  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 regularization. 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 low-field images, clearly outperforming
the reference approach. Especially patient data shows huge improvements in
visual appearance over all fields. Conclusion: The proposed reconstruction
technique largely improves FFC image quality, further pushing this new
technology towards clinical standards.
