基于生成对抗网络的无监督动态对比增强磁共振分解方法

为排除动态对比增强磁共振(DCE-MRI)中造影剂对图像定量分析的影响,可以使用图像分解的方法,将图像中造影剂造成的灰度变化分解出去,并保持其他内容不变。本文提出一种基于生成对抗网络的无监督图像分解方法,以实现对DCEMRI中造影剂造成的灰度变化的分解。该方法假设增强图像由增强成分和解剖成分组成,基于对各成分复杂度的先验设计生成器,通过双路生成网络,同时但独立地提取解剖成分和增强成分的信息。同时,引入图像重构损失作为反馈信号来约束生成。在MNIST数据集上基于MSE、SSIM、PSNR与现有的方法进行对比,验证了该方法的有效性。并在临床的肝部DCEMRI数据进行了实验。实验表明该方法可以在没有成对训练数据的情况下有效实现DCE-MRI图像分解。

Unsupervised image decomposition of DCE-MRI based on generative adversarial networks

Image decomposition can be used to separate the grayscale variations caused by contrast agent from dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI)image,with other contents unchanged,thereby eliminating the effects of contrast agent on quantitative analysis.An unsupervised image decomposition method based on generative adversarial networks is proposed to achieve the separation of grayscale variations caused by contrast agent in DCE-MRI.The method assumes that the enhanced image is composed of contrast component and anatomy component,and two generators are designed based on the complexity of each component.Through the double-generator,the information of anatomy component and contrast component can be extracted simultaneously and independently.Meanwhile,the image reconstruction loss is introduced as a feedback signal to constrain the generation.Mean-square error,peak signal to noise ratio and structural similarity index are used to compare the proposed method with the existing methods on MNIST dataset,thus verifying the effectiveness of the proposed method.Moreover,some experiments are carried out on clinical liver DCE-MRI data,and the experimental results show that the proposed method can be used to effectively achieve DCE-MRI image decomposition without paired training data.