Faster MRI finds disease with ‘fingerprints’
CASE WESTERN RESERVE (US) — A new MRI method could provide early identification of specific cancers, multiple sclerosis, heart disease, and other maladies, new research shows.
Each body tissue and disease has a unique fingerprint that can be used to diagnose problems before they become untreatable.
By using new magnetic resonance imaging (MRI) technologies to scan simultaneously for various physical properties, researchers say it may be possible to differentiate white matter from gray matter from cerebrospinal fluid in the brain in about 12 seconds—and potentially even faster in the near future.
The technology has the potential to make an MRI scan standard procedure in annual check-ups. A full-body scan lasting just minutes would provide far more information and ease interpretation of the data, making diagnostics far less expensive compared to today’s scans.
“The overall goal is to specifically identify individual tissues and diseases, to hopefully see things and quantify things before they become a problem,” says Mark Griswold, a radiology professor at Case Western Reserve University School of Medicine. “But to try to get there, we’ve had to give up everything we knew about the MRI and start over.”
Griswold has been working on this goal with Vikas Gulani, an assistant professor of radiology, and Nicole Seiberlich, assistant professor of biomedical engineering, for a decade.
As reported in Nature, a magnetic resonance imager uses a magnetic field and pulses of radio waves to create images of the body’s tissues and structures. Magnetic resonance fingerprinting, MRF for short, can obtain much more information with each measurement than a traditional MRI. Griswold likens the difference in technologies to a pair of choirs.
“In the traditional MRI, everyone is singing the same song and you can tell who is singing louder, who is off-pitch, who is singing softer,” he says. “But that’s about it.”
The louder, softer and off-pitch singing is represented by dark, light, or bright spots in the scan that a radiologist must interpret. For example, an MRI would show swelling as a bright area in an image. But brightness doesn’t necessarily equate with severity or cause.
A randomized mess
“With an MRF, we hope that with one step we can tell the severity and exactly what’s happening in that area.” The fingerprint of each tissue, each disease and each material inside the body is therefore a different song. In an MRF, each member of the choir sings a different song simultaneously, Griswold says. “What it sounds like in total is a randomized mess.”
The researchers generate unique songs by simultaneously varying different parts of the input electromagnetic fields that probe the tissues. These variations make the received signal sensitive to four physical properties that vary from tissue to tissue. These differences become evident when applying pattern recognition programs using the same math in facial recognition software.
The patterns are then charted. Instead of looking at relative measurements from an image, quantitative estimates tell one tissue from another. As the technology progresses, the results will determine whether tissue is healthy or diseased, how badly, and by what.
The scientists believe that they will be able to interrogate a total of eight or nine physical properties, which will allow them to elicit the differences from a vast array of tissues, diseases and materials.
For a patient, an MRF would seem like a quick MRI. When the scan is done, all of the patient’s songs would be compared with the songbook, which will provide doctors with a suite of diagnostic information. “If colon cancer is ‘Happy Birthday’ and we don’t hear ‘Happy Birthday,’ the patient doesn’t have colon cancer,” Griswold says.
Other researchers have tried to use multiple parameters in MRI’s, but this group was able to scan fast and with higher sensitivity than in previous attempts, Griswold says. “This research gives us hope. We can see that it’s possible the MRI can see all sorts of things.”
The research was supported in part by the National Institutes of Health.
Source: Case Western Reserve University
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