Affiliations: The CT laboratory, School of Mathematical Sciences,
Capital Normal University, Beijing, China | School of Software, Beijing Institute of Technology, Beijing, China
Note: [] Corresponding author: Xing Zhao, The CT laboratory, School of
Mathematical Sciences, Capital Normal University, Beijing 100048, China. Tel.:
+86 10 6890 7074; Fax: +86 10 6890 7074; E-mail: zhaoxing_1999@yahoo.com
Abstract: Iterative reconstruction of high-resolution cone-beam CT data is
still a difficult task due to the demand for vast amounts of computer cycles
and associated memory. In order to improve the performance of iterative
algorithms for cone-beam CT reconstruction, an acceleration approach
integrating GPU acceleration, empty space skipping and multi-resolution
technique is proposed. The approach divides the reconstructed volume into
equally sized blocks, and empty blocks are identified by reconstructing an
initial low-resolution volume and segmenting it with threshold method. Then all
non-empty blocks are packed into a new volume, which is initialized by
interpolating the low resolution volume and reconstructed at full resolution
using iterative algorithms. Finally these non-empty blocks are rearranged to
get the reconstructed high-resolution volume. The whole process is implemented
in parallel based on GPU. Since only the voxels in non-empty blocks are
calculated, the number of considered voxels is greatly reduced, which
translates directly into substantial computation, memory requirements and data
transfer savings. The method is evaluated by reconstructing images from
simulated projection data of phantom and CT datasets. The results indicate that
our approach significantly improves the performance of iterative reconstruction
while maintaining a high image quality, compared to conventional GPU-based approaches.
