A Framework For Automated And Streamlined Kv Cone Beam Computed Tomography Image Quality Assurance: A Multi-Institutional Study

The purpose of this study was to develop and evaluate a framework to support automated standardized testing and analysis of Cone Beam Computed Tomography (CBCT) image quality QA across multiple institutions. A survey was conducted among the participating institutions to understand the variability of the CBCT QA practices. A commercial, automated software platform was validated by seven institutions participating in a consortium dedicated to automated quality assurance. The CBCT image analysis framework was used to compare periodic QA results among 23 linear accelerators (linacs) from seven institutions. The CBCT image quality metrics (geometric distortion, spatial resolution, contrast, HU constancy, uniformity and noise) data are plotted as a function of means with the upper and lower control limits compared to the linac acceptance criteria and AAPM recommendations. For example, mean geometric distortion and HU constancy metrics were found to be 0.13 mm (TG142 recommendation: <= 2 mm) and 13.4 respectively (manufacturer acceptance specification: <=+/- 50).Image upload and analysis process was fully automated using a MATLAB-based platform. This analysis enabled a quantitative, longitudinal assessment of the performance of quality metrics which were also compared across 23 linacs. For key CBCT parameters such as uniformity, contrast, and HU constancy, all seven institutions used stricter goals than what would be recommended based on the analysis of the upper and lower control limits. These institutional goals were also found to be stricter than that found in AAPM published guidance. This work provides a reference that could be used to machine-specific optimized tolerance of CBCT image maintenance via control charts to monitor performance we well as the sensitivity of different tests in support of a broader quality assurance program. To ensure the daily image quality needed for patient care, the optimized statistical QA metrics recommended to using along with risk-based QA.