Rectified flow-based prediction of post-treatment brain MRI from pre-radiotherapy priors for patients with glioma
This research is significant for glioma patients and clinicians, as it offers a tool for real-time prediction of post-treatment brain changes, potentially aiding in adaptive treatment planning and personalized outcome prediction. It is an incremental step in medical image generation.
This study developed an AI model using rectified flow to predict post-radiotherapy brain MRI from pre-treatment MRI and radiotherapy dose maps in glioma patients. The model achieved a structural similarity index measure (SSIM) of 0.88, a peak signal-to-noise ratio (PSNR) of 22.82, and a mean Dice-Sørensen coefficient (DSC) of 0.91 for tissue segmentations, while also being 250x faster than DDPMs.
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 conditioning was used to incorporate temporal and chemotherapy data. The resulting images were validated with structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), Dice scores and Jacobian determinants. The resulting model generates realistic follow-up MRI for any time point, while integrating treatment information. Comparing real versus predicted images, SSIM is 0.88, and PSNR is 22.82. Tissue segmentations from real versus predicted MRI result in a mean Dice-Sørensen coefficient (DSC) of 0.91. The rectified flow (RF) model enables up to 250x faster inference than Denoising Diffusion Probabilistic Models (DDPM). The proposed model generates realistic follow-up MRI in real-time, preserving both semantic and visual fidelity as confirmed by image quality metrics and tissue segmentations. Conditional generation allows counterfactual simulations by varying treatment parameters, producing predicted morphological changes. This capability has potential to support adaptive treatment dose planning and personalized outcome prediction for patients with intracranial tumors. Code will be available upon peer-reviewed publication at: https://github.com/SelenaIHuisman/RF-GlioPREDICT