Premium
Nonlinear smoothing for reduction of systematic and random errors in diffusion tensor imaging
Author(s) -
Parker Geoffrey J.M.,
Schnabel Julia A.,
Symms Mark R.,
Werring David J.,
Barker Gareth J.
Publication year - 2000
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/1522-2586(200006)11:6<702::aid-jmri18>3.0.co;2-a
Subject(s) - diffusion mri , smoothing , fractional anisotropy , eigenvalues and eigenvectors , tensor (intrinsic definition) , anisotropic diffusion , anisotropy , nonlinear system , diffusion , noise (video) , sorting , computer science , algorithm , statistical physics , mathematics , physics , artificial intelligence , optics , computer vision , geometry , radiology , magnetic resonance imaging , medicine , image (mathematics) , quantum mechanics , thermodynamics
Calculation and sorting of the eigenvectors of diffusion using diffusion tensor imaging has previously been shown to be sensitive to noise levels in the acquired data. This sensitivity manifests as random and systematic errors in the diffusion eigenvalues and derived parameters such as indices of anisotropy. An optimized application of nonlinear smoothing techniques to diffusion data prior to calculation of the diffusion tensor is shown to reduce both random and systematic errors, while causing little blurring of anatomical structures. Conversely, filtering applied to calculated images of fractional anisotropy is shown to fail in reducing systematic errors and in recovering anatomical detail. Using both real and simulated brain data sets, it is demonstrated that this approach has the potential to allow acquisition of data that would otherwise be too noisy to be of use. J. Magn. Reson. Imaging 2000;11:702–710. © 2000 Wiley‐Liss, Inc.