Ming-Kai Chen, MD, PhD
Associate Professor of Radiology and Biomedical ImagingDownloadHi-Res Photo
Cards
Appointments
Additional Titles
Co-Medical Director, Yale University PET Center
Contact Info
Radiology & Biomedical Imaging
PO Box 208042
New Haven, CT 06519
United States
Appointments
Additional Titles
Co-Medical Director, Yale University PET Center
Contact Info
Radiology & Biomedical Imaging
PO Box 208042
New Haven, CT 06519
United States
Appointments
Additional Titles
Co-Medical Director, Yale University PET Center
Contact Info
Radiology & Biomedical Imaging
PO Box 208042
New Haven, CT 06519
United States
About
Titles
Associate Professor of Radiology and Biomedical Imaging
Co-Medical Director, Yale University PET Center
Appointments
Radiology & Biomedical Imaging
Associate Professor on TermPrimary
Other Departments & Organizations
Education & Training
- Residency
- Yale New Haven Hospital (2010)
- PhD
- Johns Hopkins University (2007)
- Internship
- Greater Baltimore Medical Center (2006)
- Research Fellowship
- Johns Hopkins Hospital (2001)
- MD
- National Taiwan University School of Medicine (1996)
Research
Overview
Medical Subject Headings (MeSH)
Diagnosis; Health Care; Neoplasms; Nervous System Diseases
Research at a Glance
Yale Co-Authors
Frequent collaborators of Ming-Kai Chen's published research.
Publications Timeline
A big-picture view of Ming-Kai Chen's research output by year.
Research Interests
Research topics Ming-Kai Chen is interested in exploring.
Richard Carson, PhD
Nabeel Nabulsi, PhD
Mika Naganawa, PhD
Jean-Dominique Gallezot, PhD
Adam Mecca, MD, PhD
Chi Liu, PhD
62Publications
2,171Citations
Publications
2024
Patlak-Guided Self-Supervised Learning for Dynamic PET Denoising
Liu Q, Guo X, Tsai Y, Gallezot J, Chen M, Guo L, Xie H, Pucar D, Young C, Panin V, Carson R, Liu C. Patlak-Guided Self-Supervised Learning for Dynamic PET Denoising. 2024, 00: 1-2. DOI: 10.1109/nss/mic/rtsd57108.2024.10655866.Peer-Reviewed Original ResearchConceptsPre-trained modelsSelf-supervised learning methodSuperior noise reductionNoise reductionDynamic framesImage quality improvementUpsampling blockSignal-to-noise ratioWeight initializationWeak supervisionDynamic PET datasetsEnhanced noise reductionUNet modelLearning methodsTraining schemeTemporal dataStatic imagesDenoisingReconstruction methodPET datasetsLesion signal-to-noise ratioSize constraintsLesion SNRImagesReconValidation of a Simplified Tissue-to-Reference Ratio Measurement Using SUVR to Assess Synaptic Density Alterations in Alzheimer Disease with [11C]UCB-J PET
Young J, O'Dell R, Naganawa M, Toyonaga T, Chen M, Nabulsi N, Huang Y, Cooper E, Miller A, Lam J, Bates K, Ruan A, Nelsen K, Salardini E, Carson R, van Dyck C, Mecca A. Validation of a Simplified Tissue-to-Reference Ratio Measurement Using SUVR to Assess Synaptic Density Alterations in Alzheimer Disease with [11C]UCB-J PET. Journal Of Nuclear Medicine 2024, jnumed.124.267419. PMID: 39299782, DOI: 10.2967/jnumed.124.267419.Peer-Reviewed Original ResearchAltmetricConceptsDistribution volume ratioSUV ratioSynaptic densityEffect sizeAlzheimer's diseaseLongitudinal study of Alzheimer's diseaseMethods:</b> ParticipantsLongitudinal studyMeasure synaptic densityAD participantsStudy of Alzheimer's diseaseNormal cognitionReference regionOlder adultsMulticenterDensity alterationsPopulation-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 ResearchCitationsConceptsDeep 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 datasetsSynaptic density patterns in early Alzheimer’s disease assessed by independent component analysis
