Title
Adaptive Noise Filtering for Accurate and Precise Diffusion Estimation in Fiber Crossings.
Abstract
Measuring the diffusion properties of crossing fibers is very challenging due to the high number of model parameters involved and the intrinsically low SNR, of Diffusion Weighted MR Images. Noise filtering aims at suppressing the noise while pertaining the data distribution. We propose an adaptive version of the Linear Minimum Mean Square Error (LMMSE) estimator to achieve tins. Our filter applies an adaptive filtering kernel that is based on a space-variant estimate of the noise level and a weight consisting of the product of a Gaussian kernel and the diffusion similarity with respect to the central voxel. The experiments show that the data distribution after filtering is still Rician and that the diffusivity values are estimated with a higher precision while pertaining an equal accuracy. We demonstrate on brain data that our adaptive approach performs better than the initial LMMSE estimator.
Year
DOI
Venue
2010
10.1007/978-3-642-15705-9_21
Lecture Notes in Computer Science
DocType
Volume
Issue
Conference
6361
Pt 1
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
7
8