Increasing the Number of Material Recognition Classes in Cargo Inspection Using the X-Ray Dual High-Energy Method
Round 1
Reviewer 1 Report
Comments and Suggestions for Authors
The tittle is not informative, please change it to (for example): “ Increasing the Number of Material Recognition Classes by the X-Ray 2 Dual High-Energy Method”
The abstract is very promising, making distinction between some important for airport security materials, like light polymers and explosives, and some metals
However, the contents of the paper are somewhat disappointing. As I understand, authors propose to use gamma-rays for detection of materials for example at airports or at customs, instead of X-rays used now. Authors should tell the reader:
1) what are energies of dual beams now in use?
2) what are the physical processes that authors included in the modelling? Creation of pairs or just the de Beer’s attenuation?
I lack comparison with some other methods – experiment and/or theory
The paper is reach in computational details but I lack a clear structure of the model.
What is the physical process of the detection? The resolution of characteristic X-ray lines of the materials? Or an algorithmic elaboration of the attenuated beam intensity? The paper does not make it clear.
Numerous repetitions on the goals of the paper, but I hardly see the advantage of authors’ results.
Please, re-do the whole structure of the paper, before possible resubmission.
Minor observations:
ADC capacity is not self explaining in key words
Please, nominate X-ray inspection in the introduction
Some more information on the dual method would be useful: which are typical energies used in commercial applications?
Line 44, “of maximum energies of X-rays”, i.e. how much in keV?
Line 50 “the pair birth”: should read: electron-positron pairs birth
Lines 97-99 and 100-102 seem to say the same thing
Table 3 is very little informative: the contents may be told in one sentence
Table 6 line with
|
10.945 |
10.735 |
10.735 |
10.84 |
10.7 |
Is the precision of the calculation so high?
Line 105 gamma-ray systems (bremsstrahlung): “bremsstrahlung” was already used. Is the meaning the same in both cases?
Too many acronyms introduced make reading difficult: why not to use Zeff instead of EAN? The same with DEM and SEM – dual and single-energy methods?
Resuming The paper is long and complicated in formulas. But it lacks a clear presentation of the physics of the processes. It also lacks comparisons with other research.
Comments on the Quality of English Language
English sufficiently clear
Author Response
We express our sincere gratitude to the reviewers for their attentive, responsible, thorough and friendly reading of the manuscript of our article. We offer special thanks for your comments, questions and recommendations.
Reviewer 1.
Comment 1: The tittle is not informative, please change it to (for example): “Increasing the Number of Material Recognition Classes by the X-Ray Dual High-Energy Method”.
Response 1: Thank you for your recommendation. We have taken your recommendation into account and changed the title of the manuscript.
Title: “Increasing the Number of Material Recognition Classes in Cargo Inspection by the X-Ray Dual High-Energy Method”
Comment 2: The abstract is very promising, making distinction between some important for airport security materials, like light polymers and explosives, and some metals. Also corrected keywords and text.
Response 2: Thank you for your appreciation of the abstract of our manuscript. To eliminate ambiguity, we have made a brief change to the Abstract to mention the class of inspection objects.
Abstract: “Issues related to increasing the number of material recognition classes in cargo inspection by the X-ray dual high-energy method through introducing a class of heavy organic materials that include basic explosives are considered. A mathematical model of material recognition by the dual-energy method based on the parameters of level lines and effective atomic numbers has been proposed. Estimates of the parameters of the level lines and effective atomic numbers of explosives and their physical counterparts for monoenergetic and classical high-energy implementations of the dual-energy method were made. The use of a simulation model to demonstrate the ability to detect and correctly identify explosives and their physical counterparts using the dual high-energy method is illustrated. An algorithmic methodological approach is proposed to improve the accuracy of effective atomic number estimation. It has been demonstrated theoretically and by simulation that it is possible to distinguish materials in cargo inspection from the following classes of materials: light organics (typical representative polyethylene); heavy organics (explosives), light minerals and heavy plastics (fluoropolymers); light metals (aluminum, Z =13), heavy minerals (calcium oxide, Z = 19); metals (iron, Z = 26); heavy metals (tin, Z = 50); radiation insensitive metals (Z > 57).”
Keywords: cargo inspection control; bremsstrahlung high energy X-rays; dual-energy method; material recognition; explosives; effective atomic number; mass thickness; pre-filtering; ADC capacity
Comment 3: However, the contents of the paper are somewhat disappointing. As I understand, authors propose to use gamma-rays for detection of materials for example at airports or at customs, instead of X-rays used now.
