用双模态深度学习自动识别多发性硬化症中慢性活跃病灶。
3D Classification of Paramagnetic Rim Lesions in Multiple Sclerosis via Asymmetric QSM-FLAIR Modeling
- 基于QSM和FLAIR图像，设计不对称融合模型捕捉磁敏感信号与结构信息。
- 在88名患者数据上达到优于以往方法的分类准确率，克服数据稀缺挑战。
- 适合神经影像分析、疾病进展预测等临床研究场景使用。
通过敏感磁共振成像识别出的顺磁性环形病灶（Rim⁺）是多发性硬化症中慢性活动性炎症的特异性生物标志物，与长期残疾进展相关。然而，磁敏感成像及专家判读仍局限于专业中心，人工评估耗时且差异大，且Rim⁺病灶罕见导致自动化分析面临严重类别不平衡问题。本文提出一种3D多模态深度学习框架，用于从定量磁化率图（QSM）和FLAIR MRI中进行病灶级Rim⁺/Rim⁻分类。该架构通过将QSM作为主要磁敏感信号，结合FLAIR提供的结构上下文来显式建模模态不对称性。为提升小样本下的鲁棒性，采用自监督多模态预训练后接对比正则化的有监督微调。在包含88名多发性硬化患者、经专家标注的临床队列上验证，结果表明性能优于先前架构，证实了不对称多模态建模在自动识别慢性活跃病灶中的有效性。
原文摘要 · Abstract (English)
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.
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