Abstract:

In this talk, recent developments in Positron Emission Tomography (PET) are explored in relation to Artificial Intelligence. The unique sensitivity of PET (picomolar) and its quantitative capabilities can be associated with the superb spatial and temporal resolution of MR as well as its excellent soft tissue contrast to provide an ideal imaging modality for many cancers as well as cardiac and brain explorations.

 Improvements in image quality and diagnostic accuracy are illustrated in specific cases and synergies between PET and MR spectroscopy are discussed in the context of guiding radiotherapy usign AI for tumor delineation.  Beyond oncology, applications in cardiac (viability, perfusion) and brain imaging (neurodegenerative disease, traumatic brain injury) are presented especially in the context of prediction of disease evolution and progression.

Dr. Georges El Fakhri 

Dr El Fakhri is the Nathaniel & Diana Alpert Professor of Radiology at Harvard Medical School (HMS) and the founding Director of the Endowed Gordon Center for Medical Imaging at Massachusetts General Hospital and HMS.  He is also a Faculty Member of the Harvard-MIT Health Sciences and Technology.  Dr El Fakhri is an internationally recognized expert in quantitative molecular imaging (SPECT, PET-CT, and PET-MR) for in vivo assessment of patho-physiology in brain, cardiac and oncologic diseases.  Current areas of research include high resolution PET/MR imaging in a range of diseases including neurodegenerative disease and traumatic brain injury (amyloid and neurofibrillary tangles), cardiac disease (mitochondrial membrane potential), as well as guiding radiotherapy planning (PET/MRSI).  He has authored or co-authored over 300 papers and mentored over 100 students, post-docs and faculty.  He has received many awards and honors, including the Mark Tetalman Award from the Society of Nuclear Medicine, the Dana Foundation Brain and Immuno-Imaging Award, the Howard Hughes Medical Institutes Training Innovation Award, The Hoffman Award, as well as significant funding from many NIH Institutes (e.g., NCI, NHLBI, NIA, NIBIB, NINDS, OD).  He was elected Fellow to the American Association of Physicists in Medicine (AAPM), the Society of Nuclear Medicine and Molecular Imaging (SNMMI), the  American Institute for Medical and Biomedical Engineering (AIMBE), The International Academy of Medical & Biological Engineering and the IEEE for contributions to quantitative biological imaging.

Abstract: 

Radiologists routinely compare current images, such as chest radiographs and CT scans, to earlier ones to assess for interval changes. Until recently, AI systems have not dealt with detecting interval changes between one radiology image and the next. In this presentation, I will summarize recent works on longitudinal assessment of radiology images, describe some datasets that include time series radiology images, and speculate about future directions.

Dr. Ron Summers

Ronald M. Summers, M.D., Ph.D. is a tenured Senior Investigator and Staff Radiologist in the Radiology and Imaging Sciences Department at the NIH Clinical Center in Bethesda, MD. He is a Fellow of the Society of Abdominal Radiologists and of the American Institute for Medical and Biological Engineering and a Senior Member of SPIE. His awards include the Presidential Early Career Award for Scientists and Engineers, the NIH Director’s Award, the NIH Ruth L. Kirschstein Mentoring Award, and the NIH Clinical Center Director’s Award. He is a member of the editorial boards of the Journal of Medical Imaging, Radiology: Artificial Intelligence and Academic Radiology and a past member of the editorial board of Radiology. He was Co-Chair of the 2018 and 2019 SPIE Medical Imaging conferences and Program Co-Chair of the 2018 IEEE ISBI symposium. He has co-authored over 600 journal, review and conference proceedings articles and is a co-inventor on 17 patents. His research interests include thoracic and abdominal imaging, large radiology image databases, and artificial intelligence.