Adaptive Noise Filtering for Accurate and Precise Diffusion Estimation in Fiber Crossings
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 this. 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.
Matthan W.A.Caan Ganesh Khedoe Dirk Poot Arjan den Dekker Silvia Olabarriaga Kees Grimbergen Lucas van Vliet Frans Vos
Academic Medical Center, University of Amsterdam, NL Delft University of Technology, NL Academic Medical Center, University of Amsterdam, NL Delft University of Technology, NL
国际会议
北京
英文
167-174
2010-09-01（万方平台首次上网日期，不代表论文的发表时间）