从噪声数据中直接构建真实人体结构模型，提升医疗影像评估可靠性。
Establishing Stochastic Object Models from Noisy Data via Ambient Measurement-Integrated Diffusion
- 通过测量融合扩散机制，分离并建模噪声耦合关系
- 在真实CT与乳腺摄影数据上生成质量更高、更符合解剖结构的样本
- 适合无需干净数据的临床医疗影像分析场景
基于任务的图像质量评价对医学成像系统至关重要，需考虑包括解剖变异在内的随机性。随机物体模型（SOMs）提供此类变异的统计描述，但传统数学方法难以刻画真实解剖结构，而数据驱动方法通常依赖罕见的干净数据。为此，我们提出无监督的环境测量集成扩散模型AMID，可直接从噪声测量中建立清洁的SOMs。AMID引入测量融合策略，将测量噪声与扩散轨迹对齐，并显式建模各步骤中测量噪声与扩散噪声的耦合关系，据此设计环境损失函数以学习纯净的SOMs。在真实CT和乳腺摄影数据集上的实验表明，AMID在生成保真度上优于现有方法，并带来更可靠的基于任务的图像质量评估，展现出在无监督医学影像分析中的潜力。
原文摘要 · Abstract (English)
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.
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