Edge Projection-Based Adaptive View Selection for Cone-Beam CT

Industrial cone-beam X-ray computed tomography (CT) uses projections acquired at multiple predetermined rotation angles about a single axis to produce a 3D reconstruction of a target object. Typically, a large number of projections are required to achieve a high-quality reconstruction, which can take several hours or days depending on the part size, material composition, and desired resolution. In this paper, we introduce an adaptive view selection procedure designed to optimize the scanning process by selecting the best next set of angles based on the current reconstruction and the computer-aided design (CAD) model. We develop a score function to identify angles aligned with long edges in the object, and we develop a tunable angle dispersion score that can be used to determine the importance of angular diversity. We combine these scores to promote measurements aligned with the part's long edges while maintaining a diverse set of overall measurements. Through simulations, we demonstrate that our algorithm notably improves reconstruction quality for a small number of views relative to traditional methods.