# Patent

## Title
Detection and/or classification of materials

## Abstract

A method processing an image dataset of radiation emergent from a test object after its irradiation by a suitable radiation source is described which comprises the steps of: generating an image dataset comprising a spatially resolved map of items of intensity data from the radiation emergent from the test object; further, resolving the intensity data items spectroscopically between at least two energy bands across the spectrum of the source; numerically processing the spectroscopically resolved intensity data items to determine a further spatially resolved dataset of data items representative of one or more orders of Compound Proton Number and/or effective mass thickness and/or a density; generating a segmented image dataset using the said dataset of data items representative of one or more orders of Compound Proton Number and/or effective mass thickness and/or a density. The method applied as part of a method for the radiological examination of an object and an apparatus for the same are also described.

## Background
This invention relates to a method of detection and/or classification of unknown materials by radiological examination of test objects in particular using X-rays, and to a method of processing of detected radiation data from such a radiological examination. The invention also relates to a detector apparatus embodying the principles of the method.
This invention relates in particular to the creation of a set of compound-specific parameters, including in certain embodiments data representative of mass thickness, and including in certain embodiments data representative of multiple orders of weighted atomic number which we call herein a Compound Proton Number Set. In their infinite form such numbers identify and depend upon the composition of a compound. The invention in a particular embodiment includes a method for calculating a number and preferably a high number of dimensions of the Compound Proton Number Set using X-ray measurements measured at multiple energies, as a method for material identification.
The invention may in particular facilitate the detection of the presence of and/or classification or identification of particular target materials within a test object, for example materials which might represent a threat to security, a breach of customs regulations or the like. The invention in particular relates to baggage screening and other security, industrial and medical applications where the detection and identification of foreign objects in an image is of benefit. However the invention is not limited to the investigation of objects inside other objects. Materials ID of stand-alone objects is also useful.
The invention may in particular relate to a method and apparatus making us of a semiconductor detector device comprising a large direct band gap semiconductor material, for example a group II-VI semiconductor material such as cadmium telluride (CdTe), cadmium zinc telluride (CZT), cadmium manganese telluride (CMT) or the like, for example formed as a bulk single crystal but is not limited to any particular class of detectors.
It is desirable to scan the contents of objects at security and customs checkpoints to gain information about content, for example to obtain an indication that the contents of the object do not constitute a threat to security or a breach of customs regulations. It is also desirable to scan the contents of objects for other purposes such as quality control, content verification, degradation monitoring etc.
It is known that information useful in this regard may be obtained from a spectroscopic analysis of radiation received at a detector after interaction with an object under test for example by scanning the object from a suitable high energy electromagnetic radiation source, collecting emergent radiation at a suitable detector after interaction with the object, and processing the emergent radiation spectroscopically, for example against reference data, to draw conclusions about the composition of the object.
The Beer-Lambert law states that for a beam of photons of energy E with intensity I0 incident on a material with thickness, t (cm), the intensity that emerges is

I=I0e−μt  1

where μ is the linear attenuation coefficient and is defined as the probability of interaction per unit distance travelled. This has units of cm−1. It is often preferable to work with a mass attenuation coefficient which is the linear attenuation coefficient (μ) divided by the material density (ρ). The mass attenuation coefficient




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    μ
    ρ
  
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therefore has the units g−1cm2. The mass attenuation coefficient, in X-ray physics is also generally denoted by the symbol α, not to be confused with the fine structure constant which also shares this symbol. As used herein α refers to the mass attenuation coefficient, unless otherwise specified. Therefore the Beer-Lambert law expressed in terms of the mass attenuation coefficient is





  
    
      
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where the product of the density and the distance (ρt) is defined as the mass thickness, x.
X-rays interact with the matter in a number of ways, which may lead to attenuation of the beam. The three most important methods of interaction are;

    
    
        Compton Scattering
        Photoelectric Effect
        Pair production
    
    


Other effects, such as Thompson Scattering, play a smaller role, but which process dominates depends upon the mass absorption characteristics of the medium, which is in turn dependent upon the energy of the photons.
Which of these processes dominates is dependent on the mass absorption characteristics of the target (directly related to the atomic number, Z) and the energy of the X-ray.
At low energies the Photoelectric Effect tends to dominate the linear absorption coefficient (μλ), as the photon energy increases the Compton Effect starts to dominate, until Pair Production occurs and dominates at energy above 1022 keV. As X-ray applications generally use X-ray up to several hundred keV, Pair Production does not occur and the attenuation of the beam is mainly caused by a combination of the other two effects.
Several attempts have been made to accurately describe the attenuation from an element, but all are approximations to real data which make a number of assumptions. One of the most widely accepted texts by Jackson and Hawkes, (D F Jackson and D J Hawkes, X-ray attenuation coefficients of elements and mixtures; Physics Reports 70 (3) pp 169-233 (1981)), present a method for estimating the linear attenuation coefficient as




  
    
      
        
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where ρ is the mass density, NA is Avagadro's number, A is the atomic mass, Z the atomic number, α in this case is the fine structure constant, m the electron rest mass, c the speed of light, φ0 is the Thomson classical cross section per atom, fnll, is a collection of terms for the Photoelectric cross section, σKN is the Compton cross section and σSCcoh is the Rayleigh scattering cross section of a standard element Z′ at energy




  
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The fitting parameter b is material dependent, thus the exponent of the atomic number varies.

