Dictionary learning and time sparsity in dynamic MRI

Authors:
Jose Caballero;Daniel Rueckert;Joseph V. Hajnal
Affiliations:
Department of Computing, Imperial College London, UK;Department of Computing, Imperial College London, UK;Institute of Clinical Sciences, Imperial College London and MRC Clinical Sciences Centre, Hammersmith Hospital, London, UK
Venue:
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Year:
2012

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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.