2025
PET Mapping of Receptor Occupancy Using Joint Direct Parametric Reconstruction
Marin T, Belov V, Chemli Y, Ouyang J, Najmaoui Y, Fakhri G, Duvvuri S, Iredale P, Guehl N, Normandin M, Petibon Y. PET Mapping of Receptor Occupancy Using Joint Direct Parametric Reconstruction. IEEE Transactions On Biomedical Engineering 2025, 72: 1057-1066. PMID: 39446540, PMCID: PMC11875991, DOI: 10.1109/tbme.2024.3486191.Peer-Reviewed Original ResearchCentral nervous systemReceptor occupancyLow-binding regionsPET scansSimulation resultsPreclinical in vivo experimentsDynamic PET scansPairs of baselineEstimation of receptor occupancyEstimation frameworkPET neuroimagingReconstruction frameworkModulating drugsTime activity curvesParametric reconstructionDevelopment of drugs
2023
Fast-MC-PET: A Novel Deep Learning-Aided Motion Correction and Reconstruction Framework for Accelerated PET
Zhou B, Tsai Y, Zhang J, Guo X, Xie H, Chen X, Miao T, Lu Y, Duncan J, Liu C. Fast-MC-PET: A Novel Deep Learning-Aided Motion Correction and Reconstruction Framework for Accelerated PET. Lecture Notes In Computer Science 2023, 13939: 523-535. DOI: 10.1007/978-3-031-34048-2_40.Peer-Reviewed Original ResearchReconstruction frameworkMotion correctionMotion-compensated reconstructionHigh-quality imagesHigh-quality reconstruction imagesReconstruction moduleFrame reconstructionReconstruction outputMotion correction methodMotion modelingReconstructed imagesReconstruction methodImage qualityMotion typesImagesPatient motionExperimental resultsMotion-induced artifactsAcquisition dataReconstruction imagesLong acquisition timesFrameworkMultiple typesLow SNRPET acquisition
2022
Event-by-Event 3D Continuous Motion Correction Based on a Data-Driven Motion Estimation Algorithm for 82Rb Myocardial Perfusion Imaging
Tsai Y, Fontaine K, Mulnix T, Armstrong I, Hayden C, Spottiswoode B, Casey M, Liu C. Event-by-Event 3D Continuous Motion Correction Based on a Data-Driven Motion Estimation Algorithm for 82Rb Myocardial Perfusion Imaging. 2022, 00: 1-4. DOI: 10.1109/nss/mic44845.2022.10399100.Peer-Reviewed Original ResearchMotion correctionData-driven motion estimationPET/CT scannerSuperior-inferior motionMotion effectsSilicon photomultipliersNEMA phantomReconstruction frameworkPET acquisitionReconstructed image qualityCardiac PETMotion estimation algorithmPatient datasetsImage qualityMyocardial perfusion imagingCorrectionMotion monitoringTemporal resolutionSiPMMotionPhotomultiplierMotion estimationMotion vectorsPhantomCorrection algorithmFree-running Simultaneous 3D Cardiac T1 Mapping and Cine Imaging Using a Linear Tangent Space Alignment Model
Djebra Y, Marin T, Han P, Bloch I, El Fakhri G, Ma C. Free-running Simultaneous 3D Cardiac T1 Mapping and Cine Imaging Using a Linear Tangent Space Alignment Model. Proceedings Of The International Society For Magnetic Resonance In Medicine ... Scientific Meeting And Exhibition. 2022 DOI: 10.58530/2022/4440.Peer-Reviewed Original Research
2021
Magnetic resonance parameter mapping using model‐guided self‐supervised deep learning
Liu F, Kijowski R, Fakhri G, Feng L. Magnetic resonance parameter mapping using model‐guided self‐supervised deep learning. Magnetic Resonance In Medicine 2021, 85: 3211-3226. PMID: 33464652, PMCID: PMC9185837, DOI: 10.1002/mrm.28659.Peer-Reviewed Original ResearchConceptsMR parameter mappingSupervised learningReconstruction qualityImaging modelSelf-supervised deep learningStandard supervised learningConventional iterative reconstructionData setsDeep learning purposesSuperior reconstruction qualityImprove reconstruction qualityQuantitative MRI applicationsUndersampled k-spacePresence of noisePhysical modeling constraintsSparsity constraintNetwork trainingReconstruction performanceDeep learningReconstruction frameworkMap extractionImprove image qualitySuppress noiseGround truthUndersampling artifacts
2020
Joint Direct Parametric Reconstruction for Pet Receptor Occupancy Mapping
Marin T, Ouyang J, Fakhri G, Normandin M, Petibon Y. Joint Direct Parametric Reconstruction for Pet Receptor Occupancy Mapping. 2020, 00: 1-4. DOI: 10.1109/nss/mic42677.2020.9507742.Peer-Reviewed Original ResearchCentral nervous systemPositron emission tomographyVariable splitting techniqueReceptor occupancyBayesian reconstruction frameworkDenoising problemDose-occupancy relationshipReconstruction frameworkCentral nervous system drugsDevelopment of central nervous systemEstimation of receptor occupancyOptimization problemDrug brain penetrationLow precisionMeasure occupancyDrug AdministrationBrain penetrationRadiation exposureSplitting techniqueEmission tomographyDynamic dataTracer bindingNervous systemConventional approachesTarget engagement
