2024
A deep learning-based approach to nuisance signal removal from MRSI data aqcuired without suppression
Lee W, Zhuo Y, Marin T, Han P, Chi D, Fakhri G, Ma C. A deep learning-based approach to nuisance signal removal from MRSI data aqcuired without suppression. Proceedings Of The International Society For Magnetic Resonance In Medicine ... Scientific Meeting And Exhibition. 2024 DOI: 10.58530/2024/0259.Peer-Reviewed Original ResearchDeep learning-based methodsLearning-based methodsU-Net structureSignal removalIn vivo MRSI dataNeural networkU-NetMRSI dataImage reconstructionSuperior performanceData processingRobust performanceHankel matrixNetworkNuisance signalsConventional methodsPerformanceMRSI signalsSignalMethodRemove nuisance signalsRemovalHankelDisentangled multimodal brain MR image translation via transformer-based modality infuser
Cho J, Liu X, Xing F, Ouyang J, Fakhri G, Park J, Woo J. Disentangled multimodal brain MR image translation via transformer-based modality infuser. Progress In Biomedical Optics And Imaging 2024, 12926: 129262h-129262h-6. DOI: 10.1117/12.3006502.Peer-Reviewed Original ResearchConvolutional neural networkBrain tumor segmentation taskModality-specific featuresTumor segmentation taskImage translationAdversarial networkSegmentation taskSynthesis qualityBrain MR imagesNeural networkMR modalitiesAcquired imagesExperimental resultsNetworkGlobal relationshipsDisease diagnosisImagesEncodingBraTSDatasetFeaturesTaskMethodSuperiorityMR imaging
2023
Attenuation correction for PET imaging using conditional denoising diffusion probabilistic model
Dong Y, Jang S, Han P, Johnson K, Ma C, Fakhri G, Li Q, Gong K. Attenuation correction for PET imaging using conditional denoising diffusion probabilistic model. 2023, 00: 1-1. DOI: 10.1109/nssmicrtsd49126.2023.10338188.Peer-Reviewed Original ResearchDiffusion probabilistic modelGenerative adversarial networkConditional encodingAttenuation correctionDenoising diffusion probabilistic modelLow-level featuresProbabilistic modelAttenuation coefficientAdversarial networkExtract featuresPET/MR systemsEncodingPET acquisitionNovel methodDiffusion encodingMagnetic resonanceImagesPET imagingCorrectionMR imagingUNetAttenuationNetworkFeaturesResonancePET image denoising based on denoising diffusion probabilistic model
Gong K, Johnson K, El Fakhri G, Li Q, Pan T. PET image denoising based on denoising diffusion probabilistic model. European Journal Of Nuclear Medicine And Molecular Imaging 2023, 51: 358-368. PMID: 37787849, PMCID: PMC10958486, DOI: 10.1007/s00259-023-06417-8.Peer-Reviewed Original ResearchConceptsDenoising diffusion probabilistic modelPET image denoisingDiffusion probabilistic modelImage denoisingDenoising methodNonlocal meansNetwork inputGenerative adversarial networkData consistency constraintsProbabilistic modelLearning-based modelsAdversarial networkData distributionDenoisingRefinement stepsIterative refinementFlexible frameworkImage qualityPhysical degrading factorsUNetNetworkDatasetImagesInputNoise levelFine-Tuning Network in Federated Learning for Personalized Skin Diagnosis
Lee K, Lee H, Cavalcanti T, Kim S, El Fakhri G, Lee D, Woo J, Hwang J. Fine-Tuning Network in Federated Learning for Personalized Skin Diagnosis. Lecture Notes In Computer Science 2023, 14222: 378-388. DOI: 10.1007/978-3-031-43898-1_37.Peer-Reviewed Original ResearchFederated learningSkin disease diagnosisMobile devicesState-of-the-art approachesUtilization of mobile devicesFine-tuning networkState-of-the-artFine-tuning methodAbstract Federated learningDeep learning networkField of medical diagnosisDeep networksLearning networkAdaptive mannerModified GADisease diagnosisGenetic algorithmSuperior performanceMedical diagnosisArchitectural designClinical datasetsExperimental resultsNetworkModel designPersonality diversitySynthesizing audio from tongue motion during speech using tagged MRI via transformer
