Open AccessAccelerating electron tomography reconstruction algorithm ICON with GPU
Author(s) -
Yu Chen,
Zihao Wang,
Jingrong Zhang,
Lun Li,
Xiaohua Wan,
Fei Sun,
Fa Zhang
Publication year - 2017
Publication title -
biophysics reports
Language(s) - English
Resource type - Journals
eISSN - 2364-3420
pISSN - 2364-3439
DOI - 10.1007/s41048-017-0041-z
Subject(s) - icon , computer science , graphics , acceleration , algorithm , computer graphics , iterative reconstruction , compressed sensing , computer vision , computer graphics (images) , computational science , physics , classical mechanics , programming language
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.