Dual U-Net based feature map algorithm for automatic projection alignment of synchrotron nano-CT

Abstract

Synchrotron radiation-based nano-CT is a non-destructive imaging technique used to study the internal structure of matter. The efficient and automatic correction of errors in nano-CT imaging has proven challenging. Accordingly, different projection alignment techniques have been employed to address these issues. However, these techniques have their limitations. Herein, we propose a novel error correction model, a U-Net, that combines an experimentally informed mathematical model, optimized deep learning algorithm, and efficient projection matching model. The results show that the generation of feature maps based on our model is effective for improving the quality of 3D reconstructions while remaining highly robust. We further propose an optimized feature map generation method, a dual U-Net. Applying it yields an almost negligible alignment error rate for low random error-shift nano-CT data and a stable decrease in errors of one order of magnitude for high random error-shift nano-CT data. This study demonstrates the feasibility of the proposed feature map replacement projection alignment method, which may prove crucial to maximizing the efficiency and generalization of automatic projection alignment tasks.