title: Reduction of noise in diffusion tensor images using anisotropic smoothing.
abstract: To improve the accuracy of tissue structural and architectural characterization with diffusion tensor imaging, a novel smoothing technique is developed for reducing noise in diffusion tensor images. The technique extends the traditional anisotropic diffusion filtering method by allowing isotropic smoothing within homogeneous regions and anisotropic smoothing along structure boundaries. This is particularly useful for smoothing diffusion tensor images in which direction information contained in the tensor needs to be restored following noise corruption and preserved around tissue boundaries. The effectiveness of this technique is quantitatively studied with experiments on simulated and human in vivo diffusion tensor data. Illustrative results demonstrate that the anisotropic smoothing technique developed can significantly reduce the impact of noise on the direction as well as anisotropy measures of the diffusion tensor images.
authors.affiliation: Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee 27232-2657, USA. zhaohua.ding@vanderbilt.edu
authors.forename: Zhaohua
authors.initials: Z
authors.lastname: Ding
authors.forename: John C
authors.initials: JC
authors.lastname: Gore
authors.forename: Adam W
authors.initials: AW
authors.lastname: Anderson
mesh_terms: D000465:Algorithms; D016880:Anisotropy; D001921:Brain / Q000033:anatomy & histology*; D003198:Computer Simulation; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D008954:Models, Biological; D015233:Models, Statistical; D009716:Numerical Analysis, Computer-Assisted; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013269:Stochastic Processes