Computational Biomechanics for Medicine

Name: Computational Biomechanics for Medicine | Solid and Fluid Mechanics for the Benefit of Patients
Brand: Springer
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Solid and Fluid Mechanics for the Benefit of Patients

Karol Miller Adam Wittek Grand Joldes Martyn P. Nash Poul M. F. Nielsen(Herausgeber*in)

Springer (Verlag)

1. Auflage

Erschienen am 17. August 2020

VIII, 200 Seiten

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978-3-030-42428-2 (ISBN)

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Inhalt

Intro
Preface
Contents
What Has Image Based Modelling of Cerebrospinal Fluid Flow in Chiari Malformation Taught Us About SyringomyeliaMechanisms?
Part I Computational Solid Mechanics
Lung Tumor Tracking Based on Patient-Specific Biomechanical Model of the Respiratory System
1 Introduction
2 Materials and Methods
2.1 Anatomy and Physiology of the Respiratory System
2.2 3D Segmentation and CAD Reconstruction
2.3 Biomechanical Patient-Specific Model of the Respiratory System
2.4 The Boundary Conditions
3 Results and Experimental Validation
3.1 Mesh Quality
4 Discussion and Conclusion
References
Design of Auxetic Coronary Stents by Topology Optimization
1 Introduction
2 Parametric Level-Set Method
3 Numerical Homogenization Method
4 The First Optimization Stage for the Design of Auxetics
4.1 The Concurrent Optimization Scheme
4.2 The Sensitivity Analysis
4.3 Numerical Results
5 The Second Optimization Stage and the Numerical Validation
6 Conclusion
References
Physics-Based Deep Neural Network for Real-Time Lesion Tracking in Ultrasound-Guided Breast Biopsy
1 Introduction
2 Methods
2.1 The U-Net Architecture
2.2 Simulation of Breast Tissue Using Hexahedral Grids
2.3 Data Generation
3 Experiments and Results
3.1 Predict Displacement on Synthetic Data Sets
3.2 Predict Displacement on Phantom Data
4 Conclusion
References
An Improved Coarse-Grained Model to Accurately Predict Red Blood Cell Morphology and Deformability
Abbreviations
1 Introduction
1.1 RBC Cellular Structure
1.2 RBC Morphology
1.3 RBC Deformability
1.4 Numerical Investigations on RBC Morphology and Deformability
1.5 Numerical Predictions of the Equilibrium RBC Morphology
2 Development of the CG-RBC Membrane Model
2.1 Free-Energy of the CG-RBC Membrane
2.2 Construction of Initial Spherical Geometry for RBC Membrane
2.2.1 Required Minimum Triangulation Quality of the RBC Membrane
2.2.2 Required Minimum Resolution for the CG-RBC Membrane
2.3 Cytoskeletal Reference State of the CG-RBC Membrane
2.4 Computational Implementation of the CG-RBC Membrane Model
2.5 The CG-RBC Membrane Model Predicted Discocyte Morphology
2.5.1 The CG-RBC Membrane Model Parameters for Discocyte Morphology Prediction
2.5.2 Evolution of Cell Shape, Membrane Free-Energy and Forces During Discocyte Morphology Prediction
2.6 Validation of the CG-RBC Membrane Model Through Predicted Discocyte Morphology
3 Deformation Behaviour of Discocyte During Optical Tweezers Stretching
3.1 Implementation of Optical Tweezers Stretching
3.2 Validation of the CG-RBC Membrane Model Predicted Discocyte Deformation Behaviour
4 Summary and Conclusions
Bibliography
Development of a Computational Modelling Platform for Patient-specific Treatment of Osteoporosis
1 Introduction
2 Methods
2.1 Mechanistic Tissue-Scale Model of Bone Remodeling
