# MR-CLIP: Efficient Metadata-Guided Learning of MRI Contrast Representations

arXiv: https://arxiv.org/abs/2507.00043 · HTML: https://mlsanity.com/paper/2507.00043/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 23 Jun 2025.

## Builds on (most cited first, 10 of 10)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [MedCLIP: Contrastive Learning from Unpaired Medical Images and Text](https://mlsanity.com/paper/2210.10163/) (2022)
- [SynthStrip: Skull-Stripping for Any Brain Image](https://mlsanity.com/paper/2203.09974/) (2022)
- [Representation Disentanglement for Multi-modal brain MR Analysis](https://mlsanity.com/paper/2102.11456/) (2021)
- [HACA3: A Unified Approach for Multi-site MR Image Harmonization](https://mlsanity.com/paper/2212.06065/) (2022)
- [Metadata-enhanced contrastive learning from retinal optical coherence tomography images](https://mlsanity.com/paper/2208.02529/) (2022)
- [ContextMRI: Enhancing Compressed Sensing MRI through Metadata Conditioning](https://mlsanity.com/paper/2501.04284/) (2025)
- [Towards General Text-guided Image Synthesis for Customized Multimodal Brain MRI Generation](https://mlsanity.com/paper/2409.16818/) (2024)

## Built on by (most cited first, 4 of 4)

- [Metadata-Aligned 3D MRI Representations for Contrast Understanding and Quality Control](https://mlsanity.com/paper/2511.00681/) (2025)
- [DIST-CLIP: Arbitrary Metadata and Image Guided MRI Harmonization via Disentangled Anatomy-Contrast Representations](https://mlsanity.com/paper/2512.07674/) (2025)
- [Understanding Sources of Demographic Predictability in Brain MRI via Disentangling Anatomy and Contrast](https://mlsanity.com/paper/2603.04113/) (2026)
- [Med-RADIO: Reducing All Medical Domains Into One via Multi-Teacher Distillation](https://mlsanity.com/paper/2609.37682/) (2026)

## Abstract (arXiv)

Accurate interpretation of Magnetic Resonance Imaging scans in clinical systems is based on a precise understanding of image contrast. This contrast is primarily governed by acquisition parameters, such as echo time and repetition time, which are stored in the DICOM metadata. To simplify contrast identification, broad labels such as T1-weighted or T2-weighted are commonly used, but these offer only a coarse approximation of the underlying acquisition settings. In many real-world datasets, such labels are entirely missing, leaving raw acquisition parameters as the only indicators of contrast. Adding to this challenge, the available metadata is often incomplete, noisy, or inconsistent. The lack of reliable and standardized metadata complicates tasks such as image interpretation, retrieval, and integration into clinical workflows. Furthermore, robust contrast-aware representations are essential to enable more advanced clinical applications, such as achieving modality-invariant representations and data harmonization. To address these challenges, we propose MR-CLIP, a multimodal contrastive learning framework that aligns MR images with their DICOM metadata to learn contrast-aware representations, without relying on manual labels. Trained on a diverse clinical dataset that spans various scanners and protocols, MR-CLIP captures contrast variations across acquisitions and within scans, enabling anatomy-invariant representations. We demonstrate its effectiveness in cross-modal retrieval and contrast classification, highlighting its scalability and potential for further clinical applications. The code and weights are publicly available at https://github.com/myigitavci/MR-CLIP.