  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.
