2022
Dual-domain self-supervised learning for accelerated non-Cartesian MRI reconstruction
Zhou B, Schlemper J, Dey N, Mohseni Salehi SS, Sheth K, Liu C, Duncan JS, Sofka M. Dual-domain self-supervised learning for accelerated non-Cartesian MRI reconstruction. Medical Image Analysis 2022, 81: 102538. PMID: 35926336, DOI: 10.1016/j.media.2022.102538.Peer-Reviewed Original ResearchMeSH KeywordsHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingMotionSupervised Machine LearningConceptsNon-Cartesian MRI reconstructionMRI reconstructionUndersampled dataPrevious baseline methodsSelf-supervised approachSelf-supervised learningHigh-quality reconstructionReconstruction networkAppearance consistencyDataset demonstrateBaseline methodsImage domainDisjoint partitionsSupervised trainingPractical adoptionReconstruction accuracyDomain partitionImproved image qualityImage qualityDDSSSampling patternK-spaceExperimental resultsNetworkMotion robustnessUsing 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
High-Resolution Magnetic Resonance Spectroscopic Imaging using a Multi-Encoder Attention U-Net with Structural and Adversarial Loss
Dong S, Hangel G, Bogner W, Trattnig S, Rössler K, Widhalm G, De Feyter HM, De Graaf RA, Duncan JS. High-Resolution Magnetic Resonance Spectroscopic Imaging using a Multi-Encoder Attention U-Net with Structural and Adversarial Loss. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2021, 00: 2891-2895. PMID: 34891851, DOI: 10.1109/embc46164.2021.9630146.Peer-Reviewed Original ResearchBrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis
Li X, Zhou Y, Dvornek N, Zhang M, Gao S, Zhuang J, Scheinost D, Staib LH, Ventola P, Duncan JS. BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis. Medical Image Analysis 2021, 74: 102233. PMID: 34655865, PMCID: PMC9916535, DOI: 10.1016/j.media.2021.102233.Peer-Reviewed Original ResearchMeSH KeywordsAutism Spectrum DisorderBrainConnectomeHumansMagnetic Resonance ImagingNeural Networks, ComputerConceptsFunctional magnetic resonance imagesGraph neural network frameworkMedical image analysisGraph neural networkGraph convolutional layersNeural network frameworkDifferent evaluation metricsSpecific task statesIndependent fMRI datasetsPooling layerConvolutional layersConsistency lossNetwork frameworkNeural networkFMRI datasetsImage analysis methodEvaluation metricsDetection resultsBrain graphsSubjects releaseROI selectionImage analysisCognitive stimuliTask statesFMRI analysisDeep learning–assisted differentiation of pathologically proven atypical and typical hepatocellular carcinoma (HCC) versus non-HCC on contrast-enhanced MRI of the liver
Oestmann PM, Wang CJ, Savic LJ, Hamm CA, Stark S, Schobert I, Gebauer B, Schlachter T, Lin M, Weinreb JC, Batra R, Mulligan D, Zhang X, Duncan JS, Chapiro J. Deep learning–assisted differentiation of pathologically proven atypical and typical hepatocellular carcinoma (HCC) versus non-HCC on contrast-enhanced MRI of the liver. European Radiology 2021, 31: 4981-4990. PMID: 33409782, PMCID: PMC8222094, DOI: 10.1007/s00330-020-07559-1.Peer-Reviewed Original ResearchMeSH KeywordsCarcinoma, HepatocellularContrast MediaDeep LearningHumansLiver NeoplasmsMagnetic Resonance ImagingRetrospective StudiesConceptsNon-HCC lesionsHepatocellular carcinomaHCC lesionsAtypical imagingGrading systemLI-RADS criteriaAtypical imaging featuresPrimary liver cancerTypical hepatocellular carcinomaAtypical hepatocellular carcinomaContrast-enhanced MRISensitivity/specificityLiver transplantMethodsThis IRBRetrospective studyLiver malignanciesImaging featuresLiver cancerAtypical featuresConclusionThis studyLesionsMRIClinical applicationCarcinomaImage-based diagnosis
2020
Molecular MRI of the Immuno-Metabolic Interplay in a Rabbit Liver Tumor Model: A Biomarker for Resistance Mechanisms in Tumor-targeted Therapy?
