标题： 比较基于条件扩散模型的对比增强乳腺MRI合成方法 显示英文标题

标题： Comparing Conditional Diffusion Models for Synthesizing Contrast-Enhanced Breast MRI from Pre-Contrast Images

摘要： 动态对比增强（DCE）MRI对于乳腺癌的诊断和治疗至关重要。 然而，其依赖对比剂带来了安全问题、禁忌症、成本增加和工作流程复杂性。 为此，我们提出了预对比条件去噪扩散概率模型来合成DCE-MRI，在单侧乳腺和全乳腺设置中总共引入、评估和比较了22种生成模型变体。 为了提高病灶保真度，我们引入了肿瘤感知损失函数和显式的肿瘤分割掩码条件。 使用公开的多中心数据集并与相应的预对比基线进行比较，我们观察到基于减影图像的模型在五个互补评估指标上始终优于基于对比后的模型。 除了评估整个图像外，我们还分别评估了感兴趣区域，其中肿瘤感知损失和分割掩码输入都提高了评估指标。 后者显著增强了定性结果，捕捉对比剂的吸收情况，尽管假设可以获得肿瘤定位输入，而在筛查环境中这些输入并不一定可用。 一项涉及2名放射科医生和4名MRI技术员的读者研究证实了合成图像的高真实性，表明生成性增强对比具有潜在的临床应用前景。 我们在https://github.com/sebastibar/conditional-diffusion-breast-MRI共享我们的代码库。 显示英文摘要

摘要： Dynamic contrast-enhanced (DCE) MRI is essential for breast cancer diagnosis and treatment. However, its reliance on contrast agents introduces safety concerns, contraindications, increased cost, and workflow complexity. To this end, we present pre-contrast conditioned denoising diffusion probabilistic models to synthesize DCE-MRI, introducing, evaluating, and comparing a total of 22 generative model variants in both single-breast and full breast settings. Towards enhancing lesion fidelity, we introduce both tumor-aware loss functions and explicit tumor segmentation mask conditioning. Using a public multicenter dataset and comparing to respective pre-contrast baselines, we observe that subtraction image-based models consistently outperform post-contrast-based models across five complementary evaluation metrics. Apart from assessing the entire image, we also separately evaluate the region of interest, where both tumor-aware losses and segmentation mask inputs improve evaluation metrics. The latter notably enhance qualitative results capturing contrast uptake, albeit assuming access to tumor localization inputs that are not guaranteed to be available in screening settings. A reader study involving 2 radiologists and 4 MRI technologists confirms the high realism of the synthetic images, indicating an emerging clinical potential of generative contrast-enhancement. We share our codebase at https://github.com/sebastibar/conditional-diffusion-breast-MRI.