Skip to main content
SpringerLink
Log in
Book cover

Advances in Electronics, Communication and Computing pp 449–457Cite as

Weighted Wavelet Tree Sparsity Regularization for Compressed Sensing Magnetic Resonance Image Reconstruction

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 443))

Abstract

Compressed sensing in magnetic resonance imaging (CS-MRI) improves the MRI scan time by acquiring only a few k-space samples and then reconstructs the image using a nonlinear procedure from the highly undersampled measurements. Besides the standard wavelet sparsity, MR images are also found to exhibit tree sparsity across various scales of the wavelet decomposition which are generally modeled as overlapping group sparsity. In this chapter, we propose a novel iteratively weighted wavelet tree sparsity based CS-MRI reconstruction technique to estimate MR images from highly undersampled Fourier measurements. Simulations on various real MR images show that the proposed technique offers significant improvements compared to the state-of-the-art either in terms of visual quality or k-space measurements with the same reconstruction time.

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

References

  1. Candes, E.J., Romberg, J.K., Tao, T.: Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inf. Theory 52, 489–509 (2006)

    Article  MathSciNet  Google Scholar 

  2. Lustig, M., Keutzer, K., Vasanawala, S.: Introduction to parallelizing compressed sensing magnetic resonance imaging. In: Patterson, D., Gannon, D., Wrinn, M. (eds) The Berkeley Par Lab: progress in the parallel computing landscape, Microsoft Corporation, pp. 105–139 (2013)

    Google Scholar 

  3. Lustig, M., Donoho, D., Pauly, J.M.: Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn. Reson. Med. 58, 1182–1195 (2007)

    Article  Google Scholar 

  4. Huang, J., Zhang, S., Metaxas, D.N.: Efficient MR image reconstruction for compressed MR imaging. Med. Image Anal. 15, 670–679 (2011)

    Article  Google Scholar 

  5. Chen, C., Huang, J.: Exploiting the wavelet structure in compressed sensing MRI. Magn. Reson. Imaging 32, 1377–1389 (2014)

    Article  Google Scholar 

  6. Chen, C., Huang, J.: The benefit of tree sparsity in accelerated MRI. Med. Image Anal. 18, 834–842 (2014)

    Article  Google Scholar 

  7. Candes, E.J., Wakin, M.B., Boyd, S.P.: Enhancing sparsity by reweighted L1 minimization. J. Fourier Anal. Appl. 14, 877–905 (2008)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Vision and Image Processing Laboratory, Department of Electronics and Communication Engineering, Tezpur University, Tezpur, 784028, Assam, India

    Bhabesh Deka & Sumit Datta

Authors
  1. Bhabesh Deka
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sumit Datta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bhabesh Deka .

Editor information

Editors and Affiliations

  1. Smart Energy Research Unit, College of Engineering and Science, Victoria University, Melbourne, VIC, Australia

    Akhtar Kalam

  2. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, West Bengal, India

    Swagatam Das

  3. Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Rangpo, Sikkim, India

    Kalpana Sharma

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Deka, B., Datta, S. (2018). Weighted Wavelet Tree Sparsity Regularization for Compressed Sensing Magnetic Resonance Image Reconstruction. In: Kalam, A., Das, S., Sharma, K. (eds) Advances in Electronics, Communication and Computing. Lecture Notes in Electrical Engineering, vol 443. Springer, Singapore. https://doi.org/10.1007/978-981-10-4765-7_48

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-4765-7_48

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-4764-0

  • Online ISBN: 978-981-10-4765-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation