In this webinar, Dr. Roger Gunn presents a domain knowledge based AI algorithm that can quantify the level of amyloid plaques in the human brain using an image based regression of two canonical images that capture non-specific and specific binding of PET amyloid tracers.
The algorithm produces a parameter, amyloid load, that shows increased power over SUVr based methods and its application to automating radiological reads, cross-sectional and longitudinal analysis are presented. Results are presented for the three commonly available amyloid tracers- florbetapir, florbetaben and flutametamol.
Roger Gunn is an international expert on imaging for disease understanding and drug development. He has a Ph.D. in Applied Mathematics from Warwick University and has worked across Academia (Imperial College London, Oxford University & McGill University), Industry (GSK & Imanova) and Government Labs (MRC Cyclotron Unit). He has made significant contributions to the development of novel imaging biomarkers, quantitative algorithms and software for a wide range of imaging applications with a focus on the neurosciences. He currently holds professorships at Imperial College London and Oxford University and has 200 publications in the field of imaging.