Metadata-Aligned 3D MRI Representations for Contrast Understanding and Quality Control

2025-11-01

AI Summary

MRI data exhibit substantial heterogeneity across scanners, acquisition protocols, and institutions, compounded by the absence of standardized contrast labels—severely hindering large-scale automated analysis. To address this, we propose MR-CLIP, the first framework enabling joint embedding and alignment of 3D MRI volumes with DICOM metadata, thereby establishing a unified, generalizable contrast representation space. Leveraging metadata as weak supervision, MR-CLIP performs sequence identification, image standardization, and quality control without manual annotations. In few-shot settings, it surpasses fully supervised baselines in sequence classification accuracy. Moreover, unsupervised detection of metadata inconsistencies is achieved via distance metrics in the shared embedding space. This work introduces a scalable, highly robust foundational representation paradigm for multi-center MRI analysis, advancing interoperability and generalizability in neuroimaging AI.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multi-instance/Multi-view Learning
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Web Mining and Content Analysis: Large pretrained models with web data
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

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.

Problem

• Addressing MRI data heterogeneity and missing contrast labels
• Creating unified MRI contrast representations using metadata alignment
• Enabling automated sequence recognition and quality control

Innovation

• Aligns volumetric MRI images with DICOM metadata
• Learns unified MRI contrast representations for classification
• Enables unsupervised quality control via embedding distances