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 resolution
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 representationA novel depth-of-interaction rebinning strategy for ultrahigh resolution PET
Kim K, Dutta J, Groll A, Fakhri G, Meng L, Li Q. A novel depth-of-interaction rebinning strategy for ultrahigh resolution PET. Physics In Medicine And Biology 2018, 63: 165011. PMID: 30040073, PMCID: PMC6375090, DOI: 10.1088/1361-6560/aad58c.Peer-Reviewed Original ResearchConceptsDepth of interactionReconstructed imagesAlternating direction methodReconstructed image qualityPoisson log-likelihoodImage qualitySub-sampling methodPositron emission tomography systemReduce noise effectsDOI layersReconstruction frameworkDetector pixel sizePoint source experimentsQuadratic surrogatesCdZnTe detectorsAnimal positron emission tomographyLog-likelihoodDirection methodSmall animal positron emission tomographySource ExperimentPhoton countingRebinning methodSystem matrixNoise effectsSinogramJoint reconstruction of rest/stress myocardial perfusion SPECT
Lai X, Petibon Y, Fakhri G, Ouyang J. Joint reconstruction of rest/stress myocardial perfusion SPECT. Physics In Medicine And Biology 2018, 63: 135019. PMID: 29897044, PMCID: PMC6245543, DOI: 10.1088/1361-6560/aacc2f.Peer-Reviewed Original ResearchConceptsMyocardial perfusion imagingSingle photon emission computed tomographyReversible defectsSignal-to-noise ratioRest/stress SPECT myocardial perfusion imagingSPECT myocardial perfusion imagingConventional subtraction methodDefect detectionJoint methodPhoton emission computed tomographySubtraction methodReverse mappingClinical dose levelsEmission computed tomographyImprove defect detectionLow noiseNon-invasive assessmentClinical dosePerfusion defectsReduced doseImprove radiologists' performanceReconstruction frameworkRest imagesPerfusion imagingDose levelsPenalized 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 imagesDeep
2017
Data-driven event-by-event respiratory motion correction using TOF PET list-mode centroid of distribution
Ren S, Jin X, Chan C, Jian Y, Mulnix T, Liu C, Carson RE. Data-driven event-by-event respiratory motion correction using TOF PET list-mode centroid of distribution. Physics In Medicine And Biology 2017, 62: 4741-4755. PMID: 28520558, PMCID: PMC6048592, DOI: 10.1088/1361-6560/aa700c.Peer-Reviewed Original ResearchConceptsData-driven eventsRespiratory motion correctionSignificant image quality improvementMotion correctionEvent respiratory motion correctionExternal motion tracking systemPET list-mode dataRespiratory motionMotion tracking systemImage quality improvementMotion correction techniqueReconstruction frameworkMotion correction methodDistribution algorithmMotion-induced blurList-mode dataTracking systemFurther processingGated reconstructionsImage noiseRespiratory gating techniqueHuman scansRadioactive eventsContrast recoveryAnzai
2015
Simultaneous CT-MRI Reconstruction for Constrained Imaging Geometries Using Structural Coupling and Compressive Sensing
Xi Y, Zhao J, Bennett J, Stacy M, Sinusas A, Wang G. Simultaneous CT-MRI Reconstruction for Constrained Imaging Geometries Using Structural Coupling and Compressive Sensing. IEEE Transactions On Biomedical Engineering 2015, 63: 1301-1309. PMID: 26672028, PMCID: PMC4930897, DOI: 10.1109/tbme.2015.2487779.Peer-Reviewed Original ResearchConceptsMRI image reconstructionImage estimation methodCompressive sensing techniquesUnified reconstruction frameworkNovel research directionsMRI reconstructionReconstruction frameworkCompressive sensingImage reconstructionPrior knowledgeDifferent modalitiesReconstruction methodologyResearch directionsInstantaneous acquisitionMRI dataPromising resultsImaging geometrySeparate reconstructionBetter CTNumerical phantomImproved resultsMRI acquisitionSensing techniquesEstimation methodApplications
2014
Quantitative simultaneous positron emission tomography and magnetic resonance imaging
Ouyang J, Petibon Y, Huang C, Reese T, Kolnick A, Fakhri G. Quantitative simultaneous positron emission tomography and magnetic resonance imaging. Journal Of Medical Imaging 2014, 1: 033502-033502. PMID: 26158055, PMCID: PMC4306197, DOI: 10.1117/1.jmi.1.3.033502.Peer-Reviewed Original Research
