SMARTT

Motivation

Small-angle X-ray scattering tensor tomography (SASTT) is a cutting-edge imaging technique that enables the non-destructive examination of nanostructures within three-dimensional (3D) macroscopic samples. Renowned for its ability to probe hierarchical structures, SASTT provides detailed insights into nanoscale features such as orientation and composition in diverse materials, including biological tissues and industrial composites. This technique involves collecting millions of scattering patterns to reconstruct the 3D structure and orientation of nanostructures. However, the process is currently resource-intensive, requiring extensive synchrotron beamtime and prolonged measurement periods, which significantly hampers its accessibility and scalability.

The smart sampling for small-angle X-ray scattering Tensor Tomography (smarTT) project is driven by the urgent need to optimize the data acquisition process in SASTT. Presently, data collection relies on empirical rules of thumb, leading to inefficiencies such as oversampling or undersampling. These inefficiencies not only squander valuable synchrotron beamtime but also increase the risk of radiation damage to samples. With growing demands for high-resolution, high-throughput analyses in fields such as biomedical research and industrial materials science, there is a critical need to make SASTT faster, more resource-efficient, and broadly accessible.

The primary aim of the smarTT project is to transform the SASTT workflow by integrating advanced data science and machine learning techniques to enable smart, data-driven sampling strategies. This will optimize synchrotron beamtime utilization, mitigate radiation damage risks, and significantly enhance the overall efficiency of the technique.

Figure 1: (a) raster scanning the full sample in x and y to obtain one projection, (b) recording one scattering pattern in each point. (c and e) 8000 SAXS pattern are measured per projection. (f) Based on an empirical rule of thumb 307 projections (red full circles) are measured in each rotation around two axis, α and β. The data suffer from a missing wedge (blue open circles) and a full sampling according to an extended Crowther criterion (black open circle) would be unfeasible.

Proposed Approach

The project is structured into three core work packages:

1. Online Reconstruction:

The development of an online reconstruction algorithm will provide real-time feedback during data acquisition. This will allow adjustments to the scanning process based on the data being collected, ensuring that sampling stops once sufficient information has been gathered. This iterative approach will enhance efficiency and reduce redundancy.

2. Smart Sampling of Projections:

By applying data-driven approaches, the project aims to optimize projection sampling, focusing on the most informative angles and orientations. For samples with known structural characteristics—such as carbon-fiber composites or layered biological tissues—this strategy will concentrate data collection on projections that yield the richest structural insights, reducing unnecessary sampling.

3. Smart Sampling Within Projections:

Efficiency in raster scanning within each projection will be enhanced by dynamically adjusting step sizes and exposure times based on real-time analysis. These adjustments will maintain an optimal signal-to-noise ratio while minimizing radiation damage, particularly for sensitive biological samples and heterogenous materials.

By addressing the key challenges of inefficiency and accessibility, the smarTT project is poised to revolutionize SASTT, enabling its broader application in statistically significant biomedical studies and industrial material assessments. This effort will directly benefit synchrotron facilities, researchers, and industries globally, fostering advancements in fields reliant on detailed structural characterization.