Computed Tomography Scanner in Airport - Coursework Example

Computed Tomography Scanner in Airports Institution Name Table of Contents Table of Contents 2 Introduction 3 Overview of computed tomography scanner 4 Computed tomography scanner working principles 5 Computational Theory 7 Analysis of CT scanner based on Computational theory 8 Detection 8 Recognition 9 Identification 11 Limitations of CT Scanners 13 Future directions 14 Conclusion 15 References 17 Introduction Computer Tomography X-ray is an explosive detection system (EDS) that uses CT scanners and image processing software to mechanically screen through luggage for explosives and other illicit materials. The device consists of dual energy x-ray imaging that presents automatic colour-coding of objects with varying atomic numbers, which enable the security personnel at the airport to identify objects categorised as threats, such as explosives, inside the baggage (Kolokytha et al, 2014). For purpose of aviation security, detection of explosives and illegal substances is a critical step towards prevention of terrorism threats (Muthukkumarasamy et al, 2004). Following the 9/11 attach on the United States in 2001, the US government made amends to its screening protocols to include screening of all baggage for explosives (Elias, 2012). Consequently, a technology was suggested that integrated Explosive Trace Detection and Explosive Detection System (EDS) (Achutan & Mueller, 2008). Drawing from the United States case, governments from across the globe have issued a directive that all passengers and their luggage, be screened (European Union, 2011). As result, airports from across the globe have instituted the use of explosive detection system (EDS) devices to serve the critical function of screening checked-baggage (Achutan and Muller, 2008). Screened of luggage has therefore become a routine procedure in major airports across the global in the war against terror (Amoore & Goede, 2005). However, the rapid advancement of security technology in addition to its application in security purposes is dependent on the capacity of the security personnel to understand the principles of its functions and operations in detecting, recognising, and identifying threats (Kolokytha et al, 2014). Accordingly, this paper offers an insight the use of computed tomography scanner in airports as a security technology to detect a contraband threat such as explosives or other contextually restricted materials (Achutan and Muller, 2008). It is expected that the insights into the operation of the technology explored in this paper will help security personnel at the airports to apply the technology effectively based on underlying security management strategies to prevent security threats. This essay argues that the significant performance requirements of the CT scanner machine at the airports should have high potential to detect all classes of explosives, high throughput rate (the rate at which it scans bags per hour) and low false alarm rates. Overview of computed tomography scanner Computed tomography (CT) scanners have found wide application in attenuation analysis, research, inspections, three-dimensional digitisation, reverse engineering and 3d digitisation. Their application in airports for purposes of augmenting security management by assisting the security personnel at the terminals to prevent carrying of explosives into plans has fast gaining acceptance due to heightened focus on preventing terrorism (Adey, 2004). According to Ying et al (2006), Explosive Detection Systems (EDS) consists of large, belt-fed machines designed for scanning luggage, where explosives are perceived as posing critical onboard risk. The EDS are in essence computer tomography (CT) x-ray machines that depend on software to interpret data from a range of x-ray diodes. They have tube-shaped gantry comprising immense lead sheath that encloses a spiral array range of eleven x-ray diodes (Castillo, 2012). During operation, the tube turns around the bags as the bags ride on a conveyor belt through the EDS equipment. The system’s software simultaneously interprets the x-rays to create detailed, three-dimensional image of the contents of the luggage, in the process identifying possible explosives. Several types of EDS have been used over the past decade. Still, the technology continues to advance. For example, the output of the traditional EDS equipment runs about 300 bags per hour (Castillo, 2012). Computed tomography scanner working principles The device consists of dual energy x-ray imaging that presents automatic colour-coding of objects with varying atomic numbers, hence allowing the security operators at the airport to identify the potential risks of the objects inside the baggage (Jin, 2011). The device generates an image of an object’s underlying physical parameters, known as x-ray attenuation co-efficient. The basic principles of DECT can be understood by exploring the mechanisms of x-ray contrasts. According to Wang et al. (2012), the fundamental contrast mechanism for the CT scanner is the ability of the scanned objects to absorb or scatter x-ray photosn. This attenuation behaviour is outlined by the Beer-Lambert Law. For I0(E) photons incidence on any homogenous material of thickness (t) and which has a linear attenuation μ(E), the Beer-Lambert suggests that the amount of transmitted photos I(E) should be expressed as (Wang et al, 2012). For instance, the x-rays may have energies ranging between 10 and 150 keV. Using this diagnostics energy range, the x-rays and matter interact as a result of two key mechanisms Compton scattering and photoelectric absorption. When it comes to photoelectric absorption, the photons of the x-ray transmit its entire energy to an atom, as a result ionising the atom before disappearing (Taguchi, 2013). In the case of Compton scattering, the photons of the x-ray lose a part of their energy to an electron. Subsequently, the x-ray photons circulate in various directions, which simultaneously conserving momentum and energy (Wells & Bradley, 2009). Still, Compton scattering and photoelectric absorption is dependent on the object being scanned. The parameters that depend on the object include the electron density (ρe) and the atomic number (Zeff), both of which drive the differences in attenuation between to dissimilar materials inside the luggage. Indeed, the likelihood of Compton scattering is relative to the density of the electron, while the rate of absorption of photoelectric rises relative to the rise in mass density and Zeff (Feuerlein et al, 2008). Consequently, object with explosives that have a Zeff of 20 will be more attenuating that objects with no explosives, which have a Zeff of 13 at a similar mass density (Wang et al, 2012). Figure 1: Mass attenuation of materials as plotted as function of energy (Wang et al, 2012) Figure 1 above shows mass attenuation of materials as plotted as function of energy. The contribution of Crompton scatter and photoelectric absorption for materials with explosives and those without are shown. According to Castillo (2012), the industrial CT is different from the human CT scanning. A single rotation may take almost 1 hour, although the captures images with resolutions of 1 to 10 μm are dependent on the system. In general, their resolution is beyond 100 times compared to that used in medical CT units. Computational Theory The concept of Computational Theory provides an appropriate framework for analysis of CT scanner technologies in airports. The theory provides a means to examine the potential of the technology to detect al explosives, and ensure high throughput rate and to low false alarm rates. Computational Theory was proposed by Marr (1982), who postulated that, a series of descriptions or representations have certain inherent attributes or details that make them recognisable or distinguishable from others. Computational Theory proposes three forms of identifications: (a )the primal sketch has tow dimensional depiction of the key changes in light intensity of the objects such as data on contours and edges, (b) two-and-a-half dimensional sketch that consists of the portrayal on depth, which categorises the texture, shadows and visual disparity (Smith & Brooks, 2012). This kind of description is observer-centred (c) Three-dimensional representation offers description of an object's shape in three dimensions with comparative positions that are autonomous of the observer's point of view (Smith & Brooks, 2012; Surre et al., 2012). Marr (1982) hypothesised that vision is essentially an information-processing undertaking. He further theorised that any such task could be described in three dimensional levels: (a) specific algorithms, (b) computational theory, and physical implementation. In essence, the three dimensions consists of (a) description of the threat or problem, (b) constructing detailed simulation of the threat and (c) constructing a working system that undertakes the interpretation and analysis of the detected threat (Glannerster, 2001). The theory is particularly relevant for the case of CT scanner technologies due to the three dimensional levels it proposes. Analysis of CT scanner based on Computational theory The significant performance requirements of the CT scanner machine include their potential to detect all classes of explosives, have high throughput rate and to have low false alarm rates (Achutan and Muller, 2008). Basing on the three dimensional levels of Computational Theory, the device has to have the capacity of performing detection, recognition, and identification of threats (Soltani & Yusof, 2012). Detection Successful application of EDS at the airport for screening and detecting threats requires a model that can perform detection and identification at