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.

本研究的目的是开发和评估一个框架，以支持跨多个机构的锥形束计算机断层扫描 (CBCT) 图像质量 QA 的自动化标准化测试和分析。在参与机构中进行了一项调查，以了解 CBCT QA 实践的可变性。一个商业自动化软件平台由参与致力于自动化质量保证的联盟的七家机构验证。CBCT 图像分析框架用于比较来自 7 个机构的 23 个直线加速器（直线加速器）的定期 QA 结果。CBCT 图像质量指标（几何失真、空间分辨率、对比度、HU 恒定性、与直线加速器验收标准和 AAPM 建议相比，将数据绘制为具有控制上限和下限的平均值的函数。例如，平均几何失真和 HU 恒常性指标分别为 0.13 mm（TG142 建议：≤2 mm）和 13.4（制造商验收规范：≤50）。图像上传和分析过程使用基于 MATLAB 的平台完全自动化. 该分析能够对质量指标的性能进行定量纵向评估，并在 23 台直线加速器之间进行比较。对于均匀性、对比度和 HU 恒定性等关键 CBCT 参数，所有七家机构都使用了比基于控制上限和下限分析建议的目标更严格的目标。这些机构目标也被发现比 AAPM 发布的指南中的目标更严格。这项工作提供了一个参考，可用于通过控制图监控 CBCT 图像维护的机器特定优化容差，以监控性能以及不同测试的灵敏度，以支持更广泛的质量保证计划。为确保患者护理所需的日常图像质量，建议将优化的统计 QA 指标与基于风险的 QA 一起使用。