Fang X, Raval N, O’Dell R, Naganawa M, Mecca A, Chen M, van Dyck C, Carson R. Synaptic density patterns in early Alzheimer’s disease assessed by independent component analysis. Brain Communications 2024, 6: fcae107. PMID: 38601916, PMCID: PMC11004947, DOI: 10.1093/braincomms/fcae107.Peer-Reviewed Original ResearchAltmetricConceptsMedial temporal brain regionsAlzheimer's diseaseTemporal brain regionsCognitive deficitsBrain regionsCognitive impairmentPostmortem studiesBinds to SV2ASynaptic densityReduction of synaptic densityIndependent component analysisSynaptic lossAlzheimerDeficitsImpairmentBrainNeocortexComponent analysisPrimary pathologySV2A
2023
The regional pattern of age-related synaptic loss in the human brain differs from gray matter volume loss: in vivo PET measurement with [11C]UCB-J
Toyonaga T, Khattar N, Wu Y, Lu Y, Naganawa M, Gallezot J, Matuskey D, Mecca A, Pittman B, Dias M, Nabulsi N, Finnema S, Chen M, Arnsten A, Radhakrishnan R, Skosnik P, D’Souza D, Esterlis I, Huang Y, van Dyck C, Carson R. The regional pattern of age-related synaptic loss in the human brain differs from gray matter volume loss: in vivo PET measurement with [11C]UCB-J. European Journal Of Nuclear Medicine And Molecular Imaging 2023, 51: 1012-1022. PMID: 37955791, DOI: 10.1007/s00259-023-06487-8.Peer-Reviewed Original ResearchCitationsAltmetricConceptsSynaptic densityAge-related decreaseMagnetic resonance imagingBlood flowAge-related synaptic lossGray matter volume lossSynaptic density lossPositron emission tomography (PET) ligandSynaptic vesicle glycoprotein 2AVivo PET measurementsMedial occipital cortexGray matter volumeAge-related neurodegenerationGray matter regionsCognitive normal subjectsAge-related changesSynaptic lossNerve terminalsWide age rangeOccipital cortexTomography ligandNormal subjectsGM volumeAge-related functional lossesMatter volumeSynaptic PET Imaging in Neurodegeneration
Chen M, Matuskey D, Finnema S, Carson R. Synaptic PET Imaging in Neurodegeneration. 2023, 157-167. DOI: 10.1007/978-3-031-35098-6_10.ChaptersConceptsAlzheimer's diseaseSynaptic densityProgressive supranuclear palsySynaptic vesicle glycoprotein 2ALarge patient cohortPositron emission tomography (PET) biomarkersInitial PET studiesMultiple neurodegenerative diseasesSupranuclear palsyCorticobasal degenerationLewy bodiesPatient cohortSynapse densityTomography biomarkersClinical valueParkinson's diseaseNeurological diseasesBrain regionsFrontotemporal dementiaPET studiesDiseaseNeurodegenerative diseasesHuntington's diseaseMultiple centersSynaptic vesicle membraneAssessment of Gray Matter Microstructure and Synaptic Density in Alzheimer's Disease: A Multimodal Imaging Study With DTI and SV2A PET
Silva-Rudberg J, Salardini E, O'Dell R, Chen M, Ra J, Georgelos J, Morehouse M, Melino K, Varma P, Toyonaga T, Nabulsi N, Huang Y, Carson R, van Dyck C, Mecca A. Assessment of Gray Matter Microstructure and Synaptic Density in Alzheimer's Disease: A Multimodal Imaging Study With DTI and SV2A PET. American Journal Of Geriatric Psychiatry 2023, 32: 17-28. PMID: 37673749, PMCID: PMC10840732, DOI: 10.1016/j.jagp.2023.08.002.Peer-Reviewed Original ResearchCitationsAltmetricConceptsSynaptic densityAlzheimer's diseaseMean diffusivitySynaptic lossGray matter microstructureGray matter mean diffusivityDisease pathologyHippocampal synaptic densityMajor pathological correlateSetting of ADAD-related neuropathologySynaptic vesicle glycoprotein 2AHippocampal mean diffusivityAlzheimer's disease pathologyAmyloid-positive participantsMatter mean diffusivityPositron emission tomography (PET) imagingEmission Tomography ImagingGray matter structuresPathological correlatesPositive participantsInverse associationAD groupCognitive impairmentDiseaseMCP-Net: Introducing Patlak Loss Optimization to Whole-Body Dynamic PET Inter-Frame Motion Correction