Savic LJ, Doemel LA, Schobert IT, Montgomery RR, Joshi N, Walsh JJ, Santana J, Pekurovsky V, Zhang X, Lin M, Adam L, Boustani A, Duncan J, Leng L, Bucala RJ, Goldberg SN, Hyder F, Coman D, Chapiro J. Molecular MRI of the Immuno-Metabolic Interplay in a Rabbit Liver Tumor Model: A Biomarker for Resistance Mechanisms in Tumor-targeted Therapy? Radiology 2020, 296: 575-583. PMID: 32633675, PMCID: PMC7434651, DOI: 10.1148/radiol.2020200373.Peer-Reviewed Original ResearchConceptsImmuno-oncologic therapiesConventional transarterial chemoembolizationTransarterial chemoembolizationIntratumoral immune cell infiltrationMR spectroscopyRabbit liver tumor modelPrussian blue iron stainingAntigen-presenting immune cellsIntra-arterial infusionImmune cell infiltrationNew Zealand white rabbitsLiver tumor modelImmune cell exclusionLiver cancer modelContrast material administrationT2-weighted MRIZealand white rabbitsT2-weighted imagingResistance mechanismsImmunosuppressive tumorHLA-DRCell infiltrationImmune cellsImmunohistochemistry stainingRing enhancementMulti-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results
Li X, Gu Y, Dvornek N, Staib LH, Ventola P, Duncan JS. Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results. Medical Image Analysis 2020, 65: 101765. PMID: 32679533, PMCID: PMC7569477, DOI: 10.1016/j.media.2020.101765.Peer-Reviewed Original ResearchConceptsDeep learning modelsFederated LearningPrivacy-preserving federated learningLearning modelFederated learning approachPrivacy-preserving strategyDomain adaptation methodsData analysis problemsLocal model weightsIterative optimization algorithmEntity dataDomain adaptationLearning approachLearning formulationMulti-site dataRandomization mechanismAdaptation methodNeuroimage analysisDifferent tasksModel weightsModel optimizationOptimization algorithmPrivate informationTraining strategyAnalysis problemMolecular Imaging of Extracellular Tumor pH to Reveal Effects of Locoregional Therapy on Liver Cancer Microenvironment
Savic LJ, Schobert I, Peters D, Walsh JJ, Laage-Gaupp F, Hamm CA, Tritz N, Doemel LA, Lin M, Sinusas A, Schlachter T, Duncan JS, Hyder F, Coman D, Chapiro J. Molecular Imaging of Extracellular Tumor pH to Reveal Effects of Locoregional Therapy on Liver Cancer Microenvironment. Clinical Cancer Research 2020, 26: 428-438. PMID: 31582517, PMCID: PMC7244230, DOI: 10.1158/1078-0432.ccr-19-1702.Peer-Reviewed Original ResearchConceptsMR spectroscopic imagingLocoregional therapyLiver cancer microenvironmentConventional transarterial chemoembolizationNew Zealand white rabbitsTumor pHMost liver tumorsZealand white rabbitsMolecular imaging paradigmsPositive therapeutic outcomesTumor residualsTransarterial chemoembolizationTumor devascularizationHistopathologic markersViable tumorSurrogate biomarkerLiver tumorsLiver cancerTumor enhancementLiver parenchymaMetabolic markersMultiparametric MRITherapeutic outcomesHIF-1αVX2 tumors
2019
Extracellular pH mapping of liver cancer on a clinical 3T MRI scanner
Coman D, Peters DC, Walsh JJ, Savic LJ, Huber S, Sinusas AJ, Lin M, Chapiro J, Constable RT, Rothman DL, Duncan JS, Hyder F. Extracellular pH mapping of liver cancer on a clinical 3T MRI scanner. Magnetic Resonance In Medicine 2019, 83: 1553-1564. PMID: 31691371, PMCID: PMC7244229, DOI: 10.1002/mrm.28035.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsBiosensing TechniquesExtracellular SpaceHydrogen-Ion ConcentrationLiver NeoplasmsMagnetic Resonance ImagingRabbitsDeep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features