Response 3: The manuscript deals with cargo control using high-energy X-ray (bremsstrahlung). The inclusion of section “2.4.1. Monoenergetic Implementations of DEM” by the authors in the text of the manuscript pursues two specific goals: the first is to find an effective (highly accurate and high-performance) algorithm for estimating the effective atomic number of a material using the level line method; the second is to evaluate the ultimate capabilities of DEM based on high-energy monoenergetic gamma ray sources in relation to estimating the effective atomic number of a material. The ultimate capabilities of DEM include the accuracy of Zeff estimation and the range of Zeff variation for which the effective atomic number is estimated with high accuracy.
We have made the following insertion before section 2.4.1.
“Let us dwell on the monoenergetic implementation of DEM. The inclusion of this subsection pursues two specific goals: the first is to find an effective (highly accurate and high-performance) algorithm for estimating the effective atomic number of a material using the levels lines method; the second is to evaluate the ultimate capabilities of DEM based on high-energy monoenergetic gamma radiation sources in relation to estimating the effective atomic number of a material. The ultimate capabilities of DEM include the accuracy of Zeff estimation and the range of Zeff variation for which the effective atomic number is estimated with high accuracy.”
Comment 4: Authors should tell the reader:
1) what are energies of dual beams now in use?
2) what are the physical processes that authors included in the modelling? Creation of pairs or just the de Beer’s attenuation?
Response 4: 1) In the Introduction, after the links to the websites of HEIS manufacturers with DEM, we made the following insertion:
“In the above-mentioned HEIS, betatrons or linear accelerators are used as sources of high-energy X-ray radiation. The objects under control are scanned by narrow beams of high-energy X-ray (bremsstrahlung) radiation with alternating maximum energies from pulse to pulse (from a group of pulses to a group of pulses). High energy is determined by the maximum possible maximum energy for the radiation source used, it is usually equal to 6 MeV, 7.5 MeV, 9 MeV. And low energy is usually 2-3 MeV less than high energy.”
2) Before point 2.3.1 we made the following insertion:
“All the models mentioned are based on the Bouguer-Lambert-Beer attenuation law, valid for monoenergetic gamma ray in parallel beam geometry, and generalized to the case of an X-ray source with a continuous spectrum and photon registration by an integrating detector that is not a total absorption detector. The models mentioned are based on tabulated dependences of the mass attenuation coefficients of gamma ray on the energy and atomic number of the attenuating substance [25, 26].”
Comment 5: I lack comparison with some other methods – experiment and/or theory.
Response 5: We have added a small subsection to the Discussions section:
“6.3. Comparison with related works
The studies presented in the paper are a natural continuation and development of the series of articles [5, 8, 10, 29, 35, 43] devoted to mathematical and simulation modeling of various DEM implementations. In this work, the mathematical and simulation models of DEM implementations were refined both in terms of level line parameters and in terms of effective atomic numbers. The refinement was reduced to introducing a highly accurate and highly productive algorithm for estimating the effective atomic number of a material using the level line method into the mathematical model, to developing an approach to determining the ultimate capabilities of high-energy DEMs, which made it possible to carry out a cycle of the necessary computational experiments and, based on the analysis of their results, draw a conclusion about the fundamental possibility of reliably distin-guishing the class of heavy organic materials from light organic materials and from light inorganic materials using the recognition parameters Q and Zeff. The available [5, 8, 10, 29, 35, 43] results of experimental estimates of the recognition parameters (Q and Zeff) are less accurate compared to those noted above. Over the last decade, the main direction of HEIS development with the dual energy material recognition function has been to in-crease inspection performance, which has held back research in other areas, such as more accurate Zeff estimation, expansion of the ranges of mass thicknesses of the TO, an in-crease in the number of classes of reliably recognized materials, etc. It should be noted that in recent years, the interest of scientists and practitioners in mathematical modeling of the analyzed systems has grown significantly, which is certainly associated with the urgent need to improve the accuracy of estimating the effective atomic numbers of TO materials and increasing the number of material recognition classes when inspecting cargo using the high-energy X-ray dual energy method. Among the latest works devoted to the above-mentioned topics, one can note [50], however, the accuracy of Zeff estimation leaves much to be desired, the error is up to several units of EAN, which is significantly worse than in the presented work. The reason for the high error in the EAN estimation, in our opinion, is the use of the Alvarez-Makovsky method in the mathematical model, which is characterized by noticeable systematic biases in the Zeff estimates. The theoret-ical model for estimating Zeff from [4] also uses the Alvarez-Makovsky method; visually, fragments from materials similar to those considered in this work are recognized well, but there are no data on Zeff. The approach used in [27] is close to the model described above, the experimental data on the Q parameter are close to those given above, but the experiments and calculations were carried out in [27] only for four classes of recognizable materials.”
