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. 24, 2020

Filed:

Nov. 08, 2018
Applicant:

General Electric Company, Schenectady, NY (US);

Inventors:

Bo Wang, Schenectady, NY (US);

Thomas Kwok-Fah Foo, Clifton Park, NY (US);

Desmond Teck Beng Yeo, Clifton Park, NY (US);

Jhimli Mitra, Niskayuna, NY (US);

Assignee:

GENERAL ELECTRIC COMPANY, Schenectady, NY (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); A61B 5/00 (2006.01); A61B 8/08 (2006.01); G06K 9/66 (2006.01); G06T 7/37 (2017.01); A61B 8/00 (2006.01); A61B 5/055 (2006.01); G01R 33/48 (2006.01); G01R 33/56 (2006.01);
U.S. Cl.
CPC ...
A61B 5/7267 (2013.01); A61B 5/055 (2013.01); A61B 5/7253 (2013.01); A61B 8/08 (2013.01); A61B 8/463 (2013.01); A61B 8/483 (2013.01); A61B 8/5261 (2013.01); G01R 33/4814 (2013.01); G01R 33/5608 (2013.01); G06K 9/66 (2013.01); G06T 7/37 (2017.01); G06T 2207/10088 (2013.01); G06T 2207/10132 (2013.01); G06T 2207/20048 (2013.01); G06T 2207/30004 (2013.01);
Abstract

A method is provided. The method includes acquiring simultaneously multiple magnetic resonance (MR) images and multiple ultrasound images of an anatomical region of a subject over a scanned duration. The method also includes training an unsupervised deep learning-based deformable registration network. This training includes training a MR registration subnetwork based on the multiple MR images to generate MR deformation and transformation vectors, training an ultrasound registration subnetwork based on the multiple ultrasound images to generate ultrasound deformation and transformation vectors, and training a MR-to-ultrasound subnetwork based the multiple MR images and the multiple ultrasound images to generate MR-to-ultrasound deformation and transformation vectors between corresponding pairs of MR images and ultrasound images at each time point.


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