Zhang Xuelan, Mao Baoyan, Che Yue, Kang Jiaheng, Luo Mingyao, Qiao Aike, Liu Youjun, Anzai Hitomi, Ohta Makoto, Guo Yuting, Li Gaoyang
School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, 100083, China.
Beijing University of Chinese Medicine, Beijing 100029, China.
Comput Biol Med. 2023 Sep;164:107287. doi: 10.1016/j.compbiomed.2023.107287. Epub 2023 Jul 29.
Hemodynamic parameters are of great significance in the clinical diagnosis and treatment of cardiovascular diseases. However, noninvasive, real-time and accurate acquisition of hemodynamics remains a challenge for current invasive detection and simulation algorithms. Here, we integrate computational fluid dynamics with our customized analysis framework based on a multi-attribute point cloud dataset and physics-informed neural networks (PINNs)-aided deep learning modules. This combination is implemented by our workflow that generates flow field datasets within two types of patient personalized models - aorta with fine coronary branches and abdominal aorta. Deep learning modules with or without an antecedent hierarchical structure model the flow field development and complete the mapping from spatial and temporal dimensions to 4D hemodynamics. 88,000 cases on 4 randomized partitions in 16 controlled trials reveal the hemodynamic landscape of spatio-temporal anisotropy within two types of personalized models, which demonstrates the effectiveness of PINN in predicting the space-time behavior of flow fields and gives the optimal deep learning framework for different blood vessels in terms of balancing the training cost and accuracy dimensions. The proposed framework shows intentional performance in computational cost, accuracy and visualization compared to currently prevalent methods, and has the potential for generalization to model flow fields and corresponding clinical metrics within vessels at different locations. We expect our framework to push the 4D hemodynamic predictions to the real-time level, and in statistically significant fashion, applicable to morphologically variable vessels.
血流动力学参数在心血管疾病的临床诊断和治疗中具有重要意义。然而，对于当前的侵入性检测和模拟算法而言，无创、实时且准确地获取血流动力学信息仍然是一项挑战。在此，我们将计算流体动力学与基于多属性点云数据集和物理信息神经网络（PINNs）辅助深度学习模块的定制分析框架相结合。这种结合是通过我们的工作流程实现的，该流程在两种患者个性化模型——带有精细冠状动脉分支的主动脉和腹主动脉——中生成流场数据集。具有或不具有先行层次结构的深度学习模块对流场发展进行建模，并完成从空间和时间维度到四维血流动力学的映射。16项对照试验中4个随机分区的88000个病例揭示了两种个性化模型内时空各向异性的血流动力学情况，这证明了PINN在预测流场时空行为方面的有效性，并在平衡训练成本和准确性维度方面为不同血管提供了最优的深度学习框架。与当前流行的方法相比，所提出的框架在计算成本、准确性和可视化方面表现出显著优势，并且具有推广到不同位置血管内流场和相应临床指标建模的潜力。我们期望我们的框架能够将四维血流动力学预测提升到实时水平，并以具有统计学意义的方式应用于形态可变的血管。
