Publication
Title
Electron tomography based on highly limited data using a neural network reconstruction technique
Author
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.
Language
English
Source (journal)
Ultramicroscopy. - Amsterdam
Publication
Amsterdam : 2015
ISSN
0304-3991
Volume/pages
158(2015), p. 81-88
ISI
000361574800011
Full text (Publisher's DOI)
Full text (open access)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
[E?say:metaLocaldata.cgzprojectinf]
Colouring Atoms in 3 Dimensions (COLOURATOM).
ESTEEM 2 - Enabling science and technology through European electron microscopy.
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 30.07.2015
Last edited 25.09.2017
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