关键词: 磁共振成像, 图像重建, 生成对抗网络, 空洞金字塔, 集成学习

Abstract: Magnetic resonance imaging（MRI） technology has become one of the commonly used auxiliary diagnosis and treatment methods in the clinic because of its non-radiation and non-trauma characteristics. However, the long scanning time and the closed environment have led to claustrophobia and the medical costs increased. MR image reconstruction algorithm based on the generative adversarial network（GAN） is proposed to solve this problem. In the GAN network, the U-net structure is used as the generator, which utilizes the residual structure in the encoding layer of U-net to alleviate the gradient disappearance and propose the atrous pyramid structure which utilizing the atrous convolutions with different dilation rates for multi-scale information fusion. In the discriminator, a series of convolutions are utilized to achieve feature down-sampling, and complete feature classification through the sigmoid function. To further improve the quality of reconstruction, the idea of ensemble learning is introduced. Comparing existing research results and mainstream reconstruction networks, the proposed model ranks first with four different sampling rates of 10%, 20%, 30%, and 50%, which shows that this model can not only effectively improve the quality of MRI reconstruction, but also has good generalization.

Key words: magnetic resonance imaging, image reconstruction, generative adversarial network, atrous pyramid structure, ensemble learning