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 levelDirect estimation of metabolite maps from undersampled k-space data using linear tangent space alignment
Ma C, Marin T, Han P, Fakhri G. Direct estimation of metabolite maps from undersampled k-space data using linear tangent space alignment. Proceedings Of The International Society For Magnetic Resonance In Medicine ... Scientific Meeting And Exhibition. 2023 DOI: 10.58530/2023/0496.Peer-Reviewed Original Research
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 evaluationDenoising
2019
Arterial spin labeling MR image denoising and reconstruction using unsupervised deep learning
Gong K, Han P, Fakhri G, Ma C, Li Q. Arterial spin labeling MR image denoising and reconstruction using unsupervised deep learning. NMR In Biomedicine 2019, 35: e4224. PMID: 31865615, PMCID: PMC7306418, DOI: 10.1002/nbm.4224.Peer-Reviewed Original ResearchConceptsSignal-to-noise ratioImage denoisingReconstruction frameworkDeep learning-based image denoisingDeep learning-based denoisersMR image denoisingLearning-based denoisingLow signal-to-noise ratioK-space dataNoisy imagesTraining labelsTraining pairsNetwork inputNeural networkDenoisingIn vivo experiment dataSuperior performanceImaging speedReconstruction processImage qualityLong imaging timesNetworkFrameworkImagesSpatial resolutionEMnet: an unrolled deep neural network for PET image reconstruction
Gong K, Wu D, Kim K, Yang J, Fakhri G, Seo Y, Li Q. EMnet: an unrolled deep neural network for PET image reconstruction. Progress In Biomedical Optics And Imaging 2019, 10948: 1094853-1094853-6. DOI: 10.1117/12.2513096.Peer-Reviewed Original ResearchDeep neural networksPET image reconstructionNeural networkExpectation maximizationImage reconstructionImage denoising applicationNeural network frameworkNeural network denoisersDenoising applicationsDenoising methodNetwork denoisingNetwork trainingNetwork frameworkWhole graphUpdate stepData consistencyIll-posedNetworkInverse problemEMNETDenoisingSimulated dataFrameworkAlgorithmGraph
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