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Records |
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Author |
Bladt, E.; Pelt, D.M.; Bals, S.; Batenburg, K.J. |
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Title |
Electron tomography based on highly limited data using a neural network reconstruction technique |
Type |
A1 Journal article |
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Year |
2015 |
Publication |
Ultramicroscopy |
Abbreviated Journal |
Ultramicroscopy |
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Volume |
158 |
Issue |
158 |
Pages |
81-88 |
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Keywords |
A1 Journal article; Electron microscopy for materials research (EMAT); Vision lab |
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Abstract |
Gold nanoparticles are studied extensively due to their unique optical and catalytical properties. Their exact shape determines the properties and thereby the possible applications. Electron tomography is therefore often used to examine the three-dimensional (3D) shape of nanoparticles. However, since the acquisition of the experimental tilt series and the 3D reconstructions are very time consuming, it is difficult to obtain statistical results concerning the 3D shape of nanoparticles. Here, we propose a new approach for electron tomography that is based on artificial neural networks. The use of a new reconstruction approach enables us to reduce the number of projection images with a factor of 5 or more. The decrease in acquisition time of the tilt series and use of an efficient reconstruction algorithm allows us to examine a large amount of nanoparticles in order to retrieve statistical results concerning the 3D shape. |
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Address |
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Corporate Author |
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Thesis |
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Publisher |
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Place of Publication |
Amsterdam |
Editor |
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Language |
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Wos |
000361574800011 |
Publication Date |
2015-07-10 |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
0304-3991; |
ISBN |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
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Impact Factor |
2.843 |
Times cited |
25 |
Open Access |
OpenAccess |
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Notes |
335078 COLOURATOM; FWO; COST Action MP1207; 312483 ESTEEM2; esteem2jra4; ECASSara; (ROMEO:green; preprint:; postprint:can ; pdfversion:cannot); |
Approved |
Most recent IF: 2.843; 2015 IF: 2.436 |
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Call Number  |
c:irua:126675 c:irua:126675 |
Serial |
988 |
| Permanent link to this record |
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Author |
van Aarle, W.; Palenstijn, W.J.; De Beenhouwer, J.; Altantzis, T.; Bals, S.; Batenburg, K.J.; Sijbers, J. |
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Title |
The ASTRA Toolbox: A platform for advanced algorithm development in electron tomography |
Type |
A1 Journal article |
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Year |
2015 |
Publication |
Ultramicroscopy |
Abbreviated Journal |
Ultramicroscopy |
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Volume |
157 |
Issue |
157 |
Pages |
35-47 |
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Keywords |
A1 Journal article; Electron microscopy for materials research (EMAT); Vision lab |
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Abstract |
We present the ASTRA Toolbox as an open platform for 3D image reconstruction in tomography. Most of the software tools that are currently used in electron tomography offer limited flexibility with respect to the geometrical parameters of the acquisition model and the algorithms used for reconstruction. The ASTRA Toolbox provides an extensive set of fast and flexible building blocks that can be used to develop advanced reconstruction algorithms, effectively removing these limitations. We demonstrate this flexibility, the resulting reconstruction quality, and the computational efficiency of this toolbox by a series of experiments, based on experimental dual-axis tilt series. |
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Address |
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Corporate Author |
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Thesis |
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Publisher |
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Place of Publication |
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Editor |
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Language |
English |
Wos |
000361002400005 |
Publication Date |
2015-05-06 |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
0304-3991; |
ISBN |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
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Impact Factor |
2.843 |
Times cited |
562 |
Open Access |
OpenAccess |
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Notes |
The authors acknowledge financial support from the iMinds ICONMetroCT project,the IWT SBO Tom Food project and from the Netherlands Organisation for Scientific Research (NWO),Project no. 639.072.005. Networking support was provided by the EXTREMA COST Action MP 1207. Sara Bals acknowledges financial support from the European Research Council (ERC Starting Grant #335078 COLOURATOMS).; ECAS_Sara; (ROMEO:green; preprint:; postprint:can ; pdfversion:cannot); |
Approved |
Most recent IF: 2.843; 2015 IF: 2.436 |
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Call Number |
c:irua:127834 |
Serial |
3974 |
| Permanent link to this record |
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Author |
Van Eyndhoven, G.; Kurttepeli, M.; van Oers, C.J.; Cool, P.; Bals, S.; Batenburg, K.J.; Sijbers, J. |
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Title |
Pore REconstruction and Segmentation (PORES) method for improved porosity quantification of nanoporous materials |
Type |
A1 Journal article |
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Year |
2015 |
Publication |
Ultramicroscopy |
Abbreviated Journal |
Ultramicroscopy |
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Volume |
148 |
Issue |
148 |
Pages |
10-19 |
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Keywords |
A1 Journal article; Electron microscopy for materials research (EMAT); Vision lab; Laboratory of adsorption and catalysis (LADCA) |
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Abstract |
Electron tomography is currently a versatile tool to investigate the connection between the structure and properties of nanomaterials. However, a quantitative interpretation of electron tomography results is still far from straightforward. Especially accurate quantification of pore-space is hampered by artifacts introduced in all steps of the processing chain, i.e., acquisition, reconstruction, segmentation and quantification. Furthermore, most common approaches require subjective manual user input. In this paper, the PORES algorithm POre REconstruction and Segmentation is introduced; it is a tailor-made, integral approach, for the reconstruction, segmentation, and quantification of porous nanomaterials. The PORES processing chain starts by calculating a reconstruction with a nanoporous-specific reconstruction algorithm: the Simultaneous Update of Pore Pixels by iterative REconstruction and Simple Segmentation algorithm (SUPPRESS). It classifies the interior region to the pores during reconstruction, while reconstructing the remaining region by reducing the error with respect to the acquired electron microscopy data. The SUPPRESS reconstruction can be directly plugged into the remaining processing chain of the PORES algorithm, resulting in accurate individual pore quantification and full sample pore statistics. The proposed approach was extensively validated on both simulated and experimental data, indicating its ability to generate accurate statistics of nanoporous materials. |
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Address |
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Corporate Author |
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Thesis |
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Publisher |
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Place of Publication |
Amsterdam |
Editor |
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Language |
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Wos |
000345973000002 |
Publication Date |
2014-08-23 |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
0304-3991; |
ISBN |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
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Impact Factor |
2.843 |
Times cited |
7 |
Open Access |
OpenAccess |
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Notes |
Colouratom; ECAS_Sara; (ROMEO:green; preprint:; postprint:can ; pdfversion:cannot); |
Approved |
Most recent IF: 2.843; 2015 IF: 2.436 |
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Call Number |
c:irua:119083 |
Serial |
2672 |
| Permanent link to this record |
