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International Journal of Biomedical Imaging
Volume 2009 (2009), Article ID 149079, 8 pages
http://dx.doi.org/10.1155/2009/149079
Research Article

GPU-Based 3D Cone-Beam CT Image Reconstruction for Large Data Volume

1School of Mathematical Sciences, Capital Normal University, Beijing 100048, China
2Department of Computer Science, Beijing Institute of Technology, Beijing 100081, China

Received 22 December 2008; Accepted 8 June 2009

Academic Editor: Seung Lee

Copyright © 2009 Xing Zhao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Currently, 3D cone-beam CT image reconstruction speed is still a severe limitation for clinical application. The computational power of modern graphics processing units (GPUs) has been harnessed to provide impressive acceleration of 3D volume image reconstruction. For extra large data volume exceeding the physical graphic memory of GPU, a straightforward compromise is to divide data volume into blocks. Different from the conventional Octree partition method, a new partition scheme is proposed in this paper. This method divides both projection data and reconstructed image volume into subsets according to geometric symmetries in circular cone-beam projection layout, and a fast reconstruction for large data volume can be implemented by packing the subsets of projection data into the RGBA channels of GPU, performing the reconstruction chunk by chunk and combining the individual results in the end. The method is evaluated by reconstructing 3D images from computer-simulation data and real micro-CT data. Our results indicate that the GPU implementation can maintain original precision and speed up the reconstruction process by 110–120 times for circular cone-beam scan, as compared to traditional CPU implementation.