2025
Increasing angular sampling for dedicated cardiac SPECT scanner: Implementation with Deep Learning and Validation with human data
Xie H, Alashi A, Thorn S, Chen X, Zhou B, Sinusas A, Liu C. Increasing angular sampling for dedicated cardiac SPECT scanner: Implementation with Deep Learning and Validation with human data. Journal Of Nuclear Cardiology 2025, 102168. PMID: 39986346, DOI: 10.1016/j.nuclcard.2025.102168.Peer-Reviewed Original ResearchLower Extremity Flow Quantification Using Dynamic 82Rb PET: a Preclinical Investigation
Guo L, Thorn S, de Rubio Cruz P, Liu Z, Gallezot J, Liu Q, Moulton E, Carson R, Sinusas A, Liu C. Lower Extremity Flow Quantification Using Dynamic 82Rb PET: a Preclinical Investigation. IEEE Transactions On Radiation And Plasma Medical Sciences 2025, PP: 1-1. DOI: 10.1109/trpms.2025.3542729.Peer-Reviewed Original Research
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, PMCID: PMC11647511, 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 accuracyImagesFrameDose-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 natureMLEMDuDoCFNet: 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 accuracyNetworkTAI-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
Self-supervised Noise-aware Network for Dynamic Rubidium-82 Cardiac PET Image Denoising
Xie H, Guo L, Guo X, Liu Q, Zhou B, Chen X, Wang G, Sinusas A, Liu C. Self-supervised Noise-aware Network for Dynamic Rubidium-82 Cardiac PET Image Denoising. 2023, 00: 1-1. DOI: 10.1109/nssmicrtsd49126.2023.10338703.Peer-Reviewed Original ResearchThe DE-SPECT System: A Hyperspectral SPECT System for in Vivo 3-D Gamma-Ray Spectrometry of Molecular Theranostics
Zannoni E, Jin Y, Sankar P, Gura D, Wu R, Zhang F, Streicher M, Yang H, He Z, Metzler S, Liu C, Sinusas A, Meng L. The DE-SPECT System: A Hyperspectral SPECT System for in Vivo 3-D Gamma-Ray Spectrometry of Molecular Theranostics. 2023, 00: 1-1. DOI: 10.1109/nssmicrtsd49126.2023.10337964.Peer-Reviewed Original ResearchCross-Domain Iterative Network for Simultaneous Denoising, Limited-Angle Reconstruction, and Attenuation Correction of Cardiac SPECT
Chen X, Zhou B, Xie H, Guo X, Liu Q, Sinusas A, Liu C. Cross-Domain Iterative Network for Simultaneous Denoising, Limited-Angle Reconstruction, and Attenuation Correction of Cardiac SPECT. Lecture Notes In Computer Science 2023, 14348: 12-22. DOI: 10.1007/978-3-031-45673-2_2.Peer-Reviewed Original ResearchSimultaneous denoisingAttenuation correctionCardiac single-photon emission computed tomographySingle-photon emission computed tomographyLimited-angleCross-domainIterative networkLow reconstruction accuracyDeep learning methodsEnd-to-endReduce hardware costIncreased image noiseAblation studiesReconstruction performanceInput featuresSingle-photonHardware costLearning methodsAttenuation mapLow-doseReconstruction accuracyLimited-angle reconstructionRadiation exposureExtra radiation exposureMultiple iterationsDual-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 dataCodeTAI-GAN: Temporally and Anatomically Informed GAN for Early-to-Late Frame Conversion in Dynamic Cardiac PET Motion Correction
Guo X, Shi L, Chen X, Zhou B, Liu Q, Xie H, Liu Y, Palyo R, Miller E, Sinusas A, Spottiswoode B, Liu C, Dvornek N. TAI-GAN: Temporally and Anatomically Informed GAN for Early-to-Late Frame Conversion in Dynamic Cardiac PET Motion Correction. Lecture Notes In Computer Science 2023, 14288: 64-74. PMID: 38464964, PMCID: PMC10923183, DOI: 10.1007/978-3-031-44689-4_7.Peer-Reviewed Original ResearchTransformer-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 amountSoftware99mTc/123I Dual-Radionuclide Correction for Self-Scatter, Down-Scatter, and Tailing Effect for a CZT SPECT With Varying Tracer Distributions
Velo A, Fan P, Xie H, Chen X, Boutagy N, Feher A, Sinusas A, Ljungberg M, Liu C. 99mTc/123I Dual-Radionuclide Correction for Self-Scatter, Down-Scatter, and Tailing Effect for a CZT SPECT With Varying Tracer Distributions. IEEE Transactions On Radiation And Plasma Medical Sciences 2023, 7: 839-850. PMID: 38745858, PMCID: PMC11090119, DOI: 10.1109/trpms.2023.3297443.Peer-Reviewed Original ResearchDuSFE: 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 informationSegmentation-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
The Impact of Additional Sampling on Hot-Spot Contrast for DE-SPECT
Metzler S, Zannoni E, Sankar P, Liu C, Sinusas A, Meng L. The Impact of Additional Sampling on Hot-Spot Contrast for DE-SPECT. 2022, 00: 1-4. DOI: 10.1109/nss/mic44845.2022.10399030.Peer-Reviewed Original ResearchHot spot phantomCollimator configurationCZT moduleAxial shiftDetector positionSensitivity dropsSample positionHot spotsNominal positionPhantomPeripheral arterial diseaseOptimal contrastCollimatorActuator switchingCZTArtery diseaseNoise levelDetectorConfigurationSmall axial shiftsShiftEvaluate contrastOptimal tradeoffQuantification 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 ResearchDual-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 emission
2017
Fully 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
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