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
Albert Sinusas, MD
Jean-Dominique Gallezot, PhD
Edward J Miller, MD, PhD
Yi-Hwa Liu, PhD
Stephanie Thorn, MSc, PhD
Ming-Kai Chen, MD, PhD
Publications
2025
Motion Management in Positron Emission Tomography/Computed Tomography and Positron Emission Tomography/Magnetic Resonance
Guo L, Liu C, Soultanidis G. Motion Management in Positron Emission Tomography/Computed Tomography and Positron Emission Tomography/Magnetic Resonance. PET Clinics 2025 PMID: 40858422, DOI: 10.1016/j.cpet.2025.07.004.Peer-Reviewed Original ResearchConceptsAxial field-of-viewLong axial field-of-viewTotal-body PET systemsMotion managementPET systemClinical positron emission tomographyMotion correctionField of viewPositron emission tomography/magnetic resonancePositron emission tomographyImage registrationData-driven motionPositron Emission Tomography/Computed TomographyEmission Tomography/Computed TomographyImage qualityPositronCorrectionTomography/Computed TomographyMotionAccuracy of motion estimationDeep learning-based methodsCorrection strategyEmission tomographyResonancePET studiesComparative Study of Bone-Avid SPECT Tracers: In Search of a Diagnostic Target of Cardiac Amyloidosis
Zohora F, Lin A, Jang S, Vermillion B, Burns R, Callegari S, DePino A, Liu C, Miller E, Thorn S, Sinusas A. Comparative Study of Bone-Avid SPECT Tracers: In Search of a Diagnostic Target of Cardiac Amyloidosis. Journal Of Nuclear Cardiology 2025, 50: 102377. DOI: 10.1016/j.nuclcard.2025.102377.Peer-Reviewed Original ResearchA generalizable diffusion framework for 3D low-dose and few-view cardiac SPECT imaging
Xie H, Gan W, Ji W, Chen X, Alashi A, Thorn S, Zhou B, Liu Q, Xia M, Guo X, Liu Y, An H, Kamilov U, Wang G, Sinusas A, Liu C. A generalizable diffusion framework for 3D low-dose and few-view cardiac SPECT imaging. Medical Image Analysis 2025, 106: 103729. PMID: 40752375, PMCID: PMC12360895, DOI: 10.1016/j.media.2025.103729.Peer-Reviewed Original ResearchCitationsMeSH Keywords and ConceptsConceptsImage qualityNetwork re-trainingDeep learning methodsCardiac SPECT imagingLow-doseSPECT imagesContextual informationDiffusion frameworkLearning methodsConsistency strategyRe-trainingAcquisition settingsLow count levelsFine-tuningSpatial informationVariation constraintCardiac catheterization resultsImage dataMyocardial perfusion imagingCoronary artery diseaseProjection dataCatheterization resultsImage volumesScanner geometryProjective measurements2.5D Multi-view Averaging Diffusion Model for 3D Medical Image Translation: Application to Low-count PET Reconstruction with CT-less Attenuation Correction
Chen T, Hou J, Zhou Y, Xie H, Chen X, Liu Q, Guo X, Xia M, Duncan J, Liu C, Zhou B. 2.5D Multi-view Averaging Diffusion Model for 3D Medical Image Translation: Application to Low-count PET Reconstruction with CT-less Attenuation Correction. IEEE Transactions On Medical Imaging 2025, PP: 1-1. PMID: 40372846, DOI: 10.1109/tmi.2025.3570342.Peer-Reviewed Original ResearchCitationsAltmetricConceptsAttenuation correctionLow-dose PETImage-to-image translationStandard-dose PETPositron emission tomographyPET reconstructionOverall radiation doseCT acquisitionState-of-the-art deep learning methodsCNN-based methodsState-of-the-artMedical image translationRadiation doseDeep learning methodsPatient studiesDiffusion modelHigh computation costHuman patient studiesClinical imaging toolBaseline methodsImage translationMulti-viewCNN-basedMultiple viewsGeneration qualityAn Investigation on Cross-Tracer Generalizability of Deep Learning-Based PET Attenuation Correction
