  The denoising of magnetic resonance (MR) images is a task of great importance
for improving the acquired image quality. Many methods have been proposed in
the literature to retrieve noise free images with good performances. Howerever,
the state-of-the-art denoising methods, all needs a time-consuming optimization
processes and their performance strongly depend on the estimated noise level
parameter. Within this manuscript we propose the idea of denoising MRI Rician
noise using a convolutional neural network. The advantage of the proposed
methodology is that the learning based model can be directly used in the
denosing process without optimization and even without the noise level
parameter. Specifically, a ten convolutional layers neural network combined
with residual learning and multi-channel strategy was proposed. Two training
ways: training on a specific noise level and training on a general level were
conducted to demonstrate the capability of our methods. Experimental results
over synthetic and real 3D MR data demonstrate our proposed network can achieve
superior performance compared with other methods in term of both of the peak
signal to noise ratio and the global of structure similarity index. Without
noise level parameter, our general noise-applicable model is also better than
the other compared methods in two datasets. Furthermore, our training model
shows good general applicability.
