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2025
Effects of lung expansion on global and regional pulmonary blood volume in a sheep model of acute lung injury.
Zang M, Zeng C, Lagier D, Leng N, Grogg K, Motta-Ribeiro G, Laine A, Winkler T, Vidal Melo M. Effects of lung expansion on global and regional pulmonary blood volume in a sheep model of acute lung injury. Anesthesiology 2025 PMID: 39946655, DOI: 10.1097/aln.0000000000005412.Peer-Reviewed Original ResearchPositive end-expiratory pressurePulmonary blood volumeLung expansionBlood volumeLung injurySystemic endotoxemiaMechanical ventilationRespiratory-gated positron emission tomographyVentilator-induced lung injuryModel of acute lung injuryDistribution of blood volumeAcute lung injuryLow-volume mechanical ventilationLung injury modelEnd-expiratory pressureQuantify blood volumePulmonary capillary blood volumeLow blood volumeRegional blood volumePositron emission tomographyVascular blood volumeCapillary blood volumeLung blood volumeCapillary closureEnd-inspirationGross tumor volume confidence maps prediction for soft tissue sarcomas from multi-modality medical images using a diffusion model
Dong Y, Marin T, Zhuo Y, Najem E, Moteabbed M, Xing F, Beddok A, Lahoud R, Rozenblum L, Ding Z, Liu X, Grogg K, Woo J, Chen Y, Lim R, Ma C, Fakhri G. Gross tumor volume confidence maps prediction for soft tissue sarcomas from multi-modality medical images using a diffusion model. Physics And Imaging In Radiation Oncology 2025, 33: 100734. PMID: 40123775, PMCID: PMC11926426, DOI: 10.1016/j.phro.2025.100734.Peer-Reviewed Original ResearchGross tumor volumeSoft tissue sarcomasTissue sarcomasGross tumor volume delineationManual GTV delineationsMagnetic resonance imagingComputed tomographyFluorodeoxyglucose positron emission tomographyGTV delineationT1-weighted magnetic resonance imagingSingle-modePositron emission tomographyMulti-modal medical imagesTumor volumeIntra-reader variabilityFDG-PETTreatment planningSarcomaEmission tomographyImaging modalitiesResonance imagingDiffusion modelDice indexReader variabilityPatients
2024
Integration 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 benefitsMechanical ventilation guided by driving pressure optimizes local pulmonary biomechanics in an ovine model
Lagier D, Zeng C, Kaczka D, Zhu M, Grogg K, Gerard S, Reinhardt J, Ribeiro G, Rashid A, Winkler T, Vidal Melo M. Mechanical ventilation guided by driving pressure optimizes local pulmonary biomechanics in an ovine model. Science Translational Medicine 2024, 16: eado1097. PMID: 39141699, DOI: 10.1126/scitranslmed.ado1097.Peer-Reviewed Original ResearchConceptsPositive end-expiratory pressureMechanical ventilationFour-dimensional computed tomographyParenchymal strainsVentilator-induced lung injuryAcute respiratory distress syndromeRespiratory system driving pressureManagement of mechanical ventilationPositive end-expiratory pressure valuesRespiratory distress syndromeTidal overdistensionEnd-expiratory pressureAssociated with mortalityPulmonary complicationsLung biomechanicsLung massDistress syndromeClinical outcomesLung injuryComputed tomographyClinical managementGeneral anesthesiaLung collapseDriving pressureLung593: Using [18F]-FDG PET Radiomics to Predict Survival After Reirradiation in Head and Neck Cancer
Beddok A, Grogg K, Rozenblum L, Nioche C, Orhlac F, Calugaru V, Crehange G, Shih H, Marin T, Fakhri G, Buvat I. 593: Using [18F]-FDG PET Radiomics to Predict Survival After Reirradiation in Head and Neck Cancer. Radiotherapy And Oncology 2024, 194: s1210-s1212. DOI: 10.1016/s0167-8140(24)01168-x.Peer-Reviewed Original Research
2023
Dynamic lung aeration and strain with positive end-expiratory pressure individualized to maximal compliance versus ARDSNet low-stretch strategy: a study in a surfactant depletion model of lung injury
