{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Li Z"],"funding":["NIBIB NIH HHS","National Cancer Institute","NCI NIH HHS","National Institutes of Health","NIH HHS","National Institute of Biomedical Imaging and Bioengineering"],"pagination":["1216-1230"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10041998"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["49(2)"],"pubmed_abstract":["<h4>Purpose</h4>Current methods for patient-specific voxel-level dosimetry in radionuclide therapy suffer from a trade-off between accuracy and computational efficiency. Monte Carlo (MC) radiation transport algorithms are considered the gold standard for voxel-level dosimetry but can be computationally expensive, whereas faster dose voxel kernel (DVK) convolution can be suboptimal in the presence of tissue heterogeneities. Furthermore, the accuracies of both these methods are limited by the spatial resolution of the reconstructed emission image. To overcome these limitations, this paper considers a single deep convolutional neural network (CNN) with residual learning (named DblurDoseNet) that learns to produce dose-rate maps while compensating for the limited resolution of SPECT images.<h4"],"journal":["Medical physics"],"pubmed_title":["DblurDoseNet: A deep residual learning network for voxel radionuclide dosimetry compensating for single-photon emission computerized tomography imaging resolution."],"pmcid":["PMC10041998"],"funding_grant_id":["R01 CA240706","R01 EB022075"],"pubmed_authors":["Li Z","Mikell JK","Dewaraja YK","Fessler JA","Wilderman SJ"],"additional_accession":[]},"is_claimable":false,"name":"DblurDoseNet: A deep residual learning network for voxel radionuclide dosimetry compensating for single-photon emission computerized tomography imaging resolution.","description":"<h4>Purpose</h4>Current methods for patient-specific voxel-level dosimetry in radionuclide therapy suffer from a trade-off between accuracy and computational efficiency. Monte Carlo (MC) radiation transport algorithms are considered the gold standard for voxel-level dosimetry but can be computationally expensive, whereas faster dose voxel kernel (DVK) convolution can be suboptimal in the presence of tissue heterogeneities. Furthermore, the accuracies of both these methods are limited by the spatial resolution of the reconstructed emission image. To overcome these limitations, this paper considers a single deep convolutional neural network (CNN) with residual learning (named DblurDoseNet) that learns to produce dose-rate maps while compensating for the limited resolution of SPECT images.<h4","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Feb","modification":"2025-04-21T15:07:59.594Z","creation":"2025-04-21T15:07:59.594Z"},"accession":"S-EPMC10041998","cross_references":{"pubmed":["34882821"],"doi":["10.1002/mp.15397"]}}