Comment 6:
The paper is reach in computational details but I lack a clear structure of the model.
Response 6: We have changed the structure of the article. Section 3 has been allocated for the mathematical model:
“3. Mathematical model of material recognition by the dual high-energy method”
In the second paragraph of this section, we have inserted a brief description of the structure of the described model:
“The mathematical model of material recognition by the dual high-energy method consists of the following blocks:
Mathematical model of material recognition by Dual Energy Method by Level Lines
Mathematical Model for Estimating the TO Thickness in Free Run Lengths
Material Recognition Criteria by Level Line Method
Recognition Parameter in the DEM Implementation by the Level Lines Method
Recognition Calibration in the DEM Implementation by the Level Lines Method
Material Recognition Criteria by Level Line Method
Estimation of the Effective Atomic Number by the Dual High-Energy Method
Monoenergetic Implementations of DEM
Non-monoenergetic DEM Implementation”
Comment 7:
What is the physical process of the detection? The resolution of characteristic X-ray lines of the materials? Or an algorithmic elaboration of the attenuated beam intensity? The paper does not make it clear.
Response 7: We made the following insert after reference [2, 4, 13] in the Introduction:
“The physical process of detecting TO fragments and recognizing their materials by one of the recognition parameters is reduced to obtaining the initial digital radiographic DEM images, calibrating the obtained images “by black” and “by white”, taking the logarithm of the calibrated images, forming an image of the recognition parameter, correlating the value of the recognition parameter at each point of the image using special predetermined calibration functions (lines) to the recognition class, coloring all points of the image according to the selected palette in accordance with the calculated recognition class. The resulting final DEM image is analyzed by the operator or a specially developed algorithm in order to establish the correspondence or non-correspondence of the final TO image to the documentation accompanying the cargo.”
Comment 8:
Numerous repetitions on the goals of the paper, but I hardly see the advantage of authors’ results.
Response 8: Thank you for your comment. We have inserted the following paragraph at the end of the Conclusion:
“The dual high-energy method improves the detection of explosives in transport and transported cargo by introducing a special class of recognizable materials, called the class of heavy organic materials, strictly taking into account all the physical laws of interaction of high-energy bremsstrahlung with matter, rational selection of technical parameters of the corresponding inspection control systems, ensuring high-quality calibrations at all stages of the formation and processing of DEM images and development of highly accurate and highly productive algorithms for assessing the effective atomic numbers of materials of control objects and their structural fragments.”
Comment 9: Please, re-do the whole structure of the paper, before possible resubmission.
Response 9: We reworked the entire structure of the manuscript.
Comment 10: Minor observations:
10.1. ADC capacity is not self explaining in key words
Response: Replaced with “ADC bit depth”
10.2. Please, nominate X-ray inspection in the introduction
Response: We have indicated. (Line 29)
10.3. Some more information on the dual method would be useful: which are typical energies used in commercial applications?
Response: We have indicated. (Line 64-70)
10.4. Line 44, “of maximum energies of X-rays”, i.e. how much in keV?
Response: Lines 71, 72. Inserted "of maximum energies of X-rays from 50 to 250 keV"
10.4. Line 50 “the pair birth”: should read: electron-positron pairs birth
Response: Corrected.
10.5. Lines 97-99 and 100-102 seem to say the same thing
Response: The first sentence was left with a link. “The problem of increasing the recognition classes of TO materials and their structural fragments by DEM is still relevant, especially in the range of EAN values from 5 to 13, due to the need to detecting and correctly recognize explosives and drugs [2], “Raw data processing techniques for material classification of objects in dual energy X-ray baggage inspection systems”
10.6. Table 3 is very little informative: the contents may be told in one sentence
Response: After some consultation, we decided to leave this table. During calibration measurements, not the entire range of arguments changes is used, since checking the quality of the solution of equations (systems of equations) is not always satisfactory. In this case, we can consider the authors lucky that there were few outliers.
10.7. Table 6 line with
10.945 10.735 10.735 10.84 10.7
Is the precision of the calculation so high?
Response: The accuracy of the solution is really that high. The advantage of the described model is that it can be implemented in any programming language and verified.
