A low-cost geometry calibration procedure for a modular cone-beam X-ray CT system

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.