DIGITAL HUMAN PHANTOM FOR USE IN THE FDTD COMPUTATION

Transcription

1 DIGITAL HUMAN PHANTOM FOR USE IN THE FDTD COMPUTATION A THESIS SUBMITTED TO THE UNIVERSITY OF MANCHESTER FOR THE DEGREE OF MASTER OF PHILOSOPHY IN THE FACULTY OF ENGINEERING AND PHYSICAL SCIENCES 2012 By Onur Sahin School of Electrical and Electronic Engineering

2 Contents Abstract 11 Declaration 12 Copyright 13 Acknowledgements 14 1 Introduction Aims and Objectives Dissertation Overview Spinal Cord Stimulation Invasive Electric Stimulation Proposed Electromagnetic Non-invasive Spinal Cord Stimulation Theoretical Study and Real Time Full Wave EM Stimulation of EM-TSCS Numerical Simulation EM Characteristics of Human Tissues Numerical Phantom Conclusion Digital Human Phantoms Historical Background of Computational Models Types of Model Mathematical Models Tomographic Models Hybrid Models

3 4.2.4 Comparison of Stylized, Voxel and Hybrid Computational Models High Spatial Resolution Look-Up Table Based Method Smoothing in Two Dimensions Smoothing in Three Dimensions Conclusion The Different Approaches to the LUT Method The Previous Approach The Present Approach Performance Evaluation The Comparison of the Accuracy of the Two Approaches The Comparison of the Speed of the Two Approaches Conclusion Conclusion and Future Work Conclusion Future Work A Spinal Cord 73 A.1 Anatomy of the Spinal Cord A.2 Causes of Spinal Cord Injury A.3 Traditional Treatment for Spinal Cord Injuries A.3.1 Surgery A Surgical Procedures A Surgical Risks A.3.2 Medication A.3.3 Rehabilitation B Calculation Methods for Phantom 82 C Method for Scaling and Smoothing 84 C.1 Smoothing in Two Dimensions C.1.1 Scaling by 6 times and Smoothing C.1.2 Scaling by 4 times and Smoothing

4 C.2 Smoothing in Three Dimensions C.2.1 Scaling by 6 times and Smoothing C.2.2 Scaling by 4 times and Smoothing C.3 The Look-Up Table C.3.1 The List of Pixel Combinations for Up-Scaling by 6 times C.3.2 The List of Pixel Combinations for Up-Scaling by 4 times Bibliography 137 4

5 List of Tables 4.1 Comparison between stylized, voxel and hybrid computational models in terms of anatomic realism and flexibility in organ and body contour changes [1] The structure of a simple pgm data file A.1 Advantages and disadvantages of anterior and posterior approaches [2]. 80 B.1 Parameters of Layered-Sphere Model (f = 2 GHz) [3]

6 List of Figures 3.1 Example of electrical characteristics of tissues over range of 1 MHz to 100 GHz at 37 o C Evolution of computational models of the human anatomy from the crude ICRU spherical model to more realistic and complex (personspecific) computational models Stylized adult male/female models showing (a) exterior view of the adult male, (b) skeleton and internal organs of the adult male/female High-resolution whole-body Japanese human voxel model Surface renderings of the (A) muscle tissue, and (B) skeleton and organs of the new 4D NCAT phantom. Anterior views are shown Comparison of various models of the human alimentary tract: (a) ORNL newborn stylized phantom, (b) UF newborn voxel phantom and (c) UF newborn hybrid phantom The structure of increasing the size of a matrix (2 2 pixels) by 2 times and 4 times First step of working principle of the Look-Up Table based method Second step of working principle of the Look-Up Table based method Cross section of 20 percent of the human phantom i-plane number 150, scaled by 6 times and smoothed with the LUT method. The left hand side of the figure is the left of DHP and the right hand side of the figure is the right of DHP The original image (50 40) was produced manually The original image is scaled by 4 times ( ) and smoothed with the LUT method The original image is scaled by 6 times ( ) and smoothed with the LUT method

7 5.8 Cross section of half of k-plane number 450 from the digital human phantom, scaled by 4 times without smoothing (top) and smoothed with the LUT method (bottom). The bottom of the figure is the left of DHP and the top of the figure is the right of DHP. The left hand side of the figure is the front of DHP and the right hand side of the figure is the back of DHP Cross section of half of k-plane number 450 from the digital human phantom, scaled by 6 times without smoothing (top), smoothed with the LUT method (bottom). The bottom of the figure is the left of DHP and the top of the figure is the right of DHP. The left hand side of the figure is the front of DHP and the right hand side of the figure is the back of DHP Procedure for the set up of the size of the digital human phantom The sample image k-plane number 10, scaled by 6 times without smoothing (on the left), smoothed with the LUT method (on the right) The sample image j-plane number 20, scaled by 6 times without smoothing (on the left), smoothed with the LUT method (on the right) The sample image i-plane number 25, scaled by 6 times without smoothing (on the left), smoothed with the LUT method (on the right) The overview of the procedure in case of 3D data processing Cross section of half of the digital human phantom k-plane number 200, scaled by 4 times without smoothing (on the left), smoothed with the LUT method (on the right).the left hand side of the figure is the right of DHP and the right hand side of the figure is the left of DHP. The top of the figure is the back of DHP and the bottom of the figure is the front of DHP Cross section of the part of the digital human phantom i-plane number 150, scaled by 4 times without smoothing (on the left), smoothed with the LUT method (on the right). The left hand side of the figure is the left of DHP and the right hand side of the figure is the right of DHP Cross section of the part of the digital human phantom j-plane number 250, scaled by 4 times without smoothing (on the left), smoothed with the LUT method (on the right). The left hand side of the figure is the front of DHP and the right hand side of the figure is the back of DHP. 55 7

8 5.18 Cross section of half of the digital human phantom k-plane number 200, scaled by 6 times without smoothing (on the left), smoothed with the LUT method (on the right). The left hand side of the figure is the right of DHP and the right hand side of the figure is the left of DHP. The top of the figure is the back of DHP and the bottom of the figure is the front of DHP Cross section of the part of the digital human phantom i-plane number 150, scaled by 6 times without smoothing (on the left), smoothed with the LUT method (on the right). The left hand side of the figure is the left of DHP and the right hand side of the figure is the right of DHP Cross section of the part of the digital human phantom j-plane number 250, scaled by 6 times without smoothing (on the left), smoothed with the LUT method (on the right). The left hand side of the figure is the front of DHP and the right hand side of the figure is the back of DHP The phantom (top) i-plane 220 from Michael s work, scaled by 6 times and smoothed with the LUT method, the phantom (bottom) i-plane 150 from my work, scaled by 6 times and smoothed with the LUT method. The bottom of the figures is the left of DHP and the top of the figures is the right of DHP A.1 The central nervous system A.2 Image shows the relationship between spinal nerve roots and vertebrae 75 A.3 This MRI scan shows bulging disks pressing on the spinal cord A.4 An anterior cervical diskectomy and fusion from the side (left) and front (right). Plates and screws are used to provide stability and increase the rate of fusion A.5 Posterior laminectomy with fusion using screws and rods B.1 Layered-sphere phantom model

9 Glossary and Acronyms CRPS Complex Regional Pain Syndromes. CT Computed Tomography. This is a method to produce a three-dimensional image from a series of two-dimensional X-ray images taken around an axis of rotation. DHP Digital Human Phantom. Model of the human body is used for computerizing analysis. EM-TSCS Electromagnetic Transcutaneous Spinal Cord Stimulation. ICRP International Commission on Radiological Protection. ICRU International Commission on Radiological Units and Measurements. LUT Look Up Table. It is a data structure, used for transforming the input data into a more desirable output form. MIRD Medical Internal Radiation Dose. MoM Method of Moments. It is a method estimating population parameters. MRI Magnetic Resonance Imaging. It is used for visualizing internal structures of the body in radiology. NCAT NURBS-based CArdiac-Torso. NURBS Non-Uniform Rational B-Splines. It is used to generate and represent curves and surfaces commonly in computer graphics. ORNL Oak Ridge National Laboratory. Pixel The smallest and controllable element of a picture. 9

10 RCT Randomized Controlled Trial. It is a scientific experiment used for testing the effects of different types of intervention within a patient population. SAR Specific Absorption Rate. It is a measure of amount of energy absorbed by the body when exposed to a radio frequency. TENS Transcutaneous Electrical Nerve Stimulation. By conveying a current through the skin, electrical stimulation of nerves for relief of pain. TSE Transcutaneous Spinal Electroanalgesia. It is a form of electrical pain relief. Vertabrae Spinal bones. VHP The Visible Human Project. Voxel A volume element. It is a form represents a pixel in three dimensions. 10

11 Abstract Digital human phantoms or computational human phantoms are anthropomorphic models which are used in computerized analysis. Even though these models have been developed for radiation dosimetry, the models are being used in more medical institutions. Scientists develop new treatment methods for various types of diseases by using digital human phantoms. This thesis focuses on the construction of a digital human phantom to up-scale and smooth in two dimensions and three dimensions in the use of the Finite-Difference Time-Domain computation for producing the high spatial resolutions. 11

12 Declaration No portion of the work referred to in this thesis has been submitted in support of an application for another degree or qualification of this or any other university or other institute of learning. 12

13 Copyright i. The author of this thesis (including any appendices and/or schedules to this thesis) owns any copyright in it (the Copyright ) and s/he has given The University of Manchester the right to use such Copyright for any administrative, promotional, educational and/or teaching purposes. ii. Copies of this thesis, either in full or in extracts, may be made only in accordance with the regulations of the John Rylands University Library of Manchester. Details of these regulations may be obtained from the Librarian. This page must form part of any such copies made. iii. The ownership of any patents, designs, trade marks and any and all other intellectual property rights except for the Copyright (the Intellectual Property Rights ) and any reproductions of copyright works, for example graphs and tables ( Reproductions ), which may be described in this thesis, may not be owned by the author and may be owned by third parties. Such Intellectual Property Rights and Reproductions cannot and must not be made available for use without the prior written permission of the owner(s) of the relevant Intellectual Property Rights and/or Reproductions. iv. Further information on the conditions under which disclosure, publication and exploitation of this thesis, the Copyright and any Intellectual Property Rights and/or Reproductions described in it may take place is available from the Head of School of Electrical and Electronic Engineering (or the Vice-President). 13

14 Acknowledgements I would like to thank my project supervisor Dr. Fumie Costen for the guidance, motivation. I would like to thank Michael Knight who had done the previous study which gave some key points for my study. I would like to warmly thank my family for their perfect support and encouragement, Irfan, Kibar, Ugur and Nuray Sahin. 14

