High-speed compressed sensing reconstruction in dynamic parallel MRI using augmented lagrangian and parallel processing

Çadaş Bilen, Yao Wang, Ivan Selesnick

Research output: Contribution to journalArticle

Abstract

Magnetic resonance imaging (MRI) is one of the fields that the compressed sensing theory is well utilized to reduce the scan time significantly leading to faster imaging or higher resolution images. It has been shown that a small fraction of the overall measurements are sufficient to reconstruct images with the combination of compressed sensing and parallel imaging. Various reconstruction algorithms have been proposed for compressed sensing, among which augmented Lagrangian based methods have been shown to often perform better than others for many different applications. In this paper, we propose new augmented Lagrangian based solutions to the compressed sensing reconstruction problem with analysis and synthesis prior formulations. We also propose a computational method which makes use of properties of the sampling pattern and the singular value decomposition of the system transfer function to significantly improve the speed of the reconstruction for the proposed algorithms in Cartesian sampled MRI. The proposed algorithms are shown to outperform earlier methods especially for the case of dynamic MRI for which the transfer function tends to be a very large matrix and significantly ill conditioned. It is also demonstrated that the proposed algorithm can be accelerated much further than other methods in case of a parallel implementation with graphics processing units.

Original languageEnglish (US)
Article number6313929
Pages (from-to)370-379
Number of pages10
JournalIEEE Journal on Emerging and Selected Topics in Circuits and Systems
Volume2
Issue number3
DOIs
StatePublished - 2012

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Compressed sensing
Magnetic resonance imaging
Processing
Transfer functions
Imaging techniques
Singular value decomposition
Image resolution
Computational methods
Sampling

Keywords

  • Compressed sensing
  • magnetic resonance imaging
  • parallel processing

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

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abstract = "Magnetic resonance imaging (MRI) is one of the fields that the compressed sensing theory is well utilized to reduce the scan time significantly leading to faster imaging or higher resolution images. It has been shown that a small fraction of the overall measurements are sufficient to reconstruct images with the combination of compressed sensing and parallel imaging. Various reconstruction algorithms have been proposed for compressed sensing, among which augmented Lagrangian based methods have been shown to often perform better than others for many different applications. In this paper, we propose new augmented Lagrangian based solutions to the compressed sensing reconstruction problem with analysis and synthesis prior formulations. We also propose a computational method which makes use of properties of the sampling pattern and the singular value decomposition of the system transfer function to significantly improve the speed of the reconstruction for the proposed algorithms in Cartesian sampled MRI. The proposed algorithms are shown to outperform earlier methods especially for the case of dynamic MRI for which the transfer function tends to be a very large matrix and significantly ill conditioned. It is also demonstrated that the proposed algorithm can be accelerated much further than other methods in case of a parallel implementation with graphics processing units.",
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