Abstract
Processing time has become increasingly a major factor in computed tomography, hence the need for reconstruction and real-time diagnostics. Since the filtered back-projection algorithm (FBP) requires significantly intensive computational time when the amount of data becomes increasingly large; which is the case of computed tomography, parallel computing technique is used in MatLab, as it enables the implementation of multi-tasking. Exploiting this advantage by partitioning the input data and allowing each core of cluster to work on its own sub-image. Therefore, FBP algorithm was applied simultaneously on all cores. All the reconstructed sub-images will be sent to client in order to be gathered. The comparison results between sequential FBP algorithm and parallel algorithm, show that the parallel computing process is up to seven times more efficient than the sequential algorithm on the voluminous images size, otherwise the small images results were poor because of the workers intercommunications that require times comparing with the sequential algorithm that takes a short computational time.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Boukhamla A, Merouani HF (2011) A multi-agents system for optimization of a digital breast tomosynthesis. In: The second international conference on complex systems. Jijel, pp 125–131
De Man B, Basu S (2004) A study of noise and spatial resolution for 2D and 3D filtered backprojection reconstruction, Nuclear Science Symposium Conference Record, 2004 IEEE, vol 6. Rome, pp 3937–3939
Deng J (2011) Parallel computing techniques for computed tomography. PhD thesis, University of Iowa
Deng J, Yu H, Ni J, He T, Zhao S, Wang L, Wang J (2006) A parallel implementation of the Katsevich algorithm for 3-D CT image reconstruction. J Supercomput 38:35–47
Juang P, Wu M (2007) X-ray image reconstruction by radon transform simulation. In: Third international conference on intelligent information hiding and multimedia signal processing, IIHMSP 2007, vol 2. Kaohsiung, pp 237–240
Jun N, Xiang L, Tao H, Ge W (2006) Review of parallel computing techniques for computed tomography image reconstruction. Curr Med Imaging Rev 2(4):405–414
Kak A, Slaney M (2001) Principles of computerized tomography imaging, Edt. SIAM
Kaur et al (2013) A comparative analysis of SIMD and MIMD architectures. Int J Adv Res Comput Sci Softw Eng 3(9):1151–1156
Kothari N, Bhateshvar YK Katariya, A Kothari S (2011) 3D image reconstruction using X-rays for CT Scan. In: International conference on computational intelligence and communication networks (CICN), 2011, Oct 7–9, 2011, Gwalior. pp 6–10
Leeser M, Mukherjee S, Brock J (2014) Fast reconstruction of 3D volumes from 2D CT projection data with GPUs. BMC Res Notes 7(1):582
MathWorks T (2013a) Distributed computing toolbox, users guide. The MathWorks Inc, Natick
MathWorks T (2013b) MATLAB distributed computing engine system administrators guide. The MathWorks Inc, Natick
Meng W, Fessler J (2011) GPU acceleration of 3D forward and backward projection using separable footprints for X-ray CT image reconstruction. In: The 3rd workshop on high performance image reconstruction. Potsdam, pp 56–59
Prabhudev SI (2014) Parallel processing in processor organization. Int J Adv Res Comput Commun Eng 3(1):5150–5153
Rao R, Kriz RD, Abbott AL, Ribbens CJ (1995) Parallel implementation of the filtered back-projection algorithm for tomographic imaging. http://www.sv.vt.edu/xray_ct/parallel/Parallel_CT.html last modified: 04/05/1995
Sheng J, Ying L (2005) A fast image reconstruction algorithm based on penalized-likelihood estimate. Med Eng Phys 27:679–686
Xiang L, Jun N, Ge W (2005) Parallel iterative cone beam CT image reconstruction on PC cluster. J X-ray Sci Technol 13:1–10
Acknowledgments
This work was financed by the Laboratory of Research on Computer Science (LRI/SRF), Annaba, Algeria.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Research involving human participants and/or animals
This research did not require any human or animals participants.
Informed consent
Since there is no human participant in this research, there was no need for informed consent.
Rights and permissions
About this article
Cite this article
Boukhamla, A., Merouani, H.F. & Sissaoui, H. Parallelization of filtered back-projection algorithm for computed tomography. Evolving Systems 7, 197–205 (2016). https://doi.org/10.1007/s12530-015-9139-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12530-015-9139-z