2024
An Investigation on Cross-Tracer Generalizability of Deep Learning-based PET Attenuation Correction
Hou J, Chen T, Zhou Y, Chen X, Xie H, Liu Q, Xia M, Panin V, Liu C, Zhou B, Toyonaga T. An Investigation on Cross-Tracer Generalizability of Deep Learning-based PET Attenuation Correction. 2024, 00: 1-1. DOI: 10.1109/nss/mic/rtsd57108.2024.10657095.Peer-Reviewed Original ResearchAttenuation correctionPET attenuation correctionQuantitative PET imagingAttenuation mapDL modelsDeep learning (DL)-based methodsTumor quantificationDL model trainingRadiation doseImmediate future workCompetitive performancePET imagingModel trainingPET signalCorrectionAnalysis of PETFuture workPreliminary resultsData availabilityRadiation
2023
Unified Noise-Aware Network for Low-Count PET Denoising With Varying Count Levels
Xie H, Liu Q, Zhou B, Chen X, Guo X, Wang H, Li B, Rominger A, Shi K, Liu C. Unified Noise-Aware Network for Low-Count PET Denoising With Varying Count Levels. IEEE Transactions On Radiation And Plasma Medical Sciences 2023, 8: 366-378. PMID: 39391291, PMCID: PMC11463975, DOI: 10.1109/trpms.2023.3334105.Peer-Reviewed Original ResearchLarge-scale dataDeep learningDynamic PET imagesLow-count dataNeural networkMultiple networksSpecific noise levelDifferent vendorsDifferent noise levelsDenoised resultsNoisy counterpartDynamic frameInput noise levelNetworkData availabilityHigher image noiseImage qualityImage noiseSuperior performanceImportant topicAdditional challengesNoise levelPET imagesLimited data availabilityVendors