The development of biophysical models for clinical applications is rapidly
advancing in the research community, thanks to their predictive nature and
their ability to assist the interpretation of clinical data. However,
high-resolution and accurate multi-physics computational models are
computationally expensive and their personalisation involves fine calibration
of a large number of parameters, which may be space-dependent, challenging
their clinical translation. In this work, we propose a new approach which
relies on the combination of physics-informed neural networks (PINNs) with
three-dimensional soft tissue nonlinear biomechanical models, capable of
reconstructing displacement fields and estimating heterogeneous
patient-specific biophysical properties. The proposed learning algorithm
encodes information from a limited amount of displacement and, in some cases,
strain data, that can be routinely acquired in the clinical setting, and
combines it with the physics of the problem, represented by a mathematical
model based on partial differential equations, to regularise the problem and
improve its convergence properties. Several benchmarks are presented to show
the accuracy and robustness of the proposed method and its great potential to
enable the robust and effective identification of patient-specific,
heterogeneous physical properties, s.a. tissue stiffness properties. In
particular, we demonstrate the capability of the PINN to detect the presence,
location and severity of scar tissue, which is beneficial to develop
personalised simulation models for disease diagnosis, especially for cardiac
applications.

基于物理信息神经网络（PINNs）与三维柔性组织非线性生物力学模型的结合，本研究提出了一种新的方法，能够重建位移场并估算异质病人个体的生物物理特性，通过在临床环境中常规获取的有限位移和应变数据，结合问题的物理性质和偏微分方程的数学模型，提高问题的稳定性和收敛性能，进而在疾病诊断等方面发展个性化的模拟模型，并演示了 PINN 能够检测瘢痕组织的存在、位置和严重程度的能力。