A Novel Method for Reconstructing CT Images in GATE/GEANT4 with Application in Medical Imaging: A Complexity Analysis Approach

• Gholami Neda

  Pattern Research Center

• Dehshibi Mohammad Mahdi

  Department of Computer Science, Multimedia and Telecommunication, Universitat Oberta de Catalunya

• Adamatzky Andrew

  Unconventional Computing Laboratory, University of the West of England

• Rueda-Toicen Antonio

  Algorithmic Nature Group, LABORES for the Natural and Digital Sciences

• Zenil Hector

  Algorithmic Nature Group, LABORES for the Natural and Digital Sciences Algorithmic Dynamics Lab, Unit of Computational Medicine, SciLifeLab, Centre for Molecular Medicine, Department of Medicine Solna, Karolinska Institute Oxford Immune Algorithmics, Oxford University Innovation

• Fazlali Mahmood

  Department of Computer Science, Shahid Beheshti University

• Masip David

  Department of Computer Science, Multimedia and Telecommunication, Universitat Oberta de Catalunya

抄録

For reconstructing CT images in the clinical setting, ‘effective energy’ is usually used instead of the total X-ray spectrum. This approximation causes an accuracy decline. We proposed to quantize the total X-ray spectrum into irregular intervals to preserve accuracy. A phantom consisting of the skull, rib bone, and lung tissues was irradiated with CT configuration in GATE/GEANT4. We applied inverse Radon transform to the obtained Sinogram to construct a Pixel-based Attenuation Matrix (PAM). PAM was then used to weight the calculated Hounsfield unit scale (HU) of each interval's representative energy. Finally, we multiplied the associated normalized photon flux of each interval to the calculated HUs. The performance of the proposed method was evaluated in the course of Complexity and Visual analysis. Entropy measurements, Kolmogorov complexity, and morphological richness were calculated to evaluate the complexity. Quantitative visual criteria (i.e., PSNR, FSIM, SSIM, and MSE) were reported to show the effectiveness of the fuzzy C-means approach in the segmenting task.

収録刊行物

• Journal of Information Processing

  Journal of Information Processing 28 (0), 161-168, 2020

  一般社団法人 情報処理学会

キーワード

• Complexity
• CT image
• FCM
• GATE/GEANT4
• Hounsfield Unit
• Pixel-based Attenuation Matrix

詳細情報

• CRID: 1390002184875350528
• NII論文ID: 130007798638
• DOI: 10.2197/ipsjjip.28.161
• ISSN: 18826652
• Web Site: https://www.jstage.jst.go.jp/article/ipsjjip/28/0/28_161/_pdf
• 本文言語コード: en
• データソース種別:
  • JaLC
  • Crossref
  • CiNii Articles
• 抄録ライセンスフラグ: 使用不可