Liu X, Xing F, Prince J, Stone M, Fakhri G, Woo J. Synthesizing audio from tongue motion during speech using tagged MRI via transformer. Proceedings Of SPIE--the International Society For Optical Engineering 2023, 12464: 1246410-1246410-5. PMID: 38009135, PMCID: PMC10669779, DOI: 10.1117/12.2653345.Peer-Reviewed Original ResearchMotion fieldAudio waveformAdversarial training approachImprove synthesis qualityConvolutional decoderAudio dataSynthesis qualityTranslation networkData structureSpeech waveformTemporal modelTagged MRITongue motionTraining approachSpectrogramMuscle deformationSource of informationSpeechIntelligible speechFrameworkDecodingInformationPredictive informationEncodingNetwork
2022
Posterior estimation using deep learning: a simulation study of compartmental modeling in dynamic positron emission tomography
Liu X, Marin T, Amal T, Woo J, Fakhri G, Ouyang J. Posterior estimation using deep learning: a simulation study of compartmental modeling in dynamic positron emission tomography. Medical Physics 2022, 50: 1539-1548. PMID: 36331429, PMCID: PMC10087283, DOI: 10.1002/mp.16078.Peer-Reviewed Original ResearchConceptsConditional variational auto-encoderDeep learning approachNeural networkDeep learningMarkov chain Monte CarloVariational Bayesian inference frameworkLearning approachDeep learning-based approachVariational auto-encoderDeep neural networksLearning-based approachDynamic brain PET imagingPosterior distributionEstimate posterior distributionsBayesian inference frameworkAuto-encoderMedical imagesInference frameworkNetworkSimulation studyBrain PET imagingLearningPosterior estimatesInferior performanceImagesStructure-aware unsupervised tagged-to-cine MRI synthesis with self disentanglement
Liu X, Xing F, Prince J, Stone M, El Fakhri G, Woo J. Structure-aware unsupervised tagged-to-cine MRI synthesis with self disentanglement. Proceedings Of SPIE--the International Society For Optical Engineering 2022, 12032: 120321q-120321q-7. PMID: 36203947, PMCID: PMC9533681, DOI: 10.1117/12.2610655.Peer-Reviewed Original ResearchStructure feature extractorImage style transferSelf-training schemeStyle transferFeature extractorAdversarial gameSynthesized imagesInformation w.Superior performanceStructural consistencyTask-specificCycle reconstructionStructural encodingImagesDiscoGANCycleGANEncodingExtractorExtraction stepNetworkSchemeGameInputSelf-Semantic Contour Adaptation for Cross Modality Brain Tumor Segmentation
Liu X, Xing F, Fakhri G, Woo J. Self-Semantic Contour Adaptation for Cross Modality Brain Tumor Segmentation. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2022, 00: 1-5. PMID: 35990931, PMCID: PMC9387767, DOI: 10.1109/isbi52829.2022.9761629.Peer-Reviewed Original ResearchUnsupervised domain adaptationAdaptive networkLow-level edge informationCross-domain alignmentEnhance segmentation performanceMulti-task frameworkCross-modality segmentationSegmentation of brain tumorsAdversarial learningDomain adaptationSemantic segmentationEdge informationSemantic alignmentPrecursor taskSegmentation performanceSpatial informationNetworkSemantic adaptationMagnetic resonance imagingTaskContour adaptationBraTS2018InformationFrameworkAdaptation
2019
Arterial spin labeling MR image denoising and reconstruction using unsupervised deep learning
Gong K, Han P, Fakhri G, Ma C, Li Q. Arterial spin labeling MR image denoising and reconstruction using unsupervised deep learning. NMR In Biomedicine 2019, 35: e4224. PMID: 31865615, PMCID: PMC7306418, DOI: 10.1002/nbm.4224.Peer-Reviewed Original ResearchConceptsSignal-to-noise ratioImage denoisingReconstruction frameworkDeep learning-based image denoisingDeep learning-based denoisersMR image denoisingLearning-based denoisingLow signal-to-noise ratioK-space dataNoisy imagesTraining labelsTraining pairsNetwork inputNeural networkDenoisingIn vivo experiment dataSuperior performanceImaging speedReconstruction processImage qualityLong imaging timesNetworkFrameworkImagesSpatial resolutionMAPEM-Net: an unrolled neural network for Fully 3D PET image reconstruction
