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Single‐step nonlinear diffusion tensor estimation in the presence of microscopic and macroscopic motion
Author(s) -
Aksoy Murat,
Liu Chunlei,
Moseley Michael E.,
Bammer Roland
Publication year - 2008
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.21558
Subject(s) - diffusion mri , tensor (intrinsic definition) , motion (physics) , diffusion , imaging phantom , nonlinear system , computer science , motion field , motion estimation , rotation around a fixed axis , reference frame , computer vision , weighting , artificial intelligence , physics , algorithm , mathematics , frame (networking) , optics , magnetic resonance imaging , geometry , classical mechanics , medicine , quantum mechanics , radiology , thermodynamics , telecommunications , acoustics
Abstract Patient motion can cause serious artifacts in diffusion tensor imaging (DTI), diminishing the reliability of the estimated diffusion tensor information. Studies in this field have so far been limited mainly to the correction of miniscule physiological motion. In order to correct for gross patient motion it is not sufficient to correct for misregistration between successive shots; the change in the diffusion‐encoding direction must also be accounted for. This becomes particularly important for multishot sequences, whereby—in the presence of motion—each shot is encoded with a different diffusion weighting. In this study a general mathematical framework to correct for gross patient motion present in a multishot and multicoil DTI scan is presented. A signal model is presented that includes the effect of rotational and translational motion in the patient frame of reference. This model was used to create a nonlinear least‐squares formulation, from which the diffusion tensors were obtained using a nonlinear conjugate gradient algorithm. Applications to both phantom simulations and in vivo studies showed that in the case of gross motion the proposed algorithm performs superiorly compared to conventional methods used for tensor estimation. Magn Reson Med 59:1138–1150, 2008. © 2008 Wiley‐Liss, Inc.