AmbientCycleGAN for Establishing Interpretable Stochastic Object Models
  Based on Mathematical Phantoms and Medical Imaging Measurements
Xichen Xu Wentao Chen Weimin Zhou
Medical imaging systems that are designed for producing diagnostically
informative images should be objectively assessed via task-based measures of
image quality (IQ). Ideally, computation of task-based measures of IQ needs to
account for all sources of randomness in the measurement data, including the
variability in the ensemble of objects to be imaged. To address this need,
stochastic object models (SOMs) that can generate an ensemble of synthesized
objects or phantoms can be employed. Various mathematical SOMs or phantoms were
developed that can interpretably synthesize objects, such as lumpy object
models and parameterized torso phantoms. However, such SOMs that are purely
mathematically defined may not be able to comprehensively capture realistic
object variations. To establish realistic SOMs, it is desirable to use
experimental data. An augmented generative adversarial network (GAN),
AmbientGAN, was recently proposed for establishing SOMs from medical imaging
measurements. However, it remains unclear to which extent the
AmbientGAN-produced objects can be interpretably controlled. This work
introduces a novel approach called AmbientCycleGAN that translates mathematical
SOMs to realistic SOMs by use of noisy measurement data. Numerical studies that
consider clustered lumpy background (CLB) models and real mammograms are
conducted. It is demonstrated that our proposed method can stably establish
SOMs based on mathematical models and noisy measurement data. Moreover, the
ability of the proposed AmbientCycleGAN to interpretably control image features
in the synthesized objects is investigated.