An Imaging-Informed Reaction-Diffusion Model of Infarct Growth
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.