Surface Quality Monitoring and Improvement for Dimensional Metrology in Inline CT by Denoising with Neural Networks and Fast Surface Quality Metric - Faizan Ahmad; Ahmed Baraka; César Cardona-Marin; Steffen Kieß; Dominik Wolfschläger; Robert H. Schmitt; Sven Simon (e-Journal of Nondestructive Testing)
Inline computed tomography (CT) is becoming increasingly important for 100% inspection in production technology. However, the short machine cycle times—often just a few minutes—require either fewer X-ray projections or shorter detector exposure times, significantly reducing photon counts by one to two orders of magnitude compared to lab-based CT systems. This reduction leads to a decrease in the signal-to-noise ratio (SNR) by at least an order of magnitude, thereby increasing noise and diminishing the accuracy of surface measurements essential for metrology. To address this challenge, denoising methods based on neural networks have been used, outperforming traditional algorithms such as BM3D. In this work, we investigate the Noise2Noise method, which trains a neural network using pairs of noisy images, eliminating the need for clean ground truth data—typically unavailable in CT. Furthermore, we propose a fast surface quality metric based on the marching cubes algorithm and Hausdorff distance to enable realtime monitoring of voxel dataset quality during continuous CT scans, as required in inline CT. This metric demonstrates a strong correlation with measurement errors associated with surface quality degradation due to noise. Experimental results show that our proposed methods significantly enhance surface quality and SNR of full CT data volumes, achieving denoising within minutes, as required for inline CT applications.