http://iet.metastore.ingenta.com
1887

Fast iterative contourlet thresholding for compressed sensing MRI

Fast iterative contourlet thresholding for compressed sensing MRI

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
Electronics Letters — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Proposed is the use of the contourlet as a sparse transform which is combined with the fast iterative shrinkage/threshold algorithm (FISTA) for compressed sensing magnetic resonance imaging reconstruction. The proposed method not only inherits the simplicity and effectiveness of the original FISTA but also has the sparse curve representation ability of the contourlet. Simulation results validate the superior performance of the proposed method in terms of reconstruction accuracy and computation time.

References

    1. 1)
      • 1. Beck, A., Teboulle, M.: ‘Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems’, IEEE Trans. Image Process., 2009, 18, (11), pp. 24192434 (doi: 10.1109/TIP.2009.2028250).
    2. 2)
      • 2. Beck, A., Teboulle, M.: ‘A fast iterative shrinkage-thresholding algorithm for linear inverse problems’, SIAM J. Imaging Sci., 2009, 2, (1), pp. 183202 (doi: 10.1137/080716542).
    3. 3)
      • 3. Huang, J., Zhang, S., Metaxas, D.: ‘Efficient MR image reconstruction for compressed MR imaging’, Med. Image Anal., 2011, 15, (5), pp. 670679 (doi: 10.1016/j.media.2011.06.001).
    4. 4)
      • 4. Qu, X., Zhang, W., Guo, D., Cai, C., Cai, S., Chen, Z.: ‘Iterative thresholding compressed sensing MRI based on contourlet transform’, Inverse Prob. Sci. Eng., 2010, 18, (6), pp. 737758 (doi: 10.1080/17415977.2010.492509).
    5. 5)
      • 5. Ma, J.: ‘Improved iterative curvelet thresholding for compressed sensing’, IEEE Trans. Instrum. Meas., 2011, 60, (1), pp. 126136 (doi: 10.1109/TIM.2010.2049221).
    6. 6)
      • 6. Bioucas-Dias, J., Figueiredo, M.: ‘A new TwIST: two step iterative shrinkage/thresholding algorithms for image restoration’, IEEE Trans. Image Process., 2007, 16, (12), pp. 29923004 (doi: 10.1109/TIP.2007.909319).
    7. 7)
      • 7. He, L., Chang, T.C., Osher, S., Fang, T., Speier, P.: ‘MR image reconstruction by using the iterative refinement method and nonlinear inverse scale space methods’. Available at ftp://ftp.math.ucla.edu/pub/camreport/cam06-35.pdf, accessed 2nd March 2013.
    8. 8)
      • 8. Candues, E., Demanet, L., Donoho, D., Ying, L.: ‘Fast discrete curvelet transforms’, Multiscale Model. Simul., 2006, 5, (3), pp. 861889 (doi: 10.1137/05064182X).
    9. 9)
      • 9. Lu, Y., Do, M.N.: ‘A new contourlet transform with sharp frequency localization’. 2006 Int. Conf. Image Processing (ICIP 2006), Atlanta, GA, USA, 2006, pp. 16291632.
http://iet.metastore.ingenta.com/content/journals/10.1049/el.2013.1483
Loading

Related content

content/journals/10.1049/el.2013.1483
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address