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Computational Cardiovascular Mechanics
Computational Cardiovascular Mechanics provides a cohesive guide to creating mathematical models for the mechanics of diseased hearts to simulate the effects of current treatments for heart failure. Clearly organized in a two part structure, this volume discusses various areas of computational modeling of cardiovascular mechanics (finite element modeling of ventricular mechanics, fluid dynamics) in addition to a description an analysis of the current applications used (solid FE modeling, CFD). Edited by experts in the field, researchers involved with biomedical and mechanical engineering will find Computational Cardiovascular Mechanics a valuable reference.
Theory of Heart
In recent years there has been a growth in interest in studying the heart from the perspective of the physical sciences: mechanics, fluid flow, electromechanics. This volume is the result of a workshop held in July 1989 at the Institute for Nonlinear Sciences at the University of California at San Diego that brought together scientists and clinicians with graduate students and postdoctoral fellows who shared an interest in the heart. The chapters were prepared by the invited speakers as didactic reviews of their subjects but also include the structure, mechanical properties, and function of the heart and the myocardium, electrical activity of the heart and myocardium, and mathematical models of heart function.
Patient-Specific Modeling of the Cardiovascular System
Peter Hunter Computational physiology for the cardiovascular system is entering a new and exciting phase of clinical application. Biophysically based models of the human heart and circulation, based on patient-specific anatomy but also informed by po- lation atlases and incorporating a great deal of mechanistic understanding at the cell, tissue, and organ levels, offer the prospect of evidence-based diagnosis and treatment of cardiovascular disease. The clinical value of patient-specific modeling is well illustrated in application areas where model-based interpretation of clinical images allows a more precise analysis of disease processes than can otherwise be achieved. For example, Chap. 6 ...
Structure-Based Mechanics of Tissues and Organs
This book portrays the commonality of tissue micro-structure that dictates physiological function in various organs (microstructure-function relation). Tissue and organ models are used to illustrate physiological functions based on microstructure. Fiber scale properties such as orientation and crimp are described in detail. Structurally-based constitutive models are given throughout the book, not only to avoid ambiguities in material characterization, but also to offer insights into the function, structure, and mechanics of tissue components. A statement of future directions of the field is also given, including how advancements, such as state-of-the-art computational modeling and optical measurements of tissue/cells structures, are taking structure-based modeling to the next level. This book also: Provides a comprehensive view of tissue modeling across multiple systems Broadens readers’ understanding of state-of-the-art computational modeling and optical measurements of tissue/cells structures Describes in detail fiber scale properties such as orientation and crimp
Functional Imaging and Modeling of the Heart
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.
