2024
Molecular MR Imaging of T-Cell Immune Response to Cryoablation in Immunologically Hot vs. Cold Hepatocellular Carcinoma.
Santana J, Shewarega A, Nam D, Duncan J, Madoff D, Hyder F, Coman D, Chapiro J. Molecular MR Imaging of T-Cell Immune Response to Cryoablation in Immunologically Hot vs. Cold Hepatocellular Carcinoma. JHEP Reports 2024, 101294. DOI: 10.1016/j.jhepr.2024.101294.Peer-Reviewed Original ResearchT cell infiltrationHepatocellular carcinomaRadiological-pathological correlationImaging mass cytometryImmune responseT1-weighted MRITumor-infiltrating CD8+ T lymphocytesAnti-tumor immune responseCD8+ T lymphocytesIncreased T lymphocyte infiltrationImaging biomarkersNon-immunogenic tumorsSystemic lymph nodesT lymphocyte infiltrationMurine tumor modelsImmune cell typesLocal tumor therapyPrimary liver cancerNon-invasive imaging biomarkerTesla MRI scannerInduce liver cirrhosisImmunogenic tumorsLocoregional therapySystemic immunotherapyHCC lesionsPrior knowledge-guided vision-transformer-based unsupervised domain adaptation for intubation prediction in lung disease at one week
Yang J, Henao J, Dvornek N, He J, Bower D, Depotter A, Bajercius H, de Mortanges A, You C, Gange C, Ledda R, Silva M, Dela Cruz C, Hautz W, Bonel H, Reyes M, Staib L, Poellinger A, Duncan J. Prior knowledge-guided vision-transformer-based unsupervised domain adaptation for intubation prediction in lung disease at one week. Computerized Medical Imaging And Graphics 2024, 118: 102442. PMID: 39515190, DOI: 10.1016/j.compmedimag.2024.102442.Peer-Reviewed Original ResearchUnsupervised domain adaptationSpatial prior informationDomain adaptationLabeled dataData-driven approachUnsupervised domain adaptation modelMedical image analysis tasksImage analysis tasksTransformer-based modelsMedical image analysisPrior informationOutcome prediction tasksAdversarial trainingDistribution alignmentDomain shiftAttention headsClass tokenPoor generalizationAnalysis tasksTarget domainPrediction taskData distributionKnowledge-guidedLocal weightsMedical imagesA Flow-based Truncated Denoising Diffusion Model for super-resolution Magnetic Resonance Spectroscopic Imaging
Dong S, Cai Z, Hangel G, Bogner W, Widhalm G, Huang Y, Liang Q, You C, Kumaragamage C, Fulbright R, Mahajan A, Karbasi A, Onofrey J, de Graaf R, Duncan J. A Flow-based Truncated Denoising Diffusion Model for super-resolution Magnetic Resonance Spectroscopic Imaging. Medical Image Analysis 2024, 99: 103358. PMID: 39353335, DOI: 10.1016/j.media.2024.103358.Peer-Reviewed Original ResearchDenoising diffusion modelsDeep learning-based super-resolution methodsLearning-based super-resolution methodsMulti-scale super-resolutionGenerative modelSuper-resolution methodsDeep learning modelsHigh-resolution magnetic resonance spectroscopic imagingHigh-quality imagesPost-processing approachSuper-resolutionFlow-based networksLearning modelsLow resolutionTruncation stepLow-resolution dataSharpness adjustmentNetworkSensitivity restrictionsUncertainty estimationDiffusion modelImagesCapabilitySampling processSpectroscopic imagingMine yOur owN Anatomy: Revisiting Medical Image Segmentation With Extremely Limited Labels
You C, Dai W, Liu F, Min Y, Dvornek N, Li X, Clifton D, Staib L, Duncan J. Mine yOur owN Anatomy: Revisiting Medical Image Segmentation With Extremely Limited Labels. IEEE Transactions On Pattern Analysis And Machine Intelligence 2024, 46: 11136-11151. PMID: 39269798, DOI: 10.1109/tpami.2024.3461321.Peer-Reviewed Original ResearchMedical image segmentationImage segmentationMedical image segmentation frameworkContext of medical image segmentationLong-tailed class distributionStrong data augmentationsIntra-class variationsSemi-supervised settingData imbalance issueImage segmentation frameworkMedical image analysisMedical image dataSupervision signalsContrastive learningBenchmark datasetsUnsupervised mannerLabel setsData augmentationSegmentation frameworkDomain expertisePseudo-codeImbalance issueModel trainingMedical imagesSegmentation modelCascaded Multi-path Shortcut Diffusion Model for Medical Image Translation
