Paramagnetic rim lesions (Rim$^+$) identified on susceptibility-sensitive MRI have recently emerged as a specific biomarker of chronic active inflammation in Multiple Sclerosis (MS) and are associated with long-term disability progression. However, susceptibility imaging and expert interpretation remain limited to specialized centers, visual assessment is time-consuming and variable, and the low prevalence of Rim$^+$ lesions poses severe class imbalance challenges for automated analysis. We propose a 3D multimodal deep learning framework for lesion-level Rim$^+$/Rim$^-$ classification from Quantitative Susceptibility Mapping (QSM) and FLAIR MRI. The architecture explicitly models modality asymmetry by treating QSM as the primary susceptibility-driven signal and conditioning it with FLAIR-derived structural context. To improve robustness under limited data, we employ self-supervised multimodal pretraining followed by supervised fine-tuning with contrastive regularization. The method was evaluated on a clinically acquired cohort of 88 people with MS with expert lesion annotations as reference standard. Results highlight improved performance compared to prior architectures, supporting the effectiveness of asymmetric multimodal modeling for automated chronic active lesion identification.

翻译：顺磁性边缘病灶(Rim$^+$)作为磁敏感加权MRI上识别的特异性生物标志物，近期被证实是多发性硬化慢性活动性炎症的特异性指标，并与长期残疾进展密切相关。然而，磁敏感成像技术与专家解读仍局限于专业中心，人工视觉评估耗时且存在主观差异，加之Rim$^+$病灶的低患病率给自动化分析带来了严重的类别不平衡挑战。我们提出了一种基于3D多模态深度学习的框架，用于从定量磁敏感成像和FLAIR MRI中实现病灶级Rim$^+$/Rim$^-$分类。该架构通过将QSM作为主磁敏感驱动信号并以FLAIR衍生的结构特征进行条件调制，显式建模了模态不对称性。为提升有限数据下的鲁棒性，我们采用自监督多模态预训练，随后结合对比正则化进行监督微调。该方法在临床采集的88名MS患者队列中进行了评估，以专家病灶注释为参考标准。结果表明，与既往架构相比性能显著提升，验证了不对称多模态建模在慢性活动性病灶自动识别中的有效性。