Deep Learning for Denoising: An Attempt to Recover the Effective Magnetic Resonance Sounding Signal in the Presence of High Level Noise

Abstract

Magnetic resonance sounding (MRS) measurements commonly suffer from a notably low signal-to-noise ratio (SNR). In recent years, many denoising methods have been developed, which have demonstrated the useful capability to improve the SNR. However, when MRS measurements are implemented on the sites with high noise levels, i.e., the human living environment, the conventional methods are helpless to recover the effective MRS signal, which is submerged in extensive environmental noise. In addition, the conventional methods that depend on signal models and the corresponding prior assumptions commonly rely on manual experience, which brings obstacles to the automation and efficiency of signal processing. To solve the above problems, we attempt to apply an intelligent denoising framework with a novel neural network as the basic tool for deep learning, called Dn-ResUnet in this article. The network extracts the features of the MRS signal through the encoder and decoder layers of the Dn-ResUnet structure, in which residual learning is adopted to accelerate the training process and improve denoising performance. Once the training is completed, the deep learning realizes adaptive denoising with no need for 1) prior assumptions of the MRS signal and noise; 2) optimal filter parameter tuning; or 3) expensive time cost. The comparison experiments demonstrate that the Dn-ResUnet model provides superior noise cancellation performance, especially it can replace the conventional methods to recover the effective MRS signals in noise levels down to an SNR of −30 dB. In addition, the noise attenuation tests are performed on synthetic and real data. The results show that the framework of deep learning yields a convincing performance in MRS signal denoising.