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Accelerating simultaneous algebraic reconstruction technique with motion compensation using CUDA-enabled GPU

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

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Abstract

Purpose

To accelerate the simultaneous algebraic reconstruction technique (SART) with motion compensation for speedy and quality computed tomography reconstruction by exploiting CUDA-enabled GPU.

Methods

Two core techniques are proposed to fit SART into the CUDA architecture: (1) a ray-driven projection along with hardware trilinear interpolation, and (2) a voxel-driven back-projection that can avoid redundant computation by combining CUDA shared memory. We utilize the independence of each ray and voxel on both techniques to design CUDA kernel to represent a ray in the projection and a voxel in the back-projection respectively. Thus, significant parallelization and performance boost can be achieved. For motion compensation, we rectify each ray’s direction during the projection and back-projection stages based on a known motion vector field.

Results

Extensive experiments demonstrate the proposed techniques can provide faster reconstruction without compromising image quality. The process rate is nearly 100 projections s −1, and it is about 150 times faster than a CPU-based SART. The reconstructed image is compared against ground truth visually and quantitatively by peak signal-to-noise ratio (PSNR) and line profiles. We further evaluate the reconstruction quality using quantitative metrics such as signal-to-noise ratio (SNR) and mean-square-error (MSE). All these reveal that satisfactory results are achieved. The effects of major parameters such as ray sampling interval and relaxation parameter are also investigated by a series of experiments. A simulated dataset is used for testing the effectiveness of our motion compensation technique. The results demonstrate our reconstructed volume can eliminate undesirable artifacts like blurring.

Conclusion

Our proposed method has potential to realize instantaneous presentation of 3D CT volume to physicians once the projection data are acquired.

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References

  1. Gordon R, Bender R, Herman GT (1970) Algebraic reconstruction techniques (art) for three-dimensional electron microscopy and x-ray photography. J Theor Biol 29: 471–481

    Article  PubMed  CAS  Google Scholar 

  2. Andersen AH, Kak AC (1984) Simultaneous algebraic reconstruction technique (sart): a superior implementation of the art algorithm. Ultrason Img 6(1): 81–94

    Article  CAS  Google Scholar 

  3. Feldkamp LA, Davis LC, Kress JW (1984) Practical cone beam algorithm. J Opt Soc Am 1(6): 612–619

    Article  Google Scholar 

  4. Kak A, Slaney M (1988) Principles of computerized tomographic imaging. IEEE Press

  5. Xu F, Mueller K (2007) Real-time 3-d computed tomographic reconstruction using commodity graphics hardware. Phys Med Biol 52(12): 3405–3419

    Article  PubMed  Google Scholar 

  6. Mueller K, Yagel R (2000) Rapid 3d cone-beam reconstruction with the simultaneous algebraic reconstruction technique (sart) using 2-d texture mapping hardware. IEEE Trans Med Imaging 19(12): 1227–1237

    Article  PubMed  CAS  Google Scholar 

  7. Huh Y, Jin SO, Park JB (1999) Fast image reconstruction from fan beam projections using paralleldigital signal processors and special purpose processors. In: Proceedings of the IEEE region 10 conference (TENCON), pp 1558–1561

  8. Gac N, Mancini S, Desvignes M (2006) Hardware/software 2d-3d backprojection on a sopc platform. In: Proceedings of 21st ACM Symp. Applied Computing (SAC). 222–228

  9. Kachelrieβ M, Knaup M, Bockenbach O (2006) Hyperfast perspective cone-beam backprojection. In: IEEE Medical Imaging Conference Records

  10. Cabral B, Cam N, Foran J (1994) Accelerated volume rendering and tomographic reconstruction using texture mapping hardware. In: VVS ’94: Proceedings of the 1994 symposium on volume visualization, New York, NY, USA, ACM, pp 91–98

  11. Chidlow K, Möller T (2003) Rapid emission tomography reconstruction. In: VG ’03: Proceedings of the 2003 Eurographics/IEEE TVCG workshop on volume graphics, New York, NY, USA, ACM 15–26

  12. Xu F, Mueller K (2005) Accelerating popular tomographic reconstruction algorithms on commodity pc graphics hardware. IEEE Trans Nuclear Sci 52(3): 654–663

    Article  Google Scholar 

  13. Riabkov D, Xue X, Tubbs D, Cheryauka A (2007) Accelerated cone-beam backprojection using GPU-CPU hardware. In: Proceedings of 9th international. Meeting fully three-dimensional image reconstruction in radiology and nuclear medicine (fully 3D 2007), pp 68–71

  14. Thomas Schiwietz, Ti-chin Chang PSRW (2006) MR image reconstruction using the GPU. In: Flynn M, Jansen HJ (eds) Proceedings of SPIE medical imaging 2006, vol 6142, San Diego, CA, SPIE, pp 1279–1290

  15. Scherl H, Keck B, Kowarschik M, Hornegger J (2007) Fast GPU-based ct reconstruction using the common unified device architecture (CUDA). In: Proceedings of nuclear science symposium and medical imaging conference (NSS/MIC), pp 4464–4466

  16. Okitsu Y, Ino F, Hagihara K (2008) Accelerating cone beam reconstruction using the CUDA-enabled GPU. In: Proceedings of high performance computing (HiPC), pp 108–119

  17. Bonnet S, Koenig A, Roux S, Hugonnard P, Guillemaud R, Grangeat P (2003) Dynamic x-ray computed tomography. In: Proceedings of the IEEE, vol 91, pp 1574–1587

  18. Balter J, Haken RT, Lawrence T, Lam K, Robertson J (1996) Uncertainties in CT-based radiation therapy treatment planning associated with patient breathing. Int J Radiat Oncol Biol Phys 36: 167–174

    PubMed  CAS  Google Scholar 

  19. Seppenwoolde Y, Shirato H, Kitamura K, Shimizu S, van Herk M, Lebesque J, Miyasaka K (2002) Precise and realtime measurement of 3d tumor motion in lung due to breathing and heartbeat, measured during radiotherapy. Int J Radiat Oncol Biol Phys 53: 822– 834

    Article  PubMed  Google Scholar 

  20. Nehmeh S, Erdi Y, Ling C, Rosenzweig K, Schroder H, Larson S, Macapinlac H, Squire O, Humm J (2002) Effect of respiratory gating on quantifying pet images of lung cancer. J Nucl Med 43: 876–881

    PubMed  Google Scholar 

  21. Kaczmarz S (1937) Angenaherte auflosung von systemen linearer glerichungen. Bull Acad Pol Sci Lett A 6-8A: 355–357

    Google Scholar 

  22. Siddon RL (1985) Fast calculation of the exact radiological path for a three-dimensional ct array. Med Phys 12(2): 252–255

    Article  PubMed  CAS  Google Scholar 

  23. Li N, Zhao HX, Cho SH, Choi JG, Kim MH (2008) A fast algorithm for voxel-based deterministic simulation of x-ray imaging. Comput Phys Commun 178(7): 518–523

    Article  CAS  Google Scholar 

  24. Rit S, Sarrut D (2005) Cone-beam projection of a deformable volume for motion compensated algebraic reconstruction. In: 29th annual international conference of the IEEE engineering in medicine and biology society (EMBS), pp 22–26

  25. Reyes M, Malandain G, Koulibaly PM, González-Ballester MAJD (2007) Model-based respiratory motion compensation for emission tomography image reconstruction. Phys Med Biol 52: 3579–3600

    Article  PubMed  CAS  Google Scholar 

  26. Sidky EY, Kao CM, Pan X (2006) Accurate image reconstruction from few-views and limited-angle data in divergent-beam ct. J X-Ray Sci Technol 14(2): 119–139

    Google Scholar 

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Authors and Affiliations

  1. Spatial Media Group, Computer Arts Lab, University of Aizu, Aizuwakamatsu, Japan

    Wai-Man Pang

  2. Department of Diagnostic Radiology, National University of Singapore, Kent Ridge, Singapore

    Jing Qin

  3. Shenzhen Institute of Advanced Integration Technology, Chinese Academy of Sciences/The Chinese University of Hong Kong, Shenzhen, China

    Yuqiang Lu

  4. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong

    Yongming Xie & Pheng-Ann Heng

  5. Department of Mechanical Engineering, National University of Singapore, Kent Ridge, Singapore

    Chee-Kong Chui

Authors
  1. Wai-Man Pang
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  2. Jing Qin
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  3. Yuqiang Lu
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  4. Yongming Xie
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  5. Chee-Kong Chui
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  6. Pheng-Ann Heng
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Correspondence to Jing Qin.

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Pang, WM., Qin, J., Lu, Y. et al. Accelerating simultaneous algebraic reconstruction technique with motion compensation using CUDA-enabled GPU. Int J CARS 6, 187–199 (2011). https://doi.org/10.1007/s11548-010-0499-3

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  • DOI: https://doi.org/10.1007/s11548-010-0499-3

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