Adaptive Beam Selection for Efficient Scanning Probe Tomography
For researchers in X-ray tomography, this method reduces acquisition time and radiation dose while maintaining reconstruction quality, though it is an incremental improvement over existing sequential design heuristics.
The paper proposes a sequential design method for X-ray tomography that selects edge-aligned beams directly from the sinogram, bypassing reconstruction, to improve computational efficiency and reduce measurement redundancy. The method dynamically balances exploration and exploitation to improve reconstruction quality while reducing the number of projections.
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.