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:
May. 31, 2022

Filed:

Mar. 12, 2020
Applicant:

Hyperfine Operations, Inc., Guilford, CT (US);

Inventors:

Jo Schlemper, Long Island City, NY (US);

Seyed Sadegh Mosheni Salehi, Bloomfield, NJ (US);

Michal Sofka, Princeton, NJ (US);

Assignee:

Hyperfine Operations, Inc., Guilford, CT (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2022.01); A61B 5/055 (2006.01); G01R 33/561 (2006.01); G06N 3/04 (2006.01); G06N 3/08 (2006.01); G06T 7/38 (2017.01); G01R 33/383 (2006.01); G01R 33/44 (2006.01); G01R 33/56 (2006.01); G06T 3/60 (2006.01); G06T 11/00 (2006.01); G16H 30/40 (2018.01); G01R 33/36 (2006.01); G06T 7/262 (2017.01); G06K 9/62 (2022.01); G06K 9/74 (2006.01); G06T 7/00 (2017.01);
U.S. Cl.
CPC ...
A61B 5/055 (2013.01); G01R 33/36 (2013.01); G01R 33/383 (2013.01); G01R 33/445 (2013.01); G01R 33/5608 (2013.01); G01R 33/5611 (2013.01); G06K 9/6203 (2013.01); G06K 9/6245 (2013.01); G06K 9/741 (2013.01); G06K 9/748 (2013.01); G06N 3/0454 (2013.01); G06N 3/08 (2013.01); G06N 3/082 (2013.01); G06T 3/60 (2013.01); G06T 7/0012 (2013.01); G06T 7/262 (2017.01); G06T 7/38 (2017.01); G06T 11/006 (2013.01); G06T 11/008 (2013.01); G16H 30/40 (2018.01); G06T 2207/10088 (2013.01); G06T 2207/20056 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20182 (2013.01); G06T 2207/20216 (2013.01); G06T 2207/20224 (2013.01); G06T 2207/30016 (2013.01); G06T 2210/41 (2013.01);
Abstract

Generating magnetic resonance (MR) images of a subject from MR data obtained by a magnetic resonance imaging (MRI) system by: generating first and second sets of one or more MR images from first and second input MR data; aligning the first and second sets of MR images using a neural network model comprising first and second neural networks, the aligning comprising: estimating, using the first neural network, a first transformation between the first and second sets of MR images; generating a first updated set of MR images from the second set of MR images using the first transformation; estimating, using the second neural network, a second transformation between the first set and the first updated set of MR images; and aligning the first set of MR images and the second set of MR images at least in part by using the first transformation and the second transformation.


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