Gong K, Wu D, Kim K, Yang J, Sun T, Fakhri G, Seo Y, Li Q. MAPEM-Net: an unrolled neural network for Fully 3D PET image reconstruction. Proceedings Of SPIE--the International Society For Optical Engineering 2019, 11072: 110720o-110720o-5. DOI: 10.1117/12.2534904.Peer-Reviewed Original ResearchPET image reconstructionNeural networkImage reconstructionImage denoising applicationDeep neural networksNeural network frameworkConvolutional neural networkDenoising applicationsDenoising methodNetwork frameworkUpdate stepData consistencyIll-posedNetworkClinical datasetsInverse problemMAPEMFrameworkAlgorithmDatasetDetected photonsReconstructionMethodSimulationGraph Convolutional Neural Networks For Alzheimer’s Disease Classification
Song T, Roy Chowdhury S, Yang F, Jacobs H, El Fakhri G, Li Q, Johnson K, Dutta J. Graph Convolutional Neural Networks For Alzheimer’s Disease Classification. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2019, 00: 414-417. PMID: 31327984, PMCID: PMC6641559, DOI: 10.1109/isbi.2019.8759531.Peer-Reviewed Original ResearchGraph convolutional neural networkConvolutional neural networkNeural networkCapabilities of convolutional neural networksGraph-structured dataNon-Euclidean domainsClassification capability of convolutional neural networksVector machine classifierGraph-based toolsData representationAudio signalsClassification capabilityMachine classifierClassifierPerformance gapImage dataNetworkConnected graphStructural connectivity graphsDisease classificationClassificationBrain connectivity studiesEuclidean domainsComplex systemsGraphEMnet: an unrolled deep neural network for PET image reconstruction
Gong K, Wu D, Kim K, Yang J, Fakhri G, Seo Y, Li Q. EMnet: an unrolled deep neural network for PET image reconstruction. Progress In Biomedical Optics And Imaging 2019, 10948: 1094853-1094853-6. DOI: 10.1117/12.2513096.Peer-Reviewed Original ResearchDeep neural networksPET image reconstructionNeural networkExpectation maximizationImage reconstructionImage denoising applicationNeural network frameworkNeural network denoisersDenoising applicationsDenoising methodNetwork denoisingNetwork trainingNetwork frameworkWhole graphUpdate stepData consistencyIll-posedNetworkInverse problemEMNETDenoisingSimulated dataFrameworkAlgorithmGraph
2018
Super-Resolution PET Using A Very Deep Convolutional Neural Network
Song T, Chowdhury S, Kim K, Gong K, Fakhri G, Li Q, Dutta J. Super-Resolution PET Using A Very Deep Convolutional Neural Network. 2018, 00: 1-2. DOI: 10.1109/nssmic.2018.8824683.Peer-Reviewed Original ResearchConvolutional neural networkNeural networkSuper-resolution convolutional neural networkDeep convolutional neural networkImage deblurring approachesInput image patchesBlur kernelResolution recovery techniquesSpatial location informationDeblurring approachDeblurring processImage patchesQuantitative accuracy of PETLocation informationSpatially-varying natureSuperior performanceNetworkRecovery techniquesDigital phantomDeblurringBrainWebInformationPartial volume effectsDeepBlurDeep networks in identifying CT brain hemorrhage
Helwan A, El-Fakhri G, Sasani H, Uzun Ozsahin D. Deep networks in identifying CT brain hemorrhage. Journal Of Intelligent & Fuzzy Systems 2018, Preprint: 1-1. DOI: 10.3233/jifs-172261.Peer-Reviewed Original ResearchConvolutional neural networkStacked autoencoderDeep networksMedical image classificationDeep learning algorithmsMedical expert's experienceImage classificationTraining timeLearning algorithmsNeural networkAutoencoderExpert experienceBrain CT imagesCT imagesNetworkHigher accuracyLess errorAlgorithmImagesAccuracyErrorClassificationAttenuation correction for brain PET imaging using deep neural network based on Dixon and ZTE MR images
