2023
FedFTN: 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 ResearchMeSH KeywordsAlgorithmsHumansImage Processing, Computer-AssistedPositron-Emission TomographySignal-To-Noise RatioConceptsFederated 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 dataJoint motion estimation and penalized image reconstruction algorithm with anatomical priors for gated TOF-PET/CT
Tsai Y, Liu C. Joint motion estimation and penalized image reconstruction algorithm with anatomical priors for gated TOF-PET/CT. Physics In Medicine And Biology 2023, 68: 025020. PMID: 36549009, DOI: 10.1088/1361-6560/acae19.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsHumansImage Processing, Computer-AssistedPhantoms, ImagingPositron Emission Tomography Computed TomographyPositron-Emission TomographyReproducibility of Results
2022
Virtual 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 ResearchMeSH KeywordsDeep LearningHumansImage Processing, Computer-AssistedPositron-Emission TomographyResearch DesignSignal-To-Noise RatioConceptsStructural 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 imagesImagesA personalized deep learning denoising strategy for low-count PET images
Liu Q, Liu H, Mirian N, Ren S, Viswanath V, Karp J, Surti S, Liu C. A personalized deep learning denoising strategy for low-count PET images. Physics In Medicine And Biology 2022, 67: 145014. PMID: 35697017, PMCID: PMC9321225, DOI: 10.1088/1361-6560/ac783d.Peer-Reviewed Original ResearchUnsupervised inter-frame motion correction for whole-body dynamic PET using convolutional long short-term memory in a convolutional neural network
Guo X, Zhou B, Pigg D, Spottiswoode B, Casey ME, Liu C, Dvornek NC. Unsupervised inter-frame motion correction for whole-body dynamic PET using convolutional long short-term memory in a convolutional neural network. Medical Image Analysis 2022, 80: 102524. PMID: 35797734, PMCID: PMC10923189, DOI: 10.1016/j.media.2022.102524.Peer-Reviewed Original ResearchMeSH KeywordsHumansImage Processing, Computer-AssistedMemory, Short-TermNeural Networks, ComputerPositron-Emission TomographyWhole Body ImagingConceptsConvolutional neural networkNeural networkConvolutional long short-term memory (ConvLSTM) layersDeep learning-based frameworkConvolutional long short-term memoryLong short-term memory layersDeep learning baselinesLong short-term memoryDynamic temporal featuresLearning-based frameworkDeep learning approachShort-term memory layersTracer distribution changeMotion estimation networkMotion prediction errorInference timeEstimation networkLearning baselinesNon-rigid registration methodLearning approachMotion correction methodMemory layerShort-term memoryTemporal featuresRegistration methodDeep-learning-based methods of attenuation correction for SPECT and PET
Chen X, Liu C. Deep-learning-based methods of attenuation correction for SPECT and PET. Journal Of Nuclear Cardiology 2022, 30: 1859-1878. PMID: 35680755, DOI: 10.1007/s12350-022-03007-3.Peer-Reviewed Original ResearchConceptsHigh computational complexityAC strategyNeural networkRaw emission dataComputational complexityLearning methodsCT imagesΜ-mapsPET imagesLow accuracySuperior performanceImagesAttenuation correctionPromising resultsMR imagesAttenuation mapPET/CT scannerHigh noise levelsArtifactsNetworkCT artifactsPET/MRI scannerIntermediate stepComplexityScannerPET respiratory motion correction: quo vadis?
Lamare F, Bousse A, Thielemans K, Liu C, Merlin T, Fayad H, Visvikis D. PET respiratory motion correction: quo vadis? Physics In Medicine And Biology 2022, 67: 03tr02. PMID: 34915465, DOI: 10.1088/1361-6560/ac43fc.Peer-Reviewed Original ResearchConceptsRespiratory motion correctionPET respiratory motion correctionMotion correctionGeneric motion modelImage reconstruction processRespiratory motion informationMotion estimationMotion informationTerms of synchronizationImage spaceSynchronization stepsReconstruction processMotion modelOverall approachPET/magnetic resonance imagingDevice systemRespiratory motionImaging devicesMRI deviceDevicesSynchronizationNumber of stepsComprehensive coverageDatasetGreat interest
2021
Automatic Inter-Frame Patient Motion Correction for Dynamic Cardiac PET Using Deep Learning
Shi L, Lu Y, Dvornek N, Weyman CA, Miller EJ, Sinusas AJ, Liu C. Automatic Inter-Frame Patient Motion Correction for Dynamic Cardiac PET Using Deep Learning. IEEE Transactions On Medical Imaging 2021, 40: 3293-3304. PMID: 34018932, PMCID: PMC8670362, DOI: 10.1109/tmi.2021.3082578.Peer-Reviewed Original ResearchMeSH KeywordsDeep LearningHumansImage Processing, Computer-AssistedMotionMovementPositron-Emission TomographyConceptsConvolutional neural networkRegistration-based methodMotion correctionDynamic frameTracer distribution changeDynamic image dataPatient motion correctionPatient scansDeep learningPatient motionMotion estimationImage dataLSTM networkNeural networkRealistic patient motionTemporal informationMotion correction methodMotion detectionCardiac PETClinical workflowRigid translational motionFlow estimationNetworkPatient datasetsSuperior performanceMDPET: 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 qualityImagesEstimationArtificial Intelligence-Based Image Enhancement in PET Imaging Noise Reduction and Resolution Enhancement
Liu J, Malekzadeh M, Mirian N, Song TA, Liu C, Dutta J. Artificial Intelligence-Based Image Enhancement in PET Imaging Noise Reduction and Resolution Enhancement. PET Clinics 2021, 16: 553-576. PMID: 34537130, PMCID: PMC8457531, DOI: 10.1016/j.cpet.2021.06.005.Peer-Reviewed Original ResearchMeSH KeywordsArtificial IntelligenceHumansImage EnhancementImage Processing, Computer-AssistedPositron-Emission TomographySignal-To-Noise RatioConceptsArtificial intelligence modelsImage enhancementIntelligence modelsArtificial intelligenceNetwork architectureEvaluation metricsLarge-scale adoptionData typesImage denoisingLoss functionPET imagesLow spatial resolutionHigh noiseResolution enhancementImagesIntelligenceDeblurringArchitectureDenoisingNoise reductionMetricsPopularityRecent effortsFuture directionsAccuracyGeneration of parametric Ki images for FDG PET using two 5‐min scans
Wu J, Liu H, Ye Q, Gallezot J, Naganawa M, Miao T, Lu Y, Chen M, Esserman DA, Kyriakides TC, Carson RE, Liu C. Generation of parametric Ki images for FDG PET using two 5‐min scans. Medical Physics 2021, 48: 5219-5231. PMID: 34287939, DOI: 10.1002/mp.15113.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsFluorodeoxyglucose F18HumansPositron-Emission TomographyRadiopharmaceuticalsWhole Body ImagingConceptsPopulation-based input functionDynamic FDG-PET scansFDG-PET scansFDG-PETSUV changesPET scansClinical practiceSolid lung nodulesClinical usefulnessLate scansBone marrowRegion of interestLung nodulesInput functionScansPatlak analysisKi imagesMin/T-testCorrelation coefficientTumorsSubjectsNodulesDynamic imagingPETGeneration of synthetic PET images of synaptic density and amyloid from 18F‐FDG images using deep learning
Wang R, Liu H, Toyonaga T, Shi L, Wu J, Onofrey JA, Tsai Y, Naganawa M, Ma T, Liu Y, Chen M, Mecca AP, O’Dell R, van Dyck C, Carson RE, Liu C. Generation of synthetic PET images of synaptic density and amyloid from 18F‐FDG images using deep learning. Medical Physics 2021, 48: 5115-5129. PMID: 34224153, PMCID: PMC8455448, DOI: 10.1002/mp.15073.Peer-Reviewed Original ResearchAlzheimer DiseaseAniline CompoundsBrainDeep LearningFluorodeoxyglucose F18HumansPositron-Emission TomographyPitfalls on PET/CT Due to Artifacts and Instrumentation
Tsai YJ, Liu C. Pitfalls on PET/CT Due to Artifacts and Instrumentation. Seminars In Nuclear Medicine 2021, 51: 646-656. PMID: 34243906, PMCID: PMC8490278, DOI: 10.1053/j.semnuclmed.2021.06.015.Peer-Reviewed Original ResearchArtifactsHumansPositron Emission Tomography Computed TomographyPositron-Emission TomographyTomography, X-Ray Computed
2020
Multi-Tracer Positron Emission Tomography Quantification of Sympathetic Innervation Tracer Similarity But Not Equivalence ∗
Sinusas AJ, Liu C. Multi-Tracer Positron Emission Tomography Quantification of Sympathetic Innervation Tracer Similarity But Not Equivalence ∗. JACC Cardiovascular Imaging 2020, 14: 1437-1439. PMID: 33221231, DOI: 10.1016/j.jcmg.2020.10.007.Peer-Reviewed Original ResearchNoise reduction with cross-tracer and cross-protocol deep transfer learning for low-dose PET
Liu H, Wu J, Lu W, Onofrey JA, Liu YH, Liu C. Noise reduction with cross-tracer and cross-protocol deep transfer learning for low-dose PET. Physics In Medicine And Biology 2020, 65: 185006. PMID: 32924973, DOI: 10.1088/1361-6560/abae08.Peer-Reviewed Original ResearchDeep LearningHumansImage EnhancementPositron-Emission TomographyRadiation DosageSignal-To-Noise RatioFeasibility study of PET dynamic imaging of [18F]DHMT for quantification of reactive oxygen species in the myocardium of large animals
Wu J, Boutagy NE, Cai Z, Lin SF, Zheng MQ, Feher A, Stendahl JC, Kapinos M, Gallezot JD, Liu H, Mulnix T, Zhang W, Lindemann M, Teng JK, Miller EJ, Huang Y, Carson RE, Sinusas AJ, Liu C. Feasibility study of PET dynamic imaging of [18F]DHMT for quantification of reactive oxygen species in the myocardium of large animals. Journal Of Nuclear Cardiology 2020, 29: 216-225. PMID: 32415628, PMCID: PMC7666654, DOI: 10.1007/s12350-020-02184-3.Peer-Reviewed Original ResearchAnimalsDogsFeasibility StudiesHumansMyocardiumPositron-Emission TomographyReactive Oxygen SpeciesSuperoxides
2019
An investigation of quantitative accuracy for deep learning based denoising in oncological PET
Lu W, Onofrey JA, Lu Y, Shi L, Ma T, Liu Y, Liu C. An investigation of quantitative accuracy for deep learning based denoising in oncological PET. Physics In Medicine And Biology 2019, 64: 165019. PMID: 31307019, DOI: 10.1088/1361-6560/ab3242.Peer-Reviewed Original ResearchData-driven voluntary body motion detection and non-rigid event-by-event correction for static and dynamic PET
Lu Y, Gallezot JD, Naganawa M, Ren S, Fontaine K, Wu J, Onofrey JA, Toyonaga T, Boutagy N, Mulnix T, Panin VY, Casey ME, Carson RE, Liu C. Data-driven voluntary body motion detection and non-rigid event-by-event correction for static and dynamic PET. Physics In Medicine And Biology 2019, 64: 065002. PMID: 30695768, DOI: 10.1088/1361-6560/ab02c2.Peer-Reviewed Original Research
2018
Improved discrimination between benign and malignant LDCT screening-detected lung nodules with dynamic over static 18F-FDG PET as a function of injected dose
Ye Q, Wu J, Lu Y, Naganawa M, Gallezot JD, Ma T, Liu Y, Tanoue L, Detterbeck F, Blasberg J, Chen MK, Casey M, Carson RE, Liu C. Improved discrimination between benign and malignant LDCT screening-detected lung nodules with dynamic over static 18F-FDG PET as a function of injected dose. Physics In Medicine And Biology 2018, 63: 175015. PMID: 30095083, PMCID: PMC6158045, DOI: 10.1088/1361-6560/aad97f.Peer-Reviewed Original ResearchMeSH KeywordsAgedAged, 80 and overAlgorithmsFemaleFluorodeoxyglucose F18HumansLung NeoplasmsMaleMiddle AgedPositron-Emission TomographyRadiation DosageRadiopharmaceuticalsROC CurveSolitary Pulmonary NoduleConceptsPopulation-based input functionStandardized uptake valueImage-derived input functionLung nodulesClinical trialsTime-activity curvesLow-dose computed tomography (LDCT) screeningLung cancer mortality ratesIndeterminate lung nodulesComputed Tomography ScreeningF-FDG PETCancer mortality ratesStatic PET acquisitionVirtual clinical trialsScan durationTomography screeningFDG injectionPET scansMortality rateUptake valueAccurate diagnosisMalignant lung nodulesROC analysisPatient dataMalignant nodules
2016
Event-by-Event Continuous Respiratory Motion Correction for Dynamic PET Imaging
Yu Y, Chan C, Ma T, Liu Y, Gallezot JD, Naganawa M, Kelada OJ, Germino M, Sinusas AJ, Carson RE, Liu C. Event-by-Event Continuous Respiratory Motion Correction for Dynamic PET Imaging. Journal Of Nuclear Medicine 2016, 57: 1084-1090. PMID: 26912437, DOI: 10.2967/jnumed.115.167676.Peer-Reviewed Original Research