2024
Patlak-Guided Self-Supervised Learning for Dynamic PET Denoising
Liu Q, Guo X, Tsai Y, Gallezot J, Chen M, Guo L, Xie H, Pucar D, Young C, Panin V, Carson R, Liu C. Patlak-Guided Self-Supervised Learning for Dynamic PET Denoising. 2024, 00: 1-2. DOI: 10.1109/nss/mic/rtsd57108.2024.10655866.Peer-Reviewed Original ResearchPre-trained modelsSelf-supervised learning methodSuperior noise reductionNoise reductionDynamic framesImage quality improvementUpsampling blockSignal-to-noise ratioWeight initializationWeak supervisionDynamic PET datasetsEnhanced noise reductionUNet modelLearning methodsTraining schemeTemporal dataStatic imagesDenoisingReconstruction methodPET datasetsLesion signal-to-noise ratioSize constraintsLesion SNRImagesReconPopulation-based deep image prior for dynamic PET denoising: A data-driven approach to improve parametric quantification
Liu Q, Tsai Y, Gallezot J, Guo X, Chen M, Pucar D, Young C, Panin V, Casey M, Miao T, Xie H, Chen X, Zhou B, Carson R, Liu C. Population-based deep image prior for dynamic PET denoising: A data-driven approach to improve parametric quantification. Medical Image Analysis 2024, 95: 103180. PMID: 38657423, DOI: 10.1016/j.media.2024.103180.Peer-Reviewed Original ResearchDeep Image PriorImage priorsSupervised modelsNoise reductionIntrinsic image featuresDeep learning techniquesU-Net architectureNovel denoising techniqueQuality of parametric imagesDenoising modelDenoising techniquesStatic datasetsBaseline techniquesEffective noise reductionData-driven approachLearning techniquesDynamic datasetsOptimization processPrior informationStatic imagesHigh noise levelsImage featuresDatasetPrior imagePET datasets
2021
Artificial Intelligence-Based Image Enhancement in PET Imaging Noise Reduction and Resolution Enhancement
Liu J, Malekzadeh M, Mirian N, Song TA, Liu C, Dutta J. Artificial Intelligence-Based Image Enhancement in PET Imaging Noise Reduction and Resolution Enhancement. PET Clinics 2021, 16: 553-576. PMID: 34537130, PMCID: PMC8457531, DOI: 10.1016/j.cpet.2021.06.005.Peer-Reviewed Original ResearchConceptsArtificial intelligence modelsImage enhancementIntelligence modelsArtificial intelligenceNetwork architectureEvaluation metricsLarge-scale adoptionData typesImage denoisingLoss functionPET imagesLow spatial resolutionHigh noiseResolution enhancementImagesIntelligenceDeblurringArchitectureDenoisingNoise reductionMetricsPopularityRecent effortsFuture directionsAccuracy