GliomaINK: A Tumor and Anatomy Conditioned Flow Matching Framework for 3D MRI Sequence Synthesis in Glioma Patients

Background: Incomplete multiparametric MR examinations limit glioma characterization and downstream AI tools. Generative models can synthesize missing MRI sequences, but anatomical structure and clinically relevant tumor features are challenging to preserve. We propose GliomaINK, an anatomy-guided conditional flow matching framework for cross-sequence 3D brain MRI synthesis. Methods: A 3D SwinUNETR predicted the conditional velocity field while an auxiliary topology-preserving loss encouraged structural consistency. The model was trained on 1,083 patients from the BraTS-AG dataset and independently tested on an in-house clinical cohort. Downstream tasks include image quality, tumor segmentation, IDH1 mutation-status prediction, and overall survival prediction. Two radiologists independently evaluated the anatomical fidelity, pathological representation, and overall image quality of the synthesized images. Results: GliomaINK exhibited the highest PSNR across all six MRI translation directions in both evaluation cohorts. In the BraTS-AG test set, the PSNR reached $25.64\pm0.55$ dB for the T2W-to-T1C synthesis, compared with $13.78\pm0.32$ dB for Pix2Pix and $18.90\pm0.53$ dB for SPADE. For downstream tumor segmentation, the method showed strong performance across tumor subregions with Dice scores reaching $0.928\pm0.014$ for edema, and $0.885\pm0.040$ for enhancing tumor. For the IDH1 mutation-status prediction, GliomaINK achieved the highest AUROC score in four of the six sequence configurations, reaching $0.827\pm0.088$. Conclusion: Anatomy-guided conditional flow matching enables high-fidelity cross-sequence glioma MRI synthesis demonstrating potential for preservation of clinically relevant information in synthesized MRI and improving downstream tasks.