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
2022
Low-Dose Tau PET Imaging Based on Swin Restormer with Diagonally Scaled Self-Attention
Jang S, Lois C, Becker J, Thibault E, Li Y, Price J, Fakhri G, Li Q, Johnson K, Gong K. Low-Dose Tau PET Imaging Based on Swin Restormer with Diagonally Scaled Self-Attention. 2022, 00: 1-3. DOI: 10.1109/nss/mic44845.2022.10399169.Peer-Reviewed Original ResearchConvolutional neural networkSelf-attention mechanismSelf-attentionTransformer architectureComputer vision tasksLocal feature extractionLong-range informationVision tasksDenoising performanceSwin TransformerFeature extractionImage datasetsUNet structureNeural networkSwinComputational costReceptive fieldsImage qualityMap calculationNetwork structureArchitecturePET image qualityChannel dimensionsQuantitative evaluationDenoisingFeasibility study of clinical target volume definition for soft-tissue sarcoma using muscle fiber orientations derived from diffusion tensor imaging
Shusharina N, Liu X, Coll-Font J, Foster A, Fakhri G, Woo J, Bortfeld T, Nguyen C. Feasibility study of clinical target volume definition for soft-tissue sarcoma using muscle fiber orientations derived from diffusion tensor imaging. Physics In Medicine And Biology 2022, 67: 155013. PMID: 35817048, PMCID: PMC9344976, DOI: 10.1088/1361-6560/ac8045.Peer-Reviewed Original ResearchConceptsClinical target volumeSoft tissue sarcomasHealthy volunteersClinical target volume definitionClinical target volume delineationRight thighGross tumor volumeTarget volume definitionEcho-planar acquisitionsTarget volumeAuto-segmentationTumor volumeVolume definitionSarcoma patientsTissue sarcomasCT planeConvolutional neural networkT2-weightedCancer CenterPlanar acquisitionImaging sessionFat suppressionMR imagingSarcomaAnatomical orientationVoxelHop: 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 ResearchConceptsConvolutional 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 fieldDetecting lumbar lesions in 99mTc‐MDP SPECT by deep learning: Comparison with physicians
Petibon Y, Fahey F, Cao X, Levin Z, Sexton‐Stallone B, Falone A, Zukotynski K, Kwatra N, Lim R, Bar‐Sever Z, Chemli Y, Treves S, Fakhri G, Ouyang J. Detecting lumbar lesions in 99mTc‐MDP SPECT by deep learning: Comparison with physicians. Medical Physics 2021, 48: 4249-4261. PMID: 34101855, DOI: 10.1002/mp.15033.Peer-Reviewed Original ResearchConceptsSingle-photon emission computed tomographyLow back painLumbar lesionsPediatric patientsTc-MDPEvaluate low back painCause of low back painTc-MDP scanLesion-presentEmission computed tomographyConvolutional neural networkClinical likelihoodBack painInterreader variabilityDeep convolutional neural networkLumbar locationLesionsStress lesionsFocal lesionsDeep learningPatientsLumbar stressPhysiciansDL systemsLROC studies
2020
High-performance rapid MR parameter mapping using model-based deep adversarial learning
Liu F, Kijowski R, Feng L, El Fakhri G. High-performance rapid MR parameter mapping using model-based deep adversarial learning. Magnetic Resonance Imaging 2020, 74: 152-160. PMID: 32980503, PMCID: PMC7669737, DOI: 10.1016/j.mri.2020.09.021.Peer-Reviewed Original ResearchConceptsConvolutional neural networkMR parameter mappingAdversarial learningState-of-the-art reconstruction methodsEnd-to-end convolutional neural networkUndersampled k-space dataConvolutional neural network approachAdversarial learning approachState-of-the-artStructural similarity indexImage reconstruction frameworkEnd-to-endImage sharpnessData consistencyConventional reconstruction approachesReconstruction approachK-space dataImprove image sharpnessImage reconstruction approachEstimated parameter mapsImage sparsityTexture restorationNetwork trainingImage datasetsReconstruction performance
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 ResearchConceptsZero 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 networkMAPEM-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 photonsReconstructionMethodSimulationDifferentiating 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, El 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 ResearchGraph 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 systemsGraph
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 effectsDeepBlurEnd-to-End Lung Nodule Detection in Computed Tomography
Wu D, Kim K, Dong B, Fakhri G, Li Q. End-to-End Lung Nodule Detection in Computed Tomography. Lecture Notes In Computer Science 2018, 11046: 37-45. DOI: 10.1007/978-3-030-00919-9_5.Peer-Reviewed Original ResearchDeep reconstruction networkLung nodule detectionReconstruction networkEnd-to-end detectorMedical imagesLung Image Database Consortium image collectionNodule detectionEfficient network trainingReconstructed imagesConvolutional neural networkEnd-to-endSuperior detection performanceRaw dataComputer visionCAD systemCNN detectorNetwork trainingImage collectionNeural networkDetection performanceImage spaceDetection taskDetection systemModern medical imagingFanbeam projectionsIterative 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 ResearchConceptsPET 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 representationDeep 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 errorAlgorithmImagesAccuracyErrorClassificationPenalized 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