title: Electron tomography based on highly limited data using a neural network reconstruction technique.
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.
authors.affiliation: Electron Microscopy for Materials Research (EMAT), University of Antwerp, Groenenborgerlaan 171, B-2020 Antwerp, Belgium.
authors.forename: Eva
authors.initials: E
authors.lastname: Bladt
authors.affiliation: CWI, Science Park 123, 1098 XG Amsterdam, The Netherlands.
authors.forename: Daniël M
authors.initials: DM
authors.lastname: Pelt
authors.affiliation: Electron Microscopy for Materials Research (EMAT), University of Antwerp, Groenenborgerlaan 171, B-2020 Antwerp, Belgium. Electronic address: sara.bals@uantwerpen.be.
authors.forename: Sara
authors.initials: S
authors.lastname: Bals
authors.affiliation: CWI, Science Park 123, 1098 XG Amsterdam, The Netherlands; Mathematical Institute, Leiden University, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands; iMinds-Visionlab, University of Antwerp, Universiteitsplein 1, B-2610 Wilrijk, Belgium.
authors.forename: Kees Joost
authors.initials: KJ
authors.lastname: Batenburg
keywords: Electron tomography; Gold nanostructures; Neural networks; Reconstruction algorithm