Evaluation of Anisotropic Filtering for DTI as a Function of SNR

J. Lee, M. K. Chung, A. L. Alexander Medical Physics, University of Wisconsin, Madison, WI, United States, Waisman Laboratory for Functional Brain Imaging and Behavior, Madison, WI, United States, Statistics, University of Wisconsin, Madison, WI, United States Introduction Diffusion tensor imaging (DTI) measures are very sensitive to noise [1]. These noise effects are even more increased as the spatial resolution of DTI is increased. Therefore, it may be desirable to use image-processing methods that will reduce noise effects in DTI. An anisotropic Gaussian filtering based on the diffusion tensor in DTI was recently proposed for preferential image blurring along the white matter tracts directions, which minimizes partial volume averaging artifacts [2]. In this study, we further improved the anisotropic Gaussian filtering by taking a higher power of diffusion tensor to accentuate the anisotropy, and evaluated the performance with higher spatial resolution DTI data with various SNR levels. Methods Data and gold standard images: A single-shot spin echo EPI sequence with diffusion-tensor encoding (12 directions (optimized using minimum energy criterion [3], b=1000s/mm), was used to get 12 sets (identical slice locations, voxels = 0.84 x 0.84 x 1.8mm, 23 cm FOV, 54 slices) of the whole brain DTI data from a single subject. “Gold standard” FA was obtained by averaging all 12 sets of diffusion weighted images. Various SNR levels (10-32) of data were derived from the twelve data sets.