# Sequential Experimental Design for X-Ray CT Using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2307.06343 · HTML: https://mlsanity.com/paper/2307.06343/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 12 Jul 2023.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning to Scan: A Deep Reinforcement Learning Approach for Personalized Scanning in CT Imaging](https://mlsanity.com/paper/2006.02420/) (2020)
- [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)

## Built on by (most cited first, 5 of 5)

- [Adaptive Compressed Sensing with Diffusion-Based Posterior Sampling](https://mlsanity.com/paper/2407.08256/) (2024)
- [Probabilistic Bayesian optimal experimental design using conditional normalizing flows](https://mlsanity.com/paper/2402.18337/) (2024)
- [Diffusion Active Learning: Towards Data-Driven Experimental Design in Computed Tomography](https://mlsanity.com/paper/2504.03491/) (2025)
- [Dynamic Angle Selection in X-Ray CT: A Reinforcement Learning Approach to Optimal Stopping](https://mlsanity.com/paper/2503.12688/) (2025)
- [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 X-ray Computed Tomography (CT), projections from many angles are acquired and used for 3D reconstruction. To make CT suitable for in-line quality control, reducing the number of angles while maintaining reconstruction quality is necessary. Sparse-angle tomography is a popular approach for obtaining 3D reconstructions from limited data. To optimize its performance, one can adapt scan angles sequentially to select the most informative angles for each scanned object. Mathematically, this corresponds to solving an optimal experimental design (OED) problem. OED problems are high-dimensional, non-convex, bi-level optimization problems that cannot be solved online, i.e., during the scan. To address these challenges, we pose the OED problem as a partially observable Markov decision process in a Bayesian framework, and solve it through deep reinforcement learning. The approach learns efficient non-greedy policies to solve a given class of OED problems through extensive offline training rather than solving a given OED problem directly via numerical optimization. As such, the trained policy can successfully find the most informative scan angles online. We use a policy training method based on the Actor-Critic approach and evaluate its performance on 2D tomography with synthetic data.