2024
The 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 success
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 ResearchConceptsStructural 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 PET