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.
Highlights
Electron tomography (ET) plays an important role in studying in situ cell ultrastructure in three-dimensional space (Yahav et al 2011; Fridman et al 2012; Rigort et al 2012; Lucicet al. 2013)
The coincident Fourier ring correlation (FRC) of iterative compressed-sensing optimized non-uniform fast Fourier transform (NUFFT) reconstruction (ICON) further demonstrate the accuracy of ICON-graphics processing units (GPU) from the perspective of restoring missing information
We analyzed the iterative framework of ICON and classified the operations of ICON reconstruction into three types
Summary
Electron tomography (ET) plays an important role in studying in situ cell ultrastructure in three-dimensional space (Yahav et al 2011; Fridman et al 2012; Rigort et al 2012; Lucicet al. 2013). Keywords Acceleration, Electron tomography, GPU, ICON, Missing wedge restoration ICON demonstrated its power in the restoration of validated missing information for low SNR biological ET dataset.
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