2024
Noise-aware dynamic image denoising and positron range correction for Rubidium-82 cardiac PET imaging via self-supervision
Xie H, Guo L, Velo A, Liu Z, Liu Q, Guo X, Zhou B, Chen X, Tsai Y, Miao T, Xia M, Liu Y, Armstrong I, Wang G, Carson R, Sinusas A, Liu C. Noise-aware dynamic image denoising and positron range correction for Rubidium-82 cardiac PET imaging via self-supervision. Medical Image Analysis 2024, 100: 103391. PMID: 39579623, DOI: 10.1016/j.media.2024.103391.Peer-Reviewed Original ResearchImage denoisingPositron range correctionDynamic framesSelf-supervised methodsSuperior visual qualityLow signal-to-noise ratioCardiac PET imagingDenoising methodSignal-to-noise ratioSelf-supervisionVisual qualityHigh-energy positronsRange correctionsDenoisingNoise levelImage spatial resolutionImage qualityDefect contrastPET imagingImage quantificationRadioactive isotopesPatient scansQuantitative accuracyImagesFrameAnatomically 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 ResearchDeep learningOver-smoothed imagesDL training processesHigh-count imagesImage denoisingDenoised imageLow-count dataSemantic informationSemantic classesSegmentation guidanceTraining processPET/CT systemHistogram distributionImage qualitySegmentation toolPositron emission tomographyImagesDenoisingDatasetHistogramPriorsRadiation exposureTAI-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 ResearchGenerative 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 disease
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. PMID: 39391291, PMCID: PMC11463975, DOI: 10.1109/trpms.2023.3334105.Peer-Reviewed Original ResearchLarge-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 availabilityVendorsFedFTN: Personalized federated learning with deep feature transformation network for multi-institutional low-count PET denoising
Zhou B, Xie H, Liu Q, Chen X, Guo X, Feng Z, Hou J, Zhou S, Li B, Rominger A, Shi K, Duncan J, Liu C. FedFTN: Personalized federated learning with deep feature transformation network for multi-institutional low-count PET denoising. Medical Image Analysis 2023, 90: 102993. PMID: 37827110, PMCID: PMC10611438, DOI: 10.1016/j.media.2023.102993.Peer-Reviewed Original ResearchConceptsFederated learning processFederated learning algorithmFederated learning strategyLarge domain shiftDifferent data distributionsTransformation networkLarge-scale datasetsDeep learningDomain shiftLearning algorithmDownstream tasksNetwork weightsFeature outputFeature transformationSecurity concernsData distributionCollaborative trainingPersonalized modelPET image qualityReconstructed imagesReconstruction methodImage qualityNetworkEfficient wayLocal dataTransformer-Based Dual-Domain Network for Few-View Dedicated Cardiac SPECT Image Reconstructions
Xie H, Zhou B, Chen X, Guo X, Thorn S, Liu Y, Wang G, Sinusas A, Liu C. Transformer-Based Dual-Domain Network for Few-View Dedicated Cardiac SPECT Image Reconstructions. Lecture Notes In Computer Science 2023, 14229: 163-172. DOI: 10.1007/978-3-031-43999-5_16.Peer-Reviewed Original ResearchDual-domain networkSPECT image reconstructionImage reconstructionDeep learning methodsPrevious baseline methodsCardiac SPECT imagesHigh-quality imagesReconstruction networkIterative reconstruction processView reconstructionBaseline methodsReconstruction outputLearning methodsClinical softwareReconstruction processImaging problemsProjection dataImage qualityNetworkImagesStationary dataSPECT scannerDiagnosis of CVDLimited amountSoftwareFast-MC-PET: A Novel Deep Learning-Aided Motion Correction and Reconstruction Framework for Accelerated PET
Zhou B, Tsai Y, Zhang J, Guo X, Xie H, Chen X, Miao T, Lu Y, Duncan J, Liu C. Fast-MC-PET: A Novel Deep Learning-Aided Motion Correction and Reconstruction Framework for Accelerated PET. Lecture Notes In Computer Science 2023, 13939: 523-535. DOI: 10.1007/978-3-031-34048-2_40.Peer-Reviewed Original ResearchReconstruction frameworkMotion correctionMotion-compensated reconstructionHigh-quality imagesHigh-quality reconstruction imagesReconstruction moduleFrame reconstructionReconstruction outputMotion correction methodMotion modelingReconstructed imagesReconstruction methodImage qualityMotion typesImagesPatient motionExperimental resultsMotion-induced artifactsAcquisition dataReconstruction imagesLong acquisition timesFrameworkMultiple typesLow SNRPET acquisitionSegmentation-Free PVC for Cardiac SPECT Using a Densely-Connected Multi-Dimensional Dynamic Network
Xie H, Liu Z, Shi L, Greco K, Chen X, Zhou B, Feher A, Stendahl J, Boutagy N, Kyriakides T, Wang G, Sinusas A, Liu C. Segmentation-Free PVC for Cardiac SPECT Using a Densely-Connected Multi-Dimensional Dynamic Network. IEEE Transactions On Medical Imaging 2023, 42: 1325-1336. PMID: 36459599, PMCID: PMC10204821, DOI: 10.1109/tmi.2022.3226604.Peer-Reviewed Original Research
2022
Event-by-Event 3D Continuous Motion Correction Based on a Data-Driven Motion Estimation Algorithm for 82Rb Myocardial Perfusion Imaging
Tsai Y, Fontaine K, Mulnix T, Armstrong I, Hayden C, Spottiswoode B, Casey M, Liu C. Event-by-Event 3D Continuous Motion Correction Based on a Data-Driven Motion Estimation Algorithm for 82Rb Myocardial Perfusion Imaging. 2022, 00: 1-4. DOI: 10.1109/nss/mic44845.2022.10399100.Peer-Reviewed Original ResearchMotion correctionData-driven motion estimationPET/CT scannerSuperior-inferior motionMotion effectsSilicon photomultipliersNEMA phantomReconstruction frameworkPET acquisitionReconstructed image qualityCardiac PETMotion estimation algorithmPatient datasetsImage qualityMyocardial perfusion imagingCorrectionMotion monitoringTemporal resolutionSiPMMotionPhotomultiplierMotion estimationMotion vectorsPhantomCorrection algorithmVirtual high‐count PET image generation using a deep learning method
Liu J, Ren S, Wang R, Mirian N, Tsai Y, Kulon M, Pucar D, Chen M, Liu C. Virtual high‐count PET image generation using a deep learning method. Medical Physics 2022, 49: 5830-5840. PMID: 35880541, PMCID: PMC9474624, DOI: 10.1002/mp.15867.Peer-Reviewed Original ResearchConceptsStructural similarity indexImage quality evaluationDeep learning-based methodsDeep learning methodsImage qualityLearning-based methodsPET datasetsStatic datasetsDL methodsNet networkImage generationPET imagesNetwork inputsImage counterpartsLearning methodsNetwork outputTraining datasetPeak signalPositron emission tomography (PET) imagesQuality evaluationDatasetCross-validation resultsMean square errorHigh-count imagesImagesIncreasing angular sampling through deep learning for stationary cardiac SPECT image reconstruction
Xie H, Thorn S, Chen X, Zhou B, Liu H, Liu Z, Lee S, Wang G, Liu YH, Sinusas AJ, Liu C. Increasing angular sampling through deep learning for stationary cardiac SPECT image reconstruction. Journal Of Nuclear Cardiology 2022, 30: 86-100. PMID: 35508796, DOI: 10.1007/s12350-022-02972-z.Peer-Reviewed Original ResearchConceptsDeep learningReconstruction qualityImage reconstructionDeep learning methodsDeep neural networksDeep learning resultsImage qualityNetwork trainingSPECT image reconstructionNeural networkLearning methodsHigh image resolutionImage volumesClinical softwareImage metricsImage resolutionReconstruction resultsImproved image qualityTesting dataLearning resultsNetwork resultsPhysical phantomStationary imagingDifferent subjectsLearning
2021
MDPET: A Unified Motion Correction and Denoising Adversarial Network for Low-Dose Gated PET
Zhou B, Tsai YJ, Chen X, Duncan JS, Liu C. MDPET: A Unified Motion Correction and Denoising Adversarial Network for Low-Dose Gated PET. IEEE Transactions On Medical Imaging 2021, 40: 3154-3164. PMID: 33909561, PMCID: PMC8588635, DOI: 10.1109/tmi.2021.3076191.Peer-Reviewed Original ResearchConceptsMotion estimationPyramid networkAdversarial networkAccurate motion estimationMotion correctionLow-noise reconstructionGated positron emission tomographyMotion correction methodMotion estimation networkGated PET dataEstimation networkRecurrent layersDenoising NetworkRespiratory motion blurringExperimental resultsLow-noise imagesMotion blurringNoise levelCorrection methodNetworkPET reconstructionPrevious methodsImage qualityImagesEstimationSuper-resolution PET Brain Imaging using Deep Learning
Ren S, Liu J, Xie H, Toyonaga T, Mirian N, Chen M, Aboian M, Carson R, Liu C. Super-resolution PET Brain Imaging using Deep Learning. 2021, 00: 1-6. DOI: 10.1109/nss/mic44867.2021.9875548.Peer-Reviewed Original ResearchDeep learning networkPET image resolutionData augmentation methodImage resolutionSuper-resolution approachMedical imaging modalitiesClinical brain imagesDeep learningLearning networkAugmentation methodPET image qualityBrain imagesImage qualityNetworkImagesMedical diagnostic technologyPET imagesHRRT imagesData generalizabilityLearningSubstantial improvementScannerTechnologyPET brain imagingAccuracy
2020
Simultaneous Denoising and Motion Estimation for Low-Dose Gated PET Using a Siamese Adversarial Network with Gate-to-Gate Consistency Learning
Zhou B, Tsai Y, Liu C. Simultaneous Denoising and Motion Estimation for Low-Dose Gated PET Using a Siamese Adversarial Network with Gate-to-Gate Consistency Learning. Lecture Notes In Computer Science 2020, 12267: 743-752. DOI: 10.1007/978-3-030-59728-3_72.Peer-Reviewed Original ResearchMotion estimationMotion estimation networkRobust motion estimationMotion correction methodRespiratory motion blurringEstimation networkNoise ratioMotion correctionMotion blurringImage volumesCorrection methodCorrection stepMotion vectorsGateSimultaneous denoisingImage qualityEstimationAdversarial network
2017
Non-Rigid Event-by-Event Continuous Respiratory Motion Compensated List-Mode Reconstruction for PET
Chan C, Onofrey J, Jian Y, Germino M, Papademetris X, Carson RE, Liu C. Non-Rigid Event-by-Event Continuous Respiratory Motion Compensated List-Mode Reconstruction for PET. IEEE Transactions On Medical Imaging 2017, 37: 504-515. PMID: 29028189, PMCID: PMC7304524, DOI: 10.1109/tmi.2017.2761756.Peer-Reviewed Original ResearchConceptsMotion-compensated image reconstructionMotion fieldImage reconstructionReconstruction algorithmRespiratory motionNon-rigid motionNon-rigid motion correctionSignificant image blurringSystem matrixMotion correctionSystem matrix calculationMotionImage blurringSuperior image qualityTracer concentrationRigid motionReference locationMatrix calculationList-mode reconstruction algorithmMotion correlationDynamics studyImage qualityFully automatic multi-atlas segmentation of CTA for partial volume correction in cardiac SPECT/CT
Liu Q, Mohy-ud-Din H, Boutagy N, Jiang M, Ren S, Stendahl J, Sinusas A, Liu C. Fully automatic multi-atlas segmentation of CTA for partial volume correction in cardiac SPECT/CT. Physics In Medicine And Biology 2017, 62: 3944-3957. PMID: 28266929, PMCID: PMC5568763, DOI: 10.1088/1361-6560/aa6520.Peer-Reviewed Original ResearchConceptsMulti-atlas segmentation methodLabel fusion methodMulti-atlas segmentationSegmentation methodConventional label fusion methodsFusion methodManual segmentationMultiple organ segmentationLabel fusion algorithmImage qualityCardiac SPECT imagesDice similarity coefficientOrgan segmentationSegmentation accuracyAutomatic segmentationCTA segmentationFusion algorithmComputed tomography angiography dataSegmentationOne-out approachCT datasetsTomography angiography dataSimilarity coefficientAngiography dataConsistent image quality
2011
Respiratory Gating for A Stationary Dedicated Cardiac SPECT System
Liu C, Chan C, Harris M, Le M, Biondi J, Volokh L, Sinusas A. Respiratory Gating for A Stationary Dedicated Cardiac SPECT System. 2011, 2898-2901. DOI: 10.1109/nssmic.2011.6152514.Peer-Reviewed Original ResearchCardiac SPECT systemDedicated cardiac SPECT systemsRespiratory motion correctionTemporal dataSlow gantry rotationData acquisitionCompressive sensorSPECT systemConventional SPECT systemImage qualityMotion correctionRespiratory gating techniquePhysical phantomRespiratory motionContrast recoveryRespiratory gatingUngated imagesImagesRespiratory triggersSystemGating techniqueMyocardial perfusion SPECTUpper abdomenLower chest
2010
Quiescent period respiratory gating for PET/CT
Liu C, Alessio A, Pierce L, Thielemans K, Wollenweber S, Ganin A, Kinahan P. Quiescent period respiratory gating for PET/CT. Medical Physics 2010, 37: 5037-5043. PMID: 20964223, PMCID: PMC2945743, DOI: 10.1118/1.3480508.Peer-Reviewed Original ResearchConceptsDisplacement signalQuiescent period gatingGating methodRespiratory motion compensationFraction of countsMotion compensationImproved signalSimulation dataNoise propertiesSignal histogramImage noiseMotion blurringImproved compromiseNoise increasesPET framesMaximum amplitudeComputer simulationsMotion artifactsReduced motionNoiseGatePhase gatingPatient respiratory tracesImage quality