2024
PET 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 reconstructionIntegration of a continuously varying image-space PSF for a dual-panel ultra-high TOF-PET scanner
Chemli Y, Marin T, Orehar M, Dolenec R, Normandin M, Gascón D, Gola A, Grogg K, Pavón G, Razdevsek G, Pestotnik R, Fakhri G. Integration of a continuously varying image-space PSF for a dual-panel ultra-high TOF-PET scanner. 2024, 00: 1-1. DOI: 10.1109/nss/mic/rtsd57108.2024.10656225.Peer-Reviewed Original ResearchGaussian mixture modelGaussian process regressionPoint spread functionAccurate image reconstructionMaximum likelihood estimation maximizationShift-variant convolutionsImage reconstructionMixture modelProcess regressionEstimation maximizationTime-of-flight (TOFPanel architectureSpread functionArchitectureParameter interpolationHigh resolution time-of-flight (TOFTOF-PET scannerBrain phantomFitting processPositron emission tomography scannerSimulated point sourcesConvolutionAlgorithmEffective diagnosisSize benefitsFlat Panel TOF-PET Detectors: a Simulation Study
Orehar M, Dolenec R, Fakhri G, Gascón D, Gola A, Korpar S, Križan P, Razdevšek G, Marin T, Chemli Y, Žontar D, Pestotnik R. Flat Panel TOF-PET Detectors: a Simulation Study. 2024, 00: 1-2. DOI: 10.1109/nss/mic/rtsd57108.2024.10658250.Peer-Reviewed Original ResearchTime resolutionAngular coverageFlat-panel detectorScintillation materialsGATE softwareAxial coverageBiograph VisionPanel detectorTotal-body coverageClinical scannerImage reconstructionDetectorReconstructed imagesHomogeneous contrastCylindrical scannerImage qualityState-of-the-artScintillationHigh-performance computingScannerPhantomResolutionCore hoursPositron emission tomographyGateThe United States Department of Energy and National Institutes of Health Collaboration: Medical Care Advances by Discovery in Radiation Detection
Buchsbaum J, Capala J, Obcemea C, Keppel C, Asai M, Chen G, Christy M, Fakhri G, Gueye P, Pogue B, Ruckman L, Tourassi G, Vetter K, Zhao W, Squires A, Saboury B, Wang G, Domurat‐Sousa K, Weisenberger A. The United States Department of Energy and National Institutes of Health Collaboration: Medical Care Advances by Discovery in Radiation Detection. Medical Physics 2024 PMID: 39177300, DOI: 10.1002/mp.17333.Peer-Reviewed Original ResearchNational Institutes of HealthState-of-the-artApplication of artificial intelligenceDOE Office of ScienceArtificial intelligenceMedical care advancesImage reconstructionIn-person workshopsOffice of ScienceRadiation detectionHealth collaborationInstitutes of HealthCare advancesIn-personAreas of successSparsity Constrained Linear Tangent Space Alignment Model (LTSA) for 3d Cardiac Extracellular Volume Mapping
Mounime I, Lee W, Marin T, Han P, Djebra Y, Eslahi S, Gori P, Angelini E, Fakhri G, Ma C. Sparsity Constrained Linear Tangent Space Alignment Model (LTSA) for 3d Cardiac Extracellular Volume Mapping. 2024, 00: 1-5. DOI: 10.1109/isbi56570.2024.10635692.Peer-Reviewed Original ResearchT)-space dataExtracellular volume mappingIntrinsic low-dimensional manifold structureCardiac tissue propertiesImproved image reconstructionLow-dimensional manifold structureExtracellular volumeFast MRI methodSparsity constraintModel-based methodsSuperior performanceSpace alignmentT1 mappingManifold structureImage reconstructionT)-spacePost-contrast T1 mappingTissue propertiesFree breathingConcentration of contrast agentLongitudinal relaxation timeAlignment modelDynamic MR imagingSparsityAlignment matrix
2023
Direct 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 ResearchArterial 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 imaging
2022
Manifold Learning via Linear Tangent Space Alignment (LTSA) for Accelerated Dynamic MRI With Sparse Sampling
Djebra Y, Marin T, Han P, Bloch I, Fakhri G, Ma C. Manifold Learning via Linear Tangent Space Alignment (LTSA) for Accelerated Dynamic MRI With Sparse Sampling. IEEE Transactions On Medical Imaging 2022, 42: 158-169. PMID: 36121938, PMCID: PMC10024645, DOI: 10.1109/tmi.2022.3207774.Peer-Reviewed Original ResearchConceptsSpace alignmentSampled k-space dataState-of-the-art methodsIntrinsic low-dimensional manifold structureNumerical simulation studyLow-dimensional manifold structureState-of-the-artLinear subspace modelSparsity modelModel-based frameworkSubspace modelManifold structureMathematical modelManifold modelSparse samplingImage reconstructionMRI applicationsDynamic magnetic resonance imagingSpatiotemporal signalsSpatial resolutionPerformanceSimulation studyImagesMethodSparsityOptimization of Design Parameters of Flat Panel Limited Angle TOF-PET Scanner: a Simulation Study
