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. 30, 2021

Filed:

Mar. 12, 2020
Applicant:

Hyperfine, 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, 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 (2006.01); 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 of a subject from MR data obtained by a magnetic resonance imaging (MRI) system, the techniques including: obtaining input MR data obtained by imaging the subject using the MRI system; generating a plurality of transformed input MR data instances by applying a respective first plurality of transformations to the input MR data; generating a plurality of MR images from the plurality of transformed input MR data instances and the input MR data using a non-linear MR image reconstruction technique; generating an ensembled MR image from the plurality of MR images at least in part by: applying a second plurality of transformations to the plurality of MR images to obtain a plurality of transformed MR images; and combining the plurality of transformed MR images to obtain the ensembled MR image; and outputting the ensembled MR image.


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