Table of Contents

Cover

Introduction

1 Main Medical Imaging Techniques

1.1. Introduction
1.2. X-ray imaging
1.3. Computed tomography
1.4. Magnetic resonance imaging
1.5. Ultrasound imaging
1.6. Comparison between the different medical imaging techniques
1.7. Conclusion

2 Medical Image Analysis and Processing

2.1. Introduction
2.2. Image compression
2.3. Image restoration
2.4. Image enhancement
2.5. Image analysis
2.6. Image segmentation
2.7. Image registration
2.8. Image fusion
2.9. Image understanding
2.10. Conclusion

3 Recent Methods of Constructing Finite Element Models Based on Medical Images

3.1. Introduction
3.2. X-ray-based finite element models
3.3. CT-based finite element models
3.4. MRI-based finite element models
3.5. Ultrasound-based finite element models
3.6. Conclusion

4 Main Bone Sites Modeled Using the Finite Element Method

4.1. Introduction
4.2. FE modeling of the calcaneus
4.3. FE modeling of phalanges
4.4. FE modeling of the metatarsal
4.5. FE modeling of the tibia
4.6. FE modeling of the knee
4.7. FE modeling of the femur
4.8. FE modeling of the vertebrae
4.9. FE modeling of the humerus
4.10. FE modeling of the elbow
4.11. FE modeling of the ulna
4.12. FE modeling of the wrist
4.13. Conclusion

Conclusion

References

Index

End User License Agreement

List of Tables

Chapter 1

Table 1.1. Advantages and disadvantages of X-ray imaging (Ceniccola et al. 2018)
Table 1.2. Advantages and disadvantages of CT imaging techniques (Ceniccola et a...
Table 1.3. Advantages and disadvantages of MRI (Ceniccola et al. 2018)
Table 1.4. Advantages and disadvantages of ultrasound imaging technique (Cenicco...
Table 1.5. Comparison between medical imaging techniques (Kasban et al. 2015)

Chapter 3

Table 3.1. Material properties of the spinal cage (Jalil et al. 2017)

List of Illustrations

Chapter 1

Figure 1.1. This famous image represents the oldest existing human X-ray image. ...
Figure 1.2. Frequencies and wavelengths of the different types of electromagneti...
Figure 1.3. An X-ray tube containing a cathode filament that, when heated, relea...
Figure 1.4. X-ray spectrum from a tungsten tube showing the continuous spectrum ...
Figure 1.5. Schematic representation of the radiographic imaging system (Suetens...
Figure 1.6. Radiographic images showing (left) fractures in the forearm and (rig...
Figure 1.7. Image of a DXA examination (Ceniccola et al. 2018). For a color vers...
Figure 1.8. Image of a fluoroscopy sequence while introducing two pacemaker elec...
Figure 1.9. Creation of a DSA, starting by creating an image of the hand without...
Figure 1.10. Two examples of DSA. The left angiogram refers to the upper mesente...
Figure 1.11. (A) Digital mammogram showing the skin line of the breast, the fat ...
Figure 1.12. Cross-section of the abdominal segment showing the third lumbar ver...
Figure 1.13. First clinical CT scan obtained in October 1971 at Atkinson Morley ...
Figure 1.14. Volume-rendered CT scan of the head (Maier et al. 2018). For a colo...
Figure 1.15. Geometry of a 1G scanner (Prince and Links 2015)
Figure 1.16. Geometry of a 2G scanner (Prince and Links 2015)
Figure 1.17. Geometry of a 3G scanner (Prince and Links 2015)
Figure 1.18. Geometry of a 4G scanner (Prince and Links 2015)
Figure 1.19. Geometry of a 6G scanner or helical scanner (Aichinger et al. 2011)
Figure 1.20. Geometry of a 7G scanner or MDCT scanner (Adam et al. 2014)
Figure 1.21. Perfusion CT showing an infarct of the left middle cerebral artery ...
Figure 1.22. Dual-energy CT showing (left) a single-energy axial CT scan, (middl...
Figure 1.23. Schematic representation of the relative positions of the different...
Figure 1.24. Representation of the hydrogen atom nucleus rotating (spinning) as ...
Figure 1.25. Hydrogen atoms schematically represented by their axes of rotation:...
Figure 1.26. Excitation and precession of the magnetization vector . This figur...
Figure 1.27. Precession and relaxation of the white substance according to the t...
Figure 1.28. Difference between the (a) T1 and (b) T2 weighted images of brain c...
Figure 1.29. Acoustic wave spectrum (Mikla and Mikla 2013). For a color version ...
Figure 1.30. Karl Dussik and his ultrasound apparatus in 1946 (Christian et al. ...
Figure 1.31. Piezoelectric effect (Maier et al. 2018)
Figure 1.32. Typical components of a conventional single-element transducer (Flo...
Figure 1.33. Schematic 2D representation of the principles of beam forming (a–c)...
Figure 1.34. Ultrasound imaging showing a normal skull (left) and fluid-filled c...
Figure 1.35. Ophthalmic ultrasound showing (left) a normal eye and (right) a col...
Figure 1.36. Ultrasound image of an eye showing a malignant melanoma at the top ...
Figure 1.37. Thyroid ultrasound showing a slight bilateral enlargement (arrows),...
Figure 1.38. Transverse ultrasound of a fetus abdomen, showing a fluid-filled an...
Figure 1.39. Doppler color flow imaging used for vascular assessment. The color ...
Figure 1.40. Transesophageal echocardiography showing an atrial septal defect (S...

