2023
Fast-MC-PET: A Novel Deep Learning-Aided Motion Correction and Reconstruction Framework for Accelerated PET
Zhou B, Tsai Y, Zhang J, Guo X, Xie H, Chen X, Miao T, Lu Y, Duncan J, Liu C. Fast-MC-PET: A Novel Deep Learning-Aided Motion Correction and Reconstruction Framework for Accelerated PET. Lecture Notes In Computer Science 2023, 13939: 523-535. DOI: 10.1007/978-3-031-34048-2_40.Peer-Reviewed Original ResearchReconstruction frameworkMotion correctionMotion-compensated reconstructionHigh-quality imagesHigh-quality reconstruction imagesReconstruction moduleFrame reconstructionReconstruction outputMotion correction methodMotion modelingReconstructed imagesReconstruction methodImage qualityMotion typesImagesPatient motionExperimental resultsMotion-induced artifactsAcquisition dataReconstruction imagesLong acquisition timesFrameworkMultiple typesLow SNRPET acquisition
2022
Unsupervised inter-frame motion correction for whole-body dynamic PET using convolutional long short-term memory in a convolutional neural network
Guo X, Zhou B, Pigg D, Spottiswoode B, Casey ME, Liu C, Dvornek NC. Unsupervised inter-frame motion correction for whole-body dynamic PET using convolutional long short-term memory in a convolutional neural network. Medical Image Analysis 2022, 80: 102524. PMID: 35797734, PMCID: PMC10923189, DOI: 10.1016/j.media.2022.102524.Peer-Reviewed Original ResearchConceptsConvolutional neural networkNeural networkConvolutional long short-term memory (ConvLSTM) layersDeep learning-based frameworkConvolutional long short-term memoryLong short-term memory layersDeep learning baselinesLong short-term memoryDynamic temporal featuresLearning-based frameworkDeep learning approachShort-term memory layersTracer distribution changeMotion estimation networkMotion prediction errorInference timeEstimation networkLearning baselinesNon-rigid registration methodLearning approachMotion correction methodMemory layerShort-term memoryTemporal featuresRegistration method
2021
Automatic Inter-Frame Patient Motion Correction for Dynamic Cardiac PET Using Deep Learning
Shi L, Lu Y, Dvornek N, Weyman CA, Miller EJ, Sinusas AJ, Liu C. Automatic Inter-Frame Patient Motion Correction for Dynamic Cardiac PET Using Deep Learning. IEEE Transactions On Medical Imaging 2021, 40: 3293-3304. PMID: 34018932, PMCID: PMC8670362, DOI: 10.1109/tmi.2021.3082578.Peer-Reviewed Original ResearchConceptsConvolutional neural networkRegistration-based methodMotion correctionDynamic frameTracer distribution changeDynamic image dataPatient motion correctionPatient scansDeep learningPatient motionMotion estimationImage dataLSTM networkNeural networkRealistic patient motionTemporal informationMotion correction methodMotion detectionCardiac PETClinical workflowRigid translational motionFlow estimationNetworkPatient datasetsSuperior performanceMDPET: A Unified Motion Correction and Denoising Adversarial Network for Low-Dose Gated PET
Zhou B, Tsai YJ, Chen X, Duncan JS, Liu C. MDPET: A Unified Motion Correction and Denoising Adversarial Network for Low-Dose Gated PET. IEEE Transactions On Medical Imaging 2021, 40: 3154-3164. PMID: 33909561, PMCID: PMC8588635, DOI: 10.1109/tmi.2021.3076191.Peer-Reviewed Original ResearchConceptsMotion estimationPyramid networkAdversarial networkAccurate motion estimationMotion correctionLow-noise reconstructionGated positron emission tomographyMotion correction methodMotion estimation networkGated PET dataEstimation networkRecurrent layersDenoising NetworkRespiratory motion blurringExperimental resultsLow-noise imagesMotion blurringNoise levelCorrection methodNetworkPET reconstructionPrevious methodsImage qualityImagesEstimation
2020
Simultaneous Denoising and Motion Estimation for Low-Dose Gated PET Using a Siamese Adversarial Network with Gate-to-Gate Consistency Learning
Zhou B, Tsai Y, Liu C. Simultaneous Denoising and Motion Estimation for Low-Dose Gated PET Using a Siamese Adversarial Network with Gate-to-Gate Consistency Learning. Lecture Notes In Computer Science 2020, 12267: 743-752. DOI: 10.1007/978-3-030-59728-3_72.Peer-Reviewed Original ResearchMotion estimationMotion estimation networkRobust motion estimationMotion correction methodRespiratory motion blurringEstimation networkNoise ratioMotion correctionMotion blurringImage volumesCorrection methodCorrection stepMotion vectorsGateSimultaneous denoisingImage qualityEstimationAdversarial network
2019
Deep Learning based Respiratory Pattern Classification and Applications in PET/CT Motion Correction
Guo Y, Dvornek N, Lu Y, Tsai Y, Hamill J, Casey M, Liu C. Deep Learning based Respiratory Pattern Classification and Applications in PET/CT Motion Correction. 2019, 00: 1-5. DOI: 10.1109/nss/mic42101.2019.9059783.Peer-Reviewed Original ResearchDeep learningNeural networkMotion correction methodDeep neural networksDeep learning modelsHybrid neural networkConvolutional layersHigh prediction accuracyRecurrent layersGeneralization capabilityData preprocessingLearning modelPattern classificationRespiratory motionAnzai systemLoss functionLinear classifierPrediction accuracyIntra-gate motionRPM systemMotion correctionTumor detectionNetworkIrregular breathersCT images
2013
Event‐by‐event respiratory motion correction for PET with 3D internal‐1D external motion correlation
Chan C, Jin X, Fung EK, Naganawa M, Mulnix T, Carson RE, Liu C. Event‐by‐event respiratory motion correction for PET with 3D internal‐1D external motion correlation. Medical Physics 2013, 40: 112507. PMID: 24320466, DOI: 10.1118/1.4826165.Peer-Reviewed Original ResearchMeSH KeywordsCarcinoma, Non-Small-Cell LungFluorine RadioisotopesHealthy VolunteersHumansHypoxiaImage Processing, Computer-AssistedImaging, Three-DimensionalInsulin-Secreting CellsKidneyLung NeoplasmsMisonidazoleMovementPancreasPositron-Emission TomographyRegression AnalysisReproducibility of ResultsRespirationSignal Processing, Computer-AssistedTetrabenazineX-Ray Microtomography