# ContextMRI: Enhancing Compressed Sensing MRI through Metadata Conditioning

arXiv: https://arxiv.org/abs/2501.04284 · HTML: https://mlsanity.com/paper/2501.04284/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- First posted on arXiv 8 Jan 2025.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Built on by (most cited first, 9 of 9)

- [Diffusion models for inverse problems](https://mlsanity.com/paper/2508.01975/) (2025)
- [MR-CLIP: Efficient Metadata-Guided Learning of MRI Contrast Representations](https://mlsanity.com/paper/2507.00043/) (2025)
- [Metadata-Aligned 3D MRI Representations for Contrast Understanding and Quality Control](https://mlsanity.com/paper/2511.00681/) (2025)
- [Diffusion Probabilistic Generative Models for Accelerated, in-NICU Permanent Magnet Neonatal MRI](https://mlsanity.com/paper/2505.15984/) (2025)
- [Inference-Time Search Using Side Information for Diffusion-Based Image Reconstruction](https://mlsanity.com/paper/2510.03352/) (2025)
- [Metadata Supervised Imaging Representations for Modelling and Controlling Acquisition Variability](https://mlsanity.com/paper/2607.11295/) (2026)
- [Clinical Metadata Guided Limited-Angle CT Image Reconstruction](https://mlsanity.com/paper/2509.01752/) (2025)
- [Predicting before Reconstruction: A generative prior framework for MRI acceleration](https://mlsanity.com/paper/2510.19472/) (2025)
- [Active Learning for Conditional Generative Compressed Sensing](https://mlsanity.com/paper/2605.05435/) (2026)

## Abstract (arXiv)

Compressed sensing MRI seeks to accelerate MRI acquisition processes by sampling fewer k-space measurements and then reconstructing the missing data algorithmically. The success of these approaches often relies on strong priors or learned statistical models. While recent diffusion model-based priors have shown great potential, previous methods typically ignore clinically available metadata (e.g. patient demographics, imaging parameters, slice-specific information). In practice, metadata contains meaningful cues about the anatomy and acquisition protocol, suggesting it could further constrain the reconstruction problem. In this work, we propose ContextMRI, a text-conditioned diffusion model for MRI that integrates granular metadata into the reconstruction process. We train a pixel-space diffusion model directly on minimally processed, complex-valued MRI images. During inference, metadata is converted into a structured text prompt and fed to the model via CLIP text embeddings. By conditioning the prior on metadata, we unlock more accurate reconstructions and show consistent gains across multiple datasets, acceleration factors, and undersampling patterns. Our experiments demonstrate that increasing the fidelity of metadata, ranging from slice location and contrast to patient age, sex, and pathology, systematically boosts reconstruction performance. This work highlights the untapped potential of leveraging clinical context for inverse problems and opens a new direction for metadata-driven MRI reconstruction.