A Parallel Implementation of the Katsevich Algorithm for 3-D CT Image Reconstruction

Authors:
Junjun Deng;Hengyong Yu;Jun Ni;Tao He;Shiying Zhao;Lihe Wang;Ge Wang
Affiliations:
Department of Radiology, the University of Iowa, Iowa City, USA 52242;Department of Radiology, the University of Iowa, Iowa City, USA 52242;Department of Radiology, the University of Iowa, Iowa City, USA 52242;Department of Radiology, the University of Iowa, Iowa City, USA 52242;Department of Radiology, the University of Iowa, Iowa City, USA 52242;Department of Radiology, the University of Iowa, Iowa City, USA 52242;Department of Radiology, the University of Iowa, Iowa City, USA 52242
Venue:
The Journal of Supercomputing
Year:
2006

Citing 2
Cited 2

Parallel programming: techniques and applications using networked workstations and parallel computers

Parallel programming: techniques and applications using networked workstations and parallel computers
A Data-Parallel Algorithm for Iterative Tomographic Image Reconstruction

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

Fast katsevich algorithm based on GPU for helical cone-beam computed tomography

IEEE Transactions on Information Technology in Biomedicine
Speedup performance analysis of parallel Katsevich algorithm for 3D CT image reconstruction

International Journal of Computational Science and Engineering

Quantified Score

Hi-index	0.01

Visualization

Abstract

Yu and Wang [1, 2] implemented the first theoretically exact spiral cone-beam reconstruction algorithm developed by Katsevich [3, 4]. This algorithm requires a high computational cost when the data amount becomes large. Here we study a parallel computing scheme for the Katsevich algorithm to facilitate the image reconstruction. Based on the proposed parallel algorithm, several numerical tests are conducted on a high performance computing (HPC) cluster with thirty two 64-bit AMD-based Opteron processors. The standard phantom data [5] is used to establish the performance benchmarks. The results show that our parallel algorithm significantly reduces the reconstruction time, achieving high speedup and efficiency.