Chi Liu, PhD
Professor of Radiology and Biomedical ImagingDownloadHi-Res Photo
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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
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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
ORCID
0000-0002-7007-1037
Research at a Glance
Yale Co-Authors
Frequent collaborators of Chi Liu's published research.
Publications Timeline
A big-picture view of Chi Liu's research output by year.
Albert Sinusas, MD
Jean-Dominique Gallezot, PhD
Edward J Miller, MD, PhD
Ming-Kai Chen, MD, PhD
Stephanie Thorn, MSc, PhD
John Onofrey, PhD
134Publications
2,045Citations
Publications
2024
Fast Energy-Based Scatter Correction for 3D TOF-PET on NeuroExplorer
Guo L, Fontaine K, Gravel P, Mulnix T, Zhang J, Liu C, Carson R. Fast Energy-Based Scatter Correction for 3D TOF-PET on NeuroExplorer. 2024, 00: 1-2. DOI: 10.1109/nss/mic/rtsd57108.2024.10657901.Peer-Reviewed Original ResearchCitationsConceptsSingle scatter simulationScatter estimationEnergy spectrumTOF binsField of viewAxial field-of-viewHigh-energy scatteringLong axial field-of-viewLow-activity regionsList-mode dataEnergy informationTOF-PETContrast phantomUniform phantomScattering phantomCounting statisticsScatter correctionOSEM reconstructionMultiple-scatteringScatteringScattering simulationsPhantomEvent distributionImproved contrastMonte-CarloGeneration of Synthetic brain PET images of synaptic density from MRI and FDG-PET using a Multi-stage U-Net
Zheng X, Worhunsky P, Liu Q, Zhou B, Chen X, Guo X, Xie H, Sun H, Zhang J, Toyonaga T, Mecca A, O’Dell R, van Dyck C, Carson R, Radhakrishnan R, Liu C. Generation of Synthetic brain PET images of synaptic density from MRI and FDG-PET using a Multi-stage U-Net. 2024, 00: 1-2. DOI: 10.1109/nss/mic/rtsd57108.2024.10655600.Peer-Reviewed Original ResearchPatlak-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 SNRImagesReconDose-aware Diffusion Model for 3D Low-count Cardiac SPECT Image Denoising with Projection-domain Consistency
Xie H, Gan W, Chen X, Zhou B, Liu Q, Xia M, Guo X, Liu Y, An H, Kamilov U, Wang G, Sinusas A, Liu C. Dose-aware Diffusion Model for 3D Low-count Cardiac SPECT Image Denoising with Projection-domain Consistency. 2024, 00: 1-1. DOI: 10.1109/nss/mic/rtsd57108.2024.10655170.Peer-Reviewed Original ResearchConceptsImage denoisingImage denoising performanceDeep learning techniquesNoise-levelDenoising performanceDenoising resultsNeural networkLearning techniquesSPECT imagesLow count levelsSPECT scansDenoisingSampling stepIterative reconstructionNoise amplitudeImagesInjected dosePatient studiesDiffusion modelRadiation exposureCardiology studiesSPECTNetworkStochastic natureMLEMExperimental Evaluation of DE-SPECT: A Hyperspectral SPECT System for Region-Selective 3-D Gamma-Ray Spectroscopy of Molecular Theragnostics
Jin Y, Zannoni E, Sankar P, Gura D, Wu R, Zhu S, Zhang F, Streicher M, Yang H, He Z, Metzler S, Liu C, Sinusas A, Meng L. Experimental Evaluation of DE-SPECT: A Hyperspectral SPECT System for Region-Selective 3-D Gamma-Ray Spectroscopy of Molecular Theragnostics. 2024, 00: 1-2. DOI: 10.1109/nss/mic/rtsd57108.2024.10657212.Peer-Reviewed Original ResearchConceptsCadmium zinc tellurideCadmium zinc telluride detectorsGamma-ray spectroscopyHigh-sensitivity imagingUniform phantomResolution phantomSPECT systemZinc telluridePhantom studyImage qualityPhantomImaging capabilitiesImaging performanceAperture systemFOVMultiple radiotracersHigh-resolutionCollimatorDual-field-of-viewPeripheral vascular diseaseDetectorState-of-the-art technologiesTellurideClinical systemsDeep Learning-based Dynamic PET Intra-frame Motion Correction and Integration with Inter-frame Motion Estimation
Guo X, Tsai Y, Liu Q, Guo L, Valadez G, Dvornek N, Liu C. Deep Learning-based Dynamic PET Intra-frame Motion Correction and Integration with Inter-frame Motion Estimation. 2024, 00: 1-1. DOI: 10.1109/nss/mic/rtsd57108.2024.10657268.Peer-Reviewed Original ResearchConceptsIntra-frame motionMotion correctionGated imagesLearning-based registration approachesDeep learning-based worksInter-frame motion estimationConventional image registrationLearning-based worksImage registrationMotion estimation processMotion estimation frameworkInter-frame registrationRespiratory gatingImprove image sharpnessInter-frameInference timeMotion estimationReconstructed framesDynamic PET datasetsGeneralization abilityPET imagingConventional registrationDynamic PET imagesImprove image qualityComputational inefficiencyAnatomically and Metabolically Informed Deep Learning Low-Count PET Image Denoising
Xia M, Xie H, Liu Q, Guo L, Ouyang J, Bayerlein R, Spencer B, Badawi R, Li Q, Fakhri G, Liu C. Anatomically and Metabolically Informed Deep Learning Low-Count PET Image Denoising. 2024, 00: 1-2. DOI: 10.1109/nss/mic/rtsd57108.2024.10657099.Peer-Reviewed Original ResearchConceptsDeep learningOver-smoothed imagesDL training processesHigh-count imagesImage denoisingDenoised imageLow-count dataSemantic informationSemantic classesSegmentation guidanceTraining processPET/CT systemHistogram distributionImage qualitySegmentation toolPositron emission tomographyImagesDenoisingDatasetHistogramPriorsRadiation exposurePOUR-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. 2024, 00: 1-1. DOI: 10.1109/nss/mic/rtsd57108.2024.10658051.Peer-Reviewed Original ResearchConceptsPET attenuation correctionLow-dose PETAttenuation correctionU-mapAttenuation mapElevated radiation doseRadiation doseEfficient feature extractionRadiation exposurePET imagingFinely detailed featuresBaseline methodsMitigate radiation exposureFeature extractionCorrectionMap generationGeneration machinesAn 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, Liu C, Zhou B, Toyonaga T. An Investigation on Cross-Tracer Generalizability of Deep Learning-based PET Attenuation Correction. 2024, 00: 1-1. DOI: 10.1109/nss/mic/rtsd57108.2024.10657095.Peer-Reviewed Original ResearchConceptsAttenuation correctionPET attenuation correctionQuantitative PET imagingAttenuation mapDL modelsDeep learning (DL)-based methodsTumor quantificationDL model trainingRadiation doseImmediate future workCompetitive performancePET imagingModel trainingPET signalCorrectionAnalysis of PETFuture workPreliminary resultsData availabilityRadiation2.5D Multi-view Averaging Diffusion Model for 3D Medical Image Translation: Application to Low-count PET Reconstruction with CT-less AC
Chen T, Hou J, Xie H, Chen X, Zhou Y, 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 AC. 2024, 00: 1-2. DOI: 10.1109/nss/mic/rtsd57108.2024.10658551.Peer-Reviewed Original ResearchCitationsConceptsLow-dose PETStandard-dose PETImage-to-image translationPositron emission tomographyAttenuation correctionPET reconstructionOverall radiation doseCT acquisitionState-of-the-art deep learning methodsRadiation hazardRadiation doseCNN-based methodsState-of-the-artMedical image translationPatient studiesDiffusion modelDeep learning methodsHigh computation costHuman patient studiesClinical imaging toolImage translationBaseline methodsMulti-viewCNN-basedMultiple views
Academic Achievements & 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
News
News
- 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
- 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
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