2.2 Osteocyte-Driven Mechanical Feedback
2.2.1 Modelling the Mechanical Feedback: Modulation of Osteocytes Biochemical Response
2.2.2 Modelling the Action of Sclerostin on Bone Remodeling
2.2.3 Modelling the Action of Nitric Oxide on Bone Remodeling
2.2.4 Closing the Feedback Loop of the Bone Remodeling `mechanostat'
2.3 Integrating Post-menopausal Osteoporosis Pathophysiology into a Mechanistic Framework of Bone Remodeling
2.4 Modelling Osteoporosis Treatment with Anti-sclerostin Antibody Therapy: From Drug Pharmacokinetics to the Quantification of Bone Remodeling
3 Results
3.1 Osteocytes Anabolic and Catabolic Responses to Mechanical Stimuli
3.1.1 Influence of Mechanically-Controlled Signalling Pathways in PMO
3.1.2 Influence of Osteocytes Density on Bone Mechanobiological Feedback: The Example of Glucocorticoid-Induced Osteoporosis
3.2 Modelling Osteoporosis-Induced Bone Loss at Different Bone Sites
3.3 Virtual Anti-sclerostin Therapy of Post-menopausal Osteoporosis
4 Discussion and Outlook
Appendix
References
Part II Topics in Patient-Specific Computations
Towards Visualising and Understanding Patient-Specific Biomechanics of Abdominal Aortic Aneurysms
1 Introduction
2 Methods
2.1 Synthetic Data
2.2 Voxel Displacement Tracking
2.3 Determining Maximum Principal Stretch
2.4 Determining Maximum Principal Tension
2.5 New Rupture Risk Index
2.6 Validation of Techniques
3 Results
3.1 Validation of Displacement Tracking
3.2 Maximum Principal Stretches
3.3 Relative Structural Integrity Index (RSII)
4 Discussion
References
Pipeline for 3D Reconstruction of Lung Surfaces Using Intrinsic Features Under Pressure-Controlled Ventilation
1 Introduction
2 Methodology
2.1 Lung Ventilation
2.2 Lung Surface Imaging
2.2.1 Stereo Rig Construction
2.2.2 Stereo Rig Calibration
2.2.3 Initial Surface Reconstruction
2.3 Lung Fixing and Micro Computed Tomography (CT) Imaging
2.4 Improving Lung Surface Reconstruction and Tracking Motion
3 Results
3.1 Tracking of Intrinsic Features
3.2 3D Reconstruction Results
4 Summary
References
A Flux-Conservative Finite Difference Scheme for Anisotropic Bioelectric Problems
1 Introduction
2 Methods
2.1 Electromagnetic Modeling Using the Flux-Conservative Finite Difference Method
2.1.1 Governing Equations
2.2 Flux-Conservative Finite Difference Method
3 Results
3.1 Verification of the FCFD Scheme
3.2 Patient-Specific Head Model
4 Conclusion
References
A Fast Method of Virtual Stent Graft Deployment for Computer Assisted EVAR
1 Introduction
2 Methods
2.1 Data Acquisition
2.2 Corotational Euler-Bernoulli Beam Elements
2.3 Stage 1 (Preliminary Stage)
2.3.1 Barycenter Positioning
2.3.2 Geometric Stent Reconstruction
2.4 Stage 2 (Refining Stage)
2.4.1 Minimization of the Rotational Difference
2.4.2 Individual Stent Deployment
3 Proof of Concept
3.1 Clinical Data
3.2 Quality Assessment of Stent Deployment
3.3 Results and Discussion
4 Conclusion
References
Efficient GPU-Based Numerical Simulation of Cryoablation of the Kidney
1 Introduction
2 Mathematical Model for Cryoablation
2.1 Temperature Model
2.2 Freezing Model
2.3 Damage Model
3 Tissue Parameter Model
4 Discretization
4.1 Temporal Discretization
4.2 Spatial Discretization
4.3 Discretization of Material Parameters
5 Numerical Implementation
6 Numerical Examples
7 Summary, Conclusions, and Future Work
References
Index

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