Wang CJ, Hamm CA, Savic LJ, Ferrante M, Schobert I, Schlachter T, Lin M, Weinreb JC, Duncan JS, Chapiro J, Letzen B. Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features. European Radiology 2019, 29: 3348-3357. PMID: 31093705, PMCID: PMC7243989, DOI: 10.1007/s00330-019-06214-8.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAlgorithmsBile Duct NeoplasmsBile Ducts, IntrahepaticCarcinoma, HepatocellularCholangiocarcinomaDeep LearningFemaleHumansImage Interpretation, Computer-AssistedLiver NeoplasmsMachine LearningMagnetic Resonance ImagingMaleMiddle AgedNeural Networks, ComputerPredictive Value of TestsProof of Concept StudyRetrospective StudiesConceptsDeep learning systemConvolutional neural networkLearning systemRelevance scoresFeature mapsPre-trained CNN modelsFeature relevance scoresMulti-phasic MRINeural network interpretationEvidence-based decision supportDeep NeuralDeep learningCNN modelLesion classifierLearning prototypeNeural networkOriginal imageSystem prototypeDecision supportLesion classificationNetwork interpretationImage voxelsIncorrect featuresLesion classesTest setDeep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI
Hamm CA, Wang CJ, Savic LJ, Ferrante M, Schobert I, Schlachter T, Lin M, Duncan JS, Weinreb JC, Chapiro J, Letzen B. Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI. European Radiology 2019, 29: 3338-3347. PMID: 31016442, PMCID: PMC7251621, DOI: 10.1007/s00330-019-06205-9.Peer-Reviewed Original ResearchAdultAgedBile Duct NeoplasmsBile Ducts, IntrahepaticCarcinoma, HepatocellularCholangiocarcinomaDeep LearningFemaleHumansImage Interpretation, Computer-AssistedLiver NeoplasmsMagnetic Resonance ImagingMaleMiddle AgedNeural Networks, ComputerReproducibility of ResultsROC CurveSensitivity and SpecificityUnited States
2018
Learning Generalizable Recurrent Neural Networks from Small Task-fMRI Datasets
Dvornek NC, Yang D, Ventola P, Duncan JS. Learning Generalizable Recurrent Neural Networks from Small Task-fMRI Datasets. Lecture Notes In Computer Science 2018, 11072: 329-337. PMID: 30873514, PMCID: PMC6411297, DOI: 10.1007/978-3-030-00931-1_38.Peer-Reviewed Original ResearchConceptsRecurrent neural networkNeural networkTask fMRI datasetsMedical image analysis problemsSuch deep networksImage analysis problemsTask fMRI scanTypical control subjectsDeep networkDeep learningTraining lossSmall datasetsLarge datasetsNumber of approachesAutism spectrum disorderAnalysis problemDatasetNetworkTraining runsImage analysisGeneralizable modelNon-imaging variablesSpectrum disorderFMRI analysisModel performancePredicting Treatment Response to Intra-arterial Therapies for Hepatocellular Carcinoma with the Use of Supervised Machine Learning—An Artificial Intelligence Concept
Abajian A, Murali N, Savic LJ, Laage-Gaupp FM, Nezami N, Duncan JS, Schlachter T, Lin M, Geschwind JF, Chapiro J. Predicting Treatment Response to Intra-arterial Therapies for Hepatocellular Carcinoma with the Use of Supervised Machine Learning—An Artificial Intelligence Concept. Journal Of Vascular And Interventional Radiology 2018, 29: 850-857.e1. PMID: 29548875, PMCID: PMC5970021, DOI: 10.1016/j.jvir.2018.01.769.