2022
Dual-Branch Squeeze-Fusion-Excitation Module for Cross-Modality Registration of Cardiac SPECT and CT
Chen X, Zhou B, Xie H, Guo X, Zhang J, Sinusas A, Onofrey J, Liu C. Dual-Branch Squeeze-Fusion-Excitation Module for Cross-Modality Registration of Cardiac SPECT and CT. Lecture Notes In Computer Science 2022, 13436: 46-55. DOI: 10.1007/978-3-031-16446-0_5.Peer-Reviewed Original ResearchConvolutional neural networkCross-modality registrationFeature fusionPrevious convolutional neural networkEarly feature fusionCross-modality informationMultiple convolutional layersMedical image registrationLow registration errorCardiac SPECTConvolutional layersCNN moduleImage featuresLate fusionSource codeNeural networkExcitation moduleInput modalitiesImage registrationSpatial featuresMultiple modalitiesRegistration errorPrevious methodsRigid registrationNetworkFederated Transfer Learning for Low-Dose PET Denoising: A Pilot Study With Simulated Heterogeneous Data
Zhou B, Miao T, Mirian N, Chen X, Xie H, Feng Z, Guo X, Li X, Zhou S, Duncan J, Liu C. Federated Transfer Learning for Low-Dose PET Denoising: A Pilot Study With Simulated Heterogeneous Data. IEEE Transactions On Radiation And Plasma Medical Sciences 2022, 7: 284-295. PMID: 37789946, PMCID: PMC10544830, DOI: 10.1109/trpms.2022.3194408.Peer-Reviewed Original ResearchLow-dose PETMedical data privacy regulationsFederated learning algorithmLarge domain shiftTransfer learning frameworkData privacy regulationsHigh-quality reconstructionFederated transferData privacyHeterogeneous dataDomain shiftLearning frameworkLearning algorithmPrivacy regulationsData distributionCollaborative trainingLow-dose dataPET reconstructionPrevious methodsFL methodEfficient wayLocal dataSuperior performanceExperimental resultsDenoising
2021
DuDoDR-Net: Dual-domain data consistent recurrent network for simultaneous sparse view and metal artifact reduction in computed tomography
Zhou B, Chen X, Zhou SK, Duncan JS, Liu C. DuDoDR-Net: Dual-domain data consistent recurrent network for simultaneous sparse view and metal artifact reduction in computed tomography. Medical Image Analysis 2021, 75: 102289. PMID: 34758443, PMCID: PMC8678361, DOI: 10.1016/j.media.2021.102289.Peer-Reviewed Original ResearchConceptsRecurrent networksSevere streak artifactsRecurrent frameworkArtifact reductionSparse viewsImage domainReconstruction qualityCT metal artifact reductionX-ray projectionsMetal artifact reductionArtifact-free imagesMedical diagnosisPrevious methodsProjection dataConsistent layerExperimental resultsMDPET: 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 qualityImagesEstimationAnatomy-Constrained Contrastive Learning for Synthetic Segmentation Without Ground-Truth
Zhou B, Liu C, Duncan J. Anatomy-Constrained Contrastive Learning for Synthetic Segmentation Without Ground-Truth. Lecture Notes In Computer Science 2021, 12901: 47-56. DOI: 10.1007/978-3-030-87193-2_5.Peer-Reviewed Original ResearchSegmentation networkContrastive learningManual segmentationSuperior segmentation performanceObject of interestSynthetic SegmentationManual effortSegmentation performanceTraining dataUnsupervised adaptationImaging dataSource modalitySegmentationNetworkPrevious methodsLearningLarge amountSuccessful applicationPET imaging dataImagesObjectsCodeDataNew imaging modalityLimited View Tomographic Reconstruction Using a Cascaded Residual Dense Spatial-Channel Attention Network With Projection Data Fidelity Layer
Zhou B, Zhou S, Duncan JS, Liu C. Limited View Tomographic Reconstruction Using a Cascaded Residual Dense Spatial-Channel Attention Network With Projection Data Fidelity Layer. IEEE Transactions On Medical Imaging 2021, 40: 1792-1804. PMID: 33729929, PMCID: PMC8325575, DOI: 10.1109/tmi.2021.3066318.Peer-Reviewed Original ResearchConceptsAttention networkView reconstructionGrand challenge datasetLimited angle reconstructionHigh-quality reconstructionNeural network methodSparse-view reconstructionExperimental resultsLimited angle acquisitionArchitecture issuesSparse viewsChallenge datasetLimited view dataView dataNeural architectureQuality reconstructionNetwork methodTomographic reconstructionReconstructed imagesProjection viewsPrevious methodsAngle reconstructionDatasetNetworkAngle acquisition