  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. This is
based on joining spatial information from all fields based on TGV
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 non-linear least squares combined with k-space
filtering. The proposed method shows excellent abilities to remove noise while
maintaining sharp image features with large SNR gains at low-field images,
clearly outperforming the reference approach. Especially patient data shows
huge improvements in visual appearance over all fields. The proposed
reconstruction technique largely improves FFC image quality, further pushing
this new technology towards clinical standards.
