In advanced tomographic experiments, large detector sizes and large numbers of acquired datasets can make it difficult to process the data in a reasonable time. At the same time, the acquired projections are often limited in some way, for example having a low number of projections or a low signal-to-noise ratio. Direct analytical reconstruction methods are able to produce reconstructions in very little time, even for large-scale data, but the quality of these reconstructions can be insufficient for further analysis in cases with limited data. Iterative reconstruction methods typically produce more accurate reconstructions, but take significantly more time to compute, which limits their usefulness in practice. In this paper, we present the application of the SIRT-FBP method to large-scale real-world tomographic data. The SIRT-FBP method is able to accurately approximate the simultaneous iterative reconstruction technique (SIRT) method by the computationally efficient filtered backprojection (FBP) method, using precomputed experiment-specific filters. We specifically focus on the many implementation details that are important for application on large-scale real-world data, and give solutions to common problems that occur with experimental data. We show that SIRT-FBP filters can be computed in reasonable time, even for large problem sizes, and that precomputed filters can be reused for future experiments. Reconstruction results are given for three different experiments, and are compared with results of popular existing methods. The results show that the SIRT-FBP method is able to accurately approximate iterative reconstructions of experimental data. Furthermore, they show that, in practice, the SIRT-FBP method can produce more accurate reconstructions than standard direct analytical reconstructions with popular filters, without increasing the required computation time.

中文翻译：

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通过过滤器优化，改进了对大型真实世界数据的层析成像重建。

在先进的层析成像实验中，较大的检测器尺寸和大量的采集数据集可能会导致难以在合理的时间内处理数据。同时，通常以某种方式限制所获取的投影，例如具有低数量的投影或低信噪比。即使对于大规模数据，直接分析重建方法也能够在极短的时间内产生重建，但是在数据有限的情况下，这些重建的质量可能不足以进行进一步的分析。迭代重建方法通常会产生更准确的重建结果，但是要花费更多的时间进行计算，这限制了它们在实践中的实用性。在本文中，我们介绍了SIRT-FBP方法在大规模真实世界断层扫描数据中的应用。SIRT-FBP方法能够使用预先计算的实验专用滤波器，通过计算效率高的滤波反投影（FBP）方法，准确地近似同时迭代重建技术（SIRT）方法。我们特别关注许多实施细节，这些细节对于在大规模现实数据中的应用非常重要，并为解决实验数据中常见的问题提供了解决方案。我们表明，即使对于较大的问题，SIRT-FBP过滤器也可以在合理的时间内计算出来，并且预先计算的过滤器可以重新用于以后的实验。给出了三个不同实验的重建结果，并将其与流行的现有方法的结果进行了比较。结果表明，SIRT-FBP方法能够准确估计实验数据的迭代重建。此外，