Literature DB >> 19785017

Accelerating SENSE using compressed sensing.

Dong Liang1, Bo Liu, Jiunjie Wang, Leslie Ying.   

Abstract

Both parallel MRI and compressed sensing (CS) are emerging techniques to accelerate conventional MRI by reducing the number of acquired data. The combination of parallel MRI and CS for further acceleration is of great interest. In this paper, we propose a novel method to combine sensitivity encoding (SENSE), one of the standard methods for parallel MRI, and compressed sensing for rapid MR imaging (SparseMRI), a recently proposed method for applying CS in MR imaging with Cartesian trajectories. The proposed method, named CS-SENSE, sequentially reconstructs a set of aliased reduced-field-of-view images in each channel using SparseMRI and then reconstructs the final image from the aliased images using Cartesian SENSE. The results from simulations and phantom and in vivo experiments demonstrate that CS-SENSE can achieve a reduction factor higher than those achieved by SparseMRI and SENSE individually and outperform the existing method that combines parallel MRI and CS. c) 2009 Wiley-Liss, Inc.

Entities:  

Mesh:

Year:  2009        PMID: 19785017     DOI: 10.1002/mrm.22161

Source DB:  PubMed          Journal:  Magn Reson Med        ISSN: 0740-3194            Impact factor:   4.668


  106 in total

1.  Fast cardiac T1 mapping in mice using a model-based compressed sensing method.

Authors:  Wen Li; Mark Griswold; Xin Yu
Journal:  Magn Reson Med       Date:  2011-12-09       Impact factor: 4.668

2.  Combination of compressed sensing and parallel imaging for highly accelerated first-pass cardiac perfusion MRI.

Authors:  Ricardo Otazo; Daniel Kim; Leon Axel; Daniel K Sodickson
Journal:  Magn Reson Med       Date:  2010-09       Impact factor: 4.668

3.  Coil compression for accelerated imaging with Cartesian sampling.

Authors:  Tao Zhang; John M Pauly; Shreyas S Vasanawala; Michael Lustig
Journal:  Magn Reson Med       Date:  2012-04-09       Impact factor: 4.668

4.  Derivative encoding for parallel magnetic resonance imaging.

Authors:  Jun Shen
Journal:  Med Phys       Date:  2011-10       Impact factor: 4.071

5.  Feasibility of high temporal resolution breast DCE-MRI using compressed sensing theory.

Authors:  Haoyu Wang; Yanwei Miao; Kun Zhou; Yanming Yu; Shanglian Bao; Qiang He; Yongming Dai; Stephanie Y Xuan; Bisher Tarabishy; Yongquan Ye; Jiani Hu
Journal:  Med Phys       Date:  2010-09       Impact factor: 4.071

6.  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

7.  Compressed sensing in quantitative determination of GAG concentration in cartilage by microscopic MRI.

Authors:  Nian Wang; Farid Badar; Yang Xia
Journal:  Magn Reson Med       Date:  2017-10-30       Impact factor: 4.668

8.  Correlated spectroscopic imaging of calf muscle in three spatial dimensions using group sparse reconstruction of undersampled single and multichannel data.

Authors:  Neil E Wilson; Brian L Burns; Zohaib Iqbal; M Albert Thomas
Journal:  Magn Reson Med       Date:  2015-09-18       Impact factor: 4.668

9.  Sparsity-promoting calibration for GRAPPA accelerated parallel MRI reconstruction.

Authors:  Daniel S Weller; Jonathan R Polimeni; Leo Grady; Lawrence L Wald; Elfar Adalsteinsson; Vivek K Goyal
Journal:  IEEE Trans Med Imaging       Date:  2013-04-09       Impact factor: 10.048

10.  A simple application of compressed sensing to further accelerate partially parallel imaging.

Authors:  Jun Miao; Weihong Guo; Sreenath Narayan; David L Wilson
Journal:  Magn Reson Imaging       Date:  2012-08-15       Impact factor: 2.546

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.