Learning Stochastic Object Models Using Ambient Adversarial Diffusion Models

Computational simulation plays an important role in the design and optimization of medical imaging systems. It is important to employ objective measures of image quality (IQ) for such purposes, but computing them requires that all sources of randomness in the measured data must be account for, including variations within the objects to-be-imaged. A stochastic object model (SOM) should be established that describes clinically realistic textures and anatomical variations. Ambient generative adversarial networks (GANs) have been explored to establish SOMs from experimental data but face limitations in representing variations in object properties due to mode collapse and premature convergence. This study proposes the Ambient Adversarial Diffusion Model (AADM), a novel ambient generative model inspired by the Adversarial Diffusion Model (ADM) and Ambient GAN frameworks. The AADM is designed to establish more advanced and comprehensive SOMs from noisy, indirect measurement data than previous AmbientGAN-based SOMs. Numerical experiments demonstrate that the performance of AADM is comparable to that of a non-ambient adversarial diffusion model that is training directly on the distribution of objects. The presented study demonstrates the significant potential to learn the distribution of realistic objects from noisy imaging measurements.