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Magnetic Resonance Materials in Physics, Biology and Medicine

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16.01.2020 | Research Article

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

verfasst von: Matthias Utzschneider, Nicolas G. R. Behl, Sebastian Lachner, Lena V. Gast, Andreas Maier, Michael Uder, Armin M. Nagel

Erschienen in: Magnetic Resonance Materials in Physics, Biology and Medicine | Ausgabe 4/2020

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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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Titel: Accelerated quantification of tissue sodium concentration in skeletal muscle tissue: quantitative capability of dictionary learning compressed sensing
verfasst von: Matthias Utzschneider
Nicolas G. R. Behl
Sebastian Lachner
Lena V. Gast
Andreas Maier
Michael Uder
Armin M. Nagel
Publikationsdatum: 16.01.2020
Verlag: Springer International Publishing
Erschienen in: Magnetic Resonance Materials in Physics, Biology and Medicine / Ausgabe 4/2020
Print ISSN: 0968-5243
Elektronische ISSN: 1352-8661
DOI: https://doi.org/10.1007/s10334-019-00819-2

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