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Beckman student develops tumor imaging method, wins international paper competition

Rong “Ronny” Guo, a 2021 Beckman Institute Graduate Fellow, developed a new method to help clinicians non-invasively locate and characterize brain tumors. His paper won first prize in the annual IEEE Engineering in Medicine & Biology Society Student Paper Competition.
Published on Nov. 17, 2021

Rong “Ronny” Guo, a 2021 Beckman Institute Graduate Fellow studying electrical and computer engineering, took home first prize at the annual IEEE Engineering in Medicine & Biology Student Paper Competition

Rong Rong "Ronny" GuoThe foundation of Guo’s research is magnetic resonance spectroscopic imaging, or MRSI, a unique technology for noninvasive label-free molecular imaging. He overcame technical barriers to enable high-speed, high-resolution MRSI of brain tumors in clinical settings.

“I have been fascinated by the capability and clinical impact of MRI since I was a senior at Tsinghua University,” Guo said. “The potential of MRSI for tumor diagnosis and characterization has been demonstrated over the last two decades. But the MRSI methods in clinical use today have low spatial resolution and require long scan times, which limit their clinical utility.

“I was attracted to solving these challenging technical problems with potential clinical impacts.”

Illustration of Guo's method to generate 3D high-resolution brain metabolite maps from the tissue intrinsic spectroscopic signals.Guo’s new approach to MRSI is calibrated for the clinic, improving upon existing technology to put real-world patients at the center. The technology reported in his paper can simultaneously map multiple molecules, such as choline and creatine, at 3-millimeter resolution. With a scan lasting just seven minutes, clinicians can generate high-resolution, 3D metabolic maps to localize and differentiate brain tumors.

When tested against patients with diagnosed brain tumors, Guo’s method performed well.

Headshot of Zhi-Pei LiangZhi-Pei Liang“Noninvasive high-resolution metabolic imaging of tumors will have many potential clinical applications, from early detection, to staging, to assessment of therapeutic effects. The technology also provides new opportunities to map the metabolic fingerprints of other brain diseases. Integrating the molecular information obtained using Ronny’s method with structural information and genetic information using machine learning will be an exciting direction to pursue,” said Zhi-Pei Liang, Guo’s adviser and the Franklin W. Woeltge professor of electrical and computer engineering.

The IEEE-EMBC is one of the largest international conferences on biomedical engineering in the world. For his first-place win, Guo received an engraved plaque and $1,000 prize. The final round of competition included an oral presentation and Q&A session with a panel of judges. Prior to the final round, he was selected to represent the North American region as a finalist.

“The ability to identify brain tumor regions by extracting molecular signatures from MRI signals — rapidly, non-invasively, without contrast — has widespread potential for clinical applications,” said Beckman Institute Director Jeff Moore. “Ronny's work could change the way that tumor identification, diagnoses, and treatment are performed.”

Guo’s work was conducted in collaboration with an interdisciplinary team consisting of members in Zhi-Pei Liang’s group at the Beckman Institute, Yao Li’s group at Shanghai Jiaotong University, and Georges El Fakhri at Harvard Medical School.

Editor’s note: Guo received a 2021 Beckman Institute Graduate Fellowship. The program offers University of Illinois graduate students at the M.A., M.S., or Ph.D. level the opportunity to pursue interdisciplinary research at the institute and is supported by funding from the Arnold and Mabel Beckman Foundation.

In this article

  • Rong Guo
    Rong Guo's directory photo.
  • Zhi-Pei Liang
    Zhi-Pei Liang's directory photo.