| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_97a2b8564412433d9a12b7cbd2e4ad4d
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_5ffdc34599f80dca91c27b3a23dd44b4
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_f41d8d0f6a128979afbf16ebf1343ae9
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_99040541819b746f0a0936e649009185
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_53c911e75ea09dd4c2705db5306dae77 |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10088 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-0012
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01R33-5611
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T11-003 |
| classificationIPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61B6-00 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T11-00 |
| filingDate |
2015-02-25-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate |
2017-01-10-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_9a7efba4a9a9acda22e44aa44a3ce940
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_ba546293e7192b8cbf0dc08d9af6fea7
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_3325c54d905afb000ef152fd1a30b156
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_2e8b0b6f07b0c1b89935ef739243de3d |
| publicationDate |
2017-01-10-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
US-9542761-B2 |
| titleOfInvention |
Generalized approximate message passing algorithms for sparse magnetic resonance imaging reconstruction |
| abstract |
A method for reconstructing magnetic resonance imaging data includes acquiring a measurement dataset using a magnetic resonance imaging device and determining an estimated image dataset based on the measurement dataset. An iterative reconstruction process is performed to refine the estimated image dataset. Each iteration of the iterative reconstruction process comprises: updating the measurement dataset and a sparse coefficient dataset based on the estimated image dataset and a plurality of belief propagation terms, incorporating a noise prior dataset into the measurement dataset, incorporating a sparsity prior dataset into the sparse coefficient dataset, updating the plurality of belief propagation terms based on the measurement dataset and the sparsity prior dataset, and updating the estimated image dataset based on the plurality of belief propagation terms. A reconstructed image and confidence map are generated using the estimated image dataset. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-10217249-B2
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-11042803-B2
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-9858689-B1
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-2017103550-A1
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-11035919-B2 |
| priorityDate |
2015-02-25-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |