Main Category: Medical Devices / Diagnostics
Also Included In: Stem Cell Research
Article Date: 05 Oct 2013 – 0:00 PDT
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.
Other researchers had previously discovered that the physical properties of cells could provide useful information about cell health, but previous techniques had been confined to academic research labs because measuring the cells of interest could take hours or even days. The new approach brings imaging flow cytometry closer to being used in a clinical setting.
University of California – San Diego. “New approach enables the sorting of cells up to 38 times faster.” Medical News Today. MediLexicon, Intl., 5 Oct. 2013. Web.
5 Oct. 2013. <http://www.medicalnewstoday.com/releases/266975.php>
University of California – San Diego
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The Interpolation step resizes the picture up to 200 by 200 pixels. It also generates a higher-contrast image of the cell. Then the Find Center module finds the center of the cell by converting the higher contrast images to binary images. It then counts the pixels with a non-zero value in each row and column of the image. The module averages the data from the two images produced by the Interpolation module to find the cell’s center point. Finally, the algorithm determines the cell’s shape and morphological properties by finding the darkest pixels on a line from the cell center at each angle of the image, which are considered to be part of the cell’s wall.
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.
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New approach enables the sorting of cells up to 38 times faster
In addition to Kastner, the papers co-authors were Dajung Lee, Pingfan Meng and Matthew Jacobsen of the Jacobs School of Engineering at UC San Diego and Henry Tse and Dino Di Carlo at the California NanoSystems Institute at UCLA.
MLA
The computer scientists presented their findings in September at the International Conference on Field Programmable Logic and Applications in Portugal.
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The ultimate goal of the algorithm is to determine the radius at every angle of the cell. This provides the necessary information to determine the cell’s key features. Ideally this process needs to be performed on every frame in about 7 microseconds per frame. The algorithm must first detect the presence of the cell, then find the center of the cell, and finally determine the distance from this center to the cell wall for every angle, finding the cell’s radius. To do this reliably, yet still meet stringent timing requirements, the algorithm was carefully modified to run faster on the FPGA.
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