2024
Fast Energy-Based Scatter Correction for 3D TOF-PET on NeuroExplorer
Guo L, Fontaine K, Gravel P, Mulnix T, Zhang J, Liu C, Carson R. Fast Energy-Based Scatter Correction for 3D TOF-PET on NeuroExplorer. 2024, 00: 1-2. DOI: 10.1109/nss/mic/rtsd57108.2024.10657901.Peer-Reviewed Original ResearchSingle scatter simulationScatter estimationEnergy spectrumTOF binsField of viewAxial field-of-viewHigh-energy scatteringLong axial field-of-viewLow-activity regionsList-mode dataEnergy informationTOF-PETContrast phantomUniform phantomScattering phantomCounting statisticsScatter correctionOSEM reconstructionMultiple-scatteringScatteringScattering simulationsPhantomEvent distributionImproved contrastMonte-CarloGeneration of Synthetic brain PET images of synaptic density from MRI and FDG-PET using a Multi-stage U-Net
Zheng X, Worhunsky P, Liu Q, Zhou B, Chen X, Guo X, Xie H, Sun H, Zhang J, Toyonaga T, Mecca A, O’Dell R, van Dyck C, Carson R, Radhakrishnan R, Liu C. Generation of Synthetic brain PET images of synaptic density from MRI and FDG-PET using a Multi-stage U-Net. 2024, 00: 1-2. DOI: 10.1109/nss/mic/rtsd57108.2024.10655600.Peer-Reviewed Original ResearchPatlak-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 natureMLEMExperimental Evaluation of DE-SPECT: A Hyperspectral SPECT System for Region-Selective 3-D Gamma-Ray Spectroscopy of Molecular Theragnostics
Jin Y, Zannoni E, Sankar P, Gura D, Wu R, Zhu S, Zhang F, Streicher M, Yang H, He Z, Metzler S, Liu C, Sinusas A, Meng L. Experimental Evaluation of DE-SPECT: A Hyperspectral SPECT System for Region-Selective 3-D Gamma-Ray Spectroscopy of Molecular Theragnostics. 2024, 00: 1-2. DOI: 10.1109/nss/mic/rtsd57108.2024.10657212.Peer-Reviewed Original ResearchCadmium zinc tellurideCadmium zinc telluride detectorsGamma-ray spectroscopyHigh-sensitivity imagingUniform phantomResolution phantomSPECT systemZinc telluridePhantom studyImage qualityPhantomImaging capabilitiesImaging performanceAperture systemFOVMultiple radiotracersHigh-resolutionCollimatorDual-field-of-viewPeripheral vascular diseaseDetectorState-of-the-art technologiesTellurideClinical systemsDIANA - Detectability Investgations using Artificial Nodal Additions
Bayerlein R, Xia M, Xie H, Spencer B, Ouyang J, Fakhri G, Nardo L, Liu C, Badawi R. DIANA - Detectability Investgations using Artificial Nodal Additions. 2024, 00: 1-1. DOI: 10.1109/nss/mic/rtsd57108.2024.10657528.Peer-Reviewed Original ResearchContrast recovery coefficientContrast-to-noise ratioLesion-to-background ratioList-mode dataTotal-body PET/CT scannerPositron emission tomographyContrast recoveryOSEM algorithmPatient motionPET/CT scannerArtificial lesionsImage quality metricsLesion detectionQuantitative accuracyPositron emission tomography scanRecovery coefficientCount densityImage contrastBody mass indexImage noisePositron emission tomography imaging techniquesFrame lengthImage smoothingActivity concentrationsAccuracy of lesion detectionAnatomically 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 exposurePOUR-Net: A Population-Prior-Aided Over-Under-Representation Network for Low-Count PET Attenuation Map Generation
Zhou B, Hou J, Chen T, Zhou Y, Chen X, Xie H, Liu Q, Guo X, Xia M, Tsai Y, Panin V, Toyonaga T, Duncan J, Liu C. POUR-Net: A Population-Prior-Aided Over-Under-Representation Network for Low-Count PET Attenuation Map Generation. 2024, 00: 1-1. DOI: 10.1109/nss/mic/rtsd57108.2024.10658051.Peer-Reviewed Original ResearchPET attenuation correctionLow-dose PETAttenuation correctionU-mapAttenuation mapElevated radiation doseRadiation doseEfficient feature extractionRadiation exposurePET imagingFinely detailed featuresBaseline methodsMitigate radiation exposureFeature extractionCorrectionMap generationGeneration machinesAn Investigation on Cross-Tracer Generalizability of Deep Learning-based PET Attenuation Correction
