Toward general text-guided multimodal brain MRI synthesis for diagnosis and medical image analysis.

ABSTRACT: Multimodal brain magnetic resonance imaging (MRI) offers complementary insights into brain structure and function, thereby improving the diagnostic accuracy of neurological disorders and advancing brain-related research. However, the widespread applicability of MRI is substantially limited by restricted scanner accessibility and prolonged acquisition times. Here, we present TUMSyn, a text-guided universal MRI synthesis model capable of generating brain MRI specified by textual imaging metadata from routinely acquired scans. We ensure the reliability of TUMSyn by constructing a brain MRI database comprising 31,407 3D images across 7 MRI modalities from 13 worldwide centers and pre-training an MRI-specific text encoder to process text prompts effectively. Experiments on diverse datasets and

SUBMITTER: Wang Y 

PROVIDER: S-EPMC12208323 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature