The research team will use video cards and leading-edge parallel processing techniques to help reduce radiation dose calculations from 10 hours to less than 60 seconds. “There is a high level of interest at the national level to quantify and reduce the amount of ionizing radiation involved in medical imaging.
With this new study, we hope to bring massively parallel computing power that is currently only available to national laboratories and major research universities, to busy and resource-limited hospitals,” said X. George Xu, Professor in the Department of Mechanical, Aerospace, and Nuclear Engineering and the Department of Biomedical Engineering at Rensselaer, plus he heads the university’s Nuclear Engineering Program.
A 2009 report by the National Council on Radiation Protection and Measurements details how the U.S. population is now exposed to seven times more radiation every year from medical imaging exams that they were in 1980. While CT scans only account for 10 percent of diagnostic radiological exams, the procedure contributes disproportionately about 67 percent to the national collective medical radiation exposure.
The radiology community is calling for new measures to avoid unjustified CT scans and to greatly reduce the radiation exposure for pediatric and pregnant patients. However, current software packages for determining and for tracking CT doses are insufficient for such a critical task, Xu said.
In the new $2.6 million study funded by NIBIB, the research team will design and test new simulation software to be run on the graphic processing units found in computer graphics cards, instead of running solely on the central processing units of a desktop computer.
The team will have to build the software from scratch, as no existing radiation dose software is compatible with extremely fast processors. Connecting a small number of video cards presents an inexpensive option for users in hospitals to tackle this “Big Data” challenge and perform massively parallel computation.
After developing and validating the software, the research team will integrate it with GE LightSpeed CT scanner models. Finally, to demonstrate and evaluate the technology’s clinical benefits, the research team will perform a series of calculations for typical diagnostic CT scanning protocols of the head, chest, and abdomen at Massachusetts General Hospital.