10.8. Line 105 gamma-ray systems (bremsstrahlung): “bremsstrahlung” was already used. Is the meaning the same in both cases?
Response: We cited the article Park J.Y., Mun J., Lee J.H., Yeon Y.H., Chae M., Lee M., Lee N.H. Development of a Dual-Modality Gamma-ray/Fast Neutron Imaging System for Air Cargo Inspection // Applied Sciences. 2022. Vol. 12. No. 19. P. 9775. https://doi.org/10.3390/app12199775. This article is in the public domain. In the mentioned article, the authors use the term “gamma-ray” (84 times) instead of the usual terms “high-energy bremsstrahlung” (3 times) or “high-energy X-rays” (none). Therefore, we decided to emphasize the equivalence of these concepts in relation to the specific cited article - the concept used by the authors of the cited article is in the first place, and the more traditional concept used in our manuscript is given in brackets. We believe that there is no need to start a dispute in the field of terminology, so we have slightly rephrased the sentence you mentioned taking into account the traditional terminology used in high-energy X-ray NDT methods. The corrected sentence is as follows:
The article [32], “Development of a Dual-Modality Gamma-ray/Fast Neutron Imaging System for Air Cargo Inspection”, argues that cargo inspection systems using high-energy bremsstrahlung sources (the equivalent term, according to the authors of [32], is gamma-ray, which is controversial) are limited in detecting objects of low-density materials, such as drugs or plastic explosives. Therefore, the combined use of gamma-ray (bremsstrahlung) and neutron sources is proposed, which will allow a strong expansion of the detection of the mentioned materials.
10.9. Too many acronyms introduced make reading difficult: why not to use Zeff instead of EAN? The same with DEM and SEM – dual and single-energy methods?
Response: Thank you for your question. When formatting the manuscript, we followed traditional publishing requirements, including MDPI, if a term is used multiple times, its abbreviation is used in the text. The abbreviation DEM is mentioned in the text of the manuscript more than fifty times, EAN more than 30 times, Zeff more than 25 times. There is no abbreviation SEM in our manuscript!
Of course, it will not be difficult for us to partially replace the abbreviation EAN with Zeff, of course, after introducing this designation in the text of the manuscript.
In scientific literature, the effective atomic number is usually denoted by the symbol Zeff [2, 4, 13].
We replaced the abbreviation EAN with Zeff in those places where it is appropriate.
Resuming. The paper is long and complicated in formulas. But it lacks a clear presentation of the physics of the processes. It also lacks comparisons with other research.
Response: We do not fully agree with your general conclusions regarding the manuscript. We will try to convince you otherwise, at least partially.
The global goal of our work is to assess the possibility of increasing the number of material recognition classes using high-energy dual energy methods, preferably for low values of effective atomic numbers. A more specific goal is to answer the question: is it possible to effectively detect and correctly recognize materials from the class of solid organic materials that are close in effective atomic number to explosives? The implementation of these goals is impossible without the development and improvement of mathematical and simulation models for the formation and processing of digital radiographic images using the dual energy method. First: the quality of material recognition using the high-energy dual energy method significantly depends on various physical and technical factors and is determined by the rational choice of parameters of high energy inspection systems (HEIS) with the function of detection of test objects materials and their fragments by dual-energy method. Therefore, the desired generalized mathematical model of HEIS with the noted function cannot be brief and based on a few formulas. Second: the physical foundations of various DEM implementations have been described in a large number of papers, some of which are cited by the authors of the manuscript in the Introduction and the subsequent text of the manuscript. The first author has been engaged in mathematical and numerical modeling of radiation methods and NDT systems, including DEM, for over forty years and strives to take into account as correctly as possible the entire set of physical laws governing the emission, interaction, and recording of gamma and X-ray, including high-energy, radiation. It is known that dual energy methods are divided into two large groups by the method of processing the original DEM images. The first group is called the Alvarez-Makovsky methods, and the second is the level line calibration methods. The Alvarez-Makovsky methods are based on the representation of the mass attenuation coefficient (MAC) of gamma radiation by the sum of the MAC of two main competing processes of interaction of gamma radiation with matter. For low-energy X-rays, such processes are the photoelectric effect and the Compton effect, and for high-energy X-rays (bremsstrahlung), the Compton effect and the effect of electron-positron pair formation. For the practical implementation of the Alvarez-Makovsky methods, an accurate description of several functions is necessary: the energy spectra of high-energy bremsstrahlung sources; the MAC dependences on the photon energy and the atomic number of the attenuating material for the Compton effects and the formation of electron-positron pairs. For various implementations of the level line calibration methods, the above functions are used in a veiled manner, i.e., in an implicit form. In these methods, at the stage of special calibrations, two-dimensional functions FL(H, Z) and FH(H, Z) are constructed, and at the next stage, Zeff and (H)eff are estimated based on their measured values. The only thing is that in the mathematical and simulation models of DEM in this case, databases on the attenuation of gamma radiation are used. Numerical modeling of level lines allows fast and accurate numerical modeling of digital radiographic images of DEM for any variations of the HEIS parameters, as well as improving the algorithms for estimating Zeff and (H)eff. Third, the text of the manuscript briefly discusses the evolution of HEIS with the function of recognizing materials of control objects and their structural fragments using high-energy DEM in terms of increasing the number of material recognition classes and assessing the possibility of correctly separating light and heavy organic materials on the one hand, and heavy organic materials and heavy plastics, oxides and salts of light metals. At the present time, we have not been able to compare our results with the data of other researchers, since no works devoted to solving the above problem have been found.
