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US Patent 10281549 Magnetic resonance imaging apparatus and image processing apparatus

Patent 10281549 was granted and assigned to Toshiba Medical Systems Corporation on May, 2019 by the United States Patent and Trademark Office.

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Patent
Patent
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Patent attributes

Patent Applicant
Toshiba Medical Systems Corporation
Toshiba Medical Systems Corporation
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Current Assignee
Toshiba Medical Systems Corporation
Toshiba Medical Systems Corporation
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
102815490
Patent Inventor Names
Hidenori Takeshima0
Date of Patent
May 7, 2019
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Patent Application Number
146665310
Date Filed
March 24, 2015
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Patent Citations Received
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US Patent 11467239 Deep learning techniques for magnetic resonance image reconstruction
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US Patent 11324418 Multi-coil magnetic resonance imaging using deep learning
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US Patent 11344219 Deep learning techniques for alignment of magnetic resonance images
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US Patent 11789104 Deep learning techniques for suppressing artefacts in magnetic resonance images
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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 11300645 Deep learning techniques for magnetic resonance image reconstruction
Patent Primary Examiner
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Rishi R Patel
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Patent abstract

A magnetic resonance imaging apparatus according to an embodiment includes a collector, a transformation module, an unfolding module and an inverse transformation module. The collector collects time-series k-space data of a plurality of channels while spatially changing a sampling position. The transformation module obtains transformed space data of the respective channels by applying, to the time-series k-space data of the respect channels, Fourier transform on a spatial axis and certain transformation on a temporal axis. The unfolding module eliminates a signal point on a basis of a certain criterion and performs unfolding using the transformed space data on the respective channels and sensitivity distribution information on the respective channels; and the inverse transformation module applies inverse transformation of the certain transformation on the temporal axis to an unfolded data on which the signal point has been eliminated and the unfolding has been performed.

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