2024
Disentangled 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
TauPETGen: Text-Conditional Tau PET Image Synthesis Based on Latent Diffusion Models
Jang S, Gomez C, Thibault E, Becker J, Dong Y, Normandin M, Price J, Johnson K, Fakhri G, Gong K. TauPETGen: Text-Conditional Tau PET Image Synthesis Based on Latent Diffusion Models. 2023, 00: 1-1. DOI: 10.1109/nssmicrtsd49126.2023.10338710.Peer-Reviewed Original ResearchPET 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 levelQuantifying velopharyngeal motion variation in speech sound production using an audio-informed dynamic MRI atlas
Xing F, Jin R, Gilbert I, El Fakhri G, Perry J, Sutton B, Woo J. Quantifying velopharyngeal motion variation in speech sound production using an audio-informed dynamic MRI atlas. Proceedings Of SPIE--the International Society For Optical Engineering 2023, 12464: 124642m-124642m-6. PMID: 37621417, PMCID: PMC10448831, DOI: 10.1117/12.2654082.Peer-Reviewed Original ResearchMotion fieldReal-time speechHigh-dimensional datasetsAudio waveformAtlas spaceTemporal alignmentMotion variationsDatasetMagnetic resonance imagingMotion atlasMotion differencesSpeech variationImage acquisitionTaskSpeechMotion characteristicsDynamic magnetic resonance imagingPrincipal componentsImages
2022
Automation of generative adversarial network-based synthetic data-augmentation for maximizing the diagnostic performance with paranasal imaging
Kong H, Kim J, Moon H, Park H, Kim J, Lim R, Woo J, Fakhri G, Kim D, Kim S. Automation of generative adversarial network-based synthetic data-augmentation for maximizing the diagnostic performance with paranasal imaging. Scientific Reports 2022, 12: 18118. PMID: 36302815, PMCID: PMC9613909, DOI: 10.1038/s41598-022-22222-z.Peer-Reviewed Original ResearchConceptsSynthetic data augmentationData augmentationLack of training dataConventional data augmentationDeep learning methodsTraining dataLearning methodsPipeline approachAlgorithm trainingGraphical dataAutomationWaters' view radiographsModel performanceAutomated pipelinePerformancePerformance parametersAlgorithmDatasetAugmentationDataMethodPipelineRulesIndustrial workersVoxelHop: 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
2019
MAPEM-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 photonsReconstructionMethodSimulation
2015
PET Point Spread Function Modeling and Image Deblurring Using a PET/MRI Joint Entropy Prior
Dutta J, Fakhri G, Zhu X, Li Q. PET Point Spread Function Modeling and Image Deblurring Using a PET/MRI Joint Entropy Prior. 2015, 1423-1426. DOI: 10.1109/isbi.2015.7164143.Peer-Reviewed Original ResearchJoint entropyDe-blurring techniqueImage deblurringBrain WebQuantitative accuracy of PETImage spaceEfficient frameworkDeconvolution problemDeblurringCost functionPoint spread function modelPenalty functionHuman datasetsSpatial resolution capabilitiesPET scannerAmplifier noisePET imagingImagesConsistent with MRIQuantitative accuracyPartial volume effectsPhantomDatasetEntropyFunction model