Substance identification method based on adaptive quantitative decomposition

Abstract

The invention discloses a substance identification method based on self-adaptive quantitative decomposition, which comprises the following steps of: establishing a polynomial relationship among coefficients, energy and effective atomic numbers participating in substance identification in the interaction of rays and substances, and scanning and reconstructing by using various known materials as calibration substances; based on the linear attenuation coefficients of different energy regions of the calibration object, the real effective atomic number and density of the calibration object and the polynomial relation obtained before, effective energy of the corresponding energy region is determined; in actual operation, after scanning reconstruction is carried out on a to-be-decomposed substance, due to the fact that effective energy is determined through calibration and linear attenuation coefficients reconstructed in different energy regions are only related to effective atomic numbers and densities, two ray and substance interaction formulas containing variable parameters are simultaneously established, and solving is carried out; and the effective atomic number and density of the scanned substance can be accurately obtained. According to the invention, correlation coefficients can be automatically adjusted according to different substances, so that the decomposition precision is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of material detection and relates to a material identification method based on adaptive quantitative decomposition. By adaptively analyzing and decomposing energy spectrum CT in an image domain, the effective atomic number and density of the scanned material can be quickly and accurately estimated on a reconstructed image using energy spectrum CT. Background Art

[0002] Spectral CT is an advanced technology based on X-ray imaging. By setting the energy threshold of the detector and utilizing the attenuation characteristics of materials to different X-ray energies, projection images of multiple energy zones are generated and reconstructed. In traditional CT imaging, the attenuation information of X-rays is recorded as a whole, and it is impossible to distinguish the attenuation of different energies. In spectral CT, the detector can segment the X-rays passing through the object according to energy, thereby obtaining attenuation data of different energy zones. These data represent the response of different materials in the object to X-rays of different energies, and can reconstruct CT images of multiple energy zones. This technology enables spectral CT to provide richer image information than traditional CT, thereby more accurately distinguishing and quantifying different materials in the object.

[0003] In order to accurately extract quantitative information of the scanned material, especially its intrinsic physical properties, the material decomposition algorithm plays an important role in spectral CT. The attenuation of X-rays by a material is mainly determined by its effective atomic number and density, and the difference in attenuation coefficients of different materials is caused by these two key factors. Therefore, in non-destructive testing, the core of material identification lies in accurately solving its effective atomic number and density. The effective atomic number means that in a mixed material, when the density is determined, the attenuation characteristics of the mixed material at the same energy are the same as the attenuation characteristics of a pure material with a certain atomic number. Therefore, the effective atomic number of a mixed material can be expressed as this specific atomic number value. By accurately solving the effective atomic number and density of the material, different materials can be better identified in non-destructive testing applications, thereby improving the accuracy and effectiveness of the detection.

[0004] In current technology, there are two main models for solving the effective atomic number and density of a substance. The first method is based on the base material decomposition model, that is, the attenuation coefficient of the scanned material is expressed as a linear combination of the attenuation characteristics of several known base materials. By calculating the combination coefficients of these base materials, the effective atomic number and density of the material can be indirectly estimated. The second method is a decomposition method based on the dual-effect model. This method directly expresses the attenuation characteristics of the material as a combination of the photoelectric effect and the Compton effect. These two effects are closely related to the effective atomic number and density of the material, respectively, and directly solves the effective atomic number and density.

[0005] These traditional decomposition algorithms perform well in dual-energy CT applications, providing reliable material composition information for clinical and industrial applications. However, most of these algorithms assume that the effective atomic number and density of the sample are within a fixed range and are used under relatively stable scanning conditions, thereby simplifying the physical model. With the development of photon counting detector (PCD) technology, the energy threshold of the detector can be flexibly adjusted to select different energy regions for imaging. This can not only effectively reduce image noise, but also improve imaging quality. Photon counting detector-based CT (PCDCT) has greater application potential than traditional dual-energy CT, especially when facing complex samples. However, the traditional fixed physical model may be inaccurate under such flexible and changeable scanning conditions and in complex samples, resulting in a decrease in the accuracy of the decomposition results. Summary of the invention

[0006] In view of the problems existing in the prior art, the purpose of the present invention is to provide a material identification method based on adaptive quantitative decomposition, which can estimate the effective atomic number and density of the scanned material more accurately and conveniently with less experimental operation in practical applications. The present invention is designed to meet the diverse needs of complex samples and can dynamically optimize the physical model of the interaction between radiation and matter under different scanning conditions. Compared with the traditional fixed model, the present invention can automatically adjust the key parameters in the model according to the different characteristics of the scanned sample, so as to adapt to different material types and scanning conditions; this adaptive mechanism greatly improves the accuracy of material decomposition and can play a role in various complex imaging scenarios.

