Dead detector element detection in flat panels using convolutional neural networks.

Journal: Medical physics
Published Date:

Abstract

BACKGROUND: Independent testing of image quality metrics is important to provide an unbiased determination of medical imaging performance. Due to the underreporting by vendors of dead detector elements, which are elements that do not function but may be corrected using information from surrounding pixels, the health of all imaging detector elements is infrequently reported and extraordinarily difficult to independently determine or verify through traditional means without vendor largesse. In many instances, dead detector data, or dead pixel maps, are only available at the discretion of the vendors, which renders these data inaccessible to many medical physicists.

Authors

  • Jon Box
    The University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA.
  • Adam Salazar
    Emory University School of Medicine Atlanta, Atlanta, Georgia, USA.
  • Dan Johnson
    University of Kansas Medical Center Radiation Oncology, Kansas City, Kansas, USA.
  • Isaac Rutel
    The University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA.