Chi Liu, PhD
Professor of Radiology and Biomedical ImagingCards
Appointments
Additional Titles
Associate Director of Biomedical Imaging Technology, Yale Biomedical Imaging Institute
Director for Research Faculty Affairs, Radiology & Biomedical Imaging
Contact Info
Appointments
Additional Titles
Associate Director of Biomedical Imaging Technology, Yale Biomedical Imaging Institute
Director for Research Faculty Affairs, Radiology & Biomedical Imaging
Contact Info
Appointments
Additional Titles
Associate Director of Biomedical Imaging Technology, Yale Biomedical Imaging Institute
Director for Research Faculty Affairs, Radiology & Biomedical Imaging
Contact Info
About
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Titles
Professor of Radiology and Biomedical Imaging
Associate Director of Biomedical Imaging Technology, Yale Biomedical Imaging Institute; Director for Research Faculty Affairs, Radiology & Biomedical Imaging
Biography
Chi Liu received his Ph.D. in 2008 from Johns Hopkins University with emphasis on quantitative SPECT/CT imaging. Following his graduate work, he was a postdoctoral fellow at University of Washington, specializing in oncological PET/CT studies with emphasis on compensation algorithms for respiratory motion. In 2010, he joined Yale University as a faculty member. He is board certified in Nuclear Medicine physics and instrumentation by the American Board of Science in Nuclear Medicine. His current research focuses on quantitative cardiac and oncological PET/CT and SPECT/CT imaging, including deep learning algorithms, reconstruction algorithms, data correction, dynamic imaging, and translational imaging. The translational and clinical applications of these projects include early detection of chemotherapy-induced cardiotoxicity, multimodality imaging of heart failure, and eliminating respiratory motion variability for assessing response to therapy. Many of the imaging technologies developed in his lab has been or is being implemented in clinical PET and SPECT scanners. In 2012, he was awarded with the Bruce Hasegawa Young Investigator Medical Imaging Science Award from the IEEE Nuclear Medical and Imaging Sciences Council for “contributions to the imaging physics of SPECT/CT and PET/CT, with emphasis in quantitative imaging and motion correction”. He was the President of Physics, Instrumentation, and Data Sciences Council (PIDSC) of the Society of Nuclear Medicine and Molecular Imaging (SNMMI) between 2022-2023.
Appointments
Radiology & Biomedical Imaging
ProfessorPrimary
Other Departments & Organizations
Education & Training
- Postdoctoral Fellow
- University of Washington (2010)
- PhD
- Johns Hopkins University (2008)
- Board Certification
- Nuclear Medicine Physics and Instrumentation, American Board of Science in Nuclear Medicine
Research
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Overview
Machine learning and deep learning in imaging applications
Radiation dose reduction methods in PET/SPECT/CT
Motion correction methods for PET/CT and SPECT/CT
ORCID
0000-0002-7007-1037
Research at a Glance
Yale Co-Authors
Publications Timeline
Bin Zhou, MS
Albert Sinusas, MD
Jean-Dominique Gallezot, PhD
Edward J Miller, MD, PhD
Yi-Hwa Liu, PhD
James Duncan, PhD
Publications
2026
Dose-aware Diffusion Model for 3D PET Image Denoising: Multi-institutional Validation with Reader Study and Real Low-dose Data
Xie H, Gan W, Bayerlein R, Zhou B, Chen M, Kulon M, Boustani A, Ko K, Wang D, Spencer B, Ji W, Chen X, Liu Q, Guo X, Xia M, Zhou Y, Liu H, Guo L, An H, Kamilov U, Wang H, Li B, Rominger A, Shi K, Wang G, Badawi R, Liu C. Dose-aware Diffusion Model for 3D PET Image Denoising: Multi-institutional Validation with Reader Study and Real Low-dose Data. Medical Image Analysis 2026, 104039. DOI: 10.1016/j.media.2026.104039.Peer-Reviewed Original ResearchConceptsPET image denoisingImage denoisingDeep learningLow-dose PETState-of-the-art generative modelsImage qualityMedical imaging tasksState-of-the-artLow-dose PET imagesFull-dose imagesDenoised imageSegmentation networkImage noise levelLow-dose dataLow-dose scansGenerative modelTraining modelLow-dose levelsImaging tasksNoise levelPET imagingHigh-quality samplesDenoisingDiffusion networksBoard-certified nuclear medicine physiciansParametric Cardiac Imaging with 18F-Flutemetamol PET to Evaluate the Impact of Tafamidis in Patients with Transthyretin Cardiac Amyloidosis.
Liu Q, Shi T, Gravel P, Sharma A, De Freitas C, Fazzone-Chettiar R, Van Laere K, Baldick A, Kattan C, Guo X, Guo L, Xie H, Chen X, Zhou B, Liu Y, Carson R, Liu C, Miller E. Parametric Cardiac Imaging with 18F-Flutemetamol PET to Evaluate the Impact of Tafamidis in Patients with Transthyretin Cardiac Amyloidosis. Journal Of Nuclear Medicine 2026, jnumed.125.270003. PMID: 41748295, DOI: 10.2967/jnumed.125.270003.Peer-Reviewed Original ResearchAltmetricConceptsTransthyretin cardiac amyloidosisATTR-CACardiac amyloidosisMethods:Results:ATTR-CA patientsImpact of tafamidisMultilinear analysis 1Blood volume fractionBlood-to-plasma ratioImage-derived input functionTreatment-related changesBlood-to-plasmaMyocardial blood flowCardiac imagingMyocardial blood volume fractionBlood flowInput functionSensitive to treatment-related changesTafamidisPET dataPatientsAmyloid burdenAmyloidosisTracer kineticsPotential of Spread Field Imaging Collimation Technology in SPECT to Differentiate Sub-endocardial and Sub-epicardial Regions: A Phantom Study
Liu Y, Palyo R, Goyal D, Liu C, Mu Z, Tao Z, Sinusas A, Miller E, Mu Z. Potential of Spread Field Imaging Collimation Technology in SPECT to Differentiate Sub-endocardial and Sub-epicardial Regions: A Phantom Study. Journal Of Medical And Biological Engineering 2026, 46: 109-122. DOI: 10.1007/s40846-026-01007-z.Peer-Reviewed Original ResearchConceptsSingle Photon Emission Computerized TomographyCardiac phantomCollimator technologyIterative ordered subsetsParallel-hole collimatorPhantom studyCollimatorSub-endocardialMyocardial perfusion defectsMode formationPhantomCoronary artery diseaseSPECT cameraField imagesNon-transmuralEmission computerized tomographyPerfusion defectsSpatial resolutionArtery diseaseSub-epicardialPerfusion gradientSub-endocardiumSub-epicardiumPurposeThis studyQuantitative comparisonPatlak-Guided Self-Supervised Learning for Dynamic PET Denoising
Liu Q, Guo X, Tsai Y, Gallezot J, Chen M, Guo L, Xie H, Panin V, Carson R, Liu C. Patlak-Guided Self-Supervised Learning for Dynamic PET Denoising. IEEE Transactions On Radiation And Plasma Medical Sciences 2026, PP: 1-1. DOI: 10.1109/trpms.2026.3656480.Peer-Reviewed Original ResearchConceptsSignal-to-noise ratioSelf-supervised deep learning frameworkSelf-supervised learningDeep learning frameworkCycleGAN-based modelDenoising NetworkConsistency lossLesion signal-to-noise ratioDynamic PET datasetsLearning frameworkSupervised methodsU-NetMulti-center datasetDenoisingCycleGANDatasetImage qualityPET datasetsDynamic framesFitting errorClinical deploymentImagesPhysiologically meaningful measuresDynamic PET dataFrame
2025
Multi-Isotope Pre-Clinical Imaging Studies with DE-SPECT: A Hyperspectral Spect System for Region-Selective 3-D Gamma-Ray Spectrometry of Cardiovascular Disease
Jin Y, Zannoni E, Thorn S, Jang S, Nazari M, Burns R, Sankar P, Zhang F, Streicher M, He Z, Metzler S, Liu C, Sinusas A, Meng L. Multi-Isotope Pre-Clinical Imaging Studies with DE-SPECT: A Hyperspectral Spect System for Region-Selective 3-D Gamma-Ray Spectrometry of Cardiovascular Disease. 2021 IEEE Nuclear Science Symposium And Medical Imaging Conference (NSS/MIC) 2025, 1-1. DOI: 10.1109/nss/mic/rtsd57106.2025.11287864.Peer-Reviewed Original ResearchConceptsSPECT systemClinical SPECT systemMulti-tracer imagingCardiovascular diseaseI-123 MIBGSystem's high sensitivityTc-99m-tetrofosminDetector panelGa-67 citratePre-clinical imaging studiesStationary geometryHigh-resolutionGa-67Sympathetic innervationCardiovascular injuryAnimal modelsImaging studiesCardiovascular imagingDynamic imagingReconstruction algorithmHighest sensitivityFOVNuclear Medicine AI in Action: The Bethesda Report (AI Summit 2024)
