Physics-informed neural network for patient-specific left ventricular finite element modelling with image motion consistency

Elucidating the biomechanical behaviour of the myocardium is important for understanding cardiac physiology. This cannot be directly inferred from clinical imaging, but requires finite element (FE) simulations. However, conventional FE simulations are computationally expensive and often do not accurately reproduce clinically observed cardiac motions. Here, we propose the IMC-PINN-FE, a physics-informed neural network (PINN) framework that integrates imaged motion consistency (IMC) with FE modelling for patient-specific simulations of left ventricular (LV) biomechanics. Cardiac motion is first estimated from magnetic resonance imaging (MRI) or echocardiography using either a pre-trained attention-based network or an unsupervised cyclic-regularised network, followed by extraction of motion modes. IMC-PINN-FE then estimates myocardial stiffness and active tension by fitting clinical pressure measurements within seconds. Based on these parameters, IMC-PINN-FE performs FE modelling throughout the cardiac cycle and achieves an overall ∼ 75-fold speed-up over conventional FE simulations. Through motion constraints, its outputs of cardiac displacements across the cardiac cycle match imaged motions much more accurately, improving Dice coefficient from 0.849 to 0.927 compared to conventional FE methods. Concurrently, it preserves a realistic pressure–volume (P–V) relationship that is very similar to that of conventional FE. IMC-PINN-FE improves on existing PINN-FE models by enabling back-computation of myocardial properties and achieving better imaged motion fidelity. This strategy of using cardiac motions of a single individual to reconstruct shape modes avoids the need for a large dataset, and enables better patient-specificity. IMC-PINN-FE thus presents a robust approach for rapid, patient-specific, and image-consistent cardiac biomechanical modelling.