We consider geometry parameter estimation in industrial sawmill fan-beam X-ray tomography. In such industrial settings, scanners do not always allow identification of the location of the source-detector pair, which creates the issue of unknown geometry. This work considers an approach for geometry estimation based on the calibration object. We parametrise the geometry using a set of 5 parameters. To estimate the geometry parameters, we calculate the maximum cross-correlation between a known-sized calibration object image and its filtered backprojection reconstruction and use differential evolution as an optimiser. The approach allows estimating geometry parameters from full-angle measurements as well as from sparse measurements. We show numerically that different sets of parameters can be used for artefact-free reconstruction. We deploy Bayesian inversion with first-order isotropic Cauchy difference priors for reconstruction of synthetic and real sawmill data with a very low number of measurements.

翻译：本文研究了工业锯木厂扇束X射线断层扫描中的几何参数估计问题。在此类工业场景中，扫描仪并不总能确定源-探测器对的位置，从而引发未知几何问题。本研究提出一种基于标定物的几何估计方法。我们采用一组5个参数对几何结构进行参数化表示。为估计几何参数，我们计算已知尺寸标定物图像与其滤波反投影重建之间的最大互相关，并采用差分进化算法作为优化器。该方法既适用于全角度测量也适用于稀疏测量中的几何参数估计。数值实验表明，可采用不同参数组合实现无伪影重建。我们采用一阶各向同性柯西差分先验的贝叶斯反演方法，对合成数据与真实锯木厂数据在极低测量数条件下进行重建。