Design of a fast field-cycling magnetic resonance imaging system, characterization and methods for relaxation dispersion measurements around 1.5 T

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

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.

Towards applying NMR relaxometry as a diagnostic tool for bone and soft tissue sarcomas: a pilot study

This work explores what Fast Field-Cycling Nuclear Magnetic Resonance (FFC-NMR) relaxometry brings for the study of sarcoma to guide future in vivo analyses of patients. We present the results of an ex vivo pilot study involving 10 cases of biopsy-proven sarcoma and we propose a quantitative method to analyse 1H NMR relaxation dispersion profiles based on a model-free approach describing the main dynamical processes in the tissues and assessing the amplitude of the Quadrupole Relaxation Enhancement effects due to 14N. This approach showed five distinct groups of dispersion profiles indicating five discrete categories of sarcoma, with differences attributable to microstructure and rigidity. Data from tissues surrounding sarcomas indicated very significant variations with the proximity to tumour, which may be attributed to varying water content but also to tissue remodelling processes due to the sarcoma. This pilot study illustrates the potential of FFC relaxometry for the detection and characterisation of sarcoma.