Ivan Klyuzhin is a postdoctoral research fellow in the Department of Medicine, Division of Neurology. He got his combined BSc/MSc degree from the Ural Federal University and University of Washington, where his research focused on the physics of water-polymer interfaces and neuronal signal transduction. He got his PhD from the department of Physics and Astronomy at UBC, where he developed novel methods of image reconstruction and analysis in positron emission tomography (PET).  Ivan’s current research is focused on the application of machine learning for outcome prediction in Parkinson’s disease (PD). He develops computational models to establish links between brain PET images and specific aspects of motor and cognitive performance in PD. This research will improve the diagnostic potential of brain imaging and help discover mechanisms of PD progression.


Application of machine learning methods of PET and SPECT image analysis

The goal of this research project is to identify new image metrics to enhance the characterization of brain PET images, and to develop novel algorithms that establish links between the imaging outcomes and clinical manifestations of neurological disorders. We use advanced machine learning techniques such as clustering, network analysis, neural networks and deep learning to relate PET imaging data to clinical measures of Parkinson’s disease.