论文标题
通过使用生成对抗网络学到的图像自适应先验的医学图像重建
Medical image reconstruction with image-adaptive priors learned by use of generative adversarial networks
论文作者
论文摘要
医疗图像重建通常是一个不适的反问题。为了解决此类不足的问题，通常通过某些刺激性的正则化来纳入所寻求的对象财产的先前分布。最近，使用生成对抗网络（GAN）估计的图像的先前分布在正规化某些图像重建问题方面表现出了巨大的希望。在这项工作中，我们采用基于图像自适应的GAN重建方法（Iagan）来从不完整的医学成像数据中重建高保真图像。据观察，iagan方法可能会在对象中恢复与医学诊断相关的细胞结构，但可能会在传统的刺激性促进正则化的重建中过度平衡。
Medical image reconstruction is typically an ill-posed inverse problem. In order to address such ill-posed problems, the prior distribution of the sought after object property is usually incorporated by means of some sparsity-promoting regularization. Recently, prior distributions for images estimated using generative adversarial networks (GANs) have shown great promise in regularizing some of these image reconstruction problems. In this work, we apply an image-adaptive GAN-based reconstruction method (IAGAN) to reconstruct high fidelity images from incomplete medical imaging data. It is observed that the IAGAN method can potentially recover fine structures in the object that are relevant for medical diagnosis but may be oversmoothed in reconstructions with traditional sparsity-promoting regularization.