Guo X, Zhou B, Chen X, Chen M, Liu C, Dvornek N. MCP-Net: Introducing Patlak Loss Optimization to Whole-Body Dynamic PET Inter-Frame Motion Correction. IEEE Transactions On Medical Imaging 2023, 42: 3512-3523. PMID: 37368811, PMCID: PMC10751388, DOI: 10.1109/tmi.2023.3290003.Peer-Reviewed Original ResearchCitationsConceptsMotion estimation blockDeep learning benchmarksGood generalization capabilityMotion correctionMotion correction frameworkMotion prediction errorGeneralization capabilityNetwork performanceNeural networkMotion correction techniqueLearning benchmarksRegistration problemLoss functionEstimation blockLoss optimizationPenalty componentDynamic frameFitting errorSpatial alignmentParametric imagesSpatial misalignmentDynamic positron emission tomographySubject motionPrediction errorCorrection frameworkPrincipal 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 ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsCognitive 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 patternsGeneration of Whole-Body FDG Parametric Ki Images From Static PET Images Using Deep Learning
Miao T, Zhou B, Liu J, Guo X, Liu Q, Xie H, Chen X, Chen M, Wu J, Carson R, Liu C. Generation of Whole-Body FDG Parametric Ki Images From Static PET Images Using Deep Learning. IEEE Transactions On Radiation And Plasma Medical Sciences 2023, 7: 465-472. PMID: 37997577, PMCID: PMC10665031, DOI: 10.1109/trpms.2023.3243576.Peer-Reviewed Original ResearchCitationsAltmetric
Clinical Trials
Current Trials
Brain Connections & HIV Status
HIC ID2000033582RoleSub InvestigatorPrimary Completion Date12/31/2027Recruiting ParticipantsGenderBothAge18 years - 80 yearsMeditation and Synaptic Density
HIC ID2000030601RoleSub InvestigatorPrimary Completion Date08/31/2024Recruiting ParticipantsGenderBothAge28 years - 70 yearsM1 Schizophrenia PET Study
HIC ID2000031171RoleSub InvestigatorPrimary Completion Date04/30/2023Recruiting ParticipantsGenderBothAge18 years - 55 yearsA Phase I/II Study of VTX-801 in Adult Patients With Wilson's Disease (GATEWAY)
HIC ID2000028887RoleSub InvestigatorPrimary Completion Date07/31/2023Recruiting ParticipantsGenderBothAge18 years - 60 yearsImaging cortisol metabolism in liver, adipose tissue and brain with a novel PET radioligand
HIC ID2000029576RoleSub InvestigatorPrimary Completion Date06/30/2024Recruiting Participants
Clinical Care
Overview
Ming-Kai Chen, MD, PhD, is a radiologist in the Department of Radiology & Biomedical Imaging and co-medical director of PET Center. He has special expertise in nuclear medicine—a type of imaging that uses radioactive materials to detect and treat disease—as well as oncologic molecular imaging, which is used to take detailed imagines of the body at a cellular level to detect and monitor cancer.
“With the advance of nuclear medicine and molecular imaging,” Dr. Chen says, “we often can detect the disease at an early stage (and detect more metastatic disease) compared to convention diagnostic imaging.”
Dr. Chen, an associate professor of radiology and biomedical imaging at Yale School of Medicine, is conducting research and a clinical trial about the use of PET imaging for synaptic density in the Alzheimer’s disease.
“We hope this new PET imaging could provide early diagnosis of Alzheimer’s disease and serve as a reliable biomarker for the evaluation of treatment response,” he says.
Clinical Specialties
Medical Oncology; Nuclear Medicine; Neuroradiology; Radiology & Biomedical Imaging
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Radiology & Biomedical Imaging
PO Box 208042
New Haven, CT 06519
United States
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