title: Dictionary learning and time sparsity in dynamic MRI.
abstract: Sparse representation methods have been shown to tackle adequately the inherent speed limits of magnetic resonance imaging (MRI) acquisition. Recently, learning-based techniques have been used to further accelerate the acquisition of 2D MRI. The extension of such algorithms to dynamic MRI (dMRI) requires careful examination of the signal sparsity distribution among the different dimensions of the data. Notably, the potential of temporal gradient (TG) sparsity in dMRI has not yet been explored. In this paper, a novel method for the acceleration of cardiac dMRI is presented which investigates the potential benefits of enforcing sparsity constraints on patch-based learned dictionaries and TG at the same time. We show that an algorithm exploiting sparsity on these two domains can outperform previous sparse reconstruction techniques.
authors.affiliation: Department of Computing, Imperial College London, UK. jose.caballero06@imperial.ac.uk
authors.forename: Jose
authors.initials: J
authors.lastname: Caballero
authors.forename: Daniel
authors.initials: D
authors.lastname: Rueckert
authors.forename: Joseph V
authors.initials: JV
authors.lastname: Hajnal
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors