Abstract
In modern computed tomography (CT) imaging, a high-quality image is produced by passing the X-ray radiation dose through the human body. The strategy for CT dose reduction is to reduce the projection views or to limit the scan angle. However, the reduction of radiation doses generates insufficient projection data which often leads the reconstruction problem. Iterative techniques are widely used addressing this issue where a prior-based method is more beneficial to reconstruct CT images. In this paper, the soft-thresholding-based reconstruction method is presented, and the use of the generalized Gaussian distribution model is considered to find the optimal thresholding value. Experimental results show that the reconstructed CT image from fewer projection views using the proposed method has higher quality compared with the other reconstruction approaches.
Similar content being viewed by others
References
Brenner, D.J., Elliston, C.D., Hall, E.J., Berdon, W.E.: Estimated risks of radiation induced fatal cancer from pediatric CT. Am. J. Roentgenol. 176(2), 289–296 (2001)
de Gonzalez, A.B., Darby, S.: Risk of cancer from diagnostic X-rays: estimates for the UK and 14 other countries. Lancet 357(22), 345–351 (2004)
Brenner, D.J., Hall, E.J.: Current concepts-computed tomography-an increasing source of radiation exposure. New Engl. J. Med. 357(22), 2277–2284 (2007)
Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. In: Proceedings of the Eleventh Annual International Conference of the Center for Nonlinear Studies on Experimental Mathematics: Computational Issues in Nonlinear Science, pp. 259–268 (1992)
Vogel, C.R., Oman, M.E.: Iterative methods for total variation denoising. SIAM J. Sci. Comput. 17(1), 227–238 (1996)
Wang, H., Wang, Y., Ren, W.: Image denoising using anisotropic second and fourth order diffusions based on gradient vector convolution. Comput. Sci. Inf. Syst. 9, 1493–1511 (2012)
Wang, Y., Ren, W., Wang, H.: Anisotropic second and fourth order diffusion models based on convolutional virtual electric field for image denoising. Comput. Math. Appl. 66(10), 1729–1742 (2013)
Daubechies, I., Defrise, M., Mol, C.D.: An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Commun. Pure Appl. Math. 57(11), 1413–1457 (2004)
Herman, G.T.: Fundamentals of Computerized Tomography: Image Reconstruction from Projections, 2nd edn. Springer, Berlin (2009)
Kak, A.C., Slaney, M.: Principles of computerized tomographic imaging. Med. Phys. 29, 107 (2001)
Shepp, L.A., Logan, B.F.: The Fourier reconstruction of a head section. IEEE Trans. Nucl. Sci. 21(3), 21–43 (1974)
Gordon, R., Herman, G.T.: Three-dimensional reconstruction from projections: a review of algorithms. Int. Rev. Cytol. 38, 111–151 (1974)
Li, C.: Modified simultaneous algebraic reconstruction technique and its application to image reconstruction. Proc. SPIE Int. Soc. Opt. Eng. 6279, 01 (2007)
Andersen, H.A., KaK, A.C.: Simultaneous algebraic reconstruction technique (SART): a superior implementation of the art algorithm. Ultrason. Imaging 6(1), 81–94 (1984)
Andersen, A.H.: Algebraic reconstruction in CT from limited views. IEEE Trans. Med. Imaging 8(1), 50–55 (1989)
Hashemi, M., Beheshti, S., Cobbold, R.S.C., Paul, N.S.: Subband-dependent compressed sensing in local ct reconstruction. Signal, Image Video Process. 10, 1009–1015 (2015)
Acunto, M.D., Benassi, A., Moroni, D., Salvetti, O.: 3d image reconstruction using radon transform. SIViP 10, 1–8 (2014)
Rosenthal, A., Jetzfellner, T., Razansky, D., Ntziachristos, V.: Efficient framework for model-based tomographic image reconstruction using wavelet packets. IEEE Trans. Med. Imaging 31(7), 1346–1357 (2012)
You, X., Du, L., Cheung, Y.-M., Chen, Q.: A blind watermarking scheme using new nontensor product wavelet filter banks. IEEE Trans. Image Process. 19, 3271–3284 (2010)
Sidky, E.Y., Pan, X.: Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization. Phys. Med. Biol. 53(17), 4777–4807 (2008)
Liu, Y., Ma, J., Fan, Y., Liang, Z.: Adaptive-weighted total variation minimization for sparse data toward low-dose x-ray computed tomography image reconstruction. Phys. Med. Biol. 57(23), 7923–7956 (2012)
Yu, H., Wang, G.: A soft-threshold filtering approach for reconstruction from a limited number of projections. Phys. Med. Biol. 55(13), 3905–3916 (2010)
Kak, A.C., Slaney, M.: Principles of computerized tomographic imaging (2001)
Hashemi, M., Beheshti, S.: Adaptive bayesian denoising for general gaussian distributed (GGD) signals in wavelet domain. IEEE Trans. Signal Process. 62(5), 1147–1156 (2014)
Bruyant, P.P.: Analytic and iterative reconstruction algorithms in SPECT. J. Nucl. Med. 43(10), 1343–1358 (2002)
Chetih, N., Messali, Z.: tomographic image reconstruction using filtered back projection (FBP) and algebraic reconstruction technique (ART). In: 3rd International Conference on Control, Engineering and Information Technology (CEIT) (2015)
Gilbert, P.F.: Iterative methods for the three-dimensional reconstruction of an object from projections. J. Theoret. Biol. 36(1), 105–117 (1972)
Selesnick, I.: Total variation denoising (an MM algorithm). Connexions, 12 (2012)
Islam, R., Lambert, A.J., Pickering, M., Scarvell, J.M., Smith, P.N.: Improved regularisation constraints for compressed sensing of multi-slice mri. Comput. Methods Biomech. Biomed. Eng. Imaging Vis. 4(1), 30–43 (2016)
Sendur, L., Selesnick, I.W.: Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency. IEEE Trans. Signal Process. 50(11), 2744–2756 (2002)
Mohamed, O.M.M., Jaidane-Saidane, M.: On the parameters estimation of the generalized gaussian mixture model. In: 17th European Signal Processing Conference (EUSIPCO 2009), pp. 2273–2277 (2009)
Gonzalez-Farias, G., Molina, J.A.D., Rodriguez-Dagnino, R.M.: Efficiency of the approximated shape parameter estimator in the generalized gaussian distribution. IEEE Trans. Veh. Technol. 58(8), 4214–4223 (2009)
Kingsbury, N.: Complex wavelets for shift invariant analysis and filtering of signals. Appl. Comput. Harmonic Anal. 10, 234–253 (2001)
Renieblas, G.P., Nogues, A.T., Gonzalez, A.M., Leon, N.G., del Castillo, E.G.: Structural similarity index family for image quality assessment in radiological images. J. Med. Imaging 4(3), 1–11 (2017)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Abascal, J.F.P.J., Abella, M., Sisniega, A., Vaquero, J.J., Desco, M.: Investigation of different sparsity transforms for the PICCS algorithm in small-animal respiratory gated CT. PLOS one 10(4), e120140 (2015)
Acknowledgements
The authors thank the Department of Computer Science and Engineering of Dhaka University of Engineering & Technology, Gazipur for providing research support to continue the research work.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Islam, M.S., Islam, R. Generalized Gaussian model-based reconstruction method of computed tomography image from fewer projections. SIViP 14, 547–555 (2020). https://doi.org/10.1007/s11760-019-01583-5
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-019-01583-5