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.

翻译：医学成像系统通常通过客观的、任务特定的图像质量度量来评估和优化，这些度量量化了观察者在特定临床相关任务上的表现。贝叶斯理想观察者的性能在所有观察者（无论是数值还是人类）中设定了上限，并被提议作为评估和优化医学成像系统的品质因数。然而，理想观察者的检验统计量对应于似然比，在大多数情况下难以计算。先前提出了一种基于采样的方法，利用马尔可夫链蒙特卡洛技术来估计理想观察者的性能。然而，当前MCMC方法在理想观察者近似中的应用仅限于少数情况，即要成像对象的分布可由相对简单的随机对象模型描述。因此，迫切需要扩展MCMC方法的适用范围，以应对各种需要基于理想观察者评估但尚无相关随机对象模型的场景。本研究描述并评估了一种新的MCMC方法，该方法采用了基于生成对抗网络的随机对象模型，称为MCMC-GAN。MCMC-GAN方法通过使用存在参考解的测试案例进行了定量验证。结果表明，MCMC-GAN方法可以扩展MCMC方法在医学成像系统理想观察者分析中的适用范围。