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Dictionary-learning-based reconstruction method for electron tomography

Authors

  • Baodong Liu,

    1. Biomedical Imaging Division, VT-WFU School of Biomedical Engineering and Sciences, Wake Forest University Health Sciences, Winston-Salem, North Carolina
    2. Key Lab of Nuclear Analysis Techniques, Institute of High Energy Physics, CAS, Beijing, China
    3. Beijing Engineering Research Center of Radiographic Techniques and Equipment, Beijing, China
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  • Hengyong Yu,

    Corresponding author
    1. Biomedical Imaging Division, VT-WFU School of Biomedical Engineering and Sciences, Wake Forest University Health Sciences, Winston-Salem, North Carolina
    • Address for reprints: Hengyong Yu, Biomedical Imaging Division, VT-WFU School of Biomedical Engineering and Sciences, Wake Forest University Health Sciences, Winston-Salem, NC 27157

      E-mail: hengyong-yu@ieee.org or

      Address for reprints: Ge Wang, Biomedical Imaging Cluster, Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180

      E-mail: ge-wang@ieee.org

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  • Scott S. Verbridge,

    1. Biomedical Imaging Division, VT-WFU School of Biomedical Engineering and Sciences, Virginia Tech, Blacksburg, Virginia
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  • Lizhi Sun,

    1. Departments of Civil & Environmental Engineering and Chemical Engineering & Materials Science, University of California, Irvine, California
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  • Ge Wang

    Corresponding author
    1. Biomedical Imaging Center/Cluster, Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, New York
    • Address for reprints: Hengyong Yu, Biomedical Imaging Division, VT-WFU School of Biomedical Engineering and Sciences, Wake Forest University Health Sciences, Winston-Salem, NC 27157

      E-mail: hengyong-yu@ieee.org or

      Address for reprints: Ge Wang, Biomedical Imaging Cluster, Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180

      E-mail: ge-wang@ieee.org

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Summary

Electron tomography usually suffers from so-called “missing wedge” artifacts caused by limited tilt angle range. An equally sloped tomography (EST) acquisition scheme (which should be called the linogram sampling scheme) was recently applied to achieve 2.4-angstrom resolution. On the other hand, a compressive sensing inspired reconstruction algorithm, known as adaptive dictionary based statistical iterative reconstruction (ADSIR), has been reported for X-ray computed tomography. In this paper, we evaluate the EST, ADSIR, and an ordered-subset simultaneous algebraic reconstruction technique (OS-SART), and compare the ES and equally angled (EA) data acquisition modes. Our results show that OS-SART is comparable to EST, and the ADSIR outperforms EST and OS-SART. Furthermore, the equally sloped projection data acquisition mode has no advantage over the conventional equally angled mode in this context. SCANNING 36:377–383, 2014. © 2013 Wiley Periodicals, Inc.

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