Task-based measures of image quality (IQ) are critical for evaluating medical imaging systems, which must account for randomness including anatomical variability. Stochastic object models (SOMs) provide a statistical description of such variability, but conventional mathematical SOMs fail to capture realistic anatomy, while data-driven approaches typically require clean data rarely available in clinical tasks. To address this challenge, we propose AMID, an unsupervised Ambient Measurement-Integrated Diffusion with noise decoupling, which establishes clean SOMs directly from noisy measurements. AMID introduces a measurement-integrated strategy aligning measurement noise with the diffusion trajectory, and explicitly models coupling between measurement and diffusion noise across steps, an ambient loss is thus designed base on it to learn clean SOMs. Experiments on real CT and mammography datasets show that AMID outperforms existing methods in generation fidelity and yields more reliable task-based IQ evaluation, demonstrating its potential for unsupervised medical imaging analysis.

翻译：基于任务的图像质量评价指标对于评估医学成像系统至关重要，这些指标必须考虑包括解剖结构变异性在内的随机性。随机对象模型为此类变异性提供了统计描述，但传统的数学模型无法捕捉真实的解剖结构，而数据驱动方法通常需要临床任务中难以获得的洁净数据。为应对这一挑战，我们提出AMID——一种具有噪声解耦的无监督环境测量融合扩散方法，可直接从含噪测量数据建立洁净随机对象模型。AMID引入测量融合策略，将测量噪声与扩散轨迹对齐，并显式建模测量噪声与扩散噪声在迭代步骤间的耦合关系，据此设计环境损失函数以学习洁净随机对象模型。在真实CT和乳腺摄影数据集上的实验表明，AMID在生成保真度方面优于现有方法，并能产生更可靠的基于任务的图像质量评估，展现了其在无监督医学影像分析中的应用潜力。