Featured Publications
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 performanceBrainGNN: 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 ResearchConceptsFunctional 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 analysisNeuropsychiatric disease classification using functional connectomics - results of the connectomics in neuroimaging transfer learning challenge
Schirmer MD, Venkataraman A, Rekik I, Kim M, Mostofsky SH, Nebel MB, Rosch K, Seymour K, Crocetti D, Irzan H, Hütel M, Ourselin S, Marlow N, Melbourne A, Levchenko E, Zhou S, Kunda M, Lu H, Dvornek NC, Zhuang J, Pinto G, Samal S, Zhang J, Bernal-Rusiel JL, Pienaar R, Chung AW. Neuropsychiatric disease classification using functional connectomics - results of the connectomics in neuroimaging transfer learning challenge. Medical Image Analysis 2021, 70: 101972. PMID: 33677261, PMCID: PMC9115580, DOI: 10.1016/j.media.2021.101972.Peer-Reviewed Original ResearchConceptsAttention-deficit/hyperactivity disorderAutism Brain Imaging Data ExchangeResting-state fMRI time seriesAutism spectrum disorder (ASD) patientsHuman Connectome ProjectADHD modelHyperactivity disorderADHD comorbidityASD classificationConnectome ProjectOpen-source image analysis platformFMRI time seriesBrain connectomicsFunctional connectomicsClassification methodologyLearning challengesOpen-source datasetField of connectomicsParcellation atlasesDisorder patientsImage analysis platformOmission rateParticipantsConnectomicsMICCAI 2019
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 ResearchConceptsPivotal Response TreatmentAutism spectrum disorderOccipital-temporal cortexAttentional systemResponse treatmentSpectrum disorderOrbitofrontal cortexPosterior cingulateHigh-level objectsBehavioral interventionsLearning mechanismPerception shiftProcessing areasNeural circuitsFunctional rewiringCortexTreatment regimenAutismInterventionNovel Bayesian frameworkCingulateFunctional changesIndividualsDisordersObjects
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
Non-rigid Registration with Missing Correspondences in Preoperative and Postresection Brain Images
Chitphakdithai N, Duncan JS. Non-rigid Registration with Missing Correspondences in Preoperative and Postresection Brain Images. Lecture Notes In Computer Science 2010, 13: 367-374. PMID: 20879252, PMCID: PMC3031159, DOI: 10.1007/978-3-642-15705-9_45.Peer-Reviewed Original ResearchConceptsTypes of datasetsNon-rigid registration methodImage alignmentNon-rigid registrationMissing correspondencesSegmentation algorithmNon-rigid registration algorithmSimilarity metricCorrespondence problemValid correspondencesRegistration algorithmRegistration methodExpectation-maximization algorithmBrain imagesJoint registrationReal dataAlgorithmImagesRegistrationError kernel