Rectified flow-based prediction of post-treatment brain MRI from pre-radiotherapy priors for patients with glioma
Brain tumors result in 20 years of lost life on average. Standard therapies induce complex structural changes in the brain that are monitored through MRI. Recent developments in artificial intelligence (AI) enable conditional multimodal image generation from clinical data. In this study, we investigate AI-driven generation of follow-up MRI in patients with intracranial tumors through conditional image generation. This approach enables realistic modeling of post-radiotherapy changes, allowing for treatment optimization. The public SAILOR dataset of 25 patients was used to create a 2D rectified flow model conditioned on axial slices of pre-treatment MRI and RT dose maps. Cross-attention con...
| Authors | Selena Huisman, Nordin Belkacemi, Vera C. Keil et al. |
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| Primary category | eess.IV |
| Published | 9 March 2026 |
| Last updated at source | 29 May 2026 |
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