Residual dense network for medical magnetic resonance images super-resolution

High-resolution magnetic resonance images (MRI) help experts to localize lesions and diagnose diseases, but it is difficult to obtain high-resolution MRI. Furthermore, image super-resolution technology based on deep learning can effectively improve image resolution.

In this work, we propose a medical magnetic resonance (MR) image super-resolution reconstruction method based on residual dense network (MRDN). Firstly, we input the convolutional features of the shallow layer into the residual dense block to obtain global and local features. Secondly, each layer in the residual dense block is directly connected to the previous layer to achieve reuse of features. Finally, we use sub-pixel convolution layer for upsampling and super-resolution reconstruction to get a clear high-resolution image.

For the 2 ×, 3 ×, and 4 × enlargement, we propose the MRDN method shows the superiority over the state-of-the-art methods on the Set5, Set14, and Urban100 benchmark datasets, extensive benchmark experiment and analysis show that the superiority of our MRDN algorithm in terms of the peak signal-to-noise ratio (PSNR) and structural similarity index indicators (SSIM).

Quantitative experiments are conducted on three public datasets: Set5, Set14 and Urban10, evaluate with commonly used evaluation metrics, and the experimental results show that the method in this paper is more effective. In addition, we reconstruct the public MR datasets, and the reconstructed high-resolution MR image has a clear structure and rich texture details.