2 January 2018 Algorithm development for intrafraction radiotherapy beam edge verification from Cherenkov imaging
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Imaging of Cherenkov light emission from patient tissue during fractionated radiotherapy has been shown to be a possible way to visualize beam delivery in real time. If this tool is advanced as a delivery verification methodology, then a sequence of image processing steps must be established to maximize accurate recovery of beam edges. This was analyzed and developed here, focusing on the noise characteristics and representative images from both phantoms and patients undergoing whole breast radiotherapy. The processing included temporally integrating video data into a single, composite summary image at each control point. Each image stack was also median filtered for denoising and ultimately thresholded into a binary image, and morphologic small hole removal was used. These processed images were used for day-to-day comparison computation, and either the Dice coefficient or the mean distance to conformity values can be used to analyze them. Systematic position shifts of the phantom up to 5 mm approached the observed variation values of the patient data. This processing algorithm can be used to analyze the variations seen in patients being treated concurrently with daily Cherenkov imaging to quantify the day-to-day disparities in delivery as a quality audit system for position/beam verification.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
Clare Snyder, Clare Snyder, Brian W. Pogue, Brian W. Pogue, Michael Jermyn, Michael Jermyn, Irwin Tendler, Irwin Tendler, Jacqueline M. Andreozzi, Jacqueline M. Andreozzi, Petr Bruza, Petr Bruza, Venkat Krishnaswamy, Venkat Krishnaswamy, David J. Gladstone, David J. Gladstone, Lesley A. Jarvis, Lesley A. Jarvis, } "Algorithm development for intrafraction radiotherapy beam edge verification from Cherenkov imaging," Journal of Medical Imaging 5(1), 015001 (2 January 2018). https://doi.org/10.1117/1.JMI.5.1.015001 . Submission: Received: 30 September 2017; Accepted: 5 December 2017
Received: 30 September 2017; Accepted: 5 December 2017; Published: 2 January 2018

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