2024
Design and development of the DE-SPECT system: a clinical SPECT system for broadband multi-isotope imaging of peripheral vascular disease
Zannoni E, Sankar P, Jin Y, Liu C, Sinusas A, Metzler S, Meng L. Design and development of the DE-SPECT system: a clinical SPECT system for broadband multi-isotope imaging of peripheral vascular disease. Physics In Medicine And Biology 2024, 69: 125016. PMID: 38815617, PMCID: PMC11167601, DOI: 10.1088/1361-6560/ad5266.Peer-Reviewed Original ResearchConceptsCadmium zinc tellurideSPECT systemField of viewExcellent spectroscopic performanceExcellent energy resolutionBroad energy rangeIntrinsic spatial resolutionSpatial resolutionClinical SPECT systemEnergy resolutionPeripheral vascular diseaseEnergy rangeMm FWHMSpectroscopic performanceZinc tellurideWide-FOVCollimatorPreliminary experimental dataPartial ringDetection systemImaging capabilitiesImaging performanceExtremity imagingVascular diseaseScout images
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 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 dataMultimodality Imaging of Aortic Valve Calcification and Function in a Murine Model of Calcific Aortic Valve Disease and Bicuspid Aortic Valve
Ahmad A, Ghim M, Toczek J, Neishabouri A, Ojha D, Zhang Z, Gona K, Raza M, Jung J, Kukreja G, Zhang J, Guerrera N, Liu C, Sadeghi M. Multimodality Imaging of Aortic Valve Calcification and Function in a Murine Model of Calcific Aortic Valve Disease and Bicuspid Aortic Valve. Journal Of Nuclear Medicine 2023, 64: 1487-1494. PMID: 37321825, PMCID: PMC10478817, DOI: 10.2967/jnumed.123.265516.Peer-Reviewed Original ResearchConceptsF-NaF PET/CTCalcific aortic valve diseaseBicuspid aortic valvePET/CTAortic valve calcificationAortic valve diseaseAortic valveAortic stenosisValve calcificationValvular calcificationValve diseaseF-NaFSubset of miceTricuspid aortic valveDevelopment of calcificationSignificant correlationUnderwent echocardiographyMedical therapyHigh prevalencePreclinical modelsMurine modelTherapeutic interventionsAge groupsAutoradiography dataMultimodality imagingDuSFE: Dual-Channel Squeeze-Fusion-Excitation co-attention for cross-modality registration of cardiac SPECT and CT
Chen X, Zhou B, Xie H, Guo X, Zhang J, Duncan J, Miller E, Sinusas A, Onofrey J, Liu C. DuSFE: Dual-Channel Squeeze-Fusion-Excitation co-attention for cross-modality registration of cardiac SPECT and CT. Medical Image Analysis 2023, 88: 102840. PMID: 37216735, PMCID: PMC10524650, DOI: 10.1016/j.media.2023.102840.Peer-Reviewed Original ResearchConceptsCross-modality registrationConvolutional layersCo-attention mechanismMultiple convolutional layersCo-attention moduleDifferent convolutional layersMedical image registrationInput data streamDeep learning strategiesLow registration errorIntensity-based registration methodCardiac SPECTΜ-mapsDeep learningFeature fusionData streamsInput imageSource codeFeature mapsNeural networkImage registrationSpatial featuresRegistration performanceRegistration methodInput informationJoint 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 Research
2022
Artificial Intelligence in Nuclear Medicine: Opportunities, Challenges, and Responsibilities Toward a Trustworthy Ecosystem
Saboury B, Bradshaw T, Boellaard R, Buvat I, Dutta J, Hatt M, Jha A, Li Q, Liu C, McMeekin H, Morris M, Scott P, Siegel E, Sunderland J, Pandit-Taskar N, Wahl R, Zuehlsdorff S, Rahmim A. Artificial Intelligence in Nuclear Medicine: Opportunities, Challenges, and Responsibilities Toward a Trustworthy Ecosystem. Journal Of Nuclear Medicine 2022, 64: jnumed.121.263703. PMID: 36522184, PMCID: PMC9902852, DOI: 10.2967/jnumed.121.263703.Peer-Reviewed Original ResearchDuDoUFNet: Dual-Domain Under-to-Fully-Complete Progressive Restoration Network for Simultaneous Metal Artifact Reduction and Low-Dose CT Reconstruction
Zhou B, Chen X, Xie H, Zhou S, Duncan JS, Liu C. DuDoUFNet: Dual-Domain Under-to-Fully-Complete Progressive Restoration Network for Simultaneous Metal Artifact Reduction and Low-Dose CT Reconstruction. IEEE Transactions On Medical Imaging 2022, 41: 3587-3599. PMID: 35816532, PMCID: PMC9812027, DOI: 10.1109/tmi.2022.3189759.Peer-Reviewed Original ResearchQuantification of intramyocardial blood volume using 99mTc-RBC SPECT/CT: a pilot human study
