MRI Rician Noise Reduction Using Recurrent Convolutional Neural Networks

Magnetic resonance images are usually corrupted by noise during the acquisition process, which can affect the results of subsequent medical image analysis and diagnosis. This paper presents a denoising recurrent convolutional neural network for Brain MRI denoising. The proposed model consists of a one-level autoencoder architecture with a shortcut, in which the standard convolutional blocks are changed for a new recurrent convolutional denoising block. This block is based on the gated recurrent units combined with local residual learning, allowing us to filter the noisy image recursively. Additionally, we adopt global residual learning to directly estimate the corrupted image's noise instead of the noise-free image. The proposed model requires less computation than other models based on neural networks and experimentally outperforms state-of-the-art models on clinical brain MRI datasets, particularly for high noise levels.