Center for Biomedical Engineering, Brown University, Providence, RI 02912, USA.
School of Engineering, Brown University, Providence, RI 02912, USA.
J R Soc Interface. 2022 Feb;19(187):20210670. doi: 10.1098/rsif.2021.0670. Epub 2022 Feb 9.
Aortic dissection progresses mainly via delamination of the medial layer of the wall. Notwithstanding the complexity of this process, insight has been gleaned by studying and the progression of dissection driven by quasi-static pressurization of the intramural space by fluid injection, which demonstrates that the differential propensity of dissection along the aorta can be affected by spatial distributions of structurally significant interlamellar struts that connect adjacent elastic lamellae. In particular, diverse histological microstructures may lead to differential mechanical behaviour during dissection, including the pressure-volume relationship of the injected fluid and the displacement field between adjacent lamellae. In this study, we develop a data-driven surrogate model of the delamination process for differential strut distributions using DeepONet, a new operator-regression neural network. This surrogate model is trained to predict the pressure-volume curve of the injected fluid and the damage progression within the wall given a spatial distribution of struts, with data generated using a phase-field finite-element model. The results show that DeepONet can provide accurate predictions for diverse strut distributions, indicating that this composite branch-trunk neural network can effectively extract the underlying functional relationship between distinctive microstructures and their mechanical properties. More broadly, DeepONet can facilitate surrogate model-based analyses to quantify biological variability, improve inverse design and predict mechanical properties based on multi-modality experimental data.
主动脉夹层的主要进展机制是中层的分层。尽管这个过程很复杂，但通过研究和在腔内空间进行准静态加压注射以驱动夹层的进展，已经获得了一些认识，这表明可以通过连接相邻弹性层的结构上重要的层间支柱的空间分布来影响主动脉夹层的不同倾向。特别是，不同的组织学微观结构可能会导致夹层过程中的不同力学行为，包括注入流体的压力-体积关系和相邻层之间的位移场。在这项研究中，我们使用 DeepONet（一种新的算子回归神经网络）为具有不同层间支柱分布的分层过程开发了一个基于数据的替代模型。该替代模型经过训练，可以根据支柱的空间分布预测注入流体的压力-体积曲线和壁内的损伤进展，数据是使用相场有限元模型生成的。结果表明，DeepONet 可以为不同的支柱分布提供准确的预测，这表明这种组合的分支-树干神经网络可以有效地提取不同微观结构及其力学性能之间的潜在功能关系。更广泛地说，DeepONet 可以促进基于代理模型的分析，以量化生物学变异性，改进基于多模态实验数据的反向设计和预测力学性能。
