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 characterizations 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 uses 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 show huge improvements in visual appearance over all fields.
Conclusion: The proposed reconstruction technique largely improves FFC image quality, further pushing this new technology toward clinical standards.
Keywords: T1 quantification; dispersion; fast field-cycling; low-field MRI; model-based reconstruction.
© 2021 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.