Joint multi-field T1 quantification for fast field-cycling MRI

Markus Bödenler, Oliver Maier, Rudolf Stollberger, Lionel Broche, James Ross, Mary Macleod

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
5 Downloads (Pure)


Purpose: Recent developments in hardware design enable the use of Fast Field-Cycling (FFC) techniques in MRI to exploit the different relaxation rates at very low field strength, achieving novel contrast. The method opens new avenues
for in vivo characterisations of pathologies but at the expense of longer acquisition times. To mitigate this we propose a model-based reconstruction method that fully exploits the high information redundancy offered by FFC methods. Methods: The proposed model-based approach utilizes joint spatial information from all fields by means of a Frobenius - total generalized variation regulariaztion. The algorithm was tested on brain stroke images, both simulated and acquired from FFC patients scans using an FFC spin echo sequences. The results are compared to three non-linear least squares fits with progressively increasing complexity.
Results: The proposed method shows excellent abilities to remove noise while maintaining sharp image features with large signal-to-noise ratio gains at lowfield images, clearly outperforming the reference approach. Especially patient data shows huge improvements in visual appearance over all fields. Conclusion: The proposed re-construction technique largely improves FFC image quality,
further pushing this new technology towards clinical standards.
Original languageEnglish
Pages (from-to)2049-2063
Number of pages15
JournalMagnetic Resonance in Medicine
Issue number4
Early online date10 Jun 2021
Publication statusPublished - Oct 2021

Bibliographical note

This article is based upon work from COST Action CA15209, supported by COST (European Cooperation in Science and Technology). Oliver Maier is a Recipient of a DOC Fellowship (24966) of the Austrian Academy of Sciences at the Institute of Medical Engineering at TU Graz. The authors would like to acknowledge the NVIDIA Corporation Hardware grant support.


  • fast field-cycling
  • Dispersion
  • T1 quantification
  • model-based reconstruction
  • low-field MRI


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