Advertisement

Blind Sparse Motion MRI with Linear Subpixel Interpolation

  • Anita Möller
  • Marco Maaß
  • Alfred Mertins
Conference paper
Part of the Informatik aktuell book series (INFORMAT)

Abstract

Vital and spontaneous motion causes major artifacts in MRI. In this paper a method is presented which reduces subpixel motion artifacts via computational post processing on a complete MR scan without additional data. On the compressed sparse MRI representation, translational subpixel motion is estimated iteratively from a fully sampled, but motion corrupted k-space, and motion free images are reconstructed by linear interpolation. Motion adjusted results are presented for the Shepp-Logan phantom and brainweb data.

Keywords

Motion Estimation Compress Sensing Motion Correction Image Domain Human Brain Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Lustig M, Donoho DL, Santos JM, et al. Compressed sensing MRl. IEEE Signal Process Mag. 2008;25:72–82.Google Scholar
  2. 2.
    Lin Wj Huang F, Börnerl P, et al. Motion correction using an enhanced floating navigator and GRAPPA operatiollJj. Magn Re50n Med. 2009;63:339–48.Google Scholar
  3. 3.
    Yang Z, Zhang C, Xie L. Sparse Mlli for motion correction. Proc IEEE Int Symp Biomed Imaging. 2013; p. 962–5.Google Scholar
  4. 4.
    Yang Z. Analysis, Algorihms and Applications of Compressed Sensing. Nanyang Technological U niversity. Singaporc; 2013.Google Scholar
  5. 5.
    Cocosco CA, Kollokian V, Kwan RKS, et al BrainWeb: anIine interface to a 3D MRl simulated brain database. NeuroIrnage. 1997;5(4):425.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Institute for Signal ProcessingUniversity of LübeckLübeckDeutschland

Personalised recommendations