We sincerely thank you for reading our manuscript, your questions and recommendations!
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsThe document presents significant results of huge interest for the scientific community and it could be an important advance of the improvement of security area in detection of potential explosive materials. There are some recommendations with the intention to improve the document:
-please provide the title or objective of the cited works in the introduction to have a better wide vision about this
-the paragraph from line 137 to 147 is not clear making reference on the tables and the materials are associated with the classification, for example water
-the superscripts and subscripts in the equations are not clear
-the text is not in accordance to figure 2 since this make mention a range from 3 to 75
-please try to improve the table 3 in the row pH/cm3, this produce a little confusion
Author Response
We express our sincere gratitude to the reviewers for their attentive, responsible, thorough and friendly reading of the manuscript of our article. We offer special thanks for your comments, questions and recommendations.
Reviewer 2.
The document presents significant results of huge interest for the scientific community and it could be an important advance of the improvement of security area in detection of potential explosive materials. There are some recommendations with the intention to improve the document:
Comment 1. Please provide the title or objective of the cited works in the introduction to have a better wide vision about this.
Response 1. Thank you for your recommendation. Although MDPI journals do not usually make such recommendations, we agreed with your recommendation and made the appropriate edits to the Introduction. For references to individual works, we provided their titles, and for references to groups of works, we noted their most general goals. Taking into account the changes made, the Introduction took the following form:
“High energy inspection systems (HEIS) with the function of detection of test objects (TO) materials and their fragments by dual-energy method (DEM) are still one of the most effective and demanded technical means of customs control and cargo inspection [1–4], the objective of the cited works is to discuss methods, approaches and algorithms for recognition (distinction, classification) of materials using the dual energy method. Multi-energy methods (MEM) [3,5–9] are an evolution of DEM and allow to increase the number of classes of recognized materials or to extend the limits of applicability of DEM for complicated structures of TO. The unifying objective of the noted works is to illustrate the additional possibilities of material recognition using MEM compared to DEM. The recognition of the TO material or its structural fragment is understood as the unique belonging of the studied material to one of the given classes of materials based on a feature called recognition parameter (RP) [5], “Limit capabilities of identifying materials by high dual- and multi-energy methods”. The main types of RP associated with high-energy DEV and MEM realizations are estimates of the effective atomic number (EAN) of the TO material or the ratio of TO thicknesses in mean free paths (MFP) for low and high energies of bremsstrahlung (high energy X-rays) [1,10–13], the objective of the cited works is to analyze the features and compare the methods of material recognition using various dual energy methods as applied to cargo inspection. In scientific literature, the effective atomic number is usually denoted by the symbol Zeff [2, 4, 13]. The physical process of detecting TO fragments and recognizing their materials by one of the recognition parameters is reduced to obtaining the initial digital radiographic DEM images, calibrating the obtained images “by black” and “by white”, taking the logarithm of the calibrated images, forming an image of the recognition parameter, correlating the value of the recognition parameter at each point of the image using special predetermined calibration functions (lines) to the recognition class, coloring all points of the image according to the selected palette in accordance with the calculated recognition class. The resulting final DEM image is analyzed by the operator or a specially developed algorithm in order to establish the correspondence or non-correspondence of the final TO image to the documentation accompanying the cargo. In the initial stage of the development of HEIS with the function of material recognition by DEM, no more than four classes of materials were distinguished [14], “Processing of interlaced images in 4–10 MeV dual energy customs system for material recognition”, and these four classes were maintained until recently [15], “A curve-based material recognition method in MeV dual-energy X-ray imaging system”. A typical representative of the first class was polyethylene, the second - aluminum or its alloys, the third — iron or its alloys, and the fourth — lead. All major HEIS manufacturers guarantee correct material identification for three to four classes [16–21], the objectives of this work is to demonstrate modern trends towards expanding the number of classes of materials recognizable by the low-energy dual energy method. In the above-mentioned HEIS, betatrons or linear accelerators are used as sources of high-energy X-ray radiation. The objects under control are scanned by narrow beams of high-energy X-ray (bremsstrahlung) radiation with alternating maximum energies from pulse to pulse (from a group of pulses to a group of pulses). High energy is determined by the maximum possible maximum energy for the radiation source used, it is usually equal to 6 MeV, 7.5 MeV, 9 MeV. And low energy is usually 2—3 MeV less