Zhang Fa, Zhang Jingrong, Lawrence Albert, Ren Fei, Wang Xuan, Liu Zhiyong, Wan Xiaohua
IEEE Trans Nanobioscience. 2015 Mar;14(2):229-36. doi: 10.1109/TNB.2015.2393377. Epub 2015 Feb 11.
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.
大视野高分辨率电子断层扫描能够在整体结构下可视化详细机制。随着视野增大，重建的失真和处理时间变得更为关键。使用曲线投影模型可以提高大视野电子断层扫描重建的质量，但其计算复杂度进一步加剧了处理时间。此外，对于具有曲线投影的迭代重建方法，在图形处理器（GPU）上没有并行策略。在此，我们提出一种用于大视野电子断层扫描重建的、具有曲线投影模型的新型块迭代同时迭代重建技术（SIRT）并行算法（BSIRT），以提高重建质量并加速重建过程。我们还开发了一些关键技术，包括具有曲线投影的块迭代方法、基于范围的数据分解方法以及基于页的数据传输方案，以在GPU平台上实现BSIRT的并行化。实验结果表明，BSIRT可以提高重建质量以及重建过程的速度。