Zeng C, Zhu M, Motta-Ribeiro G, Lagier D, Hinoshita T, Zang M, Grogg K, Winkler T, Vidal Melo M. Dynamic lung aeration and strain with positive end-expiratory pressure individualized to maximal compliance versus ARDSNet low-stretch strategy: a study in a surfactant depletion model of lung injury. Critical Care 2023, 27: 307. PMID: 37537654, PMCID: PMC10401825, DOI: 10.1186/s13054-023-04591-7.Peer-Reviewed Original ResearchConceptsAcute respiratory distress syndromeFour-dimensional computed tomographyPEEP strategyLung aerationEnd-inspirationLung injuryTidal recruitmentComputed tomographyRegistration-based techniqueEnd-expirationCyclic recruitmentMechanisms of ventilator-induced lung injuryVentilator-induced lung injuryPotential outcome benefitsBackgroundPositive end-expiratory pressureRespiratory distress syndromeHigh-resolution CTRespiratory system complianceEnd-expiratory pressureSaline lung lavageLung massDistress syndromeInspiratory increaseRecruitable lungSurfactant depletion modelImpact of motion correction on [18F]-MK6240 tau PET imaging
Tiss A, Marin T, Chemli Y, Spangler-Bickell M, Gong K, Lois C, Petibon Y, Landes V, Grogg K, Normandin M, Becker A, Thibault E, Johnson K, Fakhri G, Ouyang J. Impact of motion correction on [18F]-MK6240 tau PET imaging. Physics In Medicine And Biology 2023, 68: 105015. PMID: 37116511, PMCID: PMC10278956, DOI: 10.1088/1361-6560/acd161.Peer-Reviewed Original ResearchConceptsMotion correctionPET quantitationImpact of motion correctionList-mode reconstructionMotion correction methodList-mode dataMotion-corrected imagesEffect of motion correctionVoxel displacementsPhantom experimentsOptical tracking dataLong acquisitionBrain PET scansSlow motionImage qualityPET imagingPositron emission tomographyCorrectionMotionCorrection methodRates of tau accumulationHead motionMotion metricsPhantomPositronEffect of PEEP Setting With Minimal Driving Pressure on the Distribution of Voxel-level Lung Strain and Aeration Measured With Dynamic Computed Tomography (CT) in Large Animal Normal and Injured Lungs
Zeng C, Lagier D, Zhu M, Rashid A, Winkler T, Grogg K, Gerard S, Kaczka D, Reinhardt J, Vidal Melo M. Effect of PEEP Setting With Minimal Driving Pressure on the Distribution of Voxel-level Lung Strain and Aeration Measured With Dynamic Computed Tomography (CT) in Large Animal Normal and Injured Lungs. 2023, a6061-a6061. DOI: 10.1164/ajrccm-conference.2023.207.1_meetingabstracts.a6061.Peer-Reviewed Original ResearchTOPAS-imaging: extensions to the TOPAS simulation toolkit for medical imaging systems
Lee H, Cheon B, Feld J, Grogg K, Perl J, Ramos-Méndez J, Faddegon B, Min C, Paganetti H, Schuemann J. TOPAS-imaging: extensions to the TOPAS simulation toolkit for medical imaging systems. Physics In Medicine And Biology 2023, 68: 084001. PMID: 36930985, PMCID: PMC10164408, DOI: 10.1088/1361-6560/acc565.Peer-Reviewed Original ResearchConceptsCone-beam CTMedical imaging systemsPositron emission tomography systemProton-nuclear interactionsPatient dose calculationsIntensity of X-raysImage-guided radiotherapyImaging systemAnnihilation signalPrompt gammasPositron-electronDose calculationTOPAS toolRadiotherapy simulationDetector pixelsMedical physicsParticle simulationsPositron emission tomographySimulation toolkitPre-builtTherapy delivery systemsDetectorImaging modalitiesExperimental measurementsTOPAS
2021
Deep learning-based GTV contouring modeling inter- and intra- observer variability in sarcomas
Marin T, Zhuo Y, Lahoud R, Tian F, Ma X, Xing F, Moteabbed M, Liu X, Grogg K, Shusharina N, Woo J, Lim R, Ma C, Chen Y, El Fakhri G. Deep learning-based GTV contouring modeling inter- and intra- observer variability in sarcomas. Radiotherapy And Oncology 2021, 167: 269-276. PMID: 34808228, PMCID: PMC8934266, DOI: 10.1016/j.radonc.2021.09.034.Peer-Reviewed Original ResearchConceptsGross tumor volumeRadiation therapy treatment planningGross tumor volume contoursGross tumor volume delineationTherapy treatment planningIntra-observer variabilityConsensus contoursGTV contoursPre-operative CT imagesSoft tissue sarcomasRadiation oncologistsTumor volumeBone sarcomasTreatment planningAccurate contoursCT imagesDelineation procedureSarcomaSoft tissueConfidence levelRadiationPatientsHausdorff distanceMultiple contoursX-ray
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