Abstract: The Rician noise introduced into the diffusion tensor images can bring serious impacts on tensor calculation and fiber tracking. To decrease the effects of the Rician noise, this paper proposes a multi-channel wavelet-based method to denoise multi-channel typed diffusion weighted images. 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 metric is adopted. Based on the synthetic and real data, it calculates the apparent diffusion coefficient and tracks the fibers, makes comparisons between the presented model and the channel-by-channel smoothing method. Experimental results quantitatively and visually prove the better performance of the presented filter.

Key words: diffusion tensor imaging, restoration, multi-channel wavelet

摘要： 扩散张量图像中存在的赖斯噪声给张量计算和脑白质追踪等带来严重影响。为了减少噪声影响，该文采用多通道小波对扩散加权图像进行恢复，采用峰值信噪比来定量地评估本滤波器消除赖斯噪声的性能。基于模拟和真实数据对张量场的表面扩张系数等进行了计算并进行人脑白质纤维追踪。把该去噪方法和单通道小波方法进行比较，实验结果表明，提出的滤波器具有更好的噪声性能。

关键词: 扩散张量成像, 恢复, 多通道小波

CLC Number: 

• TP391