Researchers at Rensselaer are working to develop a new medical imaging technique designed to determine the relative stiffness of soft tissue for the diagnosis of injury and disease.
“Relative stiffness imaging could be an important diagnostic tool for such things as finding a tumour in soft tissue or detecting tissue damage from a heart attack,” said Joyce McLaughlin, director of the Center for Inverse Problems and the Ford Foundation Professor of Mathematical Sciences at Rensselaer. “Our goal is to create images depicting tissue stiffness by computing the variations of shear wave speed in biological tissue.”
McLaughlin said her research is inspired by the centuries-old medical examination in which a doctor presses on the surface of the body to detect abnormal, or stiff, tissue underneath.
After analyzing data gathered from an ultrasound-based system developed by Mathias Fink of Laboratoire Ondes et Acoustique, ESPCI, Universite Paris VII that measures the amplitude of shear waves as they pass through biological tissue, McLaughlin, along with Rensselaer research scientists Dan Renzi and Jeong-Rock Yoon, recognized that the changes of the shape and position of the wave fronts as they pass through tissue would allow them to create an image that could be used as a diagnostic tool.
Shear wave speed can more than double in abnormal, or stiff, tissue, and the high contrast helps make a high-quality image, according to McLaughlin. The researchers first developed an algorithm to identify the location of the very front of the wave as it passes through the tissue. Using only this data, the team computes the shear wave speed at each section of tissue and produces an image of stiffness variations.
“We call what we have developed the Arrival Time Algorithm, and the initial images we have created using this computation are very promising,” said McLaughlin.