Physics-informed denoising method for image reconstruction in quantitative low-field MRI
Low-field magnetic resonance imaging (MRI) is becoming increasingly important for medical imaging because it can reduce healthcare costs while ensuring high diagnostic output. Nevertheless, quantitative imaging in low-field MRI faces challenges, such as low signal-to-noise ratio and long scan durations. Deep learning approaches have been proposed for image reconstruction to overcome these challenges. Still, deep learning often requires large high-quality training datasets which are usually not available for low-field applications. Here we propose a modular unrolled end-to-end deep learning method for the denoised reconstruction of quantitative parameter maps directly from k-space data for low-field MRI. It consists of three sub-networks that are iteratively applied. They are used for the regularization of the quantitative parameter estimation, as well as for the signal estimation that is based on simulated signal curves. It generalises well and can be applied to different field strengths and even different quantitative MR sequences without the need for new training data. We applied the presented method to noisy data of knees acquired at 0.55 T for the reconstruction of $T_2$-maps and compared it to other classical and deep learning methods. We also applied the proposed approach to $T_1$-mapping of knees at 72 mT and $T_2$-mapping of brains at 0.6 T. The presented approach outperforms the other reconstruction methods with a median difference below 4 ms to the ground truth $T_2$-map. Even though the network was trained with $T_2$-maps acquired at 0.55 T, it successfully denoised data acquired at different field strengths, sequences, and of different anatomies. As a result, the proposed network and its underlying method offer an efficient and flexible solution to denoise low-field MR data and make quantitative low-field MRI a feasible diagnostic tool for clinical applications.