MR Image Reconstruction Based on Iterative Split Bregman Algorithm and Nonlocal Total Variation

This paper introduces an efficient algorithm for magnetic resonance (MR) image reconstruction. The proposed method minimizes a linear combination of nonlocal total variation and least-square data-fitting term to reconstruct the MR images from undersampled k-space data. The nonlocal total variation i...

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Detalles Bibliográficos
Autores principales: Gopi, Varun P., Palanisamy, P., Wahid, Khan A., Babyn, Paul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2013
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3753771/
https://www.ncbi.nlm.nih.gov/pubmed/23997810
http://dx.doi.org/10.1155/2013/985819
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author Gopi, Varun P.
Palanisamy, P.
Wahid, Khan A.
Babyn, Paul
author_facet Gopi, Varun P.
Palanisamy, P.
Wahid, Khan A.
Babyn, Paul
author_sort Gopi, Varun P.
collection PubMed
description This paper introduces an efficient algorithm for magnetic resonance (MR) image reconstruction. The proposed method minimizes a linear combination of nonlocal total variation and least-square data-fitting term to reconstruct the MR images from undersampled k-space data. The nonlocal total variation is taken as the L (1)-regularization functional and solved using Split Bregman iteration. The proposed algorithm is compared with previous methods in terms of the reconstruction accuracy and computational complexity. The comparison results demonstrate the superiority of the proposed algorithm for compressed MR image reconstruction.
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spelling pubmed-37537712013-09-01 MR Image Reconstruction Based on Iterative Split Bregman Algorithm and Nonlocal Total Variation Gopi, Varun P. Palanisamy, P. Wahid, Khan A. Babyn, Paul Comput Math Methods Med Research Article This paper introduces an efficient algorithm for magnetic resonance (MR) image reconstruction. The proposed method minimizes a linear combination of nonlocal total variation and least-square data-fitting term to reconstruct the MR images from undersampled k-space data. The nonlocal total variation is taken as the L (1)-regularization functional and solved using Split Bregman iteration. The proposed algorithm is compared with previous methods in terms of the reconstruction accuracy and computational complexity. The comparison results demonstrate the superiority of the proposed algorithm for compressed MR image reconstruction. Hindawi Publishing Corporation 2013 2013-08-12 /pmc/articles/PMC3753771/ /pubmed/23997810 http://dx.doi.org/10.1155/2013/985819 Text en Copyright © 2013 Varun P. Gopi et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Gopi, Varun P.
Palanisamy, P.
Wahid, Khan A.
Babyn, Paul
MR Image Reconstruction Based on Iterative Split Bregman Algorithm and Nonlocal Total Variation
title MR Image Reconstruction Based on Iterative Split Bregman Algorithm and Nonlocal Total Variation
title_full MR Image Reconstruction Based on Iterative Split Bregman Algorithm and Nonlocal Total Variation
title_fullStr MR Image Reconstruction Based on Iterative Split Bregman Algorithm and Nonlocal Total Variation
title_full_unstemmed MR Image Reconstruction Based on Iterative Split Bregman Algorithm and Nonlocal Total Variation
title_short MR Image Reconstruction Based on Iterative Split Bregman Algorithm and Nonlocal Total Variation
title_sort mr image reconstruction based on iterative split bregman algorithm and nonlocal total variation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3753771/
https://www.ncbi.nlm.nih.gov/pubmed/23997810
http://dx.doi.org/10.1155/2013/985819
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