Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration
We present the Spherical Demons algorithm for registering two spherical images. By exploiting spherical vector spline interpolation theory, we show that a large class of regularizors for the modified Demons objective function can be efficiently approximated on the sphere using iterative smoothing. B...
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2010
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Online Access: | http://hdl.handle.net/1721.1/59540 https://orcid.org/0000-0002-5002-1227 https://orcid.org/0000-0003-2516-731X |
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author | Yeo, Boon Thye Thomas Golland, Polina Sabuncu, Mert R. Fischl, Bruce Vercauteren, Tom Ayache, Nicholas |
author2 | Harvard University--MIT Division of Health Sciences and Technology |
author_facet | Harvard University--MIT Division of Health Sciences and Technology Yeo, Boon Thye Thomas Golland, Polina Sabuncu, Mert R. Fischl, Bruce Vercauteren, Tom Ayache, Nicholas |
author_sort | Yeo, Boon Thye Thomas |
collection | MIT |
description | We present the Spherical Demons algorithm for registering two spherical images. By exploiting spherical vector spline interpolation theory, we show that a large class of regularizors for the modified Demons objective function can be efficiently approximated on the sphere using iterative smoothing. Based on one parameter subgroups of diffeomorphisms, the resulting registration is diffeomorphic and fast. The Spherical Demons algorithm can also be modified to register a given spherical image to a probabilistic atlas. We demonstrate two variants of the algorithm corresponding to warping the atlas or warping the subject. Registration of a cortical surface mesh to an atlas mesh, both with more than 160 k nodes requires less than 5 min when warping the atlas and less than 3 min when warping the subject on a Xeon 3.2 GHz single processor machine. This is comparable to the fastest nondiffeomorphic landmark-free surface registration algorithms. Furthermore, the accuracy of our method compares favorably to the popular FreeSurfer registration algorithm. We validate the technique in two different applications that use registration to transfer segmentation labels onto a new image (1) parcellation of in vivo cortical surfaces and (2) Brodmann area localization in ex vivo cortical surfaces. |
first_indexed | 2024-09-23T10:47:23Z |
format | Article |
id | mit-1721.1/59540 |
institution | Massachusetts Institute of Technology |
language | en_US |
last_indexed | 2024-09-23T10:47:23Z |
publishDate | 2010 |
publisher | Institute of Electrical and Electronics Engineers |
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spelling | mit-1721.1/595402022-09-30T23:01:15Z Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration Yeo, Boon Thye Thomas Golland, Polina Sabuncu, Mert R. Fischl, Bruce Vercauteren, Tom Ayache, Nicholas Harvard University--MIT Division of Health Sciences and Technology Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Golland, Polina Yeo, Boon Thye Thomas Golland, Polina Sabuncu, Mert R. Fischl, Bruce Cortical registration Demons Diffeomorphism Spherical registration Surface registration Vector field interpolation We present the Spherical Demons algorithm for registering two spherical images. By exploiting spherical vector spline interpolation theory, we show that a large class of regularizors for the modified Demons objective function can be efficiently approximated on the sphere using iterative smoothing. Based on one parameter subgroups of diffeomorphisms, the resulting registration is diffeomorphic and fast. The Spherical Demons algorithm can also be modified to register a given spherical image to a probabilistic atlas. We demonstrate two variants of the algorithm corresponding to warping the atlas or warping the subject. Registration of a cortical surface mesh to an atlas mesh, both with more than 160 k nodes requires less than 5 min when warping the atlas and less than 3 min when warping the subject on a Xeon 3.2 GHz single processor machine. This is comparable to the fastest nondiffeomorphic landmark-free surface registration algorithms. Furthermore, the accuracy of our method compares favorably to the popular FreeSurfer registration algorithm. We validate the technique in two different applications that use registration to transfer segmentation labels onto a new image (1) parcellation of in vivo cortical surfaces and (2) Brodmann area localization in ex vivo cortical surfaces. 2010-10-27T17:49:29Z 2010-10-27T17:49:29Z 2010-03 Article http://purl.org/eprint/type/JournalArticle 0278-0062 INSPEC Accession Number: 11152914 http://hdl.handle.net/1721.1/59540 Yeo, B.T.T. et al. “Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration.” Medical Imaging, IEEE Transactions on 29.3 (2010): 650-668. © 2010, IEEE https://orcid.org/0000-0002-5002-1227 https://orcid.org/0000-0003-2516-731X en_US http://dx.doi.org/10.1109/tmi.2009.2030797 IEEE transactions on medical imaging Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Institute of Electrical and Electronics Engineers IEEE |
spellingShingle | Cortical registration Demons Diffeomorphism Spherical registration Surface registration Vector field interpolation Yeo, Boon Thye Thomas Golland, Polina Sabuncu, Mert R. Fischl, Bruce Vercauteren, Tom Ayache, Nicholas Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration |
title | Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration |
title_full | Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration |
title_fullStr | Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration |
title_full_unstemmed | Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration |
title_short | Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration |
title_sort | spherical demons fast diffeomorphic landmark free surface registration |
topic | Cortical registration Demons Diffeomorphism Spherical registration Surface registration Vector field interpolation |
url | http://hdl.handle.net/1721.1/59540 https://orcid.org/0000-0002-5002-1227 https://orcid.org/0000-0003-2516-731X |
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