Joint multi-field T$_1$ quantification for fast field-cycling MRI

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.