The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jun. 23, 2020

Filed:

Jan. 29, 2019
Applicant:

The Board of Trustees of the Leland Stanford Junior University, Stanford, CA (US);

Inventors:

Joseph Yitan Cheng, Los Altos, CA (US);

Morteza Mardani Korani, Palo Alto, CA (US);

John M. Pauly, Stanford, CA (US);

Shreyas S. Vasanawala, Stanford, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 11/00 (2006.01); G01R 33/56 (2006.01); G01R 33/48 (2006.01); G01R 33/561 (2006.01); G06N 3/08 (2006.01); G06N 3/04 (2006.01);
U.S. Cl.
CPC ...
G06T 11/005 (2013.01); G01R 33/4826 (2013.01); G01R 33/5608 (2013.01); G01R 33/5611 (2013.01); G01R 33/4824 (2013.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01);
Abstract

A method for magnetic resonance imaging acquires multi-channel subsampled k-space data using multiple receiver coils; performs singular-value-decomposition on the multi-channel subsampled k-space data to produce compressed multi-channel k-space data which normalizes the multi-channel subsampled k-space data; applies a first center block of the compressed multi-channel k-space data as input to a first convolutional neural network to produce a first estimated k-space center block that includes estimates of k-space data missing from the first center block; generates an n-th estimated k-space block by repeatedly applying an (n−1)-th estimated k-space center block combined with an n-th center block of the compressed multi-channel k-space data as input to an n-th convolutional neural network to produce an n-th estimated k-space center block that includes estimates of k-space data missing from the n-th center block; reconstructs image-space data from the n-th estimated k-space block.


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