Compressed sensing improved iterative reconstruction-reprojection algorithm for electron tomography
Open Access
- 18 November 2020
- conference paper
- conference paper
- Published by Springer Science and Business Media LLC in BMC Bioinformatics
- Vol. 21 (S6), 1-19
- https://doi.org/10.1186/s12859-020-3529-3
Abstract
Electron tomography (ET) is an important technique for the study of complex biological structures and their functions. Electron tomography reconstructs the interior of a three-dimensional object from its projections at different orientations. However, due to the instrument limitation, the angular tilt range of the projections is limited within +70∘ to −70∘. The missing angle range is known as the missing wedge and will cause artifacts. In this paper, we proposed a novel algorithm, compressed sensing improved iterative reconstruction-reprojection (CSIIRR), which follows the schedule of improved iterative reconstruction-reprojection but further considers the sparsity of the biological ultra-structural content in specimen. The proposed algorithm keeps both the merits of the improved iterative reconstruction-reprojection (IIRR) and compressed sensing, resulting in an estimation of the electron tomography with faster execution speed and better reconstruction result. A comprehensive experiment has been carried out, in which CSIIRR was challenged on both simulated and real-world datasets as well as compared with a number of classical methods. The experimental results prove the effectiveness and efficiency of CSIIRR, and further show its advantages over the other methods. The proposed algorithm has an obvious advance in the suppression of missing wedge effects and the restoration of missing information, which provides an option to the structural biologist for clear and accurate tomographic reconstruction.Funding Information
- National Key Research and Development Program of China (2017YFE0103900)
- National Key Research and Development Program of China (2017YFA0504702)
- Strategic Priority Research Program of the Chinese Academy of Sciences Grant (XDA19020400)
- Natural Science Foundation of China projects Grant (U1611263)
- Natural Science Foundation of China projects Grant (U1611261)
- Natural Science Foundation of China projects Grant (61932018)
- Natural Science Foundation of China projects Grant (61672493)
- Beijing Municipal Natural Science Foundation Grant (L182053)
- Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (the second phase)
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