Method and device for determining density distribution of material elements, electronic equipment and storage medium

Abstract

The invention provides a substance element density distribution determination method and device, electronic equipment and a storage medium, and relates to the field of physics and computers, in particular to application of physics and computer technologies in the field of radiation medical treatment. The substance element density distribution determination method comprises the steps that sample data of a sample material is obtained, the sample material is consistent with a material type of a to-be-detected substance, and the sample data describes element density distribution of the sample material; based on the distribution characteristics of the plurality of elements in the sample material, respective distribution functions of the plurality of elements are determined, and the distribution functions comprise free parameters; determining a target value from the plurality of preset values as a value of a free parameter based on an association relationship between the sample data and the plurality of preset values; and determining the element density distribution of the substance to be measured by using a distribution function taking the target value as a free parameter.

Description

Technical Field

[0001] The present disclosure relates to the fields of physics and computers, specifically to the application of physics and computer technology in the field of radiation medicine, and more specifically to a method, device, electronic device and storage medium for determining the density distribution of material elements. Background Art

[0002] Computed Tomography (CT) imaging technology has a wide range of applications in the field of medical diagnosis. CT imaging technology and image reconstruction technology can present the internal structure of the detected object. For example, the density distribution of material elements can be obtained based on CT images, which is of great significance in radiological diagnosis and radiotherapy. Summary of the invention

[0003] The present disclosure provides a method, device, electronic device, storage medium and program product for determining the density distribution of material elements.

[0004] According to one aspect of the present disclosure, a method for determining the element density distribution of a substance is provided, comprising: acquiring sample data of a sample material, the sample material being consistent with a material type of a substance to be tested, the sample data describing the element density distribution of the sample material; determining distribution functions of the multiple elements respectively based on distribution characteristics of the multiple elements in the sample material, the distribution function including free parameters; determining a target value as a value of the free parameter from the multiple preset values ​​based on a correlation between the sample data and multiple preset values; and determining the element density distribution of the substance to be tested using the distribution function with the target value as a free parameter.

[0005] According to an embodiment of the present disclosure, based on the association relationship between sample data and multiple preset values, a target value is determined from multiple preset values ​​as the value of a free parameter, including: using multiple preset values ​​as the values ​​of free parameters and using a distribution function to generate multiple test data; determining a first similarity between each of the multiple test data and the sample data; determining a second similarity between the multiple test data and the sample data; and determining the target value from the multiple preset values ​​as the value of the free parameter based on the multiple first similarities and the second similarities.

[0006] According to an embodiment of the present disclosure, based on multiple first similarities and second similarities, a target value is determined from multiple preset values ​​as the value of a free parameter, including: determining a first probability function of the multiple preset values, the probability function indicating the initial probability that the multiple preset values ​​are the target value; using the probability function, the first similarities and the second similarities, determining a second probability function of the multiple preset values, the second probability function indicating the true probability that the multiple preset values ​​are the target value under the constraints of sample data; and based on the second probability function, determining the target value from the multiple preset values ​​as the value of the free parameter.

[0007] According to an embodiment of the present disclosure, the sample data includes N first data and N second data corresponding to the N first data, the N first data and the respectively corresponding second data satisfy a divisor of a distribution function, and N is a positive integer; determining a first similarity between each of a plurality of test data and the sample data includes: obtaining test data related to each of the N first data from the plurality of test data to obtain N groups of test data; and determining a first similarity between each of the plurality of test data and the sample data based on a difference between each of the N groups of test data and the corresponding second data.

[0008] According to the embodiment of the present disclosure, the first similarity is:

[0009] ;

[0010] in, is the first similarity, is the i-th first data, is the i-th second data, is the preset value, f is the distribution function, is the standard deviation.

[0011] According to an embodiment of the present disclosure, obtaining sample data of multiple sample materials includes: obtaining reference data of the sample materials, the reference data describing characteristics of the sample materials; and generating sample data based on the reference data within a preset range, wherein the difference between the sample data and the reference data is within the preset range.

[0012] According to an embodiment of the present disclosure, the element density distribution of the material to be tested is determined using a distribution function with a target value as a free parameter, including: determining the test characteristic parameters of each of the multiple objects to be tested of the material to be tested based on a dual-energy CT image of the material to be tested; based on the test characteristic parameters, determining the element mass of each of the multiple elements in each of the multiple objects to be tested using a distribution function with a target value as a free parameter; and determining the multiple element density distributions of the material to be tested based on the multiple element masses corresponding to each of the multiple objects to be tested.

