ABSTRACT Accurate knowledge of the acquisition geometry of a CT scanning system is key for high quality tomographic imaging. Unfortunately, in modular X-ray CT setups, geometry misalignment occurs each time the setup is changed, which calls for an efficient calibration procedure to correct for geometric inaccuracies. Although many studies have been dealing with the calibration of X-ray CT systems, these are often specifically designed for one setup and/or expensive. In this work, we explore the possibilities of a low-cost, easy-to-build, and modular phantom, constructed from LEGO bricks, which serves as a structure to hold small metal beads, for geometric calibration of a tomographic X-ray system. By estimating the bead coordinates using deep learning, and minimizing center-to-center distances of the metal beads between measured and reference projection data, geometry parameters are derived. With simulated as well as real experiments, it is shown that the LEGO phantom can be used to accurately estimate the geometry of a modular X-ray CT system.

中文翻译：

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模块化锥形束 X 射线 CT 系统的低成本几何校准程序

摘要 CT 扫描系统采集几何的准确知识是高质量断层成像的关键。不幸的是，在模块化 X 射线 CT 设置中，每次更改设置时都会发生几何错位，这需要有效的校准程序来纠正几何不准确性。尽管许多研究都涉及 X 射线 CT 系统的校准，但这些通常是专门为一种设置而设计的和/或昂贵的。在这项工作中，我们探索了由乐高积木制成的低成本、易于构建的模块化模型的可能性，该模型用作容纳小金属珠的结构，用于断层扫描 X 射线系统的几何校准. 通过使用深度学习估计珠坐标，并且最小化测量和参考投影数据之间金属珠的中心到中心距离，导出几何参数。模拟和真实实验表明，乐高模型可用于准确估计模块化 X 射线 CT 系统的几何形状。