Hou J, Chen T, Zhou Y, Chen X, Xie H, Liu Q, Xia M, Panin V, Liu C, Zhou B, Toyonaga T. An Investigation on Cross-Tracer Generalizability of Deep Learning-based PET Attenuation Correction. 2024, 00: 1-1. DOI: 10.1109/nss/mic/rtsd57108.2024.10657095.Peer-Reviewed Original ResearchAttenuation correctionPET attenuation correctionQuantitative PET imagingAttenuation mapDL modelsDeep learning (DL)-based methodsTumor quantificationDL model trainingRadiation doseImmediate future workCompetitive performancePET imagingModel trainingPET signalCorrectionAnalysis of PETFuture workPreliminary resultsData availabilityRadiation2.5D Multi-view Averaging Diffusion Model for 3D Medical Image Translation: Application to Low-count PET Reconstruction with CT-less AC
Chen T, Hou J, Xie H, Chen X, Zhou Y, Xia M, Duncan J, Liu C, Zhou B. 2.5D Multi-view Averaging Diffusion Model for 3D Medical Image Translation: Application to Low-count PET Reconstruction with CT-less AC. 2024, 00: 1-2. DOI: 10.1109/nss/mic/rtsd57108.2024.10658551.Peer-Reviewed Original ResearchLow-dose PETStandard-dose PETImage-to-image translationPositron emission tomographyAttenuation correctionPET reconstructionOverall radiation doseCT acquisitionState-of-the-art deep learning methodsRadiation hazardRadiation doseCNN-based methodsState-of-the-artMedical image translationPatient studiesDiffusion modelDeep learning methodsHigh computation costHuman patient studiesClinical imaging toolImage translationBaseline methodsMulti-viewCNN-basedMultiple viewsDeep Learning-based Dynamic PET Intra-frame Motion Correction and Integration with Inter-frame Motion Estimation
Guo X, Tsai Y, Liu Q, Guo L, Valadez G, Dvornek N, Liu C. Deep Learning-based Dynamic PET Intra-frame Motion Correction and Integration with Inter-frame Motion Estimation. 2024, 00: 1-1. DOI: 10.1109/nss/mic/rtsd57108.2024.10657268.Peer-Reviewed Original ResearchIntra-frame motionMotion correctionGated imagesLearning-based registration approachesDeep learning-based worksInter-frame motion estimationConventional image registrationLearning-based worksImage registrationMotion estimation processMotion estimation frameworkInter-frame registrationRespiratory gatingImprove image sharpnessInter-frameInference timeMotion estimationReconstructed framesDynamic PET datasetsGeneralization abilityPET imagingConventional registrationDynamic PET imagesImprove image qualityComputational inefficiencyPDM: A Plug-and-Play Perturbed Multi-path Diffusion Module for Simultaneous Medical Image Segmentation Improvement and Uncertainty Estimation
Zhou B, Chen T, Hou J, Zhou Y, Xie H, Liu C, Duncan J. PDM: A Plug-and-Play Perturbed Multi-path Diffusion Module for Simultaneous Medical Image Segmentation Improvement and Uncertainty Estimation. Lecture Notes In Computer Science 2024, 15241: 259-268. DOI: 10.1007/978-3-031-73284-3_26.Peer-Reviewed Original ResearchEfficient plug-and-play moduleDenoising diffusion probabilistic modelPlug-and-play moduleDiffusion probabilistic modelState-of-the-artMedical image analysisDeep modelsSegmentation datasetUncertainty estimationSegmentation resultsImproved segmentationSegmentation modelMorphological operationsBinary segmentationSegmentation improvementProbabilistic modelUncertainty mapsDiffusion moduleReverse pathPerturbation segmentsSegmental inputsImage analysisSegmentsInputModulationDuDoCFNet: 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, 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 accuracyNetworkCascaded multi-path shortcut diffusion model for medical image translation
