Jinsong Ouyang, PhD
Associate Professor of Radiology and Biomedical ImagingDownloadHi-Res Photo
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Associate Professor of Radiology and Biomedical Imaging
Affiliated Faculty, Yale Institute for Global Health
Appointments
Radiology & Biomedical Imaging
Associate Professor on TermPrimary
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Education & Training
- PhD
- University of Colorado, Physics (1992)
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Overview
Originally trained in experimental nuclear and high-energy physics, I transitioned to medical imaging. I focus on algorithm development for PET and SPECT, including image reconstruction, parametric reconstruction, motion correction, partial-volume correction, attenuation correction, scatter correction, and lesion detection. In recent years, I have led multiple deep learning–based projects in medical imaging.
Public Health Interests
Metabolism; Modeling; Network Analysis; Cardiovascular Diseases; Cancer; Bioinformatics; Genetics, Genomics, Epigenetics; Statistical Computing; Stochastic Processes
ORCID
0000-0003-4245-170X
Research at a Glance
Yale Co-Authors
Frequent collaborators of Jinsong Ouyang's published research.
Publications Timeline
A big-picture view of Jinsong Ouyang's research output by year.
Georges El Fakhri, PhD, DABR
Thibault Marin, PhD
Xiaofeng Liu
Chao Ma, PhD
Marc David Normandin, PhD
Yanis Chemli, PhD
91Publications
1,558Citations
Publications
2026
Individualized treatment effect inference of head and neck cancer with multimodal data
Wei Y, Li Z, Woo J, Ouyang J, Fakhri G, Liu X. Individualized treatment effect inference of head and neck cancer with multimodal data. APSIPA Transactions On Signal And Information Processing 2026, 15: 3-20. PMID: 42125301, PMCID: PMC13160251, DOI: 10.1108/atsip-06-2025-0051.Peer-Reviewed Original ResearchConceptsHead and neck cancerAdversarial trainingMultimodal patient dataDeep learningHead and neck cancer casesMultimodal dataRobust estimates of causal effectsEnd-to-end deep learningMutual informationPatient-specific outcomesSelection biasEstimation of causal effectsAdaptive instance normalizationPatient dataNeck cancerMultimodal medical imagesEnd-to-endIndividual treatment effects estimationStatus featuresRetrospective observational dataInstance NormalizationTreatment effectsInformation fusionPatient characteristicsMedical imagesIndividualized Treatment Effect Inference of HNC with Multimodal Data
Wei Y, Li Z, Woo J, Ouyang J, El Fakhri G, Liu X. Individualized Treatment Effect Inference of HNC with Multimodal Data. APSIPA Transactions On Signal And Information Processing 2026, 14 DOI: 10.1561/116.20250051.Peer-Reviewed Original Research
2025
Physics-Informed List-Mode Deep Image Prior Reconstruction with Motion Correction in 3D Brain PET
Chemli Y, Najmaoui Y, Normandin M, Fakhri G, Marin T, Ouyang J. Physics-Informed List-Mode Deep Image Prior Reconstruction with Motion Correction in 3D Brain PET. 2021 IEEE Nuclear Science Symposium And Medical Imaging Conference (NSS/MIC) 2025, 1-2. DOI: 10.1109/nss/mic/rtsd57106.2025.11287851.Peer-Reviewed Original ResearchConceptsDeep Image PriorList-modeContrast recoveryMotion correctionList-mode eventsReconstructed activity distributionHoffman brain phantomNoise-resolution trade-offML-EM reconstructionLow-count dataResolution phantomBrain phantomBrain positron emission tomographySuper-resolution effectActivity distributionModel attenuationML-EMBack-projection operationsFine structureDown-sampled dataNegative log-likelihoodImage registrationUnsupervised regularizerBack-projectionImage priorsOn Hallucinations in Artificial Intelligence–Generated Content for Nuclear Medicine Imaging (the DREAM Report)
Xia M, Bayerlein R, Chemli Y, Liu X, Ouyang J, Lin M, Fakhri G, Badawi R, Li Q, Liu C. On Hallucinations in Artificial Intelligence–Generated Content for Nuclear Medicine Imaging (the DREAM Report). Journal Of Nuclear Medicine 2025, 67: 166-174. PMID: 41198241, PMCID: PMC12866389, DOI: 10.2967/jnumed.125.270653.Peer-Reviewed Original ResearchCitationsAltmetricYRT-PET: An Open-Source GPU-Accelerated Image Reconstruction Engine for Positron Emission Tomography
Najmaoui Y, Chemli Y, Toussaint M, Petibon Y, Marty B, Fontaine K, Gallezot J, Razdevsek G, Orehar M, Dhaynaut M, Guehl N, Dolenec R, Pestotnik R, Johnson K, Ouyang J, Normandin M, Tetrault M, Lecomte R, Fakhri G, Marin T. YRT-PET: An Open-Source GPU-Accelerated Image Reconstruction Engine for Positron Emission Tomography. IEEE Transactions On Radiation And Plasma Medical Sciences 2025, 10: 535-546. PMID: 41424471, PMCID: PMC12714321, DOI: 10.1109/trpms.2025.3619872.Peer-Reviewed Original ResearchConceptsOpen-sourceImage reconstructionPositron emission tomography image reconstructionReconstruction algorithmAdvanced image reconstruction algorithmsOpen-source toolkitDevelopment of advanced algorithmsTime-of-flightScanner geometryNovel reconstruction algorithmImage reconstruction algorithmMotion correctionReusable codeGPU accelerationTime-of-flight informationPython bindingsPlugin systemSoftware toolkitMeaningful imagesData formatsFast implementationRigid motion correctionAdvanced algorithmsPoint spread function modelProprietary softwareAnatomically and metabolically informed diffusion for unified denoising and segmentation in low-count PET imaging
Xia M, Ko K, Wang D, Chen M, Liu Q, Xie H, Guo L, Ji W, Ouyang J, Bayerlein R, Spencer B, Li Q, Badawi R, El Fakhri G, Liu C. Anatomically and metabolically informed diffusion for unified denoising and segmentation in low-count PET imaging. Medical Image Analysis 2025, 107: 103831. PMID: 41076965, PMCID: PMC12551811, DOI: 10.1016/j.media.2025.103831.Peer-Reviewed Original ResearchCitationsMeSH Keywords and ConceptsConceptsPET denoisingDenoising diffusion modelsSuperior performanceImage denoisingDenoised outputMulti-vendorDenoisingInformation diffusionImage informationSegmentation modelAblated versionsDiffusion strategySegmentation methodDice coefficientOrgan segmentationTest casesMulti-task functionRevision moduleTaskImagesAnalysis pipelineSegmentsClinical count levelsTotal lesion glycolysisDatasetBayesian Posterior Distribution Estimation of Kinetic Parameters in Dynamic Brain PET Using Generative Deep Learning Models
Djebra Y, Liu X, Marin T, Tiss A, Dhaynaut M, Guehl N, Johnson K, Fakhri G, Ma C, Ouyang J. Bayesian Posterior Distribution Estimation of Kinetic Parameters in Dynamic Brain PET Using Generative Deep Learning Models. IEEE Transactions On Medical Imaging 2025, 44: 5089-5102. PMID: 40663684, PMCID: PMC12318411, DOI: 10.1109/tmi.2025.3588859.Peer-Reviewed Original ResearchAltmetricConceptsPosterior distributions of kinetic parametersEfficiency of deep learningGenerative deep learning modelsConditional variational autoencoderDeep learning modelsComputational efficiencyMetropolis-Hastings MCMCPosterior distributionHyperphosphorylated tauDynamic brain positron emission tomographyWGAN-GPDual decodersWasserstein GANVariational autoencoderKinetic parametersDeep learningComputational needsBayesian inferenceP-tauLearning modelsAlzheimer's diseaseNeurodegenerative diseasesComputation timeMCMC methodsEstimation of kinetic parametersMixture-of-Shape-Experts (MoSE): End-to-End Shape Dictionary Framework to Prompt SAM for Generalizable Medical Segmentation
Wei J, Zhao X, Woo J, Ouyang J, El Fakhri G, Chen Q, Liu X. Mixture-of-Shape-Experts (MoSE): End-to-End Shape Dictionary Framework to Prompt SAM for Generalizable Medical Segmentation. 2008 IEEE Computer Society Conference On Computer Vision And Pattern Recognition Workshops 2025, 00: 6450-6460. PMID: 41069640, PMCID: PMC12506896, DOI: 10.1109/cvprw67362.2025.00642.Peer-Reviewed Original ResearchCitationsConceptsSingle domain generalizationEnd-to-endShape priorsGeneralization capabilityEnd-to-end mannerDictionary learning methodMedical image segmentationMixture-of-expertsMultiple public datasetsShape mapsDictionary atomsDictionary learningDictionary sizeShape dictionaryRepresentational powerDomain generalizationPublic datasetsGating networkImage segmentationMedical segmentationLearning methodsShape informationDictionaryBidirectional integrationOverfittingContrast-enhanced image-guided learning for nasopharyngeal carcinoma diagnosis using non-contrast MRI
Li Z, Shi Y, Liu X, Wang L, Woo J, Ouyang J, El Fakhri G, Yu H, Liu X. Contrast-enhanced image-guided learning for nasopharyngeal carcinoma diagnosis using non-contrast MRI. 2025, 11. DOI: 10.1117/12.3046863.Peer-Reviewed Original ResearchDual Prompting for Diverse Count-Level Pet Denoising
Liu X, Huang Y, Marin T, Vafay Eslahi S, Tiss A, Chemli Y, Johnson K, El Fakhri G, Ouyang J. Dual Prompting for Diverse Count-Level Pet Denoising. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2025, 00: 1-5. PMID: 40831530, PMCID: PMC12360122, DOI: 10.1109/isbi60581.2025.10980695.Peer-Reviewed Original ResearchCitations
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