The Jackson Hawkes method has proved accurate in determining the atomic number of elements, but this approach has limitations as it does not directly lead to quantitative information on the composition of the mixture under investigation. Additionally, the definition of only one effective atomic number, often called Zeff, characterising a material is not valid over wide energy ranges or crucially for mixtures or assemblies containing elements with different atomic numbers. This gives inaccuracies when measuring compounds materials, and does not provide discrimination of compounds which may be engineered to look similar in this one property. This method does provide a useful approximation for some radiation studies, however the functionality is limited.
It may be convenient to generate an image dataset of information from such a scan and process or display such a dataset. It is in particular desirable to process the image to obtain information useful in the detection and identification of particular objects in the image.
In current state of the art for threat detection in baggage screening and other security, industrial and medical applications where the detection and identification of foreign objects or components in an image of a composite object is of benefit, transmitted intensity maps are segmented, i.e. divided into contiguous regions of similar intensity levels, before detailed analysis of the defined regions. There are difficulties associated with this approach in that, for curved or irregularly shaped objects, material and mass thickness dependent interactions with the scanned object will be convoluted in a single output contrast mode. This will make it difficult to unambiguously discriminate between adjacent regions or to apply a rigorous analysis of the data within a segment if the intensity varies within regions. Similarly the output from overlapping segments will be a convolution of contributions from the atomic numbers, thicknesses and densities of each of the overlapping objects. Deconvolution of these, say, 6 independent parameters from a single measurement of two overlapping objects is not possible unless some additional information can be provided to the analysis.
For a more complete understanding of the present invention and for further objects and advantages thereof, reference may now be had to the following description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a flow diagram of a method of processing an image dataset of x-ray radiation emergent from a test object.
FIG. 1 is a flow diagram of a process 100 for processing an image dataset of x-ray radiation emergent from a test object after its irradiation by an x-ray radiation source. The process 100 begins at step 102. At step 104, an image dataset comprising a spatially resolved map of items of x-ray radiation intensity data from the x-ray radiation emergent from the test object is generated. The x-ray radiation intensity data items is further resolved spectroscopically between at least two energy bands across the spectrum of the x-ray radiation source. At step 106, a material attenuation coefficient is considered as a plural set of energy dependent polynomial equations with a set of energy dependent coefficients across the plural set of energy bands. At step 108, a measured attenuation at each energy band is determined. At step 110, a Compound Proton Number Set comprising plural order powers of weighted atomic numbers is calculated from the measured attenuation at each energy band. At step 112, a segmented image dataset is generated using the Compound Proton Number Set. The process 100 ends at step 114.
In accordance with the invention in a first aspect, a method of radiological examination of an unknown object takes the form of a method processing an image dataset of radiation emergent from a test object after its irradiation by a suitable radiation source which comprises the steps of:
generating an image dataset comprising a spatially resolved map of items of intensity data from the radiation emergent from the test object;
further, resolving the intensity data items spectroscopically between at least two energy bands across the spectrum of the source;
numerically processing the spectroscopically resolved intensity data items to determine at least one further spatially resolved dataset of data items comprising one or more orders of Compound Proton Number and/or an effective mass thickness and/or a density;
generating a segmented image dataset using the said dataset of one or more orders of Compound Proton Number and/or effective mass thickness and/or density.
The step of numerically processing the spectroscopically resolved intensity data items to determine a further spatially resolved dataset of data items comprising one or more orders of Compound Proton Number and/or an effective mass thickness and/or a density comprises for example:
numerically processing the spectroscopically resolved intensity data via the following steps:
considering a material attenuation coefficient as a plural set of energy dependent polynomial equations with a set of energy dependent coefficients across the said plural set of energy bands;
determining a measured attenuation coefficient at each said energy band;
calculating therefrom one or more orders of Compound Proton Number and/or effective mass thickness and/or density and for example a Compound Proton Number Set comprising plural order powers and preferably plural higher order powers of weighted compound atomic number.
Preferably the method comprises calculating at least two of: one or more orders of Compound Proton Number and/or effective mass thickness and/or density; and for example at least two orders of Compound Proton Number as a Compound Proton Number Set.
As has been defined herein, a Compound Proton Number Set comprises multiple orders of weighted compound atomic number. In their infinite form such numbers identify and depend upon the composition of a compound. The invention includes a method for calculating a number and preferably a high number of dimensions of the Compound Proton Number Set using X-ray measurements measured at multiple energies, as a method for material identification.
The invention comprises calculating one or more orders of Compound Proton Number and/or effective mass thickness and/or density and in the preferred case comprises calculating a Compound Proton Number Set as so defined, and making such data available for the purposes of identifying the material content of the object. Where use of such data is discussed herein, then except where the context necessarily requires otherwise the invention should be considered applicable to the use of one or more orders of Compound Proton Number and/or effective mass thickness and/or density in the general case and at least one Compound Proton Number Set as above defined in the preferred case.
The step of considering a material attenuation coefficient as a plural set of energy dependent polynomial equations comprises defining a numerical relationship comprising such a plural set of energy dependent polynomial equations with a set of energy dependent coefficients across the said plural set of energy bands, and for example making use of the formula of the general form:

α(E)=a(E)+c(E)Z2+d(E)Z3  . . . +y(E)Zn

in particular for plural higher order powers and for example at least the second and third powers. Plural powers and in particular plural higher order powers of this general form are preferred. Although plural powers and in particular plural higher order powers of this general form are preferred the invention does not exclude making use of single orders of Z.
The step of numerically processing the spectroscopically resolved intensity data items to determine a further spatially resolved dataset of data items representative of a mass thickness, may be included in the process to gather the Compound Proton Number Set.
The step of generating a segmented image dataset using the said dataset of data items representative of one or more orders of Compound Proton Number and/or effective mass thickness and/or density comprises generating a segmented image segmented in one or more orders of Compound Proton Number and/or in effective mass thickness and/or in density. Images may be segmented in multiple manner for example by applying at least two segmentations selected from one or more Compound Proton Number Sets comprising different orders of Compound Proton Number, an effective mass thickness, and a density.
Thus, the product of the method is a segmented image dataset, but the image dataset is segmented according to parameters determined by the data items calculated as a Compound Proton Number Set and/or effective mass thickness and/or density, and not based on transmitted intensity alone. Such an image dataset, either instead of or in supplement to one based on a spatial map segmented by transmitted intensity alone, may improve discrimination in the image, especially in one or more of the particular cases considered above. For example it may improve discrimination of curved or irregularly shaped objects, overlapping objects and the like.
The segmented image may be segmented in multiple manner by applying at least two segmentations selected from one or more orders of Compound Proton Number, an effective mass thickness, and a density. For example, images may be segmented by a single order Compound Proton Number (CPN) and mass thickness. Alternatively for example, instead of segmenting in a CPN and mass thickness we could segment in different orders of CPN. We may segment in a parameter derived from multiple orders of CPN, say second order/third order.
Simultaneous analysis of images segmented by one or more orders of Compound Proton Number and/or by mass thickness, and intensity can provide a more comprehensive assessment of the extent of contiguous regions with uniform elemental composition, uniform mass thickness and transmitted intensity characteristics. Logical inconsistencies in segmentation for each of these contrast modes can be highlighted for further investigation by the operator. Characteristic trends in the segmented data for each region can be used to identify common objects. In addition the availability of this more specific information permits more complex and powerful analyses to be undertaken.
For example, a contiguous region which exhibits a uniform Compound Proton Number and a positive curvature in the mass thickness along one axis can be identified as a cylindrical object for which the diameter can be derived from the mass thickness data and a more accurate Compound Proton Number value returned by averaging over the entire segmented region. The presence of a border with a different Compound Proton Number surrounding a segmented region indicates the presence of a container where an analysis of the form, dimensions and material in said container can be made to aid the material analysis for the contained segment. Image libraries and databases of characteristic features can be constructed for common objects, both threat and benign, providing, in addition to the transmitted intensity projections, both Compound Proton Number and mass thickness projections of the object. Given that the mass thickness is the product of the material density and its thickness it will be apparent to those skilled in the art that, by providing information on the thickness of the object by another technique, its density can be calculated. Materials or threat identification algorithms can also be developed from this data whereby, for instance, characteristic relationships between Compound Proton Numbers and density or mass thickness for threat and benign materials can be utilised to raise an alarm or highlight a region of the image if an increased probability of the presence of a threat is detected.
The radiation source preferably comprises one or more sources to deliver high-energy radiation such as ionizing radiation, for example high energy electromagnetic radiation such as X-rays and/or gamma rays, or subatomic particle radiation, and the detection system is adapted correspondingly to detect radiation in this spectrum. The radiation source for example is a broadband source such as a broadband X-ray or gamma-ray source capable of producing broad spectrum emission over a wide range of energies. Additionally or alternatively, multiple sources may be used to produce such a broad spectrum emission over a wide range of energies. The source(s) are such as to enable an object under test to be irradiated across a broad enough spectrum to facilitate the resolution of the emergent intensity data into plural intensity bins as required for the subsequent data processing steps.


The invention is applied to dual and/or multispectral techniques and systems where emergent intensity data is resolved spectroscopically between at least two energy bands and more preferably at least three energy bands simultaneously and/or successively. A data collection step preferably comprises resolving the intensity data items spectroscopically between at least two energy bands and more preferably at least three energy bands simultaneously and/or successively across the spectrum of the source. Dual/multispectral techniques give more detailed information on which the numerical processing steps of the method in particular can work to provide data characteristic of the composition of the object. While multispectral techniques are ideally suited to such analysis techniques it will be clear to those skilled in the art that modifications of such techniques can be applied to, for example, dual energy detection systems.
In a preferred embodiment at least a Compound Proton Number Set as above defined is generated and this is used as an image segmentation tool. A Compound Proton Number Set may comprise plural orders of Compound Proton Number. This embodiment accepts the complexity of the attenuation inherent in multi-element compounds, and treat the attenuation coefficient as a set of energy dependent high order polynomial equations, with a set of energy dependent coefficients. As the number of energy levels is measured, higher orders of the atomic number can be included in the equation. If the coefficients can be measured accurately, these fits to plural powers and in particular higher order powers of atomic number (which have been called herein Compound Proton Numbers) can be calculated, and the Compound Proton Number Set created, from which the material may be identified. As is the case with fitting techniques, the accuracy of fitting increases with the number of independent measures. In the case of dual energy techniques, only two measurements across broad energy bands are available for fitting. The greater number of data points collections using multi-spectral detection methods thus increases the accuracy of this method.
With the absorption of elements a function of the atomic number, a single-value Compound Proton Number Set may be calculated for each element. Compounds of elements will have a higher-complexity attenuation dependent upon the higher order polynomials of weighted atomic number, and each compound will have a Compound Proton Number Set. If a solution for a range of powers (or orders) of Compound Proton Number is calculated, the value of Compound Proton Number will be different for each power (as is demonstrated later in equations 13-15), which is not the case for elements. The multiple fit parameters permitted by multi-spectral techniques allow the Compound Proton Numbers to be calculated for a range of orders, unlike for dual-energy techniques, thus providing greater knowledge of the whole Compound Proton Number Set thus better identification of the material.
In the preferred case, the method step of considering a material attenuation coefficient as a plural set of energy dependent polynomial equations with a set of energy dependent coefficients across the said plural set of energy bands comprises resolving at least two orders for the polynomial equations and for example at least two higher orders.
In the preferred case, the method step of calculating therefrom plural order powers of atomic number comprises calculating at least two higher order powers and for example at least the second and third powers.
The invention is applied to dual and/or multispectral techniques and systems where emergent intensity data is resolved spectroscopically between at least two energy bands and more preferably at least three energy bands simultaneously and/or successively.
The key to the invention is that collected intensity data is resolved spectroscopically between a plurality of energy bands across the incident spectrum, more preferably at least three, and more preferably a larger plurality. This resolution is used to determine one or more orders of Compound Proton Number and/or effective mass thickness and/or density and for example a Compound Proton Number Set as above described.
In order to effect this, a predetermined incident radiation spectrum is required across a breath of spectrum/range of energies broad enough to facilitate the resolution of the emergent intensity data into plural intensity bands as required for the subsequent data processing steps. Within this general requirement such energy bands may be broad or narrow to the point of tending to be single energies, and may be adjacent or be spaced apart, and may collectively encompass any part or all of the spectrum of one or more suitable sources.
It is not specifically pertinent to the invention how, by suitable combination of sources and detectors, such a spectroscopically resolved intensity dataset is generated.
One or may radiation sources may be used to generate a predetermined incident radiation spectrum of the desired breadth across the full breadth simultaneously or across parts thereof sequentially.
The resultant predetermined incident radiation spectrum of the desired breadth may be resolved into plural energy bands simultaneously for example in that the detector system preferably exhibits a spectroscopically variable response across at least a part of the source spectrum allowing spectroscopic information to be retrieved and allowing intensity information to be detected simultaneously at a plurality of, and for example at least three, differentiated energy bands across the spectrum of the source.
A detector system may be so adapted by provision of multiple detectors calibrated to different energies or by the provision of at least one detector adapted to produce spectroscopic resolution inherently in that it exhibits a direct spectroscopic response. In particular such a detector is fabricated from a material selected to exhibit inherently as a direct material property a direct variable electrical and for example photoelectric response to different parts of the source spectrum. Such a detector may be a dual energy detector adapted to distinguish between two energy levels within the incident spectrum, or may be a genuinely multispectral detector adapted to distinguish between three or more energy levels within the incident spectrum.
The principles may be combined to distinguish a larger plurality of energy bands. For example a detector system may be used comprising a plurality of detectors that exhibit a spectroscopically variable response across at least a part of the incident spectrum with such detectors additionally calibrated to different energies. In a specific case of such a concept plural dual energy detectors calibrated to different energies may be used in order to distinguish between more than two energy levels within the incident spectrum.
Additionally or alternatively the resultant predetermined incident radiation spectrum of the desired breadth may be resolved into plural energy bands sequentially, for example using multiple detectors sequentially and/or filters and/or cycling incident radiation frequency.
In the preferred case, a multispectral X-ray technique is employed in which emergent intensity data is resolved spectroscopically between at least three energy bands simultaneously. Access to a plurality of energy bins provides information which is inaccessible to a dual energy system in particular in resolving higher orders of Compound Proton Number. As noted, this may be effected by using plural dual energy detectors calibrated to different energies and/or by using one or more multispectral detectors adapted to distinguish between three or more energy levels within the incident spectrum
Multispectral X-ray techniques whether using truly multispectral detectors, for example CdTe-type detectors, or using plural dual energy detectors calibrated to different energies, offer many advantages over traditional dual energy systems. For a dual energy system the two energy regions are not entirely discrete due to the non-zero probability of detection of high energy X-rays in the low energy detector and vice versa. In addition the cut off between high and low energy bins is not precise, resulting in an overlap between the two energy regions. The detectors used for such systems are generally scintillation detectors, which are typically operated in a current mode which records the product of the interaction rate and the charge per interaction. As such these systems do not provide a photon counting capability and instead simply give a measure of the total deposited energy. Scintillator response times are also quite slow, resulting in blurring of images and a loss of spatial resolution owing to afterglow effects.
In contrast a CdTe multispectral detector operates in pulse mode which preserves the energy and timing of individual events. The system is therefore capable of simultaneous measurement of the energy of each detected X-ray which can be measured to an accuracy fundamentally limited only by the detector resolution. As such systems use only a single detector to measure all energies each energy bin is discrete in nature with no overlapping between bins.
A suitable detector for implementation of the invention comprises one or more detector elements of a semiconductor material adapted for high energy physics applications, such as a material able to act as a detector for high energy radiation, and for example high energy electromagnetic radiation such as X-rays or gamma rays, or subatomic particle radiation. The resultant device comprises at least one layer of such material and is thus a device adapted for high energy physics applications, and for example a detector for high energy radiation such as X-rays or gamma rays, or subatomic particle radiation. The method comprises the use of such a device.
