Patent · US Active

Diffusion-weighted MRI with magnitude-based locally low-rank regularization

US11313933B2 · kind B2 · utility

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1References

5Claims

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Assignee

The Board of Trustees of the Leland Stanford Junior University · US

Inventors

Brian A. Hargreaves · Menlo Park, US
Yuxin Hu · Tangxia, CN

Key dates

Filing date	Mar 19, 2020
Grant date	Apr 26, 2022
Priority date	—
Expiry date	Oct 1, 2040

Classification

Technology area (CPC G)Physics
CPC primaryG06T2207/30016
WIPO fieldMeasurement
WIPO sectorInstruments

Abstract

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.

Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.