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:
Jan. 31, 2023

Filed:

Mar. 12, 2020
Applicant:

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

Inventors:

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

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

Michal Sofka, Princeton, NJ (US);

Prantik Kundu, Branford, CT (US);

Carole Lazarus, Paris, FR;

Hadrien A. Dyvorne, New York, NY (US);

Rafael O'Halloran, Guilford, CT (US);

Laura Sacolick, Guilford, CT (US);

Assignee:

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

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
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); G06T 7/00 (2017.01); G06V 10/88 (2022.01); G06V 10/75 (2022.01);
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 9/6245 (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); G06V 10/7515 (2022.01); G06V 10/89 (2022.01); G06V 10/92 (2022.01); 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

Techniques for generating magnetic resonance (MR) images of a subject from MR data obtained by a magnetic resonance imaging (MRI) system, the techniques include: obtaining input MR spatial frequency data obtained by imaging the subject using the MRI system; generating an MR image of the subject from the input MR spatial frequency data using a neural network model comprising: a pre-reconstruction neural network configured to process the input MR spatial frequency data; a reconstruction neural network configured to generate at least one initial image of the subject from output of the pre-reconstruction neural network; and a post-reconstruction neural network configured to generate the MR image of the subject from the at least one initial image of the subject.


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