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Optimization of Three-Dimensional (3D) Chemical Imaging by Soft X-Ray Spectro-Tomography Using a Compressed Sensing Algorithm

Published online by Cambridge University Press:  12 September 2017

Juan Wu ,
Mirna Lerotic ,
Sean Collins ,
Rowan Leary ,
Zineb Saghi ,
Paul Midgley ,
Slava Berejnov ,
Darija Susac ,
Juergen Stumper  and
Gurvinder Singh
...Show all authors
Juan Wu
Affiliation:
Department of Chemistry & Chemical Biology, McMaster University, Hamilton, ON L8S 4M1, Canada
Mirna Lerotic
Affiliation:
2nd Look Consulting, Hong Kong
Sean Collins
Affiliation:
Department of Materials Science and Metallurgy, University of Cambridge, Cambridge CB2 1TQ, UK
Rowan Leary
Affiliation:
Department of Materials Science and Metallurgy, University of Cambridge, Cambridge CB2 1TQ, UK
Zineb Saghi
Affiliation:
Department of Materials Science and Metallurgy, University of Cambridge, Cambridge CB2 1TQ, UK
Paul Midgley
Affiliation:
Department of Materials Science and Metallurgy, University of Cambridge, Cambridge CB2 1TQ, UK
Slava Berejnov
Affiliation:
Automotive Fuel Cell Cooperation (AFCC) Corporation, Burnaby, BC V5J 5J8, Canada
Darija Susac
Affiliation:
Automotive Fuel Cell Cooperation (AFCC) Corporation, Burnaby, BC V5J 5J8, Canada
Juergen Stumper
Affiliation:
Automotive Fuel Cell Cooperation (AFCC) Corporation, Burnaby, BC V5J 5J8, Canada
Gurvinder Singh
Affiliation:
Department of Materials Science and Engineering, Norwegian University of Science and Technology, Trondheim N-7491, Norway
Adam P. Hitchcock*
Affiliation:
Department of Chemistry & Chemical Biology, McMaster University, Hamilton, ON L8S 4M1, Canada
*
*Corresponding author. aph@mcmaster.ca
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Abstract

Soft X-ray spectro-tomography provides three-dimensional (3D) chemical mapping based on natural X-ray absorption properties. Since radiation damage is intrinsic to X-ray absorption, it is important to find ways to maximize signal within a given dose. For tomography, using the smallest number of tilt series images that gives a faithful reconstruction is one such method. Compressed sensing (CS) methods have relatively recently been applied to tomographic reconstruction algorithms, providing faithful 3D reconstructions with a much smaller number of projection images than when conventional reconstruction methods are used. Here, CS is applied in the context of scanning transmission X-ray microscopy tomography. Reconstructions by weighted back-projection, the simultaneous iterative reconstruction technique, and CS are compared. The effects of varying tilt angle increment and angular range for the tomographic reconstructions are examined. Optimization of the regularization parameter in the CS reconstruction is explored and discussed. The comparisons show that CS can provide improved reconstruction fidelity relative to weighted back-projection and simultaneous iterative reconstruction techniques, with increasingly pronounced advantages as the angular sampling is reduced. In particular, missing wedge artifacts are significantly reduced and there is enhanced recovery of sharp edges. Examples of using CS for low-dose scanning transmission X-ray microscopy spectroscopic tomography are presented.

Keywords

3D chemical mappingX-ray tomographycompressed sensingSTXMelectron tomography
Type
Instrumentation and Software
Information
Microscopy and Microanalysis , Volume 23 , Issue 5 , October 2017 , pp. 951 - 966
Copyright
© Microscopy Society of America 2017 

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