用AI根据放疗前影像预测脑胶质瘤患者放疗后MRI，实现实时精准模拟。
Rectified flow-based prediction of post-treatment brain MRI from pre-radiotherapy priors for patients with glioma
- 基于2D修正流模型，融合治疗前MRI与放疗剂量图生成后续影像。
- 生成图像SSIM达0.88，PSNR为22.82，组织分割Dice系数0.91。
- 支持改变治疗参数做反事实模拟，助力个性化放疗规划。
脑肿瘤平均导致20年寿命损失。标准治疗引发复杂脑部结构变化，需通过MRI监测。人工智能（AI）可基于临床数据实现条件多模态图像生成。本研究探索利用条件图像生成技术，从颅内肿瘤患者治疗前影像预测放疗后MRI，以实现对放疗后变化的逼真建模，优化治疗方案。使用公开的SAILOR数据集（25例患者）构建2D修正流模型，以轴向预治疗MRI和放疗剂量图为条件，并引入交叉注意力机制整合时间序列及化疗信息。通过结构相似性指数（SSIM）、峰值信噪比（PSNR）、Dice分数及雅可比行列式进行验证。结果表明，该模型可实时生成任意时间节点的随访MRI，保持语义与视觉保真度；真实与预测图像对比中，SSIM为0.88，PSNR为22.82；基于真实与预测图像的组织分割平均Dice-Sørensen系数（DSC）达0.91。修正流模型推理速度比去噪扩散概率模型（DDPM）快250倍。该方法支持通过调整治疗参数进行反事实模拟，生成预测的形态学变化，具有辅助自适应剂量规划与个体化预后预测的潜力。代码将在同行评审发表后开源：https://github.com/SelenaIHuisman/RF-GlioPREDICT
原文摘要 · Abstract (English)
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
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