2024
Effects of List-Mode-Based Intraframe 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 Intraframe Motion Correction in Dynamic Brain PET Imaging. IEEE Transactions On Radiation And Plasma Medical Sciences 2024, 8: 950-958. PMID: 39507127, PMCID: PMC11540417, DOI: 10.1109/trpms.2024.3432322.Peer-Reviewed Original Research
2023
Imaging Performance of the Fully Assembled Ultra-High Resolution (UHR) Brain PET scanner
Loignon-Houle F, Toussaint M, Beaudoin J, Gaudreault M, Doyon V, Leroux J, Auger E, Thibaudeau C, Arpin L, Croteau E, Espinosa-Bentancourt E, Samson A, Bouchard J, Espagnet R, Viscogliosi N, Pepin C, Labrecque V, Paulin C, Marin T, Ouyang J, Normandin M, Tétrault M, Michaud J, Fontaine R, Fakhri G, Lecomte R. Imaging Performance of the Fully Assembled Ultra-High Resolution (UHR) Brain PET scanner. 2023, 00: 1-1. DOI: 10.1109/nssmicrtsd49126.2023.10338146.Peer-Reviewed Original ResearchBrain PET scannerUltra-high resolutionPET scannerPeak noise-equivalent count rateUltra Micro Hot Spot PhantomNoise-equivalent count rateAxial field-of-viewHot spot phantomHoffman brain phantomSmall-scale structuresCount rateBrain phantomContrast recoveryReadout schemeField of viewBrain PET imagingPhantomExcellent image qualityImaging performanceSpatial resolutionSmall structuresUltrahigh resolutionImage qualityPET imagingLarger rodsEvaluation of trans- and cis-4‑[18F]Fluorogabapentin for Brain PET Imaging
Zhou Y, Normandin M, Belov V, Macdonald-Soccorso M, Moon S, Sun Y, Fakhri G, Guehl N, Brugarolas P. Evaluation of trans- and cis-4‑[18F]Fluorogabapentin for Brain PET Imaging. ACS Chemical Neuroscience 2023, 14: 4208-4215. PMID: 37947793, PMCID: PMC11485007, DOI: 10.1021/acschemneuro.3c00593.Peer-Reviewed Original ResearchConceptsNeuropathic painRodent models of neuropathic painSubunit of voltage-dependent calcium channelsModel of neuropathic painTreatment of neuropathic painMetabolite-corrected arterial input functionVoltage-dependent calcium channelsMultilinear analysis 1Brain uptakePET imagingDose of gabapentinOne-tissue compartment modelRegional time-activity curvesAdult rhesus macaquesPlasma protein bindingTime-activity curvesModerate brain uptakeCalcium channelsInjured nerveArterial input functionGabapentinRodent modelsAnticonvulsant medicationBrain PET imagingRhesus macaquesP-302 Development and evaluation of fully automated F-18 cromolyn diethyl ester labeling via multi step reaction as potential therapeutics for Alzheimer’s Disease for brain PET imaging
Moon S, Wang J, Shoup T, Normandin M, Fakhri G. P-302 Development and evaluation of fully automated F-18 cromolyn diethyl ester labeling via multi step reaction as potential therapeutics for Alzheimer’s Disease for brain PET imaging. Nuclear Medicine And Biology 2023, 126: 108763. DOI: 10.1016/j.nucmedbio.2023.108763.Peer-Reviewed Original Research
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 performanceImages
2018
Attenuation 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
2014
Motion compensation for brain PET imaging using wireless MR active markers in simultaneous PET–MR: Phantom and non-human primate studies
Huang C, Ackerman J, Petibon Y, Normandin M, Brady T, Fakhri G, Ouyang J. Motion compensation for brain PET imaging using wireless MR active markers in simultaneous PET–MR: Phantom and non-human primate studies. NeuroImage 2014, 91: 129-137. PMID: 24418501, PMCID: PMC3965607, DOI: 10.1016/j.neuroimage.2013.12.061.Peer-Reviewed Original ResearchConceptsMotion correctionWireless markersList-mode reconstructionReconstructed PET imagesMotion correction techniqueObserver signal-to-noise ratioSimultaneous PET-MRMotion artifactsPET phantomPET contrastPET reconstructionBrain PET imagingPET imagingPhantomBrain PETPET-MRIndependent noise realizationsAccurate quantitative valuesHead motionNoise realizationsPET dataSignal-to-noise ratioStatic referenceBrain PET scansActivation markers