| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_01d213f75765702302a15d513d25b85d |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B2576-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B2090-3762
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20081
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10081
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10088 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H30-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H30-40
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T5-007
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-0035
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-50
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-7267
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-7278
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T11-008
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-055
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61N5-1039
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01R33-5608 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H30-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H30-40
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H50-50
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61B5-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G01R33-56
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T11-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T5-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61B5-055
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T3-00 |
| filingDate |
2016-10-12-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_4e8e27459d96066bab7ac5a79cd96aa7 |
| publicationDate |
2022-01-19-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
EP-3940624-A1 |
| titleOfInvention |
Pseudo-ct generation from mr data using a feature regression model |
| abstract |
Systems and methods are provided for generating a pseudo-CT prediction model that can be used to generate pseudo-CT images. An exemplary system may include a processor configured to retrieve training data including at least one MR image and at least one CT image for each of a plurality of training subjects. For each training subject, the processor may extract a plurality of features from each image point of the at least one MR image, create a feature vector for each image point based on the extracted features, and extract a CT value from each image point of the at least one CT image. The processor may also generate the pseudo-CT prediction model based on the feature vectors and the CT values of the plurality of training subjects. |
| priorityDate |
2015-10-13-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |