# Edge-Aligned Beam Placement in Scanning Probe Tomography via Reconstruction-Free Sequential Design of Experiments

arXiv: https://arxiv.org/abs/2606.21713 · HTML: https://mlsanity.com/paper/2606.21713/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 19 Jun 2026.

## Builds on (most cited first, 7 of 7)

- [Sequential Bayesian optimal experimental design via approximate dynamic programming](https://mlsanity.com/paper/1604.08320/) (2016)
- [Edge-promoting adaptive Bayesian experimental design for X-ray imaging](https://mlsanity.com/paper/2104.00301/) (2021)
- [Sequential infinite-dimensional Bayesian optimal experimental design with derivative-informed latent attention neural operator](https://mlsanity.com/paper/2409.09141/) (2024)
- [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)
- [An Edge Alignment-based Orientation Selection Method for Neutron Tomography](https://mlsanity.com/paper/2212.00647/) (2022)
- [Dynamic Angle Selection in X-Ray CT: A Reinforcement Learning Approach to Optimal Stopping](https://mlsanity.com/paper/2503.12688/) (2025)

## Abstract (arXiv)

In X-ray scanning probe tomography, reconstruction quality generally improves with larger numbers of projections. However, additional projections increase experiment costs, acquisition time, and the radiation dose imparted to the sample. One mitigation to these trade-offs is to adopt a sequential design of experiments, in which each subsequent measurement is determined as a function of previously acquired data in order to maximize information gain. In scanning probe tomography, a widely used heuristic to maximize information is to align beams with the edges of the sample. A key challenge, however, is that the true sample is unknown, so identifying edge-aligned beams typically requires reconstructing the sample based on available measurements. This work proposes a novel sequential design method that identifies edge-aligned measurements directly from the sinogram, bypassing any reconstruction, thereby improving computational efficiency and reducing the experimental design's susceptibility to reconstruction errors. Our method dynamically selects the next set of measurement beams by maximizing an acquisition function that balances exploration and exploitation over the domain of all possible measurements, improving reconstruction quality while reducing measurement redundancy.