title: REGULARIZED IMAGE RECONSTRUCTION FOR PS MODEL-BASED CARDIOVASCULAR MRI.
abstract: Real-time cardiovascular MRI is a useful and challenging dynamic imaging application. The partial separability (PS) model enables reconstruction of dynamic cardiac images from highly undersampled (k,t)-space data. However, the underlying PS model-based reconstruction problem is ill-conditioned, so regularization is often necessary to stabilize its solution. It has been shown that ℓ1regularization is useful for finding sparse solutions, and ℓ2regularization is widely used to incorporate anatomical constraints. An important practical question is which regularization scheme to use for PS model-based cardiovascular imaging. We address this problem by implementing both schemes and evaluating their performances in terms of reconstruction error, image artifacts, image noise, computation time, and performance characterizability. The ℓ1-regularized results exhibit lower reconstruction error, artifact energy, and noise variance, while ℓ2regularization is much faster and produces predictable reconstruction results. This study indicates that the ℓ1scheme is preferable when image quality is the main concern.
authors.affiliation: Department of Electrical and Computer Engineering, and Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign.
authors.forename: Anthony G
authors.initials: AG
authors.lastname: Christodoulou
authors.affiliation: Department of Electrical and Computer Engineering, and Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign.
authors.forename: Bo
authors.initials: B
authors.lastname: Zhao
authors.affiliation: Department of Electrical and Computer Engineering, and Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign.
authors.forename: Zhi-Pei
authors.initials: ZP
authors.lastname: Liang
keywords: Cardiovascular imaging; Inverse problems; Magnetic resonance imaging (MRI); Regularization; Spatiotemporal modeling