Advertisement

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

  • Zigen Song
  • Melinda Baxter
  • Mingwu Jin
  • Jian-Xiong Wang
  • Ren-Cang Li
  • Talon Johnson
  • Jianzhong Su
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11309)

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.

Keywords

Imaging reconstruction Chemical shift imaging 3D Compressed Sensing Sparse sampling of MRSI data 

Notes

Acknowledgment

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

References

  1. 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)CrossRefGoogle Scholar
  2. 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)CrossRefGoogle Scholar
  3. 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)CrossRefGoogle Scholar
  4. 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)CrossRefGoogle Scholar
  5. 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. 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. 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)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Zigen Song
    • 1
    • 2
  • Melinda Baxter
    • 1
  • Mingwu Jin
    • 3
  • Jian-Xiong Wang
    • 4
  • Ren-Cang Li
    • 1
  • Talon Johnson
    • 1
  • Jianzhong Su
    • 1
  1. 1.Department of MathematicsUniversity of Texas at ArlingtonArlingtonUSA
  2. 2.College of Information TechnologyShanghai Ocean UniversityShanghaiChina
  3. 3.Department of PhysicsUniversity of Texas at ArlingtonArlingtonUSA
  4. 4.Advanced Imaging Research Center Radiology DepartmentUniversity of Texas Southwestern Medical CenterDallasUSA

Personalised recommendations