Learning stochastic object models from medical imaging measurements by use of advanced ambient generative adversarial networks

TL;DR

This paper introduces a GAN-based approach using AmbientGANs with advanced training to create stochastic object models from medical imaging data, effectively handling noise and incomplete measurements for realistic simulations.

Contribution

The paper proposes a novel AmbientGAN training procedure for deriving stochastic object models directly from medical imaging measurements, validated with MRI data.

Findings

The method generates clean images despite measurement noise.

It reliably learns object measurement distributions with incomplete data.

Visual and quantitative validations confirm the approach's effectiveness.

Abstract

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.