Towards accelerated quantitative sodium MRI at 7 T in the skeletal muscle: Comparison of anisotropic acquisition- and compressed sensing techniques.

7 tesla Anisotropic acquisition schemes Compressed sensing Image reconstruction Skeletal muscle Sodium ((23)Na) magnetic resonance imaging

Journal

Magnetic resonance imaging
ISSN: 1873-5894
Titre abrégé: Magn Reson Imaging
Pays: Netherlands
ID NLM: 8214883

Informations de publication

Date de publication:
01 2021
Historique:
received: 08 05 2020
revised: 25 08 2020
accepted: 14 09 2020
pubmed: 27 9 2020
medline: 26 2 2021
entrez: 26 9 2020
Statut: ppublish

Résumé

To compare three anisotropic acquisition schemes and three compressed sensing (CS) approaches for accelerated tissue sodium concentration (TSC) quantification using Three anisotropic 3D-radial acquisition sequences were evaluated using simulations, phantom- and in vivo TSC measurements: An anisotropic density-adapted 3D-radial sequence (3DPR-C), a 3D acquisition-weighted density-adapted stack-of-stars sampling scheme (SOS) and a SOS approach with golden-ratio rotation (SOS-GR). Eight healthy volunteers were examined at a 7 Tesla MRI system. TSC measurements of the calf were conducted with a nominal spatial resolution of Δx = (3.0 × 3.0 × 15.0) mm In simulations, the SOS-GR yielded the lowest NRMSE and mE The TA for TSC measurements could be reduced to less than 2 min by using adapted sequences such as SOS-GR and CS reconstruction approaches such as 3D-DLCS or TV-BL-CS, while the quantitative accuracy stays comparable to a fully sampled NUFFT reconstruction (approx. 8 min TA). In future, the lower TA could improve clinical applicability of TSC measurements.

Identifiants

pubmed: 32979516
pii: S0730-725X(20)30305-2
doi: 10.1016/j.mri.2020.09.019
pii:
doi:

Substances chimiques

Sodium 9NEZ333N27

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

72-88

Informations de copyright

Copyright © 2020 Elsevier Inc. All rights reserved.

Auteurs

Matthias Utzschneider (M)

Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany; Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany. Electronic address: matthias.utzschneider@uk-erlangen.de.

Max Müller (M)

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

Lena V Gast (LV)

Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany; Institute of Medical Physics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.

Sebastian Lachner (S)

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

Nicolas G R Behl (NGR)

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

Andreas Maier (A)

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

Michael Uder (M)

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

Armin M Nagel (AM)

Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany; Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; Institute of Medical Physics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.

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