Table of Contents
.- Models for Electrophysiology, Arrhythmia and Their Sequalae.
.- Multimodal Personalisation of Cardiac Electrophysiology Models combining 12-lead ECG and Computed Tomography.
.- Anatomic and Electrophysiological Biomarkers favoring Atrial Fibrillation Identified by Virtual Populations.
.- Arrhythmic Mitral Valve Syndrome: Insights from Left Ventricular Endsystolic Shape Analysis.
.- Validation of a computational model for simulating hemodynamic effects of premature ventricular complexes.
.- 3D-Shell Electromechanical Modeling of the Left Atrium.
.- Computational Modelling of Thrombogenesis during Cryoablation and Radiofrequency Ablation in the Left Atrium.
.- Influence of Oestrogen on Atrial Fibrillation Risk: An In-Silico Study.
.- Effects of the Insertion of Epicardial Anisotropy versus Isotropy in the Inverse Problem of Electrocardiography.
.- In silico Assessment of Arrhythmia Inducibility Dependence on Stimulus Location using Calibrated MR-based Infarcted Heart Models.
.- Electrophysiologically-Characterized Digital Twins from Intracavitary Recordings During Atrial Fibrillation.
.- Global Left Atrial Wall Fibrosis is Associated with Pro-thrombotic Haemodynamics in Atrial Fibrillation: A Computational Fluid Dynamics Study.
.- Towards Validation of Two Computational Models of Artificial Pacemakers.
.- Learning Cardiac Electrophysiology with Graph Neural Networks for Fast Data-driven Personalised Predictions.
.- Frugal AI for Automated Cardiac Defibrillation: Balancing Performance & Hardware Constraints.
.- A fast solver for the complex eikonal equation to initiate cardiac arrhythmias.
.- Investigation of the Left Atrial Appendage Hemodynamics by Integrating ECG-Gated CT and Mesh Morphing.
.- An Experimental Setup for the Analysis of Patient-Specific Left Atrial Appendage with Particle Image Velocimetry Investigation.
.- Multi-Therapeutic Modelling for Stroke Prevention in Atrial Fibrillation: Impact of the Pulmonary Ridge.
.- Biomechanics and Assessment of Cardiovascular Health.
.- The Impact of Left Ventricular Stiffness on Hemodynamic Responses to Mitral Regurgitation at Rest and During Exercise.
.- Multi-Scale Whole-Heart Electromechanics Modeling to Link Cellular, Tissue and Systemic Properties to Cardiac Biomarkers in the Diabetic Male and Female Heart.
.- A Multimodal Machine Learning Approach for Identifying Elevated Left Ventricular End-Diastolic Pressure.
.- The CircAdapt Framework: Create Fast Computational Models of Cardiovascular Function.
.- Atrial Constitutive Neural Networks.
.- Transformer-Based Surrogate Modeling for Efficient Left Ventricular Digital Twin.
.- High Speed Cardiac Simulations Using the JAX Framework.
.- Modeling Adaptive Fiber Reorientation in the Left Ventricle: Evaluation in an Ellipsoidal and a Patient-Specific Geometry.
.- A Fast Computational Model for Studying Interventricular Interactions.
.- Analyzing the Impact of Different Microstructure and Active Stress Models on Peak Systolic Kinematics.
.- A New Active Strain Model for Modelling Left Ventricular Contraction.
.- Identification of the Unloaded Heart Configuration Including External Interactions.
.- Cardiac Electromechanical Model Sensitivity Analysis using Causal Discovery.
.- Model-Enhanced Data Acquisition and Processing.
.- Parameter Estimation in Blood Flow Models from k-Space-Undersampled MRI Data.
.- Motion Tracking with Finite Elements Meshes and Image Models.
.- Parameter Estimation in Cardiac Fluid–Structure Interaction from Fluid and Solid Measurements.
.- A Theoretical Framework for Flow-Compatible Reconstruction of Heart Motion.
.- Comparison of Image-Driven, Patient-Specific, Direct Numerical Simulations to 4D Flow MRI in the Right Ventricle.
.- Quantification of Perfusion using Fermi Deconvolution.
.- Towards Non-Invasive Estimation of Myocardial Scar Stiffness from Cardiac Strains Using Deep Learning.
.- Intracardiac Hemodynamics Alteration in Myocardial Infarction.
