Data-driven and model-guided iterative reconstruction framework for simultaneous sparse-view and ring artifact reduction in synchrotron X-ray microtomography

Synchrotron X-ray microtomography (S-µCT) is a highly valuable technique for investigating organ function and pathologies. However, its application is often limited by high radiation doses and the occurrence of ring artifacts. While S-µCT utilizing sparse-view projections can effectively decrease radiation doses, the reconstructed images frequently exhibit severe streaking artifacts, which are exacerbated by ring artifacts, ultimately compromising reconstruction accuracy, image quality, and resolution. Previous research has primarily focused on either sparse-view CT reconstruction or ring artifact reduction, leaving the issue of simultaneous sparse-view and ring artifact reduction under-explored. In this study, we propose a data-driven and model-guided iterative reconstruction framework for S-µCT to address this issue. Specifically, this framework integrates a data prior derived from a score-based generative model to tackle the streaking artifacts introduced by sparse-view projections, along with a model prior obtained from a regularization model to suppress ring artifacts. To assess the effectiveness and capabilities of the proposed framework, simulations using foam phantoms and real S-µCT experiments involving rat liver samples were conducted. The results demonstrated that the proposed framework effectively reduces both streaking and ring artifacts, yielding high-quality S-µCT images with significant reconstruction accuracy and improved image resolution. These findings suggest that the proposed framework holds considerable promise for expanding the application of S-µCT in the future.