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

TL;DR

This paper introduces a model-based reconstruction method for fast field-cycling MRI that leverages joint multi-field information to significantly improve image quality and noise reduction, facilitating clinical application.

Contribution

The paper presents a novel joint multi-field reconstruction algorithm using Frobenius-tGTV regularization, enhancing FFC MRI image quality over existing methods.

Findings

Significant noise reduction and sharpness in low-field images.

Large SNR gains compared to traditional fitting methods.

Visual improvements in patient scan images.

Abstract

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.