http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-113933773-A
Outgoing Links
| Predicate | Object |
|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_c6655263ba4852e71280f7529b0065f3 |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01R33-54 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G01R33-54 |
| filingDate | 2021-09-30-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_a9d800b0087769087163015c9595ead2 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_0f85fa278ad7e2645f414a14a1fd7b41 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_d593fe40a14ba341fb9b113784ad9c04 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_539f5de9c08a6d4360288d39a05ea97d http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_f6852b2ac50388f56b0aee9d13c807b2 |
| publicationDate | 2022-01-14-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-113933773-A |
| titleOfInvention | Magnetic resonance imaging method, system, terminal and storage medium based on deep learning |
| abstract | The application relates to a magnetic resonance imaging method, a system, a terminal and a storage medium based on deep learning. The method comprises the following steps: undersampling the magnetic resonance full-acquisition k-space data to generate undersampled k-space data; estimating an SPIRiT convolution kernel filling the K space according to the undersampled K space data; inputting the undersampled k-space data and the SPIRiT convolution kernel into a convolution neural network for training to obtain a trained k-space-based image reconstruction model; and reconstructing a magnetic resonance image through the trained k-space-based image reconstruction model. According to the embodiment of the application, the image reconstruction is carried out by adopting the k-space-based image reconstruction model, the priori information of the image is learned by utilizing the convolutional neural network, the coil sensitivity information does not need to be estimated, the reconstruction time can be greatly shortened, and a better image reconstruction effect is obtained. |
| isCitedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-114114116-A http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-114114116-B |
| priorityDate | 2021-09-30-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
| Predicate | Subject |
|---|---|
| isDiscussedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/compound/CID23985 http://rdf.ncbi.nlm.nih.gov/pubchem/substance/SID482532689 |
Total number of triples: 19.