Deep-Learning-Based Dual Denoising Algorithm for Shielding-Free Magnetic Resonance Imaging

Electromagnetic shielding is crucial in magnetic resonance imaging (MRI), particularly at low- and ultralow-magnetic fields. However, the cost of employing physical enclosures for shielding can be prohibitively high. In this study, we introduce a comprehensive dual denoising algorithm, leveraging deep-learning techniques, specifically tailored for shielding-free MRI. This algorithm is structured into two distinct steps. In the first step, we employ a residual convolution neural network (residual CNN) to establish a mapping between the electromagnetic interference (EMI) signals detected by two EMI sensing coils and those detected by the MRI receive coils. In the subsequent step, we introduce triple generators Wasserstein generative adversarial network (TWGAN), a unique composite neural network architecture comprising three generators, to refine the denoised outputs from the first step. Furthermore, we have devised a methodology to construct a denoising dataset that comprises realistic clean-noise pairs, addressing the challenges associated with obtaining such datasets in different field (especially in low field and ultralow field) MRI scenarios. We implemented the algorithm on a shielding-free 0.11-T low-field (LF) MRI system and conducted experiments on healthy human subjects. In comparing with the convergence speed of the first step with existing algorithms, our results revealed that the utilization of residual CNN significantly accelerated the convergence process. Following the second step, we achieved cleaner images. Additionally, we applied the second step to shielding-free 0.055-T ultralow-field (ULF) MRI images, and the outcomes demonstrated that our method effectively addresses the limited denoising capabilities of existing algorithms in highly noise-contaminated MR images. These results highlight the potential of our algorithm for wider application and generalization in scenarios with complex electromagnetic noise.