Compressed sensing and the use of phased array coils in 23Na MRI: a comparison of a SENSE-based and an individually combined multi-channel reconstruction

Sebastian Lachner, Matthias Utzschneider, Olgica Zaric, Lenka Minarikova, Laurent Ruck, Štefan Zbýň, Bernhard Hensel, Siegfried Trattnig, Michael Uder, Armin M. Nagel

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: To implement and to evaluate a compressed sensing (CS) reconstruction algorithm based on the sensitivity encoding (SENSE) combination scheme (CS-SENSE), used to reconstruct sodium magnetic resonance imaging (23Na MRI) multi-channel breast data sets. Methods: In a simulation study, the CS-SENSE algorithm was tested and optimized by evaluating the structural similarity (SSIM) and the normalized root-mean-square error (NRMSE) for different regularizations and different undersampling factors (USF = 1.8/3.6/7.2/14.4). Subsequently, the algorithm was applied to data from in vivo measurements of the healthy female breast (n = 3) acquired at 7 T. Moreover, the proposed CS-SENSE algorithm was compared to a previously published CS algorithm (CS-IND). Results: The CS-SENSE reconstruction leads to an increased image quality for all undersampling factors and employed regularizations. Especially if a simple 2nd order total variation is chosen as sparsity transformation, the CS-SENSE reconstruction increases the image quality of highly undersampled data sets (CS-SENSE: SSIMUSF=7.2 = 0.234, NRMSEUSF=7.2 = 0.491 vs. CS-IND: SSIMUSF=7.2 = 0.201, NRMSEUSF=7.2 = 0.506). Conclusion: The CS-SENSE reconstruction supersedes the need of CS weighting factors for each channel as well as a method to combine single channel data. The CS-SENSE algorithm can be used to reconstruct undersampled data sets with increased image quality. This can be exploited to reduce total acquisition times in 23Na MRI.

Original languageEnglish (US)
Pages (from-to)48-57
Number of pages10
JournalZeitschrift fur Medizinische Physik
Volume31
Issue number1
DOIs
StatePublished - Feb 2021

Bibliographical note

Funding Information:
This work was supported by the Vienna Science and Technology Fund (WWTF, project LS14-096). The authors declare that they have no conflicts of interest.

Publisher Copyright:
© 2020

Keywords

  • Compressed sensing
  • Iterative reconstruction
  • Multi-channel
  • Prior knowledge
  • Sensitivity encoding
  • Sodium MRI

PubMed: MeSH publication types

  • Journal Article

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