2023
A Multiclass Radiomics Method–Based WHO Severity Scale for Improving COVID-19 Patient Assessment and Disease Characterization From CT Scans
Henao J, Depotter A, Bower D, Bajercius H, Todorova P, Saint-James H, de Mortanges A, Barroso M, He J, Yang J, You C, Staib L, Gange C, Ledda R, Caminiti C, Silva M, Cortopassi I, Dela Cruz C, Hautz W, Bonel H, Sverzellati N, Duncan J, Reyes M, Poellinger A. A Multiclass Radiomics Method–Based WHO Severity Scale for Improving COVID-19 Patient Assessment and Disease Characterization From CT Scans. Investigative Radiology 2023, 58: 882-893. PMID: 37493348, PMCID: PMC10662611, DOI: 10.1097/rli.0000000000001005.Peer-Reviewed Original ResearchMeSH KeywordsArtificial IntelligenceCOVID-19Disease ProgressionHumansLungRetrospective StudiesTomography, X-Ray ComputedConceptsCOVID-19 positive patientsClinical Progression ScaleLung lesionsLesion modelDisease severityGround-glass opacitiesCOVID-19 patientsRadiologist assessmentExpert thoracic radiologistsMulticenter cohortPleural effusionDisease extentRetrospective studyDevelopment cohortPatient assessmentTomography scanCT scanSeverity ScalePatient's diseaseTissue lesionsThoracic radiologistsLesionsPatientsRadiomics modelRadiomic features
2022
DuDoUFNet: Dual-Domain Under-to-Fully-Complete Progressive Restoration Network for Simultaneous Metal Artifact Reduction and Low-Dose CT Reconstruction
Zhou B, Chen X, Xie H, Zhou S, Duncan JS, Liu C. DuDoUFNet: Dual-Domain Under-to-Fully-Complete Progressive Restoration Network for Simultaneous Metal Artifact Reduction and Low-Dose CT Reconstruction. IEEE Transactions On Medical Imaging 2022, 41: 3587-3599. PMID: 35816532, PMCID: PMC9812027, DOI: 10.1109/tmi.2022.3189759.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsArtifactsHumansImage Processing, Computer-AssistedMetalsProstheses and ImplantsTomography, X-Ray ComputedSimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation
You C, Zhou Y, Zhao R, Staib L, Duncan JS. SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation. IEEE Transactions On Medical Imaging 2022, 41: 2228-2237. PMID: 35320095, PMCID: PMC10325835, DOI: 10.1109/tmi.2022.3161829.Peer-Reviewed Original ResearchMeSH KeywordsDistillationImage Processing, Computer-AssistedSupervised Machine LearningTomography, X-Ray ComputedConceptsMedical image segmentationImage segmentationSemi-supervised medical image segmentationRobust Medical Image SegmentationMedical image analysisUnsupervised training strategyAtrial Segmentation ChallengeLearning-based approachMedical image synthesisAverage Dice scoreSemi-supervised approachPair-wise similarityContrastive objectiveData augmentationSegmentation challengePopular datasetsDice scoreSemantic informationDistillation frameworkSegmentation accuracyDownstream tasksImage synthesisPrevious best resultSupervised counterpartMedical dataUsing Adversarial Images to Assess the Robustness of Deep Learning Models Trained on Diagnostic Images in Oncology
Joel MZ, Umrao S, Chang E, Choi R, Yang DX, Duncan JS, Omuro A, Herbst R, Krumholz HM, Aneja S. Using Adversarial Images to Assess the Robustness of Deep Learning Models Trained on Diagnostic Images in Oncology. JCO Clinical Cancer Informatics 2022, 6: e2100170. PMID: 35271304, PMCID: PMC8932490, DOI: 10.1200/cci.21.00170.Peer-Reviewed Original ResearchMeSH KeywordsBreastDeep LearningHumansMagnetic Resonance ImagingMammographyTomography, X-Ray Computed
