Adaptive Deep Image Prior Enhances Ultra-Low Dose PET Imaging with NeuroEXPLORER
Li A, Gravel P, Gallezot J, Toyonaga T, Fontaine K, Carson R, Tang J. Adaptive Deep Image Prior Enhances Ultra-Low Dose PET Imaging with NeuroEXPLORER. 2024, 00: 1-1. DOI: 10.1109/nss/mic/rtsd57108.2024.10657691.Peer-Reviewed Original ResearchContrast recovery coefficientCounting imagingLearning-based denoising methodsHead motion correctionDeep Image PriorLow-dose imagesOptimal stopping iterationsDose imagesAttenuation mapBrain phantomDeep imagingFull-count dataImage priorsMotion correctionSignal-to-noise ratioDenoising methodSequence of outputsTraining dataPET imagingStopping iterationDecreased signal-to-noise ratioNoise ratioPost-processing techniquesReconstructed imagesRecovery coefficientPopulation-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