We present a framework for modeling liver regrowth on the organ scale that is based on three components: (1) a multiscale perfusion model that combines synthetic vascular tree generation with a multi-compartment homogenized flow model, including a homogenization procedure to obtain effective parameters; (2) a poroelastic finite growth model that acts on all compartments and the synthetic vascular tree structure; (3) an evolution equation for the local volumetric growth factor, driven by the homogenized flow rate into the microcirculation as a measure of local hyperperfusion and well-suited for calibration with available data. We apply our modeling framework to a prototypical benchmark and a full-scale patient-specific liver, for which we assume a common surgical cut. Our simulation results demonstrate that our model represents hyperperfusion as a consequence of partial resection and accounts for its reduction towards a homeostatic perfusion state, exhibiting overall regrowth dynamics that correspond well with clinical observations. In addition, our results show that our model also captures local hypoperfusion in the vicinity of orphan vessels, a key requirement for the prediction of ischemia or the preoperative identification of suitable cut patterns.

翻译：我们提出了一个用于器官尺度肝脏再生建模的框架，该框架基于三个组成部分：(1) 一个多尺度灌注模型，它将合成血管树生成与多隔室均质化血流模型相结合，包括一个用于获取有效参数的均质化过程；(2) 一个作用于所有隔室及合成血管树结构的多孔弹性有限生长模型；(3) 一个局部体积生长因子的演化方程，其驱动力是进入微循环的均质化血流速率，作为局部高灌注的度量，非常适合利用现有数据进行校准。我们将我们的建模框架应用于一个原型基准案例和一个全尺寸的患者特异性肝脏，并假设了一个常见的手术切除。我们的模拟结果表明，我们的模型将部分切除导致的超灌注现象表征出来，并描述了其向稳态灌注状态的回归过程，展现出的整体再生动力学与临床观察结果吻合良好。此外，我们的结果表明，该模型还能捕捉到孤立血管附近的局部低灌注现象，这是预测缺血或术前识别合适切除模式的关键要求。