than high energy. The website [21] refers to the possible correct identification of four or more classes of materials. In the range of maximum energies of X-rays, where two effects of the interaction of gamma rays with matter compete — the photoelectric effect and the Compton effect — the number of classes of correctly recognized materials has expanded considerably in recent years [22–24]. Work [5] highlights that the increase in the number of classes of materials that can be detected during a high-energy inspection is associated with physical limitations due to the interaction of high-energy (energy above 1.022 MeV) gamma rays with matter. It is known that at energies from 2 to 10 MeV the Compton effect and the pair birth effect compete [25], “Photon cross sections, attenuation coefficients and energy absorption coefficients”, and the mass attenuation coefficient (MAC) of photons corresponding to the pair birth effect is proportional to EAN [26], “Electron—positron pair production by photons: A historical overview”, and for small values of EAN the contribution of pairs to the integral MAC is insignificant. The insignificance of the change in the integral MAC in the range of small values of EAN is the main obstacle to increasing the number of classes of correctly recognized materials [27], “Automated X-ray image analysis for cargo security: Critical review and future promise”. In [27], questions concerning the high-energy realization of DEM are formulated. The need for a comparative study of different image preprocessing methods [27] is certainly related to increasing the number of classes of correctly recognized materials. Moreover, [27] raises the question: Will machine learning-based material recognition perform better than current implementations of DEM, based on the physics of the interaction between bremsstrahlung and matter? In [27], the strong noise in the initial and final images of DEM in commercial HEISs is pointed out, which partially limits the ability to increase the number of recognition classes. The work [27] illustrates the complexity of correct material recognition of objects (fragments) with small and large thicknesses. The incorrect recognition is due to a pronounced difference in the energy spectrum of the bremsstrahlung of the delta function (monoenergetic gamma-ray source) on the one hand and a high level of noise in the original digital radiographic images and (or) a low level of digital signals on the other hand. Quantum or pseudo-quantum starvation leads to a low level of digital signals [28], “Increasing penetrating power of digital radiography systems based on analysis of low-intensity signals”. Quantum starvation is caused by insufficient photons registered by detectors behind a large TO [28], and pseudo-quantum starvation — by insufficient ADC capacity [28,29]. In order to improve the quality of recognition of small material thicknesses, it is quite effective to convert the sources of bremsstrahlung radiation into pseudo-monoenergetic ones by introducing pre-filtering of photons [29], “Physical and technical restrictions of materials recognition by the dual high energy X-ray imaging”, which, however, leads to the amplification of the effect of quantum and/or pseudo-quantum starvation. Work [14] states that the main direction of the development of HEIS with the function of material recognition by the dual and multi-energy method in the last two decades is mainly associated with an increase in the productivity and quality of material recognition. The work [30], “Security in the maritime container supply chain: what is feasible and realistic?”, emphasizes that the growth of international trade, especially through container shipping, is accompanied by the emergence of various types of smuggling, including drugs, weapons, cigarettes, explosives (EE), radioactive and nuclear substances, and the potential risks and threats associated with these activities are also increasing. The work [30] states that in order to meet the corresponding challenges and threats, customs authorities, border guards, and transport security units must significantly strengthen the monitoring of transported goods and vehicles by improving the technical means of high-energy X-ray inspection and ensuring their necessary number for the benefit of consumers and the prevention of the illegal transport of particularly dangerous goods. The work [5] assumes an increase to five classes of materials correctly recognized by high-energy dual and multi-energy methods and also demonstrates theoretically and experimentally the fundamental possibility of distinguishing the following classes of materials: light organics (Zeff = 6); minerals (Zeff = 9); light metals (Zeff = 13); calcium (Zeff = 19); metals (Zeff = 26); heavy metals (Zeff > 50). It was noted above that for high-energy DEM implementations, the competing processes of interaction of gamma radiation with matter are the Compton effect and the pair birth effect, therefore with an increase in EAN, therefore the features of recognition of materials with high EAN values are determined by the MAC dependencies for the Compton effects and pair production on EAN [4, 5, 11, 14].An article [12], “Material Estimation Method Using Dual-Energy X-Ray Image for Cargo Inspection System”, compares the quality of the material recognition algorithms of DEM with the increased number of classes of correct recognition. Theoretically, it justifies the possibility of distinguishing classes of materials associated with acrylic ((C5O2H8)n), carbon (C), water (H2O), aluminum (Al), iron (Fe), tin (Sn), and lead (Pb). It should be noted that in the results of the experiments [12], data on the test samples with water are missing, which most likely indicates insufficiently correct recognition of this material.