both sides of the spectrum (Corsi, 2012. The theory closes the gap between detecting and locating potential explosives through detection methods (Smith & Brooks, 2012). The Computational Theory conforms to an archetypical automated surveillance methodology for tracking systems, which hypothesises that detecting, tracking, representation of objects and object recognition. The CT scanners generate single energy CT images of the scanned luggage for detection of explosives (Pereira et al, 2014). The single-energy CT images estimate the density of the measurement of the objects that have been scanned. The EDS then detects the explosive basing on the mass, density or other feature of the material inside the scanned baggage (Mercury Computer Systems, 2009). According to Smith and Brooks (2012), the process of detection implies object localisation once it moves to the field of view of the imaging device. This implies that when the luggage is in the scanner's field of view, the surveillance system's analytical component is capable of indicating that the new object is a threat. Hence, a potential security threat would have been detected. Essentially therefore, the detection process has to detect the object in question using varied succeeding frames of the surveillance system in order to screen and scrutinise the luggage. Hence, the probability of detection has to be high to ensure that the target is not missed (Atrey et al, 2006; Stewart et al, 2012). The EDS devices serve the critical function of screening checked-baggage based on the principle of detection. Being a subsystem of the EDS, the CT Scanner’s Automatic Threat Detection (ATD) is central to effective operation of the EDS (Mercury Computer Systems, 2009). The ATD captures the CT images of each scanned bag as inputs, carries out analysis of the images and pattern recognition before generating the resulting image (Mercury Computer Systems, 2009). These functions are consistent with the first of the three dimensional levels of Marr’s Computational Theory: description of the threat or problem. Recognition The issue of recognition in surveillance paradigm needs the system to make out the class or category of an object based on preset parameters (Smith & Brooks, 2012). Hence, EDS visually recognises objects based on their classes, which are categorised according to the feature, attributes and characteristics that enable them to be associated with a category of potentially hazardous substances. This implies that such objects may have features of association, in which case, common characteristics or properties determine a particular category that an object should belong to (Lin, 2008). Once the ATD captures the CT image of each scanned bag, the resulting image is scrutinised for potential threat. First, the CT scanner generates cross-sectional images of the luggage. The images may either be (a) an uneven range of slices at varied slice spacing (selective slices) or (b) a combination of proximate slices referred as volumetric data or three-dimensional data (Mercury Computer Systems, 2009). The automated threat recognition (ATR) system algorithm afterwards processes the images the CT scanner generates so as to identify a potential threat inside the luggage. The luggage with no potential threat is relayed to the airplane (See Figure 1). The ATR algorithm is distinguishable by its probability of false alarm (PFA) and probability of detection (PD). It examines and interprets the images of the luggage to identify whether a threat can be made invisible from the x-rays. The ATD system is made up of algorithms software, which determines the detection performance of the EDS, including the Pd and Pfa (Michel et al, 2014). If the hidden regions are located inside the bag, the luggage and associated images are transmitted to the luggage-inspection room. St the same time, while in the course of handling the luggage a loss of identification occurs, the bag is relayed to the luggage-inspection room (Mercury Computer Systems, 2009). The images are simultaneously displayed on the computer screen of the on-screen resolution to signal that the baggage has potential threat (Michel et al, 2014). The operators may then clear the ATR's decision based on the existing protocols to allow cleared luggage to be sent to the airplane. Figure 2: Checked baggage screening process The luggage-inspection room accepts the luggage that the EDS have cleared. The operators inside the luggage inspection room apply explosive trace detection (ETD) or inspect the luggage manually and if they clear the luggage, it is sent to the airplane (Mercury Computer Systems, 2009). Identification The concept of identification describes the procedure through which an object is identified for what it is based on the positive properties and characteristics of their physiology (Smith & Brooks, 2012). When it comes to the CT scanner, the identification methods call for automated pattern matching. This implies that they are always probabilistic. This implies that decisions may be made although with some degree of uncertainty. Hence, the CT-based DTS machine has the capability to link the Transport Security Officer (TSO) at the airport to the identity of the object recorded in the database. This implies that identification of the object takes place using measurement by the technology (Straw, 2014). The CT images consist of 3-dimenstional images, a range of 2-dimensional projection images and 3 –dimensional effective atomic number. The CT images are afterwards transmitted to ATD subsystem to be processed, or through a segmentation process for purposes of identification. Mercury Computer Systems (2009) views the segmentation process as a critical step in the ATD algorithm since the segmentation’s accuracy determine the ultimate capacity to differentiate between non-threat and threat objects. Figure 3: ATD algorithm The Segmentation process segments the CT images to determine the potential threats. It also produces a label image that indicates the segmented objects. At this stage, attributes such as mass, density, and atomic number are extracted before being discriminated depending on the analysed features to determine whether they are a threat. Objects not categorised as threat are eliminated from the label image while the detected threat objects are passed to the search room based on the airport’s protocol (Chen, 2010). Since luggages that contain threats have to be handled according to existing protocol, it implies that the threat of false alarms abounds in both eth human operators and the machines. In the case of machine driven alarms, the screening algorithms will signal an alarm in case there is no threat (Committee on Engineering Aviation Security Environments, 2013). The typical causes of false alarms include materials mistaken for explosive or those aggregating various non-threat materials into a single item that may be mistaken for threat. The EDS have false alarm rates linked with detection, which may need to be cleared manually (Chen, 2010). The properties of CT rely on the x-ray energy spectrum applied in measuring an object. Conventional CT rely on one energy spectrum although they may sometimes suffer from ambiguity in a manner that different materials measured appear identical. Dual energy CT (DECT) relies on two varying energy spectra that can be applied in removing such ambiguity (Seungryong et al, 2010). Indeed, Ying et al (2006) suggests that the false alarms can be reduced using the dual energy technique, which provides atomic number of measurements of the materials that have been scanned as well as the measurements of the density. Ying et al. (2006) explains that the atomic number measurement, which is the number of single element that provides similar attenuation like mixtures or compounds measured together, offer extra dimension to the measurement of the density to characterise the scanned object’s physical features. The properties of the object can be determined effectively through the use of effective atomic number and density rather than relying solely on density (Chen, 2010). For instance, an explosive such as Ammonium Nitrate and fuel oil (ANFO) may have similar physical densities like that of water. Despite this, they have different effective atomic numbers. Accordingly, DECT scanner can effectively differentiate between ANFO and water. The effectiveness of this option was examined in an earlier research by Singh and Singh (2003) and Shi (2000) in their study of EDS for aviation security. The two confirmed that dual energy CT-based x-ray systems that use density measurements and atomic number in detecting explosives have the potential to attain minimal rate of false alarms compared to use of density measurement alone (Shi, 2000; Singh and Singh, 2003). Limitations of CT Scanners According to Mercury Computer Systems (2009), the main limitation of the CT scanning technology at the airports is their tendency to hamper the complexity of growth of automatic threat detection. In turn, this limitation affects the CT scanner machines by reducing their capacity to detect the threats. Additionally, they may also give false alarms. Additionally, not all kinds of explosives may be classified in the database. This implies that the technology cannot detect all kinds of possible explosives, such as the homemade ones (Smith, 2007; Weinberger, 2010). Additionally, technology has been criticised for leading to congestions at the airports. According to Wells and Bradley (2009), the systems such as the CTX5000 scanners have been criticised for having the capability of only handling between 150 and 200 bags each hour and having false alarm rate of nearly 30 percent. This has caused delays for passengers at the airports. As a result, it has called for extra hour to do