# Metadata-Aware Adaptation of a Generative Foundation Model for Conditional CMR Synthesis

arXiv: https://arxiv.org/abs/2608.24342 · HTML: https://mlsanity.com/paper/2608.24342/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 25 Aug 2026.

## Builds on (most cited first, 12 of 12)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Brain Imaging Generation with Latent Diffusion Models](https://mlsanity.com/paper/2209.07162/) (2022)
- [Effectively Unbiased FID and Inception Score and where to find them](https://mlsanity.com/paper/1911.07023/) (2019)
- [Generative models improve fairness of medical classifiers under distribution shifts](https://mlsanity.com/paper/2304.09218/) (2023)
- [Overcoming Barriers to Data Sharing with Medical Image Generation: A Comprehensive Evaluation](https://mlsanity.com/paper/2012.03769/) (2020)
- [Accelerating Minibatch Stochastic Gradient Descent using Stratified Sampling](https://mlsanity.com/paper/1405.3080/) (2014)
- [Feature robustness and sex differences in medical imaging: a case study in MRI-based Alzheimer's disease detection](https://mlsanity.com/paper/2204.01737/) (2022)
- [Fr'echet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging Datasets](https://mlsanity.com/paper/2412.01496/) (2024)
- [A Generalizable Deep Learning System for Cardiac MRI](https://mlsanity.com/paper/2312.00357/) (2023)
- [BatchSampler: Sampling Mini-Batches for Contrastive Learning in Vision, Language, and Graphs](https://mlsanity.com/paper/2306.03355/) (2023)
- [Fairness-Aware Data Augmentation for Cardiac MRI using Text-Conditioned Diffusion Models](https://mlsanity.com/paper/2403.19508/) (2024)

## Abstract (arXiv)

Synthetic image generation is a promising strategy to address data scarcity and the underrepresentation of clinically important phenotypes in medical imaging, yet generating images that faithfully reflect meaningful patient characteristics remains challenging. In this work, we investigate metadata-conditioned cardiac magnetic resonance (CMR) synthesis using a pretrained latent diffusion model, encoding structured clinical metadata and slice position as textual prompts to guide CMR generation. To improve metadata adherence and address the imbalance of clinical attributes, we integrate three strategies: Metadata-Free Classifier-Free Guidance (CFG), Contrastive Batching, and Inverse-Frequency Sampling. The framework was fine-tuned and evaluated on 59,058 short-axis CMR from the UK Biobank using paired image similarity, distributional fidelity, and subgroup-level analyses. The combined approach achieved a Fr'echet Inception Distance (FID) of 37.47, improving by 57.04% over the same model fine-tuned without these strategies and by 28.68% over a previous text-conditioned CMR diffusion baseline requiring cardiac geometry as additional input, while relying solely on patient metadata. This distributional gain, driven mainly by Metadata-Free CFG, came with a modest reduction in paired similarity, suggesting that the model prioritizes population-level realism over exact image reproduction. Subgroup analyses demonstrated improved alignment across demographic and acquisition-related metadata, with disease-specific conditioning being the most challenging task. These findings demonstrate the potential of generative foundation models for clinically meaningful CMR synthesis while highlighting the need for more effective metadata-aware conditioning strategies. Our code is available at https://github.com/rodriguezmarc/conditional-cmr.