发表机构
University College London; deepc GMBH（伦敦大学学院; 深度c有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究磁共振成像采集变异性问题，通过联合建模MRI图像与DICOM元数据分离变异源，学习到解纠缠表示，在此基础上引入统一协调模型，为采集感知表示学习奠定基础。
AI 中文摘要
磁共振成像存在显著的采集变异性，相同解剖结构在不同扫描仪和成像协议下可能呈现明显差异。因此，学习到的表示会将生物结构与采集相关外观纠缠在一起，限制了解释性、泛化能力和临床应用。我们表明，通过联合对MRI图像和DICOM元数据进行建模，可以分离这些变异源。利用大规模临床脑MRI数据，我们学习到将解剖结构与对比度相关外观分离的表示。所得的对比度表示可组织异构采集、支持序列理解并检测图像 - 元数据不一致，而解剖表示在保留生物学相关信息的同时抑制采集特定变异。基于这些解纠缠的表示，我们引入了一个统一的保留解剖结构的协调模型，用于跨模态和跨站点适应，以图像或采集元数据为条件。我们的研究结果表明，采集变异性是成像过程的一个结构化组成部分，可以进行建模、审核和控制，为大规模医学成像中的采集感知表示学习奠定了基础。
英文摘要
Biomedical imaging data exhibit substantial acquisition variability, where identical biological structures can appear markedly different due to differences in imaging devices, acquisition protocols, sites, and reconstruction settings. Consequently, learned representations often entangle underlying biological information with acquisition-dependent appearance, limiting interpretability, generalisation, and clinical deployment. We show that these sources of variation can be disentangled by jointly modelling medical images and acquisition metadata. Using large-scale clinical brain MRI data as a case study, we learn representations that disentangle anatomical structure from contrast-dependent appearance. The resulting framework enables the organisation of heterogeneous imaging protocols, sequence understanding, the detection of image-metadata inconsistencies and imaging artifacts, while preserving biologically relevant anatomical features across diverse acquisitions. Building on these disentangled representations, it further supports generative and translational capabilities, performing both metadata-conditioned synthesis of realistic 3D brain MRIs and anatomy-preserving harmonisation for cross-modality and cross-site adaptation. Our findings demonstrate that acquisition variability is a structured component of the imaging process that can be modeled, audited, synthesised, and controlled, establishing a foundation for acquisition-aware representation learning in large-scale biomedical imaging.
