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BSIRT: A Block-Iterative SIRT Parallel Algorithm Using Curvilinear Projection Model

机译:BSIRT:使用曲线投影模型的块迭代SIRT并行算法

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Large-field high-resolution electron tomography enables visualizing detailed mechanisms under global structure. As field enlarges, the distortions of reconstruction and processing time become more critical. Using the curvilinear projection model can improve the quality of large-field ET reconstruction, but its computational complexity further exacerbates the processing time. Moreover, there is no parallel strategy on GPU for iterative reconstruction method with curvilinear projection. Here we propose a new Block-iterative SIRT parallel algorithm with the curvilinear projection model (BSIRT) for large-field ET reconstruction, to improve the quality of reconstruction and accelerate the reconstruction process. We also develop some key techniques, including block-iterative method with the curvilinear projection, a scope-based data decomposition method and a page-based data transfer scheme to implement the parallelization of BSIRT on GPU platform. Experimental results show that BSIRT can improve the reconstruction quality as well as the speed of the reconstruction process.
机译:大范围的高分辨率电子断层扫描可以可视化全局结构下的详细机制。随着领域的扩大,重建和处理时间的扭曲变得越来越关键。使用曲线投影模型可以提高大视野ET重建的质量,但是其计算复杂性进一步加重了处理时间。而且,在GPU上还没有并行策略用于具有曲线投影的迭代重建方法。在此,我们提出了一种新的基于迭代曲线投影模型(BSIRT)的块迭代SIRT并行算法,用于大场ET重建,以提高重建质量并加快重建过程。我们还开发了一些关键技术,包括采用曲线投影的块迭代方法,基于范围的数据分解方法和基于页面的数据传输方案,以在GPU平台上实现BSIRT的并行化。实验结果表明,BSIRT可以提高重建质量和重建过程的速度。

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