利用元数据分离MRI图像中的解剖与扫描差异，提升模型泛化能力。
Metadata Supervised MRI Representations for Modelling and Controlling Acquisition Variability
- 联合建模MRI图像与DICOM元数据，解耦解剖结构与扫描参数影响。
- 在大规模临床脑MRI数据上实现对比度与解剖特征的分离表示。
- 适用于跨模态、跨站点图像标准化，适合医学影像领域研究者。
磁共振成像存在显著的采集差异，相同解剖结构在不同扫描仪和协议下外观差异明显。由此导致学习到的表示将生物结构与采集相关外观混杂，限制了可解释性、泛化性和临床应用。本文表明，通过联合建模MRI图像与DICOM元数据，可有效分离这些变化源。基于大规模临床脑MRI数据，我们学习到能区分解剖结构与对比度依赖外观的表示：对比度表示能组织异构采集数据，支持序列理解并检测图像-元数据不一致；解剖表示则抑制采集特异性变异，同时保留生物学相关信息。在此基础上，我们提出一种统一的解剖结构保持型调和模型，支持跨模态与跨站点适应，条件为图像或采集元数据。结果表明，采集变异性是成像过程的结构性成分，可被建模、审计与控制，为大规模医学影像中的采集感知表示学习奠定基础。
原文摘要 · Abstract (English)
Magnetic resonance imaging exhibits substantial acquisition variability, where identical anatomy can appear markedly different across scanners and imaging protocols. Consequently, learned representations entangle biological structure with acquisition-dependent appearance, limiting interpretability, generalisation, and clinical deployment. We show that these sources of variation can be separated by jointly modelling MRI images and DICOM metadata. Using large-scale clinical brain MRI data, we learn representations that separate anatomical structure from contrast-dependent appearance. Resulting contrast representations organise heterogeneous acquisitions, support sequence understanding, and detect image--metadata inconsistencies, whereas anatomical representations suppress acquisition-specific variation while preserving biologically relevant information. Building on these disentangled representations, we introduce a unified anatomy-preserving harmonisation model for cross-modality and cross-site adaptation, conditioned on image or acquisition metadata. Our findings suggest that acquisition variability is a structured component of the imaging process that can be modelled, audited, and controlled, providing a foundation for acquisition-aware representation learning in large-scale medical imaging.
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