Adaptive Beam Selection for Efficient Scanning Probe Tomography

2026-06-19

AI Summary

This work addresses the trade-off in X-ray computed tomography between reconstruction quality and the costs associated with increased projection numbers, including prolonged acquisition time, higher experimental expense, and elevated radiation dose. To mitigate these drawbacks, the authors propose a reconstruction-free sequential experimental design method that directly identifies edge-aligned informative measurements from sinograms. By employing an adaptive beam selection strategy, the approach dynamically balances exploration and exploitation to efficiently select the most informative projections across the full measurement space. Notably, this method circumvents the conventional reliance on iterative reconstructions for edge localization, thereby substantially improving computational efficiency and reducing sensitivity to reconstruction errors. Experimental results demonstrate that the proposed technique achieves comparable or superior reconstruction quality with significantly fewer projections, effectively lowering both experimental overhead and radiation burden.

Abstract

In X-ray tomography, reconstruction quality generally improves with larger numbers of projections. However, more 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 practice, 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.

Problem

• beam selection
• scanning probe tomography
• sequential experimental design
• information gain
• edge-aligned measurements

Innovation

• adaptive beam selection
• sequential experimental design
• sinogram-based edge detection
• X-ray tomography
• acquisition function