Medical imaging systems are often evaluated and optimized via objective, or
task-specific, measures of image quality (IQ) that quantify the performance of
an observer on a specific clinically-relevant task. The performance of the
Bayesian Ideal Observer (IO) sets an upper limit among all observers, numerical
or human, and has been advocated for use as a figure-of-merit (FOM) for
evaluating and optimizing medical imaging systems. However, the IO test
statistic corresponds to the likelihood ratio that is intractable to compute in
the majority of cases. A sampling-based method that employs Markov-Chain Monte
Carlo (MCMC) techniques was previously proposed to estimate the IO performance.
However, current applications of MCMC methods for IO approximation have been
limited to a small number of situations where the considered distribution of
to-be-imaged objects can be described by a relatively simple stochastic object
model (SOM). As such, there remains an important need to extend the domain of
applicability of MCMC methods to address a large variety of scenarios where
IO-based assessments are needed but the associated SOMs have not been
available. In this study, a novel MCMC method that employs a generative
adversarial network (GAN)-based SOM, referred to as MCMC-GAN, is described and
evaluated. The MCMC-GAN method was quantitatively validated by use of
test-cases for which reference solutions were available. The results
demonstrate that the MCMC-GAN method can extend the domain of applicability of
MCMC methods for conducting IO analyses of medical imaging systems.

通过使用生成对抗网络（GAN）模型，提出了一种新的基于随机对象模型（SOM）的基于马尔可夫链蒙特卡罗（MCMC）的理想观察者（IO）方法，扩展了 IO 评估的适用领域，从而在医学图像系统的 IO 分析方面发挥了重要作用。