小波复扩散及在DTI图像恢复中的应用

Wavelet based complex diffusion and its application in restoring DTI images

扩散张量图像中广泛存在的赖斯噪声会给张量计算和脑白质追踪等带来严重的影响.为了减少噪声影响,采用小波复扩散方法对多通道扩散加权图像进行了恢复.小波复扩散滤波方法即在小波域中进行复扩散.该方法能够有效消除噪声影响而且具有较好的边缘保持特性.采用峰值信噪比(PSNR)和信号均方差之比(SMSE)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了人脑白质纤维追踪.把去噪方法和多通道小波方法以及复扩散方法进行了比较,实验结果表明本滤波方法具有良好的去噪性能.

To decrease the effects of the Rician noise, we adopted the wavelet-based complex diffusion method to smooth diffusion weighted images （DWI）, which are of multi-channel typed. The presented smoothing strategy, which utilizes complex 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 DWI, 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, the apparent diffusion coefficients （ADC） are calculated and the fibers are tracked. Comparisons among the presented model, the wave shrinkage and complex diffusion smoothing method are made. All the experiment results prove quantitatively and visually the good performance of the presented filter.