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.

翻译：旨在生成诊断信息图像的医学成像系统，应通过基于任务的图像质量客观评估进行评价。理想情况下，基于任务的图像质量评估需考虑测量数据中所有随机性来源，包括待成像对象群体的变异性。为满足此需求，可采用能生成合成对象或体模的随机对象模型（SOM）。目前已开发出多种可解释性合成对象的数学SOM或体模，例如团块对象模型和参数化躯干体模。然而，这些纯数学定义的SOM可能无法全面捕捉真实对象的变异性。为建立真实的SOM，理想途径是使用实验数据。近期有研究提出增强生成对抗网络（GAN）——AmbientGAN，用于从医学成像测量数据中建立SOM。但AmbientGAN生成对象的可解释控制程度尚不明确。本研究提出一种名为AmbientCycleGAN的新方法，利用含噪声测量数据将数学SOM转化为真实SOM。针对聚集团块背景模型与真实乳腺X线影像开展数值研究。结果表明，所提方法能基于数学模型和含噪声测量数据稳定建立SOM。此外，还探究了AmbientCycleGAN对合成对象图像特征进行可解释性控制的能力。