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MBIR 3D Reconstruction Method Effectively Minimizes Missing Wedge Artifacts and Restores Missing Information in Cryo-electron Tomography

Published online by Cambridge University Press:  30 July 2020

Rui Yan ,
Singanallur Venkatakrishnan ,
Jun Liu ,
Charles Bouman  and
Wen Jiang
Rui Yan
Affiliation:
Howard Hughes Medical Institute, Ashburn, Virginia, United States
Singanallur Venkatakrishnan
Affiliation:
Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States
Jun Liu
Affiliation:
Yale University School of Medicine, West Haven, Connecticut, United States
Charles Bouman
Affiliation:
Purdue University, West Lafayette, Indiana, United States
Wen Jiang
Affiliation:
Purdue University, West Lafayette, Indiana, United States

Abstract

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Type
The Promise of Cryo-Electron Tomography
Information
Microscopy and Microanalysis , Volume 26 , Supplement S2 , August 2020 , pp. 3146 - 3149
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Copyright
Copyright © Microscopy Society of America 2020

References

Bharat, T.A.M., Russo, C.J., Löwe, J., Passmore, L.A., Scheres, S.H.W., 2015. Advances in Single-Particle Electron Cryomicroscopy Structure Determination applied to Sub-tomogram Averaging. Structure 23, 1743–1753.10.1016/j.str.2015.06.026CrossRefGoogle ScholarPubMed
Cardone, G., Grünewald, K., Steven, A.C., 2005. A resolution criterion for electron tomography based on cross-validation. J. Struct. Biol. 151, 117–129.10.1016/j.jsb.2005.04.006CrossRefGoogle ScholarPubMed
Deng, Y., Chen, Y., Zhang, Y., Wang, S., Zhang, F., Sun, F., 2016. ICON: 3D reconstruction with “missing-information” restoration in biological electron tomography. J. Struct. Biol. 195, 100–112.10.1016/j.jsb.2016.04.004CrossRefGoogle ScholarPubMed
Gilbert, P., 1972. Iterative methods for the three-dimensional reconstruction of an object from projections. Journal of Theoretical Biology. https://doi.org/10.1016/0022-5193(72)90180-4CrossRefGoogle ScholarPubMed
Herman, G.T., 2009. Fundamentals of Computerized Tomography: Image Reconstruction from Projections. Springer Science & Business Media.Google Scholar
Himes, B.A., Zhang, P., 2018. emClarity: software for high-resolution cryo-electron tomography and subtomogram averaging. Nat. Methods 15, 955–961.10.1038/s41592-018-0167-zCrossRefGoogle ScholarPubMed
Lucić, V., Förster, F., Baumeister, W., 2005. Structural studies by electron tomography: from cells to molecules. Annu. Rev. Biochem. 74, 833–865.10.1146/annurev.biochem.73.011303.074112CrossRefGoogle ScholarPubMed
Radermacher, M., n.d. Weighted Back-projection Methods. Electron Tomography. https://doi.org/10.1007/978-0-387-69008-7_9CrossRefGoogle Scholar
Venkatakrishnan, S.V., Drummy, L.F., Jackson, M., De Graef, M., Simmons, J., Bouman, C.A., 2015. Model-Based Iterative Reconstruction for Bright-Field Electron Tomography. IEEE Transactions on Computational Imaging. https://doi.org/10.1109/tci.2014.2371751CrossRefGoogle Scholar
Yan, R., Venkatakrishnan, S.V., Liu, J., Bouman, C.A., Jiang, W., 2019. MBIR: A cryo-ET 3D reconstruction method that effectively minimizes missing wedge artifacts and restores missing information. J. Struct. Biol. 206, 183–192.10.1016/j.jsb.2019.03.002CrossRefGoogle ScholarPubMed