Literature DB >> 17905244

Optimization of sensitivity encoding with arbitrary k-space trajectories.

Mark Bydder1, Joanna E Perthen, Jiang Du.   

Abstract

Sensitivity encoding (SENSE) is a magnetic resonance technique that unifies gradient and receive coil encoding. SENSE reconstructs the image by solving a large, ill-conditioned inverse problem, which generally requires regularization and preconditioning. The present study suggests a simple heuristic for determining the regularization parameter. Also discussed are the use of density weighting and intensity correction as preconditioners and the role that coil sensitivity estimation has in regularization. A modification to the intensity correction is proposed for use with a phase constraint.

Mesh:

Year:  2007        PMID: 17905244     DOI: 10.1016/j.mri.2007.01.003

Source DB:  PubMed          Journal:  Magn Reson Imaging        ISSN: 0730-725X            Impact factor:   2.546


  6 in total

1.  Parallel MR image reconstruction using augmented Lagrangian methods.

Authors:  Sathish Ramani; Jeffrey A Fessler
Journal:  IEEE Trans Med Imaging       Date:  2010-11-18       Impact factor: 10.048

2.  SNR and functional sensitivity of BOLD and perfusion-based fMRI using arterial spin labeling with spiral SENSE at 3 T.

Authors:  Joanna E Perthen; Mark Bydder; Khaled Restom; Thomas T Liu
Journal:  Magn Reson Imaging       Date:  2007-12-26       Impact factor: 2.546

Review 3.  Non-Cartesian parallel imaging reconstruction.

Authors:  Katherine L Wright; Jesse I Hamilton; Mark A Griswold; Vikas Gulani; Nicole Seiberlich
Journal:  J Magn Reson Imaging       Date:  2014-01-10       Impact factor: 4.813

4.  Combined parallel and partial fourier MR reconstruction for accelerated 8-channel hyperpolarized carbon-13 in vivo magnetic resonance Spectroscopic imaging (MRSI).

Authors:  Michael A Ohliger; Peder E Z Larson; Robert A Bok; Peter Shin; Simon Hu; James Tropp; Fraser Robb; Lucas Carvajal; Sarah J Nelson; John Kurhanewicz; Daniel B Vigneron
Journal:  J Magn Reson Imaging       Date:  2013-01-04       Impact factor: 4.813

5.  Scan-Specific Generative Neural Network for MRI Super-Resolution Reconstruction.

Authors:  Yao Sui; Onur Afacan; Camilo Jaimes; Ali Gholipour; Simon K Warfield
Journal:  IEEE Trans Med Imaging       Date:  2022-06-01       Impact factor: 11.037

6.  A support-based reconstruction for SENSE MRI.

Authors:  Yudong Zhang; Bradley Peterson; Zhengchao Dong
Journal:  Sensors (Basel)       Date:  2013-03-25       Impact factor: 3.576

  6 in total

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