Motion Detection in Diffusion MRI via Online ODF Estimation
The acquisition of high angular resolution diffusion MRI is particularly long and subject motion can become an issue. The orientation distribution function (ODF) can be reconstructed online incrementally from diffusion-weighted MRI with a Kalman filtering framework. This online reconstruction provid...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-01-01
|
| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2013/849363 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832558620988932096 |
|---|---|
| author | Emmanuel Caruyer Iman Aganj Christophe Lenglet Guillermo Sapiro Rachid Deriche |
| author_facet | Emmanuel Caruyer Iman Aganj Christophe Lenglet Guillermo Sapiro Rachid Deriche |
| author_sort | Emmanuel Caruyer |
| collection | DOAJ |
| description | The acquisition of high angular resolution diffusion MRI is particularly long and subject motion can become an issue. The orientation distribution function (ODF) can be reconstructed online incrementally from diffusion-weighted MRI with a Kalman filtering framework. This online reconstruction provides real-time feedback throughout the acquisition process. In this article, the Kalman filter is first adapted to the reconstruction of the ODF in constant solid angle. Then, a method called STAR (STatistical Analysis of Residuals) is presented and applied to the online detection of motion in high angular resolution diffusion images. Compared to existing techniques, this method is image based and is built on top of a Kalman filter. Therefore, it introduces no additional scan time and does not require additional hardware. The performance of STAR is tested on simulated and real data and compared to the classical generalized likelihood ratio test. Successful detection of small motion is reported (rotation under 2°) with no delay and robustness to noise. |
| format | Article |
| id | doaj-art-d462b7316da14498b3c252617b400458 |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-d462b7316da14498b3c252617b4004582025-02-03T01:31:57ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962013-01-01201310.1155/2013/849363849363Motion Detection in Diffusion MRI via Online ODF EstimationEmmanuel Caruyer0Iman Aganj1Christophe Lenglet2Guillermo Sapiro3Rachid Deriche4Athena Project-Team, INRIA, 2004 Route des Lucioles, BP93, 06902 Sophia Antipolis, INRIA Sophia Antipolis Méditerranée, FranceMartinos Center for Biomedical Imaging, Massachusetts General Hospital, Department of Radiology, Harvard Medical School, 149 13th Street, Charlestown, MA 02129, USACenter for Magnetic Resonance Research, Department of Radiology, University of Minnesota Medical School, 2021 Sixth Street SE, Minneapolis, MN 55455, USADepartment of Electrical and Computer Engineering, University of Minnesota, 200 Union Street SE, Minneapolis, MN 55455, USAAthena Project-Team, INRIA, 2004 Route des Lucioles, BP93, 06902 Sophia Antipolis, INRIA Sophia Antipolis Méditerranée, FranceThe acquisition of high angular resolution diffusion MRI is particularly long and subject motion can become an issue. The orientation distribution function (ODF) can be reconstructed online incrementally from diffusion-weighted MRI with a Kalman filtering framework. This online reconstruction provides real-time feedback throughout the acquisition process. In this article, the Kalman filter is first adapted to the reconstruction of the ODF in constant solid angle. Then, a method called STAR (STatistical Analysis of Residuals) is presented and applied to the online detection of motion in high angular resolution diffusion images. Compared to existing techniques, this method is image based and is built on top of a Kalman filter. Therefore, it introduces no additional scan time and does not require additional hardware. The performance of STAR is tested on simulated and real data and compared to the classical generalized likelihood ratio test. Successful detection of small motion is reported (rotation under 2°) with no delay and robustness to noise.http://dx.doi.org/10.1155/2013/849363 |
| spellingShingle | Emmanuel Caruyer Iman Aganj Christophe Lenglet Guillermo Sapiro Rachid Deriche Motion Detection in Diffusion MRI via Online ODF Estimation International Journal of Biomedical Imaging |
| title | Motion Detection in Diffusion MRI via Online ODF Estimation |
| title_full | Motion Detection in Diffusion MRI via Online ODF Estimation |
| title_fullStr | Motion Detection in Diffusion MRI via Online ODF Estimation |
| title_full_unstemmed | Motion Detection in Diffusion MRI via Online ODF Estimation |
| title_short | Motion Detection in Diffusion MRI via Online ODF Estimation |
| title_sort | motion detection in diffusion mri via online odf estimation |
| url | http://dx.doi.org/10.1155/2013/849363 |
| work_keys_str_mv | AT emmanuelcaruyer motiondetectionindiffusionmriviaonlineodfestimation AT imanaganj motiondetectionindiffusionmriviaonlineodfestimation AT christophelenglet motiondetectionindiffusionmriviaonlineodfestimation AT guillermosapiro motiondetectionindiffusionmriviaonlineodfestimation AT rachidderiche motiondetectionindiffusionmriviaonlineodfestimation |