标题： 扩散盲区：使用扩散模型的子宫MRI合成 显示英文标题

标题： Diffusing the Blind Spot: Uterine MRI Synthesis with Diffusion Models

摘要： 尽管在生成建模方面取得了显著进展，现有的扩散模型往往难以生成解剖结构精确的女性骨盆图像，这限制了它们在妇科影像中的应用，其中数据稀缺性和患者隐私问题至关重要。 为了克服这些障碍，我们引入了一种基于扩散的新框架，用于子宫MRI合成，结合了无条件和条件去噪扩散概率模型（DDPMs）以及二维和三维潜在扩散模型（LDMs）。 我们的方法生成解剖结构一致、高保真度的合成图像，能够紧密模仿真实扫描，并为训练稳健的诊断模型提供有价值的资源。 我们使用先进的感知和分布度量来评估生成质量，并与标准重建方法进行基准测试，在关键分类任务中展示了诊断准确性的显著提升。 盲法专家评估进一步验证了我们合成图像的临床真实性。 我们发布了带有隐私保护措施的模型和一个全面的合成子宫MRI数据集，以支持可重复的研究并推动妇科领域公平的人工智能发展。 显示英文摘要

摘要： Despite significant progress in generative modelling, existing diffusion models often struggle to produce anatomically precise female pelvic images, limiting their application in gynaecological imaging, where data scarcity and patient privacy concerns are critical. To overcome these barriers, we introduce a novel diffusion-based framework for uterine MRI synthesis, integrating both unconditional and conditioned Denoising Diffusion Probabilistic Models (DDPMs) and Latent Diffusion Models (LDMs) in 2D and 3D. Our approach generates anatomically coherent, high fidelity synthetic images that closely mimic real scans and provide valuable resources for training robust diagnostic models. We evaluate generative quality using advanced perceptual and distributional metrics, benchmarking against standard reconstruction methods, and demonstrate substantial gains in diagnostic accuracy on a key classification task. A blinded expert evaluation further validates the clinical realism of our synthetic images. We release our models with privacy safeguards and a comprehensive synthetic uterine MRI dataset to support reproducible research and advance equitable AI in gynaecology.