Advanced imaging technology may help predict MS brain lesions
New ultrafast method could detect early signs of damage that MRIs cannot
A new imaging technology may help detect early signs of brain damage in people with multiple sclerosis (MS) that aren’t visible on conventional MRI scans, a recent study shows.
The technology detects specific metabolic alterations in the brain by tracking certain metabolites and neurotransmitters — molecules that nerve cells use to communicate. It allows images to be collected much faster than conventional scans, and may also help track brain inflammation.
“Understanding the brain, how it works, and what goes wrong when it is injured or diseased is considered one of the most exciting and challenging scientific endeavors of our time,” Zhi-Pei Liang, PhD, a co-author of the study at the University of Illinois Urbana-Champaign, said in a university news story. “MRI has played major roles in unlocking the mysteries of the brain over the past four decades. Our new technology adds another dimension to MRI’s capability for brain imaging: visualization of brain metabolism, and detection of metabolic alterations associated with brain diseases.”
The study, “Ultrafast J-resolved magnetic resonance spectroscopic imaging for high-resolution metabolic brain imaging,” was published in Nature Biomedical Engineering.
Researchers target early diagnosis with metabolic imaging
MRI scans are a key tool for tracking disease activity in MS, a neurodegenerative disorder caused by the immune system mistakenly attacking the myelin sheath, a covering that protects nerve fibers. These scans allow researchers and clinicians to take detailed pictures of the brain and spinal cord, which can detect MS lesions, which are spots of damage to nerve tissue, as well as brain atrophy, the shrinkage of brain tissue as it becomes damaged.
Conventional MRI scans basically work by detecting signals from water molecules in the body’s tissues. Because the human body is mostly made of water, this can provide key information about bodily structures.
A newer technology called functional MRI (fMRI) tracks the movement of water molecules to assess changes in blood flow in the brain. This can be important for tracking the activity of different brain regions, as brain regions that are more active will have an increase in blood flow.
Metabolic and physiological changes often occur before structural and functional abnormalities are visible on conventional MRI and fMRI images. Metabolic imaging, therefore, can lead to early diagnosis and intervention of brain diseases.
A major shortcoming of conventional MRI and fMRI, however, is that these technologies only detect signals primarily from water molecules. This means these technologies can’t provide any information about the activity of other molecules in the brain, such as neurotransmitters or inflammatory molecules.
“Metabolic and physiological changes often occur before structural and functional abnormalities are visible on conventional MRI and fMRI images,” said Yibo Zhao, PhD, first author of the paper and a postdoctoral researcher at the university. “Metabolic imaging, therefore, can lead to early diagnosis and intervention of brain diseases.”
Acquiring image 100 times faster with new tech
Magnetic resonance spectroscopic imaging, or MRSI, is an evolving technology that aims to detect signals from many different molecules, not just water. Although MRSI holds a lot of promise for understanding diseases like MS, available MRSI tools have some notable drawbacks. For example, it takes a very long time to acquire the images, which are usually fuzzy, with bad resolution and a lot of background signals.
In this study, researchers developed new MRSI tech to overcome these limitations. The scientists were able to cut down the time it takes to acquire an image to only 12.5 minutes, about a hundred times faster than with earlier MRSI setups. The researchers also found ways to make the images clearer, with better resolution.
“Our technology overcomes several long-standing technical barriers to fast high-resolution metabolic imaging by synergistically integrating ultrafast data acquisition with physics-based machine learning methods for data processing,” Liang said. “As healthcare is moving [toward] personalized, predictive, and precision medicine, this high-speed, high-resolution technology can provide a timely and effective tool to address an urgent unmet need for noninvasive metabolic imaging in clinical applications.”
The scientists tested their novel MRSI system in several situations. In one test, a person with MS underwent two MRSI scans 70 days apart. At the time of the second scan, conventional MRI showed a new lesion that hadn’t been present in the first scan. Notably, the first MRSI scan showed changes in the area of the brain where this lesion developed, which implies MRSI may be able to detect changes that precede the development of lesions that are detectable by conventional MRI.
The MRSI scan in the MS patient also could provide useful information about the biology of the disease lesion, namely whether a lesion had ongoing inflammation or loss of myelin and no active inflammation. Collectively, these data indicate the new MRSI technology “may make high-resolution metabolic imaging of MS a clinical reality,” the researchers wrote.
The utility of the MRSI setup was also demonstrated in other brain diseases. For example, the researchers showed that MRSI scans could detect molecular differences in brain tumors that looked identical on conventional MRI scans. The researchers concluded that their new MRSI system “may open up a wide range of opportunities for research and clinical applications.”
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