Swiss Light Source, Paul Scherrer Institut, Forschungsstrasse 111, 5232, Villigen, Aargau, Switzerland.
Institute for Biomedical Engineering, University and ETH Zürich, 8092, Zürich, Zürich, Switzerland.
Sci Rep. 2020 Oct 2;10(1):16388. doi: 10.1038/s41598-020-73036-w.
X-ray dynamic tomographic microscopy offers new opportunities in the volumetric investigation of dynamic processes. Due to data complexity and their sheer amount, extraction of comprehensive quantitative information remains challenging due to the intensive manual interaction required. Particularly for dynamic investigations, these intensive manual requirements significantly extend the total data post-processing time, limiting possible dynamic analysis realistically to a few samples and time steps, hindering full exploitation of the new capabilities offered at dedicated time-resolved X-ray tomographic stations. In this paper, a fully automatized iterative tomographic reconstruction pipeline (rSIRT-PWC-DIFF) designed to reconstruct and segment dynamic processes within a static matrix is presented. The proposed algorithm includes automatic dynamic feature separation through difference sinograms, a virtual sinogram step for interior tomography datasets, time-regularization extended to small sub-regions for increased robustness and an automatic stopping criterion. We demonstrate the advantages of our approach on dynamic fuel cell data, for which the current data post-processing pipeline heavily relies on manual labor. The proposed approach reduces the post-processing time by at least a factor of 4 on limited computational resources. Full independence from manual interaction additionally allows straightforward up-scaling to efficiently process larger data, extensively boosting the possibilities in future dynamic X-ray tomographic investigations.
X 射线动态断层显微镜为动态过程的体积研究提供了新的机会。由于数据的复杂性和数量庞大，提取全面的定量信息仍然具有挑战性，因为需要大量的人工交互。特别是对于动态研究，这些密集的人工要求极大地延长了总数据后处理时间，实际上将可能的动态分析限制在几个样本和时间步长上，阻碍了充分利用专用时间分辨 X 射线断层扫描站提供的新功能。本文提出了一种完全自动化的迭代断层重建管道 (rSIRT-PWC-DIFF)，旨在对静态矩阵中的动态过程进行重建和分割。所提出的算法包括通过差正弦图自动进行动态特征分离、内部断层数据集的虚拟正弦图步骤、扩展到小子区域的时间正则化以提高鲁棒性以及自动停止准则。我们在动态燃料电池数据上展示了我们方法的优势，当前的数据后处理管道严重依赖于人工劳动。在有限的计算资源上，该方法将后处理时间至少缩短了 4 倍。完全不需要人工交互的另外一个优点是可以轻松扩展到处理更大的数据，从而在未来的动态 X 射线断层扫描研究中极大地提高了可能性。
