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US Patent 11344219 Deep learning techniques for alignment of magnetic resonance images

Patent 11344219 was granted and assigned to Hyperfine on May, 2022 by the United States Patent and Trademark Office.

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Is a
Patent
Patent
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Patent attributes

Patent Applicant
Hyperfine
Hyperfine
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Current Assignee
Hyperfine
Hyperfine
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
113442190
Patent Inventor Names
Seyed Sadegh Mosheni Salehi0
Jo Schlemper0
Michal Sofka0
Date of Patent
May 31, 2022
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Patent Application Number
168172690
Date Filed
March 12, 2020
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Patent Citations
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US Patent 10324147 Rotatable magnet methods and apparatus for a magnetic resonance imaging system
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US Patent 10274561 Electromagnetic shielding for magnetic resonance imaging methods and apparatus
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US Patent 10274563 Magnetic resonance imaging apparatus and method
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US Patent 10281540 Magnetic coil power methods and apparatus
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US Patent 10281541 Low-field magnetic resonance imaging methods and apparatus
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US Patent 10281549 Magnetic resonance imaging apparatus and image processing apparatus
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US Patent 10295628 Magnetic coil power methods and apparatus
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US Patent 10310037 Rotatable magnet methods and apparatus for a magnetic resonance imaging system
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Patent Citations Received
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US Patent 12105173 Self ensembling techniques for generating magnetic resonance images from spatial frequency data
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US Patent 11681000 Self ensembling techniques for generating magnetic resonance images from spatial frequency data
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US Patent 11789104 Deep learning techniques for suppressing artefacts in magnetic resonance images
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US Patent 11564590 Deep learning techniques for generating magnetic resonance images from spatial frequency data
Patent Primary Examiner
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Mahendra R Patel
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CPC Code
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G01R 33/5611
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G01R 33/4824
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G01R 33/4835
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G01R 33/56509
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G06K 9/6245
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G06N 3/0454
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G06N 3/08
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G06N 3/082
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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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