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 ResearchMeSH KeywordsAlgorithmsArtifactsHumansImage Processing, Computer-AssistedPhantoms, ImagingTomography, X-Ray ComputedConceptsRecurrent networksSevere streak artifactsRecurrent frameworkArtifact reductionSparse viewsImage domainReconstruction qualityCT metal artifact reductionX-ray projectionsMetal artifact reductionArtifact-free imagesMedical diagnosisPrevious methodsProjection dataConsistent layerExperimental resultsLimited 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 ResearchMeSH KeywordsAlgorithmsArtifactsImage Processing, Computer-AssistedNeural Networks, ComputerPhantoms, ImagingTomography, X-Ray ComputedConceptsAttention 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
2020
Automated feature quantification of Lipiodol as imaging biomarker to predict therapeutic efficacy of conventional transarterial chemoembolization of liver cancer
Stark S, Wang C, Savic LJ, Letzen B, Schobert I, Miszczuk M, Murali N, Oestmann P, Gebauer B, Lin M, Duncan J, Schlachter T, Chapiro J. Automated feature quantification of Lipiodol as imaging biomarker to predict therapeutic efficacy of conventional transarterial chemoembolization of liver cancer. Scientific Reports 2020, 10: 18026. PMID: 33093524, PMCID: PMC7582153, DOI: 10.1038/s41598-020-75120-7.Peer-Reviewed Original ResearchConceptsConventional transarterial chemoembolizationLipiodol depositionTransarterial chemoembolizationLiver cancerPeripheral depositionLipiodol depositsTherapeutic efficacyNecrotic tumor areasBaseline MRITherapy optionsTumor responseTreatment responseTumor volumeLiver lesionsLipiodolH postTumor areaH-CTHounsfield unitsBiomarkersChemoembolizationHigh rateTumorsCancerImproved responseImaging of Injectable Hydrogels Delivered into Myocardium with SPECT/CT
Uman S, Wang LL, Thorn SL, Liu Z, Duncan JS, Sinusas AJ, Burdick JA. Imaging of Injectable Hydrogels Delivered into Myocardium with SPECT/CT. Advanced Healthcare Materials 2020, 9: e2000294. PMID: 32543053, PMCID: PMC7482444, DOI: 10.1002/adhm.202000294.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsHyaluronic AcidHydrogelsMyocardiumSwineTomography, Emission-Computed, Single-PhotonTomography, X-Ray ComputedConceptsInjectable hydrogelsMyocardial infarctionSelf-healing hydrogelsHydrogel mechanical propertiesHybrid single-photon emissionHybrid SPECT/CT imagingMechanical propertiesTomography perfusion imagingAcute porcine modelSPECT/CT imagingSPECT/CTSingle photon emissionInjection forceHyaluronic acid hydrogelsPattern of deliveryTomography/HydrogelsPerfusion defectsPerfusion imagingExcellent radiopacityPorcine modelAcid hydrogelsCT imagingHydrogel deliveryHydrogel injectionSparse Data–Driven Learning for Effective and Efficient Biomedical Image Segmentation
Onofrey JA, Staib LH, Huang X, Zhang F, Papademetris X, Metaxas D, Rueckert D, Duncan JS. Sparse Data–Driven Learning for Effective and Efficient Biomedical Image Segmentation. Annual Review Of Biomedical Engineering 2020, 22: 1-27. PMID: 32169002, PMCID: PMC9351438, DOI: 10.1146/annurev-bioeng-060418-052147.Peer-Reviewed Original Research
2016
Machine learning–based 3‐D geometry reconstruction and modeling of aortic valve deformation using 3‐D computed tomography images
