Compressed sensing improved iterative reconstruction-reprojection algorithm for electron tomography
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METHODOLOGY
Open Access
Compressed sensing improved iterative reconstruction-reprojection algorithm for electron tomography Lun Li1,2 , Renmin Han3* , Zhaotian Zhang4 , Tiande Guo2 , Zhiyong Liu1 and Fa Zhang1* From 15th International Symposium on Bioinformatics Research and Applications (ISBRA’19) Barcelona, Spain. 3–6 June 2019 *Correspondence: [email protected]; [email protected] 1 High Performance Computer Research Center, Institute Of Computing Technology, Chinese Academy of Sciences, 100190 Beijing, China 3 Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, 266237 Qingdao, People’s Republic of China Full list of author information is available at the end of the article
Abstract Background: 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. Results: 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. Conclusions: 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. Keywords: Electron tomography, Compressed sensing, Matching pursuit, Iterative reconstruction-reprojection
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