title: Non-convex algorithm for sparse and low-rank recovery: application to dynamic MRI reconstruction.
abstract: In this work we exploit two assumed properties of dynamic MRI in order to reconstruct the images from under-sampled K-space samples. The first property assumes the signal is sparse in the x-f space and the second property assumes the signal is rank-deficient in the x-t space. These assumptions lead to an optimization problem that requires minimizing a combined lp-norm and Schatten-p norm. We propose a novel FOCUSS based approach to solve the optimization problem. Our proposed method is compared with state-of-the-art techniques in dynamic MRI reconstruction. Experimental evaluation carried out on three real datasets shows that for all these datasets, our method yields better reconstruction both in quantitative and qualitative evaluation.
authors.affiliation: Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada. angshulm@ece.ubc.ca
authors.forename: Angshul
authors.initials: A
authors.lastname: Majumdar
authors.forename: Rabab K
authors.initials: RK
authors.lastname: Ward
authors.forename: Tyseer
authors.initials: T
authors.lastname: Aboulnasr
mesh_terms: D000465:Algorithms; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity