Literature DB >> 18219633

High-pass GRAPPA: an image support reduction technique for improved partially parallel imaging.

Feng Huang1, Yu Li, Sathya Vijayakumar, Sarah Hertel, George R Duensing.   

Abstract

Partially parallel imaging (PPI) is a widely used technique in clinical applications. A limitation of this technique is the strong noise and artifact in the reconstructed images when high reduction factors are used. This work aims to increase the clinical applicability of PPI by improving its performance at high reduction factors. A new concept, image support reduction, is introduced. A systematic filter-design approach for image support reduction is proposed. This approach shows more advantages when used with an important existing PPI technique, GRAPPA. An improved GRAPPA method, high-pass GRAPPA (hp-GRAPPA), was developed based on this approach. The new technique does not involve changing the original GRAPPA kernel and performs reconstruction in almost the same amount of time. Experimentally, it is demonstrated that the reconstructed images using hp-GRAPPA have much lower noise/artifact level than those reconstructed using GRAPPA. (c) 2008 Wiley-Liss, Inc.

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Mesh:

Year:  2008        PMID: 18219633     DOI: 10.1002/mrm.21495

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


  9 in total

1.  Modeling non-stationarity of kernel weights for k-space reconstruction in partially parallel imaging.

Authors:  Jun Miao; Wilbur C K Wong; Sreenath Narayan; Donglai Huo; David L Wilson
Journal:  Med Phys       Date:  2011-08       Impact factor: 4.071

2.  Improving GRAPPA reconstruction by frequency discrimination in the ACS lines.

Authors:  Santiago Aja-Fernández; Daniel García Martín; Antonio Tristán-Vega; Gonzalo Vegas-Sánchez-Ferrero
Journal:  Int J Comput Assist Radiol Surg       Date:  2015-03-26       Impact factor: 2.924

3.  Error decomposition for parallel imaging reconstruction using modulation-domain representation of undersampled data.

Authors:  Yu Li
Journal:  Quant Imaging Med Surg       Date:  2014-04

4.  Self-calibrated interpolation of non-Cartesian data with GRAPPA in parallel imaging.

Authors:  Seng-Wei Chieh; Mostafa Kaveh; Mehmet Akçakaya; Steen Moeller
Journal:  Magn Reson Med       Date:  2019-11-13       Impact factor: 4.668

5.  Exploiting sparsity to accelerate noncontrast MR angiography in the context of parallel imaging.

Authors:  Pippa Storey; Ricardo Otazo; Ruth P Lim; Sooah Kim; Lazar Fleysher; Niels Oesingmann; Vivian S Lee; Daniel K Sodickson
Journal:  Magn Reson Med       Date:  2011-08-29       Impact factor: 4.668

6.  STEP: Self-supporting tailored k-space estimation for parallel imaging reconstruction.

Authors:  Zechen Zhou; Jinnan Wang; Niranjan Balu; Rui Li; Chun Yuan
Journal:  Magn Reson Med       Date:  2015-03-11       Impact factor: 4.668

7.  Data consistency criterion for selecting parameters for k-space-based reconstruction in parallel imaging.

Authors:  Roger Nana; Xiaoping Hu
Journal:  Magn Reson Imaging       Date:  2009-06-30       Impact factor: 2.546

8.  Improved k-t PCA Algorithm Using Artificial Sparsity in Dynamic MRI.

Authors:  Yiran Wang; Zhifeng Chen; Jing Wang; Lixia Yuan; Ling Xia; Feng Liu
Journal:  Comput Math Methods Med       Date:  2017-07-18       Impact factor: 2.238

9.  Instrument Variables for Reducing Noise in Parallel MRI Reconstruction.

Authors:  Yuchou Chang; Haifeng Wang; Yuanjie Zheng; Hong Lin
Journal:  Biomed Res Int       Date:  2017-01-19       Impact factor: 3.411

  9 in total

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