the check-in process before departure (Wells and Bradley 2009; Stewart & Mueller, 2011). These limitation can however be improved through the deployment of better detection algorithms to improve detection performance. Indeed, if integrated with the latest high-tech hardware technologies for pattern recognition, image segmenting and nonlinear kernel classification, improved detection and low false alarm rates are achievable (Mercury Computer Systems, 2009). Additionally, one the detection performance is improved, other advantages such as reduced operational costs and labour associated with checked-baggage screening can be realise. Future directions Due to the underlying limitations of the CT scanning technology, future technology need to be more scalable to reduce to improve their potential to detect all classes of explosives, embrace higher throughput rate and to lower the rates of false alarms (Achutan and Muller, 2008). Additionally, the technology needs constant adjustment to keep up with the changing technology and the dynamism of threat. This indicates that EDS machines will need to have the capacity to detect newer threats, including liquid and homemade explosives. Following the advent of graphic processing units (GPUs), Mercury Computer Systems (2009) suggests that it is possible that future implementation of ATD algorithms will not hinder ATD detection performance. This is since one GPU today has 1 TFLOPS that Mercury Computer Systems (2009) believes is much powerful compared to the 20 quad-core CPUs. Additionally, combination of GPUs and Fieldprogrammable gate arrays (FPGAs) will provide a more scalable hardware platform for the ATD, which would expand the CT scanner devices’ detection capacity for newly evolving threats. FPGAs provide low-power computing. Conclusion The significant performance requirements of the CT scanner machine at the airports should have high potential to detect all classes of explosives, high throughput rate (the rate at which it scans bags per hour) and low false alarm rates. This statement is supported by Marr’s Computational Theory, which specifies that effective detection systems should have the capacity of performing detection, recognition, and identification of threats. Marr’s Computational Theory proposes three dimensional levels that describe how an effective detection system should function: (a) description of the threat or problem, (b) constructing detailed simulation of the threat and (c) constructing a working system that undertakes the interpretation and analysis of the detected threat. These could be summarised as detection, recognition and identification. In regards to detection, the CT scanner has to detect the object using varied succeeding frames of the surveillance system in order to screen and scrutinise the luggage. Hence, the probability of detection has to be high to ensure that the target is not missed. In regards to recognition, the CT scanner should have the capacity to visually recognise objects based on their classes, which are categorised according to the feature, attributes and characteristics that enable them to be associated with a category of potentially hazardous substances. On the other hand, the concept of identification describes the procedure through which an object is identified for what it is based on the positive properties and characteristics of their physiology. When it comes to the CT scanner, the identification criteria call for automated pattern matching. This implies that they are always probabilistic. This implies that decisions may be made although with some degree of uncertainty. Despite their effectiveness in detecting explosives at the airports, their main limitation lies in their tendency to hamper the complexity of growth of automatic threat detection. In turn, this limitation affects the CT scanner machines by reducing their capacity to detect the threats. Additionally, they may also give false alarms. Additionally, not all kinds of explosives may be classified in the database. This implies that the technology cannot detect all kinds of possible explosives, such as the homemade ones. References Achutan, C., & Mueller, C. (2008). Evaluation of Radiation Exposure to TSA Baggage Screeners. CDC Workplace Safety and Health. Health Hazard Evaluation Report 2003-0206-3067 Adey, P. (2004). Secured and Sorted Mobilities: Examples from the Airport. Surveillance & Society 1(4), 500-519 AMOORE, L. & Goede, M. (2005). Governance, risk and dataveillance in the war on terror. Crime, Law & Social Change 43, 149–173 Atrey, K., Kankanhalli, M. & Jain, R. (2006). Information assimilation framework for event detection in multimedia surveillance systems. Multimedia Systems, 12(3), 239–253 Chen, C. (2010). Handbook of Pattern Recognition and Computer Vision. London: World Scientific Corsi, C. (2012). Infrared: A Key Technology for Security Systems. Advances in