The semiconductor device is preferably a detector device adapted to exhibit a spectroscopically variable response across at least a substantial part of the intended radiation spectrum in use. In particular the semiconductor material is used that exhibits inherently as a direct material property a direct variable electrical and for example photoelectric response to different parts of the radiation spectrum in use.
In a preferred embodiment the semiconductor material is formed as a bulk crystal, and for example as a bulk single crystal (where bulk crystal in this context indicates a thickness of at least 500 μm, and preferably of at least 1 mm).
In a preferred embodiment the semiconductor material may be selected from Group II-VI semiconductors and in particular may be selected from cadmium telluride, cadmium zinc telluride (CZT), cadmium manganese telluride (CMT), and alloys thereof, and for example, save for incidental impurities, consists essentially of crystalline Cd1−(a+b)MnaZnbTe where a+b<1 and a and/or b may be zero. A composite device may also have other detector elements of other materials for additional functionality.
In a preferred case for example the method may be applied to facilitate the detection of the presence of and/or classification or identification of particular target materials within a test object, for example materials which might represent a threat to security, a breach of customs regulations or the like.
In the preferred case, intensity data for radiation emergent from the test object comprises at least transmitted intensity data, and the numerical processing steps may comprise determining therefrom an attenuation coefficient related to attenuation of transmitted intensity.
The invention finds particularly useful application in relation to the scanning of objects comprising containers of contained materials which by their nature will be expected to have a single generally homogeneous composition, for example fluid compositions such as liquids, including mixtures, solutions, emulsions, suspensions etc, like flowable compositions such as gels, pastes, creams, fine powders, and the like, aerosols etc. However, it should be understood that the invention is not limited to such liquids applications, and many of general principles of the invention may be equally applicable to hetereogenous objects with solid and/or liquid contents.
In a more complete embodiment, the method is applied as a method for the radiological examination of an object and comprises:
providing a radiation source such as an X-ray or gamma-ray source and a radiation detector system such as an X-ray or gamma-ray detection system spaced therefrom to define a scanning zone therebetween, the detector system being capable of detecting and collecting spectroscopically resolvable information about incident radiation;
locating an object in the scanning zone and irradiating with radiation from the source;
collecting at least one spatially resolved dataset of intensity information about radiation emergent from the object and incident at the detector system after interaction with and for example transmission through the object;
resolving the spatially resolved intensity dataset across at least two and preferably at least three energy bands across the spectrum of the source to produce an intensity data item for each band;
processing the resultant dataset in accordance with the above method to generate for each measured intensity data item a further data item representative of one or more orders of Compound Proton Number and/or an effective mass thickness and/or a density;
generating a segmented image dataset using the said dataset of data items representative of one or more orders of Compound Proton Number and/or an effective mass thickness and/or a density.
Preferably the method comprises generating at least two of: one or more orders of Compound Proton Number and/or effective mass thickness and/or density; and for example at least two orders of Compound Proton Number as a Compound Proton Number Set.
The method may optionally include display of an image dataset as a visual image on suitable display means. Additionally or alternatively the image may be further processed numerically for example by any suitable image processing method.
In accordance with the invention in a second aspect, there is provided an apparatus for radiological examination of an unknown object by processing an image dataset of radiation emergent from a test object after its irradiation by a suitable radiation source comprising:
a radiation source and a radiation detector system spaced therefrom to define a scanning zone therebetween and to collect in use a spatially resolved dataset of information about radiation incident at the detector system after interaction with an object in the scanning zone;
a first data processing module to process and resolve the dataset spectroscopically across at least two and preferably at least three energy bands within the spectrum of the source;
a second data processing module adapted to generate for each measured intensity data item a further data item representative of one or more orders of Compound Proton Number and/or an effective mass thickness and/or a density, and thereby generating a spatially resolved dataset of data items representative of one or more orders of Compound Proton Number and/or an effective mass thickness and/or a density;
an image generation module adapted to generate a segmented image dataset using the said dataset of data items representative of one or more orders of Compound Proton Number and/or an effective mass thickness and/or a density.
Preferably the second data processing module is adapted to generate at least two of: one or more orders of Compound Proton Number and/or effective mass thickness and/or density; and for example at least two orders of Compound Proton Number as a Compound Proton Number Set; and the an image generation module adapted to generate a segmented image dataset using such a generated dataset.
Optionally the apparatus may include a suitable image display module to display an image generated from the segmented image dataset.
Preferred apparatus features will be understood by analogy with the description of the method.
In particular the second data processing module will be appreciated to be the means by which any data processing step associated with the generation of one or more orders of Compound Proton Number and/or an effective mass thickness and/or a density and preferred embodiments will be understood accordingly.
Any suitable form of data processing module combining suitable hardware and software, and for example a suitably programmed data processing apparatus such as a suitably programmed general purpose or special purpose computer, can be envisaged.
It will be understood generally that each numerical step in the method of the invention can be implemented by and the role of a data processing module in the method of the invention can be facilitated by a suitable set of machine readable instructions or code. These machine readable instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a means for implementing the functions specified, and in particular to produce a data processing module as herein described.
These machine readable instructions may also be stored in a computer readable medium that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in a computer readable medium produce an article of manufacture including instruction means to implement some or all of the steps in the method of the invention. Computer program instructions may also be loaded onto a computer or other programmable apparatus to produce a machine capable of implementing a computer executed process such that the instructions are executed on the computer or other programmable apparatus providing steps for implementing some or all of the steps in the method of the invention. It will be understood that a step can be implemented by, and a means of the apparatus for performing such a step composed in, any suitable combinations of special purpose hardware and/or computer instructions
In accordance with the invention in a third aspect there is provided at least one computer program comprising program instructions which when loaded onto a suitable computer will cause the computer to perform one or more and for example all of the numerical processing steps of the method of the first aspect of the invention and/or will cause a computer to serve as a data processing module in accordance with the second aspect of the invention.
The at least one computer program may without limitation be embodied on a computer-readable recording medium or read-only memory, stored in a memory of a computer, stored in a remote memory accessible by a computer for example via a distributed network, or carried on a suitable carrier signal.
An embodiment of a possible numerical analysis method in accordance with the invention for the generation of a Compound Proton Number Set will now be discussed by way of example only.
Expressed numerically, from the Compound Proton Number Set, we define the Compound Proton Number of order n as CPNnZ. A preferred Compound Proton Number Set comprises at least n=2, n=3.
One simple embodiment of the method which has been used for material identification is to use three energy bins, and using the following approximation for the mass attenuation coefficient for all elements:

