http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-10335105-B2

Outgoing Links

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classificationCPCAdditional http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20081
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classificationCPCInventive http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B6-482
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classificationIPCInventive http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T5-50
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61B6-03
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http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T11-00
filingDate 2015-04-28-04:00^^<http://www.w3.org/2001/XMLSchema#date>
grantDate 2019-07-02-04:00^^<http://www.w3.org/2001/XMLSchema#date>
inventor http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_874779e95b05c0a77e6d60c753f806cf
publicationDate 2019-07-02-04:00^^<http://www.w3.org/2001/XMLSchema#date>
publicationNumber US-10335105-B2
titleOfInvention Method and system for synthesizing virtual high dose or high kV computed tomography images from low dose or low kV computed tomography images
abstract A method and apparatus for medical image synthesis is disclosed, which synthesizes a target medical image based on a source medical image. The method can be used for synthesizing a high dose computed tomography (CT) image or a high kV CT image from a low dose CT image or a low kV image. A plurality of image patches are extracted from a source medical image. A synthesized target medical image is generated from the source medical image by calculating voxel values in the synthesized target medical image based on the image patches extracted from the source medical image using a machine learning based probabilistic model.
priorityDate 2015-04-28-04:00^^<http://www.w3.org/2001/XMLSchema#date>
type http://data.epo.org/linked-data/def/patent/Publication

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Total number of triples: 25.