2024
Diffusion-based Bayesian posterior distribution prediction of kinetic parameters in dynamic PET
Djebra Y, Liu X, Marin T, Tiss A, Dhaynaut M, Guehl N, Johnson K, Fakhri G, Ma C, Ouyang J. Diffusion-based Bayesian posterior distribution prediction of kinetic parameters in dynamic PET. 2024, 00: 1-1. DOI: 10.1109/nss/mic/rtsd57108.2024.10657955.Peer-Reviewed Original ResearchConditional variational autoencoderEfficient deep learning-based approachMarkov chain Monte CarloDenoising diffusion probabilistic modelDeep learning-based approachDiffusion probabilistic modelLearning-based approachApproximate posterior distributionPosterior distributionVariational autoencoderHeavy computationTau protein aggregationBayesian inferenceProbabilistic modelData-drivenStudy molecular processesBayesian posterior distributionProtein aggregationMetropolis-Hastings Markov chain Monte CarloMolecular processesAlzheimer's diseaseNeurodegenerative diseasesKinetic parametersEstimate posterior distributionsAutoencoder
2016
A Bayesian spatial temporal mixtures approach to kinetic parametric images in dynamic positron emission tomography
Zhu W, Ouyang J, Rakvongthai Y, Guehl N, Wooten D, Fakhri G, Normandin M, Fan Y. A Bayesian spatial temporal mixtures approach to kinetic parametric images in dynamic positron emission tomography. Medical Physics 2016, 43: 1222-1234. PMID: 26936707, PMCID: PMC5025019, DOI: 10.1118/1.4941010.Peer-Reviewed Original ResearchConceptsPositron emission tomographySpatial mixture modelNearby voxelsMixture modelEmission tomographyDynamic positron emission tomographyK-means methodKinetic modelKinetic parametric imagesOne-compartment kinetic modelNovel algorithmTemporal informationClassification purposesMeasurement of local perfusionLocal perfusionTime activity curvesNormal ROIsTemporal modelBayesian algorithmCardiac studiesMarkov chain Monte CarloParameter estimationNoise regionSimulation experimentsSimulated data sets