Skip to main content
Log in

Subband-dependent compressed sensing in local CT reconstruction

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

To achieve high-quality low-dose computed tomography (CT) images, compressed sensing (CS)-based CT reconstructions recover the images using fewer projections; and wavelet inverse Radon algorithms recover wavelet subbands of CT images from locally scanned projections. Moreover, it has been shown that subband CS algorithms accelerate the convergence of the CS recovery methods. Here, we propose an innovative combination of a newly developed accelerated wavelet inverse Radon transform and non-convex CS formulation to recover the wavelet subbands of CT images from a reduced number of locally scanned X-ray projections. Fast pseudo-polar Fourier transform is used to decrease the computational complexity of CS recovery. Therefore, the proposed method, denoted by AWiR-SISTA, reduces the radiation dose by simultaneously decreasing the X-ray exposure area and the number of projections, decreases the CS computational complexity, and accelerates the CS recovery convergence rate. Phantom-based simulations show that high-quality ultra-low-dose local CT images can be reconstructed using the proposed method in few seconds, without numerical optimization. Clinical chest CT images are used to demonstrate the practical potential of the method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. The idea of combining pseudo-polar transform and wavelet transform has been used in Ridgelet transform in a different way [22].

References

  1. Brenner, D.J., Hall, E.J.: Computed tomography—an increasing source of radiation exposure. N. Engl. J. Med. 57, 2277–2284 (2007)

    Article  Google Scholar 

  2. Rashid-Farrokhi, F., Liu, K.J.R., Berenstein, C.A., Walnut, D.: Wavelet-based multiresolution local tomography. IEEE Trans. Image Process. 6(10), 1412–1430 (1997)

    Article  Google Scholar 

  3. Olson, T., DeStefano, J.: Wavelet localization of the radon transform. IEEE Trans. Signal Process. 42(8), 2055–2067 (1994)

    Article  Google Scholar 

  4. Holschneider, M.: Inverse radon transforms through inverse wavelet transforms. Inverse Probl. 7(6), 853–861 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  5. Deng, J., Yu, H., Ni, J., Wang, L., Wang, G.: Parallelism of iterative CT reconstruction based on local reconstruction algorithm. J. Supercomput. 48(1), 1–14 (2009)

    Article  Google Scholar 

  6. Yu, H., Ye, Y., Zhao, S., Wang, G.: Local ROI reconstruction via generalized FBP and BPF algorithms along more flexible curves. Int. J. Biomed. Imaging 2006, 14989 (2006). doi:10.1155/IJBI/2006/14989

  7. Ye, Y., Yu, H., Wei, Y., Wang, G.: A general local reconstruction approach based on a truncated hilbert transform. Int. J. Biomed. Imaging 2007, 1–14 (2007)

    Google Scholar 

  8. Candès, E.J., Romberg, J., Tao, T.: Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inf. Theory 52(2), 489–509 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  9. Sidky, E.Y., Pan, X., Reiser, I.S., Nishikawa, R.M., Moore, R.H., Kopans, D.B.: Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT. Med. Phys. 36(11), 4920–4932 (2009)

    Article  Google Scholar 

  10. Lee, H., Xing, L., Davidi, R., Li, R., Qian, J., Lee, R.: Improved compressed sensing-based cone-beam CT reconstruction using adaptive prior image constraints. Phys. Med. Biol. 57(8), 2287–2307 (2012)

    Article  Google Scholar 

  11. Gottleib, D., Gustafsson, B., Forssen, P.: On the direct fourier method for computer tomography. IEEE Trans. Med. Imaging 19(3), 223–232 (2000)

    Article  Google Scholar 

  12. Bioucas-Dias, J.M., Figueiredo, M.A.T.: A new TwIST: two-step iterative shrinkage/thresholding algorithms for image restoration. IEEE Trans. Image Process. 16(12), 2992–3004 (2007)

    Article  MathSciNet  Google Scholar 

  13. Bayram, I., Selesnick, I.W.: A subband adaptive iterative shrinkage/thresholding algorithm. IEEE Trans. Signal Process. 58(3), 1131–1143 (2010)

    Article  MathSciNet  Google Scholar 

  14. Guerquin-Kern, M., Haberlin, M., Pruessmann, K.P., Unser, M.: A fast wavelet-based reconstruction method for magnetic resonance imaging. IEEE Trans. Med. Imaging 30(9), 1649–1660 (2011)

    Article  Google Scholar 

  15. Yang, J.F., Zhang, Y.: Alternating direction algorithms for \(\ell _1\)-problems in compressive sensing. SIAM J. Sci. Comput. 33(1), 250–278 (2011)

    Article  MathSciNet  Google Scholar 

  16. Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imaging Sci. 2(1), 182–202 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  17. Averbuch, A., Sedelnikov, I., Shkolnisky, Y.: CT reconstruction from parallel and fan-beam projections by a 2-D discrete radon transform. IEEE Trans. Image Process. 21(2), 733–741 (2012)

    Article  MathSciNet  Google Scholar 

  18. Averbuch, A., Coifman, R.R., Donoho, D.L., Elad, M., Israeli, M.: Fast and accurate polar Fourier transform. Appl. Comput. Harmon. Anal. 21, 145–167 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  19. Hashemi, M., Beheshti, S.: Adaptive bayesian denoising for general gaussian distributed (GGD) signals in wavelet domain. arXiv:1207.6323 (2012)

  20. Fahimian, B.P., Zhao, Y., Huang, Z., Fung, R., Mao, Y., Zhu, C., Khatonabadi, M., DeMarco, J.J., Osher, S.J., McNitt-Gray, M.F., Miao, J.: Radiation dose reduction in medical X-ray CT via fourier-based iterative reconstruction. Med. Phys. 40(3), 031914-1–031914-10 (2013)

    Article  Google Scholar 

  21. Hashemi, M., Beheshti, S., Gill, P.R., Paul, N.S., Cobbold, R.S.C.: Fast fan/parallel beam CS-based low-dose CT reconstruction. In: International Conference on Acoustics, Speech, and Signal Processing (ICASSP). IEEE (2013)

  22. Donoho, D.L., Flesia, A.G.: Digital ridgelet transform based on true ridge functions. In: Beyond Wavelets, Chui, C.K., Monk, P., Wuytack, L. (eds.) vol. 10 of Studies in Computational Mathematics, pp. 1–30. Elsevier, Amsterdam (2003)

  23. Voronin, S., Chartrand, R.: A new generalized thresholding algorithm for inverse problems with sparsity constraints. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1636–1640 (2013)

  24. Goldstein, T., Osher, S.: The split Bregman method for \(\ell _1\)-regularized problems. SIAM J. Imaging Sci. 2(2), 323–343 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  25. Lee, S.W., Wang, G.: A Grangeat-type half-scan algorithm for cone-beam CT. Med. Phys. 30(4), 689–700 (2003)

    Article  Google Scholar 

  26. Jørgensen, J.H., Jensen, T.L., Hansen, P.C., Jensen, S.H., Sidky, E.Y., Pan, X.: Accelerated gradient methods for total-variation-based CT image reconstruction. arXiv:1105.4002 (2011)

  27. Jorgensen, J.H., Sidky, E.Y., Pan, X.: Ensuring convergence in total-variation-based reconstruction for accurate microcalcification imaging in breast X-ray CT, pp. 2640–2643 (2011)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Soosan Beheshti.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hashemi, S., Beheshti, S., Cobbold, R.S.C. et al. Subband-dependent compressed sensing in local CT reconstruction. SIViP 10, 1009–1015 (2016). https://doi.org/10.1007/s11760-015-0852-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-015-0852-7

Keywords

Navigation