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：基于任务的质量评估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变异在内的随机性。随机目标模型提供了此类变异性的统计描述，但传统数学模型无法捕捉真实解剖结构，而数据驱动方法通常需要临床任务中难以获得的干净数据。为应对这一挑战，我们提出AMID方法——一种无监督的、集成了噪声解耦的环境测量集成扩散模型，能够直接从噪声测量数据中建立干净随机目标模型。AMID引入测量集成策略，将测量噪声与扩散轨迹对齐，并显式建模各步间测量噪声与扩散噪声的耦合，据此设计环境损失函数以学习干净随机目标模型。在真实CT和乳腺X线摄影数据集上的实验表明，AMID在生成保真度上优于现有方法，能够提供更可靠的任务驱动型图像质量评估，展现了其在无监督医学图像分析领域的应用潜力。