Mohammad Rezaee1, Daniel Letourneau2. 1. Department of Medical Physics, Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Canada. Electronic address: mrezaee1@jhmi.edu. 2. Department of Medical Physics, Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Canada; Department of Radiation Oncology, Faculty of Medicine, University of Toronto, Toronto, Canada.
Abstract
BACKGROUND: CT simulator for radiation therapy aims to produce high-quality images for dose calculation and delineation of target and organs at risk in the process of treatment planning. Selection of CT imaging protocols that achieve a desired image quality while minimizing patient dose depends on technical CT parameters and their relationship with image quality and radiation dose. For similar imaging protocols using comparable technical CT parameters, there are also variations in image quality metrics between different CT simulator models. Understanding the relationship and variation is important for selecting appropriate imaging protocol and standardizing QC process. Here, we proposed an automated method to determine the relationship between image quality and radiation dose for various CT technical parameters. MATERIAL AND METHOD: The impact of scan parameters on various aspects of image quality and volumetric CT dose index for a Philips Brilliance Big Bore and a Toshiba Aquilion One CT scanners were determined by using commercial phantom and automated image quality analysis software and cylindrical radiation dose phantom. RESULTS AND DISCUSSION: Both scanners had very similar and satisfactory performance based on the diagnostic acceptance criteria recommended by ACR, International Atomic Energy Agency, and American Association of Physicists in Medicine. However, our results showed a compromise between different image quality components such as low-contrast and spatial resolution with the change of scanning parameters and revealed variations between the two scanners on their image quality performance. Measurement using a generic phantom and analysis by automated software was unbiased and efficient. CONCLUSION: This method provides information that can be used as a baseline for CT scanner image quality and dosimetric QC for different CT scanner models in a given institution or across sites.
BACKGROUND: CT simulator for radiation therapy aims to produce high-quality images for dose calculation and delineation of target and organs at risk in the process of treatment planning. Selection of CT imaging protocols that achieve a desired image quality while minimizing patient dose depends on technical CT parameters and their relationship with image quality and radiation dose. For similar imaging protocols using comparable technical CT parameters, there are also variations in image quality metrics between different CT simulator models. Understanding the relationship and variation is important for selecting appropriate imaging protocol and standardizing QC process. Here, we proposed an automated method to determine the relationship between image quality and radiation dose for various CT technical parameters. MATERIAL AND METHOD: The impact of scan parameters on various aspects of image quality and volumetric CT dose index for a Philips Brilliance Big Bore and a Toshiba Aquilion One CT scanners were determined by using commercial phantom and automated image quality analysis software and cylindrical radiation dose phantom. RESULTS AND DISCUSSION: Both scanners had very similar and satisfactory performance based on the diagnostic acceptance criteria recommended by ACR, International Atomic Energy Agency, and American Association of Physicists in Medicine. However, our results showed a compromise between different image quality components such as low-contrast and spatial resolution with the change of scanning parameters and revealed variations between the two scanners on their image quality performance. Measurement using a generic phantom and analysis by automated software was unbiased and efficient. CONCLUSION: This method provides information that can be used as a baseline for CT scanner image quality and dosimetric QC for different CT scanner models in a given institution or across sites.