School of Mechanical and Manufacturing Engineering, UNSW, Sydney, NSW 2052, Australia.
School of Computer Science and Engineering, UNSW, Sydney, NSW 2052, Australia; Tyree Foundation Institute of Health Engineering (Tyree IHealthE), Sydney, Australia.
Comput Methods Programs Biomed. 2022 Oct;225:107013. doi: 10.1016/j.cmpb.2022.107013. Epub 2022 Jul 8.
Haemodynamic metrics, such as blood flow induced shear stresses at the inner vessel lumen, are associated with the development and progression of coronary artery disease. Understanding these metrics may therefore improve the assessment of an individual's coronary disease risk. However, the calculation of such luminal Wall Shear Stress (WSS) using traditional Computational Fluid Dynamics (CFD) methods is relatively slow and computationally expensive. As a result, CFD based haemodynamic computation is not suitable for integrated and large-scale use in clinical settings.
In this work, deep learning techniques are proposed as an alternative method to CFD, whereby luminal WSS magnitude can be predicted in coronary bifurcations throughout the cardiac cycle based on the steady state solution (which takes <120 seconds to calculate including preprocessing), vessel geometry and additional global features. The deep learning model is trained on a dataset of 101 patient-specific and 2626 synthetic left main bifurcation models with 26 separate patient-specific cases used as the test set.
The model showed high fidelity predictions with <5% (normalised against mean WSS magnitude) deviation to CFD derived values as the gold-standard method, while being orders of magnitude faster with on average <2 minutes versus 3 hours computation for transient CFD.
This method therefore offers a new approach to substantially reduce the computational cost involved in, for example, large-scale population studies of coronary haemodynamic metrics, and may therefore open the pathway for future clinical integration.
血流诱导的管腔内壁切应力等血流动力学指标与冠状动脉疾病的发生和发展有关。因此，了解这些指标可以提高个体冠心病风险的评估能力。然而，使用传统计算流体动力学（CFD）方法计算此类管腔壁切应力（WSS）的速度相对较慢，计算成本也很高。因此，基于 CFD 的血流动力学计算不适用于临床环境中的集成和大规模应用。
在这项工作中，提出了深度学习技术作为 CFD 的替代方法，该方法可以基于稳态解（包括预处理在内，计算时间<120 秒）、血管几何形状和其他全局特征，预测整个心动周期内冠状动脉分叉处的管腔 WSS 幅度。该深度学习模型在 101 个患者特定和 2626 个合成左主干分叉模型的数据集上进行了训练，其中 26 个患者特定的病例被用作测试集。
该模型的预测结果非常准确，与 CFD 衍生值的偏差<5%（归一化后与平均 WSS 幅度相比），而速度要快几个数量级，对于瞬态 CFD，平均计算时间<2 分钟，而不是 3 小时。
因此，该方法为例如对冠状动脉血流动力学指标进行大规模人群研究的计算成本降低提供了一种新的方法，并且可能为未来的临床应用开辟道路。