Optical Technologies, 1-15 European Union. (2011). Aviation Security and Detection Systems - Case Study. Retrieved: Feuerlein, S., Roessl, E., Proksa, R,, Martens, G.,, Klass, O., Jeltsch, M., Rasche, V., Brambs, H-J., Hoffmann M., and Schlomka, J (2008). Multienergy Photon-counting K-edge Imaging: Potential for Improved Luminal Depiction in Vascular Imaging. Radiology, 249, 1010 -1016. Castillo, M. (2012). The Industry of CT Scanning. American Journal of NeuroRadiology, 33(1), 583-585 Committee on Engineering Aviation Security Environments (2013). Engineering Aviation Security Environments--Reduction of False Alarms in Computed Tomography-Based Screening of Checked Baggage. Washington: National Academies Press Elias, B. (2012). Airport Body Scanners: The Role of advanced Imaging Technology in Airline Passenger Screening. Congressional Research Service 1-5700 Glannerster, A. (2001). Computational theories of vision. Primer Magazine R682 Jin, Y. (2011). Implementation and Optimization of Dual Energy Computed Tomography. retrieved: Kolokytha, S., Speller, R. & Robson, S. (2014). Three-Dimensional Imaging Of Hold Baggage For Airport Security. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 5(1), 331-336 Lin, S. (2008). An Introduction to Face Recognition Technology. Informing Science Special Issues on Multimeda Technology 2(1), 1-7 Marr, D. (1982). Vision : a computational investigation into the human representation and processing of visual information. , San Francisco: W.H. Freeman Mercury Computer Systems. (2009). Automated Threat Detection for Baggage Screening. Retrieved: Michel, S., Mendes, M., Ruiter, J., Koomen, G. &Schwaninger, A. (2014). Increasing X-ray image interpretation competency of cargo security screeners. International Journal of Industrial Ergonomics 44, 551e560 Muthukkumarasamy, V., Blumenstein, M. & Green, J. (2004)..Intelligent Illicit Object Detection System For Enhanced Aviation Security. Retrieved Pereira, G., Traughber, M. & Muzic, R, (2014). The Role of Imaging in Radiation Therapy Planning: Past, Present, and Future. BioMed Research International, 1-9 Surre, T., Kouh, M., Cadieu, C. & Knoblich, U. (2012). A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex. Massachusetts institute of technology. Retrieved Smith, C. (2007). The Evaluation of Security Systems: Testing Biometric and Intelligent Imaging Systems. Keynote Address: The Sixth International Workshop for Applied PKC (IWAAP2007) Seungryong, C., Sidkey, E., Bian, J. & Pan, X. (2010). Dual-Energy Technique at Low Tube Voltages for Small Animal Imaging. Tsinghua Scieince Technology, 15(1): 79–86. Shi, X. (2000). Improving object classification in x-ray luggage inspection, Ph.D. dissertation. Virginia Polytechnic Institute and State University, February 2000. Singh, S, & Singh, M. (2003). Explosives detection systems (EDS) for aviation security. Signal Processing 83, 31–55. Smith, C. & Brooks, J. (2012). Security Science : The Theory and Practice of Security. Sydney: Elsevier Science. Soltani, F. & Yusof, M. (2012). Concept of Security in the Theoretical Approaches. Research Journal of International Studies 1, 7-16 Stewart, M. & Mueller, J. (2011). Cost-Benefit Analysis of Advanced Imaging Technology Full Body Scanners for Airline Passenger Security Screening. Journal of Homeland Security and Emergency Management, 8(1), 1-18 Stewart, M., Netherton, M. & Grant, M. (2012). Probabilitsic Terrorism Risk Asessment and Risk Acceptability for Insfrastrature Protection. Australia journal of Structural Engineering 13 (1), 1-18 Straw, J. (2014). New Views on Airport Screening. Security's Web Connection. Retrieved: Taguchi, K. (2013). Vision 20/20: Single photon counting x-ray detectors in medical imaging. Med Phys. 40(10), 1-12 Wang, A., Hsieh, S. & Pelc, N. (2012). A Review of Dual Energy CT: Principles, Applications, and Future Outlook. CT Theory and Applications, 21(3), 367-386. Weinberger, S. (2010). Airport security: Intent to deceive? Nature 465, 412-415 Wells, K. & Bradley, D. (2009). A Review of X-ray Explosives Detection Techniques for Checked Baggage. Retrieved: ,http://core.kmi.open.ac.uk/download/pdf/17180209.pdf> Ying, Z., Naidu, R. & Crawford. (2006). Dual energy computed tomography for explosive detection. Journal of X-Ray Science and Technology 14 (1), 235–256 Read More

According to Ying et al (2006), Explosive Detection Systems (EDS) consists of large, belt-fed machines designed for scanning luggage, where explosives are perceived as posing critical onboard risk. The EDS are in essence computer tomography (CT) x-ray machines that depend on software to interpret data from a range of x-ray diodes. They have tube-shaped gantry comprising immense lead sheath that encloses a spiral array range of eleven x-ray diodes (Castillo, 2012). During operation, the tube turns around the bags as the bags ride on a conveyor belt through the EDS equipment.

The system’s software simultaneously interprets the x-rays to create detailed, three-dimensional image of the contents of the luggage, in the process identifying possible explosives. Several types of EDS have been used over the past decade. Still, the technology continues to advance. For example, the output of the traditional EDS equipment runs about 300 bags per hour (Castillo, 2012). Computed tomography scanner working principles The device consists of dual energy x-ray imaging that presents automatic colour-coding of objects with varying atomic numbers, hence allowing the security operators at the airport to identify the potential risks of the objects inside the baggage (Jin, 2011).

The device generates an image of an object’s underlying physical parameters, known as x-ray attenuation co-efficient. The basic principles of DECT can be understood by exploring the mechanisms of x-ray contrasts. According to Wang et al. (2012), the fundamental contrast mechanism for the CT scanner is the ability of the scanned objects to absorb or scatter x-ray photosn. This attenuation behaviour is outlined by the Beer-Lambert Law. For I0(E) photons incidence on any homogenous material of thickness (t) and which has a linear attenuation μ(E), the Beer-Lambert suggests that the amount of transmitted photos I(E) should be expressed as (Wang et al, 2012).

For instance, the x-rays may have energies ranging between 10 and 150 keV. Using this diagnostics energy range, the x-rays and matter interact as a result of two key mechanisms Compton scattering and photoelectric absorption. When it comes to photoelectric absorption, the photons of the x-ray transmit its entire energy to an atom, as a result ionising the atom before disappearing (Taguchi, 2013). In the case of Compton scattering, the photons of the x-ray lose a part of their energy to an electron.

Subsequently, the x-ray photons circulate in various directions, which simultaneously conserving momentum and energy (Wells & Bradley, 2009). Still, Compton scattering and photoelectric absorption is dependent on the object being scanned. The parameters that depend on the object include the electron density (ρe) and the atomic number (Zeff), both of which drive the differences in attenuation between to dissimilar materials inside the luggage. Indeed, the likelihood of Compton scattering is relative to the density of the electron, while the rate of absorption of photoelectric rises relative to the rise in mass density and Zeff (Feuerlein et al, 2008).

Consequently, object with explosives that have a Zeff of 20 will be more attenuating that objects with no explosives, which have a Zeff of 13 at a similar mass density (Wang et al, 2012). Figure 1: Mass attenuation of materials as plotted as function of energy (Wang et al, 2012) Figure 1 above shows mass attenuation of materials as plotted as function of energy. The contribution of Crompton scatter and photoelectric absorption for materials with explosives and those without are shown. According to Castillo (2012), the industrial CT is different from the human CT scanning.

A single rotation may take almost 1 hour, although the captures images with resolutions of 1 to 10 μm are dependent on the system. In general, their resolution is beyond 100 times compared to that used in medical CT units. Computational Theory The concept of Computational Theory provides an appropriate framework for analysis of CT scanner technologies in airports.

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