15 Chapter 1 Introduction Spinal cord injury is a common type of injuries. According to the National Spinal Cord Injury Statistical Center (NSCISC), approximately 11,000 new spinal cord injuries (SCI) from all causes are reported per year in the United States [4]. There are various reasons which cause these injuries, such as motor vehicle accidents, violence, falls, sport, industrial accidents and other kinds of causes. Many victims of SCI thus become paralyzed [4]. In addition, many individuals suffer from chronic spinal cord pain. There are some treatment methods to reduces effects of spinal cord injuries and chronic spinal cord pain such as surgery, medication, rehabilitation which are the traditionally common techniques. With the developing technology, the different types of pain treatment methods have come out. These pain treatment methods are namely invasive electric stimulation and proposed electromagnetic non-invasive spinal cord stimulation that are explained in Chapter 2. These pain treatment methods are used as alternative ways to the traditional methods for chronic spinal cord pain. Proposed electromagnetic non-invasive spinal cord stimulation is completely different from the other types of the treatment methods. This non-invasive spinal cord stimulation uses electromagnetic power which increases the temperature of the human tissues and causes action potential in the nerves. In order to understand the way the temperature increases in the spinal cord, numerical simulation is used to develop a simulation software to study the propagation of the electromagnetic wave and the heat around the spinal cord [5]. The use of numerical simulation protects living objects from harmful effects of experiments. On the other hand, numerical simulation is a complex procedure since it requires radio environment setting. For instance, in the proposed electromagnetic non-invasive spinal cord stimulation, the human body media parameter setting is required because 15

16 each human tissue has its own frequency response. Therefore, a digital human phantom, which is simulated from a human, is used in this study. 1.1 Aims and Objectives The purpose of this project is to produce data with mm spatial resolution from the currently available 1 mm resolution digital human phantom (DHP). The objectives of the project is listed below: Understanding the structure of the re-sampling and smoothing. Producing the algorithm for the re-sampling and smoothing. Improving programming skills (Fortran) to realize the algorithms. Implementation of the algorithms. Evaluation of the results. The starting point of this project is to write the program in Fortran in two dimensions by using the smaller size images produced manually. After observing the obtained results, the same process will be done in three dimensions using the sample images consisting of 10 images. In this way, more complex objects like a human phantom can be understood better. In the final part, according to the observation from the sample images, the digital human phantom will be up-scaled depending on the required spatial resolutions and then smoothed in three dimensions in order to enhance the physical realism of the human phantom. 1.2 Dissertation Overview This thesis is organized as in the following order where the project progressed: Chapter 2 defines the invasive electric stimulation and proposed electromagnetic non-invasive spinal cord stimulation. Chapter 3 explains the reason why numerical simulation is used rather than living humans. Besides, types of numerical phantoms are described with the examples. 16

17 Chapter 4 defines the digital human phantom used in this project. Furthermore, the types of model are explained and compared each other. Chapter 5 mentions the structure of high spatial resolution and presents the results of the programs coming from the sample images and the digital human phantom in two dimensions and in three dimensions. In addition, the results are obtained for each dimension in three dimensional smoothing. Chapter 6 compares the two different approaches and evaluates the performance results of these approaches. Chapter 7 summarizes the project and describes the future work. Additionally, some key points are given for the future work. Appendix A presents general information about anatomy of the spinal cord, causes of spinal cord injury, and the traditional treatment methods for spinal cord injuries. Appendix B describes numerical calculation methods such as the method of moments (MoM) and the finite-difference time domain (FDTD). Appendix C includes the Fortran source codes and the full list of pixel combinations. 17

18 Chapter 2 Spinal Cord Stimulation Spinal cord stimulation can be divided into two groups which are namely the invasive and non-invasive stimulation. The structure of these stimulations are very different from each other, that are explained in the following sections. However, the aim of the invasive and non-invasive stimulation is the same. Both of them are used to produce analgesia via the current. Therefore, invasive electric stimulation and proposed electromagnetic non-invasive spinal cord stimulation are used as pain treatment methods for chronic spinal cord pain. Additionally, there are also more information about the spinal cord given in Appendix A. 2.1 Invasive Electric Stimulation Many people are affected by chronic pain, especially elderlies who suffer from low back pain and neuropathic pain. The elderlies can use medicines for tackling the pain on condition that the people must not have overt liver and renal disease. However, their ability to analgesic medications are limited. For that reason, nonpharmacological treatment is important. There are several methods, one of them is invasive spinal cord stimulation [5]. Spinal cord stimulation (SCS) requires implanting a device which sends low currents of electrical stimulation to the spinal cord and/or its existing nerves. SCS is a minimally invasive method, because of using low currents. The working principle of SCS is that a compact generator creates small electrical impulses which are sent through thin leads, or electrical cables, to the spinal cord, in which these electrical impulses block pain signal going to the brain. After that, pain becomes a mild tingling, or a massaging sensation, which is called paresthesia. A 18

19 wireless remote control is used for adjusting the location and degree of stimulation by selecting pre-programmed settings [6]. In other words, spinal cord stimulation (neurostimulation) sends low-current electrical impulses to the spinal cord or targeted peripheral nerve to block the sensation of pain [7]. Shealby,C.N.,Mortimer,J.T.,and Resnick,J. implanted the first spinal cord stimulator to directly on the dorsal column of the spinal cord of terminal cancer patients in 1967 [6]. A couple of years later, the first successful implementation of epidural spinal cord stimulation was published by Shimoji K, Higashi H, Kano T, Asasi S, Morioka T., which is a percutaneous technique and this is a less invasive technique [6]. This technique prevents the complications of the original open surgery, which are cerebrospinal fluid leakage, localized fibrosis, and arachnoiditis. The early implantations of SCS was applied using the single, or monopolar electrode in a limited area. Since then, SCS has been improved from monopolar (1 active electrode) to bipolar (2 active electrodes), quadripolar (4 active electrodes), octapolar (8 active electrodes) leads, and 16 active electrodes that send electric current to the spinal cord [6, 5]. SCS has been used in various types of syndromes as a treatment such as cervical and lumbar post-laminectomy syndrome (failed back or neck surgery syndrome), cervical and lumbar radiculitis (neck and back radiating pain), complex regional pain syndromes (CRPS or RSD), intractable pain due to peripheral vascular disease, phantom limb pain, intractable pain due to to angina, peripheral neuropath, post-thoracotomy syndrome, neuropathic extremity pain, chronic visceral pain syndromes, and other conditions [6]. Spinal cord stimulation therapy reduces pain, and allows people who suffer from chronic pain to maintain their life. In addition, there are many study reports proving the safety and efficacy of SCS, some of them are given below. T. Cameron has done over 68 studies, on 3679 patients. According to Cameron s studies, SCS was a safe and effective treatment for different chronic neuropathic conditions. Another research was published by Kemler MA, Barendse GA, Kleef M, de Vet HC [8]. They reported in a randomized, controlled trial (RCT) of 56 patients. The result of this research is that SCS can reduce pain and offer health-related quality of life. A different research was reported by Burchiel KJ, Anderson VC, Brown FD in a RCT of 70 patients [8]. These patients were treated with spinal cord stimulation. This 1 year of SCS can bring significant long-term improvement in pain, quality of life to patients with chronic back pain and extremity pain. Another study was done by Kumar K, Toth C [8]. In this 19

20 study, SCS was applied on 182 patients with post-laminectomy pain, resulted successful management of pain in 53 % of patients [8]. However, spinal cord stimulation has some risks, when the implantation cannot be performed properly. The possible risks may include headache possibility from leakage of spinal fluid, bladder problems, scar formation around the electrodes, equipment failure that leads to intermittent or over-stimulation, disconnection, and lead migration. All these complications may require additional procedures to fix [6]. 2.2 Proposed Electromagnetic Non-invasive Spinal Cord Stimulation Non-invasive pain treatments currently exist such as Transcutaneous Spinal Electroanalgesia (TSE) and Transcutaneous Electrical Nerve Stimulation (TENS) are hampered by problems with applying the electrodes. However, TENS and TSE have limited benefit in chronic painful conditions [5]. The aim of invasive SCS and non-invasive TSE are to produce analgesia via the current, which is induced by the externally applied voltage. The exact mechanisms as why the current can produce analgesia are not completely understood. However, it is guessed that the applied current induces sensations within the spinal cord, such as tingling in SCS or spinal cord sensations in TSE, which reduces pain in the patients. A different approach is proposed in this work. Rather than applying voltage on the electrodes to force a current to flow, the proposed technique sends electromagnetic (EM) wave into the dorsal columns. When applying the EM energy on to a specific area, it is reported that nerves impulses or action potentials can be created through the mechanism of EM energy absorption. It has been demonstrated that nerve stimulation by low-intensity pulsed infrared light is possible [5]. The main point of the proposed EM Transcutaneous Spinal Cord Stimulation (EM-TSCS) is to understand the mechanism as how the temperature increase can create action potentials and how the analgesia may be produced by action potentials. The programme will include theoretical study which is mainly investigating the possible mechanism of pain relief through action potential, and real time full EM wave simulation with focused on electromagnetic propagation modelling for better understanding as how the increase of temperature may produce action potential within and around the spinal cord [5]. 20

21 2.2.1 Theoretical Study and Real Time Full Wave EM Stimulation of EM-TSCS Numerical modelling of propagation of the electromagnetic waves and temperatures. There is no doubt that by using the electromagnetic power, the temperature increase can cause the action potentials in the nerves [5]. But, the reason is not fully understood why the temperature increases in the spinal cord. Hence, theoretical study develops a stimulation software to study properly the propagation of the electromagnetic wave and the heat around the spinal cord. The following sub-tasks have to be maintained to perform the studies [5]. Study 1 Theoretical study. Increasing temperature can affect ion channel gating rates in central (mainly brain) and peripheral nerves. In addition, increased temperature has been supposed as a reason of febrile seizures. In humans, passive hyperpyrexia models have shown alterations in the cortical drive to the muscle. Shortening of action potential duration and increasing conduction speed are also related to increased temperature. Furthermore, changes in temperature affect the size and shape of excitatory and inhibitory responses at the synapse level [5]. Study 2 Human body media parameter setting. A narrowly focused point can be produced at a high frequency region, rather than the low frequency region. Thus the modulated Gaussian pulses will be used for the excitation. The excitation signal will have a wide range of frequencies at the array antenna and every single human tissue has its own frequency response. The frequency dependency of the each tissue is modelled using the Debye relaxation model in the FDTD method. The Debye parameters are data-fitted using US Air Force s measurement. There are some commercial software has frequency dependent on human tissues, but their media parameters are fixed inside the software. On the other hand, the media parameters of each human tissue show differences among the individuals. Hence the measurement data are varied %. In addition, the media parameters are identified, which become the worst case for the safety assessment [5]. Study 3 Inclusion of the Penne s bio-heat equation and thermal convection by blood flow in the 3D full-wave electromagnetic wave propagation. The electromagnetic power is an effect which increases the temperature of the human tissues. But heat flow is coming from both the thermal conduction and convection by blood perfusion. Thus these two mechanisms are introduced to the FDTD method in order to perform the stimulation of the heat propagation around the spinal cord. The outcome of Study 2 presents general understanding of the increase of the temperature around the spinal 21

