论文标题
先前基于图像的医学图像重建使用基于样式的生成对抗网络
Prior image-based medical image reconstruction using a style-based generative adversarial network
论文作者
论文摘要
计算的医学成像系统需要图像形成的计算重建程序。为了在记录的测量结果不完整时恢复对象成像的对象的有用估计,必须利用有关对象性质的先验知识。为了改善不良成像逆问题的调理,正在积极研究深度学习方法,以更好地代表对象先验和约束。这项工作建议使用基于样式的生成对抗网络(stylegan)来限制图像重建问题,如果可以使用以前的对象的先前图像形式的其他信息。优化问题是在StyleGAN的中间潜在空间中提出的,该潜在地位相对于有意义的图像属性或“样式”,例如磁共振成像(MRI)中使用的对比度。在段落和先前的图像之间的差异以脱离的潜在空间进行测量,并用于以限制的形式正规化逆问题,以限制的特定样式的分离潜在空间的特定样式。设计了受MR成像启发的程式化的数值研究,在该研究中,在结构上进行了探测和先验的图像在结构上相似,但属于不同的对比机制。与传统指标形式相比,所提出的数值研究证明了所提出的方法的优越性。
Computed medical imaging systems require a computational reconstruction procedure for image formation. In order to recover a useful estimate of the object to-be-imaged when the recorded measurements are incomplete, prior knowledge about the nature of object must be utilized. In order to improve the conditioning of an ill-posed imaging inverse problem, deep learning approaches are being actively investigated for better representing object priors and constraints. This work proposes to use a style-based generative adversarial network (StyleGAN) to constrain an image reconstruction problem in the case where additional information in the form of a prior image of the sought-after object is available. An optimization problem is formulated in the intermediate latent-space of a StyleGAN, that is disentangled with respect to meaningful image attributes or "styles", such as the contrast used in magnetic resonance imaging (MRI). Discrepancy between the sought-after and prior images is measured in the disentangled latent-space, and is used to regularize the inverse problem in the form of constraints on specific styles of the disentangled latent-space. A stylized numerical study inspired by MR imaging is designed, where the sought-after and the prior image are structurally similar, but belong to different contrast mechanisms. The presented numerical studies demonstrate the superiority of the proposed approach as compared to classical approaches in the form of traditional metrics.