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 ResearchMeSH KeywordsBayes TheoremComputer SimulationDeep LearningNeural Networks, ComputerPositron-Emission TomographyConceptsConditional 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 performanceImagesVoxelHop: Successive Subspace Learning for ALS Disease Classification Using Structural MRI
Liu X, Xing F, Yang C, Kuo C, Babu S, Fakhri G, Jenkins T, Woo J. VoxelHop: Successive Subspace Learning for ALS Disease Classification Using Structural MRI. IEEE Journal Of Biomedical And Health Informatics 2022, 26: 1128-1139. PMID: 34339378, PMCID: PMC8807766, DOI: 10.1109/jbhi.2021.3097735.Peer-Reviewed Original ResearchConceptsConvolutional neural networkLearning modelsDimension reductionSubspace learning modelConcatenation of featuresState-of-the-artUnsupervised dimension reductionDeep learning modelsMedical image dataSupervised dimension reductionImage dataClassification of amyotrophic lateral sclerosisSubspace learningClassification taskDeep learningDataset sizeNeural networkSubspace approximationMemory requirementsTraining datasetClassification approachAUC scoreAccurate classificationDatasetExperimental results
2021
Segmentation of Cardiac Structures via Successive Subspace Learning with Saab Transform from Cine MRI
Liu X, Xing F, Gaggin H, Wang W, Kuo C, Fakhri G, Woo J. Segmentation of Cardiac Structures via Successive Subspace Learning with Saab Transform from Cine MRI. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2021, 00: 3535-3538. PMID: 34892002, DOI: 10.1109/embc46164.2021.9629770.Peer-Reviewed Original ResearchMeSH KeywordsHeartHeart VentriclesImage Processing, Computer-AssistedMagnetic Resonance Imaging, CineNeural Networks, ComputerConceptsConvolutional neural networkSegmentation of cardiac structuresSaab transformSubspace approximationAccurate segmentation of cardiac structuresDimension reductionConvolutional neural network modelConcatenation of featuresUnsupervised dimension reductionConditional random fieldPixel-wise classificationSupervised dimension reductionU-Net modelMachine learning modelsSubspace learningChannel-wiseSegmentation databaseSegmentation frameworkNeural networkU-NetEfficient segmentationAccurate segmentationLearning modelsCardiac MR imagesRandom fieldA deep joint sparse non-negative matrix factorization framework for identifying the common and subject-specific functional units of tongue motion during speech
Woo J, Xing F, Prince J, Stone M, Gomez A, Reese T, Wedeen V, El Fakhri G. A deep joint sparse non-negative matrix factorization framework for identifying the common and subject-specific functional units of tongue motion during speech. Medical Image Analysis 2021, 72: 102131. PMID: 34174748, PMCID: PMC8316408, DOI: 10.1016/j.media.2021.102131.Peer-Reviewed Original ResearchConceptsNon-negative matrix factorizationSparse Non-negative Matrix FactorizationIterative shrinkage-thresholding algorithmNon-negative matrix factorization frameworkDeep neural networksMatrix factorization frameworkDeep learning frameworkTongue motionIdentified functional unitsGraph regularizationClustering performanceWeight mapLearning frameworkSpectral clusteringNeural networkMatrix factorizationModular architectureIncreased interpretabilityMotion dataFactorization frameworkConvoluted natureComparison methodTagged magnetic resonance imagingMuscle coordination patternsSpeech
2019
Attenuation correction using 3D deep convolutional neural network for brain 18F-FDG PET/MR: Comparison with Atlas, ZTE and CT based attenuation correction
Blanc-Durand P, Khalife M, Sgard B, Kaushik S, Soret M, Tiss A, Fakhri G, Habert M, Wiesinger F, Kas A. Attenuation correction using 3D deep convolutional neural network for brain 18F-FDG PET/MR: Comparison with Atlas, ZTE and CT based attenuation correction. PLOS ONE 2019, 14: e0223141. PMID: 31589623, PMCID: PMC6779234, DOI: 10.1371/journal.pone.0223141.Peer-Reviewed Original ResearchMeSH KeywordsAgedAlgorithmsAnalysis of VarianceBrainFemaleFluorodeoxyglucose F18HumansMagnetic Resonance ImagingMaleMultimodal ImagingNeural Networks, ComputerPositron-Emission TomographyConceptsZero echo timeAC mapsAttenuation correctionPET attenuation correctionCT-based ACComputed tomographyAC methodPhoton attenuationZTE-ACInvestigation of suspected dementiaMR imagingBrain computed tomographyAtlas-ACBrain metabolismZTE-MRIConvolutional neural networkEcho timeHead atlasFDG-PET/MRPET imagingLow biasRegions-of-interestPatientsCorrectionNeural networkDifferentiating post-cancer from healthy tongue muscle coordination patterns during speech using deep learning
Woo J, Xing F, Prince J, Stone M, Green J, Goldsmith T, Reese T, Wedeen V, Fakhri G. Differentiating post-cancer from healthy tongue muscle coordination patterns during speech using deep learning. The Journal Of The Acoustical Society Of America 2019, 145: el423-el429. PMID: 31153323, PMCID: PMC6530633, DOI: 10.1121/1.5103191.Peer-Reviewed Original ResearchMeSH KeywordsDeep LearningFacial MusclesHumansMagnetic Resonance ImagingMovementNeoplasmsNeural Networks, ComputerSpeechTongue
2018
Iterative PET Image Reconstruction Using Convolutional Neural Network Representation
