Motion Detection in Diffusion MRI via Online ODF Estimation
Name
IJBI.2013.849363.pdf
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1.85 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Caruyer, Emmanuel
Aganj, Iman
Lenglet, Christophe
Sapiro, Guillermo
Deriche, Rachid
Date Issued
January 2013
Journal
International Journal of Biomedical Imaging
Publisher
Hindawi Publishing Corporation
Citation
Caruyer, Emmanuel, Iman Aganj, Christophe Lenglet, Guillermo Sapiro, and Rachid Deriche. Motion Detection in Diffusion MRI via Online ODF Estimation. International Journal of Biomedical Imaging 2013 (2013): 1-8.
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1155/2013/849363
