2024
DIANA - Detectability Investgations using Artificial Nodal Additions
Bayerlein R, Xia M, Xie H, Spencer B, Ouyang J, Fakhri G, Nardo L, Liu C, Badawi R. DIANA - Detectability Investgations using Artificial Nodal Additions. 2024, 00: 1-1. DOI: 10.1109/nss/mic/rtsd57108.2024.10657528.Peer-Reviewed Original ResearchContrast recovery coefficientContrast-to-noise ratioLesion-to-background ratioList-mode dataTotal-body PET/CT scannerPositron emission tomographyContrast recoveryOSEM algorithmPatient motionPET/CT scannerArtificial lesionsImage quality metricsLesion detectionQuantitative accuracyPositron emission tomography scanRecovery coefficientCount densityImage contrastBody mass indexImage noisePositron emission tomography imaging techniquesFrame lengthImage smoothingActivity concentrationsAccuracy of lesion detectionAnatomically and Metabolically Informed Deep Learning Low-Count PET Image Denoising
Xia M, Xie H, Liu Q, Guo L, Ouyang J, Bayerlein R, Spencer B, Badawi R, Li Q, Fakhri G, Liu C. Anatomically and Metabolically Informed Deep Learning Low-Count PET Image Denoising. 2024, 00: 1-2. DOI: 10.1109/nss/mic/rtsd57108.2024.10657099.Peer-Reviewed Original ResearchDeep learningOver-smoothed imagesDL training processesHigh-count imagesImage denoisingDenoised imageLow-count dataSemantic informationSemantic classesSegmentation guidanceTraining processPET/CT systemHistogram distributionImage qualitySegmentation toolPositron emission tomographyImagesDenoisingDatasetHistogramPriorsRadiation exposurePoint-supervised Brain Tumor Segmentation with Box-prompted Medical Segment Anything Model
Liu X, Woo J, Ma C, Ouyang J, Fakhri G. Point-supervised Brain Tumor Segmentation with Box-prompted Medical Segment Anything Model. 2024, 00: 1-1. DOI: 10.1109/nss/mic/rtsd57108.2024.10656071.Peer-Reviewed Original ResearchAblation 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. 2024, 00: 1-2. 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 informationAutoencoderTaskDiffusion-based Bayesian posterior distribution prediction of kinetic parameters in dynamic PET
Djebra Y, Liu X, Marin T, Tiss A, Dhaynaut M, Guehl N, Johnson K, Fakhri G, Ma C, Ouyang J. Diffusion-based Bayesian posterior distribution prediction of kinetic parameters in dynamic PET. 2024, 00: 1-1. DOI: 10.1109/nss/mic/rtsd57108.2024.10657955.Peer-Reviewed Original ResearchConditional variational autoencoderEfficient deep learning-based approachMarkov chain Monte CarloDenoising diffusion probabilistic modelDeep learning-based approachDiffusion probabilistic modelLearning-based approachApproximate posterior distributionPosterior distributionVariational autoencoderHeavy computationTau protein aggregationBayesian inferenceProbabilistic modelData-drivenStudy molecular processesBayesian posterior distributionProtein aggregationMetropolis-Hastings Markov chain Monte CarloMolecular processesAlzheimer's diseaseNeurodegenerative diseasesKinetic parametersEstimate posterior distributionsAutoencoderSubject-aware PET Denoising with Contrastive Adversarial Domain Generalization
Liu X, Marin T, Eslahi S, Tiss A, Chemli Y, Johson K, Fakhri G, Ouyang J. Subject-aware PET Denoising with Contrastive Adversarial Domain Generalization. 2024, 00: 1-1. DOI: 10.1109/nss/mic/rtsd57108.2024.10656150.Peer-Reviewed Original ResearchDomain generalizationDenoising performanceDenoising moduleDeep learningSubject-independent mannerSubject-invariant featuresSuperior denoising performanceAdversarial learning frameworkSubject-related informationConventional UNetBottleneck featuresTrustworthy systemsLearning frameworkDL modelsDL model performanceDenoisingNoise realizationsNegative samplesList-mode dataImage volumesModel performancePerformancePerformance of positron emission tomographyUNetFraction of eventsPET motion correction using subspace-based real-time MR imaging in simultaneous PET/MR
Mounime I, Marin T, Han P, Ouyang J, Gori P, Angelini E, Fakhri G, Ma C. PET motion correction using subspace-based real-time MR imaging in simultaneous PET/MR. 2024, 00: 1-1. DOI: 10.1109/nss/mic/rtsd57108.2024.10657647.Peer-Reviewed Original ResearchOrdered-subset expectation maximizationMotion correctionGated reconstructionsMotion-corrected PET reconstructionsPET eventsCardiac motion phasesMotion correction methodCardiac motionMotion phaseReconstructed dynamic imagesPET reconstructionReal-time MR imagingSimultaneous PET/MRPatient motionSoft tissue contrastDynamic MR image reconstructionReference phaseMitigate artifactsLow-rank propertyMR image reconstructionPositron emission tomographyManifold learning frameworkSpatial resolutionBlurring artifactsImage reconstructionEffects of List-Mode Based Intra-Frame Motion Correction in Dynamic Brain PET Imaging
Tiss A, Chemli Y, Guehl N, Marin T, Johnson K, Fakhri G, Ouyang J. Effects of List-Mode Based Intra-Frame Motion Correction in Dynamic Brain PET Imaging. IEEE Transactions On Radiation And Plasma Medical Sciences 2024, PP: 1-1. DOI: 10.1109/trpms.2024.3432322.Peer-Reviewed Original ResearchDiffusion Model-Based Posterior Distribution Prediction for Kinetic Parameter Estimation in Dynamic PET
Djebra Y, Liu X, Marin T, Tiss A, Dhaynaut M, Guehl N, Johnson K, Fakhri G, Ma C, Ouyang J. Diffusion Model-Based Posterior Distribution Prediction for Kinetic Parameter Estimation in Dynamic PET. 2024, 00: 1-5. DOI: 10.1109/isbi56570.2024.10635805.Peer-Reviewed Original ResearchPosterior distributions of kinetic parametersDenoising diffusion probabilistic modelHyperphosphorylated tauP-tauDiffusion probabilistic modelAlzheimer's diseaseNeurodegenerative diseasesKinetic parametersPosterior distributionInference efficiencyComputational needsEstimate kinetic parametersProbabilistic modelComputation timeCross noise level PET denoising with continuous adversarial domain generalization
Liu X, Eslahi S, Marin T, Tiss A, Chemli Y, Huang Y, Johnson K, Fakhri G, Ouyang J. Cross noise level PET denoising with continuous adversarial domain generalization. Physics In Medicine And Biology 2024, 69: 085001. PMID: 38484401, PMCID: PMC11195012, DOI: 10.1088/1361-6560/ad341a.Peer-Reviewed Original ResearchDomain generalization techniqueDomain generalizationDenoising performanceSuperior denoising performanceLatent feature representationGeneral techniqueDistribution shiftsAdversarial trainingDenoised imageFeature representationDomain labelsDistribution divergenceNoise levelDeep learningImage spaceDenoisingPerformance degradationCore ideaNoise realizationsCD methodNoiseImage volumesPerformanceImagesPSNRDisentangled 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
Balanced Steady-State Free Precession and Radial Sampling for Arterial Spin Labeled Perfusion Imaging
Han P, Marin T, Zhuo Y, Ouyang J, Fakhri G, Ma C. Balanced Steady-State Free Precession and Radial Sampling for Arterial Spin Labeled Perfusion Imaging. Proceedings Of The International Society For Magnetic Resonance In Medicine ... Scientific Meeting And Exhibition. 2023 DOI: 10.58530/2023/0373.Peer-Reviewed Original ResearchImpact of motion correction on [18F]-MK6240 tau PET imaging
Tiss A, Marin T, Chemli Y, Spangler-Bickell M, Gong K, Lois C, Petibon Y, Landes V, Grogg K, Normandin M, Becker A, Thibault E, Johnson K, Fakhri G, Ouyang J. Impact of motion correction on [18F]-MK6240 tau PET imaging. Physics In Medicine And Biology 2023, 68: 105015. PMID: 37116511, PMCID: PMC10278956, DOI: 10.1088/1361-6560/acd161.Peer-Reviewed Original ResearchConceptsMotion correctionPET quantitationImpact of motion correctionList-mode reconstructionMotion correction methodList-mode dataMotion-corrected imagesEffect of motion correctionVoxel displacementsPhantom experimentsOptical tracking dataLong acquisitionBrain PET scansSlow motionImage qualityPET imagingPositron emission tomographyCorrectionMotionCorrection methodRates of tau accumulationHead motionMotion metricsPhantomPositronArterial spin labeled perfusion imaging with balanced steady-state free precession readout and radial sampling