Hou J, Chen T, Zhou Y, Chen X, Xie H, Liu Q, Xia M, Panin V, Toyonaga T, Liu C, Zhou B. An Investigation on Cross-Tracer Generalizability of Deep Learning-Based PET Attenuation Correction. IEEE Transactions On Radiation And Plasma Medical Sciences 2025, PP: 1-1. DOI: 10.1109/trpms.2025.3566630.Peer-Reviewed Original ResearchConceptsAttenuation mapAttenuation correctionPET attenuation correctionAC PET imagesPET imagingQuantitative PET imagingPET reconstructionNon-attenuationAttenuation-corrected (ACTracer typeRadiation doseDL modelsDeep learning (DL)-based methodsMLAADL model trainingCorrectionPET signalNetwork performanceCompetitive performanceModel trainingAttenuationRadiationTracer activityM-CTPETPOUR-Net: A Population-Prior-Aided Over-Under-Representation Network for Low-Count PET Attenuation Map Generation
Zhou B, Hou J, Chen T, Zhou Y, Chen X, Xie H, Liu Q, Guo X, Xia M, Tsai Y, Panin V, Toyonaga T, Duncan J, Liu C. POUR-Net: A Population-Prior-Aided Over-Under-Representation Network for Low-Count PET Attenuation Map Generation. IEEE Transactions On Medical Imaging 2025, 44: 1699-1710. PMID: 40030468, PMCID: PMC12126958, DOI: 10.1109/tmi.2024.3514925.Peer-Reviewed Original ResearchCitationsConceptsGenerating synthetic brain PET images of synaptic density based on MR T1 images using deep learning
Zheng X, Worhunsky P, Liu Q, Guo X, Chen X, Sun H, Zhang J, Toyonaga T, Mecca A, O’Dell R, van Dyck C, Angarita G, Cosgrove K, D’Souza D, Matuskey D, Esterlis I, Carson R, Radhakrishnan R, Liu C. Generating synthetic brain PET images of synaptic density based on MR T1 images using deep learning. EJNMMI Physics 2025, 12: 30. PMID: 40163154, PMCID: PMC11958861, DOI: 10.1186/s40658-025-00744-5.Peer-Reviewed Original ResearchCitationsAltmetricConceptsCannabis use disorderStructural similarity indexPET imagingImages of higher qualityMR-T1 imagesMean square errorUse disorderEncoder-decoderDeep learningCross-validation processData-driven approachDiagnostic categoriesLow-dose scansPredicted imageTemporal regionsBrain disordersGround truthT1-weighted MRISynaptic densityHuman brainSimilarity indexDisordersSevere neurological disordersTranslation accuracyNoise reductionIncreasing angular sampling for dedicated cardiac single photon emission computed tomography scanner: Implementation with deep learning and validation with human data
Xie H, Alashi A, Thorn S, Chen X, Zhou B, Sinusas A, Liu C. Increasing angular sampling for dedicated cardiac single photon emission computed tomography scanner: Implementation with deep learning and validation with human data. Journal Of Nuclear Cardiology 2025, 49: 102168. PMID: 39986346, PMCID: PMC12227299, DOI: 10.1016/j.nuclcard.2025.102168.Peer-Reviewed Original ResearchCitationsLower Extremity Flow Quantification Using Dynamic ⁸²Rb PET: A Preclinical Investigation
Guo L, Thorn S, de Rubio Cruz P, Liu Z, Gallezot J, Liu Q, Moulton E, Carson R, Sinusas A, Liu C. Lower Extremity Flow Quantification Using Dynamic ⁸²Rb PET: A Preclinical Investigation. IEEE Transactions On Radiation And Plasma Medical Sciences 2025, 9: 918-926. DOI: 10.1109/trpms.2025.3542729.Peer-Reviewed Original ResearchCitationsCollagen Hybridizing Peptide-Based Radiotracers for Molecular Imaging of Collagen Turnover in Pulmonary Fibrosis.
Ahmad A, Ghim M, Kukreja G, Neishabouri A, Zhang Z, Li J, Salarian M, Toczek J, Gona K, Hedayatyanfard K, Morrison T, Zhang J, Huang Y, Liu C, Yu S, Sadeghi M. Collagen Hybridizing Peptide-Based Radiotracers for Molecular Imaging of Collagen Turnover in Pulmonary Fibrosis. Journal Of Nuclear Medicine 2025, 66: 425-433. PMID: 39915119, PMCID: PMC11876730, DOI: 10.2967/jnumed.124.268832.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsPulmonary fibrosisTracer uptakeLung uptakeMurine model of pulmonary fibrosisModel of pulmonary fibrosisMice 3 wkEffect of antifibrotic therapyCollagen turnoverInterstitial lung diseaseClinical diagnostic methodsSPECT/CT imagingHybrid tracersLung histologyAntifibrotic therapyControl miceDisease activityMurine modelLung diseaseMice 8Tissue fibrosisPatient managementLiver uptakeSPECT/CTFibrosisSPECT images
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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