  The objective optimization of medical imaging systems requires full
characterization of all sources of randomness in the measured data, which
includes the variability within the ensemble of objects to-be-imaged. This can
be accomplished by establishing a stochastic object model (SOM) that describes
the variability in the class of objects to-be-imaged. Generative adversarial
networks (GANs) can be potentially useful to establish SOMs because they hold
great promise to learn generative models that describe the variability within
an ensemble of training data. However, because medical imaging systems record
imaging measurements that are noisy and indirect representations of object
properties, GANs cannot be directly applied to establish stochastic models of
objects to-be-imaged. To address this issue, an augmented GAN architecture
named AmbientGAN was developed to establish SOMs from noisy and indirect
measurement data. However, because the adversarial training can be unstable,
the applicability of the AmbientGAN can be potentially limited. In this work,
we propose a novel training strategy---Progressive Growing of AmbientGANs
(ProAGAN)---to stabilize the training of AmbientGANs for establishing SOMs from
noisy and indirect imaging measurements. An idealized magnetic resonance (MR)
imaging system and clinical MR brain images are considered. The proposed
methodology is evaluated by comparing signal detection performance computed by
use of ProAGAN-generated synthetic images and images that depict the true
object properties.
