Literature DB >> 14755659

Noquist: reduced field-of-view imaging by direct Fourier inversion.

Marijn E Brummer1, David Moratal-Pérez, Chung-Yi Hong, Roderic I Pettigrew, José Millet-Roig, W Thomas Dixon.   

Abstract

A novel technique called "Noquist" is introduced for the acceleration of dynamic cardiac magnetic resonance imaging (CMRI). With the use of this technique, a more sparsely sampled dynamic image sequence is reconstructed correctly, without Nyquist foldover artifact. Unlike most other reduced field-of-view (rFOV) methods, Noquist does not rely on data substitution or temporal interpolation to reconstruct the dynamic image sequence. The proposed method reduces acquisition time in dynamic MRI scans by eliminating the data redundancy associated with static regions in the dynamic scene. A reduction of imaging time is achieved by a fraction asymptotically equal to the static fraction of the FOV, by omitting acquisition of an appropriate subset of phase-encoding views from a conventional equidistant Cartesian acquisition grid. The theory behind this method is presented along with sample reconstructions from real and simulated data. Noquist is compared with conventional cine imaging by retrospective selection of a reduced data set from a full-grid conventional image sequence. In addition, a comparison is presented, using real and simulated data, of our technique with an existing rFOV technique that uses temporal interpolation. The experimental results confirm the theory, and demonstrate that Noquist reduces scan time for cine MRI while fully preserving both spatial and temporal resolution, but at the cost of a reduced signal-to-noise ratio (SNR). Copyright 2004 Wiley-Liss, Inc.

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Year:  2004        PMID: 14755659     DOI: 10.1002/mrm.10694

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


  10 in total

1.  Optimal sampling for "Noquist" reduced-data cine magnetic resonance imaging.

Authors:  David Moratal; W Thomas Dixon; Senthil Ramamurthy; Stamatios Lerakis; W James Parks; Marijn E Brummer
Journal:  Med Phys       Date:  2013-01       Impact factor: 4.071

2.  Correlation imaging for multiscan MRI with parallel data acquisition.

Authors:  Yu Li; Charles Dumoulin
Journal:  Magn Reson Med       Date:  2012-02-28       Impact factor: 4.668

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 correlation imaging with k-space variant correlation functions.

Authors:  Yu Li; Masoud Edalati; Xingfu Du; Hui Wang; Jie J Cao
Journal:  Magn Reson Med       Date:  2017-07-07       Impact factor: 4.668

5.  Wavelet-space correlation imaging for high-speed MRI without motion monitoring or data segmentation.

Authors:  Yu Li; Hui Wang; Jean Tkach; David Roach; Jason Woods; Charles Dumoulin
Journal:  Magn Reson Med       Date:  2014-12-02       Impact factor: 4.668

6.  Patient-adaptive reconstruction and acquisition in dynamic imaging with sensitivity encoding (PARADISE).

Authors:  Behzad Sharif; J Andrew Derbyshire; Anthony Z Faranesh; Yoram Bresler
Journal:  Magn Reson Med       Date:  2010-08       Impact factor: 4.668

7.  A 2D MTF approach to evaluate and guide dynamic imaging developments.

Authors:  Tzu-Cheng Chao; Hsiao-Wen Chung; W Scott Hoge; Bruno Madore
Journal:  Magn Reson Med       Date:  2010-02       Impact factor: 4.668

8.  Kalman filter techniques for accelerated Cartesian dynamic cardiac imaging.

Authors:  Xue Feng; Michael Salerno; Christopher M Kramer; Craig H Meyer
Journal:  Magn Reson Med       Date:  2012-08-27       Impact factor: 4.668

9.  Correlation imaging with arbitrary sampling trajectories.

Authors:  Yu Li
Journal:  Magn Reson Imaging       Date:  2014-02-10       Impact factor: 2.546

Review 10.  Sparse Reconstruction Techniques in Magnetic Resonance Imaging: Methods, Applications, and Challenges to Clinical Adoption.

Authors:  Alice C Yang; Madison Kretzler; Sonja Sudarski; Vikas Gulani; Nicole Seiberlich
Journal:  Invest Radiol       Date:  2016-06       Impact factor: 6.016

  10 in total

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