From instruments that can peer deep inside cells and tissues, revealing previously unknown processes, to methods that can find signs of disease before any clinical symptoms arise, many tools and devices routinely used in the laboratory and clinic began their lives as challenging technical problems.
Scott Reeder, currently at the University of Wisconsin–Madison, and colleagues at the NCRR-funded Center for Advanced Magnetic Resonance Technology at Stanford University, developed a new method for enhancing magnetic resonance imaging (MRI). Through a collaboration with General Electric (GE), the technology has been further developed into an easy-to-use option on many of GE’s MRI instruments. Photo courtesy of General Electric.
Researchers at NCRR-funded Biomedical Technology Research Resources (BTRRs) focus on finding solutions to such problems. Over the years, BTRRs have been the source of countless breakthrough technologies with wide applications in biomedical research and medicine. But as difficult as it is to develop a useful new method or instrument, it is also challenging to build it into a finished, easy-to-use product and put it in the hands of researchers and clinicians worldwide. This process sometimes requires collaborating with an established company or, in some cases, starting a new one.
Many BTRR discoveries have followed commercial paths. Two recent examples—a technique to enhance clinical imaging and another to detect changes in oxygen in different tissues—illustrate how some inventions make it to the clinic.
As a radiology resident at Stanford University School of Medicine, Scott Reeder set out to overcome a problem that had long vexed researchers working with magnetic resonance imaging (MRI).
MRI works by applying a strong, constant magnetic field to a sample and then measuring how the nuclei of hydrogen atoms—found in water, fat, and other body tissues—respond to a short burst of radio waves. The method can be thought of as ringing a bell: exposing the body to energy waves is the “ding,” and the resulting echoes are used to construct an image.
The problem is that echoes from fat are very “loud” and can obscure those from tumors and inflamed or infected tissues. And although there are ways to eliminate fat signals from an image, they are difficult to implement in breast tissue, extremities, and the head and neck. As a result, these remain problem areas of the body for imaging.
To better distinguish between fat and other tissues, Gary Glover, head of the NCRR-funded Center for Advanced Magnetic Resonance Technology (CAMRT) at Stanford University, developed in 1991 a method that records MR signals at three different time intervals. Glover’s technique provided considerable improvements to image quality in difficult areas, but it was not easy to implement routinely.
Reeder decided to work with CAMRT scientists to build on Glover’s approach. “I wrote my first algorithm late at night while on call at the hospital, when the ER was slow,” remembers Reeder. “We came up with a more general and flexible method to acquire and analyze the echoes.”