Compressed sensing reconstruction of 7 Tesla 23Na multi-channel breast data using 1H MRI constraint

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

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Purpose: To reduce acquisition time and to improve image quality in sodium magnetic resonance imaging (23Na MRI)using an iterative reconstruction algorithm for multi-channel data sets based on compressed sensing (CS)with anatomical 1H prior knowledge. Methods: An iterative reconstruction for 23Na MRI with multi-channel receiver coils is presented. Based on CS it utilizes a second order total variation (TV(2)), adopted by anatomical weighting factors (AnaWeTV(2))obtained from a high-resolution 1H image. A support region is included as additional regularization. Simulated and measured 23Na multi-channel data sets (n = 3)of the female breast acquired at 7 T with different undersampling factors (USF = 1.8/3.6/7.2/14.4)were reconstructed and compared to a conventional gridding reconstruction. The structural similarity was used to assess image quality of the reconstructed simulated data sets and to optimize the weighting factors for the CS reconstruction. Results: Compared with a conventional TV(2), the AnaWeTV(2) reconstruction leads to an improved image quality due to preserving of known structure and reduced partial volume effects. An additional incorporated support region shows further improvements for high USFs. Since the decrease in image quality with higher USFs is less pronounced compared to a conventional gridding reconstruction, proposed algorithm is beneficial especially for higher USFs. Acquisition time can be reduced by a factor of 4 (USF = 7.2), while image quality is still similar to a nearly fully sampled (USF = 1.8)gridding reconstructed data set. Conclusion: Especially for high USFs, the proposed algorithm allows improved image quality for multi-channel 23Na MRI data sets.

Original languageEnglish (US)
Pages (from-to)145-156
Number of pages12
JournalMagnetic Resonance Imaging
Volume60
DOIs
StatePublished - Jul 2019

Bibliographical note

Funding Information:
This work was supported by the Vienna Science and Technology Fund (WWTF, project LS14-096 ).

Keywords

  • 7 Tesla
  • Compressed sensing (CS)
  • Iterative reconstruction
  • Multi-channel
  • Prior knowledge
  • Sodium (Na)breast MRI

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