Liang L, Kong F, Martin C, Pham T, Wang Q, Duncan J, Sun W. Machine learning–based 3‐D geometry reconstruction and modeling of aortic valve deformation using 3‐D computed tomography images. International Journal For Numerical Methods In Biomedical Engineering 2016, 33 PMID: 27557429, PMCID: PMC5325825, DOI: 10.1002/cnm.2827.Peer-Reviewed Original ResearchMeSH KeywordsAortic ValveComputer SimulationFinite Element AnalysisHumansImaging, Three-DimensionalMachine LearningTomography, X-Ray ComputedConceptsHuman expertsGeometry reconstructionHuman errorMean discrepancyPreoperative planning systemComputational modeling processReconstructed geometryFinite element model generationModel generationPatient-specific computational modelingCardiac imagesComputational modeling methodsFast feedbackComputational modeling frameworkModeling processMesh correspondencePlanning systemModeling methodMachineModeling frameworkAortic valveImagesDisease diagnosisLarge patient cohortIndividual patient needs
2012
Precise Segmentation of Multiple Organs in CT Volumes Using Learning-Based Approach and Information Theory
Lu C, Zheng Y, Birkbeck N, Zhang J, Kohlberger T, Tietjen C, Boettger T, Duncan JS, Zhou SK. Precise Segmentation of Multiple Organs in CT Volumes Using Learning-Based Approach and Information Theory. Lecture Notes In Computer Science 2012, 15: 462-469. PMID: 23286081, DOI: 10.1007/978-3-642-33418-4_57.Peer-Reviewed Original ResearchConceptsMarginal Space LearningCT volumesChallenging segmentation problemInformation-theoretic schemesLearning-based approachComputer-aided diagnosisExcellent segmentation accuracyRobust boundary detectionInformation theoryPelvic organ segmentationSteerable featuresChallenging datasetArt solutionsOrgan segmentationSegmentation problemSpace learningSegmentation performanceSegmentation accuracyPrecise segmentationBoundary detectionJensen-Shannon divergenceTheoretic schemeInference processDiverse sourcesPrevious state
2011
An integrated approach to segmentation and nonrigid registration for application in image-guided pelvic radiotherapy
Lu C, Chelikani S, Papademetris X, Knisely JP, Milosevic MF, Chen Z, Jaffray DA, Staib LH, Duncan JS. An integrated approach to segmentation and nonrigid registration for application in image-guided pelvic radiotherapy. Medical Image Analysis 2011, 15: 772-785. PMID: 21646038, PMCID: PMC3164526, DOI: 10.1016/j.media.2011.05.010.Peer-Reviewed Original ResearchAlgorithmsBayes TheoremFemaleHumansImaging, Three-DimensionalMaleProstatic NeoplasmsRadiographic Image EnhancementRadiographic Image Interpretation, Computer-AssistedRadiotherapy, Computer-AssistedReproducibility of ResultsSensitivity and SpecificitySubtraction TechniqueSystems IntegrationTomography, X-Ray ComputedUterine Cervical NeoplasmsTargeted Imaging of the Spatial and Temporal Variation of Matrix Metalloproteinase Activity in a Porcine Model of Postinfarct Remodeling
Sahul ZH, Mukherjee R, Song J, McAteer J, Stroud RE, Dione DP, Staib L, Papademetris X, Dobrucki LW, Duncan JS, Spinale FG, Sinusas AJ. Targeted Imaging of the Spatial and Temporal Variation of Matrix Metalloproteinase Activity in a Porcine Model of Postinfarct Remodeling. Circulation Cardiovascular Imaging 2011, 4: 381-391. PMID: 21505092, PMCID: PMC3140564, DOI: 10.1161/circimaging.110.961854.Peer-Reviewed Original Research
2010
Integrated Segmentation and Nonrigid Registration for Application in Prostate Image-Guided Radiotherapy
