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Author Bladt, E.; Pelt, D.M.; Bals, S.; Batenburg, K.J. pdf  url
doi  openurl
  Title Electron tomography based on highly limited data using a neural network reconstruction technique Type A1 Journal article
  Year (down) 2015 Publication Ultramicroscopy Abbreviated Journal Ultramicroscopy  
  Volume 158 Issue 158 Pages 81-88  
  Keywords A1 Journal article; Electron microscopy for materials research (EMAT); Vision lab  
  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.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Amsterdam Editor  
  Language Wos 000361574800011 Publication Date 2015-07-10  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0304-3991; ISBN Additional Links UA library record; WoS full record; WoS citing articles  
  Impact Factor 2.843 Times cited 25 Open Access OpenAccess  
  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  
  Call Number c:irua:126675 c:irua:126675 Serial 988  
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