2024
Ablation Study of Diffusion Model with Transformer Backbone for Low-count PET Denoising
Huang Y, Liu X, Miyazaki T, Omachi S, Fakhri G, Ouyang J. Ablation Study of Diffusion Model with Transformer Backbone for Low-count PET Denoising. 2011 IEEE Nuclear Science Symposium Conference Record 2024, 00: 1-2. PMID: 39445309, PMCID: PMC11497477, DOI: 10.1109/nss/mic/rtsd57108.2024.10655179.Peer-Reviewed Original ResearchIR tasksImage restorationImage super-resolution taskField of image restorationSuper-resolution taskLatent feature spaceConventional UNetDenoising iterationDenoising taskTransformer backboneDenoising autoencoderTexture restorationVision transformerFeature spaceAblation studiesLearning schemeBackbone networkImage generationDenoisingUNetIR modelPSNRSpatial informationAutoencoderTaskDisentangled 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
Quantifying 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
Principal Component Characterization of Deformation Variations Using Dynamic Imaging Atlases
Xing F, Jin R, Gilbert I, Fakhri G, Perry J, Sutton B, Woo J. Principal Component Characterization of Deformation Variations Using Dynamic Imaging Atlases. Proceedings Of The International Society For Magnetic Resonance In Medicine ... Scientific Meeting And Exhibition. 2022 DOI: 10.58530/2022/2849.Peer-Reviewed Original ResearchSelf-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
2021
Adapting Off-the-Shelf Source Segmenter for Target Medical Image Segmentation
Liu X, Xing F, Yang C, El Fakhri G, Woo J. Adapting Off-the-Shelf Source Segmenter for Target Medical Image Segmentation. Lecture Notes In Computer Science 2021, 12902: 549-559. PMID: 34734216, PMCID: PMC8562716, DOI: 10.1007/978-3-030-87196-3_51.Peer-Reviewed Original ResearchUnsupervised domain adaptationSegmentation taskSource domainTarget domainUnsupervised domain adaptation methodsLabeled source domainSource domain dataUnsupervised learning methodDomain adaptationUDA methodsPrivacy issuesLearning methodsAdaptation frameworkDomain dataData storageTransfer knowledgeBatch statisticsSource dataOptimization objectivesAdaptation stageTaskFrameworkPrivacyDomainBraTSSubtype-aware Unsupervised Domain Adaptation for Medical Diagnosis
Liu X, Liu X, Hu B, Ji W, Xing F, Lu J, You J, Kuo C, Fakhri G, Woo J. Subtype-aware Unsupervised Domain Adaptation for Medical Diagnosis. Proceedings Of The AAAI Conference On Artificial Intelligence 2021, 35: 2189-2197. DOI: 10.1609/aaai.v35i3.16317.Peer-Reviewed Original ResearchA Unified Conditional Disentanglement Framework For Multimodal Brain Mr Image Translation
Liu X, Xing F, Fakhri G, Woo J. A Unified Conditional Disentanglement Framework For Multimodal Brain Mr Image Translation. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2021, 00: 10-14. PMID: 34567419, PMCID: PMC8460116, DOI: 10.1109/isbi48211.2021.9433897.Peer-Reviewed Original Research
2002
Optimization of Ga‐67 imaging for detection and estimation tasks: Dependence of imaging performance on spectral acquisition parameters
Fakhri G, Moore S, Kijewski M. Optimization of Ga‐67 imaging for detection and estimation tasks: Dependence of imaging performance on spectral acquisition parameters. Medical Physics 2002, 29: 1859-1866. PMID: 12201433, DOI: 10.1118/1.1493214.Peer-Reviewed Original ResearchConceptsIdeal signal-to-noise ratioEnergy windowSignal-to-noise ratioMonte Carlo programDetection of spheresTorso phantomPhantom acquisitionsSphere of radiusEstimation taskPhantom dataLower-energyGa-67 imagingPhantomAcquisition parametersActivity concentrationsSpectral acquisition parametersGa-67Sphere sizeEnergyPhotopeakTumor imagingOptimal windowTaskClinicMonte
2001
Optimization of Ga-67 Imaging for Detection and Estimation Tasks: Dependence of Imaging Performance on Choice of Energy Windows
Fakhri G, Moore S, Kijewski M. Optimization of Ga-67 Imaging for Detection and Estimation Tasks: Dependence of Imaging Performance on Choice of Energy Windows. 2001, 3: 1355-1357. DOI: 10.1109/nssmic.2001.1008588.Peer-Reviewed Original ResearchEnergy windowSignal-to-noise ratioMonte Carlo programDetection of spheresIdeal signal-to-noise ratioTorso phantomEstimation taskDetection signal-to-noise ratioPhantom acquisitionsSphere of radiusPhantom dataLower-energyPhotopeakImaging performancePhantomGa-67Detection metricsGa-67 imagingSphere sizeOptimal window widthWindow widthEnergyTaskQuantitative tasksOptimal window