2022
Posterior estimation using deep learning: a simulation study of compartmental modeling in dynamic positron emission tomography
Liu X, Marin T, Amal T, Woo J, Fakhri G, Ouyang J. Posterior estimation using deep learning: a simulation study of compartmental modeling in dynamic positron emission tomography. Medical Physics 2022, 50: 1539-1548. PMID: 36331429, PMCID: PMC10087283, DOI: 10.1002/mp.16078.Peer-Reviewed Original ResearchMeSH KeywordsBayes TheoremComputer SimulationDeep LearningNeural Networks, ComputerPositron-Emission TomographyConceptsConditional variational auto-encoderDeep learning approachNeural networkDeep learningMarkov chain Monte CarloVariational Bayesian inference frameworkLearning approachDeep learning-based approachVariational auto-encoderDeep neural networksLearning-based approachDynamic brain PET imagingPosterior distributionEstimate posterior distributionsBayesian inference frameworkAuto-encoderMedical imagesInference frameworkNetworkSimulation studyBrain PET imagingLearningPosterior estimatesInferior performanceImages
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