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAntineoplastic AgentsCarcinoma, HepatocellularChemoembolization, TherapeuticContrast MediaDoxorubicinEthiodized OilFemaleHumansLiver NeoplasmsMachine LearningMagnetic Resonance ImagingMaleMiddle AgedNeoplasm StagingPredictive Value of TestsRetrospective StudiesSensitivity and SpecificityTreatment OutcomeConceptsTransarterial chemoembolizationHepatocellular carcinomaTreatment responseLogistic regressionClinical patient dataPatient dataIntra-arterial therapyQuantitative European AssociationMagnetic resonance imagingLiver criteriaBaseline imagingClinical variablesTumor responseTherapeutic featuresTreatment respondersBaseline MRClinical informationImaging variablesChemoembolizationTherapeutic outcomesResonance imagingResponse criteriaEuropean AssociationPatientsMR imaging
2017
The impact of antiangiogenic therapy combined with Transarterial Chemoembolization on enhancement based quantitative tumor response assessment in patients with hepatocellular carcinoma
Smolka S, Chapiro J, Manzano W, Treilhard J, Reiner E, Deng Y, Zhao Y, Hamm B, Duncan JS, Gebauer B, Lin M, Geschwind JF. The impact of antiangiogenic therapy combined with Transarterial Chemoembolization on enhancement based quantitative tumor response assessment in patients with hepatocellular carcinoma. Clinical Imaging 2017, 46: 1-7. PMID: 28668723, PMCID: PMC5720941, DOI: 10.1016/j.clinimag.2017.05.007.Peer-Reviewed Original ResearchConceptsEarly response assessmentTransarterial chemoembolizationImaging-based criteriaResponse assessmentHepatocellular carcinomaTumor response assessmentAnti-angiogenic therapyQuantitative European AssociationTherapy armOverall survivalLiver criteriaAntiangiogenic therapyTreatment groupsPatientsSimilar associationEuropean AssociationBevacizumabChemoembolizationCarcinomaTherapyAssociationAssessmentFollowCriteriaBaseline
2016
Brain responses to biological motion predict treatment outcome in young children with autism
Yang D, Pelphrey KA, Sukhodolsky DG, Crowley MJ, Dayan E, Dvornek NC, Venkataraman A, Duncan J, Staib L, Ventola P. Brain responses to biological motion predict treatment outcome in young children with autism. Translational Psychiatry 2016, 6: e948-e948. PMID: 27845779, PMCID: PMC5314125, DOI: 10.1038/tp.2016.213.Peer-Reviewed Original ResearchConceptsAutism spectrum disorderYoung childrenSocial information processingMultivariate pattern analysisMotivation/rewardBiological motionCore deficitComplex neurodevelopmental disorderBrain responsesResponse treatmentSpectrum disorderNeurobiological markersNeural predictorsInformation processingBehavioral interventionsIndividual childrenNeurodevelopmental disordersCurrent findingsNeural circuitsBehavioral deficitsEarly childhoodChildrenUnsuccessful interventionsNeurobiomarkersPattern analysisPivotal response treatment prompts a functional rewiring of the brain among individuals with autism spectrum disorder
Venkataraman A, Yang D, Dvornek N, Staib LH, Duncan JS, Pelphrey KA, Ventola P. Pivotal response treatment prompts a functional rewiring of the brain among individuals with autism spectrum disorder. Neuroreport 2016, 27: 1081-1085. PMID: 27532879, PMCID: PMC5007196, DOI: 10.1097/wnr.0000000000000662.Peer-Reviewed Original ResearchMeSH KeywordsAutism Spectrum DisorderBayes TheoremBehavior TherapyBrainChildChild, PreschoolFemaleHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingMaleNeural PathwaysOxygenConceptsPivotal Response TreatmentAutism spectrum disorderOccipital-temporal cortexAttentional systemResponse treatmentSpectrum disorderOrbitofrontal cortexPosterior cingulateHigh-level objectsBehavioral interventionsLearning mechanismPerception shiftProcessing areasNeural circuitsFunctional rewiringCortexTreatment regimenAutismInterventionNovel Bayesian frameworkCingulateFunctional changesIndividualsDisordersObjects
