Towards Metadata-customized Brain MR image Synthesis for Disease Diagnosis

Motivation: Multi-modal MR images are vital to various disease diagnosis. Lengthy acquisition time limits its clinical applications. MRI synthesis can be an alternative to mitigating this issue. Goal(s): We aim to develop a general multi-modal MRI synthesis model capable of generating metadata-specified brain MR images from acquired scans. Approach: We compile a dataset of 31,407 3D brain MR scans. An MRI-dedicated text encoder is pre-trained to extract features from textual metadata, empowering the MR image synthesis model to precisely yield metadata-specified images. Results: Our generative foundation model provides reliable MRI sequences according to specified scanning parameters, and demonstrates superior generalizability across multi-center data. Impact: Our general multimodal MRI synthesis foundation model is capable of quickly and cost-effectively providing metadata-tailored multiple MR sequences, enabling clinicians and researchers to customize the desired MR images using this convenient AI technology, thereby enhancing diagnostic precision and efficiency.