Alejandro Gonzalo

This study investigates how fibrosis burden and spatial distribution affect left atrial appendage hemodynamics using multi-physics simulations.

This paper develops two methods to estimate left atrial blood residence time from 4D CT contrast data, showing that indicator dilution theory provides faster, more accurate results than a physics-informed neural network.

This study uses a computational model to assess how fibrosis-related changes in electrophysiological and mechanical properties affect left atrial function, finding that reduced L-type calcium current most impairs function while reduced inward rectifier current improves it.

This study evaluates how different temporal resolutions affect CFD-derived left atrial flow metrics, finding that higher frame rates improve accuracy while lower rates preserve patient ranking.

This study simulates blood flow and coagulation in patient-specific left atrium models to assess how hemodynamics affect the efficacy of factor XI/XII inhibitors in preventing thrombosis.

This paper uses multi-physics simulations to show how fibrosis in left atrial tissue affects hemodynamics and increases stroke risk by altering flow patterns and reducing emptying fractions.

This paper introduces AI-VFM, a physics-informed neural network method for super-resolution left ventricular flow and pressure mapping from echocardiographic data.