The problem of increasing the recognition classes of TO materials and their structural fragments by DEM is still relevant, especially in the range of EAN values from 5 to 13, due to the need to identify explosives and drugs correctly.
To confirm the relevance of solving the above problem, we can mention the conclusion from [2], “Raw data processing techniques for material classification of objects in dual energy X-ray baggage inspection systems”, where the authors talk about the need for work related to detecting materials with a low value of EAN, such as explosives and drugs. In a recent work [31], “Dose Evaluation of a Car Occupant in Dual Energy X-Ray Automobile Inspection System”, it is pointed out that it is necessary to diagnose the nature of the substance in the case of drugs with low atomic number, as well as in the case of many explosives, such as plastic explosives, which can be masked in various ways. The article [32], “Development of a Dual-Modality Gamma-ray/Fast Neutron Imaging System for Air Cargo Inspection”, argues that cargo inspection systems using high-energy bremsstrahlung sources (the equivalent term, according to the authors of [32], is gamma-ray, which is controversial) are limited in detecting objects of low-density materials, such as drugs or plastic explosives. Therefore, the combined use of gamma-ray (bremsstrahlung) and neutron sources is proposed, which will allow a strong expansion of the detection of the mentioned materials. The conclusion of the work [32] on the achievement of bremsstrahlung (high energy X-rays) HEISs its limit in terms of material recognition seems not sufficiently argued, so theoretical, simulation, and experimental studies on the evaluation and achievement of DEM limits in terms of correct material recognition in cargo inspection in the range of small values of EAN are needed.”
Comment 2: The paragraph from line 137 to 147 is not clear making reference on the tables and the materials are associated with the classification, for example water.
Response 2: It is possible that the insertions we made made the paragraph in question more understandable. The final appearance of the paragraph:
“From the above and the purpose of this work, it follows that it is necessary to divide the class of organic materials into two classes — light and heavy organic materials. From Table 1, we can conclude that it is logical to assign polyethylene or polypropylene to the class of light organic materials. According to Table 1, water can be associated with the class of heavy organic materials, which includes the main explosives. Sugar (the lower limit of the class of heavy organic materials according to EAN) and borax (the upper limit of the class of heavy organic materials according to EAN) can also be used for imitation explosives. It should be noted that using water as a calibration material for DEM is difficult. For low-energy DEM realizations, non-explosive explosion simulators [39] that correspond to actual samples in density and elemental composition are used. However, creating such simulators for high-energy DEM implementations is quite difficult, since it is a liquid in the temperature range from 1 to 100 degrees Celsius. An alternative could be the approach proposed in [35], based on B-Al equivalents of explosives by effective atomic number.”
Comment 3: The superscripts and subscripts in the equations are not clear.
Response 3: There are some indexes in the text of the manuscript that do not require additional explanation. We have made several inserts with explanations of some indexes.
3.1. Here and below, the index L relates the indexed value to the low maximum energy of bremsstrahlung EL (Low-Energy), and the index H corresponds to the high maximum energy of bremsstrahlung EH (High-Energy). (Line 204)
3.2. (subscript “1”). (Line 236)
3.3. The superscript “*” in (1) denotes the transformation of the numerical energy spectrum. (Line 245)
3.4. , here and below the subscript “d” is associated with the detector (RSE). (Line 250)
3.5. Here the subscript “W” corresponds to calibration in white. (Line 296)
3.6. (subscript B). (Line 300)
3.7. Here the subscript Q is associated with the transformation of functions from system (15) to (9), and the subscript H indicates the greater significance of the corresponding function on the H. (line 577)
Comment 4: The text is not in accordance to figure 2 since this make mention a range from 3 to 75.
