How will HPC power personalized medicine in the future? With help from XSEDE consulting and computing resources, researchers have developed finite-element computational protocols to assess of the risk for aortic rupture for individual patients, and thereby to help guide decisions about surgical intervention.
We have software to make computational models from medical images of individual patients, which takes into account their aortic wall thickness, slice by slice, in vivo, and from that to predict wall-stress distribution,” said Ender Finol from University of Texas at San Antonio. “No one else has done this before with this level of accuracy.”
Finol is currently conducting further research on the Blacklight supercomputer at the Pittsburgh Supercomputer Center. Each patient analysis requires geometry reconstruction and meshing with nearly three-million degrees of freedom for a CSS simulation. Using the shared-memory version of ADINA, Jana has found that the problem optimizes at eight cores with up to 32 cores for faster time to solution. Read the Full Story.