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US Patent 11681000 Self ensembling techniques for generating magnetic resonance images from spatial frequency data

Patent 11681000 was granted and assigned to Hyperfine on June, 2023 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
116810000
Date of Patent
June 20, 2023
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Patent Application Number
174781270
Date Filed
September 17, 2021
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Patent Citations
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US Patent 8354844 Method for data acquisition acceleration in magnetic resonance imaging (MRI) with N-dimensional spatial encoding using two or more receiver coil arrays and non-linear phase distributions
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US Patent 8384383 Method and device for reconstructing a sequence of magnetic resonance images
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US Patent 8473028 K-space sample density compensation for magnetic resonance image reconstruction
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US Patent 8494905 Audience response analysis using simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI)
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US Patent 8692549 Method for reconstructing images of an imaged subject from a parallel MRI acquisition
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US Patent 8781197 Tool for accurate quantification in molecular MRI
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US Patent 9208263 System and method for image segmentation in generating computer models of a joint to undergo arthroplasty
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US Patent 9224210 Systems and methods for accelerated dynamic magnetic resonance imaging
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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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Patent Primary Examiner
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Manav Seth
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CPC Code
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G06V 10/92
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G06V 10/431
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G06V 10/454
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G06V 10/82
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G06V 10/30
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G06V 10/52
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G01R 33/36
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G01R 33/383
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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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