Title
Electron tomography based on highly limited data using a neural network reconstruction technique Electron tomography based on highly limited data using a neural network reconstruction technique
Author
Faculty/Department
Faculty of Sciences. Physics
Publication type
article
Publication
Amsterdam ,
Subject
Physics
Chemistry
Source (journal)
Ultramicroscopy. - Amsterdam
Volume/pages
158(2015) , p. 81-88
ISSN
0304-3991
ISI
000361574800011
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
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.
E-info
https://repository.uantwerpen.be/docman/iruaauth/793c36/6d90f7f2883.pdf
Full text (open access)
https://repository.uantwerpen.be/docman/irua/924ef8/10510.pdf
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