2023
Deep learning-based attenuation map generation with simultaneously reconstructed PET activity and attenuation and low-dose application
Shi L, Zhang J, Toyonaga T, Shao D, Onofrey J, Lu Y. Deep learning-based attenuation map generation with simultaneously reconstructed PET activity and attenuation and low-dose application. Physics In Medicine And Biology 2023, 68: 035014. PMID: 36584395, DOI: 10.1088/1361-6560/acaf49.Peer-Reviewed Original Research
2022
Deep learning–based attenuation correction for whole-body PET — a multi-tracer study with 18F-FDG, 68 Ga-DOTATATE, and 18F-Fluciclovine
Toyonaga T, Shao D, Shi L, Zhang J, Revilla EM, Menard D, Ankrah J, Hirata K, Chen MK, Onofrey JA, Lu Y. Deep learning–based attenuation correction for whole-body PET — a multi-tracer study with 18F-FDG, 68 Ga-DOTATATE, and 18F-Fluciclovine. European Journal Of Nuclear Medicine And Molecular Imaging 2022, 49: 3086-3097. PMID: 35277742, PMCID: PMC10725742, DOI: 10.1007/s00259-022-05748-2.Peer-Reviewed Original ResearchConceptsNeural networkNovel deep learningNet neural networkPET/CT datasetsImage analysis metricsPhysics-based loss functionDeep learningCT-derived attenuation mapAttenuation mapLoss functionAnalysis metricsDetail recoveryTumor volume estimationMLAACT datasetsOSEM algorithmNetworkAlgorithmAttenuation correctionCorrection framework
2017
Players of ‘hypoxia orchestra’ – what is the role of FMISO?
Toyonaga T, Hirata K, Shiga T, Nagara T. Players of ‘hypoxia orchestra’ – what is the role of FMISO? European Journal Of Nuclear Medicine And Molecular Imaging 2017, 44: 1679-1681. PMID: 28634683, DOI: 10.1007/s00259-017-3754-9.Commentaries, Editorials and Letters