Han P, Marin T, Zhuo Y, Ouyang J, El Fakhri G, Ma C. Arterial spin labeled perfusion imaging with balanced steady-state free precession readout and radial sampling. Magnetic Resonance Imaging 2023, 102: 126-132. PMID: 37187264, PMCID: PMC10524790, DOI: 10.1016/j.mri.2023.05.005.Peer-Reviewed Original ResearchConceptsOff-resonance effectsBalanced steady-state free precessionPhase-cycling techniqueTemporal SNRBalanced steady-state free precession acquisitionRadial sampling schemeSpoiled gradient-recalled acquisitionRadial samplingCartesian sampling schemeBalanced steady-state free precession readoutK-space dataSampling schemeSpin labelingSteady-state free precessionK-spaceImage readoutBanding artifactsMotion-related artifactsReadoutFree precessionArterial spin labelingImage reconstructionParallel imagingImaging timePerfusion-weighted imagingSuper-resolution in brain positron emission tomography using a real-time motion capture system
Chemli Y, Tétrault M, Marin T, Normandin M, Bloch I, El Fakhri G, Ouyang J, Petibon Y. Super-resolution in brain positron emission tomography using a real-time motion capture system. NeuroImage 2023, 272: 120056. PMID: 36977452, PMCID: PMC10122782, DOI: 10.1016/j.neuroimage.2023.120056.Peer-Reviewed Original ResearchConceptsBrain positron emission tomographySuper-resolutionEvent-by-event basisReal-time motion capture systemSR reconstruction methodTracking cameraVisualization of small structuresPET reconstruction algorithmMoving phantomMeasure target motionLine profilesPET/CT scannerMeasured shiftsImprove image resolutionMotion capture systemMotion tracking devicePositron emission tomographyReconstruction algorithmSpatial resolutionMeasured linesPhantomReal-timeEstimation frameworkIncreased spatial resolutionReconstruction method
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 performanceImagesCardiac PET/MR Basics
Petibon Y, Ma C, Ouyang J, El Fakhri G. Cardiac PET/MR Basics. 2022, 21-35. DOI: 10.1007/978-3-031-09807-9_2.Peer-Reviewed Original ResearchMeasuring organ motionPET motion correctionFront-end electronicsScintillation detectorOrgan motionMotion correctionAttenuation correctionPET/MR scannersAbility of MRISimultaneous imaging modalityPET/MR technologyAttenuation propertiesHybrid PET/MR scannerPET/MRPre-clinicalImaging modalitiesClinical applicationMRIOrgan systemsCardiac applicationsImaging opportunitiesCorrectionDetectorSpatiotemporal resolutionElectron
2021
Impact of reconstruction parameters on lesion detection and localization in joint ictal/inter-ictal SPECT reconstruction
Onwanna J, Chantadisai M, Tepmongkol S, Fahey F, Ouyang J, Rakvongthai Y. Impact of reconstruction parameters on lesion detection and localization in joint ictal/inter-ictal SPECT reconstruction. Annals Of Nuclear Medicine 2021, 36: 24-32. PMID: 34559366, DOI: 10.1007/s12149-021-01680-x.Peer-Reviewed Original ResearchConceptsLocalization receiver operating characteristicsDetection performanceSubtraction methodJoint methodOptimization iterationsLesion detection performanceSPECT reconstructionEpileptic focus localizationLocalization receiver operating characteristic curvesDifferential imagingInter-ictalLesion detectionIterationPhantom dataConventional subtraction methodPerformanceImagesPatient datasetsReconstruction parametersImpact of reconstruction parametersFocus localizationSubtractionDetectionSPECT dataJoint reconstructionDomain Generalization under Conditional and Label Shifts via Variational Bayesian Inference
Liu X, Hu B, Jin L, Han X, Xing F, Ouyang J, Lu J, El Fakhri G, Woo J. Domain Generalization under Conditional and Label Shifts via Variational Bayesian Inference. 2021, 881-887. DOI: 10.24963/ijcai.2021/122.Peer-Reviewed Original ResearchDomain generalizationDomain-invariant feature learningVariational Bayesian inference frameworkLabel shiftCross-domain accuracyLabeled source domainVariational Bayesian inferenceFeature learningBayesian inference frameworkLatent spaceSource domainTarget domainDistribution matchingTransfer knowledgeInference frameworkSuperior performanceP(x|yBayesian inferenceLabelingDomainFrameworkGeneralizationBenchmarksPosterior alignmentLearningDetecting 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