Orehar M, Dolenec R, Fakhri G, Gascón D, Gola A, Korpar S, Križan P, Razdevšek G, Pestotnik R. Optimization of Design Parameters of Flat Panel Limited Angle TOF-PET Scanner: a Simulation Study. 2022, 00: 1-4. DOI: 10.1109/nss/mic44845.2022.10399330.Peer-Reviewed Original ResearchImage quality phantomFlat-panel detectorGATE softwareRing scannerPanel detectorDetection chainState-of-the-artNEMA standardsOpen geometryImage reconstructionReconstructed imagesPositron emission tomographyState-of-the-art scannersScannerPhantomDetectorPhotodetectorsElectronNEMAMaterial costGatePositron
2021
First Investigation of List mode MLEM Reconstruction for Fast DC-SPECT System Design Optimization
Feng Y, Bläckberg L, Fakhri G, Worstell W, Sabet H. First Investigation of List mode MLEM Reconstruction for Fast DC-SPECT System Design Optimization. 2021, 00: 1-3. DOI: 10.1109/nss/mic44867.2021.9875864.Peer-Reviewed Original ResearchFWHM spatial resolutionCorrection of scatterAsymmetric geometryMaximum likelihood expectation maximizationLikelihood expectation maximizationSystem's imaging resolutionLM-MLEMMonte Carlo implementationMLEM reconstructionDetector headMonte-CarloSystem matrix modelSimultaneous high resolutionDesign optimizationCardiac SPECTMatrix modelSystem resolutionMC methodSpatial resolutionSystem design optimizationImage reconstructionDynamic cardiac SPECTDesign iterationsSystem sensitivityImage resolutionQuantitative PET in the 2020s: a roadmap
Meikle S, Sossi V, Roncali E, Cherry S, Banati R, Mankoff D, Jones T, James M, Sutcliffe J, Ouyang J, Petibon Y, Ma C, El Fakhri G, Surti S, Karp J, Badawi R, Yamaya T, Akamatsu G, Schramm G, Rezaei A, Nuyts J, Fulton R, Kyme A, Lois C, Sari H, Price J, Boellaard R, Jeraj R, Bailey D, Eslick E, Willowson K, Dutta J. Quantitative PET in the 2020s: a roadmap. Physics In Medicine And Biology 2021, 66: 06rm01. PMID: 33339012, PMCID: PMC9358699, DOI: 10.1088/1361-6560/abd4f7.Peer-Reviewed Original ResearchMeSH KeywordsArtificial IntelligenceHistory, 20th CenturyHistory, 21st CenturyHumansImage Processing, Computer-AssistedImaging, Three-DimensionalKineticsMedical OncologyNeoplasmsPositron Emission Tomography Computed TomographyPositron-Emission TomographyPrognosisRadiopharmaceuticalsSystems BiologyTomography, X-Ray ComputedConceptsTime-of-flight positron emission tomographyStatistical image reconstructionTotal-body positron emission tomographyPositron emission tomographyQuantitative positron emission tomographyImage reconstructionWhole-body positron emission tomographySensitivity of positron emission tomographyCapabilities of positron emission tomographyImage qualityClinical applicationTracer principleRelevant parametersOncology applicationsPhysicsStatistical qualityExpansion of applicationsEmission tomographyClinical practicePET/MRBiologically relevant parametersSensitive biomarkerPositron
2020
PET imaging of neurotransmission using direct parametric reconstruction
Petibon Y, Alpert N, Ouyang J, Pizzagalli D, Cusin C, Fava M, Fakhri G, Normandin M. PET imaging of neurotransmission using direct parametric reconstruction. NeuroImage 2020, 221: 117154. PMID: 32679252, PMCID: PMC7800040, DOI: 10.1016/j.neuroimage.2020.117154.Peer-Reviewed Original ResearchConceptsSignal-to-noise ratioImage reconstructionPositron emission tomography image reconstructionLow signal-to-noise ratioPoisson log-likelihood functionScattered coincidencesDetector sensitivityPET sinogramsStatistical fluctuationsEstimate parametric imagesGradient-based optimizationParametric reconstructionLog-likelihood functionEffects of head movementVoxel scalePositron emission tomographySimplified reference region modelActivity concentration dataMR‐based PET attenuation correction using a combined ultrashort echo time/multi‐echo Dixon acquisition