22 cord [5]. Study 4 Investigation on the creation of action potentials caused by the increase of the temperature. The aim is to mathematically modelling the action potentials and how they react with electromagnetic wave. Effects of the frequency, power, polarization will be examined in near and far field scenarios. The outcomes of the study will be used to provide guideline for the antenna design. The designed antenna effectively increases the temperature at a certain location in the spinal cord. The parameters related to the antenna array such as the excitation waveform, power, frequency range, polarization and gain of the antenna array will be identified by optimization procedure using the time-reversal algorithm [5]. 22

23 Chapter 3 Numerical Simulation With the development of the wireless communications technology and the use of mobile phone and other wireless devices that have caused increasing public concern about the hazardous effect of electromagnetic radiation on the human phantom. On the other hand, electromagnetic radiation can also be used for positive aims. For instance, bioelectromagnetic therapy, where certain tissues of the human body are simulated by electromagnetic fields, is used as a treatment method for some neurological diseases. Recently researches on the effect of mobile phone radiation applied on mice put forward an idea that, mobile phones might protect against Alzheirmer s diseases. However, these researches cannot be applied on the living human being for studying dosimetry or other bioelectromagnetic aspects, because of ethical restrictions. For that reason, numerical stimulation is the alternative way to do experiments [9]. The finitedifference time-domain (FDTD) method is the most popular electromagnetic numerical technique, because of its robustness and simplicity. Additionally, FDTD is well suited in order to model complex and irregularly shaped objects such as the human body [9, 10]. The FDTD method can be easily parallelized with small modification to the algorithm, which is one of the most remarkable features of the FDTD method. The parallel FDTD algorithm is based on the space decomposition technique, for the FDTD method solves Maxwell equations in the time-space domain. The message passing interface (MPI) library provides the data transfer functionality between processors (PCs). Data exchange is required only for the adjacent cells at the interface between different subdomains and it is performed at each time step. Therefore, the FDTD algorithm is a self-synchronized process. At the end of parallel FDTD simulations, the results are 23

24 calculated at each note and then these results are combined to obtain the final simulation result [10]. In order to develop body-centric wireless communication devices, interactions between the human body and the electromagnetic (EM) waves, which are radiated from the devices, have to be evaluated. The interactions mean two ways that are an influence of the human body on the performance of the devices and an influence of EM waves on the human body. These interactions are estimated using numerical simulation [11]. 3.1 EM Characteristics of Human Tissues The knowledge of the dielectric characteristics of tissues is extremely important to use biological tissue-equivalent phantoms in the evaluation of the interactions. Depending on the frequency, tissue, position, temperature, water content, etc, electrical and thermal characteristics of biological tissues show some changes. Inaccurate dielectric properties badly affect various parameters such as radiation pattern, EM distributions, specific absorption rate (SAR), etc. Therefore, it is necessary to use accurate properties of human tissues in order to realize the precision evaluation of interactions [11]. To understand electrical characteristics of tissues, there is an example shown in Figure 3.1. The example illustrates dielectric properties of muscle (high water-content tissue) and that of fat (low water-content tissue) over frequency range of 1 MHz to 100 GHz at 37 o C. In Figure 3.1, it is shown that the electrical properties (permittivity and conductivity) of tissues depend on their frequencies and tissue types. The characteristics of the human tissues are measured and then mathematically data-fitted using Cole- Cole models or Debye models [11]. Here, this work uses the 4-Cole-Cole equation. As it is shown in Figure 3.1, there are remarkable differences in relative permittivity and conductivity across a wide frequency range for muscle and fat. The reason is the difference of water content. However, relatively small changes are observed in the range 100 MHz to around 1 GHz [11]. 3.2 Numerical Phantom There are many numerical phantoms used for the theoretical analysis and computational simulations. In theoretical analysis, simple-shaped phantoms, which are called theoretical phantoms, are mostly used. On the other hand, use of realistic numerical phantoms is also necessary in order to calculate the characteristics of the antennas close 24

25 Figure 3.1: Example of electrical characteristics of tissues over range of 1 MHz to 100 GHz at 37 o C [11]. to the human body in the actual situation. These realistic numerical phantoms consist of many voxels. For that reason, these phantoms are called voxel phantoms [11]. These models are explained in Chapter Conclusion Here it has been seen that numerical models can be obtained using electrical parameters which simulate the human tissue. In order to achieve whole-body human model, electrical parameters of each human tissue have to be considered so that numerical simulation requires radio environment setting depending on the human tissue. Therefore, 1 mm digital human phantom produced from MRI data is used in this work for the radio environment setting. 25

26 Chapter 4 Digital Human Phantoms Digital human phantoms (DHP) are one of the main data input for computation. Moreover, DHP is a method of providing anthropomorphic, bio-physical models that can be used and operated on by computer software. The biggest advantage of digital modelling is to allow us to perform experiments which are inherently dangerous to apply on live subjects [12, 13]. The general procedure for producing DHP is shown: Firstly, a whole human body is MRI-scanned. Each scan shows the cross section of the human body orthogonal to the direction of the backbone. In our research group, the digital human phantom we use is produced by scanning a human body from the head to the feet every 1 mm or 2 mm [13]. Based on knowledge of medical doctors, the MRI scanned image is segmented. Using the segmentation in an MRI image, a tissue can be identified such as bone, fat, muscle. In our digital human phantoms, the size of the pixel is either 1 mm 1 mm or 2 mm 2 mm. In this way, each pixel has a tissue number for example 10 for bone and 11 for heart muscle. The MRI scanned image is replaced with a stream of integers without the Cartesian coordinate. This stream of integers, which is in a file, has a file name that gives the height of each MRI scan phantom from the ground [13]. The spatial resolution of the digital human phantom, especially in some practical biomedical applications, has to be mm voxels. In such cases, the original digital human phantom has to be re-scaled. After that, in order to produce the digital human phantom with the required spatial resolution, smoothing techniques have to be applied [13]. 26

27 Our group has the cooperation with RIKEN Advantages Science Institute. The digital human phantom we use was provided from RIKEN Bio-research Infrastructure Construction Team 1 where the human body was scanned at each 1 mm using MRI. In this way, 1 mm 3 voxel DHP has been produced, that is also the spatial resolution of the DHP. The size of the data is voxels, which requires a large memory space to process. Our current resources are not suitable for these big data. Therefore, a head part of the phantom is used in this work to reduce the required memory capacity. 4.1 Historical Background of Computational Models Early computational models were historically based on stylized models that were represented by regularly shaped continuous mathematical objects. These objects were defined by combinations of simple surface equations such as right circular cylinders, sphere, or disks. In the late 1950 s, the human body was modelled using a sphere (International Commission on Radiation Units and Measurements (ICRU) sphere), with a lot of little spherical organs. Figure 4.1 shows the historical evolution of computational models from the crude ICRU spherical model to person-specific models that are expected to become available in the near future [1]. The Development of the Fisher-Snyder heterogeneous, hermaphrodite, anthropomorphic model of human body was the first breakthrough in the history of computational models in the late 1960 s. This model and its revised version represented an average adult male in good physical shape. Thus they described typical working population. The model comprised both male and female organs [1]. 4.2 Types of Model Computational models have developed from simple homogeneous tissue-equivalent spheres or slabs to progressively more realistic anthropomorphic models. These models emulate the anatomy and physiology of living subjects such as humans and laboratory animals [1]. Currently available computational models are produced based on one of three major sections: mathematical equation-based stylized models are produced from a series of geometrically defined regions which are used to describe the placement, shape and properties of various organs; image-based tomographic models are 1 Bio-research Infrastructure Construction Team, Advanced Technology Support Division, Advanced Science Institute, RIKEN 27

28 Figure 4.1: Evolution of computational models of the human anatomy from the crude ICRU spherical model to more realistic and complex (person-specific) computational models [1]. developed from segmented high-resolution medical images (MRI, CT or photographic data) that are taken from human subjects; and equation-voxel based hybrid models, where the mathematical description of organ boundaries is derived from definitions that are obtained from voxel data [12, 1] Mathematical Models Mathematical models are mostly of the Medical Internal Radiation Dose (MIRD)-type models. The model was first produced by MIRD committee of the US Society of Nuclear Medicine in Since then it has been repeatedly updated and developed. Every single generation of model adds to the structures and information in the phantom. Thus the level of complexity of the phantom has increased, but that can be managed by computational systems increases [12]. Regularly shaped continuous mathematical objects, which are defined by a mixture of simple surface equations, represent stylized models. The usual geometrical shapes of these models include spheres, cylinders, ellipsoids, slabs, cones, tori, and subsections of such objects that are mostly combined to approximate the geometry of typical irregularly shaped regions of the body and its internal structures [1]. Internal organs, which are represented by simple mathematical objects, are mostly very crude. Because, simple equations can only capture the most general description of an organ s position and geometry. These simple geometries are effective in studying fundamental issues of imaging systems performance characteristics and dosimetry for radiation protection purposes [1]. Nevertheless, such simple geometries cannot 28

29 evaluate clinically realistic distributions. A precise modelling of human body requires suitable information on the location, shape, density, and elemental composition of the organs or tissues [1]. The most basic theoretical phantoms are homogeneous or layered flat phantoms to evaluate EM dosimetry. These phantoms are radiated from simple sources, e.g., plane wave, half-wave dipole antenna, small dipole antenna, etc. Spherical models are generally used for EM dosimetry inside the human head and dosimetry in the eyes. Cylindroid phantoms are used as whole-body models. Besides, these models can be utilized in order to confirm the validity of calculated results using the FDTD method, the method of moment (MoM), etc [11]. There are more information about the MoM and the FDTD method given in Appendix B. A mathematical model is mostly intended to match the characteristic of a Reference Man, when a model is being produced or updated. The reference man was defined by the International Commission on Radiological Protection (ICRP) [12]. To understand the characteristic of a reference man, here it is a concrete data for early stylized models; the original model developed was intended to represent a healthy average male, that defined the working population of its time. The model had both male and female organs. However, most structures represented the organs of the Reference Man, who was a years-old Caucasian, 70 kg in weight and 1.70 in height (the height was later changed to be 1.74 m) [1]. In addition, matching the characteristics of a reference man is a collection of information that describes many of the anatomical features of the human body, with information available for the both sexes and a variety of ages [12]. Figure 4.2 shows the early stylized model which was described in three principal sections: an elliptical cylinder representing the arms, torso, and hips; a truncated elliptical cone representing the legs and feet; and an elliptical cylinder representing the head and neck [14]. The development of computational models of the human anatomy is also shown historically in Figure Tomographic Models Tomographic or voxel-based models were announced in Before then, by using simple mathematical equations, the modelling of the human body produced unrealistic anatomical geometries. After the introduction of major tomographic medical imaging techniques such as the magnetic resonance imaging (MRI) and X-ray computed tomography (CT), more realistic models were produced [1]. Thanks to progress in medical imaging technologies (MRI, CT) and high computer performance have enabled us to 29

