Adaptive smoothing of multi-shell diffusion weighted magnetic resonance data by msPOAS
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Adaptive smoothing of multi-shell diffusion weighted magnetic resonance data by msPOAS. / Becker, S M A; Tabelow, K; Mohammadi, S; Weiskopf, N; Polzehl, J.
in: NEUROIMAGE, Jahrgang 95, 15.07.2014, S. 90-105.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
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TY - JOUR
T1 - Adaptive smoothing of multi-shell diffusion weighted magnetic resonance data by msPOAS
AU - Becker, S M A
AU - Tabelow, K
AU - Mohammadi, S
AU - Weiskopf, N
AU - Polzehl, J
N1 - Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.
PY - 2014/7/15
Y1 - 2014/7/15
N2 - We present a novel multi-shell position-orientation adaptive smoothing (msPOAS) method for diffusion weighted magnetic resonance data. Smoothing in voxel and diffusion gradient space is embedded in an iterative adaptive multiscale approach. The adaptive character avoids blurring of the inherent structures and preserves discontinuities. The simultaneous treatment of all q-shells improves the stability compared to single-shell approaches such as the original POAS method. The msPOAS implementation simplifies and speeds up calculations, compared to POAS, facilitating its practical application. Simulations and heuristics support the face validity of the technique and its rigorousness. The characteristics of msPOAS were evaluated on single and multi-shell diffusion data of the human brain. Significant reduction in noise while preserving the fine structure was demonstrated for diffusion weighted images, standard DTI analysis and advanced diffusion models such as NODDI. MsPOAS effectively improves the poor signal-to-noise ratio in highly diffusion weighted multi-shell diffusion data, which is required by recent advanced diffusion micro-structure models. We demonstrate the superiority of the new method compared to other advanced denoising methods.
AB - We present a novel multi-shell position-orientation adaptive smoothing (msPOAS) method for diffusion weighted magnetic resonance data. Smoothing in voxel and diffusion gradient space is embedded in an iterative adaptive multiscale approach. The adaptive character avoids blurring of the inherent structures and preserves discontinuities. The simultaneous treatment of all q-shells improves the stability compared to single-shell approaches such as the original POAS method. The msPOAS implementation simplifies and speeds up calculations, compared to POAS, facilitating its practical application. Simulations and heuristics support the face validity of the technique and its rigorousness. The characteristics of msPOAS were evaluated on single and multi-shell diffusion data of the human brain. Significant reduction in noise while preserving the fine structure was demonstrated for diffusion weighted images, standard DTI analysis and advanced diffusion models such as NODDI. MsPOAS effectively improves the poor signal-to-noise ratio in highly diffusion weighted multi-shell diffusion data, which is required by recent advanced diffusion micro-structure models. We demonstrate the superiority of the new method compared to other advanced denoising methods.
KW - Algorithms
KW - Artifacts
KW - Brain Mapping
KW - Diffusion Magnetic Resonance Imaging
KW - Humans
KW - Image Processing, Computer-Assisted
KW - Models, Theoretical
U2 - 10.1016/j.neuroimage.2014.03.053
DO - 10.1016/j.neuroimage.2014.03.053
M3 - SCORING: Journal article
C2 - 24680711
VL - 95
SP - 90
EP - 105
JO - NEUROIMAGE
JF - NEUROIMAGE
SN - 1053-8119
ER -
