Skip to main content
SpringerLink
Log in

Sparse Sampling and Fully-3D Fast Total Variation Based Imaging Reconstruction for Chemical Shift Imaging in Magnetic Resonance Spectroscopy

  • Conference paper
  • First Online:
Brain Informatics (BI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11309))

Included in the following conference series:

  • 1100 Accesses

Abstract

We propose a 3-dimensional sparse sampling reconstruction method, aiming for chemical shift imaging in magnetic resonance spectroscopy. The method is a Compressed Sensing (CS) method based on the interior point optimization technique that can substantially reduce the number of sampling points required, and the method has been tested successfully in hyperpolarized 13C experimental data using two different sampling strategies.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Posse, S., Otazo, R., Dager, S.R., Alger, J.: MR spectroscopic imaging: principles and recent advances. J. Magn. Reson. Imaging 37(6), 1301–1325 (2013)

    Article  Google Scholar 

  2. Wang, J.X., Merritt, M.E., Dean Sherry, A., Malloy, C.R.: Accelerated chemical shift imaging of hyperpolarized 13C metabolites. Magn. Reson. Med. 76, 1033–1038 (2016)

    Article  Google Scholar 

  3. Hourani, R., et al.: Proton magnetic resonance spectroscopic imaging to differentiate between nonneoplastic lesions and brain tumors in children. J. Magn. Reson. Imaging 23(2), 99–107 (2006)

    Article  Google Scholar 

  4. 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 

  5. Candes, E., Romberg, J.: L1-magic: recovery of sparse signals via convex programming (2005). http://www.users.ece.gatech.edu/justin/l1magic/downloads/l1magic.pdf

  6. Melinda, M.A.: Three dimensional image reconstruction (3DIRECT) of sparse signal with MRI application. Ph.D. thesis, The University of Texas at Arlington (2016)

    Google Scholar 

  7. 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, 600–612 (2004)

    Article  Google Scholar 

Download references

Acknowledgment

Zigen Song’s research is supported in part by the National Natural Science Foundation of China under Grant No. 11672177.

Author information

Authors and Affiliations

  1. Department of Mathematics, University of Texas at Arlington, Arlington, USA

    Zigen Song, Melinda Baxter, Ren-Cang Li, Talon Johnson & Jianzhong Su

  2. College of Information Technology, Shanghai Ocean University, Shanghai, China

    Zigen Song

  3. Department of Physics, University of Texas at Arlington, Arlington, USA

    Mingwu Jin

  4. Advanced Imaging Research Center Radiology Department, University of Texas Southwestern Medical Center, Dallas, USA

    Jian-Xiong Wang

Authors
  1. Zigen Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Melinda Baxter
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mingwu Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jian-Xiong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ren-Cang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Talon Johnson
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jianzhong Su
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jianzhong Su .

Editor information

Editors and Affiliations

  1. University of Texas at Arlington, Arlington, TX, USA

    Shouyi Wang

  2. University of Southern California, West Hollywood, USA

    Vicky Yamamoto

  3. Department of Mathematics, University of Texas at Arlington, Arlington, TX, USA

    Jianzhong Su

  4. Maebashi Institute of Technology, Gunma, Japan

    Yang Yang

  5. The University of Texas at Arlington, Arlington, USA

    Erick Jones

  6. Louisiana Tech University, Arlington, TX, USA

    Leon Iasemidis

  7. Carnegie Mellon University, Pittsburgh, PA, USA

    Tom Mitchell

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Song, Z. et al. (2018). Sparse Sampling and Fully-3D Fast Total Variation Based Imaging Reconstruction for Chemical Shift Imaging in Magnetic Resonance Spectroscopy. In: Wang, S., et al. Brain Informatics. BI 2018. Lecture Notes in Computer Science(), vol 11309. Springer, Cham. https://doi.org/10.1007/978-3-030-05587-5_45

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-05587-5_45

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05586-8

  • Online ISBN: 978-3-030-05587-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation