An Imaging-Informed Reaction-Diffusion Model of Infarct Growth

2026-08-21

AI Summary

本文提出了一种基于临床急性MRI的Fisher-KPP反应扩散模型，以预测缺血性梗死体积，解决了现有方法在可解释性和实用性上的不足。

Abstract

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.

Problem

• imaging
• infarct growth
• stroke management
• machine learning
• electrophysiological models

Innovation

• Imaging-informed
• Reaction-diffusion model
• Fisher-KPP PDE
• Biophysical propagation constraints
• Physiological interpretability