discrete Registration of Clinical Brain Images |
| title_short | Patch-Based Discrete Registration of Clinical Brain Images |
| title_sort | patch based discrete registration of clinical brain images |
| url | https://hdl.handle.net/1721.1/137570 |
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