Zhou Y, Chen T, Hou J, Xie H, Dvornek N, Zhou S, Wilson D, Duncan J, Liu C, Zhou B. Cascaded Multi-path Shortcut Diffusion Model for Medical Image Translation. Medical Image Analysis 2024, 98: 103300. PMID: 39226710, DOI: 10.1016/j.media.2024.103300.Peer-Reviewed Original ResearchGenerative adversarial networkMedical image translationImage translationState-of-the-art methodsImage-to-image translationMedical image datasetsImage translation tasksImage-to-imageState-of-the-artMedical image processingHigh-quality translationsUncertainty estimationCascaded pipelineAdversarial networkImage datasetsSub-tasksTranslation qualityTranslation performanceTranslation tasksImage processingTranslation resultsDM methodPrior imageRobust performanceExperimental resultsTumor response assessment in hepatocellular carcinoma treated with immunotherapy: imaging biomarkers for clinical decision-making
Sobirey R, Matuschewski N, Gross M, Lin M, Kao T, Kasolowsky V, Strazzabosco M, Stein S, Savic L, Gebauer B, Jaffe A, Duncan J, Madoff D, Chapiro J. Tumor response assessment in hepatocellular carcinoma treated with immunotherapy: imaging biomarkers for clinical decision-making. European Radiology 2024, 1-11. PMID: 39033181, DOI: 10.1007/s00330-024-10955-6.Peer-Reviewed Original ResearchMedian overall survivalTumor response criteriaTumor response assessmentHepatocellular carcinoma patientsHepatocellular carcinomaTumor responseOverall survivalResponse criteriaResponse assessmentNon-respondersPoorer median overall survivalPrediction of tumor responsePredictive valueHepatocellular carcinoma immunotherapyDisease controlPrognostic of survivalClinical baseline parametersLog-rank testKaplan-Meier curvesMultivariate Cox regressionPredicting overall survivalCox regression modelsSurvival benefitStratify patientsMRI pre-Medical image registration via neural fields
Sun S, Han K, You C, Tang H, Kong D, Naushad J, Yan X, Ma H, Khosravi P, Duncan J, Xie X. Medical image registration via neural fields. Medical Image Analysis 2024, 97: 103249. PMID: 38963972, DOI: 10.1016/j.media.2024.103249.Peer-Reviewed Original ResearchLearning-based methodsNeural fieldsNeural networkImage registrationMedical image analysis tasksMini-batch gradient descentImage analysis tasksDeep neural networksMedical image registrationDiffeomorphic image registrationImage registration frameworkOptimization-based methodDomain shiftAnalysis tasksGradient descentCompetitive performanceImage pairsRegistration taskOptimal deformationShort computation timeRegistration frameworkDesign choicesDisplacement vector fieldComputation timeModel optimizationSpectral Brain Graph Neural Network for Prediction of Anxiety in Children with Autism Spectrum Disorder
Duan P, Dvornek N, Wang J, Eilbott J, Du Y, Sukhodolsky D, Duncan J. Spectral Brain Graph Neural Network for Prediction of Anxiety in Children with Autism Spectrum Disorder. 2024, 00: 1-5. DOI: 10.1109/isbi56570.2024.10635753.Peer-Reviewed Original ResearchGraph neural networksFunctional magnetic resonance imagingAutism spectrum disorderNeural networkCurrent graph neural networksSpectrum disorderMASC-2Spectral analysis algorithmAnalysis algorithmGraph-based networkMultidimensional Anxiety ScaleFast Fourier transformPredictive of anxietyDaily anxiety levelsExtract hidden informationBrain functional networksPower spectrum densityNode featuresNetwork performanceComorbid anxietyBrain mechanismsHidden informationCorrelated featuresAnxiety ScaleTotal scoreHigh‐resolution extracellular pH imaging of liver cancer with multiparametric MR using Deep Image Prior
Dong S, Shewarega A, Chapiro J, Cai Z, Hyder F, Coman D, Duncan J. High‐resolution extracellular pH imaging of liver cancer with multiparametric MR using Deep Image Prior. NMR In Biomedicine 2024, 37: e5145. PMID: 38488205, DOI: 10.1002/nbm.5145.Peer-Reviewed Original ResearchDeep Image PriorU-NetUnsupervised deep learning techniquesU-Net parametersDeep learning techniquesHigh-resolution ground truthU-Net architectureSuper-resolution imagingImage priorsSuper-resolutionGround truthMean absolute errorDeepSpatial resolutionPostprocessing methodDeep imagingAbsolute errorImagesAnatomical MR imagesMR spectroscopic imagingAnatomical informationSpectroscopic imagingInformationAcquisition timeErrorGrand Challenges at the Interface of Engineering and Medicine
