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A Reconstruction Approach to CT with Cauchy RBFs Network

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Book cover Advances in Neural Networks - ISNN 2004 (ISNN 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3174))

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Abstract

A new reconstruction approach to computerized tomography(CT) with Cauchy Radial Basis Functions network is presented. The distribution of material parameters is represented by the weighting sum of Cauchy functions. The analytical formula of the line integral of Cauchy functions along any straight-line path is deduced, and the theoretical projection data along a bent ray is computed by optimization algorithm. The parameters in RBFs network are found by the learning rule based on the gradient decent method. The new reconstruction approach is suitable for the CT with a relatively small number of bent ray paths or projection dada, such as seismic tomography. Computer simulations show its good effects.

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References

  1. Herman, G.T., Langenberg, K.G., Sabatier, P.C.: Basic Methods of Tomography and Inverse Problems. IOP Publishing Limited and Individual Contributor (1987)

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  3. Tabuchi, H., Miyoshi, T., Ichihashi, H., Ohno, K.: Computerized Tomography with Radial Basis Functions Network: A Neuro-fuzzy Approach. In: IEEE Int. Conf. Neural Network Conf. Proc., vol. 5, pp. 2258–2263 (1995)

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Author information

Authors and Affiliations

  1. College of Computer and Information Engineering, Xiamen University, Xiamen, 361005, China

    Jianzhong Zhang & Haiyang Li

Authors
  1. Jianzhong Zhang
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  2. Haiyang Li
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Editor information

Editors and Affiliations

  1. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, China

    Fu-Liang Yin

  2. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  3. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, Liaoning, China

    Chengan Guo

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© 2004 Springer-Verlag Berlin Heidelberg

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Zhang, J., Li, H. (2004). A Reconstruction Approach to CT with Cauchy RBFs Network. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_85

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  • DOI: https://doi.org/10.1007/978-3-540-28648-6_85

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22843-1

  • Online ISBN: 978-3-540-28648-6

  • eBook Packages: Springer Book Archive

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