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“Automatic correction of nonlinear damping effects in HAADF-STEM tomography for nanomaterials of discrete compositions”. Zhong Z, Aveyard R, Rieger B, Bals S, Palenstijn WJ, Batenburg KJ, Ultramicroscopy 184, 57 (2018). http://doi.org/10.1016/J.ULTRAMIC.2017.10.013
Abstract: <script type='text/javascript'>document.write(unpmarked('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 \u0022cupping artifacts\u0022, 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. (C) 2017 Elsevier B.V. All rights reserved.'));
Keywords: A1 Journal article; Electron microscopy for materials research (EMAT)
Impact Factor: 2.843
Times cited: 8
DOI: 10.1016/J.ULTRAMIC.2017.10.013
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“A bimodal tomographic reconstruction technique combining EDS-STEM and HAADF-STEM”. Zhong Z, Goris B, Schoenmakers R, Bals S, Batenburg KJ, Ultramicroscopy 174, 35 (2017). http://doi.org/10.1016/j.ultramic.2016.12.008
Abstract: A three-dimensional (3D) chemical characterization of nanomaterials can be obtained using tomography based on high angle annular dark field (HAADF) scanning transmission electron microscopy (STEM) or energy dispersive X-ray spectroscopy (EDS) STEM. These two complementary techniques have both advantages and disadvantages. The Z-contrast images have good image quality but lack robustness in the compositional analysis, while the elemental maps give more element-specific information, but at a low signal-to-noise ratio and a longer exposure time. Our aim is to combine these two types of complementary information in one single tomographic reconstruction process. Therefore, an imaging model is proposed combining both HAADF-STEM
and EDS-STEM. Based on this model, the elemental distributions can be reconstructed using both types of information simultaneously during the reconstruction process. The performance of the new technique is evaluated using simulated data and real experimental data. The results demonstrate that combining two imaging modalities leads to tomographic reconstructions with suppressed noise and enhanced contrast.
Keywords: A1 Journal article; Electron microscopy for materials research (EMAT)
Impact Factor: 2.843
Times cited: 26
DOI: 10.1016/j.ultramic.2016.12.008
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“Automated discrete electron tomography &ndash, Towards routine high-fidelity reconstruction of nanomaterials”. Zhuge X, Jinnai H, Dunin-Borkowski RE, Migunov V, Bals S, Cool P, Bons A-J, Batenburg KJ, Ultramicroscopy 175, 87 (2017). http://doi.org/10.1016/j.ultramic.2017.01.009
Abstract: Electron tomography is an essential imaging technique for the investigation of morphology and 3D structure of nanomaterials. This method, however, suffers from well-known missing wedge artifacts due to a restricted tilt range, which limits the objectiveness, repeatability and efficiency of quantitative structural analysis. Discrete tomography represents one of the promising reconstruction techniques for materials science, potentially capable of delivering higher fidelity reconstructions by exploiting the prior knowledge of the limited number of material compositions in a specimen. However, the application of discrete tomography to practical datasets remains a difficult task due to the underlying challenging mathematical problem. In practice, it is often hard to obtain consistent reconstructions from experimental datasets. In addition, numerous parameters need to be tuned manually, which can lead to bias and non-repeatability. In this paper, we present the application of a new
iterative reconstruction technique, named TVR-DART, for discrete electron tomography. The technique is capable of consistently delivering reconstructions with significantly reduced missing wedge artifacts for a variety of challenging data and imaging conditions, and can automatically estimate its key parameters. We describe the principles of the technique and apply it to datasets from three different types of samples acquired under diverse imaging modes. By further reducing the available tilt range and number of projections, we show that the
proposed technique can still produce consistent reconstructions with minimized missing wedge artifacts. This new development promises to provide the electron microscopy community with an easy-to-use and robust tool for high-fidelity 3D characterization of nanomaterials.
Keywords: A1 Journal article; Electron microscopy for materials research (EMAT); Laboratory of adsorption and catalysis (LADCA)
Impact Factor: 2.843
Times cited: 22
DOI: 10.1016/j.ultramic.2017.01.009
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