Geometry parameter estimation for sparse X-ray log imaging
Abstract
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.
Keywords
Cite
@article{arxiv.2206.14444,
title = {Geometry parameter estimation for sparse X-ray log imaging},
author = {Angelina Senchukova and Jarkko Suuronen and Jere Heikkinen and Lassi Roininen},
journal= {arXiv preprint arXiv:2206.14444},
year = {2023}
}