Dictionary learning and time sparsity in dynamic MRI

Jose Caballero, Daniel Rueckert, Joseph V. Hajnal

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

31 Scopus citations


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.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings
EditorsNicholas Ayache, Herve Delingette, Polina Golland, Kensaku Mori
PublisherSpringer Verlag
Number of pages8
ISBN (Print)9783642334146
StatePublished - 2012
Externally publishedYes
Event15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012 - Nice, France
Duration: 1 Oct 20125 Oct 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7510 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012


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