Generation 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 ResearchDeep 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 inefficiencyPOUR-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 availabilityRadiationComparative 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 tomography