用神经网络快速校准血流模型参数，支持噪声数据和未知测量位置。
Data-driven Neural Networks for Windkessel Parameter Calibration
- 基于仿真血压数据训练神经网络，模拟全域压力波形。
- 仅需少量实测数据，通过扩展神经元即可完成参数校准。
- 适用于测量位置不明确或数据含噪的临床场景。
本文提出一种新型方法，用于在降维的1D-0D耦合血流模型中校准Windkessel（WK）参数。为此，我们设计了一个数据驱动的神经网络（NN），并在左肱动脉的仿真血压数据上进行训练。训练完成后，该神经网络可几乎无误差地模拟整个仿真域内的时间、空间及不同WK参数下的压力脉搏波，计算开销极低。针对实测脉搏波的参数校准，通过引入虚拟神经元对神经网络进行扩展，并仅在实测数据上重新训练。本研究的主要目标是评估该方法在多种场景下的有效性，特别是当测量位置不明确或数据受噪声影响时的表现。
原文摘要 · Abstract (English)
In this work, we propose a novel method for calibrating Windkessel (WK) parameters in a dimensionally reduced 1D-0D coupled blood flow model. To this end, we design a data-driven neural network (NN)trained on simulated blood pressures in the left brachial artery. Once trained, the NN emulates the pressure pulse waves across the entire simulated domain, i.e., over time, space and varying WK parameters, with negligible error and computational effort. To calibrate the WK parameters on a measured pulse wave, the NN is extended by dummy neurons and retrained only on these. The main objective of this work is to assess the effectiveness of the method in various scenarios -- particularly, when the exact measurement location is unknown or the data are affected by noise.
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