An Accelerated Iterative Cone Beam Computed Tomography Image Reconstruction Approach

Authors

  • Shimaa Abdulsalam Khazal Electronic and Communication Engineering, Al-Nahrain University, Baghdad-Iraq.
  • Mohammed Hussein Ali Electronic and Communication Engineering, Al-Nahrain University, Baghdad-Iraq.

DOI:

https://doi.org/10.29194/NJES.22040307

Keywords:

Cone-Beam Computed Tomography, ART, MART, GPU, Shepp-Logan, 3D, Iterative

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.

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Published

20-12-2019

How to Cite

[1]
S. A. Khazal and M. H. Ali, “An Accelerated Iterative Cone Beam Computed Tomography Image Reconstruction Approach”, NJES, vol. 22, no. 4, pp. 307–314, Dec. 2019, doi: 10.29194/NJES.22040307.

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