2015
An unbiased Bayesian approach to functional connectomics implicates social-communication networks in autism
Venkataraman A, Duncan JS, Yang D, Pelphrey KA. An unbiased Bayesian approach to functional connectomics implicates social-communication networks in autism. NeuroImage Clinical 2015, 8: 356-366. PMID: 26106561, PMCID: PMC4474177, DOI: 10.1016/j.nicl.2015.04.021.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAutism Spectrum DisorderBayes TheoremCerebral CortexChildCommunicationConnectomeHumansMagnetic Resonance ImagingMaleNerve NetSocial PerceptionConceptsAutism spectrum disorderAutism Brain Imaging Data ExchangeSuperior temporal sulcusMiddle temporal gyrusTemporal sulcusTemporal gyrusRight posterior superior temporal sulcusPosterior superior temporal sulcusFunctional magnetic resonance imaging studyFunctional connectomicsTemporo-parietal junctionResting-state functional magnetic resonance imaging studyRight temporal poleIntrinsic functional networksDefault mode networkPossible neural mechanismsPosterior cingulate cortexMeta-analytic databaseIntra-hemispheric connectivityInter-hemispheric connectivityMagnetic resonance imaging studyASD patientsResonance imaging studyNeural mechanismsSpectrum disorder
2012
Simultaneous Nonrigid Registration, Segmentation, and Tumor Detection in MRI Guided Cervical Cancer Radiation Therapy
Lu C, Chelikani S, Jaffray DA, Milosevic MF, Staib LH, Duncan JS. Simultaneous Nonrigid Registration, Segmentation, and Tumor Detection in MRI Guided Cervical Cancer Radiation Therapy. IEEE Transactions On Medical Imaging 2012, 31: 1213-1227. PMID: 22328178, PMCID: PMC3889159, DOI: 10.1109/tmi.2012.2186976.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsFemaleHumansImage EnhancementImage Interpretation, Computer-AssistedImaging, Three-DimensionalMagnetic Resonance ImagingPattern Recognition, AutomatedRadiotherapy, ConformalRadiotherapy, Image-GuidedReproducibility of ResultsSensitivity and SpecificitySubtraction TechniqueUterine Cervical Neoplasms
2011
Non-rigid Registration of Longitudinal Brain Tumor Treatment MRI
Chitphakdithai N, Chiang VL, Duncan JS. Non-rigid Registration of Longitudinal Brain Tumor Treatment MRI. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2011, 2011: 4893-4896. PMID: 22255435, PMCID: PMC3753806, DOI: 10.1109/iembs.2011.6091212.Peer-Reviewed Original Research
2010
Image-Guided Intraoperative Cortical Deformation Recovery Using Game Theory: Application to Neocortical Epilepsy Surgery
DeLorenzo C, Papademetris X, Staib LH, Vives KP, Spencer DD, Duncan JS. Image-Guided Intraoperative Cortical Deformation Recovery Using Game Theory: Application to Neocortical Epilepsy Surgery. IEEE Transactions On Medical Imaging 2010, 29: 322-338. PMID: 20129844, PMCID: PMC2824434, DOI: 10.1109/tmi.2009.2027993.Peer-Reviewed Original ResearchConceptsDeformation estimationSurface deformationBrain surface deformationSurface deformation estimationPreoperative brain imagesCortical surface deformationSurface trackingCamera calibration parametersDisplacement errorStereo vision systemBrain deformationDeformationCalibration parametersBiomechanical modelIntraoperative brainCalibration errorsPhysical processesVision systemVivo casesCamera calibrationStereo systemInitial conditionsImage acquisitionErrorEstimation