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        Mathematical Modeling of the Human Brain

        From Magnetic Resonance Images to Finite Element Simulation

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        Author(s)
        Mardal, Kent-André
        Rognes, Marie E.
        Thompson, Travis B.
        Valnes, Lars Magnus
        Language
        English
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        Abstract
        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.
        URI
        https://library.oapen.org/handle/20.500.12657/53362
        Keywords
        magnetic resonance imaging; Mesh generation; mathematical modeling; finite element methods; scientific computing
        DOI
        10.1007/978-3-030-95136-8
        ISBN
        9783030951368, 9783030951368
        Publisher
        Springer Nature
        Publisher website
        https://www.springernature.com/gp/products/books
        Publication date and place
        Cham, 2022
        Imprint
        Springer
        Series
        Simula SpringerBriefs on Computing, 10
        Classification
        Mathematical modelling
        Maths for scientists
        Physiology
        Numerical analysis
        Pages
        118
        Rights
        http://creativecommons.org/licenses/by/4.0/
        • Imported or submitted locally

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        License

        • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

        Credits

        • logo EU
        • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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