2024
Cross 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 volumesPerformanceImagesPSNR
2023
PET 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 level
2018
Penalized 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 imagesDeepMR-based motion correction for cardiac PET parametric imaging: a simulation study
Guo R, Petibon Y, Ma Y, El Fakhri G, Ying K, Ouyang J. MR-based motion correction for cardiac PET parametric imaging: a simulation study. EJNMMI Physics 2018, 5: 3. PMID: 29388075, PMCID: PMC5792384, DOI: 10.1186/s40658-017-0200-9.Peer-Reviewed Original ResearchNon-motion-correctedPositron emission tomography-magnetic resonanceMR-based motion correctionRespiratory motionMotion correctionActivity distributionMotion correction methodRespiratory gatingPET sinogramsNoise levelCardiac gatingMRI simulationOne-tissue compartment modelNoise realizationsMC methodPET dataCardiac motionReduce motion blurParametric imagesIncreased noise levelConclusionsThis simulation studyArterial input functionPET imagingGateMyocardial regions
2014
Myocardial Defect Detection Using PET-CT: Phantom Studies
Mananga E, Fakhri G, Schaefferkoetter J, Bonab A, Ouyang J. Myocardial Defect Detection Using PET-CT: Phantom Studies. PLOS ONE 2014, 9: e88200. PMID: 24505429, PMCID: PMC3914931, DOI: 10.1371/journal.pone.0088200.Peer-Reviewed Original ResearchConceptsMyocardial defect detectionFiltered back projectionChannelized Hotelling observerPhantom studyActivity distributionSubset expectation maximizationDefect detectionCardiac PET studiesMyocardial defectsHotelling observerNoise levelBack-projectionPET-CTPhantomExpectation maximizationOP-OSEMReconstruction schemePET studiesOSEMDefectsNoise
2011
A Nonlocal Averaging Technique for Kinetic Parameter Estimation from Dynamic PET Data
Dutta J, Fakhri G, Leahy R, Li Q. A Nonlocal Averaging Technique for Kinetic Parameter Estimation from Dynamic PET Data. 2011, 3562-3566. DOI: 10.1109/nssmic.2011.6153669.Peer-Reviewed Original ResearchNonlocal meansDenoising schemeGaussian filtering techniquePatlak parametric imagesDenoised time seriesDenoising frameworkClustering stepDistributed natureGaussian filterLocal neighborhoodVoxel-wise estimationHigh noise levelsData setsDenoisingFiltering techniqueSchemeDataLow varianceNoise levelTime activity curvesEstimation of kinetic parametersDynamic PET imagesParameter estimationVoxelImages