The University of Texas at Austin
  • Improving Brain Tumor Imaging

    By Aaron Dubrow, Texas Advanced Computing Center
    Published: March 27, 2013

    Research by George Biros, professor of mechanical engineering and computer science at The University of Texas at Austin.

    brain images

    (left) MRI image; (second from left) probability maps of tumor infiltration; (right two) biophysical models with different fidelity.

    When it comes to gliomas, a type of primary brain tumor, even the most experienced doctors often disagree on the best approach to treatment. The problem lies in the difficulty of determining the full extent of the tumor’s invasion into normal tissue.

    Texas Unleashes Stampede

    Go back to the KNOW story on Stampede.

    For a patient with a glioma, surgeons need to understand how aggressive the tumor is to be able to plan for surgery, radiotherapy and other treatment options. George Biros, a professor of mechanical engineering and computer science at The University of Texas at Austin, is using Stampede to answer these questions and to improve the quality of brain tumor imaging so surgeons can make more informed decisions about treatment options.

    Working with Christos Davatzikos, a professor radiology at the University of Pennsylvania School of Medicine, Biros is creating new methods to quickly and accurately assimilate MRI scans and other imaging modalities, and then to combine these images with biophysical models that represent tumor growth. They’ve found that the addition of these math-driven biophysical tumor models increases the accuracy and effectiveness of the interpretation of images.

    “Just looking at a single MRI scan is not enough,” Biros said. “We need to combine images acquired using several imaging modalities, apply pattern recognition and statistical inference tools and integrate them with sophisticate biophysical models to be able to quantitatively interpret the images. A machine like Stampede makes this possible.”

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