| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_9d6f7da1e425c8a3ef1da21e08f4c196 |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-4872
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10088 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01R33-5608
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H30-40
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-055
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01R33-24
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01R33-56536
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-0042
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-70 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T11-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G01R33-56
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61B5-055
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H30-40 |
| filingDate |
2021-08-19-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_ccdb8fbf62d0440749c05631f30e9056
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_cf27ae88f2794d6fe56f59357734c54d
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_09aa640c21b5a615923140dfb0e3a3ff
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_742b160ed6ff4049101755477be5e892
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_1138334bedccade7d799f00ce8ed77b7
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_d4c4ab1cc2267dae604e9a22a600509f
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_38d8e44df31c54604d191e22da607694 |
| publicationDate |
2022-02-24-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
WO-2022040449-A1 |
| titleOfInvention |
System and method of accurate quantitative mapping of biophysical parameters from mri data |
| abstract |
Quantitative susceptibility mapping methods, systems and computer-accessible medium generate images of tissue magnetism property from complex magnetic resonance imaging data using the Bayesian inference approach, which minimizes a cost function comprising of a data fidelity term and regularization terms. The data fidelity term is constructed directly from the multiecho complex magnetic resonance imaging data. The regularization terms include a prior constructed from matching structures or information content in known morphology, and a prior constructed from regions of low susceptibility contrasts characterized on image features. The quantitative susceptibility map can be determined by minimizing the cost function that involves nonlinear functions in modeling the obtained signals, and the corresponding inverse problem is solved using nonconvex optimization using a scaling approach or deep neural network. The nonconvex optimization is also developed for solving other inverse problems of nonlinear signal models in fat-water separation, tissue transport and oxygen extraction fraction. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-115530820-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-115530820-B |
| priorityDate |
2020-08-20-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |