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ACCELERATED ITERATIVE RECONSTRUCTION OF PHANTOM «ROZI» BY OS-SART METHOD USING ORDERED SUBSET PROJECTIONS

https://doi.org/10.21122/2309-4923-2017-2-4-11

Abstract

The statistical maximum likelihood (EM) method and the algebraic reconstruction method with simultaneous iterations (SART) are two methods of iterative tomographic reconstruction. These algorithms are often used when the projection data contains a large amount of statistical noise or has been obtained from a limited range of angles. One of the popular approaches used to increase the rate of convergence of these algorithms is to perform a correction of the current approximation of the reconstructed object on subsets of the projection data. The desire to increase the convergence rate of the iterative methods led to the use of ordered subsets of projections for both the maximum likelihood method of EM (OS-EM) and for the algebraic reconstruction method with simultaneous iterations of SART (OS-SART). The efficiency of using ordered subsets of projections was first established for sequential programs that run on the central processor of the computer (CPU). In this work, both these methods have been accelerated by using the OpenGL graphics library by mirroring them on the graphics processor architecture of the video card.

About the Authors

S. A. Zolotarev
Institute of Applied Physics of the NAS of Belarus
Belarus

Zolotarev Sergei Alekseevich

220072 Minsk, Akademicheskaya str. 16, 


M. M. Mieteeg
Belarusian National Technical University
Belarus

Mieteeg Muhamed Mukhtar Abdulla, Graduate student 

220092 Minsk, Odoevskogo st., 20 bldg. 2, ap. 77, Mob. tel. 8 (029) 7878641, Velcom, E-mail: mieteeg@gmail.com   



A. N. Al-Nadfa
Belarusian National Technical University
Belarus

Alnadfa Antoine Nabiljevich, Graduate student  

220051 Minsk, Slobodskaya, 79, apt. 54, Mob. tel. 8 (029) 3560677, Velcom email: antwan.tiger@gmail.com



References

1. Троицкий, И. Н. Статистическая теория томографии. / И.Н. Троицкий // Москва, Изд-во Радио и связь, – 1989. – 240 с.

2. Kak, A. C., Principles of computerized tomographic imaging / A.C. Kak and M. Slaney, – Piscataway, NJ: IEEE Press, – 1988. – 327 p.

3. Feldkamp, L. A., Davis, L. C., Kress, J. W., Practical cone beam algorithm / L.A. Feldkamp, L.C. Davis, and J.W. Kress // Journal of the Optical Society of America A: Optics, Image Science, and Vision. – 1984.– P. 612–619.

4. Венгринович, В. Л. Итерационные методы томографии / В.Л. Венгринович, С.А. Золотарев // Минск: «Белорусская наука», – 2009. – 227 с.

5. Andersen, A., Kak, A. Simultaneous Algebraic Reconstruction Technique (SART): a superior implementation of the ART algorithm / A. Andersen, A. Kak // Ultrasonic Imaging. – 1984. – Vol. 6. – P. 81-94.

6. Gilbert P. Iterative methods for the 3D reconstruction of an object from projections / P. Gilbert // Journal of Theoretical Biology. – 1972.Vol. 76. – P. 105–117.

7. Yang, L., Zhao, J., Wang, G. Few-view image reconstruction with dual dictionaries / L. Yang, J. Zhao, G. Wang // Phys. Med. Biol. – 2012. – Vol. 57. – P. 173–189.

8. Shepp, L., Vardi, Y. Maximum likelihood reconstruction for emission tomography / L. Shepp, Y. Vardi // IEEE Trans. on Medical Imaging. – 1982. – Vol. 1. – P. 113–122.

9. Hudson, H., Larkin, R. Accelerated Image Reconstruction Using Ordered Subsets of Projection Data / H. Hudson and R. Larkin // IEEE Trans. Medical Imaging. – 1994. – Vol. 13. – P. 601–609.

10. Lagendijk, R. L., Biemond, J. Iterative Identification and Restoration of Images / R.L. Lagendijk and J. Biemond // Boston, MA: Kluwer. – 1991.

11. Andersen, A. H. Algebraic reconstruction in CT from limited views / A.H. Andersen // IEEE Trans. Med. Imag. – 1989. – Vol. 8. – P. 50–55.

12. Xu, F., Mueller K. Real-Time 3D Computed Tomographic Reconstruction Using Commodity Graphics Hardware / Xu, and K. Mueller // Physics in Medicine and Biology. – 2007. – Vol. 52. – P. 3405–3419.


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Zolotarev S.A., Mieteeg M.M., Al-Nadfa A.N. ACCELERATED ITERATIVE RECONSTRUCTION OF PHANTOM «ROZI» BY OS-SART METHOD USING ORDERED SUBSET PROJECTIONS. «System analysis and applied information science». 2017;(2):4-11. (In Russ.) https://doi.org/10.21122/2309-4923-2017-2-4-11

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ISSN 2309-4923 (Print)
ISSN 2414-0481 (Online)