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Parker, G.J.M.* ; Schnabel, J.A.* ; Symms, M.R.* ; Werring, D.J.* ; Barker, G.J.*

Nonlinear smoothing for reduction of systematic and random errors in diffusion tensor imaging.

J. Magn. Reson. Imaging 11, 702-710 (2000)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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. (C) 2000 Wiley-Liss, Inc.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Diffusion Tensor Imaging ; Noise Reduction ; Signal-to- Noise Ratio ; Systematic Errors
ISSN (print) / ISBN 1053-1807
e-ISSN 1522-2586
Zeitschrift Journal of Magnetic Resonance Imaging
Quellenangaben Band: 11, Heft: 6, Seiten: 702-710 Artikelnummer: , Supplement: ,
Verlag Wiley
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)