Dynamic angle selection in X-Ray CT: A reinforcement learning approach to optimal stopping

In industrial X-ray Computed Tomography (CT), the need for rapid in-line inspection is critical. Sparse-angle tomography plays a significant role in this by reducing the required number of projections, thereby accelerating processing and conserving resources. Most existing methods aim to balance reconstruction quality and scanning time, typically relying on fixed scan durations. Adaptive adjustment of the number of angles is essential; for instance, more angles may be required for objects with complex geometries or noisier projections. The concept of optimal stopping, which dynamically adjusts this balance according to varying industrial needs, remains overlooked. Building on our previous work, we integrate optimal stopping into sequential Optimal Experimental Design (sOED) and Reinforcement Learning (RL). We propose a novel method for computing the policy gradient within the Actor–Critic framework, enabling the development of adaptive policies for informative angle selection and scan termination. Additionally, we evaluate whether policies trained in simulation transfer to experimental X-ray CT data and provide initial evidence on laboratory data. Trained on synthetic data, the model shows consistent behavior on experimental scans. This supports flexible CT operation and expands the applicability of sparse-view tomography in industrial settings.