Download Computational Cardiology: Modeling of Anatomy, by Frank B. Sachse PDF

By Frank B. Sachse

This booklet is dedicated to computer-based modeling in cardiology, through taking an instructional standpoint, and through summarizing wisdom from a number of, often thought of delimited parts of cardiac learn in a constant way.

First, the principles and numerical ideas from arithmetic are supplied, with a specific specialise in the finite point and finite modifications equipment. Then, the idea of electrical fields and continuum mechanics is brought with recognize to numerical calculations in anisotropic organic media. as well as the presentation of electronic snapshot processing options, the subsequent chapters care for specific features of cardiac modeling: cardiac anatomy, cardiac electro body structure, cardiac mechanics, modeling of cardiac electro mechanics.

This booklet was once written for researchers in modeling and cardiology, for medical cardiologists, and for complex students.

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Additional resources for Computational Cardiology: Modeling of Anatomy, Electrophysiology, and Mechanics

Example text

One-dimensional shape-functions. (a) Linear, (b) quadratic, and (c,d) cubic shape-functions allow the determination of the solution function u at an arbitrary point x in a one-dimensional domain Ω starting from values of node variables ui . The shape-functions shown in (a), (b) and (c) result from node variables specifying the function u at two, three and four, respectively, node points. The shape-functions depicted in (d) accrues from nodes variables defining the functions u as well as its first derivatives at two node points.

The minimum of the 1 function F corresponds to the solution of the equation system Ax + b = 0. 4) ∂α(k) Hence, the factor α(k) is determined by: α(k) = − r (k−1)T r(k−1) r (k−1)T Ar(k−1) The iteration is commonly stopped, if some norm of the residuum r(k) becomes smaller than a given threshold or the iteration number k is larger than a given value. The steepest descent method necessitates positive-definiteness of the matrix A. Commonly, the method shows a small convergence rate [5]. The matrix A is unchanged during the solving process.

Substituting the step of exact solution by a recursive application leads to multigrid methods. Multigrid methods can be classified by the number of domains and by the strategy traversing through these domains [5]. Full-Multigrid Method. g. by direct methods. The solution is transferred to the next finer domain with an appropriate prolongation operator. Here, the twogrid method is applied. The scheme is iteratively applied until a solution in the finest domain is achieved. In each domain the multigrid method is applied.

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