Response 4: Corrected.
Comment 5: Please try to improve the table 3 in the row H, g/cm2, this produce a little confusion.
Response 5:
We have changed the table, we have underlined the line with the values of H, g/cm2. I hope this has cleared up little confusion.
Author Response File: Author Response.pdf
Reviewer 3 Report
Comments and Suggestions for AuthorsThe text discusses the evaluation and calibration of material recognition using a dual-energy method (DEM) in radiographic imaging, focusing on the identification of various materials, including explosives, based on their effective atomic number and mass thickness. It highlights the challenges in recognizing materials with higher atomic numbers and emphasizes the importance of preliminary filtering and accurate calibration to improve recognition accuracy. The study presents mathematical models for estimating material properties and discusses advancements in detection technologies, particularly for enhancing transport security against threats like explosives.
--What challenges are associated with recognizing materials with atomic numbers of 56 or higher?
--How does the dual-energy method improve the detection of explosives in security contexts?
--Write some on the effectiveness of the dual-energy method in distinguishing between organic and inorganic materials based on their effective atomic numbers.
Comments on the Quality of English LanguageGood English
Author Response
We express our sincere gratitude to the reviewers for their attentive, responsible, thorough and friendly reading of the manuscript of our article. We offer special thanks for your comments, questions and recommendations.
Reviewer 3.
The text discusses the evaluation and calibration of material recognition using a dual-energy method (DEM) in radiographic imaging, focusing on the identification of various materials, including explosives, based on their effective atomic number and mass thickness. It highlights the challenges in recognizing materials with higher atomic numbers and emphasizes the importance of preliminary filtering and accurate calibration to improve recognition accuracy. The study presents mathematical models for estimating material properties and discusses advancements in detection technologies, particularly for enhancing transport security against threats like explosives.
Comment 1: What challenges are associated with recognizing materials with atomic numbers of 56 or higher?
Response 1: Thank you for your question. We have made two insertions into the manuscript:
“It was noted above that for high-energy DEM implementations, the competing processes of interaction of gamma radiation with matter are the Compton effect and the pair birth effect, therefore with an increase in EAN, therefore the features of recognition of materials with high EAN values are determined by the MAC dependencies for the Compton effects and pair production on EAN [4, 5, 11, 14].”
“The data presented in Figure 1 indicate that starting from a certain value of Z, the function Q(Z) becomes flat. This factor leads to an increase in the error in estimating the atomic number Z based on the experimentally measured value of Q and to insufficient quality of recognition of materials with Z exceeding a certain level, for example, Z = 56.”
Comment 2: How does the dual-energy method improve the detection of explosives in security contexts?
Response 2: Thank you for your question. At the end of the conclusion we have made a summary insert:
“The dual high-energy method improves the detection of explosives in transport and transported cargo by introducing a special class of recognizable materials, called the class of heavy organic materials, strictly taking into account all the physical laws of interaction of high-energy bremsstrahlung with matter, rational selection of technical parameters of the corresponding inspection control systems, ensuring high-quality calibrations at all stages of the formation and processing of DEM images and development of highly accurate and highly productive algorithms for assessing the effective atomic numbers of materials of control objects and their structural fragments.”
Comment 3: Write some on the effectiveness of the dual-energy method in distinguishing between organic and inorganic materials based on their effective atomic numbers.
Response 3: Thank you for this recommendation. I will try to implement it. Supplemented the Conclusion with the following phrase:
“In the future, we plan to implement a number of research topics related to the substantiation of the possibility of increasing the efficiency of high-energy dual and multi-energy methods, as well as spectral methods for the correct (separate) recognition of organic and inorganic materials based on the assessment of their effective atomic numbers.”
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
Comments and Suggestions for AuthorsThe manuscript has been improved and can be accepted.
Minor: still 5 significant digits in tantalium. I suppose that the precision of the model is less.
Author Response
I express my sincere gratitude to you for your attentive, responsible, thorough and benevolent reading of the corrected version of our manuscript. We express special gratitude to you for your decision and recommendations.
Comment 1: Still 5 significant digits in tantalium. I suppose that the precision of the model is less.
Response 1: Thank you for your recommendation. We have taken your recommendation into account and reduced the number of significant digits in tables 4 and 6 to 4.
Author Response File: Author Response.pdf