[0007] The method relates to an image domain decomposition technology, which reconstructs an image based on the dual energy zones of the scanned material, and obtains the effective atomic number and density through a calibration. First, the present invention establishes a polynomial relationship between the coefficient involved in the material identification in the interaction between rays and matter, the energy and the effective atomic number. Next, two or more known materials are used as calibration objects for scanning and reconstruction, and the effective energy of the corresponding energy zone is determined based on the linear attenuation coefficient (μ) of the calibration object in different energy zones, the real effective atomic number and density of the calibration object, and the polynomial relationship obtained previously. In actual operation, after the material to be decomposed is scanned and reconstructed, since the effective energy has been determined through calibration, the μ value reconstructed in different energy zones is only related to the effective atomic number and density in the interaction model between rays and matter. By combining two ray-matter interaction formulas containing variable parameters, formula (12) is obtained, and an optimization algorithm such as Newton's method or gradient descent method is used to solve formula (12), so that the effective atomic number and density of the scanned material can be accurately obtained. In formula (12), except for the atomic number Z and density to be solved, only energy is an unknown quantity, so it is necessary to obtain the value of energy before calculation. Here, the average energy is used to represent E in two energy zones. Therefore, the present invention uses a calibration object with known atomic number and density to solve E. Through formula (11), after scanning and obtaining μ of the calibration object, E can be calculated by knowing the effective atomic number and density.

[0008] The present invention no longer relies solely on a simple dual-effect model to solve the effective atomic number and density in constructing a model of the interaction between radiation and matter. By introducing the consideration of coherent scattering, the model of the interaction between radiation and matter is made more complete and comprehensive, thereby better reflecting the real physical properties of complex materials. For the key parameters in the interaction model, a polynomial fitting method is used to establish the relationship between the scanned effective atomic number and energy, as shown in formulas (7)-(11).

[0009] The present invention can dynamically adjust the coefficients of the photoelectric effect and Compton scattering: in the photoelectric effect part, the present invention no longer uses a fixed exponential coefficient, but introduces a dynamic function related to the energy and the effective atomic number of the substance. Similarly, in the Compton scattering part, the present invention also introduces this function that depends on the energy and material properties. This method allows the model to automatically adjust the correlation coefficient according to different substances during the decomposition process, thereby improving the accuracy of the decomposition and avoiding the error caused by using fixed coefficients.

[0010] The technical solution of the present invention is:

[0011] A method for identifying substances based on adaptive quantitative decomposition, the steps of which include:

[0012] 1) Construct a model of interaction between ray and matter μ = ρm (σ′ De_Compt +σ Compt ), where ρ m is the mass density, σ′ De_Compt is the deCompton model including coherent scattering, σ Compt is the Compton scattering cross section between the scanning ray and the material; C(E,Z eff )=Σ ij h ij Z eff i E j ,0≤i≤4,0≤j≤4; α1 is a constant, E is the energy of the scanning ray, Z eff is the effective atomic number of the scanned material, i is C(E,Z eff )Z eff The index of E, j is the index of E, h is the index of ij is the corresponding term Z in the polynomial relation eff i E j The coefficient of Compt =α2D(E,Z eff )f KN (E), D(E,Z eff )=Σ st g st Z eff s E t ,0≤s≤4,0≤t≤4, where s is D(E,Z eff )Z eff The exponent of t is D(E,Z eff ) is the index of E, g st is the corresponding term Z in the polynomial relation eff s E t coefficient, α2 is a constant, f KN (E) is the Klein-Cecil coefficient;

[0013] 2) Using the relevant data of the selected calibration object, the variable parameters C(E, Z eff )、D(E,Z eff ) to fit and establish variable parameters C(E,Z eff ) and energy E, effective atomic number Z eff The specific relationship between them, and the variable parameters D(E,Z eff ) and energy E, effective atomic number Z eff The specific relationship between them;

[0014] 3) Using C(E,Z eff)、D(E,Z eff ), and combined with the known effective atomic number and density information of various materials in the selected calibration object, the effective energy of each energy zone is solved to obtain the effective energy E of each material in the low energy zone. L and the effective energy E in the high energy region H ;

[0015] 4) Scan the object to be inspected according to the scanning parameters of the calibration object to obtain the energy spectrum CT of the object to be inspected; then, based on the energy spectrum CT of the object to be inspected, combined with C(E, Z eff )、D(E,Z eff ) and step 3) to obtain the effective energy E of each known material in the low energy region L , effective energy E in high energy zone H , obtain the linear attenuation coefficient of the detected object in the low energy zone and the linear attenuation coefficient of the high energy zone and substitute them into the ray-matter interaction model to solve the effective atomic number and material density of the detected object.

[0016] Furthermore, the effective energy E of each material in the low energy region is obtained. L and the effective energy E in the high energy region H The method is as follows: firstly, the selected calibration object is scanned and reconstructed to obtain the linear attenuation coefficient μ of each material in the calibration object in the high energy region. h , Linear attenuation coefficient μ in low energy region l ; Using the known physical properties of the calibration material, and then solving Get the effective energy E of the corresponding material in the low energy zone L and the effective energy E in the high energy region H .

[0017] Furthermore, in step 4), the method for solving the effective atomic number and material density of the detected object is: scanning and reconstructing the detected object using energy spectrum CT to obtain the linear attenuation coefficient μ′ of the detected object in the low energy region l , linear attenuation coefficient μ′ in the high energy region h ; Use the effective energy of the two energy regions to estimate the effective energy E of the low energy region L and the effective energy E in the high energy region H , and then use the Newton gradient method to Solve pixel by pixel to obtain the effective atomic number Z of the object being detected eff and the material density ρ m .

[0018] The main steps of the present invention include:

[0019] 1) Construct the interaction model between radiation and matter, and fit the variable parameters in the interaction model between radiation and matter using the attenuation coefficient data of the material: Use the known data such as the linear attenuation coefficient, effective atomic number and density of the material to fit the variable coefficients in the interaction model between radiation and matter. Through these data, a polynomial relationship between the variable coefficients and the energy and effective atomic number is established. On this basis, the only unknown parameters in the interaction model between radiation and matter are energy, effective atomic number and density.

[0020] 2) Estimate the effective energy of the dual energy zone: Use two or more known materials as calibration objects to estimate the effective energy of the dual energy zone. The material of the calibration object does not have to be the same as the decomposed material, and it does not need to meet the requirement that the linear attenuation coefficient of the calibration object in the base material decomposition should include the scanned material. However, during the scanning process, the voltage, current, amplification ratio and other conditions should be consistent with the scanning conditions of the decomposed material. First, using the polynomial relationship established in step (1), combined with the known effective atomic number and density information of various materials in the calibration object, the effective energy of each energy zone is solved. Specifically, for a single energy zone, the attenuation data of these known materials are fitted by the least squares method to obtain the effective energy of the energy zone. The same operation is also applicable to another energy zone, and finally the effective energy of the two energy zones under the current scanning conditions is obtained. At this time, the unknown coefficients in the interaction model between rays and matter are only the effective atomic number and density.

[0021] 3) Adaptive decomposition of the material scanned by spectral CT in the image domain: Perform CT reconstruction on the scanned material to obtain reconstructed images in two energy zones. Combining the polynomial relationship of the variable parameters fitted in step (1) and the effective energy in different energy zones estimated in step (2), the linear attenuation coefficient data of the scanned material in these two energy zones are used to substitute them into the radiation-matter interaction model. By using simultaneous equations and optimization algorithms such as gradient descent, the remaining unknown variables - effective atomic number and density - can be solved.

[0022] The interaction model between radiation and matter is expressed as: μ = ρ m (σ ph +σ Compt +σ coherent ), where μ represents the linear attenuation coefficient, ρ m represents the mass density, σ ph is the photoelectric effect cross section of the scanning ray interacting with matter, σ Compt is the Compton scattering cross section between the scanning ray and the material, σ coherentis the coherent scattering cross section between the scanning ray and the material. In the comprehensive consideration of the model, after including coherent scattering, because the scattering model is more complicated, and in order to better analyze and calculate the variable index m, we include the de-Compton effect σ′ with coherent scattering De_Compt Still expressed in the form of the photoelectric effect, the index m is expressed as a variable C(E,Z) with respect to the effective atomic number and energy eff ):

[0023]

[0024] Where α1 is a constant, E represents the scanning energy, Z eff is the effective atomic number of the scanned material. In order to make it easier for the model to solve the effective atomic number and density, we will C(E,Z eff ) is expanded using the following polynomial series: C(E,Z eff )=Σ ij h ij Z eff i E j ,0≤i≤4,0≤j≤4, where i is C(E,Z eff )Z eff The index of E, j is the index of E, h is the index of ij Expressed as the corresponding term Z in the polynomial relation eff i E j In the description of the Compton effect, the Compton effect is proportional to the atomic number of the scanned material, and the Klein-Xi Island coefficient f is used in the energy. KN (E), together represent the influence of the Compton effect. However, through in-depth study of NIST data, we found that this approximate Compton model based on mass density is not accurate enough. In order to better describe the Compton effect, we introduced an additional eff The variable D(E,Z eff ) to enrich the model: σ Compt =α2D(E,Z eff )f KN (E), where α2 is a constant. And for D(E,Z eff ) uses the same polynomial series expansion fitting representation: D(E,Z eff )=Σ st g st Z eff s E t ,0≤s≤4,0≤t≤4, where s is D(E,Z eff ) where Z is the index, t is the index of E, g is the index of stis the coefficient of the corresponding term in this polynomial relationship. Therefore, the linear attenuation coefficient of the material can be expressed as:

[0025] Select two or more known materials as calibration objects and scan the calibration objects. During the scanning process, the voltage, current, amplification ratio and other conditions should be consistent with the scanning conditions of the decomposed material (i.e. the unknown component of the object to be detected). Scan and reconstruct the calibration object to obtain the linear attenuation coefficient value of the calibration material in two energy zones. The energy of the two energy zones is estimated using the interaction model between radiation and matter and the fitting formula of the variable coefficient obtained through NIST data, and the effective energy E in the low energy zone is obtained using the least squares method. L and the effective energy E in the high energy region H .

[0026] The scanned object is reconstructed to obtain the linear attenuation coefficients of the object in the low and high energy zones, which are recorded as μ′ l , μ′ h , establish a joint solution equation, and bring the formula obtained in the above steps into the equation where μ l ′ and μ h ′ respectively represent the linear attenuation coefficient of the detected object in the low and high energy regions, Z eff is the effective atomic number of the scanned material to be determined. The Newton gradient method is used to solve the effective atomic number Z pixel by pixel for the reconstructed image. eff and material density ρ, and finally achieve the ability to eff The parameter adaptation in the process of solving the effective atomic number and density is realized to improve the decomposition accuracy.

[0027] The advantages of the present invention are as follows:

[0028] This method is based on the image domain decomposition method, and can estimate the effective atomic number and density of the scanned material more accurately and conveniently with less experimental operation in practical applications. The present invention avoids the phenomenon of inaccurate decomposition results caused by constantization of coefficients in the double effect. By using NIST real data, an effective polynomial estimation is established and brought into the double effect decomposition, which effectively realizes the adaptation of coefficients when solving the effective atomic number and density, and realizes the ability of energy spectrum CT to accurately identify materials. With a relatively simple physical process and relatively convenient experimental operation, the present invention has a relatively broad application prospect in practical applications such as non-destructive testing. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 Based on NIST data, C(E,Z eff ) and D(E,Z eff) with respect to the fitted surface of energy and effective atomic number;

[0030] (a)C(E,Z eff ) with respect to the fitting surface of energy and effective atomic number, (b) D(E,Z eff ) versus energy and effective atomic number.

[0031] Figure 2 It is a flow chart of the present invention.

[0032] Figure 3 To scan material images and reconstruct tomograms, the material components are marked in the images;

[0033] (a) is a picture display of the scanned object, and (b) is a tomogram reconstructed in the low-energy area.

[0034] Figure 4 This is a comparison chart of the decomposition result images;

[0035] (a) is a comparison chart of 40-75keV, (b) is a comparison chart of 70-150keV, (a) is a comparison chart of effective atomic number, and (b) is a comparison chart of mass density. DETAILED DESCRIPTION

[0036] The present invention is further described in detail below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention but not to limit the scope of the present invention.

[0037] The adaptive quantitative decomposition method of the present invention comprises the following steps:

[0038] Step (1) The exponential variable parameter C(E, Z) in the photoelectric effect of the interaction model between radiation and matter eff ) and D(E,Z introduced in Compton scattering eff ) Use the material attenuation coefficient data for fitting: For example, use the known data such as the linear attenuation coefficient, effective atomic number and density of the material at different energies to fit the variable coefficients in the interaction model between radiation and matter. Through these data, the variable coefficients C(E, Z eff )、D(E,Z eff ) and the specific relationship between energy and effective atomic number, that is, the coefficients h are obtained by fitting ij , g st On this basis, the only unknown parameters in the interaction model between radiation and matter are energy, effective atomic number and density;

[0039] Step (2) estimates the effective energy of the dual energy zone: two or more substances of known materials are used as calibration objects to estimate the effective energy of the dual energy zone. The material of the calibration object does not have to be the same as the decomposed substance, but during the scanning process, the voltage, current, amplification ratio and other conditions should be consistent with the scanning conditions of the decomposed substance. First, using the polynomial relationship established in step (1), and combining the known effective atomic number and density information of various materials in the calibration object, the effective energy of each energy zone is solved. Specifically, for a single energy zone, the attenuation data of these known materials are fitted by the least squares method to obtain the effective energy of the energy zone. The same operation is also applied to another energy zone, and finally the effective energy of the two energy zones under the current scanning conditions is obtained. At this time, the unknown coefficients in the interaction model between rays and matter are only the effective atomic number and density;

[0040] Step (3) performs image domain double-effect decomposition on the material scanned by spectral CT: perform CT reconstruction on the scanned material to obtain reconstructed images in two energy zones. Combining the polynomial relationship of the variable parameters fitted in step (1) and the effective energy in different energy zones estimated in step (2), the linear attenuation coefficient data of the scanned material in these two energy zones are used and substituted into the interaction model between the ray and the material. By using simultaneous equations and optimization algorithms such as gradient descent, the remaining unknown variables - effective atomic number and density - can be solved.

[0041] Furthermore, step (1) includes: based on the fact that the energy spectrum X-rays mainly undergo photoelectric effect and Compton scattering with matter, Alvarez proposed an approximate fitting representation method to be used in a wider range of practical applications. The linear attenuation coefficient of the material at energy E can be approximated as:

[0042]

[0043] where ρ m represents the mass density of the material, ρ e Represents the electron density of the material The index m in the photoelectric effect is m′-1, which is usually set to a constant of 3-4. ph ,σ Compt They represent the photoelectric effect cross section and Compton scattering cross section respectively, A is the atomic mass, Z is the atomic number, and k1 and k2 are constants. KN (E) is the Klein-Cecil coefficient, which is expressed as:

[0044]

[0045] Heismann proposed a further approximation. In the case of medium and low atomic numbers, A≈2Z, the interaction expression can be further approximated as:

[0046]

[0047] Using PCDCT to obtain dual-energy zone data to establish a system of simultaneous equations can solve the Z and E of the material. Or in the basis material decomposition, the attenuation coefficient of the scanned material can be expressed as a linear combination of the basis materials:

[0048] μ=Σ p w p μ p #(4)

[0049] where w p Represents the decomposition coefficient corresponding to the pth base substance. Using the above double effect formula, the effective atomic number formula can be expressed as:

[0050]

[0051] where ρ p It can be expressed as the electron density or mass density corresponding to the p-th base material, Z p is the atomic number corresponding to the p-th base material.

[0052] The existing effective atomic number calculation model based on dual effects (photoelectric effect and Compton scattering) is widely used in the analysis of physical properties of material decomposition. This model can usually simplify the calculation and show high accuracy when the photoelectric effect and Compton scattering are the main interactions. However, this model often ignores the relatively small coherent scattering effect. Although the effect of coherent scattering is relatively weak at higher energies, the contribution of this effect cannot be ignored in the medium and low energy range.

