Posted By News On October 2, 2013 - 6:30pm
A team of engineers led by computer scientists at the University of California, San Diego, has developed a new approach that marries computer vision and hardware optimization to sort cells up to 38 times faster than is currently possible.
"Previous techniques simply could not keep up with the image data streaming off of this high speed camera," said Ryan Kastner, a professor of computer science at the Jacobs School of Engineering at UC San Diego.
The microscope-mounted camera used in imaging flow cytometry operates at 140,000 frames per second. But algorithms currently in use take anywhere from 10 seconds to 0.4 seconds to analyze a single frame, depending on the programming language used—making the technique impractical.
The researchers' new approach speeds processing speeds up to 11.94 milliseconds and 151.7 milliseconds depending on the type of hardware used. For the fastest results, engineers developed a custom hardware solution using a field-gate programmable array, or FPGA, which speeds up the process considerably. The slower results, which are still much faster than what's currently available, were obtained using a graphics processing unit, or GPU.