Functional Imaging and Modeling of the Heart

13th International Conference, FIMH 2025, Dallas, TX, USA, June 2-4, 2025, Proceedings, Part I
Herausgegeben:Chabiniok, Radomír; Zou, Qing; Hussain, Tarique; Nguyen, Hoang H.; Zaha, Vlad G.; Gusseva, Maria

• Broschiertes Buch

Produktbeschreibung

This two-volume set, LNCS 15672 and LNCS 15673, constitutes the refereed proceedings of the 13th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2025, held in Dallas, Texas, USA, during June 2 4, 2025.

The 79 full papers presented in this book were carefully reviewed and selected from 93 submissions. These papers have been organized in the following topical sections:-

Part I: Models for Electrophysiology, Arrhythmia and Their Sequalae; Biomechanics and Assessment of Cardiovascular Health; Model-Enhanced Data Acquisition and Processing.

Part II: Multiscale & Multimodality Imaging; Image Processing and Visualization; Clinical Translations of Computational Modeling across Medical Specialties.

Produktdetails

• Lecture Notes in Computer Science 15672
• Verlag: Springer / Springer Nature Switzerland / Springer, Berlin
• Artikelnr. des Verlages: 978-3-031-94558-8
• Seitenzahl: 467
• Erscheinungstermin: Juli 2025
• Englisch
• Abmessung: 235mm x 155mm
• ISBN-13: 9783031945588
• Artikelnr.: 73949896

Inhaltsangabe

.- 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.