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Mathematical Modeling of the Human Brain - From Magnetic Resonance Images to Finite Element Simulation (Paperback, 1st ed.... Mathematical Modeling of the Human Brain - From Magnetic Resonance Images to Finite Element Simulation (Paperback, 1st ed. 2022)
Kent-Andre Mardal, Marie E. Rognes, Travis B. Thompson, Lars Magnus Valnes
R1,075 Discovery Miles 10 750 Ships in 10 - 15 working days

This open access book bridges common tools in medical imaging and neuroscience with the numerical solution of brain modelling PDEs. The connection between these areas is established through the use of two existing tools, FreeSurfer and FEniCS, and one novel tool, the SVM-Tk, developed for this book. The reader will learn the basics of magnetic resonance imaging and quickly proceed to generating their first FEniCS brain meshes from T1-weighted images. The book's presentation concludes with the reader solving a simplified PDE model of gadobutrol diffusion in the brain that incorporates diffusion tensor images, of various resolution, and complex, multi-domain, variable-resolution FEniCS meshes with detailed markings of anatomical brain regions. After completing this book, the reader will have a solid foundation for performing patient-specific finite element simulations of biomechanical models of the human brain.

Modeling Excitable Tissue - The EMI Framework (Paperback, 1st ed. 2021): Aslak Tveito, Kent-Andre Mardal, Marie E. Rognes Modeling Excitable Tissue - The EMI Framework (Paperback, 1st ed. 2021)
Aslak Tveito, Kent-Andre Mardal, Marie E. Rognes
R1,429 Discovery Miles 14 290 Ships in 10 - 15 working days

This open access volume presents a novel computational framework for understanding how collections of excitable cells work. The key approach in the text is to model excitable tissue by representing the individual cells constituting the tissue. This is in stark contrast to the common approach where homogenization is used to develop models where the cells are not explicitly present. The approach allows for very detailed analysis of small collections of excitable cells, but computational challenges limit the applicability in the presence of large collections of cells.

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