2024
Population-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
2023
Unsupervised Deep Learning with Self-Validation in Dynamic PET Dose Reduction
Li A, Syed M, Naganawa M, Matuskery D, Carson R, Tang J. Unsupervised Deep Learning with Self-Validation in Dynamic PET Dose Reduction. 2023, 00: 1-1. DOI: 10.1109/nssmicrtsd49126.2023.10337886.Peer-Reviewed Original ResearchNoise reductionSingle-frame methodsImage featuresImage framesTraining dataUnsupervised methodSpatiotemporal informationKinetic modelingDeep imageDynamic PETHard thresholdComposite imageBetter performanceImagesRobust performanceHigh noiseImaging dataFrame methodVast numberKinetic modeling analysisNoise levelFrameSelf-ValidationDynamic PET imagingCapability