30 Figure 4.2: Stylized adult male/female models showing (a) exterior view of the adult male, (b) skeleton and internal organs of the adult male/female [14]. improve precision head and whole-body voxel models. These models can be used in order to evaluate the interactions employing the computational simulations [11]. Human-head models are mostly used for the evaluation of EM dosimetry and also characteristic of antennas are used, when cellular phones are placed close to the head. These models were segmented into dozen of tissues and the resolution of these models was a few mm. Additionally, the evaluation of SAR inside child-head models has been proposed. These child-head models were transformed by adult-head models. But, the internal structure of child-head models produced by adult models is different from the real child [11]. Tomographic models are described by digital (voxel-based) volume arrays from segmented high-resolution structural imaging data. In other words, tomographic models are built from high-resolution CT or MRI scan, with live volunteers or donated cadavers. Recently, the National Library of Medicine s Visible Human Project (VHP) provided photographic data that provides much greater resolution than MRI or CT. 30

31 Models have been built using MRI, CT and other photographic data. In addition, cadavers and animals in some groups were used to obtain the required anatomical information through MRI or CT. The use of cadavers brings some advantages. The cadaver imaging makes it possible to scan the whole body at the desired slice thickness and with optimal X-ray tube settings. One example of the cadaver imaging is the National Library of Medicine s Visible Human Project (VHP) [12, 1]. On the other hand, there are also some disadvantages of cadaver imaging, such as clinically related deformation of organs (e.g., trauma) or procedure-related issues (e.g., body preparation for imaging) [1]. When comes to the development of human voxel models, whole-body human voxel models have been proposed. An anatomically realistic voxel model of an whole body were developed by Dimbylow in order to simulate reference man, whose height is 170 cm and the weight is 70 kg described in ICRP 23 (International Commission on Radiological Protection). This model, which was obtained using MRI images of an adult man, is named NORMAN (NORmalized MAN) and it was segmented into 37 different types of tissues. The resolution of NORMAN is mm 3. Apart from NORMAN, the female voxel model of an whole body was also developed by Dimbylow. This model, which was obtained using MRI images of an adult female, was named NAOMI (anatomical model) and it was segmented into 41 different types of tissues. The resolution of NAOMI is 2 3 mm 3. The female was 23-years-old, 165 cm tall and 58 kg in weight. The model was rescaled to a height of 163 cm and a weight of 60 kg, the dimensions of ICRP adult female [11]. A high spatial resolution whole-body human voxel model was proposed by Mason et al. The original x, y, z voxel dimensions were mm (1 mm 3 ) for the man. Photographic male data were obtained from the Visible Human Project (National Library of Medicine, Bethesda, MD) [15, 11]. This model was segmented into 40 different types of tissues. The original data of the Visible Human Project (VHP) were obtained from a 38-years-old male cadaver, whose height is 186 cm and the weight is 90 kg [11]. Due to the process of modelling, the model weights 105 kg. This model has been widely used in the world for numerical simulation of interactions [11]. Nagaoka et al. developed realistic high resolution whole-body Japanese human voxel models. The resolution of the developed model is 2 3 mm 3. The models were segmented into 51 different types of tissues, based on MRI images of male and those of female. The average height and weight of Japanese 18 to 30 years old are cm and 63.3 kg for males and cm and 52.6 kg for females (National Institute 31

32 of Bioscience and Human Technology, NIBH 1996). According to the average data, a male and female were chosen whose sizes were similar to the Japanese average values. The whole-body Japanese human voxel models are shown in Figure 4.3 (a) and (b). The male volunteer was 22 years old, his height was cm and his weight 65 kg. The female volunteer was 22 years old, her height was cm and her weight was 53.0 kg [11, 16]. Additionally, this adult female model was the first of its kind in the world and also these two models are the first Asian voxel models that enable numerical evaluation of electromagnetic dosimetry at high frequencies up to 3 GHz [11]. (a)taro (male) (b)hanako (female) Figure 4.3: High-resolution whole-body Japanese human voxel model [11]. The development of tomographic models from anatomical imaging modalities encountered some technical challenges; whole-body models are mostly necessary, but patient scans are generally applied for a limited portion, hence producing partial scans of the body (patient exposure because of CT examinations is substantial, and MRI is a long procedure); experienced radiologists or using dedicated software should recognize and segment individual tissues and organs; voxel models consist of many voxels and thus image data size of a whole-body tomographic model mostly requires a very large computer memory to run [1]. 32

33 The complexity of the human body restricts the the construction of tomographic models, and the limitations of the information capture medium. MRI and CT data may bring difficulty in the separation of tissues with similar responses or regions in which similar tissues are intertwined such as blood vessels [12]. For example, CT scans of the abdomen display very poor soft-tissue contrast, that makes the delineation of anatomical structures very difficult. Therefore, the abdomen imaging should be achieved without the use of contrast media if not impossible [1]. Photographic data is based on the ability to visually describe the boundaries between structures. Many models are limited to containing the organs and tissues for reducing the complexity and construction time [12]. There are many countries where their own voxel phantoms are being produced. These phantoms are being developed according to their own populace in order to correctly model racial variations in size, weight and organ structure. The sources are mostly chosen for these models, and the results sometimes need to be scaled to approach the characteristics of a reference man. In this way, the obtained reference man well defines the local population rather than the ICRP specifications [12] Hybrid Models Hybrid equation-voxel modelling is an approach to incorporate the best features of mathematical (equation-based) and tomographic (image-based) phantoms [1, 17]. The basic modelling primitives used to define hybrid models which are Non-Uniform Rational B-Splines (NURBS) and subdivision (SD) surfaces, both of these are widely used in computer graphics [1]. Subdivision surfaces were utilized for modelling structures with an arbitrary topological type, such as the brain, skull, muscle tissue and vasculature. NURBS surfaces can only model such structures by segmenting the model into a collection of individual NURBS patches, that presents a large number of parameters in order to define the model [18]. NURBS fits that are a mathematical modelling technique used in computer graphics to generate smooth 3D surfaces. NURBS presents a mathematical form to represent not only standard analytic shapes, but freeform curves and surfaces that are very suitable for defining 3D human anatomy. Furthermore, NURBS brings the flexibility to design a large variety of shapes by manipulating control points. Thanks to this feature, the organ volumes and body contours can easily be modified, when compared to either mathematical (stylized) or tomographic (voxel) phantoms [17]. A NURBS model, such as the 4D NURBS-based CArdiac-Torso (NCAT), which 33

34 was developed to provide both realistic anatomical structures and the flexibility in order to be used in medical imaging research [12, 17, 18]. A NURBS model is constructed in stages. First the body and organ surfaces are extracted from a pre-existing tomographic model or medical data and then converted into polygonal meshes. As it is in tomographic phantoms, this may require manual segmentation of medical images. The polygonal meshes are converted into NURBS-based surfaces and then combined into a single body framework. The NURBS system is not capable of handling regions of high geometric complexity such as anatomical structures which can only be defined by a large number of parameters. Therefore, some regions may be left in their defined state [12, 1]. Once the NURBS surfaces are integrated, the result can be voxelised for producing a model which is compatible with most simulation software [12]. Figure 4.4: Surface renderings of the (A) muscle tissue, and (B) skeleton and organs of the new 4D NCAT phantom. Anterior views are shown [18]. 34

35 4.2.4 Comparison of Stylized, Voxel and Hybrid Computational Models There are both advantages and disadvantages of stylized, voxel-based and hybrid models depending on various criteria for simulation of imaging systems shown in Table 4.1. Table 4.1: Comparison between stylized, voxel and hybrid computational models in terms of anatomic realism and flexibility in organ and body contour changes [1]. Features Stylized Voxel Hybrid Simple geometric descriptions Realistic patient-specific anatomical descriptions Memory requirements +++ Discretization errors Modelling dynamic processes Computational burden (ray-tracing) Flexibility regarding organ and body contour changes Modelling software complexity Body contour and internal organs of mathematical (stylized) phantoms were designed and positioned using information from literature sources on the human anatomy [17]. These models have a simple structure, and are based on geometric primitives. The advantages of anatomic realism of stylized phantoms are to offer smooth organ surfaces and the flexible phantoms. Stylized phantoms are better than voxel phantoms at modification. In stylized phantoms, it is possible to non-uniformly modify organ shape and body contour by adjusting each parameter of mathematical equation which describes their surfaces. On the other hand, stylized models offer anatomically unrealistic description of organ depth, position and shape because of geometric limitation of quadratic equations. In addition, stylized models have some difficulties in scaling nonuniform models and posture changes. As a result of all these factors, the physiological accuracy of stylized phantoms is very poor [12, 17]. 35

36 Voxel phantoms are constructed from medical images taken from a human subject. Therefore, voxel models offer a remarkable anatomical result, that means realistic description of organ depth, position and shape can be obtained using voxel models. There are also some disadvantages of voxel phantoms. Tomographic representation of anatomical models depends on manual image segmentation. The quality of source images directly affects the result of voxel phantoms. Additionally, slice-to-slice discontinuities are seen in coronal and sagittal views [12, 17]. Hybrid models combine the best features of the stylized and voxel models. Hybrid models present anatomically more realistic description of organ depth, position and shape in NURBS format and offer smooth organ surfaces. Besides, continuity is seen in coronal and sagittal views. Another advantage of hybrid models is to scale properly non-uniform organ and body contour. However, hybrid models require more complex software [17]. This is thus the main drawback of hybrid models. To emphasize the advantages of hybrid models over stylized and voxel models, Figure 4.5 illustrates the comparison of alimentary tract in the Oak Ridge National Laboratory (ORNL) newborn stylized phantom, UF (the University of Florida) newborn voxel phantom and UF newborn hybrid phantom. As shown in Figure 4.5, the hybrid model produces much better realistic result and represent a serious improvement as compared to either the stylized and voxel phantom [1]. Figure 4.5: Comparison of various models of the human alimentary tract: (a) ORNL newborn stylized phantom, (b) UF newborn voxel phantom and (c) UF newborn hybrid phantom [17]. 36

37 Chapter 5 High Spatial Resolution High spatial resolution has an important role to detect the edges of structures, margins of tumors, small foreign bodies, and small bony structures. Therefore, high spatial resolution is a measure of detail resolution [19]. Many scaling techniques are primarily focused on decreasing image sizes while keeping as much information as possible in the reduced space. On the other hand, increasing the size of an image only extends spaces between pixels without adding any new information in the increased space, that reduces the aesthetic quality of the image [12]. However, by using some filtering techniques, it is possible to improve the quality of an up-scaled image. In this study, a specific filtering technique, which is called look-up table (LUT) method, has been used to obtain the smoothed results. Figure 5.1 shows the structure of increasing the size of the image. The bigger size images are produced from the original shown in Figure 5.1. Every single value in the bigger size images is taken from the original, and replaced with the required number of values that depend on the scaling rate. These bigger size images in Figure 5.1 thus look the same as the original after the size of the original was increased by 2 times and 4 times. Each number represents a pixel in Figure 5.1. Up-scaling techniques cause some problems in images such as the sharp corners and square elements called blocky effects, hence the produced images look jagged [12]. However, these effects can be reduced by filtering techniques. The LUT method is one of these techniques, which eliminates the blocky effects and produce the smoothed results. This LUT method is visually explained in the following part. The digital human phantom used in this work consists of two dimensional data. By treating the data as two dimensional images, interpretation and alteration become relatively simple. In fact, each pixel is a three dimensional voxel in the DHP. In order to produce a three dimensional image, two dimensional images are combined [12]. 37

