# Dynamic Angle Selection in X-Ray CT: A Reinforcement Learning Approach to Optimal Stopping

arXiv: https://arxiv.org/abs/2503.12688 · HTML: https://mlsanity.com/paper/2503.12688/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for clinical treatment and surgery, one of 15 directions in Deep reinforcement learning methods.
- Builds on 16 of its 37 references that are in the map.
- First posted on arXiv 16 Mar 2025.

## Placed in

- Deep reinforcement learning methods / [RL for clinical treatment and surgery](https://mlsanity.com/deeprl/rl-for-clinical-treatment-and-surgery/)

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [The Option-Critic Architecture](https://mlsanity.com/paper/1609.05140/) (2016)
- [A Closer Look at Invalid Action Masking in Policy Gradient Algorithms](https://mlsanity.com/paper/2006.14171/) (2020)
- [Modern Bayesian Experimental Design](https://mlsanity.com/paper/2302.14545/) (2023)
- [Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design](https://mlsanity.com/paper/2103.02438/) (2021)
- [Optimizing Sequential Experimental Design with Deep Reinforcement Learning](https://mlsanity.com/paper/2202.00821/) (2022)
- [Optimal experimental design: Formulations and computations](https://mlsanity.com/paper/2407.16212/) (2024)
- [Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learning](https://mlsanity.com/paper/2110.15335/) (2021)
- [Learning to Scan: A Deep Reinforcement Learning Approach for Personalized Scanning in CT Imaging](https://mlsanity.com/paper/2006.02420/) (2020)
- [Adaptive Compressed Sensing with Diffusion-Based Posterior Sampling](https://mlsanity.com/paper/2407.08256/) (2024)
- [Edge-promoting adaptive Bayesian experimental design for X-ray imaging](https://mlsanity.com/paper/2104.00301/) (2021)
- [Bayesian Experimental Design for Computed Tomography with the Linearised Deep Image Prior](https://mlsanity.com/paper/2207.05714/) (2022)
- [Sequentially optimized projections in X-ray imaging](https://mlsanity.com/paper/2006.12579/) (2020)
- [Sequential Experimental Design for X-Ray CT Using Deep Reinforcement Learning](https://mlsanity.com/paper/2307.06343/) (2023)
- [Real-Time Tilt Undersampling Optimization during Electron Tomography of Beam Sensitive Samples using Golden Ratio Scanning and RECAST3D](https://mlsanity.com/paper/2304.01221/) (2023)
- [X-ray Image Generation as a Method of Performance Prediction for Real-Time Inspection: a Case Study](https://mlsanity.com/paper/2401.16847/) (2024)

## Built on by (most cited first, 1 of 1)

- [Edge-Aligned Beam Placement in Scanning Probe Tomography via Reconstruction-Free Sequential Design of Experiments](https://mlsanity.com/paper/2606.21713/) (2026)

## Abstract (arXiv)

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 investigate the gap between simulation and real-world applications in the context of the developed learning-based method. Our trained model, developed using synthetic data, demonstrates reliable performance when applied to experimental X-ray CT data. This approach enhances the flexibility of CT operations and expands the applicability of sparse-angle tomography in industrial settings.