Researchers at The University of Texas at Arlington and the University
of Washington are working on a solution and have developed a new,
personalized respiratory-motion system that uses mathematical modeling
to capture images of a patient's lung when it is depressed, offering a
clearer, more precise image of the tumor to be destroyed.
The work is supported by a three-year, $250,000 National Science
Foundation grant and promises to lead to improved, more precise
radiation therapy. Shouyi Wang, an assistant professor in UTA's
Industrial, Manufacturing and Systems Engineering Department and a data
analytics expert, is the principal investigator on the grant.
Wang's approach monitors respiratory gating, or a patient's motion
breath-by-breath, and uses the data collected to focus a radiology beam
on the targeted area when the chest cavity is relaxed -- the stage that
provides the best picture of a cancerous site.
"We will develop a powerful new mathematical model that considers
different factors and takes into account all of the major variables, and
predicts performance and the best method for a particular patient,"
Wang said. "Respiratory gating is a readily available technology, but it
has been very slow to gain acceptance in managing respiratory motion in
radiation therapy.
"We are going to build evidence that it works, that it can be better
utilized, easily implemented and that it can be cost-effective."
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