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
Principal component analysis of synaptic density measured with [11C]UCB-J PET in early Alzheimer’s disease
O'Dell R, Higgins-Chen A, Gupta D, Chen M, Naganawa M, Toyonaga T, Lu Y, Ni G, Chupak A, Zhao W, Salardini E, Nabulsi N, Huang Y, Arnsten A, Carson R, van Dyck C, Mecca A. Principal component analysis of synaptic density measured with [11C]UCB-J PET in early Alzheimer’s disease. NeuroImage Clinical 2023, 39: 103457. PMID: 37422964, PMCID: PMC10338149, DOI: 10.1016/j.nicl.2023.103457.Peer-Reviewed Original ResearchConceptsCognitive domainsCognitive performanceSubjects' scoresCortical regionsNeuropsychological batteryEarly Alzheimer's diseaseAD groupBilateral regionsNormal participantsNegative loadingsCognitive impairmentCN participantsAlzheimer's diseaseParticipantsStructural correlatesStrong contributionParticipant characteristicsScoresPositive loadingsData-driven approachTotal variancePrincipal component analysisSpecific spatial patterns