An experimental and theoretical approach to correct for the scattered radiation in an X-ray computer tomography system for industrial applications

The main problem connected to cone beam computed tomography (CT) systems for industrial applications employing 450 kV X-ray tubes is the high amount of scattered radiation which is added to the primary radiation (signal). This stray radiation leads to a significant degradation of the image quality. A better understanding of the scattering and methods to reduce its effects are therefore necessary to improve the image quality. Several studies have been carried out in the medical field at lower energies, whereas studies in industrial CT, especially for energies up to 450 kV, are lacking. Moreover, the studies reported in literature do not consider the scattered radiation generated by the CT system structure and the walls of the X-ray room (environmental scatter). In order to investigate the scattering on CT projections a GEANT4-based Monte Carlo (MC) model was developed. The model, which has been validated against experimental data, has enabled the calculation of the scattering including the environmental scatter, the optimization of an anti-scatter grid suitable for the CT system, and the optimization of the hardware components of the CT system. The investigation of multiple scattering in the CT projections showed that its contribution is 2.3 times the one of primary radiation for certain objects. The results of the environmental scatter showed that it is the major component of the scattering for aluminum box objects of front size 70 x 70 mm2 and that it strongly depends on the thickness of the object and therefore on the projection. For that reason, its correction is one of the key factors for achieving high quality images. The anti-scatter grid optimized by means of the developed MC model was found to reduce the scatter-toprimary ratio in the reconstructed images by 20 %. The object and environmental scatter calculated by means of the simulation were used to improve the scatter correction algorithm which could be patented by Empa. The results showed that the cupping effect in the corrected image is strongly reduced. The developed CT simulation is a powerful tool to optimize the design of the CT system and to evaluate the contribution of the scattered radiation to the image. Besides, it has offered a basis for a new scatter correction approach by which it has been possible to achieve images with the same spatial resolution as state-of-the-art well collimated fan-beam CT with a gain in the reconstruction time of a factor 10. This result has a high economic impact in non-destructive testing and evaluation, and reverse engineering.

In defence of modeling simultaneity for a correct approximation of cultural aspects: implications for food consumers studies with latent variables

Dealing with latent constructs (loaded by reflective and congeneric measures) cross-culturally compared means studying how these unobserved variables vary, and/or covary each other, after controlling for possibly disturbing cultural forces. This yields to the so-called ‘measurement invariance’ matter that refers to the extent to which data collected by the same multi-item measurement instrument (i.e., self-reported questionnaire of items underlying common latent constructs) are comparable across different cultural environments. As a matter of fact, it would be unthinkable exploring latent variables heterogeneity (e.g., latent means; latent levels of deviations from the means (i.e., latent variances), latent levels of shared variation from the respective means (i.e., latent covariances), levels of magnitude of structural path coefficients with regard to causal relations among latent variables) across different populations without controlling for cultural bias in the underlying measures. Furthermore, it would be unrealistic to assess this latter correction without using a framework that is able to take into account all these potential cultural biases across populations simultaneously. Since the real world ‘acts’ in a simultaneous way as well. As a consequence, I, as researcher, may want to control for cultural forces hypothesizing they are all acting at the same time throughout groups of comparison and therefore examining if they are inflating or suppressing my new estimations with hierarchical nested constraints on the original estimated parameters. Multi Sample Structural Equation Modeling-based Confirmatory Factor Analysis (MS-SEM-based CFA) still represents a dominant and flexible statistical framework to work out this potential cultural bias in a simultaneous way. With this dissertation I wanted to make an attempt to introduce new viewpoints on measurement invariance handled under covariance-based SEM framework by means of a consumer behavior modeling application on functional food choices.