  Purpose: To objectively assess new medical imaging technologies via
computer-simulations, it is important to account for the variability in the
ensemble of objects to be imaged. This source of variability can be described
by stochastic object models (SOMs). It is generally desirable to establish SOMs
from experimental imaging measurements acquired by use of a well-characterized
imaging system, but this task has remained challenging. Approach: A generative
adversarial network (GAN)-based method that employs AmbientGANs with modern
progressive or multiresolution training approaches is proposed. AmbientGANs
established using the proposed training procedure are systematically validated
in a controlled way using computer-simulated magnetic resonance imaging (MRI)
data corresponding to a stylized imaging system. Emulated single-coil
experimental MRI data are also employed to demonstrate the methods under less
stylized conditions. Results: The proposed AmbientGAN method can generate clean
images when the imaging measurements are contaminated by measurement noise.
When the imaging measurement data are incomplete, the proposed AmbientGAN can
reliably learn the distribution of the measurement components of the objects.
Conclusions: Both visual examinations and quantitative analyses, including
task-specific validations using the Hotelling observer, demonstrated that the
proposed AmbientGAN method holds promise to establish realistic SOMs from
imaging measurements.
