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. 10, 2022

Filed:

Mar. 12, 2020
Applicant:

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

Inventors:

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

Seyed Sadegh Moshen 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 (2006.01); 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

Techniques for generating magnetic resonance (MR) images from MR data obtained by a magnetic resonance imaging (MRI) system comprising a plurality of RF coils configured to detect RF signals. The techniques include: obtaining a plurality of input MR datasets obtained by the MRI system to image a subject, each of the plurality of input MR datasets comprising spatial frequency data and obtained using a respective RF coil in the plurality of RF coils; generating a respective plurality of MR images from the plurality of input MR datasets by using an MR image reconstruction technique; estimating, using a neural network model, a plurality of RF coil profiles corresponding to the plurality of RF coils; generating an MR image of the subject using the plurality of MR images and the plurality of RF coil profiles; and outputting the generated MR image.


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