Accelerating electron tomography reconstruction algorithm ICON with GPU

Yu Chen; Zihao Wang; Jingrong Zhang; Lun Li; Xiaohua Wan; Fei Sun; Fa Zhang

doi:10.1007/s41048-017-0041-z

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Yu Chen, Zihao Wang, Jingrong Zhang, Lun Li, Xiaohua Wan, Fei Sun, Fa Zhang. Accelerating electron tomography reconstruction algorithm ICON with GPU. Biophysics Reports, 2017, 3(1-3): 36-42. doi: 10.1007/s41048-017-0041-z

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Yu Chen, Zihao Wang, Jingrong Zhang, Lun Li, Xiaohua Wan, Fei Sun, Fa Zhang. Accelerating electron tomography reconstruction algorithm ICON with GPU. Biophysics Reports, 2017, 3(1-3): 36-42. doi: 10.1007/s41048-017-0041-z

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Accelerating electron tomography reconstruction algorithm ICON with GPU

doi: 10.1007/s41048-017-0041-z

Yu Chen^1,2,
Zihao Wang^1,2,
Jingrong Zhang^1,2,
Lun Li^1,3,
Xiaohua Wan¹,
Fei Sun^{2,4,5
,
,},
Fa Zhang¹

1.
Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
4 National Key Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
5 Center for Biological Imaging, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China

Funds: This work was supported by the National Natural Science Foundation of China (U1611263,U1611261,61232001,61472397,61502455,61672493),Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (the second phase),the Strategic Priority Research Program of Chinese Academy of Sciences (XDB08030202),and the "973" Program of Ministry of Science and Technology of China (2014CB910700).

More Information

Corresponding author: Fei Sun,feisun@ibp.ac.cn;Fa Zhang,zhangfa@ict.ac.cn
Received Date: 09 February 2017
Rev Recd Date: 07 April 2017
Publish Date: 31 March 2017

Abstract

Abstract

Electron tomography (ET) plays an important role in studying in situ cell ultrastructure in threedimensional 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.
- Acceleration,
- Electron tomography,
- GPU,
- ICON,
- Missing wedge restoration

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References(29)

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