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