2024
Patlak-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 SNRImagesRecon
2017
Data-driven event-by-event respiratory motion correction using TOF PET list-mode centroid of distribution
Ren S, Jin X, Chan C, Jian Y, Mulnix T, Liu C, Carson RE. Data-driven event-by-event respiratory motion correction using TOF PET list-mode centroid of distribution. Physics In Medicine And Biology 2017, 62: 4741-4755. PMID: 28520558, PMCID: PMC6048592, DOI: 10.1088/1361-6560/aa700c.Peer-Reviewed Original ResearchConceptsData-driven eventsRespiratory motion correctionSignificant image quality improvementMotion correctionEvent respiratory motion correctionExternal motion tracking systemPET list-mode dataRespiratory motionMotion tracking systemImage quality improvementMotion correction techniqueReconstruction frameworkMotion correction methodDistribution algorithmMotion-induced blurList-mode dataTracking systemFurther processingGated reconstructionsImage noiseRespiratory gating techniqueHuman scansRadioactive eventsContrast recoveryAnzai