HemoPIC：用于脑灌注的物理信息脑血流动力学数字孪生模型
HemoPIC: A Physics-Informed Cerebral Hemodynamics Digital Twin for Brain Perfusion
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中文总结 AI 辅助
研究提出物理信息脑血流动力学数字孪生模型HemoPIC，通过解决约束反问题，联合估计数字孪生参数和潜在状态，消除常规灌注定量中的手动AIF选择和反卷积，直接生成临床可行的灌注汇总图，还能进行正向模拟和反事实干预分析。
中文摘要 AI 辅助
灌注成像通过表征组织水平的血流动力学来指导中风和脑肿瘤的临床评估。常规定量依赖于手动选择动脉输入函数（AIF）并进行反卷积，生成的汇总图没有可执行的时间模型用于模拟或机理洞察。基于示踪动力学的模型从灌注时间序列推断传输或隔室参数，但无法产生用于指导诊断和治疗决策的临床可行灌注指标（如CBF、CBV、MTT）。在这项工作中，我们提出了HemoPIC，这是一个物理信息脑血流动力学数字孪生模型，它通过示踪剂质量守恒和集总参数血流动力学模型来解释灌注时间序列。具体而言，HemoPIC解决了一个约束反问题，从灌注成像中联合估计数字孪生模型参数和潜在状态，消除了常规灌注定量中的手动AIF选择和反卷积，同时直接生成临床可行的灌注汇总图。实验表明，HemoPIC能够重建示踪动力学，生成具有病变灌注不足模式的生理一致灌注图，满足中心容积一致性，并产生一个能够进行正向模拟和反事实干预分析的机理血流动力学数字孪生模型。代码可在指定网址公开获取。
英文摘要
Perfusion imaging guides clinical evaluation of stroke and brain tumors by characterizing tissue-level hemodynamics. Routine quantification relies on manual arterial input function (AIF) selection followed by deconvolution, producing summary maps without an executable temporal model for simulation or mechanistic insight. Tracer-dynamics-based models infer transport or compartmental parameters from perfusion time series, but do not yield clinically actionable perfusion indices (e.g., CBF, CBV, MTT) that inform diagnosis and treatment decisions. In this work, we propose HemoPIC, a physics-informed cerebral hemodynamics digital twin that explains perfusion time series through tracer mass conservation and a lumped parameter hemodynamic model. Specifically, HemoPIC solves a constrained inverse problem that jointly estimates digital twin parameters and latent states from perfusion imaging, eliminating manual AIF selection and deconvolution from routine perfusion quantification while directly producing clinically actionable perfusion summary maps. Experiments demonstrate that HemoPIC reconstructs tracer dynamics, generates physiologically consistent perfusion maps with lesion hypoperfusion patterns, satisfies central volume consistency, and yields a mechanistic hemodynamic digital twin that enables forward simulation and counterfactual intervention analysis. Code is publicly available at https://github.com/jhuldr/HemoPIC.
