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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

@inproceedings{lu2009accelerating,

   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},

   pages={480–485},

   year={2009},

   organization={IEEE}

}

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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.
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