Rahmim A, Bradshaw T, Davidzon G, Dutta J, El Fakhri G, Ghesani M, Karakatsanis N, Li Q, Liu C, Roncali E, Saboury B, Yusufaly T, Jha A. Nuclear Medicine AI in Action: The Bethesda Report (AI Summit 2024). Journal Of Nuclear Medicine 2025, 67: 348-351. PMID: 41290370, DOI: 10.2967/jnumed.125.269540.Peer-Reviewed Original ResearchOn 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 ResearchCitationsAltmetricCritical review of partial volume correction methods in PET and SPECT imaging: benefits, pitfalls, challenges, and future outlook
Azimi M, Rahmim A, Arabi H, Sanaat A, Zeraatkar N, Bouchareb Y, Liu C, Alavi A, King M, Boellaard R, Zaidi H. Critical review of partial volume correction methods in PET and SPECT imaging: benefits, pitfalls, challenges, and future outlook. European Journal Of Nuclear Medicine And Molecular Imaging 2025, 53: 2830-2861. PMID: 41188529, PMCID: PMC12920307, DOI: 10.1007/s00259-025-07612-5.Peer-Reviewed Original ResearchCitationsMeSH Keywords and ConceptsConceptsSingle-photon Emission Computed TomographyPartial volume correctionPartial volume correction approachesPositron emission tomographyPartial volume correction methodQuantitative single-photon emission computed tomographySingle-photon emission computed tomography imagingClinical practiceEmission Computed TomographyQuantitative accuracyRoutine clinical usePost-reconstruction methodsComputed tomographyClinical scenariosClinical useCardiovascular imagingClinical translationEmission tomographyMethodsThis reviewRoutine practiceClinical applicationLesion detectionClinical readinessDosimetryPVC methodAnatomically 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 glycolysisDatasetLeqMod: Adaptable Lesion-Quantification-Consistent Modulation for Deep Learning Low-Count PET Image Denoising
Xia M, Xie H, Liu Q, Zhou B, Wang H, Li B, Rominger A, Li Q, Badawi R, Shi K, Fakhri G, Liu C. LeqMod: Adaptable Lesion-Quantification-Consistent Modulation for Deep Learning Low-Count PET Image Denoising. IEEE Transactions On Medical Imaging 2025, 45: 1115-1126. PMID: 41052161, PMCID: PMC12910708, DOI: 10.1109/tmi.2025.3618247.Peer-Reviewed Original ResearchCitationsConceptsPeak signal-to-noise ratioImage denoisingPET image denoisingLow-count imagesDenoising frameworkDenoised imageInference phaseSignal-to-noise ratioSegmentation networkModel architectureModel trainingDenoisingPositron emission tomography datasetsComputational burdenOptimization procedureNoise levelImagesAuxiliary toolFrameworkModulationDeepQumodesDatasetArchitectureVendors
Academic Achievements & Community Involvement
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Honors
honor Bruce Hasegawa Young Investigator Medical Imaging Science Award
10/31/2012International AwardIEEE Nuclear Medical and Imaging Sciences CouncilDetailsUnited States
News
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News
- June 16, 2025
Yale Announces New Biomedical Imaging Institute
- June 13, 2025
NeuroEXPLORER Paper wins Journal of Nuclear Medicine's Editors' Choice Award as the best clinical article
- October 15, 2024
CMITT presentations at upcoming IEEE NSS/MIC/RTSD conference
- April 01, 2024
Yale Faculty Present Groundbreaking Clinical Research at the 2024 American College of Cardiology Scientific Sessions
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