Deng Junjun, Yu Hengyong, Ni Jun, Wang Lihe, Wang Ge
Department of Mathematics, University of Iowa, Iowa City, IA 52242, USA.
J Supercomput. 2009 Apr;48(1):1-14. doi: 10.1007/s11227-008-0198-9.
An iterative algorithm is suited to reconstruct CT images from noisy or truncated projection data. However, as a disadvantage, the algorithm requires significant computational time. Although a parallel technique can be used to reduce the computational time, a large amount of communication overhead becomes an obstacle to its performance (Li et al. in J. X-Ray Sci. Technol. 13:1-10, 2005). To overcome this problem, we proposed an innovative parallel method based on the local iterative CT reconstruction algorithm (Wang et al. in Scanning 18:582-588, 1996 and IEEE Trans. Med. Imaging 15(5):657-664, 1996). The object to be reconstructed is partitioned into a number of subregions and assigned to different processing elements (PEs). Within each PE, local iterative reconstruction is performed to recover the subregion. Several numerical experiments were conducted on a high performance computing cluster. And the FORBILD head phantom (Lauritsch and Bruder http://www.imp.uni-erlangen.de/phantoms/head/head.html) was used as benchmark to measure the parallel performance. The experimental results showed that the proposed parallel algorithm significantly reduces the reconstruction time, hence achieving a high speedup and efficiency.
迭代算法适用于从噪声或截断的投影数据中重建CT图像。然而，该算法的一个缺点是需要大量的计算时间。虽然可以使用并行技术来减少计算时间，但大量的通信开销成为其性能的障碍（Li等人，《X射线科学与技术杂志》13:1-10，2005年）。为了克服这个问题，我们基于局部迭代CT重建算法提出了一种创新的并行方法（Wang等人，《扫描》18:582-588，1996年；《IEEE医学成像杂志》15(5):657-664，1996年）。将待重建的对象划分为多个子区域，并分配给不同的处理单元（PE）。在每个PE内，进行局部迭代重建以恢复子区域。在高性能计算集群上进行了几次数值实验。并使用FORBILD头部模型（Lauritsch和Bruder，http://www.imp.uni-erlangen.de/phantoms/head/head.html）作为基准来衡量并行性能。实验结果表明，所提出的并行算法显著减少了重建时间，从而实现了高加速比和效率。
