Li Hechao, Kaira Shashank, Mertens James, Chawla Nikhilesh, Jiao Yang
Mechanical Engineering, Arizona State University, Tempe, Arizona, U.S.A.
Materials Science and Engineering, Arizona State University, Tempe, Arizona, U.S.A.
J Microsc. 2016 Dec;264(3):339-350. doi: 10.1111/jmi.12449. Epub 2016 Jul 21.
An accurate knowledge of the complex microstructure of a heterogeneous material is crucial for its performance prediction, prognosis and optimization. X-ray tomography has provided a nondestructive means for microstructure characterization in 3D and 4D (i.e. structural evolution over time), in which a material is typically reconstructed from a large number of tomographic projections using filtered-back-projection (FBP) method or algebraic reconstruction techniques (ART). Here, we present in detail a stochastic optimization procedure that enables one to accurately reconstruct material microstructure from a small number of absorption contrast x-ray tomographic projections. This discrete tomography reconstruction procedure is in contrast to the commonly used FBP and ART, which usually requires thousands of projections for accurate microstructure rendition. The utility of our stochastic procedure is first demonstrated by reconstructing a wide class of two-phase heterogeneous materials including sandstone and hard-particle packing from simulated limited-angle projections in both cone-beam and parallel beam projection geometry. It is then applied to reconstruct tailored Sn-sphere-clay-matrix systems from limited-angle cone-beam data obtained via a lab-scale tomography facility at Arizona State University and parallel-beam synchrotron data obtained at Advanced Photon Source, Argonne National Laboratory. In addition, we examine the information content of tomography data by successively incorporating larger number of projections and quantifying the accuracy of the reconstructions. We show that only a small number of projections (e.g. 20-40, depending on the complexity of the microstructure of interest and desired resolution) are necessary for accurate material reconstructions via our stochastic procedure, which indicates its high efficiency in using limited structural information. The ramifications of the stochastic reconstruction procedure in 4D materials science are also discussed.
准确了解异质材料的复杂微观结构对于其性能预测、预后和优化至关重要。X射线断层扫描为3D和4D（即随时间的结构演变）微观结构表征提供了一种无损方法，其中材料通常使用滤波反投影（FBP）方法或代数重建技术（ART）从大量断层投影中重建。在此，我们详细介绍一种随机优化程序，该程序能够从少量吸收对比X射线断层投影中准确重建材料微观结构。这种离散断层扫描重建程序与常用的FBP和ART形成对比，后者通常需要数千个投影才能准确呈现微观结构。我们的随机程序的实用性首先通过从锥束和平行束投影几何中的模拟有限角度投影重建包括砂岩和硬颗粒堆积在内的广泛的两相异质材料来证明。然后将其应用于从通过亚利桑那州立大学的实验室规模断层扫描设备获得的有限角度锥束数据以及在阿贡国家实验室的先进光子源获得的平行束同步加速器数据中重建定制的锡球-粘土-基体系统。此外，我们通过依次纳入更多数量的投影并量化重建的准确性来检查断层扫描数据的信息含量。我们表明，通过我们的随机程序进行准确的材料重建仅需要少量投影（例如20 - 40个，具体取决于感兴趣的微观结构的复杂性和所需分辨率），这表明其在使用有限结构信息方面的高效率。还讨论了随机重建程序在4D材料科学中的影响。
