Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network
This addresses the problem of time-consuming optimization and parameter dependency in MRI denoising for medical imaging applications, representing an incremental improvement over existing methods.
The paper tackled denoising 3D magnetic resonance images by proposing a convolutional neural network with residual learning and multi-channel strategy, achieving superior performance in peak signal-to-noise ratio and structural similarity index compared to other methods without needing noise level parameters.
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.