关键词: 医学图像, 超分辨重建, 卷积神经网络, 特征损失

Abstract: High-resolution Magnetic Resonance（MR） images can provide a clearer anatomical image to facilitate early diagnosis of the disease. However, the inherent defects of medical imaging system make the acquisition of high-resolution medical images face many problems, and one of the methods to solve these problems is to use the super-resolution reconstruction technique. In view of the problem of medical image super-resolution reconstruction, a feedforward full connection convolution neural network is designed, which includes five-layer convolution layer and five residual blocks, and the loss function based on feature is also used to solve the problem caused by mean square error loss function which cannot meet the human visual sense. This method realizes the image 4 times magnification reconstruction in the network and avoids the checkerboard artifacts, which are often occurred when using deconvolution layers to up-sample images. The effectiveness of the method is verified by experiments, and both the visual and numerical results are improved.

Key words: medical image, super-resolution reconstruction, convolution neural network, feature-based loss