Chapter 2

Figure 2.1. Window and level effect on a slice obtained using a C-arm system, sh...
Figure 2.2. Examples of grayscale histogram textures: (a) dispersion, (b) varian...
Figure 2.3. Examples of edge detection: (a) Sobel filter magnitude, (b) Sobel fi...
Figure 2.4. Original MRI of slices 62 and 90 with user-defined initial pixels (t...
Figure 2.5. MRI before (a) and after (b) the application of K-means clustering (...
Figure 2.6. Development process from the initial contour curve (the first column...
Figure 2.7. Atlas-based segmentation process (Cuadra 2003). For a color version ...
Figure 2.8. Examples of rigid transforms: (a) reference image, (b) similarity tr...
Figure 2.9. Example of local (non-rigid) deformation (Duy 2009)

Chapter 3

Figure 3.1. Loading/constraint conditions used to assess the risk of femur fract...
Figure 3.2. Process for constructing a DXA-based FE model: (a) contour of the pr...
Figure 3.3. Loading conditions of the proximal femur used to simulate a sideways...
Figure 3.4. The proximal femur plate model with a constant thickness (Yang et al...
Figure 3.5. Patient-specific mesh design: (a) EOS images and semi-automatic 3D r...
Figure 3.6. Preparation and imaging of samples. (a) Location of the diaphyseal s...
Figure 3.7. Segmentation of the lacuno–canalicular network and generation of the...
Figure 3.8. Boundary conditions used for the FE analyses, allowing the study of ...
Figure 3.9. Cadaveric mandible and the corresponding CT scans shown in two diffe...
Figure 3.10. FE model and boundary conditions (Marques et al. 2014)
Figure 3.11. Protocol of CT-based FE modeling of the hip cartilage (Osawa et al....
Figure 3.12. 3D CT scan showing the femur, the coxal bone and the cartilage. (a)...
Figure 3.13. Multilayered FE model of the joint cartilage and the boundary condi...
Figure 3.14. Patient-specific FE model. (A) Readjustment procedures. (B) BMD dis...
Figure 3.15. Boundary conditions applied to the patient-specific FE models in th...
Figure 3.16. Final 3D model showing a human scapula, a keeled implant, bone ceme...
Figure 3.17. (A) Glenoidal region FE mesh and (B) keeled streamlined implant (Po...
Figure 3.18. (A) FE model assembly meshing with the different components. (B) Cr...
Figure 3.19. Coordinate system and boundary condition definition. The femur head...
Figure 3.20. Load distribution across the superior endplate for boundary conditi...
Figure 3.21. (a) Profiled CT scan with posterior elements. (b) CT-based aBMD (Ch...
Figure 3.22. (a) Reconstruction diagram of the FE model. (b) The loading directi...
Figure 3.23. Reconstruction image showing fracture loads and fracture initiation...
Figure 3.24. Radiographic measurement of femoral bowing, with the center of the ...
Figure 3.25. CT-based nonlinear FE model. (A) Full-length FE models were created...
Figure 3.26. FE analysis of the proximal femur plate model (e), sloped at 20% to...
Figure 3.27. Simulated L4-L5 bone (Jalil et al. 2017). For a color version of th...
Figure 3.28. Boundary and loading conditions of the FE model, where the red node...
Figure 3.29. Schematic representation of the algorithm used for reconstructing t...
Figure 3.30. X-ray measurement of the surgical parameters of the two axial lumbo...
Figure 3.31. FE models of the axial lumbosacral axial interbody fusion surgery. ...
Figure 3.32. Axial, sagittal and coronal slice, as well as the 3D plots of the t...
Figure 3.33. High spatial resolution MRI analysis workflow. (A) Generation of a ...
Figure 3.34. (a) Boundary condition diagram simulating the forces exerted on the...
Figure 3.35. Schematic flowchart showing the different steps of the numerical st...
Figure 3.36. (a) A pre-cut sample of aluminum foam. (b) μΟΤ reconstructed from a...
Figure 3.37. Removal of geometric artifacts from the reconstructed geometry of a...
Figure 3.38. (a) Hemispherical artificial boundary with the virtual cube and the...
Figure 3.39. (a) UCT-based 2D attenuation map, with the pixel number shown along...

