Geometry parameter estimation for sparse X-ray log imaging

TL;DR

This paper presents a method for estimating the geometry parameters in sparse X-ray tomography using calibration objects, differential evolution, and Bayesian inversion, enabling artefact-free reconstructions even with limited measurements.

Contribution

It introduces a novel approach combining cross-correlation and differential evolution for geometry estimation in sparse tomography settings.

Findings

Accurate geometry parameters can be estimated from sparse measurements.

The method achieves artefact-free reconstructions with limited data.

It is applicable to real industrial sawmill data.

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.