[0053] Through detailed analysis of NIST data, we found that in the commonly used energy range of 40-80keV, for materials with atomic numbers between 7 and 20, the contribution of coherent scattering to the total attenuation coefficient is usually more than 10%, and can even reach 16%. This shows that under low and medium energy conditions, coherent scattering accounts for a considerable proportion, and ignoring this effect may lead to inaccurate calculation results of the effective atomic number, especially in the application scenarios of medium atomic number materials.

[0054] The interaction model between rays and matter is expressed as:

[0055] μ=ρ m (σ ph +σ Compt +σ coherent )#(6)

[0056] In the calculation of the effective atomic number, the coefficient m plays a vital role, so the analysis of m can better reflect the difference between the two models. Related studies have shown that the exponent m of the photoelectric effect is closely related to the atomic number of the material and the energy of the radiation. Our research also found that m is actually a function of energy and effective atomic number. And in real scanning, the reconstructed linear attenuation coefficient is the common result of multiple effects. Therefore, in order to cope with the limitations of the simplified dual-effect model and obtain more accurate material decomposition results under the corresponding scanning conditions, the exponent m of the photoelectric effect is usually empirically adjusted. For example, in clinical applications such as human body inspection or explosives detection in security applications, the spectral conditions of X-rays are relatively fixed, and the detection objects are usually specific. In this case, m is usually set to a constant between 3 and 4.

[0057] In terms of comprehensive model consideration, after including coherent scattering, since the scattering model is more complicated, and in order to better analyze and calculate the variable index m, we include the de-Compton model σ′ with coherent scattering De_Compt Still expressed in the form of photoelectric effect,

[0058] The index m is expressed as a variable C(E,Z) with respect to the effective atomic number and energy. eff ):

[0059]

[0060] Where α1 is a constant, E represents the scanning energy, Z eff is the effective atomic number of the scanned material. In order to make it easier for the model to solve the effective atomic number and density, we will C(E,Z eff ) is expanded using the following polynomial series:

[0061] C(E,Z eff )=Σ ij h ij Z eff i E j ,0≤i≤4,0≤j≤4#(8)

[0062] where i is C(E,Z eff )Z eff The index of E, j is the index of E, h is the index of ij Expressed as the corresponding term Z in the polynomial relation eff i E j In the description of the Compton effect, the Compton effect is proportional to the atomic number of the scanned material, and the KN coefficient f is used on the energy. KN(E), together represent the influence of the Compton effect. However, through in-depth study of NIST data, we found that this approximate Compton model based on mass density is not accurate enough. In order to better describe the Compton effect, we introduced an additional eff The variable D(E,Z eff ) to enrich the model:

[0063] σ Compt =α2D(E,Z eff )f KN (E)#(9)

[0064] Where α2 is a constant. The same polynomial series expansion fitting representation is used for D(E,Z):

[0065] D(E,Z eff )=Σ st g st Z eff s E t ,0≤s≤4,0≤t≤4#(10)

[0066] Where s is D(E,Z eff ) where Z is the index, t is the index of E, g is the index of st is the coefficient of the corresponding term in this polynomial relationship. Therefore, the linear attenuation coefficient μ of the material can be expressed as:

[0067]

[0068] Through this polynomial fitting method, we can more flexibly capture the changing characteristics of C(E,Z) and D(E,Z) under different energy and atomic number conditions (such as Figure 1 ). This dynamic fitting method not only further improves the accuracy of the model, but also more realistically reflects the complexity of physical effects as conditions change.

[0069] Further, step (2) includes: The purpose of dual energy calibration is to obtain the effective energy E in two energy regions. Since X-rays have a wide energy spectrum, we use the average energy As a representation of the effective energy in each energy region, this is equivalent to assuming that the linear attenuation coefficient of the object is obtained by the average energy of the monoenergetic X-rays. For this purpose, the calibration process requires a known base material phantom.

[0070] When using known calibration materials to obtain the effective energy E, special attention should be paid to the type and amount of the selected materials. Compounds or mixtures should be avoided as calibration materials to avoid complicating the solution of the effective atomic number. At the same time, a single material may lead to energy estimation errors, so at least two or more material types are required. It is best to choose a single substance as the calibration material, and avoid using materials with too high density or atomic number to avoid introducing additional errors due to the X-ray hardening effect.

