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math formula-corrected water–fat imaging using compressed sensing and parallel imaging

Authors

  • Curtis N. Wiens,

    Corresponding author
    1. Department of Physics and Astronomy, Faculty of Science, University of Western Ontario, London, Canada
    • Correspondence to: Curtis Wiens, B.Sc., Department of Physics and Astronomy, University of Western Ontario, Natural Science Room 9, 1151 Richmond Street, London, Ontario, Canada N6A 5B7. E-mail: cwiens3@uwo.ca

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  • Colin M. McCurdy,

    1. Department of Medical Biophysics, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada
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  • Jacob D. Willig-Onwuachi,

    1. Department of Physics, Grinnell College, Grinnell, Iowa, USA
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  • Charles A. McKenzie

    1. Department of Physics and Astronomy, Faculty of Science, University of Western Ontario, London, Canada
    2. Department of Medical Biophysics, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada
    3. Robarts Research Institute, University of Western Ontario, London, Canada
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Abstract

Purpose

To demonstrate an approach to water–fat separation with math formula correction using compressed sensing and parallel imaging.

Methods

Acquisition times for chemical shift based water–fat separation imaging are lengthy, and many applications rely on image acceleration techniques. In this study, we present an integrated compressed sensing, parallel imaging, math formula corrected water–fat separation technique for water–fat imaging of highly accelerated acquisitions. Reconstruction times are reduced using coil compression.

Results

The proposed technique is demonstrated using a customized IDEAL-SPGR pulse sequence to acquire retrospectively and prospectively undersampled datasets of the liver, calf, knee, and abdominal cavity. This technique is shown to offer comparable image quality relative to fully sampled reference images for a range of acceleration factors. At high acceleration factors, this technique is shown to offer improved image quality over parallel imaging.

Conclusion

A technique is described that uses compressed sensing and parallel imaging to reconstruct math formula-corrected water and fat images from accelerated datasets. Acceleration factors as high as 7.0 are shown with excellent image quality. These high acceleration factors enable water–fat separation with higher resolution or greater anatomical coverage in breath-hold applications. Magn Reson Med 71:608–616, 2014. © 2013 Wiley Periodicals, Inc.

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