A sparse Bayesian learning for highly accelerated dynamic MRI

  • Authors:

  • Hong Jung;Jong Chul Ye

  • Affiliations:

  • Bio-Imaging & Signal Processing Lab., Dept. of Bio and Brain engineering, Korea Adv. Inst. of Science & Technology, Daejon, Republic of Korea;Bio-Imaging & Signal Processing Lab., Dept. of Bio and Brain engineering, Korea Adv. Inst. of Science & Technology, Daejon, Republic of Korea

  • Venue:
  • ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
  • Year:
  • 2010

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Abstract

In dynamic MRI, spatio-temporal resolution is a very important issue. Recently, compressed sensing approach has become a highly attracted imaging technique since it enables accelerated acquistion without aliasing artifacts. Our group has proposed an l1-norm based compressed sensing dynamic MRI called k-t FOCUSS, which outperforms existing methods. However, it is known that the restrictive conditions for l1 exact reconstruction usually cost more measurements than l0 minimization. In this paper, we adopts a sparse Bayesian learning approach to improve k-t FOCUSS and achieve l0 solution. We demonstrated the improved image quality using in vivo cardiac cine imaging.