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Improved compressed sensing reconstruction for \(^{19}\)F magnetic resonance imaging

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Magnetic Resonance Materials in Physics, Biology and Medicine Aims and scope Submit manuscript

Abstract

Objective

In magnetic resonance imaging (MRI), compressed sensing (CS) enables the reconstruction of undersampled sparse data sets. Thus, partial acquisition of the underlying k-space data is sufficient, which significantly reduces measurement time. While 19F MRI data sets are spatially sparse, they often suffer from low SNR. This can lead to artifacts in CS reconstructions that reduce the image quality. We present a method to improve the image quality of undersampled, reconstructed CS data sets.

Materials and methods

Two resampling strategies in combination with CS reconstructions are presented. Numerical simulations are performed for low-SNR spatially sparse data obtained from 19F chemical-shift imaging measurements. Different parameter settings for undersampling factors and SNR values are tested and the error is quantified in terms of the root-mean-square error.

Results

An improvement in overall image quality compared to conventional CS reconstructions was observed for both strategies. Specifically spike artifacts in the background were suppressed, while the changes in signal pixels remained small.

Discussion

The proposed methods improve the quality of CS reconstructions. Furthermore, because resampling is applied during post-processing, no additional measurement time is required. This allows easy incorporation into existing protocols and application to already measured data.

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Acknowledgements

This work was partially supported by the Deutsche Forschungsgemeinschaft SFB 630 (C2) and SFB 688 (B 5), Deutsche Forschungsgemeinschaft (contract grant number: DFG ZI 1295/2-1 and DFG KU 3555/1-1). L. R. Buschle was supported by a scholarship of the German Academic Scholarship Foundation (Studienstiftung des deutschen Volkes). F. T. Kurz was supported by a postdoctoral fellowship from the medical faculty of Heidelberg University and the Hoffmann-Klose foundation of Heidelberg University. V.J.F Sturm was supported by the German Research Council (DFG, SFB 1118/2).

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Author notes

  1. Thomas Kampf and Volker J. F. Sturm contributed equally.

Authors and Affiliations

  1. Department of Neuroradiology, University Hospital Würzburg, 97080, Würzburg, Germany

    Thomas Kampf & Mirko Pham

  2. Experimental Physics V, University of Würzburg, 97074, Würzburg, Germany

    Thomas Kampf, Thomas C. Basse-Lüsebrink, André Fischer & Peter M. Jakob

  3. Experimental Neuroradiology, University Hospital Heidelberg, 69120, Heidelberg, Germany

    Volker J. F. Sturm, Felix T. Kurz & Sabine Heiland

  4. German Cancer Research Center, 69120, Heidelberg, Germany

    Lukas R. Buschle, Felix T. Kurz, Heinz-Peter Schlemmer & Christian H. Ziener

  5. Department of Neuroradiology, University Hospital Heidelberg, 69120, Heidelberg, Germany

    Martin Bendszus

  6. Department of Neurology, University Hospital Würzburg, 97080, Würzburg, Germany

    Guido Stoll

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  1. Thomas Kampf
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  2. Volker J. F. Sturm
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Corresponding author

Correspondence to Thomas Kampf.

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Conflict of interest

TBL is currently employed by Bruker BioSpin MRI GmbH, Ettlingen, Germany. AF is currently employed by GE Healthcare. However, the main part of their contribution to this work steems from their time at the department of EP5, Würzburg, Germany.

Ethical approval

All animal experiments presented in this work were performed according to institutional guidelines and were approved by the Ethics Committee for Animal Welfare of the University Hospital of Würzburg, Germany.

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Kampf, T., Sturm, V.J.F., Basse-Lüsebrink, T.C. et al. Improved compressed sensing reconstruction for \(^{19}\)F magnetic resonance imaging. Magn Reson Mater Phy 32, 63–77 (2019). https://doi.org/10.1007/s10334-018-0729-1

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