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arXiv · 2206.14444

Geometry parameter estimation for sparse X-ray log imaging

Angelina Senchukova·Jarkko Suuronen·Jere Heikkinen·Lassi Roininen
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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.

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Angelina Senchukova, Jarkko Suuronen, Jere Heikkinen, Lassi Roininen. 2022-06-29. Geometry parameter estimation for sparse X-ray log imaging. https://doi.org/10.1007/s10851-023-01167-6

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