York mathematician brings clarity to MRI scans

While functional magnetic resonance imaging (fMRI) scans allow doctors to directly observe and assess the brain functionality of their patients, the accuracy of fMRI studies can sometimes be low. For example, a patient’s movements – even breathing – can complicate a signal, making it difficult for doctors to detect brain activities.

Left: Hongmei Zhu

York University’s Hongmei Zhu is working to correct all of that. An assistant professor in the Department of Mathematics & Statistics, Zhu is applying her mathematical research on analyzing wave patterns or frequencies to human health – with startling results.

“When patients move during an fMRI scan,” says Zhu, “the scan’s signal can become degraded, complex and hard to read.” Zhu’s innovative research brings clarity to the component parts of a signal, enabling researchers to improve signal quality by identifying and removing undesired frequency components. This leads to better diagnosis as well as more precise treatment assessments.

“We can also produce a texture map of the human brain from patients’ MR scans,” says Zhu. “This may help doctors detect multiple sclerosis at its very early stage, and then follow the disease’s progress through time.”

Since joining York last year, Zhu has continued to refine her technology – first developed with an interdisciplinary team of medical physicists, neurologists, computer scientists, and mathematicians at the University of Calgary’s Seaman Family MR Research Centre. More specifically, Zhu’s research is related to the work of Joseph Fourier, a mathematician who developed the Fourier Transform in 1804, a tool to help study frequencies and waves. Since then, the tool has been further refined into a class of time-frequency analysis techniques, which reveal signal frequency content over time and/or space. One technique, the Stockwell-transform, was first developed in 1996 by researchers at the University of Western Ontario. But Zhu is the first to apply it to MRI.

“Theory, of course, is immensely important,” says Zhu, “but application brings life to the theory.”

And Zhu’s innovative mathematical work has seemingly endless applications, especially in areas like culture and entertainment. Her research, for example, will enable researchers to break down a piece of digital music into its component sound waves, and determine when different notes or instruments come into play. It also has the potential to improve music quality by reducing noise and removing undesirable sounds, and can be used to improve digital photo quality. Zhu’s interdisciplinary research even has applications to disciplines as diverse as finance and remote sensing.

This article was submitted to YFile by Jason Guriel, a York alumnus who writes on research and innovation.