基于整流流的胶质瘤患者放疗前先验信息治疗后脑MRI预测
Rectified flow-based prediction of post-treatment brain MRI from pre-radiotherapy priors for patients with glioma
AI总结：
提出一种基于整流流的条件图像生成模型，利用放疗前MRI和剂量图预测治疗后任意时间点的脑MRI，实现快速推理并保持语义和视觉保真度。
摘要：
脑肿瘤平均导致20年的寿命损失。标准疗法会引起大脑复杂的结构变化，这些变化通过MRI监测。人工智能的最新进展使得从临床数据中进行条件多模态图像生成成为可能。在本研究中，我们通过条件图像生成探索了颅内肿瘤患者随访MRI的AI驱动生成。该方法能够对放疗后变化进行真实建模，从而优化治疗。使用公开的SAILOR数据集（25名患者）创建了一个二维整流流模型，该模型以治疗前MRI轴向切片和RT剂量图为条件。采用交叉注意力条件化来整合时间和化疗数据。通过结构相似性指数（SSIM）、峰值信噪比（PSNR）、Dice分数和雅可比行列式对生成的图像进行验证。所生成的模型能够生成任意时间点的真实随访MRI，同时整合治疗信息。比较真实图像与预测图像，SSIM为0.88，PSNR为22.82。真实与预测MRI的组织分割平均Dice-Sørensen系数（DSC）为0.91。整流流（RF）模型的推理速度比去噪扩散概率模型（DDPM）快250倍。所提出的模型能够实时生成真实的随访MRI，并通过图像质量指标和组织分割确认其保持了语义和视觉保真度。条件生成允许通过改变治疗参数进行反事实模拟，产生预测的形态学变化。该能力有望支持颅内肿瘤患者的适应性治疗剂量规划和个性化预后预测。代码将在同行评审发表后提供：https://github.com/SelenaIHuisman/RF-GlioPREDICT
英文摘要：
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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