PDF(Pubmed)
Abstract：
The spatial resolution attainable in diffusion magnetic resonance (MR) imaging is inherently limited by noise. The weaker signal associated with a smaller voxel size, especially at a high level of diffusion sensitization, is often buried under the noise floor owing to the non-Gaussian nature of the MR magnitude signal. Here, we show how the noise floor can be suppressed remarkably via optimal shrinkage of singular values associated with noise in complex-valued k-space data from multiple receiver channels. We explore and compare different low-rank signal matrix recovery strategies to utilize the inherently redundant information from multiple channels. In combination with background phase removal, the optimal strategy reduces the noise floor by 11 times. Our framework enables imaging with substantially improved resolution for precise characterization of tissue microstructure and white matter pathways without relying on expensive hardware upgrades and time-consuming acquisition repetitions, outperforming other related denoising methods.
摘要：
在扩散磁共振(MR)成像中可获得的空间分辨率固有地受到噪声的限制。较弱的信号与较小的体素大小相关联，特别是在高水平的扩散敏化下，由于MR幅度信号的非高斯性质,通常被埋在噪声层之下。这里,我们展示了如何通过最佳收缩与来自多个接收器通道的复值k空间数据中的噪声相关的奇异值，显着抑制本底噪声。我们探索和比较不同的低秩信号矩阵恢复策略，以利用来自多个通道的固有冗余信息。结合背景相位去除，最优策略将噪声本底降低了11倍。我们的框架使成像具有显著提高的分辨率，用于精确表征组织微结构和白质通路，而不依赖于昂贵的硬件升级和耗时的采集重复。优于其他相关的去噪方法。
