3D FLAIR MRI is widely recommended as one of the standard MRI sequences for brain imaging in multiple sclerosis (MS), but publicly available MS datasets remain relatively small and vary across scanners, acquisition protocols, and lesion patterns. This scarcity and variability hinder the development of robust neuroimaging machine learning models and are particularly challenging for generative models that aim to synthesize images while preserving small, sparse lesions. We propose Lesion-DDPM, a 3D conditional diffusion framework for lesion-aware FLAIR synthesis that incorporates multi-level anatomical mask injection together with a lesion-weighted reconstruction loss to emphasize lesion voxels while maintaining global brain structure. Using a curated subset of the MSLesSeg dataset, we compare Lesion-DDPM with representative state-of-the-art GAN- and diffusion-based models, assessing both image-generation metrics and downstream 3D U-Net segmentation. In our experiments, Lesion-DDPM achieved the lowest lesion-region reconstruction error among all methods. In a downstream 3D U-Net lesion segmentation task, a model trained only on Lesion-DDPM-generated scans and evaluated on real MRIs reached a Dice score of 0.616 compared with 0.569 for the best competing synthetic dataset. When Lesion-DDPM images were added to the real training set, the Dice score further increased to 0.685.

翻译：[摘要] 3D FLAIR MRI被广泛推荐为多发性硬化症（MS）脑部成像的标准MRI序列之一，但公开可用的MS数据集规模相对较小，且在不同扫描仪、采集协议和病灶模式间存在差异。这种稀缺性和异质性阻碍了稳健的神经影像机器学习模型的发展，尤其对旨在合成图像时保留稀疏微小病灶的生成模型构成了挑战。我们提出Lesion-DDPM，一种用于病灶感知FLAIR合成的3D条件扩散框架，该框架融合了多层级解剖掩膜注入与病灶加权重建损失，以在保持整体大脑结构的同时强调病灶体素。利用MSLesSeg数据集的一个精选子集，我们将Lesion-DDPM与代表性的最先进GAN和扩散模型进行比较，评估了图像生成指标与下游3D U-Net分割性能。实验中，Lesion-DDPM在所有方法中实现了最低的病灶区域重建误差。在下游3D U-Net病灶分割任务中，仅基于Lesion-DDPM生成扫描训练的模型在真实MRI上评估时达到0.616的Dice分数，而最佳竞争合成数据集为0.569。当Lesion-DDPM图像加入真实训练集后，Dice分数进一步升至0.685。