Bioengineering Professor Brad Sutton wins Beckman Image Contest

Scientists at the Beckman Institute for Advanced Science and Technology recently showed off their research through the annual Beckman Research Image Contest.

This year, the contest features four winners in the following categories: undergraduate students, graduate students, postdoctoral researchers, and faculty. The four framed images are being featured in the Beckman director’s conference room. Last year’s winning images will be hung throughout the Beckman’s halls. 

“Researchers at the Beckman Institute use our state-of-the-art tools to work together across disciplines and break new barriers,” said Jeff Moore, the director of the Beckman Institute and an Ikenberry Endowed Chair in the Department of Chemistry. “These images show that research is not only important, but also visually beautiful. I continue to be amazed and inspired by the entries in the Beckman Research Image Contest.”

Bioengineering Professor Brad Sutton is the winner of the faculty category. Sutton leads the Magnetic Resonance Functional Imaging Lab and serves as the technical director of the Biomedical Imaging Lab at Beckman. 

Two techniques in magnetic resonance imaging enable researchers to see highly sensitive information about the structure of brain tissue: diffusion tensor imaging and magnetic resonance elastography. DTI looks at the cabling in the brain. These white matter fiber pathways transmit information from one part of the brain to another. MRE looks at the mechanical properties of the brain tissue, including stiffness. It provides information about the interconnections and complexity of cells in the brain. 

In order to make a clinically feasible protocol and save time, the Sutton Group and Carle Foundation Hospital-Beckman Institute Postdoctoral Fellow Aaron Anderson, in collaboration with professors Dieter Klatt and Richard Magin at the University of Illinois at Chicago, developed and implemented a DTI-MRE sequence that acquires both DTI and MRE data at the same time. This data was collected on the Biomedical Imaging Center 3 Tesla Prisma MRI system. Grant funding: NIH/NIBIB 5R21EB026238-02.