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 distributionsAutoencoderDiffusion Model-Based Posterior Distribution Prediction for Kinetic Parameter Estimation in Dynamic PET
Djebra Y, Liu X, Marin T, Tiss A, Dhaynaut M, Guehl N, Johnson K, Fakhri G, Ma C, Ouyang J. Diffusion Model-Based Posterior Distribution Prediction for Kinetic Parameter Estimation in Dynamic PET. 2024, 00: 1-5. DOI: 10.1109/isbi56570.2024.10635805.Peer-Reviewed Original ResearchPosterior distributions of kinetic parametersDenoising diffusion probabilistic modelHyperphosphorylated tauP-tauDiffusion probabilistic modelAlzheimer's diseaseNeurodegenerative diseasesKinetic parametersPosterior distributionInference efficiencyComputational needsEstimate kinetic parametersProbabilistic modelComputation time
2023
Attenuation correction for PET imaging using conditional denoising diffusion probabilistic model
Dong Y, Jang S, Han P, Johnson K, Ma C, Fakhri G, Li Q, Gong K. Attenuation correction for PET imaging using conditional denoising diffusion probabilistic model. 2023, 00: 1-1. DOI: 10.1109/nssmicrtsd49126.2023.10338188.Peer-Reviewed Original ResearchDiffusion probabilistic modelGenerative adversarial networkConditional encodingAttenuation correctionDenoising diffusion probabilistic modelLow-level featuresProbabilistic modelAttenuation coefficientAdversarial networkExtract featuresPET/MR systemsEncodingPET acquisitionNovel methodDiffusion encodingMagnetic resonanceImagesPET imagingCorrectionMR imagingUNetAttenuationNetworkFeaturesResonance