Gong K, Guan J, Kim K, Zhang X, Yang J, Seo Y, Fakhri G, Qi J, Li Q. Iterative PET Image Reconstruction Using Convolutional Neural Network Representation. IEEE Transactions On Medical Imaging 2018, 38: 675-685. PMID: 30222554, PMCID: PMC6472985, DOI: 10.1109/tmi.2018.2869871.Peer-Reviewed Original ResearchMeSH KeywordsComputer SimulationHumansImage Processing, Computer-AssistedLikelihood FunctionsNeural Networks, ComputerPositron-Emission TomographyConceptsPET image reconstructionNeural networkConvolutional neural network representationsDeep residual convolutional neural networkImage reconstructionResidual convolutional neural networkComputer vision tasksDeep neural networksConvolutional neural networkNeural network denoisersAlternating direction methodNeural network representationIterative reconstruction frameworkNeural network methodVision tasksImage representationNetwork denoisingReconstruction frameworkMultipliers algorithmMedical imagesOptimization problemNetwork methodPost-processing toolDirection methodNetwork representationAttenuation 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 ResearchMeSH KeywordsBrainHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingNeural Networks, ComputerPositron-Emission TomographyConceptsU-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 errors
2017
Iterative Low-Dose CT Reconstruction With Priors Trained by Artificial Neural Network
Wu D, Kim K, Fakhri G, Li Q. Iterative Low-Dose CT Reconstruction With Priors Trained by Artificial Neural Network. IEEE Transactions On Medical Imaging 2017, 36: 2479-2486. PMID: 28922116, PMCID: PMC5897914, DOI: 10.1109/tmi.2017.2753138.Peer-Reviewed Original ResearchMeSH KeywordsAbdomenAlgorithmsHumansNeural Networks, ComputerRadiation DosageRadiographic Image Interpretation, Computer-AssistedThoraxTomography, X-Ray ComputedConceptsArtificial neural networkIterative reconstruction algorithmNeural networkLow-dose CT reconstructionReconstruction algorithmUnsupervised feature learningReconstructed imagesFeatures of imagesImprove reconstruction qualityNormal-dose imagesDecreasing radiation riskDevelopment of artificial neural networksFeature learningComplex featuresAuto-encoderReconstruction qualityData fidelityMachine learningSuppress noiseSmoothness constraintPhoton fluxPreservation abilityGrand ChallengeNoise reductionPriors
2010
Dual-Radionuclide Brain SPECT for the Differential Diagnosis of Parkinsonism
El Fakhri G, Ouyang J. Dual-Radionuclide Brain SPECT for the Differential Diagnosis of Parkinsonism. Methods In Molecular Biology 2010, 680: 237-246. PMID: 21153385, DOI: 10.1007/978-1-60761-901-7_16.Peer-Reviewed Original ResearchConceptsDopamine transporter functionBrain SPECTDifferential diagnosis of parkinsonismDifferential diagnosisDiagnosis of parkinsonismIdiopathic Parkinson's diseaseDifferential diagnosis of idiopathic Parkinson’s diseaseDiagnosis of idiopathic Parkinson's diseaseCorticobasal degenerationProgressive supranuclear palsyParkinson's diseaseMultiple system atrophyParkinsonSPECT protocol
2001
Absolute activity quantitation in simultaneous 123I/99mTc brain SPECT.
El Fakhri G, Moore S, Maksud P, Aurengo A, Kijewski M. Absolute activity quantitation in simultaneous 123I/99mTc brain SPECT. Journal Of Nuclear Medicine 2001, 42: 300-8. PMID: 11216530.Peer-Reviewed Original ResearchMeSH KeywordsAlzheimer DiseaseBenzamidesBrainCerebellumCerebral CortexCerebrovascular CirculationCorpus CallosumHumansIodine RadioisotopesMonte Carlo MethodNeural Networks, ComputerParkinson DiseasePhantoms, ImagingPutamenPyrrolidinesRadiopharmaceuticalsSensitivity and SpecificityTechnetium Tc 99m ExametazimeTomography, Emission-Computed, Single-PhotonConceptsOrdered-subset expectation maximizationDistance-dependent collimator responseActivity quantitationActivity distributionIterative ordered-subsets expectation maximizationZubal brain phantomAsymmetric windowsSimulated normal populationPathological studiesDecay photonsBrain SPECTCollimator responseNonuniform attenuationDual-isotope imagingBrain phantomMonte Carlo simulationsCorpus callosumNormal populationPartial volume effectsCarlo simulationsCortical lobesAssessment of brain perfusionScatteringCross-talkBrain perfusion
1998
Artificial neural network as a tool to compensate for scatter and attenuation in radionuclide imaging.
Maksud P, Fertil B, Rica C, El Fakhri G, Aurengo A. Artificial neural network as a tool to compensate for scatter and attenuation in radionuclide imaging. Journal Of Nuclear Medicine 1998, 39: 735-45. PMID: 9544691.Peer-Reviewed Original ResearchMeSH KeywordsBone and BonesHumansImage EnhancementMaleMiddle AgedMonte Carlo MethodNeural Networks, ComputerPhantoms, ImagingRadionuclide ImagingScattering, RadiationConceptsEnergy spectrumCompton scatteringRadioactive sourcesImages of radioactive sourcesScatter correctionArtificial neural networkNeural networkNumerical Monte Carlo simulationsMonte Carlo simulationsPelvis scansIncident photonsMultilayer neural networkProjection imagesScatteringComptonCarlo simulationsDiffusion mediaSource distributionSpectrum acquisitionEnergyGeometric sourcesHomogeneous mediumCorrectionSpectraNetwork