Wavelet-Based Diffusion Approach for DTI Image Restoration
ZHANG Xiang-fen
CHEN Wu-fan
TIAN Wei-feng
YE Hong
摘要：The Rician noise introduced into the diffusion tensor images (DTIs) can bring serious impacts on tensor calculation and fiber tracking. To decrease the effects of the Rician noise, we propose to consider the wavelet-based diffusion method to denoise multichannel typed diffusion weighted (DW) images. The presented smoothing strategy, which utilizes anisotropic nonlinear diffusion in wavelet domain, successfully removes noise while preserving both texture and edges. To evaluate quantitatively the efficiency of the presented method in accounting for the Rician noise introduced into the DW images, the peak-to-peak signal-to-noise ratio (PSNR) and signal-to-mean squared error ratio (SMSE) metrics are adopted. Based on the synthetic and real data, we calculated the apparent diffusion coefficient (ADC) and tracked the fibers. We made comparisons between the presented model,the wave shrinkage and regularized nonlinear diffusion smoothing method. All the experiment results prove quantitatively and visually the better performance of the presented filter.
机标关键词：nonlinear diffusionapparent diffusion coefficient
分类号：R445.2(诊断学)
资助基金：国家重点基础研究发展计划(973计划)(2003CB716103)基金Shanghai Normal University Project(SK200734)
论文发表日期：2008-01-01
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：8( 26-33 )
英文信息