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• W2726779852 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." @default.
• W2726779852 created "2017-07-14" @default.
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• W2726779852 date "2017-06-01" @default.
• W2726779852 modified "2023-10-16" @default.
• W2726779852 title "Accelerating electron tomography reconstruction algorithm ICON with GPU" @default.
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• W2726779852 doi "https://doi.org/10.1007/s41048-017-0041-z" @default.
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