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Jonathan Doucette Receives UBC Killam Doctoral Scholarship
His work revolves around using MRI physics models coupled with machine learning in order to extract quantitative tissue parameters of the brain. In quantitative imaging, the goal is to measure these tissue parameters as accurately as possible, leading to images which are easy to interpret and which are reproducible across MRI centres.
"A challenge in quantitative MRI is that computer models of the MRI signal, which are required to produce images, are never able to perfectly capture all of the complexities of the MRI signal," says Doucette. "In my work, we use machine learning to add small corrections to these imperfect models. This results in more accurate estimates of underlying tissue parameters and improves our understanding of brain microstructure."
Ultimately, Doucette's work will help map brain tissue damage and repair in conditions such as multiple sclerosis or traumatic brain injury.
Congratulations to Jonathan!