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Accelerated quantification of tissue sodium concentration in skeletal muscle tissue: quantitative capability of dictionary learning compressed sensing

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A Correction to this article was published on 12 February 2020

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Abstract

Objective

To accelerate tissue sodium concentration (TSC) quantification of skeletal muscle using 23Na MRI and 3D dictionary-learning compressed sensing (3D-DLCS).

Materials and methods

Simulations and in vivo 23Na MRI examinations of calf muscle were performed with a nominal spatial resolution of \(\Delta x = \left( {3.0 \times 3.0 \times 15.0} \right){\text{ mm}}^{3}\). Fully sampled and three undersampled 23Na MRI data sets (undersampling factors (USF) = 3, 4.4, 6.7) were evaluated. Ten healthy subjects were examined on a 3 Tesla MRI system. Results of the simulation study and the in vivo measurements were compared to the ground truth (GT) and the fully sampled fast Fourier transform (NUFFT) reconstruction, respectively.

Results

Reconstruction results of simulated data with optimized 3D-DLCS yielded a lower deviation (< 4%) from the GT than results of the NUFFT reconstruction (> 5%) and a lower standard deviation (SD). For in vivo measurements, a TSC of \(17 \pm 2.7 {\text{ mMol/l}}\) was observed. The mean deviation from the reference is lower for the undersampled 3D-DLCS reconstructions (3.4%) than for NUFFT reconstructions (4.6%). SD is reduced using 3D-DLCS. Compared to a fully sampled NUFFT reconstruction, acquisition time could be reduced by a factor of 4.4 while maintaining similar quantitative accuracy.

Discussion

The optimized 3D-DLCS reconstruction enables accelerated TSC measurements with high quantification accuracy.

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  • 12 February 2020

    The original version of this article unfortunately contained a mistake. Title was incorrect.

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Acknowledgements

We thank the Imaging Science Institute (Erlangen, Germany) for providing us with measurement time.

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Authors and Affiliations

  1. Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Maximiliansplatz 3, 91054, Erlangen, Germany

    Matthias Utzschneider, Sebastian Lachner, Lena V. Gast, Michael Uder & Armin M. Nagel

  2. Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany

    Matthias Utzschneider & Andreas Maier

  3. Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany

    Nicolas G. R. Behl & Armin M. Nagel

  4. Institute of Medical Physics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany

    Armin M. Nagel

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  1. Matthias Utzschneider
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Contributions

MU: responsible for study conception and design, acquisition of data, analysis and interpretation of data and drafting of manuscript. NGRB: involved in study conception and design and critical revision of the manuscript. SL: advised and contributed to/for study conception and design, analysis and interpretation of data and critical revision of the manuscript. LVG: advised and developed of methods for acquisition of data and contributed to analysis and interpretation of data, and critical revision of the manuscript. AM: contributions in analysis and interpretation of data and critical revision of the manuscript. MU: contributed to critical revision of the manuscript. AMN: responsible for study conception and design, analysis and interpretation of data, drafting of manuscript, and critical revision of the manuscript.

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Correspondence to Matthias Utzschneider.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee (Ethics Committee of the Friedrich-Alexander Universität Erlangen Nürnberg, Reference number: 119_17 B).

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The original version of this article was revised: Title was incorrect.

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Utzschneider, M., Behl, N.G.R., Lachner, S. et al. Accelerated quantification of tissue sodium concentration in skeletal muscle tissue: quantitative capability of dictionary learning compressed sensing. Magn Reson Mater Phy 33, 495–505 (2020). https://doi.org/10.1007/s10334-019-00819-2

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