CHAMPAIGN, Ill. — New multiplexed imaging technology using standard clinical MRI systems can simultaneously map more than 20 biomarkers in high resolution, providing a comprehensive view of the brain with a single scan.
Researchers at the University of Illinois Urbana-Champaign demonstrated the multiplexed MRI technology, or MRx, by characterizing brain tumors and multiple sclerosis lesions — revealing different structural, physiological and molecular changes associated with the diseases. Led by Zhi-Pei Liang, a professor of electrical and computer engineering and a member of the Beckman Institute for Advanced Science and Technology at the U. of I., the team reported its findings in the journal Nature.
“MRx can be a powerful tool for noninvasive tissue characterization, helping to advance personalized, precision and predictive medicine,” Liang said. “By providing rich, multidimensional biomarkers to capture disease progression and treatment response, this capability could open new opportunities for more precise diagnosis, individualized treatment planning and improved patient outcomes.”
Conventional MRI generates high-resolution images using magnetic resonance signals from water molecules in biological tissues. These images are primarily used in clinical practice to visualize and assess tissue structure and pathology, Liang said. More than 100 million MRI scans are performed worldwide each year.
MRx expands the capabilities of conventional MRI by enabling simultaneous imaging of signals from multiple molecules, such as brain metabolites and neurotransmitters, in addition to water. Building on previous work on high-resolution metabolic imaging, Liang’s group developed the MRx technology to enable clinical MRI scanners to capture more than 20 biomarkers at once.
“MRx is a new artificial-intelligence-powered imaging framework that can measure many markers without the need for contrast agents,” said study coauthor Rong Guo, a former student in Liang’s group who now is a senior scientist at Siemens Healthineers.
“With our integration of ultrafast data acquisition and physics-based machine learning methods for data processing, MRx overcomes several longstanding bottlenecks to fast, high-resolution multiplexed imaging,” Guo said. “This gives clinicians and researchers better understanding of not only the brain’s structure, but also its physiology and molecular processes, within a single imaging framework.”
A whole-brain MRx scan can be completed in approximately 14 minutes — well within a clinically acceptable time frame and significantly shorter than conventional multicontrast clinical MRI protocols, which can take up to an hour, the researchers said.
The researchers demonstrated the power of MRx to provide more accurate characterization of diseases by applying it to patients with brain tumors and multiple sclerosis. By capturing structural, physiological and molecular changes in brain tumors, MRx provided a multifaceted assessment of the tumor microenvironment and underlying physiological processes. The researchers observed metabolic alterations, edema, axonal damage and demyelination. This information could allow clinicians and researchers to distinguish tumor states more precisely, even when they appear similar on conventional MRI, Liang said.
For multiple sclerosis, the combined MRx biomarkers enabled characterization of lesions at different stages by detecting changes related to inflammation, demyelination, gliosis, axonal injury and metabolic activity — without the need for contrast agents. The researchers also found that subtle biomarker patterns could reveal early tissue alterations and help predict lesion progression, suggesting that MRx may improve both diagnosis and prognosis for the disease.
“Diseases such as tumors, multiple sclerosis and neurodegenerative disorders are highly heterogeneous. The rich set of biomarkers obtained using MRx has the potential to provide deeper insights into brain function and disease processes, while also improving the sensitivity and specificity of detection and diagnosis,” said Yudu Li, a U. of I. professor of bioengineering and the first author of the paper.
The Grainger College of Engineering and the Beckman Institute supported this work.
