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Diffusion imaging with prospective motion correction and reacquisition
Author(s) -
Benner Thomas,
van der Kouwe André J. W.,
Sorensen A. Gregory
Publication year - 2011
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.22837
Subject(s) - artificial intelligence , computer vision , diffusion mri , computer science , rotation (mathematics) , signal (programming language) , diffusion , translation (biology) , residual , scanner , tracking (education) , motion (physics) , pattern recognition (psychology) , mathematics , algorithm , magnetic resonance imaging , physics , medicine , radiology , thermodynamics , psychology , pedagogy , biochemistry , chemistry , messenger rna , gene , programming language
A major source of artifacts in diffusion‐weighted imaging is subject motion. Slow bulk subject motion causes misalignment of data when more than one average or diffusion gradient direction is acquired. Fast bulk subject motion can cause signal dropout artifacts in diffusion‐weighted images and results in erroneous derived maps, e.g., fractional anisotropy maps. To address both types of artifacts, a fully automatic method is presented that combines prospective motion correction with a reacquisition scheme. Motion correction is based on the prospective acquisition correction method modified to work with diffusion‐weighted data. The images to reacquire are determined automatically during the acquisition from the imaging data, i.e., no extra reference scan, navigators, or external devices are necessary. The number of reacquired images, i.e., the additional scan duration can be adjusted freely. Diffusion‐weighted prospective acquisition correction corrects slow bulk motion well and reduces misalignment artifacts like image blurring. Mean absolute residual values for translation and rotation were <0.6 mm and 0.5°. Reacquisition of images affected by signal dropout artifacts results in diffusion maps and fiber tracking free of artifacts. The presented method allows the reduction of two types of common motion related artifacts at the cost of slightly increased acquisition time. Magn Reson Med, 2011. © 2011 Wiley‐Liss, Inc.