Lu C, Chelikani S, Chen Z, Papademetris X, Staib LH, Duncan JS. Integrated Segmentation and Nonrigid Registration for Application in Prostate Image-Guided Radiotherapy. Lecture Notes In Computer Science 2010, 13: 53-60. PMID: 20879214, DOI: 10.1007/978-3-642-15705-9_7.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsHumansImaging, Three-DimensionalMaleProstatic NeoplasmsRadiographic Image EnhancementRadiographic Image Interpretation, Computer-AssistedRadiotherapy, Computer-AssistedReproducibility of ResultsSensitivity and SpecificitySubtraction TechniqueSystems IntegrationTomography, X-Ray ComputedConceptsManual segmentationAutomatic segmentationImportant treatment parametersNonrigid registrationImage-guided radiotherapy systemReal patient dataNon-rigid registrationIntegrated SegmentationRegistration partRadiotherapy linear acceleratorSegmentationTreatment imagesImage qualityCone-beam CTTreatment parametersImagesPromising resultsPatient dataKey anatomical structuresLinear acceleratorRegistrationPrevious workRadiotherapy system
2009
Constrained non-rigid registration for use in image-guided adaptive radiotherapy
Greene WH, Chelikani S, Purushothaman K, Knisely JP, Chen Z, Papademetris X, Staib LH, Duncan JS. Constrained non-rigid registration for use in image-guided adaptive radiotherapy. Medical Image Analysis 2009, 13: 809-817. PMID: 19682945, PMCID: PMC2771756, DOI: 10.1016/j.media.2009.07.004.Peer-Reviewed Original ResearchAlgorithmsArtificial IntelligenceHumansMalePattern Recognition, AutomatedProstatic NeoplasmsRadiographic Image EnhancementRadiographic Image Interpretation, Computer-AssistedRadiotherapy, Computer-AssistedRadiotherapy, ConformalReproducibility of ResultsSensitivity and SpecificitySubtraction TechniqueTomography, X-Ray Computed
2008
Automated 2D–3D registration of portal images and CT data using line‐segment enhancement
Munbodh R, Chen Z, Jaffray DA, Moseley DJ, Knisely JP, Duncan JS. Automated 2D–3D registration of portal images and CT data using line‐segment enhancement. Medical Physics 2008, 35: 4352-4361. PMID: 18975681, PMCID: PMC3910153, DOI: 10.1118/1.2975143.Peer-Reviewed Original ResearchAlgorithmsArtificial IntelligenceHumansImaging, Three-DimensionalMalePattern Recognition, AutomatedProstatic NeoplasmsRadiographic Image EnhancementRadiographic Image Interpretation, Computer-AssistedRadiotherapy, Computer-AssistedReproducibility of ResultsSensitivity and SpecificitySubtraction TechniqueTomography, X-Ray Computed
2003
Entropy-Based Dual-Portal-to-3-DCT Registration Incorporating Pixel Correlation
Bansal R, Staib LH, Chen Z, Rangarajan A, Knisely J, Nath R, Duncan JS. Entropy-Based Dual-Portal-to-3-DCT Registration Incorporating Pixel Correlation. IEEE Transactions On Medical Imaging 2003, 22: 29. PMID: 12703758, DOI: 10.1109/tmi.2002.806430.Peer-Reviewed Original ResearchConceptsRegistration frameworkImage dataMutual information-based registration algorithmRegistration parametersPortal imagesUltrasound image dataReal patient dataTomography image dataImage pixelsPixel correlationRegistration algorithmPatient setup verificationSegmentationPixel intensityMarkov random processInitial versionTransformation parametersAppropriate entropyImagesAlgorithmPatient dataFrameworkCT imagesLine processSetup verification
2000
Point-tracked quantitative analysis of left ventricular surface motion from 3-D image sequences
Shi P, Sinusas AJ, Constable RT, Ritman E, Duncan JS. Point-tracked quantitative analysis of left ventricular surface motion from 3-D image sequences. IEEE Transactions On Medical Imaging 2000, 19: 36-50. PMID: 10782617, DOI: 10.1109/42.832958.Peer-Reviewed Original Research