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Real-Time Compressive Sensing MRI Reconstruction Using GPU Computing and Split Bregman Methods
Compressive sensing (CS) has been shown to enable dramatic acceleration of MRI acquisition in some applications. Being an iterative reconstruction technique, CS MRI reconstructions can be more time-consuming than traditional inverse Fourier reconstruction. We have accelerated our CS MRI reconstructi...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3296267/ https://www.ncbi.nlm.nih.gov/pubmed/22481908 http://dx.doi.org/10.1155/2012/864827 |
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author | Smith, David S. Gore, John C. Yankeelov, Thomas E. Welch, E. Brian |
author_facet | Smith, David S. Gore, John C. Yankeelov, Thomas E. Welch, E. Brian |
author_sort | Smith, David S. |
collection | PubMed |
description | Compressive sensing (CS) has been shown to enable dramatic acceleration of MRI acquisition in some applications. Being an iterative reconstruction technique, CS MRI reconstructions can be more time-consuming than traditional inverse Fourier reconstruction. We have accelerated our CS MRI reconstruction by factors of up to 27 by using a split Bregman solver combined with a graphics processing unit (GPU) computing platform. The increases in speed we find are similar to those we measure for matrix multiplication on this platform, suggesting that the split Bregman methods parallelize efficiently. We demonstrate that the combination of the rapid convergence of the split Bregman algorithm and the massively parallel strategy of GPU computing can enable real-time CS reconstruction of even acquisition data matrices of dimension 4096(2) or more, depending on available GPU VRAM. Reconstruction of two-dimensional data matrices of dimension 1024(2) and smaller took ~0.3 s or less, showing that this platform also provides very fast iterative reconstruction for small-to-moderate size images. |
format | Online Article Text |
id | pubmed-3296267 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-32962672012-04-05 Real-Time Compressive Sensing MRI Reconstruction Using GPU Computing and Split Bregman Methods Smith, David S. Gore, John C. Yankeelov, Thomas E. Welch, E. Brian Int J Biomed Imaging Research Article Compressive sensing (CS) has been shown to enable dramatic acceleration of MRI acquisition in some applications. Being an iterative reconstruction technique, CS MRI reconstructions can be more time-consuming than traditional inverse Fourier reconstruction. We have accelerated our CS MRI reconstruction by factors of up to 27 by using a split Bregman solver combined with a graphics processing unit (GPU) computing platform. The increases in speed we find are similar to those we measure for matrix multiplication on this platform, suggesting that the split Bregman methods parallelize efficiently. We demonstrate that the combination of the rapid convergence of the split Bregman algorithm and the massively parallel strategy of GPU computing can enable real-time CS reconstruction of even acquisition data matrices of dimension 4096(2) or more, depending on available GPU VRAM. Reconstruction of two-dimensional data matrices of dimension 1024(2) and smaller took ~0.3 s or less, showing that this platform also provides very fast iterative reconstruction for small-to-moderate size images. Hindawi Publishing Corporation 2012 2012-02-01 /pmc/articles/PMC3296267/ /pubmed/22481908 http://dx.doi.org/10.1155/2012/864827 Text en Copyright © 2012 David S. Smith 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 Smith, David S. Gore, John C. Yankeelov, Thomas E. Welch, E. Brian Real-Time Compressive Sensing MRI Reconstruction Using GPU Computing and Split Bregman Methods |
title | Real-Time Compressive Sensing MRI Reconstruction Using GPU Computing and Split Bregman Methods |
title_full | Real-Time Compressive Sensing MRI Reconstruction Using GPU Computing and Split Bregman Methods |
title_fullStr | Real-Time Compressive Sensing MRI Reconstruction Using GPU Computing and Split Bregman Methods |
title_full_unstemmed | Real-Time Compressive Sensing MRI Reconstruction Using GPU Computing and Split Bregman Methods |
title_short | Real-Time Compressive Sensing MRI Reconstruction Using GPU Computing and Split Bregman Methods |
title_sort | real-time compressive sensing mri reconstruction using gpu computing and split bregman methods |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3296267/ https://www.ncbi.nlm.nih.gov/pubmed/22481908 http://dx.doi.org/10.1155/2012/864827 |
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