title: PSF model-based reconstruction with sparsity constraint: algorithm and application to real-time cardiac MRI.
abstract: The partially separable function (PSF) model has been successfully used to reconstruct cardiac MR images with high spatiotemporal resolution from sparsely sampled (k,t)-space data. However, the underlying model fitting problem is often ill-conditioned due to temporal undersampling, and image artifacts can result if reconstruction is based solely on the data consistency constraints. This paper proposes a new method to regularize the inverse problem using sparsity constraints. The method enables both partial separability (or low-rankness) and sparsity constraints to be used simultaneously for high-quality image reconstruction from undersampled (k,t)-space data. The proposed method is described and reconstruction results with cardiac imaging data are presented to illustrate its performance.
authors.affiliation: Department of Electrical and Computer Engineering and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, 1406 West Green Street, IL 61801, USA. bozhaol@illinois.edu
authors.forename: Bo
authors.initials: B
authors.lastname: Zhao
authors.forename: Justin P
authors.initials: JP
authors.lastname: Haldar
authors.forename: Zhi-Pei
authors.initials: ZP
authors.lastname: Liang
mesh_terms: D000465:Algorithms; D000818:Animals; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D019028:Magnetic Resonance Imaging, Cine / Q000379:methods*; D051381:Rats; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity