Assessment of Spatially-Varying Arterial Wall Stiffness and Pressure Using a Physics-Informed Neural Network and Pulse Wave Imaging: An in Silico and Experimental Phantom Study of Stenotic Vessels.

objectiveArterial stiffness is a key predictor of cardiovascular mortality. This study utilizes a physics-informed neural network (PINN) model to estimate spatially varying arterial stiffness by leveraging ultrasound-based Pulse Wave Imaging (PWI) and Vector Flow Imaging (VFI).

methodsThe PWI and VFI frameworks provide high-frame-rate wall displacement and blood flow velocity data, which are incorporated into a PINN constrained by linearized 1D differential equations modeling pulse wave propagation in heterogeneous vessels. The model was validated using in-silico simulations and physical plaque phantoms.

resultsThe proposed PINN model effectively captured variations in localized compliance, which is indicative of arterial wall stiffness, in both in-silico and phantom experiments, and remained robust under varying inlet and outlet boundary conditions. Notably, incorporating flow velocity data enhanced reconstruction accuracy by 13.84% over displacement-only methods. The approach yielded low bias in homogeneous cases (1.41%), with higher biases for stiffer (3.95%) and softer (8.10%) plaque scenarios, mainly due to limitations in the 1D modeling and lack of explicit boundary condition integration.

conclusionThe findings presented herein indicate the PINN framework has strong potential for non-invasive assessment of focal arterial stiffness, such as in atherosclerotic plaques. Future work aims to include nonlinear vascular dynamics and extend the model to 2D or 3D to better capture complex blood flow behavior seen in stenotic arteries.