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
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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)
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
Ming-Kai Chen, MD, PhD
Edward J Miller, MD, PhD
Yi-Hwa Liu, PhD
Publications
2026
Generation of brain PET synaptic density imaging from MRI and FDG-PET using a 3D Multi-stage Residual U-Net
Zheng X, Worhunsky P, Toyonaga T, Liu Q, Guo X, Zhou Y, Chen X, Zhou B, Mecca A, Chen M, O'Dell R, Van Dyck C, Carson R, Radhakrishnan R, Liu C. Generation of brain PET synaptic density imaging from MRI and FDG-PET using a 3D Multi-stage Residual U-Net. IEEE Transactions On Radiation And Plasma Medical Sciences 2026, PP: 1-1. DOI: 10.1109/trpms.2026.3690934.Peer-Reviewed Original ResearchUnsupervised Adaptation from FDG to PSMA PET/CT for 3D Lesion Detection Under Label Shift
Liu X, Xia M, Chemli Y, Fakhri G, Liu C, Ouyang J. Unsupervised Adaptation from FDG to PSMA PET/CT for 3D Lesion Detection Under Label Shift. 2026, 00: 1-5. DOI: 10.1109/isbi61048.2026.11515918.Peer-Reviewed Original ResearchConceptsUnsupervised domain adaptationLabel shiftPseudo-labelsSupervised learningSelf-trainingPseudo-label selectionBox regressionDomain adaptationUnsupervised adaptationCovariate shiftConfidence thresholdLesion detectionLearningLabelingDetectionAnchor shapeUnsupervisedFROCHistogramAdaptationSize compositionPseudoAI‑driven multi-lesion detection in whole‑body FDG PET/CT
Liu X, Xia M, Chemli Y, Fakhri G, Liu C, Ouyang J. AI‑driven multi-lesion detection in whole‑body FDG PET/CT. Progress In Biomedical Optics And Imaging 2026, 13928: 7. DOI: 10.1117/12.3087729.Peer-Reviewed Original ResearchCitationsConceptsWhole-body FDG PET/CTFDG-PET/CTLesion detectionFDG-PET/CT studiesIntersection-over-unionOncologic PET/CTLesion detection networkDetection of lesionsDeep learning modelsCT informationDiagnostic accuracyPET-onlyTreatment planningLesion sizePET/CTObject detectorsEfficiency of radiologistsIoU thresholdLesionsDetection modelPublic datasetsDetectorDetection networkNumerous lesionsLocalization performanceExploring the limits of deep-learning‑based PET image denoising for lesion detectability
Bayerlein R, Xia M, Ouyang J, Chemli Y, Melnichuk D, Fakhri G, Nardo L, Liu C, Badawi R. Exploring the limits of deep-learning‑based PET image denoising for lesion detectability. Progress In Biomedical Optics And Imaging 2026, 13928: 5. DOI: 10.1117/12.3085222.Peer-Reviewed Original ResearchConceptsDenoised imageDL-based denoisersLesion contrastDetectability of low-contrast lesionsDL-basedInformation diffusion modelDeep learning denoisingPET image qualityImage qualityVisual image qualityArea under the ROC curveActivity concentration ratioLow-contrast lesionsOverall image appearanceNoisy imagesImage representationLearning denoisingDenoisingHigh-contrast featuresNoise levelLesion uptakeLesion-to-background ratioInput noise levelTOF-OSEMDetection task26-A-20816-ACC RISKS IN THE STUDY AND CARE OF PATIENTS WITH PERIPHERAL ARTERY DISEASE: LOWER EXTREMITY CUFF OCCLUSION INCREASES LEFT VENTRICULAR PRESSURE AND REDUCES MYOCARDIAL BLOOD FLOW
Nazari M, Cho S, Tapia C, Jang S, Zohora F, Burns R, Lima M, Depino A, Stendahl J, Jin Y, Meng L, Liu C, Thorn S, Sinusas A. 26-A-20816-ACC RISKS IN THE STUDY AND CARE OF PATIENTS WITH PERIPHERAL ARTERY DISEASE: LOWER EXTREMITY CUFF OCCLUSION INCREASES LEFT VENTRICULAR PRESSURE AND REDUCES MYOCARDIAL BLOOD FLOW. Journal Of The American College Of Cardiology 2026, 87: a1032. DOI: 10.1016/j.jacc.2026.02.2532.Peer-Reviewed Original ResearchMolecular Imaging of Collagen Turnover in Myocardial Infarction
Neishabouri A, Ghim M, Varli O, Ahmad A, Kukreja G, Zhang Z, Li J, Toczek J, Salarian M, Gona K, Hedayatyanfard K, Zhang J, Alshaeba D, Akar F, Liu C, Huang H, Yu S, Sadeghi M. Molecular Imaging of Collagen Turnover in Myocardial Infarction. Journal Of Nuclear Medicine 2026, 67: jnumed.125.271721. PMID: 41887734, PMCID: PMC13224913, DOI: 10.2967/jnumed.125.271721.Peer-Reviewed Original ResearchAltmetricMeSH Keywords and ConceptsConceptsTracer uptakeMyocardial infarctionInfarct zoneCardiac fibrosisLeft anterior descending arteryContrast-enhanced CTManagement of cardiomyopathyCollagen turnoverCardiac tissueBiomarkers of collagen turnoverPersonalized managementNoninvasive detectionMyocardial perfusion imagingEx vivo autoradiographyAnterior descending arteryMolecular imagingHuman cardiac tissueControl tracerCardiac uptakeSPECT/CT imagingAntifibrotic treatmentSham surgeryFibrotic changesDescending arteryPerfusion imagingDose-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, 111: 104039. PMID: 41930496, PMCID: PMC13332693, DOI: 10.1016/j.media.2026.104039.Peer-Reviewed Original ResearchCitationsConceptsPET 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, 67: 780-787. PMID: 41748295, DOI: 10.2967/jnumed.125.270003.Peer-Reviewed Original ResearchThis study investigates dynamic 18F-flutemetamol PET imaging for measuring amyloid burden in transthyretin cardiac amyloidosis, showing significant reductions after 6 months of tafamidis treatment.Patlak-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
On 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 ResearchCitationsAltmetric
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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