Chapter 4

Figure 4.1. FE model of the ankle–foot complex showing the top and side views of...
Figure 4.2. (A) FE models of the human foot and the supporting surface, consisti...
Figure 4.3. The displacements of the fracture line with (top) the medial plate a...
Figure 4.4. The results of shear and compressive stress on the femoral cartilage...
Figure 4.5. Boundary conditions of the FE model of the proximal femur (Ben Kahla...
Figure 4.6. (A) Posteroanterior radiographs and FE modeling of the initial curve...
Figure 4.7. Cortical thickness mapping workflow, showing (above) the transformat...
Figure 4.8. Boundary conditions applied to the FE proximal humerus model. The bo...
Figure 4.9. Subject-specific FE ulno-humeral joint model (Renani et al. 2017). F...
Figure 4.10. (a) Healthy distribution and (b) the distribution of von Mises stre...

First published 2019 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.

Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address:

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The rights of Rabeb Ben Kahla, Abdelwahed Barkaoui and Tarek Merzouki to be identified as the authors of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988.

Library of Congress Control Number: 2019948686

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ISBN 978-1-78630-518-3

Introduction

Investigation of the inside of the human body can be carried out directly, through surgical intervention, or indirectly, using, for example, autonomous encapsulated cameras or endoscopes. The latter mainly consists of flexible or rigid optical tubes, taking images at the distal end while passing into the body through an orifice or surgical opening and transmitting these images to the proximal end. However, these two investigation methods are invasive techniques, causing potential damage or injuries.

The need for less invasive methods to visualize the body anatomy and internal composition has led to the development of various medical imaging techniques. These techniques have enjoyed a spectacular expansion due to the significant contributions from computer science, engineering, medical physics, applied mathematics, biology and chemistry. In general, a medical imaging process involves the use of an energy source moving through the body to create images of its internal anatomy or of the region of interest, and to provide relevant information on the composition and functioning of different biological structures. X-ray imaging, computed tomography (CT), magnetic resonance imaging (MRI), as well as ultrasound (US) imaging are the most commonly used medical imaging techniques. Each has its advantages and weaknesses, as well as its specific indications and application areas.

Furthermore, medical images obtained using these techniques may subsequently be used to reconstruct two-dimensional (2D) or three-dimensional (3D) models of the target organs or structures. To do this, several numerical methods may be used, such as the finite element (FE) method, which is one of the most widely used. The FE method involves the construction of models according to a three-step planning process, consisting of pre-mesh, mesh and post-mesh. During the pre-mesh planning, medical images are segmented and registered, surfaces are smoothed and the regions of interest are extracted, using specific scientific software. The meshes are then generated using several approaches, based on different algorithms depending on the element size and type (triangular, tetrahedral, hexahedral, brick, etc.). Creating tetrahedral elements for 3D volume meshing is relatively easy, as the construction of the raw elements requires the connection of several vertices. From a computational perspective, the brick elements are more accurate, but their construction requires complicated computer-assisted design surface topology, in addition to other efforts. After the preliminary mesh generation, verifying element quality is important to provide a strong solution for the FE model. Precise material property assignment must be monitored to ensure the rigidity required for tissue meshes. However, segmentation is considered as the most important and difficult step in processing and analyzing medical images, because addressing the intensity inhomogeneity in the regions to segment is challenging, which influences the outcome of the entire analysis. More specifically, the intensity inhomogeneity usually results from flaws in the image acquisition process, while the background represents the basic anatomical structure of many medical images. The segmentation process consists of extracting the region of interest using an automatic or semi-automatic process, and various segmentation methods have been used in several medical applications.

Digital models based on data from medical images have recently been widely used in biomechanics. In particular, digital bone models are largely used in biomechanical engineering to better understand bone behavior and the fracturing process by evaluating the fracture risk based on image diagnosis. Digital bone fracture modeling is not an easy task to perform, since bone is a heterogeneous composite material and its modeling depends mainly on its mechanical properties. It should be noted that accurate predictions are tightly linked to realistic characterization of the material behavior. Digital models can be obtained from volumetric images using two main categories of procedure, namely the voxel-based method and the geometry-based method, both of which are based on the FE method. Developed in mechanical engineering, the FE analysis was first introduced in orthopedic biomechanics in the early 1970s, in order to evaluate the stresses to which human bones are subjected. Since then, this analysis method has been widely used to study bone mechanics.

In this book, Chapter 1 covers the physical principles and applications, as well as the advantages and limitations, of the four most common medical imaging techniques used in bone diagnosis and investigation. Chapter 2 covers the main steps of processing medical images generated using these four techniques. Chapter 3 is a review of recent studies on the FE model of reconstruction of bone structure using medical images, and finally Chapter 4 is a review of recent studies using the FE method to model and analyze bone behavior at several sites on the human skeleton.