用生物物理模型生成胶质母细胞瘤生长的3D MRI，更真实地预测肿瘤进展。
TumorFlow: Physics-Guided Longitudinal MRI Synthesis of Glioblastoma Growth
- 结合生物物理模型与生成网络，从连续肿瘤浓度场合成3D MRI。
- 纵向外推时肿瘤轮廓与真实模型保持75%的Dice重合度，周围组织PSNR稳定在25。
- 适合神经肿瘤学研究者用于个性化治疗模拟和合成数据生成。
胶质母细胞瘤具有多样、浸润性和患者特异性的生长模式，常规MRI仅部分显示其真实范围，难以可靠评估肿瘤实际扩展并实现个性化治疗规划与随访。本文提出一种生物物理引导的生成框架，从估计的空间连续肿瘤浓度场合成生物学真实的3D脑部MRI体积。该方法将生成模型与肿瘤浸润图结合，可通过生物物理生长模型进行时间传播，实现对肿瘤形状和生长的细粒度控制，同时保留患者解剖结构。由此可在真实患者空间中直接合成一致的肿瘤生长轨迹，提供超越影像显性观察的可解释、可控的肿瘤浸润与进展估计。我们在纵向胶质母细胞瘤病例上评估该框架，结果表明其能生成时间连贯的序列，呈现肿瘤外观及周围组织反应的真实变化。结果表明，将机制性肿瘤生长先验与现代生成建模结合，可为个体化进展可视化提供实用工具，并生成受控的合成数据以支持下游神经肿瘤学工作流。在纵向外推中，我们实现了与生物物理模型75%的稳定Dice重合度，同时保持周围组织恒定的PSNR为25。代码已开源：https://github.com/valentin-biller/lgm.git
原文摘要 · Abstract (English)
Glioblastoma exhibits diverse, infiltrative, and patient-specific growth patterns that are only partially visible on routine MRI, making it difficult to reliably assess true tumor extent and personalize treatment planning and follow-up. We present a biophysically-conditioned generative framework that synthesizes biologically realistic 3D brain MRI volumes from estimated, spatially continuous tumor-concentration fields. Our approach combines a generative model with tumor-infiltration maps that can be propagated through time using a biophysical growth model, enabling fine-grained control over tumor shape and growth while preserving patient anatomy. This enables us to synthesize consistent tumor growth trajectories directly in the space of real patients, providing interpretable, controllable estimation of tumor infiltration and progression beyond what is explicitly observed in imaging. We evaluate the framework on longitudinal glioblastoma cases and demonstrate that it can generate temporally coherent sequences with realistic changes in tumor appearance and surrounding tissue response. These results suggest that integrating mechanistic tumor growth priors with modern generative modeling can provide a practical tool for patient-specific progression visualization and for generating controlled synthetic data to support downstream neuro-oncology workflows. In longitudinal extrapolation, we achieve a consistent 75% Dice overlap with the biophysical model while maintaining a constant PSNR of 25 in the surrounding tissue. Our code is available at: https://github.com/valentin-biller/lgm.git
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