CNN-DMRI: A Convolutional Neural Network for Denoising of Magnetic Resonance Images

Abstract

Magnetic Resonance Images (MRI) are often contaminated by rician noise at the acquisition time. This type of noise typically deteriorates the performance of disease diagnosis by a human observer or an automated system. Thus, it is necessary to remove the rician noise from MRI scans as a preprocessing step. In this letter, we propose a novel Convolutional Neural Network (CNN), viz. CNN-DMRI, for denoising of MRI scans. The network uses a set of convolutions to separate the image features from the noise. The network also employs encoder-decoder structure for preserving the prominent features of the image while ignoring unnecessary ones. The training of the network is carried out in an end-to-end way by utilizing residual learning scheme. The performance of the proposed CNN has been tested qualitatively and quantitatively on one simulated and four real MRI datasets. Extensive experimental findings suggest that the proposed network can denoise MRI images effectively without losing crucial image details.