Vol. 22 No. 4 (2019) Cover Image
Vol. 22 No. 4 (2019)

Published: December 31, 2019

Pages: 307-314

Articles

An Accelerated Iterative Cone Beam Computed Tomography Image Reconstruction Approach

Abstract

Cone-beam computed tomography (CBCT) is an indispensable method that reconstructs three dimensional (3D) images. CBCT employs a mathematical technique of reconstruction, which reveals the anatomy of the patient’s body through the measurements of projections. The mathematical techniques employed in the reconstruction process are classified as; analytical, and iterative. The iterative reconstruction methods have been proven to be superior over the analytical methods, but due to their prolonged reconstruction time those methods are excluded from routine use in clinical applications. The aim of this research is to accelerate the iterative methods by performing the reconstruction process using a graphical processing unit (GPU). This method is tested on two iterative-reconstruction algorithms (IR), the algebraic reconstruction technique (ART), and the multiplicative algebraic reconstruction technique (MART). The results are compared against the traditional ART, and MART. A 3D test head phantom image is used in this research to demonstrate results of the proposed method on the reconstruction algorithms. The simulation results are executed using MATLAB (version R2018b) programming language and computer system with the following specifications: CPU core i7 (2.40 GHz) for the processing, with a NIVDIA GEFORCE GPU. Experimental results indicate, that this method reduces the reconstruction time for the iterative algorithms.

References

  1. P. Allisy-Robets and J. Williams, “Farr's physics for medical imaging”, 2nd ed. Edinburgh: Elsevier, 2008.
  2. L. Romans, “Computed tomography for technologists”. Philadelphia [etc.]: Wollters Kluwer Health/Lippincott Williams & Wilkins, 2011.
  3. “Exxim computing corporation. Conventional Cone-Beam Setup”, Exxim-cc.com, 2019. [Online]. Available: https://www.exxim-cc.com/conventional_cone_beam_setup.html. [Accessed: 26- Apr- 2019].
  4. P. Caruso, E. Silvestri and L. Sconfienza, “Cone beam CT and 3D imaging”. Italy: Springer, 2014.
  5. A. Kak and M. Slaney, “Principles of computerized tomographic imaging”. Philadelphia, Pa: IEEE, 1999.
  6. G. Zeng, “Medical Image Reconstruction”. Berlin, Heidelberg: Springer, 2010.
  7. M. Aurumskjöld, “Optimisation of image quality and radiation dose in computed tomography using iterative image reconstruction”, MSc, Lund University, 2017.
  8. D. Qiu and E. Seeram, “Does Iterative Reconstruction Improve Image Quality and Reduce Dose in Computed Tomography?”, Radiology - Open Journal, vol. 1, no. 2, pp. 42-54, 2016. Available: 10.17140/roj-1-108.
  9. F. Xu and K. Mueller, “Accelerating popular tomographic reconstruction algorithms on commodity PC graphics hardware”, IEEE Transactions on Nuclear Science, vol. 52, no. 3, pp. 654-663, 2005. Available: 10.1109/tns.2005.851398.
  10. T. Van Hemelryck, S. Wuyts, M. Goossens, J. Batenburg Kees and J. Sijbers, “Iterative Reconstruction Algorithms The implementation of iterative reconstruction algorithms in MATLAB”, 2007.
  11. X. Zhao, J. Hu and P. Zhang, “GPU-Based 3D Cone-Beam CT Image Reconstruction for Large Data Volume”, International Journal of Biomedical Imaging, vol. 2009, pp. 1-8, 2009. Available: 10.1155/2009/149079.
  12. C. de Molina, E. Serrano, J. Garcia-Blas, J. Carretero, M. Desco and M. Abella, “GPU-accelerated iterative reconstruction for limited-data tomography in CBCT systems”, BMC Bioinformatics, vol. 19, no. 1, 2018. Available: 10.1186/s12859-018-2169-3.
  13. T. Valencia Pérez, J. Hernández López, E. Moreno Barbosa, M. Martínez Hernández, G. Tejeda Muñoz and B. de Celis Alonso, “Parallel approach to tomographic reconstruction algorithm using a Nvidia GPU”, AIP Conference Proceedings, vol. 2090, no. 1, 2019. Available: 10.1063/1.5095930.
  14. A. Biguri, “Iterative Reconstruction and Motion compensation in Computed Tomography on GPUs”, MSc. University of Bath, 2017.
  15. Y. Du, G. Yu, X. Xiang and X. Wang, “GPU accelerated voxel-driven forward projection for iterative reconstruction of cone-beam CT”, BioMedical Engineering OnLine, vol. 16, no. 1, 2017. Available: 10.1186/s12938-016-0293-8.
  16. H. Scherl, “Evaluation of State-of-the-Art Hardware Architectures for Fast Cone-Beam CT Reconstruction”, 1st ed. Germany: Vieweg+Teubner Verlag, 2011.
  17. L. Geyer et al., “State of the Art: Iterative CT Reconstruction Techniques”, Radiology, vol. 276, no. 2, pp. 339-357, 2015. Available: 10.1148/radiol.2015132766.
  18. M. Willemink and P. Noël, “The evolution of image reconstruction for CT from filtered back projection to artificial intelligence”, European Radiology, vol. 29, no. 5, pp. 2185-2195, 2018. Available: 10.1007/s00330-018-5810-7.
  19. T. Buzug, “Introduction to Computed Tomography”. Dordrecht: Springer, 2008.
  20. E. Oliveira, S. Melo, C. Dantas, D. Vasconcelos and L. Cadiz, “Comparison Among Tomographic Reconstruction Algorithms With A Limited Data”, in International Nuclear Atlantic Conference, Belo Horizonte,MG,Brazil, 2011.
  21. M. Al-masni, M. Al-antari, M. Metwally, Y. Kadah, S. Han and T. Kim, “A rapid algebraic 3D volume image reconstruction technique for cone beam computed tomography”, Biocybernetics and Biomedical Engineering, vol. 37, no. 4, pp. 619-629, 2017. Available: 10.1016/j.bbe.2017.07.001.
  22. “2D and 3D Shepp-Logan Phantom in the Fourier and Image Domains - HI-SPEED Software Packets”, Sites.google.com, 2010. [Online]. Available: https://sites.google.com/site/hispeedpackets/Home/shepplogan. [Accessed: 22- Sep- 2019].
  23. N. H. Fallooh Al-anbari and M. H. Ali Al-Hayani, “Design and Construction Three-Dimensional Head Phantom Test Image for the Algorithms of 3D Image Reconstruction”, Journal of Emerging Trends in Computing and Information Sciences, vol. 6, no. 2, 2015.
  24. N. H. Fallooh Al-anbari and M. H. Ali Al-Hayani, “Evaluation Performance of Iterative Algorithms for 3D Image Reconstruction in Cone Beam Geometry”, Al-Nahrain jounal of engineering sciences, vol. 20, no. 1, pp. 149-157, 2017.