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US Patent 11564590 Deep learning techniques for generating magnetic resonance images from spatial frequency data

Patent 11564590 was granted and assigned to Hyperfine on January, 2023 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent

Patent attributes

Patent Applicant
Hyperfine
Hyperfine
Current Assignee
Hyperfine
Hyperfine
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
11564590
Date of Patent
January 31, 2023
Patent Application Number
16817370
Date Filed
March 12, 2020
Patent Citations
‌
US Patent 10222434 Portable magnetic resonance imaging methods and apparatus
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US Patent 10222435 Magnetic coil power methods and apparatus
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US Patent 10241177 Ferromagnetic augmentation for magnetic resonance imaging
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US Patent 10274561 Electromagnetic shielding for magnetic resonance imaging methods and apparatus
‌
US Patent 10274563 Magnetic resonance imaging apparatus and method
‌
US Patent 10281540 Magnetic coil power methods and apparatus
‌
US Patent 10281541 Low-field magnetic resonance imaging methods and apparatus
‌
US Patent 10281549 Magnetic resonance imaging apparatus and image processing apparatus
...
Patent Citations Received
‌
US Patent 12105173 Self ensembling techniques for generating magnetic resonance images from spatial frequency data
0
Patent Primary Examiner
‌
Michael S Osinski
CPC Code
‌
G01R 33/445
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G01R 33/5608
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G01R 33/5611
‌
G01R 33/4824
‌
G01R 33/4835
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G01R 33/56509
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G06K 9/6245
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G06N 3/0454
...

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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