Physics-informed Neural Network Estimation of Material Properties in Soft Tissue Nonlinear Biomechanical Models
This work addresses the clinical translation bottleneck for personalized biomechanical models, particularly in cardiac applications, though it represents an incremental advancement of existing PINN methodology.
The researchers tackled the challenge of personalizing computationally expensive biomechanical models by developing a physics-informed neural network approach that estimates patient-specific material properties from limited clinical displacement data. They demonstrated the method's ability to accurately detect scar tissue location and severity, showing potential for cardiac disease diagnosis.
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.