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US Patent 11324418 Multi-coil magnetic resonance imaging using deep learning

Patent 11324418 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
113244180
Patent Inventor Names
Michal Sofka0
Seyed Sadegh Moshen Salehi0
Jo Schlemper0
Date of Patent
May 10, 2022
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Patent Application Number
168174020
Date Filed
March 12, 2020
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Patent Citations
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US Patent 10663551 Method for constructing image from MRI data
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US Patent 10605878 System, method and computer-accessible medium for highly-accelerated dynamic magnetic resonance imaging using golden-angle radial sampling and compressed sensing
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US Patent 10635943 Systems and methods for noise reduction in medical images with deep neural networks
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US Patent 10650532 Single- and multi-modality alignment of medical images in the presence of non-rigid deformations using phase correlation
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US Patent 10667691 Compressed sensing high resolution functional magnetic resonance imaging
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US Patent 10705170 Methods and systems for removing spike noise in magnetic resonance imaging
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US Patent 10709387 Radio frequency coil methods and apparatus
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US Patent 10712416 Methods and systems for magnetic resonance image reconstruction using an extended sensitivity model and a deep neural network
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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 11564590 Deep learning 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
Patent Primary Examiner
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Oneal R Mistry
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CPC Code
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A61B 5/055
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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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