38 Figure 5.1: The structure of increasing the size of a matrix (2 2 pixels) by 2 times and 4 times. 5.1 Look-Up Table Based Method Up-scaling process always requires some smoothing methods to eliminate unwanted effects in images such as the blocky effects. As mentioned before, Look-Up Tables (LUTs) are a method to reduce the unwanted effects in an up-scaled image. The lookup table method used in this work is also described in [12]. The look-up table method uses the arrangement of similar pixels around an element in the original to determine the layout of the 36 (6 6) pixels or 16 (4 4) pixels that depend on the scaling rates. This pixel layout from the original is used for defining the shape in the 36 (6 6) or 16 (4 4) pixel region, which are the defined scaling rates. This shape (value data) in the defined region is then combined with value data from the original in order to produce the output region [12]. With the LUT method, there is no need to consider the contents of the local region for a huge amount of new pixels coming from the scaling process 38

39 since the LUT method uses the arrangement of similar pixels around an element in the original. In order words, the LUT method uses 8 pixels which surround a centre pixel in the original. In the scaled phantom, the nearness of surrounding pixel values to the centre pixel is irrelevant. That means pixel values are not dependent on their nearness. Thus, the only pixels which characterize as similar are those with the same value. The centre is surrounded by 8 pixels, that makes 256 (2 8 ) potential combinations of similar pixel arrangement [12]. LUT methods have previously been used in the field of computer gaming to upgrade old games with low resolution. These types of games require scaling and smoothing in order to be clearly viewed. LUT methods meet all these requirements and reduce the blocky effect which can be seen in low resolution [12]. Thus, the aesthetic quality of these games can be improved with LUT methods. The LUT method is explained in Figure 5.2, Figure 5.3 which illustrate the working principle of the LUT method. Figure 5.2 shows the first step of the LUT method. The original in Figure 5.2 is scaled by 6 times so that every single original element (pixel) has 36 (6 6) pixel region in the output region. Figure 5.2 shows only the first element (element 1) which covers the 36 (6 6) pixel region in the output region. This scaling process of the first element is represented in red area in Figure 5.2. In the following process, the scaled image is smoothed using the LUT method. As it was mentioned before, the LUT method uses the original 8 surrounding pixels which are 1, 2, 3, 4, 6, 7, 8, 9 in Figure 5.2. These pixels surround the centre pixel which is element 5 represented in green area (3 3) in Figure 5.2. The original 8 surrounding pixels are combined with all the values (element 1) in red area to define the shape. The second step of the LUT method is also shown in Figure 5.3. In this case, 3 3 pixel region moves 1 step forward in the original and 6 6 pixel region also moves 6 steps forward in the output region in order to scan the whole pixel regions. The process in the second step changes the original 8 surrounding pixel values which are now 2, 3, 10, 5, 11, 8, 9, 12 shown in Figure 5.3. These pixels surround element 6 which is in the centre shown in green area in Figure 5.3. The first element in the original is now element 2 which covers the 36 (6 6) pixel region represented in red area in Figure 5.3. The same process in the first step is then performed. The original 8 surrounding pixels are combined with all the values (element 2) in red area in order to define the shape. The whole process continues until the whole scaled region is scanned. In this way, the scaled image will be smoothed. 39

40 Original Smoothed Green Area: Local Region (3 x 3) Red Area: 36 Pixel Region (6 x 6) Output Region Figure 5.2: First step of working principle of the Look-Up Table based method. Original Smoothed Green Area: Local Region (3 x 3) Red Area: 36 Pixel Region (6 x 6) Output Region Figure 5.3: Second step of working principle of the Look-Up Table based method. 40

41 There is a concrete example for the LUT method shown in Figure 5.4. This result is produced from the DHP which was scaled by 6 times and smoothed with the LUT method in three dimensions. In order to smooth the DHP with the LUT method, the arrangement of similar pixels around an element in the original is used to determine the layout of the 36 pixels. This pixel layout is used to define the shape in the 36 pixel region. This shape (value data) in the defined region are then combined with the value data from the original to produce the output region. The result produced from the LUT method is shown in Figure 5.4. The code and the full list of the pixel combinations for the two dimensional and three dimensional smoothing are given in Appendix C. Figure 5.4: Cross section of 20 percent of the human phantom i-plane number 150, scaled by 6 times and smoothed with the LUT method. The left hand side of the figure is the left of DHP and the right hand side of the figure is the right of DHP. The human body has more smooth curves than sharp edges, we cannot directly infer this from the information provided. By using the phantom we have, no more information can be directly drawn. For that reason, to fully match the contents of any one plane in the final phantom to the expected tissues show in a plane from a human phantom. The segmentation of the phantom is therefore necessary at that level [12]. 41

42 With the LUT method, it is possible to obtain visually remarkable results. However, there are some points which have to be considered when up-scaling. These points are memory capacity and computing time. In this study, the LUT method has been applied to the two different image sizes depending on scaling rates (4 times and 6 times). The digital human phantom (DHP) we use is 1 mm, that is segmented 1687 PGM (Portable Graymap Format) data files on k-planes. To scale this whole DHP by 4 times and 6 times in three dimensions, it requires a large amount of memory space which is 13,7 GB and 30,82 GB respectively. However, the available resource only has 8 GB free space. Therefore, 20 percent of the DHP (the head part) has been up-scaled, that is almost equal to 337 pgm data files. In this way, the required memory capacity reduces 2,73 GB and 6,15 GB for scaling by 4 times and 6 times respectively. Furthermore, use of less data (20 percent of the whole DHP) also reduces the running time of the programs. In addition, when the DHP (1 mm) is scaled by 4 times and 6 times, the resolution would increase to 0.25 mm and mm respectively. These resolutions are in the desired region which is between 0.1 mm and 0.3 mm. These scaled DHP are then smoothed using with the LUT method in order to produce the required spatial resolutions. 5.2 Smoothing in Two Dimensions The resolution of the DHP we use (1 mm) consists of pgm data files which are two dimensional. Firstly, the structure of a pgm data file should therefore be understood. There is a simple example for a pgm data file explained in Table 5.1. A pgm data file consists of numbers of gray between black and white. Minimum value (0) is black, maximum value is white, thus gray scale of a pgm data file can be adjusted by changing maximum value. However, there is no meaning to use a bigger value than an effective maximum value since only the number of effective maximum pixel values can be shown in the output. If a value is smaller than an effective maximum value, the output shows only the number of the small pixel values and the rest of the pixel values would be filled with black pixel values until the total number of pixel values is equal to the effective maximum value [20]. In this study, for better understanding of the scaling and smoothing process, a small size sample image was used before the DHP due to the complexity of the human phantom. If there are any mistake in the smoothed DHP, it is generally hard to find them. The use of a small size image makes it easier to find possible mistakes which are likely 42

43 Table 5.1: The structure of a simple pgm data file P2 data is pgm format # test introducing a comment 2 4 width = 2, height = 4 (dimensions) 8 effective maximum value 1 black.. ( gray scale). 8 white to come from the list of pixel combinations. The small size sample image, which was produced manually, has 50 width, 40 height (50 40). This image is firstly scaled by 4 times and 6 times, and then smoothed with the LUT method in two dimensions (2D) shown in Figure 5.5, Figure 5.6, Figure 5.7). Figure 5.5: The original image (50 40) was produced manually. 43

44 Figure 5.6: The original image is scaled by 4 times ( ) and smoothed with the LUT method. Figure 5.7: The original image is scaled by 6 times ( ) and smoothed with the LUT method. 44

45 The output of Figure 5.7 may visually appear little better than Figure 5.6. However, in reality, there is no difference since Figure 5.7 ( ) has normally the bigger size than Figure 5.6 ( ). Hence, the size of the images was adjusted to the same size to appear properly on the page. On the other hand, the difference between the original and the smoothed images can be seen clearly in Figure 5.5, Figure 5.6, Figure 5.7. By using the small size image, the code and the list of pixel combinations have been improved and then the same scaling and smoothing process are applied to the chosen segmentation (one of the pgm data files) of the DHP. All the pgm data files, which have two dimensions namely i-axis, j-axis, were produced on k-planes (k-axis). In addition to the pgm data files, their size is 265 width (i-axis) and 490 height (j-axis). Figure 5.8 and Figure 5.9 show the outputs of the chosen segmentation of the DHP (number 450) which was scaled by 4 times and 6 times and then smoothed using the LUT method in 2D. The resolution of the produced results is mm and mm respectively which are obtained from 1 1 mm resolution DHP. Number of the pgm data files, which represents the third dimension, does not affect the required memory capacity for the two dimensional smoothing. The program uses only one of all the pgm data files in 2D. For that reason, only the size of the pgm data files determines the required memory capacity for the program in 2D. The LUT method produces remarkably good smooth surfaces shown in Figure 5.8 and Figure 5.9. The difference between the smoothed and the non-smoothed images can be visually seen from Figure 5.8 and Figure 5.9. The codes and the full list of pixel combinations are given in Appendix C. 45

46 Figure 5.8: Cross section of half of k-plane number 450 from the digital human phantom, scaled by 4 times without smoothing (top) and smoothed with the LUT method (bottom). The bottom of the figure is the left of DHP and the top of the figure is the right of DHP. The left hand side of the figure is the front of DHP and the right hand 46 side of the figure is the back of DHP.

47 Figure 5.9: Cross section of half of k-plane number 450 from the digital human phantom, scaled by 6 times without smoothing (top), smoothed with the LUT method (bottom). The bottom of the figure is the left of DHP and the top of the figure is the right of DHP. The left hand side of the figure is the front of DHP and the right hand side of 47 the figure is the back of DHP.

