2024
Dose-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 natureMLEMA Review on Low-Dose Emission Tomography Post-Reconstruction Denoising With Neural Network Approaches
Bousse A, Kandarpa V, Shi K, Gong K, Lee J, Liu C, Visvikis D. A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising With Neural Network Approaches. IEEE Transactions On Radiation And Plasma Medical Sciences 2024, 8: 333-347. PMID: 39429805, PMCID: PMC11486494, DOI: 10.1109/trpms.2023.3349194.Peer-Reviewed Original Research
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 availabilityVendorsMCP-Net: Introducing Patlak Loss Optimization to Whole-Body Dynamic PET Inter-Frame Motion Correction
Guo X, Zhou B, Chen X, Chen M, Liu C, Dvornek N. MCP-Net: Introducing Patlak Loss Optimization to Whole-Body Dynamic PET Inter-Frame Motion Correction. IEEE Transactions On Medical Imaging 2023, 42: 3512-3523. PMID: 37368811, PMCID: PMC10751388, DOI: 10.1109/tmi.2023.3290003.Peer-Reviewed Original ResearchMotion estimation blockDeep learning benchmarksGood generalization capabilityMotion correctionMotion correction frameworkMotion prediction errorGeneralization capabilityNetwork performanceNeural networkMotion correction techniqueLearning benchmarksRegistration problemLoss functionEstimation blockLoss optimizationPenalty componentDynamic frameFitting errorSpatial alignmentParametric imagesSpatial misalignmentDynamic positron emission tomographySubject motionPrediction errorCorrection frameworkDuSFE: 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 information
2022
Dual-Branch Squeeze-Fusion-Excitation Module for Cross-Modality Registration of Cardiac SPECT and CT
Chen X, Zhou B, Xie H, Guo X, Zhang J, Sinusas A, Onofrey J, Liu C. Dual-Branch Squeeze-Fusion-Excitation Module for Cross-Modality Registration of Cardiac SPECT and CT. Lecture Notes In Computer Science 2022, 13436: 46-55. DOI: 10.1007/978-3-031-16446-0_5.Peer-Reviewed Original ResearchConvolutional neural networkCross-modality registrationFeature fusionPrevious convolutional neural networkEarly feature fusionCross-modality informationMultiple convolutional layersMedical image registrationLow registration errorCardiac SPECTConvolutional layersCNN moduleImage featuresLate fusionSource codeNeural networkExcitation moduleInput modalitiesImage registrationSpatial featuresMultiple modalitiesRegistration errorPrevious methodsRigid registrationNetworkDeep-Learning-Based Few-Angle Cardiac SPECT Reconstruction Using Transformer
Xie H, Thorn S, Liu Y, Lee S, Liu Z, Wang G, Sinusas A, Liu C. Deep-Learning-Based Few-Angle Cardiac SPECT Reconstruction Using Transformer. IEEE Transactions On Radiation And Plasma Medical Sciences 2022, 7: 33-40. PMID: 37397179, PMCID: PMC10312390, DOI: 10.1109/trpms.2022.3187595.Peer-Reviewed Original ResearchConvolutional neural networkLimitations of CNNMedical imaging tasksDeep U-NetImage reconstruction taskCardiac SPECT imagesComputer visionVision TransformerConvolutional kernelsTransformer networkAttention blockInput imageU-NetNeural networkMemory burdenImage sizeInductive biasInformative featuresImage volumesImaging tasksTesting dataNetworkWhole 3D volumeNetwork structureCardiac single photon emissionUnsupervised 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 subjectsLearning
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 performanceInvestigation of Direct and Indirect Approaches of Deep-Learning-Based Attenuation Correction for General Purpose and Dedicated Cardiac SPECT Scanners
Chen X, Zhou B, Xie H, Shi L, Liu H, Liu C. Investigation of Direct and Indirect Approaches of Deep-Learning-Based Attenuation Correction for General Purpose and Dedicated Cardiac SPECT Scanners. 2021, 00: 1-2. DOI: 10.1109/nss/mic44867.2021.9875517.Peer-Reviewed Original ResearchNovel neural networkConventional U-NetMulti-channel inputDeep learningU-NetAttenuation mapNeural networkMap generationCardiac SPECTGeneral purposeSuperior performanceImagesDatasetIterative reconstructionAttenuation-corrected imagesCT transmission scanningAveraged errorNovel methodParallel-hole SPECTAttenuation correctionSPECT scannerMapsEmission imagesDirect approachScanner
2020
Deep learning-based attenuation map generation for myocardial perfusion SPECT
Shi L, Onofrey JA, Liu H, Liu YH, Liu C. Deep learning-based attenuation map generation for myocardial perfusion SPECT. European Journal Of Nuclear Medicine And Molecular Imaging 2020, 47: 2383-2395. PMID: 32219492, DOI: 10.1007/s00259-020-04746-6.Peer-Reviewed Original Research
2019
Deep Learning based Respiratory Pattern Classification and Applications in PET/CT Motion Correction
Guo Y, Dvornek N, Lu Y, Tsai Y, Hamill J, Casey M, Liu C. Deep Learning based Respiratory Pattern Classification and Applications in PET/CT Motion Correction. 2019, 00: 1-5. DOI: 10.1109/nss/mic42101.2019.9059783.Peer-Reviewed Original ResearchDeep learningNeural networkMotion correction methodDeep neural networksDeep learning modelsHybrid neural networkConvolutional layersHigh prediction accuracyRecurrent layersGeneralization capabilityData preprocessingLearning modelPattern classificationRespiratory motionAnzai systemLoss functionLinear classifierPrediction accuracyIntra-gate motionRPM systemMotion correctionTumor detectionNetworkIrregular breathersCT images