Subramaniam S, Akay M, Anastasio M, Bailey V, Boas D, Bonato P, Chilkoti A, Cochran J, Colvin V, Desai T, Duncan J, Epstein F, Fraley S, Giachelli C, Grande-Allen K, Green J, Guo X, Hilton I, Humphrey J, Johnson C, Karniadakis G, King M, Kirsch R, Kumar S, Laurencin C, Li S, Lieber R, Lovell N, Mali P, Margulies S, Meaney D, Ogle B, Palsson B, Peppas N, Perreault E, Rabbitt R, Setton L, Shea L, Shroff S, Shung K, Tolias A, van der Meulen M, Varghese S, Vunjak-Novakovic G, White J, Winslow R, Zhang J, Zhang K, Zukoski C, Miller M. Grand Challenges at the Interface of Engineering and Medicine. IEEE Open Journal Of Engineering In Medicine And Biology 2024, 5: 1-13. PMID: 38415197, PMCID: PMC10896418, DOI: 10.1109/ojemb.2024.3351717.Peer-Reviewed Original ResearchEffect of Incomplete Cryoablation and Matrix Metalloproteinase Inhibition on Intratumoral CD8+ T-Cell Infiltration in Murine Hepatocellular Carcinoma.
Shewarega A, Santana J, Nam D, Berz A, Tefera J, Kahl V, Mishra S, Coman D, Duncan J, Roberts S, Wetter A, Madoff D, Chapiro J. Effect of Incomplete Cryoablation and Matrix Metalloproteinase Inhibition on Intratumoral CD8+ T-Cell Infiltration in Murine Hepatocellular Carcinoma. Radiology 2024, 310: e232365. PMID: 38349244, PMCID: PMC10902598, DOI: 10.1148/radiol.232365.Peer-Reviewed Original ResearchConceptsT cell infiltrationCD8<sup>+</sup> T cellsMatrix metalloproteinase inhibitionT cellsHepatocellular carcinomaMatrix metalloproteinase inhibitorsMatrix metalloproteinasesResidual tumorCXCR3<sup>+</sup> CD8<sup>+</sup> T cellsCytotoxic CD8<sup>+</sup> T cell infiltrationIntratumoral CD8+ T cell infiltrationCD8+ T cell infiltrationCD8<sup>+</sup> T cell infiltrationMouse model of hepatocellular carcinomaEarly-stage hepatocellular carcinomaImage-guided tumor ablationUnpaired Student's <i>t</i> testModel of hepatocellular carcinomaFirst-line therapyMurine hepatocellular carcinomaT cell subsetsTumor-associated macrophagesMurine HCC modelLocal immune responseFemale BALB/c micePatient-Specific Heart Geometry Modeling for Solid Biomechanics Using Deep Learning
Pak D, Liu M, Kim T, Liang L, Caballero A, Onofrey J, Ahn S, Xu Y, McKay R, Sun W, Gleason R, Duncan J. Patient-Specific Heart Geometry Modeling for Solid Biomechanics Using Deep Learning. IEEE Transactions On Medical Imaging 2024, 43: 203-215. PMID: 37432807, PMCID: PMC10764002, DOI: 10.1109/tmi.2023.3294128.Peer-Reviewed Original ResearchConceptsFinite element analysisDeep learning methodsSpatial accuracyElement analysisDeep learningStress estimationLearning methodsSimulation accuracyDeployment simulationHigh spatial accuracyThin structuresMesh generationVolumetric meshingDeformation energyGeometry modelingVolumetric meshMesh qualityElement qualitySimultaneous optimizationMain noveltyBiomechanics studiesMeshModeling characteristicsAccuracyDownstream analysis
2023
Unsupervised Domain Adaptation by Cross-Prototype Contrastive Learning for Medical Image Segmentation
Cai Z, Xin J, Dong S, You C, Shi P, Zeng T, Zhang J, Onofrey J, Zheng N, Duncan J. Unsupervised Domain Adaptation by Cross-Prototype Contrastive Learning for Medical Image Segmentation. 2023, 00: 819-824. DOI: 10.1109/bibm58861.2023.10386055.Peer-Reviewed Original ResearchUnsupervised domain adaptationDistribution alignmentDomain adaptationContrastive learningUnsupervised domain adaptation methodsMedical image segmentation tasksDomain distribution alignmentGlobal distribution alignmentContrastive learning methodDomain adaptation performanceIntra-class distancePixel-level featuresImage segmentation tasksInter-class distancePublic cardiac datasetsCategory centroidDiscrimination of classesClass prototypesSegmentation taskSource domainTarget domainCardiac datasetsLearning methodsGlobal prototypesCentroid alignmentRethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective.
