用3D扩散模型预测脑胶质瘤放疗后MRI，更精准还原组织变化。
3D-GlioPREDICT: 3D Latent Diffusion for Post-Radiotherapy Brain MRI Prediction in Patients with Glioma
- 基于3D潜空间和剂量分布图生成放疗后脑MRI，支持空间动态建模。
- 在257例患者数据上，图像相似度提升，解剖结构匹配度高。
- 适合放射治疗评估、影像预后研究及临床决策支持场景。
放疗是胶质瘤治疗的核心手段，但其引起的脑组织复杂变化难以预测。仅凭治疗前影像与放疗信息预测这些变化，有助于理解治疗效应并推动基于影像的预后评估。现有方法多基于单张2D切片，且将放疗视为全局参数，缺乏空间动态性。本文提出一种3D潜空间扩散模型，以治疗前影像、随访时间及体素级剂量分布为条件，实现三维体积生成。为降低计算成本，模型结合潜空间压缩与ControlNet空间控制机制。在包含257例扫描的公开数据集上训练与评估，使用均方误差、峰值信噪比、结构相似性指数衡量图像质量，通过脑脊液、灰质、白质分割的Dice分数、海马体积预测误差及基于对数雅可比行列式图的形变分析评估解剖一致性。相比此前2D方法，3D模型在图像相似度上表现更优，同时保持与真实解剖结构和形变模式的良好一致。结果验证了仅用治疗前信息进行3D治疗感知生成建模的可行性。代码已开源。
原文摘要 · Abstract (English)
Radiotherapy is a cornerstone of glioma treatment inducing complex structural changes in brain tissue that are difficult to anticipate. Predicting these changes from pretreatment data could improve understanding of treatment-related effects and support the development of image-based outcome prediction methods. Recent studies have shown that follow-up brain magnetic resonance imaging can be synthesized from baseline imaging and treatment information, but most existing approaches operate on single 2D slices and represent treatment as a global parameter, rather than a spatially dynamic variable. In this work, we address both limitations with a 3D latent diffusion framework that conditions image generation on the spatially resolved voxel-wise dose distribution, alongside a pretreatment image and follow-up time. To make volumetric synthesis computationally feasible, the model combines latent-space compression with ControlNet-based spatial conditioning. The method was trained and evaluated on a public dataset comprising 257 scans from 25 glioma patients. Prediction quality was assessed using mean squared error, peak signal-to-noise ratio, and structural similarity index. Anatomical consistency was further evaluated using Dice scores for cerebrospinal fluid, gray matter, and white matter segmentations, together with hippocampus volume prediction error and deformation analysis based on log Jacobian determinant maps. Compared with our previously proposed 2D approach, the 3D model achieved improved image similarity while maintaining good agreement with ground truth anatomy and deformation patterns. Overall, these results support the feasibility of 3D treatment-aware generative modeling for predicting post-radiotherapy brain MRI using only pretreatment information. Code is available at https://github.com/nordinbelkacemi/fu-pred-3d
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