@inproceedings{4e6642ea9b3a448aad2e5d64d95b801f,
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.",
author = "Jose Caballero and Daniel Rueckert and Hajnal, {Joseph V.}",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2012.; 15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012 ; Conference date: 01-10-2012 Through 05-10-2012",
year = "2012",
doi = "10.1007/978-3-642-33415-3_32",
language = "English",
isbn = "9783642334146",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "256--263",
editor = "Nicholas Ayache and Herve Delingette and Polina Golland and Kensaku Mori",
booktitle = "Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings",
}