Yousefi H, Shi L, Soufer A, Tsatkin V, Bruni W, Avendano R, Greco K, McMahon D, Thorn S, Miller E, Sinusas A, Liu C. Quantification of intramyocardial blood volume using 99mTc-RBC SPECT/CT: a pilot human study. Journal Of Nuclear Cardiology 2022, 30: 292-297. PMID: 36319815, DOI: 10.1007/s12350-022-03123-0.Peer-Reviewed Original ResearchDuDoSS: Deep‐learning‐based dual‐domain sinogram synthesis from sparsely sampled projections of cardiac SPECT
Chen X, Zhou B, Xie H, Miao T, Liu H, Holler W, Lin M, Miller EJ, Carson RE, Sinusas AJ, Liu C. DuDoSS: Deep‐learning‐based dual‐domain sinogram synthesis from sparsely sampled projections of cardiac SPECT. Medical Physics 2022, 50: 89-103. PMID: 36048541, PMCID: PMC9868054, DOI: 10.1002/mp.15958.Peer-Reviewed Original ResearchConceptsLow reconstruction accuracySynthetic projectionsAbsolute percent errorImage predictionSPECT image reconstructionImage domainSinogram synthesisGround truthReconstruction accuracyImage reconstructionSinogram domainProjection angleData acquisitionMean square errorFast data acquisitionImagesReconstruction artifactsSPECT imagesSquare errorVirtual 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 imagesImagesUnsupervised 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 ResearchConceptsConvolutional 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 stepComplexityScannerIncreasing 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 subjectsLearningCross-vender, cross-tracer, and cross-protocol deep transfer learning for attenuation map generation of cardiac SPECT
Chen X, Pretorius P, Zhou B, Liu H, Johnson K, Liu YH, King MA, Liu C. Cross-vender, cross-tracer, and cross-protocol deep transfer learning for attenuation map generation of cardiac SPECT. Journal Of Nuclear Cardiology 2022, 29: 3379-3391. PMID: 35474443, PMCID: PMC11407548, DOI: 10.1007/s12350-022-02978-7.Peer-Reviewed Original ResearchDirect and indirect strategies of deep-learning-based attenuation correction for general purpose and dedicated cardiac SPECT
Chen X, Zhou B, Xie H, Shi L, Liu H, Holler W, Lin M, Liu YH, Miller EJ, Sinusas AJ, Liu C. Direct and indirect strategies of deep-learning-based attenuation correction for general purpose and dedicated cardiac SPECT. European Journal Of Nuclear Medicine And Molecular Imaging 2022, 49: 3046-3060. PMID: 35169887, PMCID: PMC9253078, DOI: 10.1007/s00259-022-05718-8.Peer-Reviewed Original ResearchPET 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 ResearchConceptsConvolutional 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 performanceDuDoDR-Net: Dual-domain data consistent recurrent network for simultaneous sparse view and metal artifact reduction in computed tomography
Zhou B, Chen X, Zhou SK, Duncan JS, Liu C. DuDoDR-Net: Dual-domain data consistent recurrent network for simultaneous sparse view and metal artifact reduction in computed tomography. Medical Image Analysis 2021, 75: 102289. PMID: 34758443, PMCID: PMC8678361, DOI: 10.1016/j.media.2021.102289.Peer-Reviewed Original ResearchConceptsRecurrent networksSevere streak artifactsRecurrent frameworkArtifact reductionSparse viewsImage domainReconstruction qualityCT metal artifact reductionX-ray projectionsMetal artifact reductionArtifact-free imagesMedical diagnosisPrevious methodsProjection dataConsistent layerExperimental resultsMDPET: 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 qualityImagesEstimationPost-reconstruction attenuation correction for SPECT myocardium perfusion imaging facilitated by deep learning-based attenuation map generation
Liu H, Wu J, Shi L, Liu Y, Miller E, Sinusas A, Liu YH, Liu C. Post-reconstruction attenuation correction for SPECT myocardium perfusion imaging facilitated by deep learning-based attenuation map generation. Journal Of Nuclear Cardiology 2021, 29: 2881-2892. PMID: 34671940, DOI: 10.1007/s12350-021-02817-1.Peer-Reviewed Original Research