Gong K, Yang J, Kim K, Fakhri G, Seo Y, Li Q. Attenuation correction for brain PET imaging using deep neural network based on Dixon and ZTE MR images. Physics In Medicine And Biology 2018, 63: 125011. PMID: 29790857, PMCID: PMC6031313, DOI: 10.1088/1361-6560/aac763.Peer-Reviewed Original ResearchConceptsU-Net structureU-NetModified U-net structureAttenuation correctionDeep neural network methodBrain PET imagingPET attenuationDeep neural networksPatient data setsAttenuation coefficientDixon-based methodNeural network methodData setsConvolution moduleNetwork inputNeural networkDixon MRPET/MR hybrid systemImage reconstructionPET imagingNetwork methodNetworkNetwork approachNetwork structureQuantification errorsPenalized PET Reconstruction Using Deep Learning Prior and Local Linear Fitting
Kim K, Wu D, Gong K, Dutta J, Kim J, Son Y, Kim H, Fakhri G, Li Q. Penalized PET Reconstruction Using Deep Learning Prior and Local Linear Fitting. IEEE Transactions On Medical Imaging 2018, 37: 1478-1487. PMID: 29870375, PMCID: PMC6375088, DOI: 10.1109/tmi.2018.2832613.Peer-Reviewed Original ResearchConceptsDeep learningDenoising convolutional neural networkConvolutional neural networkDeep learning-basedPerformance of iterative reconstructionPotential of deep learningDeep networksNoise levelLearning-basedReconstruction frameworkDegradation of performanceNeural networkDnCNNMedical imagesDownsampled dataFitness functionPoisson thinningFull-dose imagesLow dose imagesNoise conditionsNetworkImage qualityPET reconstructionDose imagesDeep
2016
Localizing Sources of Brain Disease Progression with Network Diffusion Model
Hu C, Hua X, Ying J, Thompson P, Fakhri G, Li Q. Localizing Sources of Brain Disease Progression with Network Diffusion Model. IEEE Journal Of Selected Topics In Signal Processing 2016, 10: 1214-1225. PMID: 28503250, PMCID: PMC5423678, DOI: 10.1109/jstsp.2016.2601695.Peer-Reviewed Original ResearchMild cognitive impairmentEfficient gradient descent methodGradient descent methodAlzheimer's Disease Neuroimaging InitiativeBrain connectivity networksDescent methodOrigin of dementiaDementia sourcesOrigin of Alzheimer's diseaseAlzheimer's diseaseEffective treatment of neurodegenerative diseasesDementiaMagnetic resonance imagingBrain atrophy patternsProgression of brain atrophyCognitive impairmentInverse problemNetworkSource of dementiaConnectivity networksTreatment of neurodegenerative diseasesAtrophy patterns
2015
A Spectral Graph Regression Model for Learning Brain Connectivity of Alzheimer’s Disease
Hu C, Cheng L, Sepulcre J, Johnson K, Fakhri G, Lu Y, Li Q. A Spectral Graph Regression Model for Learning Brain Connectivity of Alzheimer’s Disease. PLOS ONE 2015, 10: e0128136. PMID: 26024224, PMCID: PMC4449104, DOI: 10.1371/journal.pone.0128136.Peer-Reviewed Original ResearchConceptsNetwork featuresAlzheimer's diseaseConsistent with known pathologyUnknown graphConnection weightsReconstruction networkCortical hubsDegree statisticsData modelSmooth signalsFeatures of brain pathologyOptimization frameworkAmyloid-bPartial correlation estimationImage dataNetworkGraphGlobal connectivity measuresPositron emission tomographyConnectivity measuresNeurodegenerative diseasesConnectivity patternsSample correlationClinical ADSimulated data
2013
A GRAPH THEORETICAL REGRESSION MODEL FOR BRAIN CONNECTIVITY LEARNING OF ALZHEIMER'S DISEASE
Hu C, Cheng L, Sepulcre J, Fakhri G, Lu Y, Li Q. A GRAPH THEORETICAL REGRESSION MODEL FOR BRAIN CONNECTIVITY LEARNING OF ALZHEIMER'S DISEASE. 2013, 616-619. DOI: 10.1109/isbi.2013.6556550.Peer-Reviewed Original Research