Automatic correction of nonlinear damping effects in HAADF–STEM tomography for nanomaterials of discrete compositions

Journal Article (2018)
Author(s)

Zhichao Zhong (Centrum Wiskunde & Informatica (CWI))

R.A. Aveyard (TU Delft - ImPhys/Quantitative Imaging)

B Rieger (TU Delft - ImPhys/Quantitative Imaging)

Sara Bals (Universiteit Antwerpen)

Willem Jan Palenstijn (Centrum Wiskunde & Informatica (CWI))

K. Joost Batenburg (Universiteit Leiden, Centrum Wiskunde & Informatica (CWI))

Research Group
ImPhys/Quantitative Imaging
Copyright
© 2018 Zhichao Zhong, R.A. Aveyard, B. Rieger, Sara Bals, Willem Jan Palenstijn, K. Joost Batenburg
DOI related publication
https://doi.org/10.1016/j.ultramic.2017.10.013
More Info
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Publication Year
2018
Language
English
Copyright
© 2018 Zhichao Zhong, R.A. Aveyard, B. Rieger, Sara Bals, Willem Jan Palenstijn, K. Joost Batenburg
Research Group
ImPhys/Quantitative Imaging
Volume number
184
Pages (from-to)
57-65
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Abstract

HAADF-STEM tomography is a common technique for characterizing the three-dimensional morphology of nanomaterials. In conventional tomographic reconstruction algorithms, the image intensity is assumed to be a linear projection of a physical property of the specimen. However, this assumption of linearity is not completely valid due to the nonlinear damping of signal intensities. The nonlinear damping effects increase w.r.t the specimen thickness and lead to so-called “cupping artifacts”, due to a mismatch with the linear model used in the reconstruction algorithm. Moreover, nonlinear damping effects can strongly limit the applicability of advanced reconstruction approaches such as Total Variation Minimization and discrete tomography. In this paper, we propose an algorithm for automatically correcting the nonlinear effects and the subsequent cupping artifacts. It is applicable to samples in which chemical compositions can be segmented based on image gray levels. The correction is realized by iteratively estimating the nonlinear relationship between projection intensity and sample thickness, based on which the projections are linearized. The correction and reconstruction algorithms are tested on simulated and experimental data.

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