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迭代X射线光谱叠层成像术

Iterative X-ray spectroscopic ptychography.

作者信息

Chang Huibin, Rong Ziqin, Enfedaque Pablo, Marchesini Stefano

机构信息

School of Mathematical Sciences, Tianjin Normal University, Tianjin, People's Republic of China.

Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.

出版信息

J Appl Crystallogr. 2020 Jul 8;53(Pt 4):937-948. doi: 10.1107/S1600576720006354. eCollection 2020 Aug 1.

Abstract

Spectroscopic ptychography is a powerful technique to determine the chemical composition of a sample with high spatial resolution. In spectro-ptychography, a sample is rastered through a focused X-ray beam with varying photon energy so that a series of phaseless diffraction data are recorded. Each chemical component in the material under investigation has a characteristic absorption and phase contrast as a function of photon energy. Using a dictionary formed by the set of contrast functions of each energy for each chemical component, it is possible to obtain the chemical composition of the material from high-resolution multi-spectral images. This paper presents SPA (spectroscopic ptychography with alternating direction method of multipliers), a novel algorithm to iteratively solve the spectroscopic blind ptychography problem. First, a nonlinear spectro-ptychography model based on Poisson maximum likelihood is designed, and then the proposed method is constructed on the basis of fast iterative splitting operators. SPA can be used to retrieve spectral contrast when considering either a known or an incomplete (partially known) dictionary of reference spectra. By coupling the redundancy across different spectral measurements, the proposed algorithm can achieve higher reconstruction quality when compared with standard state-of-the-art two-step methods. It is demonstrated how SPA can recover accurate chemical maps from Poisson-noised measurements, and its enhanced robustness when reconstructing reduced-redundancy ptychography data using large scanning step sizes is shown.

摘要

光谱叠层成像术是一种强大的技术,能够以高空间分辨率确定样品的化学成分。在光谱叠层成像术中,样品在聚焦的X射线束下以不同的光子能量进行逐行扫描,从而记录一系列无相位衍射数据。被研究材料中的每种化学成分都具有作为光子能量函数的特征吸收和相位对比度。利用由每种化学成分在每种能量下的对比度函数集形成的字典,可以从高分辨率多光谱图像中获得材料的化学成分。本文提出了SPA(交替方向乘子法光谱叠层成像术),这是一种用于迭代解决光谱盲叠层成像问题的新算法。首先,设计了一种基于泊松最大似然的非线性光谱叠层成像模型,然后基于快速迭代分裂算子构建了所提出的方法。当考虑已知或不完整(部分已知)的参考光谱字典时,SPA可用于检索光谱对比度。通过耦合不同光谱测量之间的冗余信息,与标准的最先进两步法相比,该算法可以实现更高的重建质量。展示了SPA如何从泊松噪声测量中恢复准确的化学图谱,并展示了其在使用大扫描步长重建低冗余叠层成像数据时增强的鲁棒性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e46/7401786/7d1b071affec/j-53-00937-fig1.jpg

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