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Modeling Blood Flow
Kenneth Jansen, professor of mechanical, aerospace, and nuclear engineering, is using the power of Rensselaer’s Computational Center for Nanotechnology Innovations (CCNI) and innovative computational methods to speed his human blood flow models sufficiently to move them from the laboratory to clinical practice.
The research, funded by the National Science Foundation, aims to give surgeons better insight and quantitative data when deciding which of several procedures would be safest and most effective for a given cardiovascular condition.
Jansen says surgeons often rely on instinct and past experience when deciding what type of surgery to perform on occluded or partially occluded arteries. They can widen the artery with a stent, bypass the blockage, or jump over the occlusion and connect the artery to another artery nearby.
Jansen uses blood flow data from high-resolution magnetic resonance images collected at Stanford University’s School of Medicine and simulates the outcomes of possible procedures to identify which one will best restore healthy circulation. The model includes and keeps track of about 40 million different variables. Without supercomputers, he says, the project would be impossible.
Five years ago, these simulations took a year to complete. Recently, the time was trimmed down to a day. Spreading calculations across 8,000 of CCNI’s 32,000 processors should narrow the time to 15 minutes, which should be a reasonable amount of time for a surgeon to wait for the data to be analyzed before deciding on a particular surgical plan. In a few years, the cost of owning a supercomputer to perform these calculations should be affordable for most major hospitals.
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