  It has been advocated that medical imaging systems and reconstruction
algorithms should be assessed and optimized by use of objective measures of
image quality that quantify the performance of an observer at specific
diagnostic tasks. One important source of variability that can significantly
limit observer performance is variation in the objects to-be-imaged. This
source of variability can be described by stochastic object models (SOMs). A
SOM is a generative model that can be employed to establish an ensemble of
to-be-imaged objects with prescribed statistical properties. In order to
accurately model variations in anatomical structures and object textures, it is
desirable to establish SOMs from experimental imaging measurements acquired by
use of a well-characterized imaging system. Deep generative neural networks,
such as generative adversarial networks (GANs) hold great potential for this
task. However, conventional GANs are typically trained by use of reconstructed
images that are influenced by the effects of measurement noise and the
reconstruction process. To circumvent this, an AmbientGAN has been proposed
that augments a GAN with a measurement operator. However, the original
AmbientGAN could not immediately benefit from modern training procedures, such
as progressive growing, which limited its ability to be applied to
realistically sized medical image data. To circumvent this, in this work, a new
Progressive Growing AmbientGAN (ProAmGAN) strategy is developed for
establishing SOMs from medical imaging measurements. Stylized numerical studies
corresponding to common medical imaging modalities are conducted to demonstrate
and validate the proposed method for establishing SOMs.