Han P, Horng D, Gong K, Petibon Y, Kim K, Li Q, Johnson K, Fakhri G, Ouyang J, Ma C. MR‐based PET attenuation correction using a combined ultrashort echo time/multi‐echo Dixon acquisition. Medical Physics 2020, 47: 3064-3077. PMID: 32279317, PMCID: PMC7375929, DOI: 10.1002/mp.14180.Peer-Reviewed Original ResearchConceptsLinear attenuation coefficientPositron emission tomography attenuation correctionPhysical compartmental modelAttenuation correctionShort T<sub>2</sub> componentPET attenuation correctionRadial k-space trajectoryMagnetic resonance (MR)-based methodK-space trajectoriesRadial trajectoryK-spaceAttenuation coefficientDixon acquisitionsPositron emission tomographyWhole white matterMuting methodImage reconstructionImaging speedMR signalMRAC methodPositron emission tomography imagingCorrectionGray matter regionsPhantomMatter regions
2019
Body motion detection and correction in cardiac PET: Phantom and human studies
Sun T, Petibon Y, Han P, Ma C, Kim S, Alpert N, Fakhri G, Ouyang J. Body motion detection and correction in cardiac PET: Phantom and human studies. Medical Physics 2019, 46: 4898-4906. PMID: 31508827, PMCID: PMC6842053, DOI: 10.1002/mp.13815.Peer-Reviewed Original ResearchConceptsList-mode dataMotion-compensated image reconstructionMotion correctionCenter of massPET list-mode dataMotion correction methodMotion detectionMotion estimationImage reconstructionPatient body motionDegrade image qualityNonrigid registrationImage qualityMotion transformationCoincident distributionBody motion detectionCardiac positron emission tomographyBack-projection techniqueCovariance matrixImage volumesBody motionPositron emission tomographyBack-projectionReference framePhantomMAPEM-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 photonsReconstructionMethodSimulationComputational-efficient cascaded neural network for CT image reconstruction
Wu D, Kim K, Fakhri G, Li Q. Computational-efficient cascaded neural network for CT image reconstruction. Progress In Biomedical Optics And Imaging 2019, 10948: 109485z-109485z-6. DOI: 10.1117/12.2511526.Peer-Reviewed Original ResearchCascaded neural networkNeural networkImage reconstructionCT image reconstructionMemory consumptionDevelopment of deep learningDeep artificial neural networksState-of-the-artMedical image reconstructionReduce memory consumptionImage reconstruction qualitySparse-view samplingTraining ground truthUnrolling networkImage priorsImage quality improvementImage patchesReconstruction qualityDeep learningArtificial neural networkImage domainUndersampled projectionsTraining phaseTraining processParameter tuningEMnet: 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
2018
Iterative 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 representationAttenuation correction for brain PET imaging using deep neural network based on Dixon and ZTE MR images
Gong K, Yang J, Kim K, Fakhri G, Seo Y, Li Q. Attenuation correction for brain PET imaging using deep neural network based on Dixon and ZTE MR images. Physics In Medicine And Biology 2018, 63: 125011. PMID: 29790857, PMCID: PMC6031313, DOI: 10.1088/1361-6560/aac763.Peer-Reviewed Original ResearchConceptsU-Net structureU-NetModified U-net structureAttenuation correctionDeep neural network methodBrain PET imagingPET attenuationDeep neural networksPatient data setsAttenuation coefficientDixon-based methodNeural network methodData setsConvolution moduleNetwork inputNeural networkDixon MRPET/MR hybrid systemImage reconstructionPET imagingNetwork methodNetworkNetwork approachNetwork structureQuantification errors
2016
GATE Simulation of a High-Performance Stationary SPECT System for Cardiac Imaging
Uzun-Özşahin D, Bläckberg L, Moghadam N, Fakhri G, Sabet H. GATE Simulation of a High-Performance Stationary SPECT System for Cardiac Imaging. 2016, 1-3. DOI: 10.1109/nssmic.2016.8069814.Peer-Reviewed Original ResearchPoint spread functionDerenzo-like phantomGATE simulated resultsGATE simulation studiesSystem spatial resolutionStationary SPECT systemGATE simulationsSPECT systemMultiple simultaneous viewsCardiac imaging applicationsActive rodsSpread functionSpatial resolutionImaging applicationsImage reconstructionGateCorrection techniqueFWHMContouring systemPhantomResolutionLaserCardiac imaging