Hu Yuxin, Wang Xiaole, Tian Qiyuan, Yang Grant, Daniel Bruce, McNab Jennifer, Hargreaves Brian
Department of Electrical Engineering, Stanford University, Stanford, California.
Department of Radiology, Stanford University, Stanford, California.
Magn Reson Med. 2020 May;83(5):1596-1607. doi: 10.1002/mrm.28025. Epub 2019 Oct 8.
To resolve the motion-induced phase variations in multi-shot multi-direction diffusion-weighted imaging (DWI) by applying regularization to magnitude images.
A nonlinear model was developed to estimate phase and magnitude images separately. A locally low-rank regularization (LLR) term was applied to the magnitude images from all diffusion-encoding directions to exploit the spatial and angular correlation. In vivo experiments with different resolutions and b-values were performed to validate the proposed method.
The proposed method significantly reduces the noise level compared to the conventional reconstruction method and achieves submillimeter (0.8mm and 0.9mm isotropic resolutions) DWI with a b-value of 1,000 and 1-mm isotropic DWI with a b-value of 2,000 without modification of the sequence.
A joint reconstruction method with spatial-angular LLR regularization on magnitude images substantially improves multi-direction DWI reconstruction, simultaneously removes motion-induced phase artifacts, and denoises images.
通过对幅值图像应用正则化来解决多次激发多方向扩散加权成像（DWI）中运动引起的相位变化。
开发了一种非线性模型来分别估计相位和幅值图像。对来自所有扩散编码方向的幅值图像应用局部低秩正则化（LLR）项，以利用空间和角度相关性。进行了不同分辨率和b值的体内实验，以验证所提出的方法。
与传统重建方法相比，所提出的方法显著降低了噪声水平，并且在不修改序列的情况下实现了b值为1000时的亚毫米（0.8毫米和0.9毫米各向同性分辨率）DWI以及b值为2000时的1毫米各向同性DWI。
对幅值图像进行空间-角度LLR正则化的联合重建方法显著改善了多方向DWI重建，同时消除了运动引起的相位伪影并对图像进行了去噪。
