Open AccessComparing isotropic and anisotropic smoothing for voxel‐based DTI analyses: A simulation study
Author(s) -
Van Hecke Wim,
Leemans Alexander,
De Backer Steve,
Jeurissen Ben,
Parizel Paul M.,
Sijbers Jan
Publication year - 2010
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.20848
Subject(s) - smoothing , voxel , diffusion mri , isotropy , anisotropy , kernel (algebra) , gaussian , fractional anisotropy , computer science , artificial intelligence , gaussian function , pattern recognition (psychology) , mathematics , computer vision , physics , magnetic resonance imaging , optics , medicine , combinatorics , quantum mechanics , radiology
Voxel‐based analysis (VBA) methods are increasingly being used to compare diffusion tensor image (DTI) properties across different populations of subjects. Although VBA has many advantages, its results are highly dependent on several parameter settings, such as those from the coregistration technique applied to align the data, the smoothing kernel, the statistics, and the post‐hoc analyses. In particular, to increase the signal‐to‐noise ratio and to mitigate the adverse effect of residual image misalignments, DTI data are often smoothed before VBA with an isotropic Gaussian kernel with a full width half maximum up to 16 × 16 × 16 mm 3 . However, using isotropic smoothing kernels can significantly partial volume or voxel averaging artifacts, adversely affecting the true diffusion properties of the underlying fiber tissue. In this work, we compared VBA results between the isotropic and an anisotropic Gaussian filtering method using a simulated framework. Our results clearly demonstrate an increased sensitivity and specificity of detecting a predefined simulated pathology when the anisotropic smoothing kernel was used. Hum Brain Mapp, 2010. © 2009 Wiley‐Liss, Inc.