MRI Image Generation Based on Text Prompts

TL;DR

This paper demonstrates that fine-tuned Stable Diffusion models can generate realistic MRI images from text prompts, aiding data augmentation and addressing dataset scarcity issues in medical imaging.

Contribution

The study introduces a method for text-guided MRI image generation using a fine-tuned SD model, enhancing image quality and semantic relevance for medical AI applications.

Findings

Improved image quality and semantic consistency with text prompts.

Synthetic images effectively augment training datasets.

Enhanced MRI classification performance with generated data.

Abstract

This study explores the use of text-prompted MRI image generation with the Stable Diffusion (SD) model to address challenges in acquiring real MRI datasets, such as high costs, limited rare case samples, and privacy concerns. The SD model, pre-trained on natural images, was fine-tuned using the 3T fastMRI dataset and the 0.3T M4Raw dataset, with the goal of generating brain T1, T2, and FLAIR images across different magnetic field strengths. The performance of the fine-tuned model was evaluated using quantitative metrics,including Fr\'echet Inception Distance (FID) and Multi-Scale Structural Similarity (MS-SSIM), showing improvements in image quality and semantic consistency with the text prompts. To further evaluate the model's potential, a simple classification task was carried out using a small 0.35T MRI dataset, demonstrating that the synthetic images generated by the fine-tuned SD model can effectively augment training datasets and improve the performance of MRI constrast classification tasks. Overall, our findings suggest that text-prompted MRI image generation is feasible and can serve as a useful tool for medical AI applications.