48 5.3 Smoothing in Three Dimensions Smoothing process in three dimensions (3D) is a complex procedure, that is commonly achieved in human phantoms by converting the three dimensional voxel surface into a three dimensional mathematical surface. With this method, only a single surface of a single material is smoothed without considering the other structures. Therefore, this requires the extraction of each individual material, its smoothing and its subsequent re-integration into the phantom [12]. On the other hand, three dimensional smoothing may seem to be similar to two dimensional smoothing since three dimensional smoothing uses one plane number like in two dimensional smoothing and then the process is operated on that plane. That plane can be one of i-planes or j-planes or k-planes depending on a chosen surface in three dimensions. However, in two dimensions only k- planes can be chosen because the provided pgm data files are already two dimensional and produced on k-planes. Besides, two dimensional smoothing does not require the subsequent re-integration. This difference makes three dimensional smoothing more detailed and more complicated when compared to two dimensional smoothing. Additionally, three dimensional data have three surfaces that enables us to observe the phantom in each dimension. The three dimensional DHP consists of pgm data files which are two dimensional. By combining these pgm data files, the whole DHP can be obtained in 3D. The code that defines the DHP is given in Appendix C. In order to scale the whole DHP by 4 times and 6 times in 3D, the program requires 13,7 GB and 30,82 GB free spaces respectively. As mentioned before, the memory capacity of the available resource has 8 GB free space. The head part of the DHP (20 percent of the whole DHP) is therefore used for scaling and smoothing process in 3D so that the required memory capacity reduces 2,73 GB and 6,15 GB respectively. To allocate the required space in the memory for the DHP, the allocatable array was determined according to the desired size of the DHP. This allocation statement and all the programs were written in Fortran [21,22]. The whole DHP consists of 1687 pgm data files. After setting the size of the DHP in 3D, the produced DHP consists 337 pgm data files, which are almost equal to 20 percent of the whole DHP. In this way, the required memory capacity for the three dimensional smoothing is reduced. This procedure is illustrated in Figure

49 human body data 265 a head part j i k Figure 5.10: Procedure for the set up of the size of the digital human phantom. The small size sample images (50 40) are used for the three dimensional smoothing in the same manner used in the two dimensional smoothing. The three dimensional image is produced by integrating the two dimensional small size sample images that have the same size. Number of two dimensional images can be increased depending on how big a three dimensional image is desired. In this work, 10 two dimensional images, which are in pgm format, were produced manually. To observe changes on the different dimensions, the size of the figure in these two dimensional images gets bigger from image number 1 to image number 10 and then the three dimensional image ( ) were produced. This three dimensional image is scaled by 6 times and smoothed using the LUT method in each dimension (i-planes, j-planes, k-planes) and the results are shown in Figure 5.11, Figure 5.12, Figure

50 Figure 5.11: The sample image k-plane number 10, scaled by 6 times without smoothing (on the left), smoothed with the LUT method (on the right). Figure 5.12: The sample image j-plane number 20, scaled by 6 times without smoothing (on the left), smoothed with the LUT method (on the right). Figure 5.13: The sample image i-plane number 25, scaled by 6 times without smoothing (on the left), smoothed with the LUT method (on the right). 50

51 The aim of using the small size images is to easily evaluate the results. In this way, if there is a mistake in the outcomes, that can be easily observed and fixed. The remarkable outcomes were obtained from the small size images and the difference between the smoothed images and non-smoothed images can be seen clearly in Figure 5.11, Figure 5.12, Figure5.13. After these results were produced, the same process (scaling and smoothing) has been applied to the DHP in 3D. Indeed, the original size of the DHP we use is (219 million) voxels. However, as mentioned before, the size of the DHP was adjusted to (43,8 million) voxels depending on the memory capacity of the available resources. This DHP has been scaled by 4 times and 6 times and smoothed with the LUT method in each dimension. The LUT method produces effective results in each dimension of the DHP. Thus, the DHP can be observed from the different perspectives in three dimensional smoothing. Figure 5.15, Figure 5.16, Figure 5.17, Figure 5.18, Figure 5.19, Figure 5.20 show the difference between the images in each dimension. In these Figures, the results on the left hand side show the scaled image without smoothing, the results on the right hand side show the scaled images with smoothing. When the scaled (non-smoothed) images are compared to the smoothed images, the difference between these results can be visually seen from these Figures. In addition, the flowchart of the overview of the procedure in case of 3D data processing is shown in Figure

52 Start Read all the pgm data files from 1 to 337 Send all the pgm data into a file Open the file in which all the pgm data files exist Read the orginal pgm data files (i-planes, j-planes) Open a new file where the result is written Allocate the space in the memory and choose a segmentation (plane number) Define corners of the segmentation (image) Scale the chosen segmentation by 4 times or 6 times Operate on the defined corners in the mask where the pattern of the original 8 surrounding pixels are checked Send the code and information to the mask subroutine for overwriting the previously scaled segmentation Write out the result End Include subroutine table where the full list of pixel combinations is defined Figure 5.14: The overview of the procedure in case of 3D data processing. 52

53 Figure 5.15: Cross section of half the digital human phantom k-plane number 200, scaled by 4 times without smoothing (on the left), smoothed with the LUT method (on the right). The left hand side of the figure is the right of DHP and the right hand side of the figure is the left of DHP. The top of the figure is the back of DHP and the bottom of the figure is the front of DHP. 53

54 Figure 5.16: Cross section of the part of the digital human phantom i-plane number 150, scaled by 4 times without smoothing (on the left), smoothed with the LUT method (on the right). The left hand side of the figure is the left of DHP and the right hand side of the figure is the right of DHP. 54

55 Figure 5.17: Cross section of the part of the digital human phantom j-plane number 250, scaled by 4 times without smoothing (on the left), smoothed with the LUT method (on the right). The left hand side of the figure is the front of DHP and the right hand side of the figure is the back of DHP. 55

56 Figure 5.18: Cross section of half of the digital human phantom k-plane number 200, scaled by 6 times without smoothing (on the left), smoothed with the LUT method (on the right). The left hand side of the figure is the right of DHP and the right hand side of the figure is the left of DHP. The top of the figure is the back of DHP and the bottom of the figure is the front of DHP. 56

57 Figure 5.19: Cross section of the part of the digital human phantom i-plane number 150, scaled by 6 times without smoothing (on the left), smoothed with the LUT method (on the right). The left hand side of the figure is the left of DHP and the right hand side of the figure is the right of DHP. 57

58 Figure 5.20: Cross section of the part of the digital human phantom j-plane number 250, scaled by 6 times without smoothing (on the left), smoothed with the LUT method (on the right). The left hand side of the figure is the front of DHP and the right hand side of the figure is the back of DHP. 58

59 5.4 Conclusion The small size images, which were produced manually, have been scaled by 4 and 6 times and smoothed using the the LUT method in 2D and 3D in order to easily observe the results. After producing the successful results, the digital human phantom has been scaled by 4 and 6 times and smoothed using the LUT method in 2D and 3D for producing the required spatial resolutions. The results of the small size image ( ) and the digital human phantom ( ) in 3D have been produced for each dimension (i-planes, j-planes, k-planes). 59

60 Chapter 6 The Different Approaches to the LUT Method The previous work was done by Michael Knight in 2008 at the University of Manchester. His study is based on the comparison of the different smoothing methods which are nearest neighbor, modal filters (5 5 and 9 9) and the look-up table. According to the results of these methods, the best output was obtained from the LUT method in the previous work. In the present work (my work), before the LUT method had been decided to use, the nearest neighbor method was also tried to smooth the DHP. However, the obtained result from the nearest neighbor method was very blurred due to the structure of the method. The nearest neighbor method takes the mean average of the pixels in a certain region using 3 3 mean filter and then this average is assigned to the position of the centre pixel in a new image. Hence this process changes the pixel values in a new image so that the produced result in nearest neighbor method are different from the original. Therefore, the nearest neighbor method was not preferred in the present work. The LUT method was decided to use in the present work since the LUT method uses the original values without taking the mean average. Thus, the produced results are very similar to the original image. The same smoothing method (LUT) is used in the previous and present work. There are, however, some differences coming out about the performance evaluation and flexibility between these two works. These differences are explained by comparing the previous and present approaches in this chapter. 60

61 6.1 The Previous Approach The previous approach is based on Michael Knight s work. In this study, the DHP, which is 2 mm and consists of 866 data, was scaled by 6 times and smoothed using the LUT method in three dimensions. The program in the previous work has some limitations and disadvantages. The main limitation is that the program can work in only one dimension (i-planes). It does not allow us to observe the DHP either from j- planes (j-dimension) or k-planes (k-dimension) in three dimensions so that we cannot obtain any result on j-planes or k-planes. In addition, the running time of the program is another weak point in the previous work because the program takes very long time to run. The input and output data were defined as the two dimensional for three dimensional smoothing in the previous work. Therefore, the program can work in only one dimension. Every single data (segmentation) of the DHP is scaled by 6 times and smoothed with the LUT method and then written in a file. This process repeats 866 times which represent the number of data in k-planes. When all the data are obtained, the whole smoothed phantom in three dimensions (3D) is produced by combining the obtained data. As it is seen that each segmentation of the DHP has to be scaled and smoothed in order to produce the whole DHP in 3D. However, the scaling and smoothing process for each segmentation are pointless and a waste of time. Because the result can only be shown on one chosen segmentation. That means the rest of the scaled and smoothed segmentations are not used. Hence, this whole process takes an extremely long time to produce the result from the DHP in 3D. The program in the previous work is currently located on in /local/michael/nict/codes/ and the Fortran source code is presented in Listing 6.1. The result of this program is shown in Figure 6.1. PROGRAM lookup T h i s program s c a l e s t h e human phantom by 6 t i m e s and smooths a t t h e same t i m e u s i n g a look up t a b l e o f p o t e n t i a l r e s u l t s. I t o p e r a t e s on t h e i p l a n e s o f t h e TARO phantom. IMPLICIT NONE INTEGER : : i, j, k, ni, nj, pix,m, l c o u n t i n g and h o l d i n g v a r i a b l e s INTEGER : : d2, d1, d0 naming numbers INTEGER : : i n p ( 3 2 0, ), outp ( , ) INTEGER : : mask ( 3 2 0, ) CHARACTER ( l e n =30) : : num CHARACTER (LEN=1) : : a 61

62 The TARO phantom i s opened and a s i n g l e v o x e l l a y e r i s read OPEN( 1 0 1, FILE = / l o c a l / m i c h a e l / NICT /MALE V1.RAW,& ACCESS= STREAM, STATUS= OLD ) DO k =1,866 DO j =1,160 DO i =1,320 READ( ) a i n p ( i, j ) = ICHAR( a ) w r i t e (, ) k An o u t p u t f i l e f o r t h i s l e v e l i s c r e a t e d d2=k / ; d1 =( k d ) / 1 0 ; d0 =( k d2 100) d1 10; num = / l o c a l / m i c h a e l / NICT / b i g / big / / CHAR(48+ d2 ) & / / CHAR(48+ d1 ) / / CHAR(48+ d0 ) OPEN( 2 0 1, FILE=num, ACCESS= STREAM, STATUS= NEW ) Corners i n t h e image are l o c a t e d and s t o r e d DO j =2,159 DO i =2,319 IF ( ( i n p ( i, j 1) == i n p ( i, j ). and. & i n p ( i, j +1) == i n p ( i, j ) ).OR. & ( i n p ( i 1, j ) == i n p ( i, j ). and. & i n p ( i +1, j ) == i n p ( i, j ) ) ) THEN mask ( i, j ) = 0 mask ( i, j ) = 1 The o u t p u t base i s s c a l e d from t h e i n p u t DO j =1,960 DO i =1,1920 outp ( i, j ) = i n p ( c e i l i n g ( i / 6. 0 ), c e i l i n g ( j / 6. 0 ) ) Operate on t h e c o r n e r s found i n t h e mask DO i =2,319 DO j =2,159 IF ( mask ( i, j ) == 1) THEN l =0 c o u n t e r p i x =0 p i x e l code c o m b i n a t i o n 62

