Physics-Informed Neural Network for Modeling the Pulmonary Artery Blood Pressure from Magnetic Resonance Images: A Reduced-Order Navier-Stokes Model.

Pulmonary arterial pressure is a key parameter for diagnosing cardiovascular and pulmonary diseases. Its measurement through right heart catheterization is considered the gold standard, and it is an invasive procedure that entails significant risks for patients. This has motivated the development of non-invasive techniques based on patient-specific imaging, such as Physics-Informed Neural Networks (PINNs), which integrate clinical measurements with physical models, such as the 1D reduced Navier-Stokes model, enabling biologically plausible predictions with limited data. This work implements a PINN model that uses velocity and area measurements in the main bifurcation of the pulmonary artery, comprising the main artery and its secondary branches, to predict pressure, velocity, and area variations throughout the bifurcation. The model training includes penalties to satisfy the laws of flow and momentum conservation. The results show that, using 4D Flow MRI images from a healthy patient as clinical data, the pressure estimates provided by the model are consistent with the expected ranges reported in the literature, reaching a mean arterial pressure of 21.5 mmHg. This model presents an innovative approach that avoids invasive methods, being the first study to apply PINNs to estimate pulmonary arterial pressure in bifurcations. In future work, we aim to validate the model in larger populations and confirm pulmonary hypertension cases diagnosed through catheterization.