Metadata Supervised MRI Representations for Modelling and Controlling Acquisition Variability

• URL: http://arxiv.org/abs/2607.11295v1
• Date: Mon, 13 Jul 2026 09:12:23 GMT
• Title: Metadata Supervised MRI Representations for Modelling and Controlling Acquisition Variability
• Abstract summary: learned representations entangle biological structure with acquisition-dependent appearance, limiting interpretability, generalisation, and clinical deployment.<n>We show that these sources of variation can be separated by jointly modelling MRI images and DICOM metadata.<n>Resulting contrast representations organise heterogeneous acquisitions, support sequence understanding, and detect image--metadata inconsistencies.<n>We introduce a unified anatomy-preserving harmonisation model for cross-modality and cross-site adaptation, conditioned on image or acquisition metadata.
• Score: 0.6940876618878119
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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