Kees Joost Batenburg

3D imaging of nanomaterials by discrete tomography

The field of discrete tomography focuses on the reconstruction of samples that consist of only a few different materials. Ideally, a three-dimensional (3D) reconstruction of such a sample should contain only one grey level for each of the compositions in the sample. By exploiting this property in the reconstruction algorithm, either the quality of the reconstruction can be improved significantly, or the number of required projection images can be reduced. The discrete reconstruction typically contains fewer artifacts and does not have to be segmented, as it already contains one grey level for each composition. Recently, a new algorithm, called discrete algebraic reconstruction technique (DART), has been proposed that can be used effectively on experimental electron tomography datasets. In this paper, we propose discrete tomography as a general reconstruction method for electron tomography in materials science. We describe the basic principles of DART and show that it can be applied successfully to three different types of samples, consisting of embedded ErSi(2) nanocrystals, a carbon nanotube grown from a catalyst particle and a single gold nanoparticle, respectively.

DART: A Practical Reconstruction Algorithm for Discrete Tomography

In this paper, we present an iterative reconstruction algorithm for discrete tomography, called discrete algebraic reconstruction technique (DART). DART can be applied if the scanned object is known to consist of only a few different compositions, each corresponding to a constant gray value in the reconstruction. Prior knowledge of the gray values for each of the compositions is exploited to steer the current reconstruction towards a reconstruction that contains only these gray values. Based on experiments with both simulated CT data and experimental μCT data, it is shown that DART is capable of computing more accurate reconstructions from a small number of projection images, or from a small angular range, than alternative methods. It is also shown that DART can deal effectively with noisy projection data and that the algorithm is robust with respect to errors in the estimation of the gray values.

Performance improvements for iterative electron tomography reconstruction using graphics processing units (GPUs)

Iterative reconstruction algorithms are becoming increasingly important in electron tomography of biological samples. These algorithms, however, impose major computational demands. Parallelization must be employed to maintain acceptable running times. Graphics Processing Units (GPUs) have been demonstrated to be highly cost-effective for carrying out these computations with a high degree of parallelism. In a recent paper by Xu et al. (2010), a GPU implementation strategy was presented that obtains a speedup of an order of magnitude over a previously proposed GPU-based electron tomography implementation. In this technical note, we demonstrate that by making alternative design decisions in the GPU implementation, an additional speedup can be obtained, again of an order of magnitude. By carefully considering memory access locality when dividing the workload among blocks of threads, the GPUs cache is used more efficiently, making more effective use of the available memory bandwidth.

Advanced reconstruction algorithms for electron tomography : from comparison to combination

In this work, the simultaneous iterative reconstruction technique (SIRT), the total variation minimization (TVM) reconstruction technique and the discrete algebraic reconstruction technique (DART) for electron tomography are compared and the advantages and disadvantages are discussed. Furthermore, we describe how the result of a three dimensional (3D) reconstruction based on TVM can provide objective information that is needed as the input for a DART reconstruction. This approach results in a tomographic reconstruction of which the segmentation is carried out in an objective manner.

Three‐Dimensional Characterization of Helical Silver Nanochains Mediated by Protein Assemblies

[*] Dr. M. Gysemans, Prof. J. Snauwaert, Prof. C. Van Haesendonck Laboratory of Solid-State Physics and Magnetism Katholieke Universiteit Leuven Celestijnenlaan 200 D, BE-3001 Leuven (Belgium) E-mail: Chris.Van.Haesendonck@fys.kuleuven.be F. Leroux, Prof. S. Bals, Prof. G. Van Tendeloo EMAT, University of Antwerp Groenenborgerlaan 171, BE-2020 Antwerp (Belgium) E-mail: Sara.Bals@ua.ac.be Dr. K. J. Batenburg Vision Lab, University of Antwerp Universiteitsplein 1, BE-2020 Wilrijk (Belgium)

Dart: A Fast Heuristic Algebraic Reconstruction Algorithm for Discrete Tomography

Discrete tomography (DT) is concerned with the tomographic reconstruction of images that consist of only a small number of gray levels. DT reconstruction problems are usually underdetermined. Therefore, incorporation of heuristic rules to guide the reconstruction algorithm towards an optimal as well as intuitive solution would be valuable. In this paper, we introduce DART: a new, heuristic DT algorithm that is based on an iterative algebraic reconstruction method. Starting from a continuous reconstruction, a discrete image is reconstructed by consistent updating of border pixels. Using simulation experiments, it is shown that the DART algorithm is capable of computing high quality reconstructions from substantially fewer projections than required for conventional continuous tomography.

Optimal Threshold Selection for Tomogram Segmentation by Projection Distance Minimization

Grey value thresholding is a segmentation technique commonly applied to tomographic image reconstructions. Many procedures have been proposed to optimally select the grey value thresholds based on the tomogram data only (e.g., using the image histogram). In this paper, a projection distance minimization (PDM) method is presented that uses the tomographic projection data to determine optimal thresholds. These thresholds are computed by minimizing the distance between the forward projection of the segmented image and the measured projection data. An important contribution of the current paper is the efficient implementation of the forward projection method, which makes the use of the original projection data as a segmentation criterion feasible. Simulation experiments applied to various phantom images show that our proposed method obtains superior results compared to established histogram-based projection data methods.

Adaptive thresholding of tomograms by projection distance minimization

Segmentation is an important step to obtain quantitative information from tomographic data sets. However, it is usually not possible to obtain an accurate segmentation based on a single, global threshold. Instead, local thresholding schemes can be applied that use a varying threshold. Selecting the best local thresholds is not a straightforward task, as local image features often do not provide sufficient information for choosing a proper threshold. Recently, the concept of projection distance was proposed by the authors as a new criterion for evaluating the quality of a tomogram segmentation [K.J. Batenburg, J. Sijbers, Automatic threshold selection for tomogram segmentation by reprojection of the reconstructed image, in: Computer Analysis of Images and Patterns, in: Lecture Notes in Computer Science, vol. 4673, Springer, Berlin/Heidelberg, 2007, pp. 563-570.]. In this paper, we describe how projection distance minimization (PDM) can be used to select local thresholds, based on the available projection data from which the tomogram was initially computed. The results of several experiments are presented in which our local thresholding approach is compared with alternative thresholding methods. These results demonstrate that the local thresholding approach yields segmentations that are significantly more accurate compared to previously published methods, in particular when the initial reconstruction contains artifacts.

Accurate segmentation of dense nanoparticles by partially discrete electron tomography

Accurate segmentation of nanoparticles within various matrix materials is a difficult problem in electron tomography. Due to artifacts related to image series acquisition and reconstruction, global thresholding of reconstructions computed by established algorithms, such as weighted backprojection or SIRT, may result in unreliable and subjective segmentations. In this paper, we introduce the Partially Discrete Algebraic Reconstruction Technique (PDART) for computing accurate segmentations of dense nanoparticles of constant composition. The particles are segmented directly by the reconstruction algorithm, while the surrounding regions are reconstructed using continuously varying gray levels. As no properties are assumed for the other compositions of the sample, the technique can be applied to any sample where dense nanoparticles must be segmented, regardless of the surrounding compositions. For both experimental and simulated data, it is shown that PDART yields significantly more accurate segmentations than those obtained by optimal global thresholding of the SIRT reconstruction.

Barrier efficiency of sponge-like La2Zr2O7 buffer layers for YBCO-coated conductors

Solution derived La2Zr2O7 films have drawn much attention for potential applications as thermal barriers or low-cost buffer layers for coated conductor technology. Annealing and coating parameters strongly affect the microstructure of La2Zr2O7, but different film processing methods can yield similar microstructural features such as nanovoids and nanometer-sized La2Zr2O7 grains. Nanoporosity is a typical feature found in such films and the implications for the functionality of the films are investigated by a combination of scanning transmission electron microscopy (STEM), electron energy-loss spectroscopy (EELS) and quantitative electron tomography. Chemical solution based La2Zr2O7 films deposited on flexible Ni‐5 at.%W substrates with a {100}� 001� biaxial texture were prepared for an in-depth characterization. A sponge-like structure composed of nanometer-sized voids is revealed by high-angle annular dark-field scanning transmission electron microscopy in combination with electron tomography. A three-dimensional quantification of nanovoids in the La2Zr2O7 film is obtained on a local scale. Mostly non-interconnected highly faceted nanovoids compromise more than one-fifth of the investigated sample volume. The diffusion barrier efficiency of a 170 nm thick La2Zr2O7 film is investigated by STEM-EELS, yielding a 1.8 ± 0.2 nm oxide layer beyond which no significant nickel diffusion can be detected and intermixing is observed. This is of particular significance for the functionality of YBa2Cu3O7−δ coated conductor architectures based on solution derived La2Zr2O7 films as diffusion barriers. (Some figures in this article are in colour only in the electronic version)