Grant: Project Grant

Approximately 4-5% of people with a primary cancer diagnosis will go on to develop secondary brain tumors, with annual incidence rates around 24 cases for every 100,000 Canadians. As patients with metastatic cancer are living longer than ever before, thanks to the improving efficacy of cancer treatments, there is increasing pressure on the healthcare infrastructure that is supporting the management of the disease. However, while magnetic resonance imaging is effective for diagnosis and monitoring of brain cancer, the increasing volume of patients coupled with the relatively long MRI scan times results in a significant burden on medical imaging resources. This contributes to the relatively long wait times for diagnostic MRI overall in Canada, with national averages estimated to be 133 days, with consequences for patient wellbeing. Therefore, there is a clear need for the development of methods to reduce MRI scan times and increase patient throughput. The overall goal of the proposed research is to speed up diagnostic brain cancer imaging through the development of novel MRI techniques, using machine learning tools. In particular, the proposed research has a target goal of reducing imaging times from 22 minutes down to 12 minutes per patient, while maintaining image quality and contrast. The developed technology will be deployed in the clinic, reducing imaging times by 44%, and has the potential to be generalized to other clinical brain imaging contexts (e.g. dementia, stroke, brain injury), for broad impact across a range of domains.