基于自适应低秩去噪的磁共振图像重构

本文提出了一种基于自适应低秩去噪的磁共振图像重构算法.该方法使用去噪近似消息传递算法重构磁共振图像，将自适应加权Schatten-p范数最小化方法（Weighted Schatten p-Norm Minimization，WSNM）作为其降噪模型，研究图像的重构性能.根据算法迭代过程中估计的噪声标准差自适应的设定WSNM的图像块大小及相似块个数.实验表明，与近几年提出的磁共振图像重构算法比较，本文提出的算法可以获得更高的峰值信噪比（Peak Signal to Noise Ratio，PSNR）和更低的相对${L_2}$范数误差（Relative${L_2}$Norm Error，RLNE），得到更好的重建效果.

MRI Reconstruction Method Using Adaptive Low Rank Denoising

In this study, the adaptive low rank denoising based Magnetic Resonance Imaging (MRI) reconstruction method is proposed. This method uses denoising-based approximate message passing algorithm to reconstruct MR images. The adaptive Weighted Schatten p-Norm Minimization (WSNM) method is used as its noise reduction model to study the reconstruction performance of the MR images. And the image block size and the number of similar blocks of WSNM are set adaptively according to the noise standard deviation estimated during the algorithm iteration process. Compared with the MR image reconstruction algorithms proposed in recent years, the experimental results show that the proposed method can get higher Peak Signal-to-Noise Ratio (PSNR) and lower Relative L₂ Norm Error (RLNE) and have the best reconstruction performance.