α(E)=a(E)+c(E)Z2+d(E)Z3   4

For a compound material the mass attenuation coefficient is given by the sum of the individual attenuation coefficients (αi) weighted by their mass fraction, wi, so that




  
    
      
        
          α
          compound
        
        =
        
          
            ∑
            i
            
              
            
          
          ⁢
          
            
          
          ⁢
          
            
              w
              i
            
            ⁢
            
              α
              i
            
          
        
      
    
    
      5
    
  




Therefore:




  
    
      
        
          R
          ⁡
          
            (
            E
            )
          
        
        =
        
          
            ln
            ⁡
            
              (
              
                
                  
                    I
                    0
                  
                  ⁡
                  
                    (
                    E
                    )
                  
                
                /
                
                  I
                  ⁡
                  
                    (
                    E
                    )
                  
                
              
              )
            
          
          =
          
            x
            ⁢
            
              
                ∑
                j
                
                  
                
              
              ⁢
              
                
              
              ⁢
              
                
                  w
                  j
                
                ⁡
                
                  (
                  
                    
                      a
                      ⁡
                      
                        (
                        E
                        )
                      
                    
                    +
                    
                      
                        c
                        ⁡
                        
                          (
                          E
                          )
                        
                      
                      ⁢
                      
                        Z
                        j
                        2
                      
                    
                    +
                    
                      
                        d
                        ⁡
                        
                          (
                          E
                          )
                        
                      
                      ⁢
                      
                        Z
                        j
                        3
                      
                    
                  
                  )
                
              
            
          
        
      
    
    
      6
    
  




where wj is the mass fraction of the element j within the material under investigation.
Rearranging Equation 6 gives:




  
    
      
        
          R
          ⁡
          
            (
            E
            )
          
        
        =
        
          x
          [
          
            
              
                a
                ⁡
                
                  (
                  E
                  )
                
              
              ⁢
              
                
                  ∑
                  j
                  
                    
                  
                
                ⁢
                
                  
                
                ⁢
                
                  w
                  j
                
              
            
            +
            
              
                c
                ⁡
                
                  (
                  E
                  )
                
              
              ⁢
              
                
                  ∑
                  j
                  
                    
                  
                
                ⁢
                
                  
                
                ⁢
                
                  
                    w
                    j
                  
                  ⁢
                  
                    Z
                    j
                    2
                  
                
              
            
            +
            
              
                d
                ⁡
                
                  (
                  E
                  )
                
              
              ⁢
              
                
                  ∑
                  j
                  
                    
                  
                
                ⁢
                
                  
                
                ⁢
                
                  
                    w
                    j
                  
                  ⁢
                  
                    Z
                    j
                    3
                  
                
              
            
          
          ]
        
      
    
    
      7
    
  




So

R(E)=x[a(E)+c(E)Z2+d(E)Z3]  8

where Z2 and Z3 are the simple weighted mean square and mean cube of the atomic number respectively. Here, the second order Compound Proton Number




  
    
       
      CPN
      2
    
    ⁢
    Z
  
  =
  
    
      (
      
        
          Z
          2
        
        _
      
      )
    
    2
  




and the third order Compound Proton Number





  
    
       
      CPN
      3
    
    ⁢
    Z
  
  =
  
    
      (
      
        
          Z
          3
        
        _
      
      )
    
    3
  




The components a(E), c(E) and d(E) can be found empirically either by experiment or by simulation in Geant4.This is done by carrying out I and I0 measurements on a range of calibration items of known atomic mass and mass thickness. Equation 8 can then be solved for coefficients a(E), c(E) and d(E) across the energy bins.
The simplest way of doing this fit is to use just three energy bins, which we label 1, 2 and 3. To shorten the equations we define REnergy Bin 1=R1, aEnergy Bin 3=a3, etc. Then we rewrite equation 8 for the three energy bins to get the simultaneous equations;

R1=x[a1+c1Z2+d1Z3]

R2=x[a2+c2Z2+d2Z3]

R3=x[a3+c3Z2+d3Z3]  9

This is a matrix equation. If we know the matrix




  
    
      
        M
        =
        
          (
          
            
              
                
                  a
                  1
                
              
              
                
                  c
                  1
                
              
              
                
                  d
                  1
                
              
            
            
              
