10158105Addressing beam hardening and photon starvation in micro-computed tomographyClosedGrant for R&DInnovate UKX-ray micro-computed tomography (micro-CT) is a powerful imaging technique used across industries such as energy, automotive, and advanced manufacturing to examine the internal structure of materials and devices. However, imaging certain components---particularly those containing metals---presents significant challenges due to beam hardening. This effect occurs when low-energy X-rays are disproportionately absorbed by dense materials, leading to artefacts that obscure critical details. In extreme cases, photon starvation can completely mask regions of interest, reducing the reliability of the final reconstructions. To address these issues, this project will develop advanced computational techniques for improving the quality of micro-CT images acquired from multi-orientational scans. A common approach to mitigating beam hardening is to capture multiple scans of the sample at different orientations and combine them to enhance the reconstructed image. However, this process is currently manual, time-consuming, and prone to errors. The goal of this project is to create a more efficient and semi-automated pipeline for processing these datasets. The key innovations in this project include: * Improved Image Combination Strategies -- In collaboration with NPL (National Physical Laboratory), we will investigate methods for optimally integrating data from multiple orientations, reducing beam hardening effects while preserving fine structural details. * (Semi-)Automated Registration -- We will develop a method using physical reference markers embedded in the sample, such as small metallic spheres, to ensure accurate alignment of multi-orientational scans. This approach will improve reproducibility and reduce the need for manual adjustments. * Self-Supervised Denoising -- We will apply cutting-edge self-supervised machine learning techniques to enhance the signal quality of micro-CT reconstructions without requiring extensive training datasets. This will allow for improved image clarity while maintaining computational efficiency. * Advanced Segmentation Tools -- The project will focus on automatically distinguishing different material components in reconstructed volumes. A specific application will be the segmentation of copper (Cu) tracing in printed circuit boards (PCBs), a challenge that is highly relevant to industries dealing with electronics inspection, where beam hardening significantly affects image quality. By improving the accuracy and efficiency of micro-CT data processing, this project will enable faster and more reliable analysis of complex materials and electronic components. The outcomes will support industries that rely on high-quality imaging for quality control, failure analysis, and product development, ultimately advancing the capabilities of non-destructive testing techniques.