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Functional Imaging and Modeling of the Heart: 11th International Conference, FIMH 2021, Stanford, CA, USA, June 21-25, 2021, Proceedings de Daniel B. Ennis

Functional Imaging and Modeling of the Heart: 11th International Conference, FIMH 2021, Stanford, CA, USA, June 21-25, 2021, Proceedings: Lecture Notes in Computer Science, cartea 12738

Editat de Daniel B. Ennis, Luigi E. Perotti, Vicky Y. Wang
en Limba Engleză Paperback – 18 iun 2021
This book constitutes the refereed proceedings of the 11th International Conference on Functional Imaging and Modeling of the Heart, which took place online during June 21-24, 2021, organized by the University of Stanford.
The 65 revised full papers were carefully reviewed and selected from 68 submissions. They were organized in topical sections as follows: advanced cardiac and cardiovascular image processing; cardiac microstructure: measures and models; novel approaches to measuring heart deformation; cardiac mechanics: measures and models; translational cardiac mechanics; modeling electrophysiology, ECG, and arrhythmia; cardiovascular flow: measures and models; and atrial microstructure, modeling, and thrombosis prediction.

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Specificații

ISBN-13: 9783030787097
ISBN-10: 3030787095
Pagini: 690
Ilustrații: XVIII, 690 p. 326 illus., 301 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.98 kg
Ediția:1st ed. 2021
Editura: Springer International Publishing
Colecția Springer
Seriile Lecture Notes in Computer Science, Image Processing, Computer Vision, Pattern Recognition, and Graphics

Locul publicării:Cham, Switzerland

Cuprins

Population-based personalization of geometric models of myocardial infarction.- Impact of Image Resolution and Resampling on Motion Tracking of the Left Chambers from Cardiac Scans.- Shape Constraints in Deep Learning for Robust 2D Echocardiography Analysis.- Image-Derived Geometric Characteristics Predict Abdominal Aortic Aneurysm Growth in a  Machine Learning Model.- Cardiac MRI Left Ventricular Segmentation and Function Quantification Using Pre-trained Neural Networks.- Three-Dimensional Embedded Attentive RNN (3D-EAR) Segmentor for Left Ventricle Delineation from Myocardial Velocity Mapping.- Whole Heart Anatomical Refinement from CCTA using Extrapolation and Parcellation.- Optimisation of Left Atrial Feature Tracking using Retrospective Gated Computed Tomography Images.- Assessment of geometric models for the approximation of aorta cross-sections.- Improved High Frame Rate Speckle Tracking for Echocardiography.- Efficient Model Monitoring for Quality Control in Cardiac ImageSegmentation.- Domain adaptation for automatic aorta segmentation of 4D flow magnetic resonance imaging data from multiple vendor scanners.- A multi-step machine learning approach for short axis MR images segmentation.- Diffusion biomarkers in chronic myocardial infarction.- Spatially constrained Deep Learning approach for myocardial T1 mapping.- A methodology for accessing the local arrangement of the sheetlets that make up the extracellular heart tissue.- A High-Fidelity 3D Micromechanical Model of Ventricular Myocardium.- Quantitative Interpretation of Myocardial Fiber Structure in the Left and Right Ventricle of an Equine Heart using Diffusion Tensor Cardiovascular Magnetic Resonance Imaging.- Analysis of Location-Dependent Cardiomyocyte Branching.- Systematic Study of Joint Influence of Angular Resolution and Noise in Cardiac Diffusion Tensor Imaging.- Arbitrary Point Tracking with Machine Learning to Measure Cardiac Strain in Tagged MRI.- Investigation of the impact of normalization on the study of interactions between myocardial shape and deformation.- Reproducibility of Left Ventricular CINE DENSE Strain in Pediatric Subjects with Duchenne Muscular Dystrophy.- M-SiSSR: Regional Endocardial Function using Multilabel Simultaneous Subdivision Surface Registration.- CNN-based Cardiac Motion Extraction to Generate Deformable Geometric Left Ventricle Myocardial Models from Cine MRI.- Multiscale Graph Convolutional Networks for Cardiac Motion Analysis.- An image registration framework to estimate 3D myocardial strains from cine cardiac MRI in mice.- Sensitivity of Myocardial Stiffness Estimates to Inter-observer Variability in LV Geometric Modelling.- A computational approach on sensitivity of left ventricular wall strains to fiber orientation.- A Framework for Evaluating Myocardial Stiffness Using 3D-Printed Heart Phantoms.- Modeling patient-specific periaortic interactions with static and dynamic structures using a moving heterogeneous elastic foundation boundarycondition.- An Exploratory Assessment of Focused Septal Growth in Hypertrophic Cardiomyopathy.- Parameter Estimation in a Rule-Based Fiber Orientation model from End Systolic Strains Using the Reduced Order Unscented Kalman Filter.- Effects of fibre orientation on electrocardiographic and mechanical functions in a computational human biventricular model.- Model-assisted time-synchronization of cardiac MR image and catheter pressure data.- From clinical imaging to patient-specific computational model: Rapid adaptation of the Living Heart Human Model to a case of aortic stenosis.- Cardiac support for the right ventricle: effects of timing on hemodynamics-biomechanics tradeoff.- In vivo pressure-volume loops and chamber stiffness estimation using real-time 3D echocardiography and left ventricular catheterization – application to post-heart transplant patients .- In silico mapping of the omecamtiv mecarbil effects from the sarcomere to the whole-heart and back again.- High-Speed Simulation of the 3D Behavior of Myocardium Using a Neural Network PDE Approach.- On the interrelationship between left ventricle infarction geometry and ischemic mitral regurgitation grade.- Cardiac modeling for Multisystem Inflammatory Syndrome in Children (MIS-C, PIMS-TS).- Personal-by-design: a 3D Electromechanical Model of the Heart Tailored for Personalisation.- Scar-Related Ventricular Arrhythmia Prediction from Imaging using Explainable Deep Learning.- Deep Adaptive Electrocardiographic Imaging with Generative Forward Model for Error Reduction.- EP-Net 2.0: Out-of-Domain Generalisation for Deep Learning Models of Cardiac Electrophysiology.- Simultaneous Multi-Heartbeat ECGI Solution with a Time-Varying Forward Model: a Joint Inverse Formulation.- The Effect of Modeling Assumptions on the ECG in Monodomain and Bidomain Simulations.- Uncertainty Quantification of the Effects of Segmentation Variability in ECGI.- Spiral Waves Generation using an Eikonal-reaction Cardiac Electrophysiology Model.- Simplified Electrophysiology Modeling Framework to Assess Ventricular Arrhythmia Risk in Infarcted Patients.- Sensitivity analysis of a smooth muscle cell electrophysiological model..- A volume source method for solving ECGI inverse problem.- Fast and Accurate Uncertainty Quantification for the ECG with Random Electrodes Location.- Quantitative Hemodynamics in Aortic Dissection: Comparing in vitro MRI with FSI Simulation in a Compliant Model.- 3-D Intraventricular Vector Flow mapping Using Triplane Doppler Echo.- The role of extra-coronary vascular conditions that affect coronary fractional flow reserve estimation..- In-silico analysis of the influence of pulmonary vein configuration on left atrial haemodynamics and thrombus formation in a large cohort.- Shape analysis and computational fluid simulations to assess feline left atrial function and thrombogenesis.- Using the Universal Atrial Coordinate system for MRI and electroanatomic data registration in patient-specific left atrial model construction and simulation.- Geometric Deep Learning for the Assessment of Thrombosis Risk in the Left Atrial Appendage.- Learning atrial fiber orientations and conductivity tensors from intracardiac maps using physics-informed neural networks.- The Effect of Ventricular Myofibre Orientation on Atrial Dynamics.- Intra-Cardiac Signatures of Atrial Arrhythmias Identified By Machine Learning and Traditional Features.- Computational Modelling of the Role of Atrial Fibrillation on Cerebral Blood Perfusion..