Accelerating Algebraic Reconstruction Using CUDA-Enabled GPU

Yuqiang Lu, Weiming Wang, Shifu Chen, Yongming Xie, Jing Qin, Wai-Man Pang, Pheng-Ann Heng
Shenzhen Inst. of Adv. Integration Technol., Chinese Univ. of Hong Kong, Shenzhen, China
Sixth International Conference on Computer Graphics, Imaging and Visualization, 2009. CGIV ’09


   title={Accelerating Algebraic Reconstruction Using CUDA-Enabled GPU},

   author={Lu, Y. and Wang, W. and Chen, S. and Xie, Y. and Qin, J. and Pang, W.M. and Heng, P.A.},

   booktitle={2009 Sixth International Conference on Computer Graphics, Imaging and Visualization},





Source Source   



In this paper, we apply the compute unified device architecture (CUDA) to the 3D cone-beam CT reconstruction using simultaneous algebraic reconstruction technique (SART). With the hardware acceleration, the computationally complex SART can run at speed comparable to the commonly used filtered back-projection, and provide even better quality volume with less samples. The main contributions include two novel techniques to accelerate the reconstruction. We introduce a ray-driven projection along with hardware built-in trilinear interpolation, as well as a voxel-driven back-projection that can avoid redundant computation by combining CUDA shared memory. Significant performance boost is reported from experiments using our techniques. A real-time reconstruction is achieved within 3 seconds for a 128*128*128 volume from 80 128*128 projections, without compromising image quality. Our proposed method realizes the instantaneous presentation of CT volume to the physician once projection images are acquired.
No votes yet.
Please wait...

Recent source codes

* * *

* * *

HGPU group © 2010-2019 hgpu.org

All rights belong to the respective authors

Contact us: