Manuel Guerrero‐Hurtado

Fibrosis burden and spatial pattern affect left atrial appendage stasis in multi-beat multi-physics simulations

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

Joint Vector Flow Mapping and Segmentation: Ill-Posedness,Differentiable Bayesian Inference, and Synthetic Vortex-FlowBenchmarks

This paper introduces Bayesian VFM (B-VFM) to address ill-posedness in vector flow mapping by jointly inferring velocity fields, segmentation masks, and uncertainties using Bayesian inference.

Left atrial flow and thrombosis risk from 4D CT contrast dynamics by physics-informed neural network and indicator dilution theory

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.

Hemodynamics Affects Factor XI/XII Anticoagulation Efficacy in Patient-Specific Left Atrial Models

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.

Multi-physics simulations reveal hemodynamic impacts of patient-derived fibrosis-related changes in left atrial tissue mechanics

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.

Super-resolution Left Ventricular Flow and Pressure Mapping by Navier-Stokes-Informed Neural Networks

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