http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-115187528-A
Outgoing Links
| Predicate | Object |
|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_a074588fa8fd3238c724d4c1482213d1 |
| classificationCPCAdditional | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10104 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-30048 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10081 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20084 |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-82 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-25 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T3-4007 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-30 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-136 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-0012 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-136 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T3-40 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-30 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06V10-82 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06V10-25 |
| filingDate | 2022-06-24-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_22cd11c18af3e6ac5d6064ddc8e65196 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_98e81010f6143a438400a5ce1a3ee3ad http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_08836478189239de58c804d2bba8ffc6 |
| publicationDate | 2022-10-14-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-115187528-A |
| titleOfInvention | A PET/CT-based myocardial metabolism assessment system |
| abstract | The invention discloses a PET/CT-based myocardial metabolism evaluation system, which performs 11 C-Acetate PET/CT myocardial metabolism imaging on a patient to obtain chest PET and CT images of the patient; preprocesses the images, including the PET images and CT images Image registration, image sampling and SUV transformation; use deep learning to segment the pericardium of the CT image to obtain the pericardial region of interest, and use the threshold method to obtain the pericardial fat ROI; use the threshold method to segment the PET image to obtain the myocardial ROI; According to the spatial position information, the main axis of the left ventricle is automatically positioned, and the left ventricular myocardium is segmented according to the 17-segment method; the above ROI is used to measure and analyze the image results of PET and CT, including CT calcification fraction and CT pericardial fat statistical parameters , PET pericardial fat metabolism, PET myocardial statistical parameters, PET myocardial 17-segment statistical parameters and PET metabolic bullseye map; make full use of the clear anatomical structure of CT images and the high myocardial metabolic signal of PET, which can quickly and simply segment the pericardium and myocardium. |
| priorityDate | 2022-06-24-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 33.