Computational Modeling and Simulation Examples in Bioengineering

NENAD D. FILIPOVIC, PhD, is a Professor in the Faculty of Engineering and Head of the Center for Bioengineering at the University of Kragujevac, Serbia. He also leads national and international projects in bioengineering and software development, including joint research projects with Harvard University and the University of Texas. He is a Managing Editor for the Journal of the Serbian Society for Computational Mechanics and a member of IEEE, European Society of Biomechanics (ESB).

• Editor Biography xiAuthor Biographies xiiPreface xv1 Computational Modeling of Abdominal Aortic Aneurysms 1Nenad D. Filipovic1.1 Background 11.2 Clinical Trials for AAA 21.3 Computational Methods Applied for AAA 31.4 Experimental Testing to Determine Material Properties 61.5 Material Properties of the Aorta Wall 81.6 ILT Modeling 91.7 Finite Element Procedure and Fluid–Structure Interaction 121.7.1 Displacement Force Calculations 121.7.2 Shear Stress Calculation 131.7.3 Modeling the Deformation of Blood Vessels 131.7.4 FSI Interaction 151.8 Data Mining and Future Clinical Decision Support System 161.9 Conclusions 19References 232 Modeling the Motion of Rigid and Deformable Objects in Fluid Flow 33Tijana Djukic and Nenad D. Filipovic2.1 Introduction 332.2 Numerical Model 352.2.1 Modeling Blood Flow 362.2.2 Modeling Solid–Fluid Interaction 402.2.2.1 Modeling the Motion of Rigid Particle 422.2.2.2 Modeling the Motion of Deformable Particle 452.2.3 Modeling Deformation of the Particle 462.2.3.1 Force Caused by the Surface Strain of Membrane 472.2.3.2 Force Caused by the Bending of the Membrane 512.2.3.3 Force Caused by the Change of Surface area of the Membrane 512.2.3.4 Force Caused by the Change of Volume 522.2.4 Modeling the Flow of Two Fluids with Different Viscosity that are Separated by the Membrane of the Solid 522.3 Results 542.3.1 Modeling the Behavior of Particles in Poiseuille Flow 552.3.2 Modeling the Behavior of Particles in Shear Flow 572.3.3 Modeling Behavior of Particles in Stenotic Artery 742.3.4 Modeling Behavior of Particles in Artery with Bifurcation 772.4 Conclusion 81References 823 Application of Computational Methods in Dentistry 87Ksenija Zelic Mihajlovic, Arso M. Vukicevic, and Nenad D. Filipovic3.1 Introduction 873.2 Finite Element Method in Dental Research 883.2.1 Development of FEM in Dental Research 893.2.1.1 Morphology and Dimensions of the Structures – Application of Digital Imaging Systems 903.2.1.2 FE Model – Required/Composing Structures 913.2.1.3 Simulating Occlusal Load 923.2.1.4 Boundary Conditions 943.2.1.5 Importance of Periodontal Ligament, Spongious, and Cortical Bone 953.2.2 Overview of FEM in Dental Research – Most Important Topics in the Period 2010–2020 963.2.2.1 FEM in the Research Related to Implants, Restorative Dentistry, and Prosthodontics 973.2.2.2 FEM in Analysis of Biomechanical Behavior of Structures in Masticatory Complex 1013.2.2.3 FEM in Orthodontic Research 1023.2.2.4 FEM in Studies of Trauma in the Dentoalveolar Region 1033.3 Examples of FEA in Clinical Research in Dentistry 1033.3.1 Example 1– Assessment of Critical Breaking Force and Failure Index 1043.3.1.1 Background 1043.3.1.2 Materials and Methods 1043.3.1.3 Results and Discussion 1113.3.2 Example 2 – Assessment of the Dentine Fatigue Failure 1183.3.2.1 Background 1183.3.2.2 Materials and Methods 1193.3.2.3 Results and Discussion 124References 1314 Determining Young’s Modulus of Elasticity of Cortical Bone from CT Scans 141Aleksandra Vulović and Nenad D. Filipovic4.1 Introduction 1414.2 Bone Structure 1434.3 Young’s Modulus of Elasticity of Bone Tissue 1454.3.1 Factors Influencing Elasticity Modulus 1454.3.2 Experimental Calculation of Elasticity Modulus 1464.4 Tool for Calculating the Young’s Modulus of Elasticity of Cortical Bone from CT Scans 1514.4.1 Theoretical Background 1514.4.2 Practical Application 1524.5 Numerical Analysis of Femoral Bone Using Calculated Elasticity Modulus 1574.5.1 Femoral Bone Model 1574.5.2 Material Properties 1594.5.3 Boundary