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. 30, 2020

Filed:

Jun. 14, 2018
Applicant:

Siemens Healthcare Gmbh, Erlangen, DE;

Inventors:

Sandro Braun, Karlsruhe, DE;

Boris Mailhe, Plainsboro, NJ (US);

Xiao Chen, Princeton, NJ (US);

Benjamin L. Odry, West New York, NJ (US);

Pascal Ceccaldi, Princeton, NJ (US);

Mariappan S. Nadar, Plainsboro, NJ (US);

Assignee:

Siemens Healthcare GmbH, Erlangen, DE;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G01R 33/565 (2006.01); G06T 5/00 (2006.01); G06N 5/04 (2006.01); G06T 7/20 (2017.01); G06N 3/08 (2006.01);
U.S. Cl.
CPC ...
G01R 33/56509 (2013.01); G06N 3/08 (2013.01); G06N 5/046 (2013.01); G06T 5/003 (2013.01); G06T 7/20 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/20081 (2013.01);
Abstract

Systems and methods are provided for correcting motion artifacts in magnetic resonance images. An image-to-image neural network is used to generate motion corrected magnetic resonance data given motion corrupted magnetic resonance data. The image-to-image neural network is coupled within an adversarial network to help refine the generated magnetic resonance data. The adversarial network includes a generator network (the image-to-image neural network) and a discriminator network. The generator network is trained to minimize a loss function based on a Wasserstein distance when generating MR data. The discriminator network is trained to differentiate the motion corrected MR data from motion artifact free MR data.


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