Chi Liu, PhD
Professor of Radiology and Biomedical ImagingCards
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Professor of Radiology and 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, is currently the Immediate Past President of PIDSC.
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
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
Research at a Glance
Yale Co-Authors
Publications Timeline
Albert Sinusas, MD
Jean-Dominique Gallezot, PhD
Edward J Miller, MD, PhD
Ming-Kai Chen, MD, PhD
John Onofrey, PhD
Yi-Hwa Liu, PhD
Publications
2024
Comparative study of functional and structural muscle changes in peripheral artery disease: rubidium-82 positron emission tomography and histological correlation
Alashi A, Vermillion B, Callegari S, Burns R, Guo L, Moulton E, Guerrera N, Depino A, Papademetris X, Zeiss C, Thorn S, Liu C, Sinusas A. Comparative study of functional and structural muscle changes in peripheral artery disease: rubidium-82 positron emission tomography and histological correlation. European Heart Journal - Cardiovascular Imaging 2024, 25: jeae142.087. DOI: 10.1093/ehjci/jeae142.087.Peer-Reviewed Original ResearchConceptsPeripheral arterial diseaseStandardized uptake valueHindlimb ischemia modelReactive hyperemiaSkeletal muscle perfusionPerfusion reserveCapillary densityPET imagingArtery diseaseNon-ischemicRubidium-82 positron emission tomographyType 2 muscle fibersRelevant pre-clinical modelIndicative of fibrosisManagement of peripheral arterial diseaseCapillary to muscle fiber ratioClinically relevant pre-clinical modelPre-clinical modelsMuscle perfusionFast myosinWeeks post-ligationRb-82 uptakeEvaluate treatment strategiesRabbit hindlimb ischemia modelPositron emission tomographyDesign and development of the DE-SPECT system: a clinical SPECT system for broadband multi-isotope imaging of peripheral vascular disease
Zannoni E, Sankar P, Jin Y, Liu C, Sinusas A, Metzler S, Meng L. Design and development of the DE-SPECT system: a clinical SPECT system for broadband multi-isotope imaging of peripheral vascular disease. Physics In Medicine And Biology 2024, 69: 125016. PMID: 38815617, PMCID: PMC11167601, DOI: 10.1088/1361-6560/ad5266.Peer-Reviewed Original ResearchMeSH Keywords and ConceptsConceptsCadmium zinc tellurideSPECT systemField of viewExcellent spectroscopic performanceExcellent energy resolutionBroad energy rangeIntrinsic spatial resolutionSpatial resolutionClinical SPECT systemEnergy resolutionPeripheral vascular diseaseEnergy rangeMm FWHMSpectroscopic performanceZinc tellurideWide-FOVCollimatorPreliminary experimental dataPartial ringDetection systemImaging capabilitiesImaging performanceExtremity imagingVascular diseaseScout imagesTAI-GAN: A Temporally and Anatomically Informed Generative Adversarial Network for early-to-late frame conversion in dynamic cardiac PET inter-frame motion correction
Guo X, Shi L, Chen X, Liu Q, Zhou B, Xie H, Liu Y, Palyo R, Miller E, Sinusas A, Staib L, Spottiswoode B, Liu C, Dvornek N. TAI-GAN: A Temporally and Anatomically Informed Generative Adversarial Network for early-to-late frame conversion in dynamic cardiac PET inter-frame motion correction. Medical Image Analysis 2024, 96: 103190. PMID: 38820677, PMCID: PMC11180595, DOI: 10.1016/j.media.2024.103190.Peer-Reviewed Original ResearchConceptsGenerative adversarial networkAdversarial networkMotion estimation accuracyInter-frame motionIntensity-based image registration techniqueAll-to-oneSegmentation masksImage registration techniquesOriginal frameTemporal informationDiagnosis accuracyMyocardial blood flowEstimation accuracyFrame conversionPositron emission tomographyNovel methodImage qualityPET datasetsRegistration techniqueNetworkCardiac positron emission tomographyBlood flowDynamic cardiac positron emission tomographyMotion correctionCoronary artery diseasePopulation-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 ResearchConceptsDeep 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 datasetsDuDoCFNet: Dual-Domain Coarse-to-Fine Progressive Network for Simultaneous Denoising, Limited-View Reconstruction, and Attenuation Correction of Cardiac SPECT
