ISSN 1004-4140
CN 11-3017/P
Volume 32 Issue 2
Mar.  2023
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Abstract
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ZHANG J H, QIAO Z W. Computed Tomography Reconstruction Algorithm Based on Relative Total Variation Minimization[J]. CT Theory and Applications, 2023, 32(2): 153-169. DOI: 10.15953/j.ctta.2022.190. (in Chinese).
Citation: ZHANG J H, QIAO Z W. Computed Tomography Reconstruction Algorithm Based on Relative Total Variation Minimization[J]. CT Theory and Applications, 2023, 32(2): 153-169. DOI: 10.15953/j.ctta.2022.190. (in Chinese).
shu

Computed Tomography Reconstruction Algorithm Based on Relative Total Variation Minimization

  • School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China

More Information
  • Received Date: September 24, 2022
  • Revised Date: October 12, 2022
  • Accepted Date: October 16, 2022
  • Available Online: November 03, 2022
  • Published Date: March 30, 2023
    Graphical Abstract
    • Abstract
  • The total variation (TV) minimization algorithm is an effective CT image reconstruction algorithm that can reconstruct sparse or noisy projection data with high accuracy. However, in some cases, the TV algorithm produces stepped artifacts. The relative TV algorithm outperforms TV algorithm in the field of image denoising. In view of this, the relative TV model is introduced into image reconstruction, a relative TV minimum optimization model is proposed, and the corresponding solution algorithm is designed under the framework of adaptive gradient descent projection to the convex set (ASD-POCS) to further improve reconstruction accuracy. The reconstruction experiments were conducted with Shepp Logan, Forbild, and real CT image simulation models to verify the anti-crime ability of the algorithm and evaluate the sparse reconstruction and anti-noise abilities of the algorithm. The experimental findings reveal that the algorithm outperforms the TV method in terms of anti-crime capability and the ability to reconstruct sparse or noisy projection data with high precision. Compared with the TV algorithm, the algorithm can achieve higher reconstruction accuracy.
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    • References (22)
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