Conditions 1594.5.4 Obtained Results 1614.5.4.1 Case 1 1654.5.4.2 Case 2 1654.5.4.3 Case 3 1664.5.4.4 Comparison of the Obtained Results 1664.6 Conclusion 169Acknowledgements 169References 1705 Parametric Modeling of Blood Flow and Wall Interaction in Aortic Dissection 175Igor B. Saveljic and Nenad D. Filipovic5.1 Introduction 1755.2 Medical Background 1775.2.1 Circulatory System 1775.2.2 Aorta 1785.2.3 Structure and Function of the Arterial Wall 1795.2.4 Aortic Dissection 1815.2.5 History of Aortic Dissection 1825.2.6 Classification of Aortic Dissection 1825.2.7 Diagnostic Techniques 1855.2.7.1 Aortography 1855.2.7.2 Computed Tomography 1855.2.7.3 Echocardiography 1865.2.7.4 Magnetic Resonance 1865.2.7.5 Intravascular Ultrasound 1875.2.8 Treatment of Acute Aortic Dissection 1875.2.8.1 Drug Therapy 1875.2.8.2 Surgical Treatment 1885.3 Theoretical Background 1895.3.1 Continuum Mechanics 1895.3.1.1 Lagrange and Euler’s Formulation of the Material Derivative 1895.3.1.2 Law of Conservation of Mass 1915.3.1.3 Navier–Stokes Equations 1925.3.1.4 Equations of Solid Motion 1935.3.2 Solid–Fluid Interaction 1965.4 Blood Flow in the Arteries 1965.4.1 Stationary Flow 1975.4.2 Oscillatory (Pulsating) Flow 1985.4.3 Flow in Curved Pipes 1995.4.4 Blood Flow in Bifurcations 2005.5 Numerical Simulations 2015.6 Conclusions 213References 2136 Application of AR Technology in Bioengineering 219Dalibor D. Nikolic and Nenad D. Filipovic6.1 Introduction 2196.2 Review of AR Technology 2206.2.1 Augmented Reality Devices 2206.2.2 AR Screen Based on the Monitor 2216.2.3 AR Screen Based on Mobile Devices 2216.2.4 Head Mounting Screen 2216.2.5 AR in Biomedical Engineering 2246.3 Marker-based AR Simple Application, Based on the OpenCV Framework 2276.3.1 Generating ArUco Markers in OpenCV 2296.4 Marker-less AR Simple Application, Based on the OpenCV Framework 2356.4.1 Use Feature Descriptors to Find the Target Image in a Video 2366.4.2 Calculating the Camera-intrinsic Matrix 2476.4.3 Rendering AR with a Simple OpenGL Object (Cube) 2506.5 Conclusion 255References 2557 Augmented Reality Balance Physiotherapy in HOLOBALANCE Project 259Nenad D. Filipovic and Zarko Milosevic7.1 Introduction 2597.2 Motivation 2617.3 Holograms-Based Balance Physiotherapy 2657.4 Mock-ups 2657.4.1 Meta 2 2667.4.2 HoloLens 2687.4.3 Holobox 2707.4.4 Modeling of BP in Unity 3D 2727.5 Final Version 2737.5.1 Balance Physiotherapy Hologram (BPH) 2787.5.2 BPH–MCWS Communication 2797.5.3 Speech Recognition 2867.5.4 Localization 2887.5.5 Motion Capturing 2887.5.6 Marker-less Motion Capture 2897.5.7 Marker-based Motion Capture 2907.5.8 Optical Systems 2917.5.9 World Tracking 2917.6 Biomechanical Model of Avatar Based on the Muscle Modeling 2957.6.1 Muscle Modeling 298References 3018 Modeling of the Human Heart – Ventricular Activation Sequence and ECG Measurement 305Nenad D. Filipovic8.1 Introduction 3058.2 Materials and Methods 3078.2.1 Material Model Based on Holzapfel Experiments 3098.2.2 Biaxial Loading: Experimental Curves 3098.3 Determination of Stretches in the Material Local Coordinate System 3108.4 Determination of Normal Stresses from Current Stretches 3138.4.1 Determination of Shear Stresses from Current Shear Strains 3148.5 Results and Discussion 3168.6 Conclusion 317Acknowledgements 320References 3209 Implementation of Medical Image Processing Algorithms on FPGA Using Xilinx System Generator 323Tijana I. Šušteršicˇ and Nenad D. Filipovic9.1 Brief Introduction to FPGA 3239.1.1 Xilinx System Generator 325Algorithm Exploration 326Implementing Part of a Larger Design 327Implementing a Complete Design 3279.1.2 Image Processing on FPGAs Using XSG 3279.2 Building a Simple Model Using XSG 329 Prerequisites 3309.3 Medical Image Processing Using XSG 3349.3.1 Image Pre- and Post-Processing 3349.3.2 Algorithms for Image Preprocessing 3359.3.2.1 Algorithm for Negative Image 3359.3.2.2 Algorithm for Image Contrast Stretching 3379.3.2.3 Image Edge Detection 3379.3.3 Hardware Co-Simulation 3519.4 Results and Discussion 3529.5 Conclusions 359Acknowledgments 359References 360Index 363