AI 中文总结
该研究针对卒中梗死体积预测的临床痛点，提出首个从急性MRI参数化Fisher-KPP反应-扩散PDE的影像驱动框架，在ISLES 2017数据集上验证其性能优于传统rCBF阈值法，兼具生理可解释性，推动卒中预测从纯数据驱动转向患者特异性生物物理预测。
AI 中文摘要
从急性影像预测最终缺血性梗死体积是个性化卒中管理的核心环节，但当前策略在不可解释的机器学习架构与过于详细、难以在临床影像场景中应用的电生理模型之间存在两极分化。我们通过引入首个直接从临床急性MRI参数化Fisher-KPP反应-扩散偏微分方程（PDE）的影像驱动框架，弥合这一临床差距。连续状态变量$u(\boldsymbol{x},t)\u2208[0,1]$模拟组织损伤，捕捉离子应力通过细胞外空间的向前扩展，以及在基线灌注缺陷（$T_{\text{max}}>6$秒）内的局部细胞死亡代谢决定。通过 oracle 框架在ISLES 2017数据集的一个子集（$N=29$）上评估该范式，结果显示，纳入生物物理传播约束的平均Dice评分为$0.46\pm0.24$，而标准rCBF阈值法的评分为$0.25\pm0.21$。大量消融实验表明，对源自临床灌注图的空间异质性扩散场进行建模可准确捕捉半暗带扩展，同时提供完整的生理可解释性。这一概念验证表明，第一性原理物理学可直接在临床扫描网格上捕捉复杂的缺血进展，将范式从纯数据驱动模型转向患者特异性生物物理预测。
英文摘要
Predicting final ischemic infarct volumes from acute imaging is a cornerstone of personalized stroke management, yet current strategies remain polarized between uninterpretable machine learning architectures and overly detailed electrophysiological models that are intractable in clinical imaging settings. We bridge this clinical gap by introducing the first imaging-driven framework that parameterizes a Fisher-KPP reaction-diffusion partial differential equation (PDE) directly from clinical acute MRI. The continuous state variable $u(\mathbf{x},t)\in[0,1]$ models tissue damage, capturing the forward expansion of ionic stress through the extracellular space alongside a localized metabolic commitment to cell death gated within the baseline perfusion deficit ($T_{\max}>6$\,s). Evaluating this paradigm on a subset of the ISLES 2017 dataset ($N=29$) via an oracle framework reveals that incorporating biophysical propagation constraints yields a mean Dice score of $0.46 \pm 0.24$, compared to standard rCBF thresholding ($0.25 \pm 0.21$). Extensive ablations demonstrate that modeling spatially heterogeneous diffusion fields derived from clinical perfusion maps accurately captures penumbral expansion, while providing full physiological interpretability. This proof-of-concept establishes that first-principles physics could capture complex ischemic progression directly on clinical scan grids, shifting the paradigm from purely data-driven models toward patient-specific biophysical forecasting.
CommentsAccepted at MICCAI 2026 Satellite Events (LNCS). 11 pages, 1 figure, 2 tables
