Multi-modal robust inverse-consistent linear registration
Registration performance can significantly deteriorate when image regions do not comply with model assumptions. Robust estimation improves registration accuracy by reducing or ignoring the contribution of voxels with large intensity differences, but existing approaches are limited to monomodal regis...
| Main Authors: | , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Wiley Blackwell
2017
|
| Online Access: | http://hdl.handle.net/1721.1/111604 https://orcid.org/0000-0002-3652-1874 https://orcid.org/0000-0003-2516-731X |
| _version_ | 1826211074124283904 |
|---|---|
| author | Magnain, Caroline Wachinger, Christian Golland, Polina Fischl, Bruce Reuter, Klaus Martin |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Magnain, Caroline Wachinger, Christian Golland, Polina Fischl, Bruce Reuter, Klaus Martin |
| author_sort | Magnain, Caroline |
| collection | MIT |
| description | Registration performance can significantly deteriorate when image regions do not comply with model assumptions. Robust estimation improves registration accuracy by reducing or ignoring the contribution of voxels with large intensity differences, but existing approaches are limited to monomodal registration. In this work, we propose a robust and inverse-consistent technique for cross-modal, affine image registration. The algorithm is derived from a contextual framework of image registration. The key idea is to use a modality invariant representation of images based on local entropy estimation, and to incorporate a heteroskedastic noise model. This noise model allows us to draw the analogy to iteratively reweighted least squares estimation and to leverage existing weighting functions to account for differences in local information content in multimodal registration. Furthermore, we use the nonparametric windows density estimator to reliably calculate entropy of small image patches. Finally, we derive the Gauss–Newton update and show that it is equivalent to the efficient second-order minimization for the fully symmetric registration approach. We illustrate excellent performance of the proposed methods on datasets containing outliers for alignment of brain tumor, full head, and histology images. |
| first_indexed | 2024-09-23T15:00:09Z |
| format | Article |
| id | mit-1721.1/111604 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:00:09Z |
| publishDate | 2017 |
| publisher | Wiley Blackwell |
| record_format | dspace |
| spelling | mit-1721.1/1116042022-10-01T23:54:51Z Multi-modal robust inverse-consistent linear registration Magnain, Caroline Wachinger, Christian Golland, Polina Fischl, Bruce Reuter, Klaus Martin Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Wachinger, Christian Golland, Polina Fischl, Bruce Reuter, Klaus Martin Registration performance can significantly deteriorate when image regions do not comply with model assumptions. Robust estimation improves registration accuracy by reducing or ignoring the contribution of voxels with large intensity differences, but existing approaches are limited to monomodal registration. In this work, we propose a robust and inverse-consistent technique for cross-modal, affine image registration. The algorithm is derived from a contextual framework of image registration. The key idea is to use a modality invariant representation of images based on local entropy estimation, and to incorporate a heteroskedastic noise model. This noise model allows us to draw the analogy to iteratively reweighted least squares estimation and to leverage existing weighting functions to account for differences in local information content in multimodal registration. Furthermore, we use the nonparametric windows density estimator to reliably calculate entropy of small image patches. Finally, we derive the Gauss–Newton update and show that it is equivalent to the efficient second-order minimization for the fully symmetric registration approach. We illustrate excellent performance of the proposed methods on datasets containing outliers for alignment of brain tumor, full head, and histology images. National Cancer Institute (U.S.) (Grant K25-CA181632-01A1) National Center for Research Resources (U.S.) (Grant P41-RR13218) National Center for Research Resources (U.S.) (Grant P41-RR14075) National Center for Research Resources (U.S.) (Grant U24-RR021382) National Institute of Biomedical Imaging and Bioengineering (U.S.) (Grant R01EB006758) National Alliance for Medical Image Computing (U.S.) (Grant U54-EB005149) National Institute on Aging (Grant AG022381) National Institute on Aging (Grant 5R01AG008122-22) National Center for Complementary and Alternative Medicine (U.S.) (Grant RC1 AT005728-01) National Institute of Neurological Diseases and Stroke (U.S.) (Grant R01 NS052585-01) National Institute of Neurological Diseases and Stroke (U.S.) (Grant 1R21NS072652-01) National Institute of Neurological Diseases and Stroke (U.S.) (Grant 1R01NS070963) National Institutes of Health (U.S.) (Grant 5U01-MH093765) 2017-09-19T15:07:08Z 2017-09-19T15:07:08Z 2015-03 2014-10 Article http://purl.org/eprint/type/JournalArticle 1065-9471 1097-0193 http://hdl.handle.net/1721.1/111604 Wachinger, Christian, et al. “Multi-Modal Robust Inverse-Consistent Linear Registration.” Human Brain Mapping 36, 4 (December 2014): 1365–1380 © 2014 Wiley Periodicals https://orcid.org/0000-0002-3652-1874 https://orcid.org/0000-0003-2516-731X en_US http://dx.doi.org/10.1002/hbm.22707 Human Brain Mapping Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Wiley Blackwell PMC |
| spellingShingle | Magnain, Caroline Wachinger, Christian Golland, Polina Fischl, Bruce Reuter, Klaus Martin Multi-modal robust inverse-consistent linear registration |
| title | Multi-modal robust inverse-consistent linear registration |
| title_full | Multi-modal robust inverse-consistent linear registration |
| title_fullStr | Multi-modal robust inverse-consistent linear registration |
| title_full_unstemmed | Multi-modal robust inverse-consistent linear registration |
| title_short | Multi-modal robust inverse-consistent linear registration |
| title_sort | multi modal robust inverse consistent linear registration |
| url | http://hdl.handle.net/1721.1/111604 https://orcid.org/0000-0002-3652-1874 https://orcid.org/0000-0003-2516-731X |
| work_keys_str_mv | AT magnaincaroline multimodalrobustinverseconsistentlinearregistration AT wachingerchristian multimodalrobustinverseconsistentlinearregistration AT gollandpolina multimodalrobustinverseconsistentlinearregistration AT fischlbruce multimodalrobustinverseconsistentlinearregistration AT reuterklausmartin multimodalrobustinverseconsistentlinearregistration |