Zhou Y, Chen T, Hou J, Xie H, Dvornek N, Zhou S, Wilson D, Duncan J, Liu C, Zhou B. Cascaded multi-path shortcut diffusion model for medical image translation. Medical Image Analysis 2024, 98: 103300. PMID: 39226710, DOI: 10.1016/j.media.2024.103300.Peer-Reviewed Original ResearchGenerative adversarial networkMedical image translationImage translationState-of-the-art methodsImage-to-image translationMedical image datasetsImage translation tasksImage-to-imageState-of-the-artMedical image processingHigh-quality translationsUncertainty estimationCascaded pipelineAdversarial networkImage datasetsSub-tasksTranslation qualityTranslation performanceTranslation tasksImage processingTranslation resultsDM methodPrior imageRobust performanceExperimental resultsMolecular Imaging of Fibroblast Activation in Lower Extremity Skeletal Muscles Using a 99mTc-Labeled Inhibitor of Fibroblast Activation Protein in Porcine Model of Peripheral Artery Disease
Jang S, Thorn S, Zohora F, Porcaro O, Burns R, Vermillion B, Callegari S, Guerrera N, DePino A, Liu C, Luna-Gutierrez M, Ferro-Flores G, Sinusas A. Molecular Imaging of Fibroblast Activation in Lower Extremity Skeletal Muscles Using a 99mTc-Labeled Inhibitor of Fibroblast Activation Protein in Porcine Model of Peripheral Artery Disease. Journal Of Nuclear Cardiology 2024, 38: 101997. DOI: 10.1016/j.nuclcard.2024.101997.Peer-Reviewed Original ResearchHybrid SPECT/CT Imaging of Fibroblast Activation Protein Post Myocardial Infarction on Novel 360° CZT Scanner
Thorn S, Burns R, Zohora F, Jang S, Porcaro O, Guerrera N, DePino A, Vermillion B, Liu C, Luna-Gutiérrez M, Ferro-Flores G, Sinusas A. Hybrid SPECT/CT Imaging of Fibroblast Activation Protein Post Myocardial Infarction on Novel 360° CZT Scanner. Journal Of Nuclear Cardiology 2024, 38: 101972. DOI: 10.1016/j.nuclcard.2024.101972.Peer-Reviewed Original ResearchComparative study of functional and structural muscle changes in peripheral artery disease: rubidium-82 positron emission tomography and histological correlation
Alashi A, Vermillion B, Callegari S, Burns R, Guo L, Moulton E, Guerrera N, Depino A, Papademetris X, Zeiss C, Thorn S, Liu C, Sinusas A. Comparative study of functional and structural muscle changes in peripheral artery disease: rubidium-82 positron emission tomography and histological correlation. European Heart Journal - Cardiovascular Imaging 2024, 25: jeae142.087. DOI: 10.1093/ehjci/jeae142.087.Peer-Reviewed Original ResearchPeripheral arterial diseaseStandardized uptake valueHindlimb ischemia modelReactive hyperemiaSkeletal muscle perfusionPerfusion reserveCapillary densityPET imagingArtery diseaseNon-ischemicRubidium-82 positron emission tomographyType 2 muscle fibersRelevant pre-clinical modelIndicative of fibrosisManagement of peripheral arterial diseaseCapillary to muscle fiber ratioClinically relevant pre-clinical modelPre-clinical modelsMuscle perfusionFast myosinWeeks post-ligationRb-82 uptakeEvaluate treatment strategiesRabbit hindlimb ischemia modelPositron emission tomographyDesign 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 imagesTAI-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 diseasePopulation-based deep image prior for dynamic PET denoising: A data-driven approach to improve parametric quantification
Liu Q, Tsai Y, Gallezot J, Guo X, Chen M, Pucar D, Young C, Panin V, Casey M, Miao T, Xie H, Chen X, Zhou B, Carson R, Liu C. Population-based deep image prior for dynamic PET denoising: A data-driven approach to improve parametric quantification. Medical Image Analysis 2024, 95: 103180. PMID: 38657423, DOI: 10.1016/j.media.2024.103180.Peer-Reviewed Original ResearchDeep Image PriorImage priorsSupervised modelsNoise reductionIntrinsic image featuresDeep learning techniquesU-Net architectureNovel denoising techniqueQuality of parametric imagesDenoising modelDenoising techniquesStatic datasetsBaseline techniquesEffective noise reductionData-driven approachLearning techniquesDynamic datasetsOptimization processPrior informationStatic imagesHigh noise levelsImage featuresDatasetPrior imagePET datasets