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 accuracyImagesFramePatlak-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 ResearchPre-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 ResearchImage denoisingImage denoising performanceDeep learning techniquesNoise-levelDenoising performanceDenoising resultsNeural networkLearning techniquesSPECT imagesLow count levelsSPECT scansDenoisingSampling stepIterative reconstructionNoise amplitudeImagesInjected dosePatient studiesDiffusion modelRadiation exposureCardiology studiesSPECTNetworkStochastic natureMLEMAnatomically 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 exposureDuDoCFNet: Dual-Domain Coarse-to-Fine Progressive Network for Simultaneous Denoising, Limited-View Reconstruction, and Attenuation Correction of Cardiac SPECT
Chen X, Zhou B, Guo X, Xie H, Liu Q, Duncan J, Sinusas A, Liu C. DuDoCFNet: Dual-Domain Coarse-to-Fine Progressive Network for Simultaneous Denoising, Limited-View Reconstruction, and Attenuation Correction of Cardiac SPECT. IEEE Transactions On Medical Imaging 2024, 43: 3110-3125. PMID: 38578853, PMCID: PMC11539864, DOI: 10.1109/tmi.2024.3385650.Peer-Reviewed Original ResearchMulti-task learning methodCross-domainLimited-viewLearning methodsCoarse-to-fine estimationProgressive networkDual domainCross-modal feature fusionDual-domain networkProgressive learning strategyCross-modal informationSimultaneous denoisingFeature fusionSingle-photon emission computed tomographyImage domainCardiac single-photon emission computed tomographyReconstruction accuracyDenoisingHardware expenseFusion mechanismAccelerated scansImage noiseM-mapSuperior accuracyNetwork
2023
Dual-Domain Iterative Network with Adaptive Data Consistency for Joint Denoising and Few-Angle Reconstruction of Low-Dose Cardiac SPECT
Chen X, Zhou B, Xie H, Guo X, Liu Q, Sinusas A, Liu C. Dual-Domain Iterative Network with Adaptive Data Consistency for Joint Denoising and Few-Angle Reconstruction of Low-Dose Cardiac SPECT. Lecture Notes In Computer Science 2023, 14307: 49-59. DOI: 10.1007/978-3-031-44917-8_5.Peer-Reviewed Original ResearchIterative networkAuxiliary modulesJoint denoisingLow reconstruction accuracySource codeData consistencyNetwork performanceAblation studiesReconstruction accuracyCardiac SPECTConsistency moduleHardware expensePrediction accuracyAngle reconstructionNetworkDenoisingImage noiseAngle projectionsModuleADC moduleAccuracyReconstructionImagesMPI dataCode
2022
An Adaptive Patch Sampling Scheme for Deep Learning Based PET Image Denoising
Wu J, Tan H, Liu H, Liu C, Onofrey J. An Adaptive Patch Sampling Scheme for Deep Learning Based PET Image Denoising. 2022, 00: 1-3. DOI: 10.1109/nss/mic44845.2022.10399313.Peer-Reviewed Original ResearchOver-smoothing effectSignal-to-noise ratioU-NetImage denoisingDeep learning-based approachPET image denoisingL1 loss functionPatch-based strategyLearning-based approachSampling schemeMean square errorHigh signal-to-noise ratioDenoising performanceLow-dose PET imagesNetwork trainingWeight mapData augmentationDenoisingLoss functionNetworkImage noiseSquare errorPatch samplesSampling rateSchemeFederated Transfer Learning for Low-Dose PET Denoising: A Pilot Study With Simulated Heterogeneous Data
Zhou B, Miao T, Mirian N, Chen X, Xie H, Feng Z, Guo X, Li X, Zhou S, Duncan J, Liu C. Federated Transfer Learning for Low-Dose PET Denoising: A Pilot Study With Simulated Heterogeneous Data. IEEE Transactions On Radiation And Plasma Medical Sciences 2022, 7: 284-295. PMID: 37789946, PMCID: PMC10544830, DOI: 10.1109/trpms.2022.3194408.Peer-Reviewed Original ResearchLow-dose PETMedical data privacy regulationsFederated learning algorithmLarge domain shiftTransfer learning frameworkData privacy regulationsHigh-quality reconstructionFederated transferData privacyHeterogeneous dataDomain shiftLearning frameworkLearning algorithmPrivacy regulationsData distributionCollaborative trainingLow-dose dataPET reconstructionPrevious methodsFL methodEfficient wayLocal dataSuperior performanceExperimental resultsDenoising
2021
Artificial 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 ResearchConceptsArtificial intelligence modelsImage enhancementIntelligence modelsArtificial intelligenceNetwork architectureEvaluation metricsLarge-scale adoptionData typesImage denoisingLoss functionPET imagesLow spatial resolutionHigh noiseResolution enhancementImagesIntelligenceDeblurringArchitectureDenoisingNoise reductionMetricsPopularityRecent effortsFuture directionsAccuracy