Parallelism of iterative CT reconstruction based on local reconstruction algorithm

Authors:
Junjun Deng;Hengyong Yu;Jun Ni;Lihe Wang;Ge Wang
Affiliations:
Department of Mathematics, University of Iowa, Iowa City, USA 52242;VT-WFU School of Biomedical Engineering and Sciences, Virginia Polytechnic Institute and State University, Blacksburg, USA 24061;Department of Radiology, University of Iowa, Iowa City, USA 52242;Department of Mathematics, University of Iowa, Iowa City, USA 52242;VT-WFU School of Biomedical Engineering and Sciences, Virginia Polytechnic Institute and State University, Blacksburg, USA 24061
Venue:
The Journal of Supercomputing
Year:
2009

Citing 3
Cited 1

Statistical approaches in quantitative positron emissiontomography

Statistics and Computing
On Parallelizing the EM Algorithm for PET Image Reconstruction

IEEE Transactions on Parallel and Distributed Systems
A Data-Parallel Algorithm for Iterative Tomographic Image Reconstruction

FRONTIERS '99 Proceedings of the The 7th Symposium on the Frontiers of Massively Parallel Computation

Analytical Performance Prediction for Iterative Reconstruction Techniques in Electron Tomography of Biological Structures

International Journal of High Performance Computing Applications

Quantified Score

Hi-index	0.01

Visualization

Abstract

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.