A sparse Bayesian learning for highly accelerated dynamic MRI

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Hong Jung, Jong Chul Ye

Published: 2010, Last Modified: 05 Mar 2026ISBI 2010EveryoneRevisionsBibTeXCC BY-SA 4.0
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 ℓ1 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.