63 Check t h e p a t t e r n f o r t h e o r i g i n a l 8 s u r r o u n d i n g p i x e l s t o s e e which match DO n j = 1,1 DO n i = 1,1 IF ( n i == 0.AND. n j == 0) THEN IF ( i n p ( i +ni, j + n j ) == i n p ( i, j ) ) THEN p i x = p i x +2 l l = l +1 l = l +1 Give a v a l u e t o i s o l a t e d p i x e l s IF ( p i x == 0) THEN p i x = 254 Revalue t h e p o s i t i o n i n t h e mask w i t h t h e code f o r t h e p i x e l p a t t e r n mask ( i, j ) = p i x Send t h e code and t h e i n f o r m a t i o n t o t h e mask s u b r o u t i n e t o d e t e r m i n e t h e p a t t e r n o f t h e r e s u l t i n g 36 p i x e l s. These o v e r w r i t e t h e p r e v i o u s l y g e n e r a t e d s c a l e d image. DO i =2,319 DO j =2,159 IF ( mask ( i, j ) /= 0) THEN CALL t a b l e ( mask ( i, j ), i n p ( i 1: i +1, j 1: j + 1 ), o utp (6 i 5:6 i, 6 j 5:6 j ) ) W r i t e o u t t h e r e s u l t DO j =1,960 DO i =1,1920 m = o utp ( i, j ) WRITE( ) a c h a r (m) Close t h e c u r r e n t o u t p u t f i l e and s t a r t on t h e n e x t l a y e r 63

64 CLOSE( ) CLOSE( ) END PROGRAM Listing 6.1: According to the program in the previous work the DHP is scaled by 6 times and smoothed using the LUT method 6.2 The Present Approach The present approach is based on my work where the LUT method is used differently from the previous (Michael Knight s) work. The DHP was scaled by 6 times and smoothed with the LUT method in the present work. This process is the same as the process in the previous work. Additionally, there is another process has been done in the present work. The DHP was also scaled by 4 times and smoothed with the same method (LUT) in the present work. Therefore, the full list of the pixel combinations and the parameters in the main program were also rewritten for up-scaling by 4 times. Even though the same smoothing method (LUT) was used in the both approaches, the structure of the main programs is very different from each other. That makes some fundamental differences in the performance evaluation. As mentioned in the previous approach, the program can work in only one dimension in 3D due to the limitations of the program. The present approach does not have these types of limitations in 3D, because the program was written based on the structure of a three dimensional data. The input and output data in the present work were defined as the three dimensional for three dimensional smoothing. In this way, the DHP can be scaled and smoothed for each dimension (i-planes, j-planes, k-planes) and also the program takes less time to run. These are the main advantages of the present approach when compared to the previous approach. These advantages come from the working principle of the program in the present work. Rather than repeating the scaling and smoothing process by a large number of times, which depends on the size of the third dimension, all the segmentations of the head part of the DHP are combined in 3D before up-scaling process. In the following process, we choose one plane number (segmentation) and then the scaling and smoothing process in 3D operate on that chosen plane in the present work. In other words, the head part of the DHP is produced by combining all the segmentations in 3D before up-scaling process. Then the scaling and smoothing 64

65 process are operated on one chosen segmentation. Consequently there is no need to scale and smooth all the segmentations since the result can only be shown on one chosen segmentation. With this method, we can reduce the running time of the program significantly in the present work. The part of this Fortran source code, which includes the chosen plane number, is presented in Listing 6.2. The result of this program is shown in Figure 6.1 and the original source code is given in Appendix C. PROGRAM lookup T h i s program s c a l e s t h e human phantom by 6 t i m e s and smooths a t t h e same t i m e w i t h t h e look up t a b l e method. I t o p e r a t e s on i p l a n e number IMPLICIT NONE INTEGER : : i, j, l i m i t j, l i m i t k, k, i p l a n e Holding v a r i a b l e s and c o u n t i n g. CHARACTER( l e n =2) : : magic = P2 D e f i n i n g pgm f o r m a t. CHARACTER( l e n =6) : : comment = # t e s t W r i t i n g a comment i n pgm f o r m a t. CHARACTER( l e n =128) : : f i l e n a m e Name o f t h e f i l e. CHARACTER( l e n =128) : : k c h a r Holding a d e f i n e d c h a r a c t e r. CHARACTER( l e n =10) : : v1 0 D e f i n i n g a c h a r a c t e r depending on t h e name o f pgm data f i l e s. INTEGER, DIMENSION( 1 : 2 6 5, 1 : 4 9 0, 1 : ) : : a The i n p u t t h r e e d i m e n s i o n a l. INTEGER,ALLOCATABLE, DIMENSION ( :, :, : ) : : b S t r u c t u r e o f t h e o u t p u t. INTEGER : : mask ( 4 9 0, ) T e s t i n g t h e v a l u e s i n t h e a r r a y. INTEGER : : width1 =265, h e i g h t 1 =490 Naming i n p u t v a l u e s. INTEGER : : width2 =2940, h e i g h t 2 =2022 Naming o u t p u t v a l u e s. INTEGER : : maxv=94 Maximum number i n t h e pgm data f i l e s. INTEGER : : r, t Counter and p i x e l code c o m b i n a t i o n. Read a l l t h e pgm data f i l e s one by one. DO k = 1,337 IF ( k. l t. 10) THEN WRITE( k c h a r, ( A4, I1 ) ), 000, k IF ( k. l t. 100) THEN WRITE( k c h a r, ( A3, I2 ) ), 00, k IF ( k. l t. 338) THEN WRITE( k c h a r, ( A1, I3 ) ), 0, k f i l e n a m e = v1 0 / / t r i m ( a d j u s t l ( k c h a r ) ) / /. pgm D i s p l a y a l l t h e pgm data f i l e s r e s p e c t i v e l y. PRINT, f i l e n a m e OPEN( 1 1 0, FILE = f i l e n a m e, ACTION= READ, STATUS= OLD ) 65

66 READ( 1 1 0, ( A2 ) ), magic READ( 1 1 0, ( A6 ) ), comment READ( 1 1 0, ), width1, h e i g h t 1 READ( 1 1 0, ), maxv Read i n f o r m a t i o n i n a r r a y. DO j =1,490 DO i =1,265 READ( 1 1 0, ) a ( i, j, k ) CLOSE( ) OPEN( 3 0 0, FILE= humansmoothingjkby6, ACTION= WRITE, STATUS= NEW ) WRITE( 3 0 0, ( A2 ) ), magic WRITE( 3 0 0, ( A6 ) ), comment WRITE( 3 0 0, ), width2,, h e i g h t 2 WRITE( 3 0 0, ), maxv A l l o c a t e t h e space i n t h e memory. ALLOCATE ( b ( 1 : 2 6 5, 1 : , 1 : ) ) Operate on i p l a n e number i p l a n e =150 Corners o f t h e image are d e f i n e d. DO k =2,336 DO j =2,489 IF ( ( a ( i p l a n e, j 1,k ) == a ( i p l a n e, j, k ).AND.& a ( i p l a n e, j +1, k ) == a ( i p l a n e, j, k ) ).OR. & ( a ( i p l a n e, j, k 1) == a ( i p l a n e, j, k ).AND. & a ( i p l a n e, j, k +1) == a ( i p l a n e, j, k ) ) ) THEN mask ( j, k ) = 0 mask ( j, k ) = 1 Listing 6.2: The part of the main program from the present work the DHP is scaled by 66

67 6 times and smoothed using the LUT method. 6.3 Performance Evaluation Performance evaluation is one of the most important part of programming. It commonly consists of the two mean criteria that are accuracy and speed. The comparison of the accuracy and speed of the present and previous approach is given in the following sections The Comparison of the Accuracy of the Two Approaches The accuracy of the results in the two approaches depends on directly the smoothing method. In the both approaches, the look-up table method is used for smoothing the DHP. Even though the main programs are different from each other, their full list of the pixel combinations for smoothing process is almost the same. However, only a couple of changes in the full list of pixel combinations of the present work was made in order to produce a better output. These changes were made using the small size images, otherwise that would be very difficult to observe in the DHP. The result of the both approaches appears almost the same because the both DHP are smoothed with the same method (LUT). Hence, the aesthetic quality of these two results are almost the same shown in Figure 6.1. On the other hand, the two different DHP are used so that the produced outcomes seem to look different. However, when the quality of these two smoothed DHP are compared to each other, it can be visually seen that the results are almost the same. 67

68 Figure 6.1: The phantom (top) i-plane 220 from Michael s work, scaled by 6 times and smoothed with the LUT method, the phantom (bottom) i-plane 150 from my work, scaled by 6 times and smoothed with the LUT method. The bottom of the figures is the left of DHP and the top of the figures is the right of DHP. 68

69 6.3.2 The Comparison of the Speed of the Two Approaches Speed is the other main point which has to be considered when programming. The running times of the programs in the both approaches are compared to each other in this section and the remarkable differences have been obtained. In the previous work, all the segmentations of the DHP are scaled by 6 times and smoothed using the LUT method in 3D in order to produce the whole DHP. This whole process is a waste of time since the result can only be shown on one chosen segmentation. Therefore, the program in the previous work takes about 7 days for three dimensional smoothing. In the present work, all the segmentation of the head part of the DHP are combined to produce the whole DHP in 3D before up-scaling process. In the following process, one chosen segmentation is scaled by 6 times or 4 times and then smoothed using the LUT method in 3D. The result is shown on that chosen segmentation. Hence, there is no need to consider all the segmentations in the present work. With this method, the running time of the program for the head part of the DHP in 3D is about 2 minutes and 1 minute respectively. The head part of the DHP is equal to 20 percent of the whole DHP. With this information, the running time of the program for the whole DHP in 3D can be estimated, that is about 10 minutes for up-scaling by 6 times and 5 minutes for up-scaling by 4 times in the present work. These running times measured from the present work are extremely good when compared to the running time of the program in the previous work. This work shows that the use of the same smoothing method, where the same pixel combinations are operated, produces the same results, even though the approaches are different from each other. However, that does not mean their running time and flexibility are also the same. Depending on the structure of the main programs, the speed and flexibility of the program in the present work have been improved significantly. Thus, the program in the present work can produce a result in each dimension Conclusion In Chapter 6, the result of the present approach are compared to the result of the previous (Michael Knight s) approach in the three dimensional smoothing. The accuracy of these two approaches are almost the same as shown in Figure 6.1. However, the result of these two approaches may seem to look different since the DHP used are different from each other. On the other hand, there is a significant difference has been measured in the speed of these two approaches. The running time of the program in the 69

70 present work is about 10 minutes for up-scaling the whole phantom by 6 times, but in the previous work, the same process takes about 7 days. In addition to the differences of these two approaches, the program in the previous approach can work in only one dimension (i-planes) in 3D, which is the main limitation of the previous work. In the present approach, there is no such limitations so that the program can work in each dimension in 3D. The produced results for each dimension are given in Chapter 5. 70

71 Chapter 7 Conclusion and Future Work 7.1 Conclusion This thesis has defined the structure of the digital human phantom (DHP) that has been constructed of the 1687 pgm data. The DHP we use consists of 1 mm 3 voxels. The size of the whole DHP is 265 x 490 x In this work, the head part of the DHP has been used and its size was set 265 x 490 x 337 due to the limited memory capacity of the available resources. This produced DHP has been up-scaled and smoothed in the FDTD computation in order to achieve the required spatial resolutions. The main aim of this work is to up-scale the DHP by keeping the aesthetic quality intact. First of all, the small size images were used for the scaling and smoothing processes in 2D and 3D. By observing the results from these small size images, the code and the pixel combinations in the look-up table have been improved. After this observation, the up-scaling process was applied to the more complex object which is the digital human phantom. This up-scaling process was a must to increase the image size. The size of the DHP was increased towards the desired resolutions which are between 0.1 mm and 0.3 mm. Considering these desired resolutions, the DHP was scaled by 4 and 6 times in 2D and 3D. Thus, the produced DHP has 0.25 mm and mm resolution respectively. As a result of the up-scaling process, the produced DHP has many unwanted effects such as sharp corners and square elements. Therefore, the smoothing technique called the look-up table based method was used in 2D and 3D to reduce these unwanted effects. In this way, the physical realism of the DHP is remarkably enhanced. This whole process can work in each dimension in 3D without any limitations and the program becomes applicable to any DHP or images by changing the parameters depending on data in the code. 71

72 7.2 Future Work In this work, the size of the digital human phantom has been increased and smoothed in 2D and 3D. In other words, the digital human phantom has been up-scaled. The purpose of the future work is to downscale the DHP. However, there are some important points which have to be considered when downscaling. Downscaling process does not allow us to smooth a DHP (or an image) since the size of a DHP is decreased instead of increasing. The other fundamental point for this future work is to retain as much information as possible in the reduced space when a DHP is downscaled. Otherwise, a downscaled DHP would be very different from an original and consequently a downscaled DHP may appear deformed. These two points thus have a significant role in this future work. 72

73 Appendix A Spinal Cord The central nervous system is composed of the brain and spinal cord that control all body movements. Specifically, the role of the spinal cord in the nervous system is to control the voluntary muscles of limbs, and that receives sensory information from these regions. Spinal cord also controls most of the viscera and blood vessels of the thorax abdomen and pelvis. Figure A.1: The central nervous system [23]. 73

74 A.1 Anatomy of the Spinal Cord Spinal cord has a key role in a human body. It has the largest nerve in the body. These nerve fibres, which is also called spinal nerves or nerve roots, send the messages to and from the brain to the rest of the body. Spinal cord is surrounded by protective bone segments, which are named the vertebral column [24]. This column is divided into five groups, named cervical vertebrae, thoracic vertebrae, lumbar vertebrae, sacral vertebrae, and coccygeal vertebrae shown in Figure A.2. The vertebral column is comprised by 8 cervical vertebrae located in the neck, 12 thoracic vertebrae in the upper back, 5 lumbar vertebrae in the lower back, 5 sacral vertebrae in the hip area, and 3 or 4 little vertebrae fused into 1 coccygeal vertebrae in the tailbone. The whole structure shows that spinal column includes 31 vertebrae bones in the human, and the average length of the spinal cord is 45 cm in males, cm in females. However, this structure differs depending on a type of animals such as in the rat, spinal cord has 34 vertebrae bones [25]. The nerves in the spinal cord is divided into two groups called ascending (sensory) and descending (motor) tracts. Ascending tracts transfer sensory information from the body, upwards to the brain. In other words, ascending tracts in spinal cord carry environmental information from the body to the brain such as touch, skin temperature, pain and joint position. Descending tracts send information from the brain downwards to initiate movement and body functions which are controlled in the spinal cord. Spinal nerves, which are namely cervical, thoracic, lumbar and sacral nerves, carry information from the spinal cord to the body and from the body to the brain. For instance, cervical nerves (C) in neck control movement, feeling to the arms, neck and upper trunk, breathing functions. Thoracic nerves (T) in the upper back control the trunk and abdomen. Lumbar nerves (L) and sacral nerves (S) in the lower back control the legs, the bladder, bowel and sexual organs. All these four main groups of spinal nerves are in descending order down the vertebral column, which exist different levels of the spinal cord [26]. A.2 Causes of Spinal Cord Injury Spinal cord injuries result in damage to surrounding bones (vertebrae), tissues, blood vessels. There are numerous ways to get caused spinal cord trauma, such as motor vehicle accidents, fall, industrial accidents, sports injuries and other kinds of causes. 74

75 Figure A.2: Image shows the relationship between spinal nerve roots and vertebrae [26]. All these spinal cord injuries occur different symptoms depending on the location of injury. The locations are called cervical, thoracic, lumbar and sacral respectively. How the locational injuries affect the human body is described below. Cervical (neck) injuries occur in the neck area, the arms, legs and, middle of the body can be affected by symptoms, these symptoms can cause some diseases such as, breathing difficulties, loss of normal bowel and bladder control, numbness, sensory changes, spasticity, pain, weakness and paralysis. Thoracic (chest level) injuries occur at the neck level and symptoms can bring some problems about the legs. The symptoms can include loss of normal bowel and bladder control, numbness, sensory changes, spasticity, pain, weakness and paralysis. In addition, the cervical and high thoracic spinal cord injuries may cause different symptoms as well, such as blood pressure problems, abnormal 75

76 sweating and trouble keeping normal body temperature. Lumbar sacral (lower back) injuries occur at lower back level, and symptoms of these injuries can affect different parts of the body such as, one or both legs and the muscles controlling your bowels and bladder. These injuries result in loss of normal bowel and bladder control, numbness, sensory changes, spasticity, pain, weakness and paralysis. As it is seen that injuries to the cervical, thoracic, lumbar and sacral spinal cord can cause many serious health problems [27]. A.3 Traditional Treatment for Spinal Cord Injuries When spinal cord injury (SCI) occurs, the first precaution which has to be taken is to protect the injured tissue of the cord. Besides, the injured person has to be protected the bodily functions, for some types of SCI may cause long-term health problems such as paralysis [28]. There are several types of treatment methods which are surgery, medication, rehabilitation. In addition, there are also some different pain treatment methods for chronic spinal cord pain. These pain treatment methods are namely invasive electric stimulation and proposed electromagnetic non-invasive spinal cord stimulation which are explained in Chapter 2. A.3.1 Surgery Surgery is a treatment method for SCI, which is necessary especially in some cases. When the spine is compressed by fragments of bones, foreign objects, herniated disk or fractured vertebrae, the surgery becomes necessary. The surgery may also be needed to stabilize the spine in order to prevent future pain or deformity [29]. There are many spinal cord disorders, but cervical spondylotic myelopathy (CSM) is slightly different from other kinds of injuries in that the ageing process causes this disorder, which results in degenerative changes in the cervical spine. Therefore, this is the most common disorder generally seen in people who are more than 55 years of age in North America and perhaps in the world [30]. CSM can be diagnosed using magnetic resonance imaging (MRI) and computed tomography (CT). MRI of the cervical spine is the procedure of choice during the initial screening process of patients who have risk of CSM. MRI is non-invasive and the aim is to provide images of the spine and spinal cord in different planes. Besides giving an assessment of the degree of the spinal canal stenosis, an MRI enables to identify intrinsic spinal cord lesions which 76

77 can present with myelopathy (e.g., tumors). In addition, high signal changes may cause myelomalacia or permanent spinal cord damage in the spinal cord of patients with CSM. CT is the other technique differently from MRI, which is complementary to MRI. In order to get a more accurate assessment of amount of canal compromise, CT should be used since it produces better information than MRI in evaluating bones (osteophytes) [30]. Diagnostic criteria of cervical spondylotic myelopathy include characteristic symptoms (leg stiffness, hand weakness), characteristic signs (hyperreflexia, atrophy of hands), MRI or CT (showing spinal stenosis cord compression as a result of osteophyte overgrowth, disc herniation, ligamentum hypertrophy) [30]. A Surgical Procedures Many successful surgical techniques exist for treating CSM. The aim is to open the space for the spinal cord, or decompress the spinal canal. The decompression surgery involves operating either from the front of the neck (anterior) or the back (posterior). However, none of these two approaches are ideal for every patient. There are also pros and cons of each approach shown in Table A.1 [2]. Anterior Approach This surgical approach is performed from the front of the neck through 1 to 2 inch incision a long the neck crease. The purpose of neck surgery from the front is to remove the disks (diskectomy) and the bones (corpectomy) in order to reduce the pressure on the spinal cord. After that, the bones are fused back together with a bone graft [2]. Anterior cervical diskectomy and fusion; in this procedure, the problem disk is removed and then the area of the problem disk left is stretched so that the height is similar to the problem disk which used to exist. At the end of this procedure, a bone graft is placed in the space of the removed disk [2]. Anterior cervical corpectomy and fusion; this procedure has only one difference from diskectomy that the vertebrae is removed and then bone graft material replaces the vertebrae. In some cases, the spinal cord may be compressed by both disk and bone. In such cases, a combination of diskectomy and corpectomy may be applied. In the surgical operation such as removing a disk or vertebrae, the spine must be stabilized through fusion. The purpose of the stabilization is to fuse together spinal bones (vertebrae). In this way, spinal bones heal into a single and solid bone. However, after this operation, fusion will restrict spinal flexibility. In addition to fusion, metal plates and screws are mostly used to keep the bones in place [2]. 77

78 Figure A.3: This MRI scan shows bulging disks pressing on the spinal cord [2]. Figure A.4: An anterior cervical diskectomy and fusion from the side (left) and front (right). Plates and screws are used to provide stability and increase the rate of fusion [2]. 78

79 Bone graft; the role of bone graft material is to fill in the space where the disk was removed. Furthermore, bone graft material is placed along the side of vertebrae in order to assist the fusion. A bone graft is originally used for stimulating bone healing. This increases bone production and helps the vertebrae heal together into a solid bone [2]. Posterior Approach This approach requires an incision along the midline of the back of the neck. There are two procedure that are namely laminectomy and laminoplasty. Spinal fusion generally accompanies these two procedure [2]. Laminectomy; it is a procedure that removes the bony arch (lamina), any bone spurs, and ligament which are compressing the spinal cord. Laminectomy surgery reduces pressure on the spinal cord by providing extra space for the spinal cord to drift backward. However, this decompression of the spinal cord makes the bone less stable. Thus, this procedure requires fusion with a bone graft and possibly screws and rods [2]. Figure A.5: Posterior laminectomy with fusion using screws and rods [2]. Laminectomy surgery can be performed for people with very small spinal canals, enlarged or swollen soft tissues at the back of the spine, and problems in more than four spine segments or levels. On the other hand, it is not possible to use these approaches in patients with kyphotic (bent forward) spine, because the cord will not be moved backwards [2]. Laminoplasty; this procedure is an alternative to laminectomy, which hinges the 79