Improving geometry element regression analysis for dimensional X-ray computed tomography measurements using locally determined quality values

Abstract

X-ray computed tomography (CT) enables the determination of numerous dimensional measurands with a single scan. However, measurements are often affected by artefacts, which are mainly caused by the effects of the complex physical interactions between the used radiation, the measurement object and the detector as well as the algorithms used for measurement data processing. Surface regions affected by artefacts lead to an inaccurate surface determination and therefore to increased measurement deviations and uncertainties of geometric measurements. This contribution aims to demonstrate the possibility of detecting negatively affected surface regions by a qualitative examination of the underlying volume data in the region of each determined surface point. This classification is used to improve different kinds of sphere measurements, which are evaluated by performing regression analysis onto the measured point clouds. Because of the available qualitative classification for each surface point, it is possible to apply a suitable weighting metric before applying the regression analysis in order to reduce the influence of lowly classified surface areas onto the measurement result. The workflow is demonstrated with a calibrated multi-sphere specimen. The results show that a significant reduction of the measurement errors associated with the evaluation of sphere centre distances, sphere form and radius deviations, respectively, can be achieved, while using the regression analysis tools of the commercial software VGStudio Max (Volume Graphics GmbH).