随机对象模型学习的AmbientGAN改进
Advancing the AmbientGAN for learning stochastic object models
摘要：
医学成像系统通常通过使用量化观察者在特定任务中的表现的图像质量（IQ）的客观测量来评估和优化。待成像对象的变化是可变性的重要来源，其可以显著地限制观察者的性能。这种对象的可变性可以用随机对象模型（SOM）来描述。为了建立可以精确地模拟现实对象变化的SOM，期望使用实验数据。为了实现这一点，一个增强的生成对抗网络（GAN）架构称为环境GAN已开发和研究。然而，AmbientGAN不能通过使用高级GAN训练方法（例如GANs的渐进式增长（ProGANs））来立即训练。因此，AmbientGANs建立逼真对象模型的能力有限。为了解决这个问题，已经提出了一个渐进式增长的AmbientGAN（ProAmGAN）。然而，ProAmGAN被设计用于生成二维（2D）图像，而医学成像模态通常用于对三维（3D）对象进行成像。此外，采用传统生成器架构的ProAmGAN缺乏控制特定图像特征的能力，例如在优化成像系统时经常考虑的精细纹理。在这项研究中，我们通过提出两种先进的AmbientGAN架构来解决这些限制：3D ProAmGAN和Style-AmbientGAN（StyAmGAN）。进行涉及磁共振（MR）成像系统的程式化数值研究。证明了3D ProAmGANs从成像测量中学习3D SOM的能力以及StyAmGANs控制合成对象的精细尺度纹理的能力。
英文摘要：
Medical imaging systems are commonly assessed and optimized by use of objective-measures of image quality (IQ) that quantify the performance of an observer at specific tasks. Variation in the objects to-be-imaged is an important source of variability that can significantly limit observer performance. This object variability can be described by stochastic object models (SOMs). In order to establish SOMs that can accurately model realistic object variability, it is desirable to use experimental data. To achieve this, an augmented generative adversarial network (GAN) architecture called AmbientGAN has been developed and investigated. However, AmbientGANs cannot be immediately trained by use of advanced GAN training methods such as the progressive growing of GANs (ProGANs). Therefore, the ability of AmbientGANs to establish realistic object models is limited. To circumvent this, a progressively-growing AmbientGAN (ProAmGAN) has been proposed. However, ProAmGANs are designed for generating two-dimensional (2D) images while medical imaging modalities are commonly employed for imaging three-dimensional (3D) objects. Moreover, ProAmGANs that employ traditional generator architectures lack the ability to control specific image features such as fine-scale textures that are frequently considered when optimizing imaging systems. In this study, we address these limitations by proposing two advanced AmbientGAN architectures: 3D ProAmGANs and Style-AmbientGANs (StyAmGANs). Stylized numerical studies involving magnetic resonance (MR) imaging systems are conducted. The ability of 3D ProAmGANs to learn 3D SOMs from imaging measurements and the ability of StyAmGANs to control fine-scale textures of synthesized objects are demonstrated.
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