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Method | Open Access

Accelerating electron tomography reconstruction algorithm ICON with GPU

Yu Chen1,2, Zihao Wang1,2, Jingrong Zhang1,2, Lun Li1,3, Xiaohua Wan1, Fei Sun2,4,5( ), Fa Zhang1( )
Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
University of Chinese Academy of Sciences, Beijing 100049, China
School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
National Key Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
Center for Biological Imaging, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China

Yu Chen and Zihao Wang have contributed equally to this work.

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Abstract

Electron tomography (ET) plays an important role in studying in situ cell ultrastructure in three-dimensional space. Due to limited tilt angles, ET reconstruction always suffers from the “missing wedge” problem. With a validation procedure, iterative compressed-sensing optimized NUFFT reconstruction (ICON) demonstrates its power in the restoration of validated missing information for low SNR biological ET dataset. However, the huge computational demand has become a major problem for the application of ICON. In this work, we analyzed the framework of ICON and classified the operations of major steps of ICON reconstruction into three types. Accordingly, we designed parallel strategies and implemented them on graphics processing units (GPU) to generate a parallel program ICON-GPU. With high accuracy, ICON-GPU has a great acceleration compared to its CPU version, up to 83.7×, greatly relieving ICON’s dependence on computing resource.

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Keywords

AccelerationElectron tomographyGPUICONMissing wedge restoration

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Biophysics Reports
Volume 3 Issue 1-3,
June 2017
Pages 36-42
DOI: 10.1007/s41048-017-0041-z

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Cite this article:
Chen Y, Wang Z, Zhang J, et al. Accelerating electron tomography reconstruction algorithm ICON with GPU. Biophysics Reports, 2017, 3(1-3): 36-42. https://doi.org/10.1007/s41048-017-0041-z

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Received: 09 February 2017
Accepted: 07 April 2017
Published: 04 July 2017
© The Author(s) 2017

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.