【摘要】 随着医学成像技术的快速发展,医学图像在临床检测及科研领域得到了广泛的应用。针对临床图像数据集不完备的情况,本文提出了基于密集连接的自逆生成对抗网络用于实现核磁共振T1加权图像和T2加权图像相互生成的模型。该模型在自逆循环对抗生成网络的生成器模块中引入密集连接块结构,并采用U-net的多尺度融合框架,实现了T1与T2加权图像的互相生成。实验采用BraTS2018数据集进行验证,生成图像的峰值信噪比与结构相似度最高分别可以达到22.78和0.8。基于密集连接块的生成器与基于U-net及ResNet的生成器模型的对比实验结果表明,基于密集连接块的生成模型性能更优。本文提出的基于密集连接自逆生成对抗网络的MR图像生成方法可以较好地改善T1或T2加权像缺失的问题,为临床论断提供更多的信息。更多还原

【Abstract】 With the rapid development of medical imaging technology,medical images have been widely used in clinical detection and scientific research. In view of the insufficient clinical image data set,this paper proposes a generation model based on dense connection self-inverse generative adversarial network(GAN)to realize the mutual generation of T1-and T2-weighted MR images. Especially,the dense block is introduced into the generator module of self-inverse GAN model,and the multi-scale fusion framework of U-net is adopted to realize the mutual generation of T1 and T2 weighted MR images. The BraTS 2018 data set is used for validation and the peak signal-to-noise ratio and structure similarity of the generated images could reach 22.78 and 0.8,respectively. Contrast experimental results of different generators show that the model with the generator based on dense block has better performance than the model with the generator based on U-net or ResNet. The MR image generation method based on dense connection selfinverse GAN proposed in this paper can reduce the negative influence brought from missing T1 or T2 weighted images and provide more information for clinical judgment.更多还原