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Diffusion-weighted MRI with magnitude-based locally low-rank regularization
专利权人:
The Board of Trustees of the Leland Stanford Junior University
发明人:
Brian A. Hargreaves,Yuxin Hu
申请号:
US16824582
公开号:
US20200300956A1
申请日:
2020.03.19
申请国别(地区):
US
年份:
2020
代理人:
摘要:
A diffusion-weighted magnetic resonance imaging (MRI) method acquires MRI scan data from a multi-direction, multi-shot, diffusion-weighted MRI scan, and jointly reconstructs from the MRI scan data 1) magnitude images for multiple diffusion-encoding directions and 2) phase images for multiple shots and multiple diffusion-encoding directions using an iterative reconstruction method. Each iteration of the iterative reconstruction method comprises a gradient calculation, a phase update to update the phase images, and a magnitude update to update the magnitude images. Each iteration minimizes a cost function comprising a locally low-rank (LLR) regularization constraint on the magnitude images from the multiple diffusion-encoding directions.
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