用扫描元数据训练3D MRI表示，实现自动对比度识别与质量检测。
Metadata-Aligned 3D MRI Representations for Contrast Understanding and Quality Control
- 利用DICOM元数据引导图像表征学习，对齐三维MRI与采集参数。
- 少样本下序列分类性能超越监督基线，且能无监督发现异常元数据。
- 适合需要低标注成本的医学影像分析与多中心数据统一处理的研究者。
磁共振成像存在显著的数据异质性，且不同扫描仪、协议和机构间缺乏标准化的对比度标签，严重制约了大规模自动化分析。建立统一的MRI对比度表征可支持自动序列识别、数据调和与质量控制，而无需依赖人工标注。为此，我们提出MR-CLIP框架，通过将体积分层图像与对应的DICOM采集参数对齐，学习MRI对比度表征。生成的嵌入在序列聚类中表现出明显分离，且在数据稀缺条件下，少样本序列分类性能优于监督型3D基线模型。此外，MR-CLIP可通过图像-元数据嵌入距离实现无监督数据质量控制，识别损坏或不一致的元数据。通过将常规获取的采集元数据转化为监督信号，MR-CLIP为跨多种临床数据集的高效标注分析提供了可扩展基础。
原文摘要 · Abstract (English)
Magnetic Resonance Imaging suffers from substantial data heterogeneity and the absence of standardized contrast labels across scanners, protocols, and institutions, which severely limits large-scale automated analysis. A unified representation of MRI contrast would enable a wide range of downstream utilities, from automatic sequence recognition to harmonization and quality control, without relying on manual annotations. To this end, we introduce MR-CLIP, a metadata-guided framework that learns MRI contrast representations by aligning volumetric images with their DICOM acquisition parameters. The resulting embeddings shows distinct clusters of MRI sequences and outperform supervised 3D baselines under data scarcity in few-shot sequence classification. Moreover, MR-CLIP enables unsupervised data quality control by identifying corrupted or inconsistent metadata through image-metadata embedding distances. By transforming routinely available acquisition metadata into a supervisory signal, MR-CLIP provides a scalable foundation for label-efficient MRI analysis across diverse clinical datasets.
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