| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_ff81a650b70e913d3b2524b45e4c7320 |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20081
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-30016
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10104
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-048
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20016
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20084 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T3-60
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B6-037
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T9-002
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T5-002
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B6-5205
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T5-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B6-5258 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T5-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61B6-03
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61B6-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T3-60
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T9-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08 |
| filingDate |
2020-08-28-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_3a97b86e4771cef3a770c6e21ced7883
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_101ae7ad29f649721f9fb1a4375b7f90
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_f3454d8b6ffcd1c7dafb17842b260178 |
| publicationDate |
2022-09-15-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
US-2022287671-A1 |
| titleOfInvention |
Dilated convolutional neural network system and method for positron emission tomography (pet) image denoising |
| abstract |
A method for performing positron emission tomography (PET) image denoising using a dilated convolutional neural network system includes: obtaining, as an input to the dilated convolutional neural network system, a noisy image; performing image normalization to generate normalized image data corresponding to the noisy image; encoding the normalized image data using one or more convolutions in the dilated convolutional neural network, whereby a dilation rate is increased for each encoding convolution performed to generate encoded image data; decoding the encoded image data using one or more convolutions in the dilated convolutional neural network, whereby dilation rate is decreased for each decoding convolution performed to generate decoded image data; synthesizing the decoded image data to construct a denoised output image corresponding to the noisy image; and displaying the denoised output image on an image display device, the denoised output image having enhanced image quality compared to the noisy image. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-2022292679-A1
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-11756197-B2
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-11756243-B1 |
| priorityDate |
2019-08-30-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |