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:
Nov. 26, 2019

Filed:

Feb. 28, 2018
Applicant:

General Electric Company, Schenectady, NY (US);

Inventors:

Itzik Malkiel, Givaatayim, IL;

Sangtae Ahn, Guilderland, NY (US);

Christopher Judson Hardy, Schenectady, NY (US);

Assignee:

General Electric Company, Schenectady, NY (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
A61B 5/00 (2006.01); G06T 11/00 (2006.01); G01R 33/48 (2006.01); G01R 33/561 (2006.01);
U.S. Cl.
CPC ...
G06T 11/008 (2013.01); G01R 33/4822 (2013.01); G01R 33/561 (2013.01); G06T 2211/424 (2013.01);
Abstract

A method for sparse image reconstruction includes acquiring coil data from a magnetic resonance imaging device. The coil data includes undersampled k-space data corresponding to a subject. The method further includes processing the coil data using an image reconstruction technique to generate an initial undersampled image. The method also includes generating a reconstructed image based on the coil data, the initial undersampled image, and a plurality of iterative blocks of a flared network. A first iterative block of the flared network receives the initial undersampled image. Each of the plurality of iterative blocks includes a data consistency unit and a regularization unit and the iterative blocks are connected both by direct connections from one iterative block to the following iterative block and by a plurality of dense skip connections to non-adjacent iterative blocks. The flared network is based on a neural network trained using previously acquired coil data.


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