2013
Direct 4D PET reconstruction of parametric images into a stereotaxic brain atlas for [<sup>11</sup>C]raclopride
Gravel P, Verhaeghe J, Reader A. Direct 4D PET reconstruction of parametric images into a stereotaxic brain atlas for [11C]raclopride. 2011 IEEE Nuclear Science Symposium Conference Record 2013, 3994-3998. DOI: 10.1109/nssmic.2012.6551915.Peer-Reviewed Original ResearchKinetic parameter estimationKey processing stagesParameter estimationImage resolution degradationKinetic parametersInaccurate modelingVoxel-wise kinetic parametersPerformance of reconstructionResolution degradationSpatial transformation parametersMaximum likelihood expectation maximization reconstruction algorithmMotion correctionRoot mean squared errorImage resolutionSub-optimal estimatesRoot-MeanExpectation maximization reconstruction algorithmReconstruction frameworkInterpolation effectReconstruction algorithmReconstruction methodError analysisMean squared errorProcessing stagesParametersSpatially varying regularization for motion compensated PET reconstruction
Dutta J, Fakhri G, Lin Y, Huang C, Petibon Y, Reese T, Leahy R, Li Q. Spatially varying regularization for motion compensated PET reconstruction. 2011 IEEE Nuclear Science Symposium Conference Record 2013, 2156-2160. DOI: 10.1109/nssmic.2012.6551493.Peer-Reviewed Original ResearchSimulated lung lesionsRegularization schemeSpatially varying regularizationUngated reconstructionTorso phantomImage reconstruction problemContrast recoverySacrificing signal-to-noise ratioPET reconstructionSignal-to-noise ratioAnalytical approximationReconstruction frameworkQuadratic penaltyDiagonal elementsReconstruction problemLocal impulse responseCardiac motionRegularization approachReconstructed imagesFisher information matrixAutomated fashionPET imagingImpulse responseDegree of smoothnessInformation matrixCardiac motion compensation and resolution modeling in simultaneous PET-MR: a cardiac lesion detection study
Petibon Y, Ouyang J, Zhu X, Huang C, Reese T, Chun S, Li Q, Fakhri G. Cardiac motion compensation and resolution modeling in simultaneous PET-MR: a cardiac lesion detection study. Physics In Medicine And Biology 2013, 58: 2085-2102. PMID: 23470288, PMCID: PMC3657754, DOI: 10.1088/0031-9155/58/7/2085.Peer-Reviewed Original ResearchConceptsContrast recoveryDetector point spread functionPartial volume effectsK-spaceMotion compensationB-spline registrationLesion-detection studiesCardiac motion compensationPET-MR scannersSimultaneous PET-MRPET contrastIterative reconstruction frameworkPoint spread functionMotion correctionPET countsNon-rigid B-spline registrationCardiac phantomPSF modelPET-MRMotion deblurringReconstruction frameworkSystem matrixCardiac PETSpread functionDefect detection
2011
Direct 3D PET Image Reconstruction into MR Image Space
Gravel P, Verhaeghe J, Reader A. Direct 3D PET Image Reconstruction into MR Image Space. 2011, 3955-3962. DOI: 10.1109/nssmic.2011.6153752.Peer-Reviewed Original ResearchImage spaceTransformation parametersPET image reconstructionRigid body transformation parametersDifferent image spacesImage resolution degradationMotion correctionQuality of reconstructionRegistration transformation parametersReconstruction frameworkAbsolute error analysisImage registrationMean absolute errorInterpolation effectRegistration methodImage reconstructionImage resolutionFinal imageMLEM algorithmScanner geometrySimilarity criteriaAbsolute errorSpatial atlasDirect reconstructionImages
2008
Physical-Space Refraction-Corrected Transmission Ultrasound Computed Tomography Made Computationally Practical
Li S, Mueller K, Jackowski M, Dione D, Staib L. Physical-Space Refraction-Corrected Transmission Ultrasound Computed Tomography Made Computationally Practical. Lecture Notes In Computer Science 2008, 11: 280-288. PMID: 18982616, DOI: 10.1007/978-3-540-85990-1_34.Peer-Reviewed Original ResearchConceptsUltrasound Computed TomographyHigh-quality image reconstructionIterative reconstruction frameworkReconstruction frameworkReconstruction qualityComputational platformInteractive demandsTracking approachImage reconstructionConsiderable computational expenseComputational expenseCT frameworkImaged tissueFrameworkEikonal solverArchitectureTrackingPlatformProper modelingSolverComputationallyWave-front trackingCapabilityEikonal equation
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