2024
POUR-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 machines
2023
Cross-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 iterationsJoint motion estimation and penalized image reconstruction algorithm with anatomical priors for gated TOF-PET/CT
Tsai Y, Liu C. Joint motion estimation and penalized image reconstruction algorithm with anatomical priors for gated TOF-PET/CT. Physics In Medicine And Biology 2023, 68: 025020. PMID: 36549009, DOI: 10.1088/1361-6560/acae19.Peer-Reviewed Original Research
2022
Deep-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 stepComplexityScanner
2021
Investigation 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
A Novel Loss Function Incorporating Imaging Acquisition Physics for PET Attenuation Map Generation Using Deep Learning
Shi L, Onofrey J, Revilla E, Toyonaga T, Menard D, Ankrah J, Carson R, Liu C, Lu Y. A Novel Loss Function Incorporating Imaging Acquisition Physics for PET Attenuation Map Generation Using Deep Learning. Lecture Notes In Computer Science 2019, 11767: 723-731. DOI: 10.1007/978-3-030-32251-9_79.Peer-Reviewed Original ResearchAttenuation mapAttenuation correctionCT-based attenuation mapAnnihilation eventsPET attenuation correctionLine integral projectionsPET raw dataInaccurate attenuation correctionCT attenuation mapsPhysicsMaximum likelihood reconstructionAC errorsMotion resultsLikelihood reconstructionLoss functionLarge biasΜ-CT
2011
Respiratory motion correction for quantitative PET/CT using all detected events with internal—external motion correlation
Liu C, Alessio AM, Kinahan PE. Respiratory motion correction for quantitative PET/CT using all detected events with internal—external motion correlation. Medical Physics 2011, 38: 2715-2723. PMID: 21776808, PMCID: PMC3107832, DOI: 10.1118/1.3582692.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsArtifactsHumansImage EnhancementImage Interpretation, Computer-AssistedMotionMovementNeoplasmsPositron-Emission TomographyReproducibility of ResultsRespiratory MechanicsRespiratory-Gated Imaging TechniquesSensitivity and SpecificityStatistics as TopicSubtraction TechniqueTomography, X-Ray ComputedConceptsPET listmode dataInternal motionsExternal motion signalExternal respiratory signalListmode dataTumor motion informationRespiratory-gated PET imagesCT attenuation mapsMotion correlationPhantom experimentsRespiratory motion signalMotion degradationMotion correctionTumor motionSUVmax increaseAttenuation mapResidual motionAttenuation correctionSinogramRespiratory motion correctionQuantitative PET/CTMotionReference frameRespiratory motionPET images