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 segmen...
| Authors | Moinak Bhattacharya, Annie Singh, Mohammad Chaudhry et al. |
|---|---|
| Primary category | eess.IV |
| Published | 16 October 2026 |
| Last updated at source | 16 October 2026 |
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