New PDF release: Computational Cardiovascular Mechanics: Modeling and

By Jonathan F. Wenk, Choon-Sik Jhun, Zhihong Zhang, Kay Sun, Mike Burger, Dan Einstein (auth.), Julius M. Guccione, Ghassan S. Kassab, Mark B. Ratcliffe (eds.)

ISBN-10: 1441907297

ISBN-13: 9781441907295

Computational Cardiovascular Mechanics promotes the appliance of patient-specific cardiovascular mechanics types to scientific medication, which relief scientific prognosis and increase therapy for cardiovascular disease.

Organized in a two-part constitution, this quantity provides a entire assessment of computational modeling from either stable mechanics and fluid dynamics views. half I bargains chapters dedicated to quite a few options concerning finite point modeling of ventricular mechanics and computational fluid dynamics, with a spotlight in cardiovascular mechanics. half II covers middle failure purposes which make the most of strategies in reliable mechanics and fluid dynamics. within the former, either diagnostic (i.e., international and nearby indices of myocardial contractility) in addition to healing ways (surgical ventricular home improvement techniques, passive ventricular constraint units, ventricular implantation of biomaterials and cardiac resynchronization treatment) are mentioned. within the latter, the fluid mechanics of center valves is simulated, as are surgeries and middle failure-related units within the type of coronary artery pass grafting and ventricular help devices.

Computational Cardiovascular Mechanics is an important source for heart problems researchers who are looking to the way to practice computational fluid and/or reliable mechanics to the analysis and therapy of center failure.

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Extra info for Computational Cardiovascular Mechanics: Modeling and Applications in Heart Failure

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User input is encapsulated by « » and explained below. The important user parameters are the element anisotropy and the number of layers. The latter is self-explanatory. The anisotropy parameter is relative to the gradient-limited local feature size, as explained in Chapter 7. 16 illustrates the results of applying the input shown in Fig. 15 and the application in MATLAB of the boundary prism algorithm described in [2]. The original surface was provided by the NYU Medical Center and Zhang et al.

It is assumed that source and target grids occupy approximately the same geometry within a certain tolerance, such as would be the case if DTI data and computational grid were either both based on ex vivo data or both based on in vivo data. The tolerance is necessary because geometry in an image is implicit, while the geometry of a computational grid is explicit. Furthermore, operations of smoothing and surface adaptivity typical of grid generation imply some deformation of the geometry. The algorithms presented are embodied in the code I2G, available from the Pacific Northwest National Laboratory.

Kuprat Abstract A precise knowledge of the microstructures of the myocardium such as myocyte organization and myofiber orientation is necessary to better understand material and functional properties of the tissue. By characterizing the diffusion of water exerted by its molecular environment, magnetic resonance diffusion tensor imaging has emerged as a viable alternative to conventional histology for mapping tissue fibers and offers advantages of being nondestructive, relatively convenient, and inherently 3D.

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Computational Cardiovascular Mechanics: Modeling and Applications in Heart Failure by Jonathan F. Wenk, Choon-Sik Jhun, Zhihong Zhang, Kay Sun, Mike Burger, Dan Einstein (auth.), Julius M. Guccione, Ghassan S. Kassab, Mark B. Ratcliffe (eds.)


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