Three Gordon Center members, Felicitas Detmer, Gengyang Yuan, and Yanis Chemli, have received Young Investigator Award (YIA) recognition for their work presented at the Society of Nuclear Medicine and Molecular Imaging Annual Conference.
Hak Soo Choi, Ph.D., received the 2021 Distinguished Investigator Award from the Academy for Radiology & Biomedical Imaging Research.
Dr. Ramos-Torres, a post-doctoral research fellow at the Gordon Center, was awarded an MGH Physician/Scientist Development award for “Imaging brain injury in animal models using PET.”
Two Gordon Center students and a post-doctoral fellow have been recognized for their excellent work on recent conference abstracts.
Ruth Lim, MD, of the Divisions of Pediatric Radiology and Nuclear Medicine & Molecular Imaging and Medical Director of the Gordon Center for Medical Imaging, has been elected chair of the American Board of Nuclear Medicine (ABNM)
Dr. Johnson’s Lab has reported on recent work, “The cortical origin and initial spread of medial temporal tauopathy in Alzheimer’s disease assessed with positron emission tomography”, using Positron emission tomography (PET) to image tau protein deposition in the brain in Alzheimer’s disease (AD).
Sanchez, J.S., et al. (2021) The cortical origin and initial spread of medial temporal tauopathy in Alzheimer’s disease assessed with positron emission tomography. Science Translational Medicine. doi.org/10.1126/scitranslmed.abc0655.
The BRAIN Initiative will be holding two virtual workshops in February 18-19 and March 9-11 to review and assess current projects and future opportunities to develop and disseminate technologies for non-invasive imaging of human brain function. Several Gordon Center Faculty members will be contributing to the workshop through presentations, moderation, and panel discussions.
In Magnetic Resonance Imaging, Georges El Fakhri, PhD, director of the Gordon Center for Medical Imaging in the Department of Radiology at Massachusetts General Hospital, and Fang Liu, PhD, a researcher in the center, and colleagues describe their newest effort, a method that rapidly creates realistic MRI parameter maps by incorporating adversarial training into a previously created deep learning framework.
