2024
Data-driven non-rigid motion detection and correction for NeuroEXPLORER
Zhang J, Sun C, Volpi T, Zeng T, Fontaine K, Du Y, Toyonaga T, Onofrey J, Lu Y, Carson R. Data-driven non-rigid motion detection and correction for NeuroEXPLORER. 2024, 00: 1-2. DOI: 10.1109/nss/mic/rtsd57108.2024.10658289.Peer-Reviewed Original ResearchNon-rigid motionNon-rigid motion estimationMotion dataNon-rigid regionsHead motion dataTracking capabilityMotion estimationMotion detectionRigid transformationImage-derived input functionMotion tracking systemImage blurringCarotid arteryEffective MCMotion patternsPatient movementTracking systemMotion correction frameworkBrain PET systemRigid motionMotion-corrected reconstructionFacial surfaceRigid motion correctionCorrect reconstructionCorrection framework
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