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|a 9783030951368
|q (electronic bk.)
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|a (MiAaPQ)EBC6897088
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|a (Au-PeEL)EBL6897088
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|a (OCoLC)1301515755
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|a MiAaPQ
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|a TA342-343
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|a Mardal, Kent-André.
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|a Mathematical Modeling of the Human Brain :
|b From Magnetic Resonance Images to Finite Element Simulation.
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| 250 |
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|a 1st ed.
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| 264 |
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|a Cham :
|b Springer International Publishing AG,
|c 2022.
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| 264 |
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|c {copy}2022.
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| 300 |
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|a 1 online resource (129 pages)
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 490 |
1 |
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|a Simula SpringerBriefs on Computing Series ;
|v v.10
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| 505 |
0 |
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|a Intro -- Series Foreword -- Foreword -- Preface -- Contents -- Chapter 1 Introduction -- 1.1 A model problem -- 1.2 On reading this book -- 1.3 Datasets and scripts -- 1.4 Other software -- 1.5 Book outline -- Chapter 2 Working with magnetic resonance images of the brain -- 2.1 Human brain anatomy -- 2.2 Magnetic resonance imaging -- 2.2.1 Structural MRI: T1- and T2-weighted images -- 2.2.2 Diffusion-weighted imaging and diffusion tensor imaging -- 2.3 Viewing and working with MRI datasets -- 2.3.1 The DICOM file format -- 2.3.2 Working with the contents of an MRI dataset -- 2.4 From images to simulation: A software ecosystem -- 2.4.1 FreeSurfer for MRI processing and segmentation -- 2.4.2 NiBabel: A python tool for MRI data -- 2.4.3 SVM-Tk for volume mesh generation -- 2.4.4 The FEniCS Project for finite element simulation -- 2.4.5 ParaView and other visualization tools -- 2.4.6 Meshio for data and mesh conversion -- 2.4.7 Testing the software pipeline -- Chapter 3 Getting started: from T1 images to simulation -- 3.1 Generating a volume mesh from T1-weighted MRI -- 3.1.1 Extracting a single series from an MRI dataset -- 3.1.2 Creating surfaces from T1-weighted MRI -- 3.1.3 Creating a volume mesh from a surface -- 3.2 Improved volume meshing by surface preprocessing -- 3.2.1 Remeshing a surface -- 3.2.2 Smoothing a surface file -- 3.2.3 Preventing surface intersections and missing facets -- 3.3 Simulation of diffusion into the brain hemisphere -- 3.3.1 Research question and model formulation -- 3.3.2 Numerical solution of the diffusion equation -- 3.3.3 Implementation using FEniCS -- 3.3.4 Visualization of solution fields -- 3.4 Advanced topics for working with larger cohorts -- 3.4.1 Scripting the extraction of MRI series -- 3.4.2 More about FreeSurfer's recon-all -- Chapter 4 Introducing heterogeneities.
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|a 4.1 Hemisphere meshing with gray and white matter -- 4.1.1 Converting pial and gray/white surface files to STL -- 4.1.2 Creating the gray and white matter mesh -- 4.1.3 More about defining SVM-Tk subdomain maps -- 4.2 Separating the ventricles from the gray and white matter -- 4.2.1 Extracting a ventricular surface from MRI data -- 4.2.2 Removing the ventricular volume -- 4.3 Combining the hemispheres -- 4.3.1 Repairing overlapping surfaces -- 4.3.2 Combining surfaces to create a brain mesh -- 4.4 Working with parcellations and finite element meshes -- 4.4.1 Mapping a parcellation onto a finite element mesh -- 4.4.2 Mapping parcellations respecting subdomains -- 4.5 Refinement of parcellated meshes -- 4.5.1 Extending the Python interface of DOLFIN/FEniCS -- 4.5.2 Refining certain regions of parcellated meshes -- Chapter 5 Introducing directionality with diffusion tensors -- 5.1 Extracting mean diffusivity and fractional anisotropy -- 5.1.1 Extracting and converting DTI data -- 5.1.2 DTI reconstruction with FreeSurfer -- 5.1.3 Mean diffusivity and fractional anisotropy -- 5.2 Finite element representation of the diffusion tensor -- 5.2.1 Preprocessing the diffusion tensor data -- 5.2.2 Representing the DTI tensor in FEniCS -- 5.2.3 A note on co-registering DTI and T1 data -- Chapter 6 Simulating anisotropic diffusion in heterogeneous brain regions -- 6.1 Molecular diffusion in one dimension -- 6.1.1 Analytical solution -- 6.1.2 Numerical solution and handling numerical artifacts -- 6.2 Anisotropic diffusion in 3D brain regions -- 6.2.1 Regional distribution of gadobutrol -- 6.2.2 Accuracy and convergence of computed quantities -- Chapter 7 Concluding remarks and outlook -- References -- Index.
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| 588 |
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|a Description based on publisher supplied metadata and other sources.
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| 590 |
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|a Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2023. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
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| 655 |
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4 |
|a Electronic books.
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| 700 |
1 |
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|a Rognes, Marie E.
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| 700 |
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|a Thompson, Travis B.
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| 700 |
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|a Valnes, Lars Magnus.
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| 776 |
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8 |
|i Print version:
|a Mardal, Kent-André
|t Mathematical Modeling of the Human Brain
|d Cham : Springer International Publishing AG,c2022
|z 9783030951351
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| 797 |
2 |
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|a ProQuest (Firm)
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| 830 |
|
0 |
|a Simula SpringerBriefs on Computing Series
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| 856 |
4 |
0 |
|u https://ebookcentral.proquest.com/lib/matrademy/detail.action?docID=6897088
|z Click to View
|
