Parallelism of iterative CT reconstruction based on local reconstruction algorithm

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.