Chen X, Zhou B, Guo X, Xie H, Liu Q, Duncan J, Sinusas A, Liu C. DuDoCFNet: Dual-Domain Coarse-to-Fine Progressive Network for Simultaneous Denoising, Limited-View Reconstruction, and Attenuation Correction of Cardiac SPECT. IEEE Transactions On Medical Imaging 2024, PP: 1-1. PMID: 38578853, DOI: 10.1109/tmi.2024.3385650.Peer-Reviewed Original ResearchConceptsMulti-task learning methodCross-domainLimited-viewLearning methodsCoarse-to-fine estimationProgressive networkDual domainCross-modal feature fusionDual-domain networkProgressive learning strategyCross-modal informationSimultaneous denoisingFeature fusionSingle-photon emission computed tomographyImage domainCardiac single-photon emission computed tomographyReconstruction accuracyDenoisingHardware expenseFusion mechanismAccelerated scansImage noiseM-mapSuperior accuracyNetworkASSESSING VASCULAR FLOW IN ACUTE AND CHRONIC RABBIT MODELS OF HINDLIMB ISCHEMIA USING HIGH-RESOLUTION 2D ANGIOGRAPHY: IMPLICATIONS FOR PET FLOW QUANTIFICATION
Callegari S, Vermillion B, Alashi A, Burns R, Jang S, Guerrera N, Guo L, Depino A, Zohora F, Smolderen K, Hurtado C, Liu C, Thorn S, Sinusas A. ASSESSING VASCULAR FLOW IN ACUTE AND CHRONIC RABBIT MODELS OF HINDLIMB ISCHEMIA USING HIGH-RESOLUTION 2D ANGIOGRAPHY: IMPLICATIONS FOR PET FLOW QUANTIFICATION. Journal Of The American College Of Cardiology 2024, 83: 2251. DOI: 10.1016/s0735-1097(24)04241-4.Peer-Reviewed Original ResearchA Review on Low-Dose Emission Tomography Post-Reconstruction Denoising With Neural Network Approaches
Bousse A, Kandarpa V, Shi K, Gong K, Lee J, Liu C, Visvikis D. A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising With Neural Network Approaches. IEEE Transactions On Radiation And Plasma Medical Sciences 2024, 8: 333-347. PMID: 38313194, PMCID: PMC10836084, DOI: 10.1109/trpms.2023.3349194.Peer-Reviewed Original ResearchCitations
2023
Unified Noise-Aware Network for Low-Count PET Denoising With Varying Count Levels
Xie H, Liu Q, Zhou B, Chen X, Guo X, Wang H, Li B, Rominger A, Shi K, Liu C. Unified Noise-Aware Network for Low-Count PET Denoising With Varying Count Levels. IEEE Transactions On Radiation And Plasma Medical Sciences 2023, 8: 366-378. DOI: 10.1109/trpms.2023.3334105.Peer-Reviewed Original ResearchCitationsConceptsLarge-scale dataDeep learningDynamic PET imagesLow-count dataNeural networkMultiple networksSpecific noise levelDifferent vendorsDifferent noise levelsDenoised resultsNoisy counterpartDynamic frameInput noise levelNetworkData availabilityHigher image noiseImage qualityImage noiseSuperior performanceImportant topicAdditional challengesNoise levelPET imagesLimited data availabilityVendorsSelf-supervised Noise-aware Network for Dynamic Rubidium-82 Cardiac PET Image Denoising
Xie H, Guo L, Guo X, Liu Q, Zhou B, Chen X, Wang G, Sinusas A, Liu C. Self-supervised Noise-aware Network for Dynamic Rubidium-82 Cardiac PET Image Denoising. 2023, 00: 1-1. DOI: 10.1109/nssmicrtsd49126.2023.10338703.Peer-Reviewed Original ResearchThe DE-SPECT System: A Hyperspectral SPECT System for in Vivo 3-D Gamma-Ray Spectrometry of Molecular Theranostics
Zannoni E, Jin Y, Sankar P, Gura D, Wu R, Zhang F, Streicher M, Yang H, He Z, Metzler S, Liu C, Sinusas A, Meng L. The DE-SPECT System: A Hyperspectral SPECT System for in Vivo 3-D Gamma-Ray Spectrometry of Molecular Theranostics. 2023, 00: 1-1. DOI: 10.1109/nssmicrtsd49126.2023.10337964.Peer-Reviewed Original Research
Academic Achievements and Community Involvement
honor Bruce Hasegawa Young Investigator Medical Imaging Science Award
International AwardIEEE Nuclear Medical and Imaging Sciences CouncilDetails10/31/2012United Stateshonor Junior Faculty Scholar Award
Yale University AwardYale Center for Clinical InvestigationDetails07/01/2011United Stateshonor Young Investigators Award Honorable Mention
International AwardSociety of Nuclear Medicine, Computer and Instrumentation CouncilDetails06/05/2010United States
Links & Media
News
- April 01, 2024
Yale Faculty Present Groundbreaking Clinical Research at the 2024 American College of Cardiology Scientific Sessions
- February 20, 2023Source: Yale Ventures
Roberts Innovation Fund to Support 10 Bold SEAS Faculty Inventions
- October 02, 2020
Yale PET Center Receives $10.2M BRAIN Initiative Grant to Build New Scanner
- June 18, 2020
Will Chemo Cause Heart Failure? A New Test May Have Answers