Hong Jung1, Jong Chul Ye1
1KAIST, Yuseong-Gu, Daejon, Korea, Republic of
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 acquisition without aliasing artifacts. Our group has proposed an l1-norm based compressed sensing dynamic MRI called k-t FOCUSS which outperforms the 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 adopt a sparse Bayesian learning approach to improve k-t FOCUSS and achieve l0 solution. We demonstrated the improved image quality using cardiac cine imaging.