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2008 | 18 | 2 | 147-157

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A new approach to image reconstruction from projections using a recurrent neural network


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A new neural network approach to image reconstruction from projections considering the parallel geometry of the scanner is presented. To solve this key problem in computed tomography, a special recurrent neural network is proposed. The reconstruction process is performed during the minimization of the energy function in this network. The performed computer simulations show that the neural network reconstruction algorithm designed to work in this way outperforms conventional methods in the obtained image quality.








Opis fizyczny




  • Technical University of Czestochowa, Department of Computer Engineering, Al. Armii Krajowej 36, 42-200, Częstochowa, Poland


  • Averbuch A., Coifman R.R., Donoho D.L., Israeli M. and Waldén J. (2001). A notion of Radon transform for data in a Cartesian grid, which is rapidly computable, algebraically exact, geometrically faithful and invertible, Technical report TR No. 2001-11, Department of Statistics, Stanford University, USA.
  • Censor Y. (1983). Finite series-expansion reconstruction methods, Proceeding of the IEEE 71(3): 409-419.
  • Cichocki A., Unbehauen R., Lendl M. and Weinzierl K. (1995). Neural networks for linear inverse problems with incomplete data especially in application to signal and image reconstruction, Neurocomputing 8: 7-41.
  • Cierniak R. and Rutkowski L. (2000). On image compression by competitive neural networks and optimal linear predictors, Signal Processing: Image Communication 15(6): 559-565.
  • Cierniak R. (2002). Image reconstruction from projection using unsupervised neural network, Proceedings of the Joint 1st International Conference on Soft Computing and Intelligent Systems and 3-rd International Symposium on Advanced Intelligent Systems, Tsukuba, Japan.
  • Cierniak R. (2006). A novel approach to image reconstruction from projections using Hopfield-type neural network, in Rutkowski L., Tadeusiewicz R., Zadeh L. A., Żurada J. (Eds.), Proceedings of the 8-th International Conference on Artificial Intelligence and Soft Computing, Zakopane, Poland, LNAI, Springer, Berlin, pp. 890-898.
  • Cormack A.M. (1963). Representation of a function by its line integrals with some radiological application, Journal of Applied Physics 34: 2722-2727.
  • Gordon R., Bender R. and Herman G.T. (1970). Algebraic reconstruction techniques (ART) for three-dimensional electron microscopy and X-ray photography, Journal of Theoretical Biology 29: 471-481.
  • Hopfield J.J. (1982). Neural networks and physical systems with emergent collective computational abilities, Proceedings of the National Academy of Sciences of the USA 79: 2554-2558.
  • Ingman D. and Merlis Y. (1992). Maximum entropy signal reconstruction with neural networks, IEEE Transactions on Neural Networks 3: 195-201.
  • Jaene B. (1991). Digital Image Processing - Concepts, Algoritms and Scientific Applications, Springer, Berlin, Heidelberg.
  • Jain A.K. (1989). Fundamentals of Digital Image Processing, Prentice Hall, Englewood Cliffs, NJ.
  • Kaczmarz S. (1937). Angeneaherte Aufloesung von Systemen Linearer Gleichungen, Bulletin de l'Académie Polonaise des Sciences et Lettres 35: 355-357.
  • Kak A.C. and Slanley M. (1988). Principles of Computerized Tomographic Imaging, IEEE Press, New York.
  • Kerr J.P. and Barlett E.B. (1995a). A statistically tailored neural network approach to tomographic image reconstruction, Medical Physics 22: 601-610.
  • Kerr J.P. and Barlett E.B. (1995b). Medical image processing utilizing neural networks trained on a massively parallel computer, Computers in Biology and Medicine 25: 393-403.
  • Kerr J.P. and Barlett E.B. (1995c). Neural network reconstruction of single-photon emission computed tomography images, Journal of Digital Imaging 8: 116-126.
  • Kingston A. and Svalbe I. (2003). Mapping between digital and continuous projections via the discrete Radon transform in Fourier space, Proceedings of the 7-th Conference on Digital Image Computing: Techniques and Applications, Sydney, pp. 263-272.
  • Knoll P., Mirzaei S., Muller A., Leitha T., Koriska K., Kohn H. and Neumann M. (1999). An artificial neural net and error backpropagation to reconstruct single photon emission computerized tomography data, Medical Physics 26: 244-248.
  • Lewitt R.M. (1983). Reconstruction algorithms: Transform methods, Proceeding of the IEEE 71(3): 390-408.
  • Luo F.-L. and Unbehauen R. (1998). Applied Neural Networks for Signal Processing, Cambridge University Press, Cambridge, UK.
  • Munllay M.T., Floyd C.E., Bowsher J.E. and Coleman R. E. (1994). An artificial neural network approach to quantitative single photon emission computed tomographic reconstruction with collimator, attenuation and scatter compensation, Medical Physics 21: 1889-1899.
  • Ramachandran G.N. and Lakshminarayanan A.V. (1971). Threedimensional reconstruction from radiographs and electron micrographs: II. Application of convolutions instead of Fourier transforms, Proceedings of the National Academy of Sciences of the USA 68: 2236-2240.
  • Srinivasan V., Han Y.K. and Ong S.H. (1993). Image reconstruction by a Hopfield neural network, Image and Vision Computing 11(5): 278-282.
  • Wang Y. and Wahl F.M. (1997). Vector-entropy optimizationbased neural-network approach to image reconstruction from projections, IEEE Transaction on Neural Networks 8(5): 1008-1014.

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