Mark Chiew

Computational methods are facilitating the next generation of biomedical imaging technologies, using new approaches for learning and leveraging information about the signals of interest to enable rapid and more robust imaging. These approaches are complementary to the development of new imaging hardware or the discovery of new imaging physics, and provide another avenue for extracting more information from imaging data. Dr. Chiew’s research uses these computational tools to develop new strategies for magnetic resonance imaging (MRI) in the human brain and body.

Dr. Chiew’s group has several key research themes. These include:

1. Development of novel MRI acquisition and image reconstruction strategies for accelerated (faster) imaging
2. Investigation of self-supervised deep learning methods for neural network-based image reconstruction
3. Improvement the robustness of MR imaging to factors such as noise, motion, and physiological instabilities
4. Multi-modal integration of information from different MRI contrasts and other imaging modalities

Education

• B.ASc., 2007, Engineering Physics, University of British Columbia
• Ph.D., 2012, Medical Biophysics, University of Toronto

Appointments & Affiliations

• Scientist, Physical Sciences Platform, Hurvitz Brain Sciences Program, Sunnybrook Research Institute
• Associate Professor, Department of Medical Biophysics, University of Toronto