Effect of iterative sparse-view CT reconstruction with task-specific projection angles on dimensional measurements

Abstract

X-ray computed tomography based on a small number of radiographic projections (sparse-view CT) is relevant for several application cases in industry, where reducing the measurement time plays a major role (e.g. for in-line CT). Because of low data quality of sparse-view CT scans due to insufficient sampling, new concepts are needed to reduce measurement uncertainties for dimensional metrology. Iterative image reconstruction techniques provide a promising opportunity for this purpose as they are in principle able to handle limited data or noise well and give the possibility to select specific projection angles (i.e. perspective views) for circular scan trajectories. In this paper, the influence of task-specific projection angles on dimensional measurements is discussed using algebraic and statistical iterative reconstruction of simulated and experimentally obtained sparse-view scans. It is shown that measurement deviations for specific dimensional measurands can be reduced by selecting projection angles which are expected to have a high contribution to a good imaging of relevant geometric features.