[0071] Furthermore, step (3) includes: reconstructing the scanned material to obtain the linear attenuation coefficients in two energy zones, establishing the double-effect equation in two energy zones using formula (10), and solving the effective atomic number and density:

[0072]

[0073] The effective atomic number Z is solved pixel by pixel for the reconstructed image using the Newton gradient method. eff and material density ρ, and finally achieve the ability to eff The realization coefficient C(E,Z eff ) and (E,Z eff ) Adaptively solve for effective atomic number and density.

[0074] In the accuracy evaluation of decomposition, the absolute value of the decomposition error percentage APE is used for evaluation:

[0075]

[0076] where w T represents the true value, and w represents the value obtained after decomposition.

[0077] Table 1 shows the comparison of the decomposition results.

[0078]

[0079] Although the specific embodiments of the present invention are disclosed for the purpose of illustration, the purpose is to help understand the content of the present invention and implement it accordingly, those skilled in the art will understand that various substitutions, changes and modifications are possible without departing from the spirit and scope of the present invention and the appended claims. Therefore, the present invention should not be limited to the content disclosed in the best embodiment, and the scope of the present invention is subject to the scope defined in the claims.

Claims

1. A method for identifying substances based on adaptive quantitative decomposition, comprising the steps of: 1) Construct a model of interaction between ray and matter μ = ρ m (σ′ De_Compt +σ Compt ), where h m is the mass density, σ′ De_Compt is the deCompton model including coherent scattering, σ Compt is the Compton scattering cross section between the scanning ray and the material; C(E,Z eff )=Σ ij h ij Z eff i E j ,0≤i≤4,0≤j≤4; α1 is a constant, E is the energy of the scanning ray, Z eff is the effective atomic number of the scanned material, i is C(E,Z eff )Z eff The index of E, j is the index of E, h is the index of ij is the corresponding term Z in the polynomial relation eff i E j The coefficient of Compt = α2D(E,Z eff )f KN (E), D(E,Z eff )=Σ st g st Z eff s E t ,0≤s≤4,0≤t≤4, where s is D(E,Z eff )Z eff The exponent of t is D(E,Z eff ) is the index of E, g st is the corresponding term Z in the polynomial relation eff s E t coefficient, α2 is a constant, f KN (E) is the Klein-Cecil coefficient; 2) Using the relevant data of the selected calibration object, the variable parameters C(E, Z eff )、D(E,Z eff ) to fit and establish variable parameters C(E,Z eff ) and energy E, effective atomic number Z eff The specific relationship between them, and the variable parameters D(E,Z eff ) and energy E, effective atomic number Z eff The specific relationship between them; 3) Using C(E,Z eff )、D(E,Z eff ), and combined with the known effective atomic number and density information of various materials in the selected calibration object, the effective energy of each energy zone is solved to obtain the effective energy E of each material in the low energy zone. L and the effective energy E in the high energy region H ; 4) Scan and reconstruct the object to be detected according to the scanning parameters of the calibration object to obtain the linear attenuation coefficient of the object to be detected; then, based on the linear attenuation coefficient of the object to be detected, combined with C(E, Z eff )、D(E,Z eff ) and step 3) to obtain the effective energy E of each known material in the low energy region L , effective energy E in high energy zone H , obtain the linear attenuation coefficient of the detected object in the low energy zone and the linear attenuation coefficient of the high energy zone and substitute them into the ray-matter interaction model to solve the effective atomic number and material density of the detected object.

2. The method according to claim 1, characterized in that Get the effective energy E of each material in the low energy zone L and the effective energy E in the high energy region H The method is as follows: firstly, the selected calibration object is scanned and reconstructed to obtain the linear attenuation coefficient μ of each material in the calibration object in the high energy region. h , Linear attenuation coefficient μ in low energy region l ; Using the known physical properties of the calibration material, and then solving Get the effective energy E of the corresponding material in the low energy zone L and the effective energy E in the high energy region H .

3. The method according to claim 2, characterized in that In step 4), the method for solving the effective atomic number and material density of the detected object is: scanning and reconstructing the detected object using energy spectrum CT to obtain the linear attenuation coefficient μ of the detected object in the low energy region l ′ , linear attenuation coefficient μ in high energy region ′ h ; Use the effective energy of the two energy regions to estimate the effective energy E of the low energy region L and the effective energy E in the high energy region H , and then use the Newton gradient method to Solve pixel by pixel to obtain the effective atomic number Z of the object being detected eff and the material density ρ m .

Images