Motion detection in diffusion MRI via online ODF estimation

Emmanuel Caruyer, Iman Aganj, Christophe Lenglet, Guillermo Sapiro, Rachid Deriche

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

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.

Original languageEnglish (US)
Article number849363
JournalInternational Journal of Biomedical Imaging
Volume2013
DOIs
StatePublished - 2013

Center for Magnetic Resonance Research (CMRR) tags

  • IRP
  • BFC

Fingerprint

Dive into the research topics of 'Motion detection in diffusion MRI via online ODF estimation'. Together they form a unique fingerprint.

Cite this