2023
Unified Noise-Aware Network for Low-Count PET Denoising With Varying Count Levels
Xie H, Liu Q, Zhou B, Chen X, Guo X, Wang H, Li B, Rominger A, Shi K, Liu C. Unified Noise-Aware Network for Low-Count PET Denoising With Varying Count Levels. IEEE Transactions On Radiation And Plasma Medical Sciences 2023, 8: 366-378. PMID: 39391291, PMCID: PMC11463975, DOI: 10.1109/trpms.2023.3334105.Peer-Reviewed Original ResearchLarge-scale dataDeep learningDynamic PET imagesLow-count dataNeural networkMultiple networksSpecific noise levelDifferent vendorsDifferent noise levelsDenoised resultsNoisy counterpartDynamic frameInput noise levelNetworkData availabilityHigher image noiseImage qualityImage noiseSuperior performanceImportant topicAdditional challengesNoise levelPET imagesLimited data availabilityVendorsDirect respiratory motion correction of whole-body PET images using a deep learning framework incorporating spatial information
Miao T, Tsai Y, Zhou B, Menard D, Schleyer P, Hong I, Casey M, Liu C. Direct respiratory motion correction of whole-body PET images using a deep learning framework incorporating spatial information. Progress In Biomedical Optics And Imaging 2023, 12463: 124633x-124633x-9. DOI: 10.1117/12.2654472.Peer-Reviewed Original ResearchDeep learning frameworkRespiratory motion correctionMotion-corrected imagesLearning frameworkImage domainSpatial informationData-driven gating methodMotion correctionMotion detection techniqueGround truth imagesU-NetTruth imagesPET imagesData driving methodImage reconstructionWhole-body PET imagesMotion sensorsDetection techniquesExternal motion sensorsCross validationImagesConvenient mannerFrameworkRespiratory motionInformation
2022
Virtual high‐count PET image generation using a deep learning method
Liu J, Ren S, Wang R, Mirian N, Tsai Y, Kulon M, Pucar D, Chen M, Liu C. Virtual high‐count PET image generation using a deep learning method. Medical Physics 2022, 49: 5830-5840. PMID: 35880541, PMCID: PMC9474624, DOI: 10.1002/mp.15867.Peer-Reviewed Original ResearchConceptsStructural similarity indexImage quality evaluationDeep learning-based methodsDeep learning methodsImage qualityLearning-based methodsPET datasetsStatic datasetsDL methodsNet networkImage generationPET imagesNetwork inputsImage counterpartsLearning methodsNetwork outputTraining datasetPeak signalPositron emission tomography (PET) imagesQuality evaluationDatasetCross-validation resultsMean square errorHigh-count imagesImagesDeep-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
PET Image Denoising Using a Deep-Learning Method for Extremely Obese Patients
Liu H, Yousefi H, Mirian N, Lin M, Menard D, Gregory M, Aboian M, Boustani A, Chen M, Saperstein L, Pucar D, Kulon M, Liu C. PET Image Denoising Using a Deep-Learning Method for Extremely Obese Patients. IEEE Transactions On Radiation And Plasma Medical Sciences 2021, 6: 766-770. PMID: 37284026, PMCID: PMC10241407, DOI: 10.1109/trpms.2021.3131999.Peer-Reviewed Original ResearchSuper-resolution PET Brain Imaging using Deep Learning
Ren S, Liu J, Xie H, Toyonaga T, Mirian N, Chen M, Aboian M, Carson R, Liu C. Super-resolution PET Brain Imaging using Deep Learning. 2021, 00: 1-6. DOI: 10.1109/nss/mic44867.2021.9875548.Peer-Reviewed Original ResearchDeep learning networkPET image resolutionData augmentation methodImage resolutionSuper-resolution approachMedical imaging modalitiesClinical brain imagesDeep learningLearning networkAugmentation methodPET image qualityBrain imagesImage qualityNetworkImagesMedical diagnostic technologyPET imagesHRRT imagesData generalizabilityLearningSubstantial improvementScannerTechnologyPET brain imagingAccuracyArtificial Intelligence-Based Image Enhancement in PET Imaging Noise Reduction and Resolution Enhancement
Liu J, Malekzadeh M, Mirian N, Song TA, Liu C, Dutta J. Artificial Intelligence-Based Image Enhancement in PET Imaging Noise Reduction and Resolution Enhancement. PET Clinics 2021, 16: 553-576. PMID: 34537130, PMCID: PMC8457531, DOI: 10.1016/j.cpet.2021.06.005.Peer-Reviewed Original ResearchConceptsArtificial intelligence modelsImage enhancementIntelligence modelsArtificial intelligenceNetwork architectureEvaluation metricsLarge-scale adoptionData typesImage denoisingLoss functionPET imagesLow spatial resolutionHigh noiseResolution enhancementImagesIntelligenceDeblurringArchitectureDenoisingNoise reductionMetricsPopularityRecent effortsFuture directionsAccuracy
2018
Respiratory Motion Compensation for PET/CT with Motion Information Derived from Matched Attenuation-Corrected Gated PET Data
Lu Y, Fontaine K, Mulnix T, Onofrey JA, Ren S, Panin V, Jones J, Casey ME, Barnett R, Kench P, Fulton R, Carson RE, Liu C. Respiratory Motion Compensation for PET/CT with Motion Information Derived from Matched Attenuation-Corrected Gated PET Data. Journal Of Nuclear Medicine 2018, 59: 1480-1486. PMID: 29439015, PMCID: PMC6126443, DOI: 10.2967/jnumed.117.203000.Peer-Reviewed Original ResearchConceptsMotion correction frameworkMotion informationReference gatePET reconstructionMotion estimation accuracyGated PET dataMotion compensation approachMotion correctionMotion compensation methodMotion estimationRespiratory motion compensationAttenuation correction artifactsLung cancer datasetMotion compensationCT imagesNAC approachReconstruction algorithmPET dataPET imagesNew frameworkInaccurate localizationCancer datasetsBreathing variationsAttenuation correction mapsHuman datasets
2013
Enhancing clinical utility of respiratory-gated PET/CT using patient respiratory trace classification
Bowen S, Pierce L, Alessio A, Liu C, Kinahan P. Enhancing clinical utility of respiratory-gated PET/CT using patient respiratory trace classification. 2011 IEEE Nuclear Science Symposium Conference Record 2013, 2881-2885. DOI: 10.1109/nssmic.2012.6551657.Peer-Reviewed Original ResearchLesion locationDiagnostic CTMaximum standardized uptake valueUpper lung lesionSelection of patientsPatient selection guidelinesStandardized uptake valueLiver cancer patientsPET/CTPET/CT examsLower lungPatient groupCancer patientsLung lesionsPET parametersClinical utilityLiver lesionsResponse assessmentPatientsPET imagesUptake valueCT examsLinear associationAbdominal displacementCT
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