Towards General Text-guided Image Synthesis for Customized Multimodal Brain MRI Generation

TL;DR

This paper introduces TUMSyn, a versatile text-guided model capable of synthesizing diverse brain MRI images from routine scans, enhancing clinical diagnosis and research with improved accuracy and flexibility.

Contribution

The paper presents TUMSyn, a novel universal MRI synthesis model guided by text prompts, trained on a large multi-center dataset, enabling high-quality, customizable brain MRI generation.

Findings

TUMSyn generates clinically meaningful MR images with specified metadata.

It performs well in supervised and zero-shot scenarios.

Physician assessments confirm the quality and utility of generated images.

Abstract

Multimodal brain magnetic resonance (MR) imaging is indispensable in neuroscience and neurology. However, due to the accessibility of MRI scanners and their lengthy acquisition time, multimodal MR images are not commonly available. Current MR image synthesis approaches are typically trained on independent datasets for specific tasks, leading to suboptimal performance when applied to novel datasets and tasks. Here, we present TUMSyn, a Text-guided Universal MR image Synthesis generalist model, which can flexibly generate brain MR images with demanded imaging metadata from routinely acquired scans guided by text prompts. To ensure TUMSyn's image synthesis precision, versatility, and generalizability, we first construct a brain MR database comprising 31,407 3D images with 7 MRI modalities from 13 centers. We then pre-train an MRI-specific text encoder using contrastive learning to effectively control MR image synthesis based on text prompts. Extensive experiments on diverse datasets and physician assessments indicate that TUMSyn can generate clinically meaningful MR images with specified imaging metadata in supervised and zero-shot scenarios. Therefore, TUMSyn can be utilized along with acquired MR scan(s) to facilitate large-scale MRI-based screening and diagnosis of brain diseases.