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:
Aug. 27, 2019

Filed:

Feb. 20, 2018
Applicant:

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

Inventors:

Joseph Y. Cheng, Los Altos, CA (US);

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

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

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G01R 33/56 (2006.01); G01R 33/48 (2006.01); G01R 33/565 (2006.01); G01R 33/561 (2006.01);
U.S. Cl.
CPC ...
G01R 33/5608 (2013.01); G01R 33/4824 (2013.01); G01R 33/5611 (2013.01); G01R 33/56509 (2013.01); G01R 33/56545 (2013.01); G06T 2207/10088 (2013.01);
Abstract

A method for magnetic resonance imaging (MRI) scans a field of view and acquires sub-sampled multi-channel k-space data U. An imaging model A is estimated. Sub-sampled multi-channel k-space data U is divided into sub-sampled k-space patches, each of which is processed using a deep convolutional neural network (ConvNet) to produce corresponding fully-sampled k-space patches, which are assembled to form fully-sampled k-space data V, which is transformed to image space using the imaging model adjoint Ato produce an image domain MRI image. The processing of each k-space patch upreferably includes applying the k-space patch uas input to the ConvNet to infer an image space bandpass-filtered image y, where the ConvNet comprises repeated de-noising blocks and data-consistency blocks; and estimating the fully-sampled k-space patch vfrom the image space bandpass-filtered image yusing the imaging model A and a mask matrix.


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