You C, Dai W, Min Y, Liu F, Clifton D, Zhou S, Staib L, Duncan J. Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective. Advances In Neural Information Processing Systems 2023, 36: 9984-10021. PMID: 38813114, PMCID: PMC11136570.Peer-Reviewed Original ResearchMedical image segmentationContrastive learningImage segmentationSemi-supervised medical image segmentationSemi-supervised contrastive learningSelf-supervised objectiveSemantic segmentation datasetsSemi-supervised methodGround-truth labelsQuality of visual representationSafety-critical tasksSegmentation datasetTail classesSegmentation taskLabel setsTruth labelsCL frameworkNegative examplesModel collapseVariance-reductionVariance-reduction techniquesVisual representationTaskLearningPairs of samplesFedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET Denoising
Zhou B, Zhou B, Xie H, Liu Q, Chen X, Guo X, Zhou K, Li B, Rominger A, Shi K, Duncan J, Liu C. FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET Denoising. 2023, 00: 1-1. DOI: 10.1109/nssmicrtsd49126.2023.10338446.Peer-Reviewed Original ResearchA 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 ResearchConceptsCOVID-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 ResearchFlow-Based Visual Quality Enhancer for Super-Resolution Magnetic Resonance Spectroscopic Imaging
Dong S, Hangel G, Chen E, Sun S, Bogner W, Widhalm G, You C, Onofrey J, de Graaf R, Duncan J. Flow-Based Visual Quality Enhancer for Super-Resolution Magnetic Resonance Spectroscopic Imaging. Lecture Notes In Computer Science 2022, 13609: 3-13. DOI: 10.1007/978-3-031-18576-2_1.Peer-Reviewed Original ResearchAdversarial networkVisual qualityDeep learning-based super-resolution methodsLearning-based super-resolution methodsFlow-based modelImage visual qualityGenerative adversarial networkHigh visual qualitySuper-resolution methodSuper-resolved imagesGenerative modelHigh-resolution imagesImage modalitiesFlow-based methodNetworkLow spatial resolutionUncertainty estimationImagesPromising resultsEnhancer networkAnatomical informationHigh fidelityEssential toolDatasetQuality adjustmentIncremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation
You C, Xiang J, Su K, Zhang X, Dong S, Onofrey J, Staib L, Duncan J. Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation. Lecture Notes In Computer Science 2022, 13573: 3-16. PMID: 37415747, PMCID: PMC10323962, DOI: 10.1007/978-3-031-18523-6_1.Peer-Reviewed Original ResearchIncremental learningMedical image segmentation tasksMulti-site datasetImage segmentation tasksMedical image segmentationProstate MRI SegmentationComputation resourcesMedical datasetsSegmentation taskImage segmentationSegmentation frameworkEmbedding featuresBenchmark datasetsMRI segmentationTraining dataTarget domainLearning approachPractical deploymentDomain-specific expertiseCompetitive performanceDatasetTraining schemePrior workSegmentationSingle modelMulti-scale Super-Resolution Magnetic Resonance Spectroscopic Imaging with Adjustable Sharpness
Dong S, Hangel G, Bogner W, Widhalm G, Rössler K, Trattnig S, You C, de Graaf R, Onofrey J, Duncan J. Multi-scale Super-Resolution Magnetic Resonance Spectroscopic Imaging with Adjustable Sharpness. Lecture Notes In Computer Science 2022, 13436: 410-420. DOI: 10.1007/978-3-031-16446-0_39.Peer-Reviewed Original Research