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Performance analysis of regularization algorithms used for image reconstruction in computed tomography

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Języki publikacji
EN
Abstrakty
EN
Computed tomography is one of the most significant diagnostic techniques in medicine. This work is focused on hard-field imaging, where signals take a form of straight rays and the reconstructed image can be presented as a matrix with unknown pixels. Algebraic methods for direct computation of the image have not been used in practice because of the scale of the problem and numerical errors appearing in the solution. The aim of this work was to analyse the performance of direct algebraic algorithms for tomographic image reconstruction including regularisation mechanism such as: generalised regularisation, Tikhonov regularisation, Twomey regularisation and ridge regression (RR), as well as comparing the results with the filtered backprojection (FBP) as the reference method. The performed analyses demonstrated that the regularised algebraic methods are more accurate than the commonly used FBP, and RR appeared the most precise among them. Additionally it was shown that the invariant system matrix (inverted during calculations) can be easily determined by solving the forward problem. Finally, potential directions of further research have been pointed out.
Rocznik
Strony
467--474
Opis fizyczny
Bibliogr. 32 poz., rys., wykr.
Twórcy
  • Chair of Electronic and Photonic Metrology, Faculty of Electronics, Wroclaw University of Technology, 53/55 Prusa St., 50-317 Wrocław, Poland
autor
  • Chair of Electronic and Photonic Metrology, Faculty of Electronics, Wroclaw University of Technology, 53/55 Prusa St., 50-317 Wrocław, Poland
autor
  • Chair of Electronic and Photonic Metrology, Faculty of Electronics, Wroclaw University of Technology, 53/55 Prusa St., 50-317 Wrocław, Poland
Bibliografia
  • [1] A.C. Kak and M. Slaney, Principles of Computerized TomographicImaging, IEEE Press, New York, 1988.
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  • [8] J.A. Fessler, “Penalized weighted least-squares image reconstruction for positron emission tomography”, IEEE Trans. Med. Imaging 13, 290-300 (1994).
  • [9] K. Sauer and C. Bouman, “A local update strategy for iterative reconstruction from projections”, IEEE Trans. Signal Proc. 41, 534-548 (1993).
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  • [12] E.Y. Sidky and X. Pan, “Image reconstruction in circular conebeam computed tomography by constrained, total-variation minimization”, Phys. Med. Biol. 53, 4777-4807 (2008).
  • [13] H.R. Shi and J.A. Fessler, “Quadratic regularization design for 2-D CT”, IEEE Trans. Med. Imaging 28 (5), 645-656 (2009).
  • [14] J.W. Stayman and J.A. Fessler, “Regularization for uniform spatial resolution properties in penalized-likelihood image reconstruction”, IEEE Trans. Med. Imaging 19 (6), 601-615 (2000).
  • [15] S.S. Stone, J.P. Haldar, S.C. Tsao, W.W. Hwu, B.P. Sutton, and Z.P. Liang, “Accelerating Advanced MRI Reconstructions on GPUs.”, J. Parallel Distrib. Comput. 68 (10), 1307-131 (2008).
  • [16] J. Sunneg˚ardh and P.E. Danielsson, “Regularized iterative weighted filtered backprojection for helical cone-beam CT”, Med. Phys. 35 (9), 4173-4185 (2008).
  • [17] J.B. Thibault, K.D. Sauer, C.A. Bouman, and J. Hsieh, “A three-dimensional statistical approach to improved image quality for multislice helical CT”, Med. Phys. 34 (11), 4526-4544 (2007).
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  • [19] L. Ying, D. Xu, and Z.P. Liang, “On Tikhonov regularization for image reconstruction in parallel MRI”, Conf. Proc. IEEEEMBS 2, 1056-1059 (2004).
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  • [21] E.A. Rashed and H. Kudo, ”Statistical image reconstruction from limited projection data with intensity priors”, Phys. Med. Biol. 57 (7), 2039-2061 (2012).
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  • [23] A.G. Polak, “An error-minimizing approach to regularization in indirect measurements”, IEEE Trans. Instrum. Meas. 59 (2), 379-386 (2010).
  • [24] J. Mroczka and D. Szczuczyński, “Inverse problems formulated in terms of first-kind Fredholm integral equations in indirect measurements”, Metrol. Meas. Syst. 16 (3), 333-357 (2009).
  • [25] J. Mroczka and D. Szczuczyński, “Simulation research on improved regularized solution of the inverse problem in spectral extinction measurements”, Applied Optics 51 (11), 1715-1723 (2012).
  • [26] D.L. Phillips, “A technique for the numerical solution of certain integral equations of the first kind”, J. ACM 9, 84-97 (1962).
  • [27] A.N. Tikhonov, “Solution of incorrectly formulated problems and the regularization method”, Sov. Math. Dokl. 4, 1035-1038 (1963).
  • [28] S. Twomey, “On the numerical solution of Fredholm integral equations of the first kind by the inversion of the linear system produced by quadrature”, J. ACM 10, 97-101 (1963).
  • [29] A.E. Hoerl, “Application of ridge analysis to regression problems”, Chem. Eng. Progress 58, 54-59 (1962).
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  • [32] J. Mroczka and D. Szczuczyński, “Improved regularized solution of the inverse problem in turbidimetric measurements”, Applied Optics 49 (24), 4591-4603 (2010).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c39eb024-8b83-4984-9aa3-b7e8417ee63f
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