2023
The United States Department of Energy and National Institutes of Health Collaboration: Medical Care Advances via Discovery in Physical Sciences
Keppel C, Weisenberger A, Atanasijevic T, Wang S, Zubal G, Buchsbaum J, Brechbiel M, Capala J, Escorcia F, Obcemea C, Boehnlein A, Heyes G, Bourne P, Cherry S, Colby E, Fakhri G, Gillo J, Gropler R, Gueye P, Tourassi G, Peggs S, Woody C. The United States Department of Energy and National Institutes of Health Collaboration: Medical Care Advances via Discovery in Physical Sciences. Medical Physics 2023, 50: e53-e61. PMID: 36705550, PMCID: PMC10033422, DOI: 10.1002/mp.16252.Peer-Reviewed Original ResearchMeSH KeywordsArtificial IntelligenceBiomedical ResearchLaboratoriesNational Institutes of Health (U.S.)Natural Science DisciplinesUnited StatesConceptsNational Institutes of HealthHigh-energy particle detectionSuperconducting particle acceleratorsMedical care advancesHigh-speed electronicsNational health carePhysical Sciences LaboratoryParticle physicsParticle accelerationBeam generationRadioisotope productionSuperconducting magnetsHealth collaborationHealth careInstitutes of HealthParticle detectionCare advancesScientific missionsScience LaboratoryMedical healthcareInstrumental advancesPhysicsJoint workshopUnited States DepartmentCollaborative workshops
2021
A cross-scanner and cross-tracer deep learning method for the recovery of standard-dose imaging quality from low-dose PET
Xue S, Guo R, Bohn K, Matzke J, Viscione M, Alberts I, Meng H, Sun C, Zhang M, Zhang M, Sznitman R, El Fakhri G, Rominger A, Li B, Shi K. A cross-scanner and cross-tracer deep learning method for the recovery of standard-dose imaging quality from low-dose PET. European Journal Of Nuclear Medicine And Molecular Imaging 2021, 49: 1843-1856. PMID: 34950968, PMCID: PMC9015984, DOI: 10.1007/s00259-021-05644-1.Peer-Reviewed Original ResearchMeSH KeywordsArtificial IntelligenceBrainDeep LearningFluorodeoxyglucose F18HumansImage Processing, Computer-AssistedPositron-Emission TomographyConceptsStructural similarity index measurePET imagingGenerative adversarial networkNuclear medicine physiciansArtificial intelligenceLow-dose scansBaseline image qualityDose reductionConditional generative adversarial networkClinical imaging assessmentSimilarity index measureDiversity of clinical practiceDevelopment of AI technologyDeep learning developmentDose acquisitionImaging assessmentMedicine physiciansImage qualityResultsThe improvementPatientsClinical acceptanceClinical practiceClinical settingAdversarial networkLow-dose PETQuantitative 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
2014
Pattern recognition for rapid T2 mapping with stimulated echo compensation
Huang C, Altbach M, Fakhri G. Pattern recognition for rapid T2 mapping with stimulated echo compensation. Magnetic Resonance Imaging 2014, 32: 969-974. PMID: 24853466, PMCID: PMC4099291, DOI: 10.1016/j.mri.2014.04.014.Peer-Reviewed Original Research