  X-ray imaging can be efficiently used for high-throughput in-line inspection
of industrial products. However, designing a system that satisfies industrial
requirements and achieves high accuracy is a challenging problem. The effect of
many system settings is application-specific and difficult to predict in
advance. Consequently, the system is often configured using empirical rules and
visual observations. The performance of the resulting system is characterized
by extensive experimental testing. We propose to use computational methods to
substitute real measurements with generated images corresponding to the same
experimental settings. With this approach, it is possible to observe the
influence of experimental settings on a large amount of data and to make a
prediction of the system performance faster than with conventional methods. We
argue that a high accuracy of the image generator may be unnecessary for an
accurate performance prediction. We propose a quantitative methodology to
characterize the quality of the generation model using POD curves. The proposed
approach can be adapted to various applications and we demonstrate it on the
poultry inspection problem. We show how a calibrated image generation model can
be used to quantitatively evaluate the effect of the X-ray exposure time on the
performance of the inspection system.