                
                  a
                  2
                
              
              
                
                  c
                  2
                
              
              
                
                  d
                  2
                
              
            
            
              
                
                  a
                  3
                
              
              
                
                  c
                  3
                
              
              
                
                  d
                  3
                
              
            
          
          )
        
      
    
    
      10
    
  




then we can invert it and multiply M−1 by three measured R values to get the vector (x, xZ2, xZ3) for the material sample. This allows us to obtain the second and third orders of the Compound Proton Number Set in this embodiment, along with the mass thickness, x. The matrix M depends only on our choice of energy bins. Once we have found M we can use it for any material as long as our starting assumption of equation 4 is valid.
It would be possible to find M by taking numbers from the NIST database for example. But in reality it is better to base it on our own measurements of materials of known composition. This way we can expect that the biases of our measurement system will be (at least partly) absorbed into the matrix and when we apply it to our own measurements of an unknown material the measurement biases will be reduced. We refer to measurement of M as calibration. Calibration is particularly simple if we use pure elements. We measure the absorption in, say, energy bin 1 of samples of three different elements of atomic numbers ZA, ZB, ZC and mass thicknesses xA, xB and xC. The result is three simultaneous equations again

R1(ZA)/xA=a1+c1ZA2+d1ZA3 

R1(ZB)/xb=a1+c1ZB2+d1ZB3 

R1(ZC)/xC=a1+c1ZC2+d1ZC3  11

So once again we solve for (a1, c1, d1) by multiplying the vector of measured R/x values by X−1, where




  
    
      
        X
        =
        
          (
          
            
              
                1
              
              
                
                  Z
                  A
                  2
                
              
              
                
                  Z
                  A
                  3
                
              
            
            
              
                1
              
              
                
                  Z
                  B
                  2
                
              
              
                
                  Z
                  B
                  3
                
              
            
            
              
                1
              
              
                
                  Z
                  C
                  2
                
              
              
                
                  Z
                  C
                  3
                
              
            
          
          )
        
      
    
    
      12
    
  




And repeat for the other two energy bins to get the whole of matrix M.
The calibration elements can be any elements covering the range of atomic numbers likely to be encountered in the analysis e.g. carbon, aluminium and copper as these cover the range of atomic numbers expected in a test object. The use of such an analysis method shows the potential for more advanced techniques than straightforward matching of spectra.
As a simple example to show how the second and third order Compound Proton Numbers will be different in a compound, but be the same in an element, consider a compound made up of two items in an atomic number of 2, and a second with an atomic number of 5, with a 50:50 combination by weight. Therefore, the second order Compound Proton Number




  
    
      
        
          
             
            CPN
            2
          
          ⁢
          Z
        
        =
        
          
            
              
                (
                
                  
                    1
                    2
                  
                  ⁢
                  
                    2
                    2
                  
                
                )
              
              +
              
                (
                
                  
                    1
                    2
                  
                  ⁢
                  
                    5
                    2
                  
                
                )
              
            
            2
          
          =
          3.81
        
      
    
    
      13
    
  




And for the third order Compound Proton Number




  
    
      
        
          
             
            CPN
            3
          
          ⁢
          Z
        
        =
        
          
            
              
                (
                
                  
                    1
                    2
                  
                  ⁢
                  
                    2
                    3
                  
                
                )
              
              +
              
                (
                
                  
                    1
                    2
                  
                  ⁢
                  
                    5
                    3
                  
                
                )
              
            
            3
          
          =
          4.05
        
      
    
    
      14
    
  




However for a single element of atomic number 5 both second and third order Compound Proton Numbers are identical (as indeed are any orders of Compound Proton Number).

CPN2Z==5=√{square root over ((53))}=CPN3Z   15

As each material will have a different set of Compound Proton Numbers, the greater the dimensions of the Compound Proton Number that can be calculated the more information about the material can be gathered, and the material better identified. Further orders will readily be derivable using the same basic principles of the invention and sufficient plural energy bins of radiation data.
As an example measured on real apparatus, the calculation of mass thickness and the ability to measure both CPN2Z and CPN3Z can be exploited in order to distinguish powdered aluminium from a solid aluminium block. The oxide content is significantly greater in powdered aluminium due to the increased surface area over that of a solid block of aluminium. This then results in a compound which can be identified by the difference between the CPN2Z and CPN3Z. Additionally, with the aid of a tomographic thickness measurement, the density of the material can be derived by dividing the mass thickness by the measured thickness. Densities deviating significantly from that of a solid block of aluminium are clearly powders.
Table 1 shows the resulting density, CPN2Z and CPN3Z. The CPN2Z and CPN3Z are identical for the aluminium block and the density is over 95% of that expected for a solid block. In contrast the powdered aluminium shows a variation in CPN2Z and CPN3Z indicating the sample is a compound rather than a single element and the densities of both powder samples are significantly lower than expected for a solid block of material.







TABLE 1







Second and third order Compound Proton Numbers, CPN2Z and CPN3Z,


measured for aluminium powder and an aluminium block.


The density was derived here from knowledge of the physical thickness


of the samples and measurement of the mass thickness.














Mass








thick-
Thick-



% of



ness
ness
Density


Expected


Material
(gcm−2)
(cm)
(gcm−3)
CPN2Z
CPN3Z
Density
















Al powder in
3.08
6.5
0.47
12.71
11.65
17.55


an


aluminium


can


Al block
4.02
1.56
2.58
12.92
12.92
95.46


1.56 cm

## Claims

1. A method of processing an image dataset of x-ray radiation emergent from a test object after its irradiation by an x-ray radiation source, the method comprising:
generating an image dataset comprising a spatially resolved map of items of x-ray radiation intensity data from the x-ray radiation emergent from the test object;
further, resolving the x-ray radiation intensity data items spectroscopically between at least two energy bands across the spectrum of the x-ray radiation source;
numerically processing the spectroscopically resolved x-ray radiation intensity data items by:
considering a material attenuation coefficient as a plural set of energy dependent polynomial equations with a set of energy dependent coefficients across the said plural set of energy bands;
determining a measured attenuation at each said energy band;
calculating from the measured attenuation at each said energy band a Compound Proton Number Set comprising plural order powers of weighted atomic numbers, the Compound Proton Number Set comprising solutions to the plural set of energy dependent polynomial equations; and
generating a segmented image dataset using the Compound Proton Number Set.

2. The method in accordance with claim 1 wherein the method step of considering a material attenuation coefficient as a plural set of energy dependent polynomial equations with a set of energy dependent coefficients across the said plural set of energy bands comprises defining a numerical relationship comprising such a plural set of energy dependent polynomial equations with a set of energy dependent coefficients across the said plural set of energy bands.

3. The method in accordance with claim 1 comprising resolving at least two higher orders for the polynomial equations.

4. The method in accordance with claim 1 wherein the step of numerically processing the spectroscopically resolved x-ray radiation intensity data items to determine an effective mass thickness comprises calculating the mass thickness alongside the Compound Proton Number Set.

5. The method in accordance with claim 1 wherein the step of numerically processing the spectroscopically resolved x-ray radiation intensity data items comprises deriving data items representative of a Compound Proton Number Set and of an effective mass thickness, and the step of generating a segmented image dataset comprises generating an image dataset for simultaneous analysis segmented by Compound Proton Number(s), mass thickness and intensity.

6. The method in accordance with claim 1 wherein the source is an X-ray source, and a detection system is adapted correspondingly to detect and resolve X-rays between a plural set of energy bands.

7. The method in accordance with claim 1 wherein a detector system is provided that exhibits a spectroscopically variable response across at least a part of the incident spectrum allowing spectroscopic information to be retrieved and allowing intensity information to be detected simultaneously at a plurality of differentiated energy bands.

8. The method in accordance with claim 1 wherein emergent x-ray radiation intensity data is resolved spectroscopically between at least three energy bands simultaneously.

9. The method in accordance with claim 8 wherein a detector system is provided comprising plural dual energy detectors calibrated to different energies in order to distinguish between more than two energy levels within the incident spectrum.

10. The method in accordance with claim 8 wherein a detector system is provided comprising at least one multispectral detector inherently adapted to distinguish between three or more energy levels within the incident spectrum.

11. The method in accordance with claim 1 wherein intensity data for x-ray radiation emergent from the test object comprises at least transmitted x-ray radiation intensity data, and the numerical processing steps comprises determining an attenuation coefficient related to attenuation of transmitted intensity.

12. The method in accordance with claim 1 further comprising display of an image dataset as a visual image on suitable display means.

13. The method in accordance with claim 1 applied as part of a method for the radiological examination of an object.

14. A method for the radiological examination of an object, the method comprising:
providing an x-ray radiation source and an x-ray radiation detector system spaced therefrom to define a scanning zone therebetween, the detector system being capable of detecting and collecting spectroscopically resolvable information about incident x-ray radiation;
locating an object in the scanning zone and irradiating with x-ray radiation from the source;
collecting at least one spatially resolved dataset of intensity information about x-ray radiation emergent from the object and incident at the detector system after interaction with and transmission through the object;
resolving the spatially resolved x-ray radiation intensity dataset across at least two energy bands across the spectrum of the source to produce an x-ray radiation intensity data item for each band;
processing the x-ray intensity data item for each band in accordance with claim 1 to generate for each measured x-ray radiation intensity data item a Compound Proton Number Set; and
generating a segmented image dataset using the Compound Proton Number Set.

15. An apparatus for radiological examination of an unknown object by processing an image dataset of x-ray radiation emergent from a test object after its irradiation by a suitable x-ray radiation source, the apparatus comprising:
an x-ray radiation source and an x-ray radiation detector system spaced therefrom to define a scanning zone therebetween and to collect in use a spatially resolved dataset of information about x-ray radiation incident at the detector system after interaction with an object in the scanning zone;
a first data processing module to process and resolve the dataset spectroscopically across at least two energy bands within the spectrum of the source;
a second data processing module to generate for each measured x-ray radiation intensity data item a Compound Proton Number Set via the following steps:
considering material attenuation coefficient as a plural set of energy dependent polynomial equations with a set of energy dependent coefficients across the said plural set of energy bands;
determining a measured attenuation at each said energy band;
calculating from the measured attenuation at each energy band a Compound Proton Number Set comprising plural order powers of weighted atomic number, the Compound Proton Number Set comprising solutions to the plural set of energy dependent polynomial equations; and
an image generation module to generate a segmented image dataset using the said Compound Proton Number Set.

16. An apparatus in accordance with claim 15 further comprising an image display module to display an image generated from the segmented image dataset.

17. A computer program product comprising a non-transitory computer-usable medium having computer-readable program code embodied therein, the computer-readable program code adapted to by executed to implement the method of claim 1.