[0013] According to another aspect of the present disclosure, there is provided a device for determining the element density distribution of a substance, comprising: an acquisition module for acquiring sample data of a sample material, the sample material being consistent with the material type of a substance to be tested, the sample data describing the element density distribution of the sample material; a first determination module for determining the distribution functions of the multiple elements respectively based on the distribution characteristics of the multiple elements in the sample material, the distribution function including free parameters; a second determination module for determining a target value as a value of a free parameter from multiple preset values ​​based on an association between the sample data and multiple preset values; and determining the element density distribution of the substance to be tested using the distribution function with the target value as a free parameter.

[0014] Another aspect of the present disclosure provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method for determining the density distribution of material elements provided by the present disclosure.

[0015] According to another aspect of an embodiment of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable a computer to execute the method for determining the material element density distribution provided by the present disclosure.

[0016] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0018] Figure 1 is a flow chart of a method for determining the density distribution of material elements according to an embodiment of the present disclosure;

[0019] Figure 2 is a scene principle diagram of a method for determining density distribution of material elements according to an embodiment of the present disclosure;

[0020] Figure 3 is a structural block diagram of a device for determining material element density distribution according to an embodiment of the present disclosure; and

[0021] Figure 4 is a schematic block diagram of an example electronic device for implementing an embodiment of the present disclosure. DETAILED DESCRIPTION

[0022] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0023] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0024] In the technical solution of the present disclosure, the collection, storage, use, processing, transmission, provision, disclosure and application of the data involved (including but not limited to user personal information) comply with the provisions of relevant laws and regulations, take necessary confidentiality measures, and do not violate public order and good morals.

[0025] In the technical solution of the present disclosure, the "test" in "test characteristic parameter" and the "sample" in "sample characteristic parameter" are only used to distinguish two "characteristic parameters". For example, the "test characteristic parameter" is obtained by testing the substance to be tested during the execution of the method of the present disclosure, and the "sample characteristic parameter" is a known characteristic parameter obtained in advance. For the sake of simplicity, similar parts are not repeated.

[0026] Figure 1 It is a flowchart of a method for determining the density distribution of material elements according to an embodiment of the present disclosure.

[0027] like Figure 1 As shown, the method 100 for determining the material element density distribution of this embodiment may include operations S110 to S140.

[0028] In operation S110 , sample data of a sample material is acquired.

[0029] In the disclosed embodiment, the sample material is consistent with the material type of the substance to be tested, and the sample data describes the element density distribution of the sample material. For example, the substance to be tested may be an object of CT scanning, and a CT image of the substance to be tested is obtained by performing CT scanning on the substance to be tested. The CT image includes the distribution of HU values ​​of the substance to be tested, and the HU value can reflect the interaction between the substance and the photons.

[0030] In the embodiment of the present disclosure, the material type of the substance to be tested and the sample material can be determined based on the effective atomic number, and the element composition within the material can be determined based on the material type.

[0031] For example, if the effective atomic number Z eff If the effective atomic number Z is greater than 8.2, the material type of the substance to be tested is determined to be bone tissue. eff If the value is less than or equal to 8.2, the material type of the substance to be tested is determined to be soft tissue. For example, the substance to be tested may be animal tissue. Bone tissue may be bone, etc., in which a large amount of calcium and phosphorus are distributed, as well as a certain amount of carbon, oxygen, hydrogen and nitrogen. Soft tissue includes a large amount of fat and protein, etc., in which a large amount of carbon, oxygen, hydrogen and nitrogen are distributed, as well as a small amount of calcium and phosphorus. The content of calcium and phosphorus in soft tissue is usually less than 0.2%, so the calcium and phosphorus included in the soft tissue can be ignored.

[0032] Effective atomic number Z eff It is the characteristic parameter of the substance to be tested. Effective atomic number Z eff It can be determined by CT images. For example, the effective atomic number Z of the substance to be tested is determined based on the dual-energy CT image of the substance to be tested. eff .

[0033] Dual-energy CT images are formed based on two radiation sources, and the dual-energy CT images include two energy spectra with different energy components. The HU values ​​of the same substance may be the same or different under the irradiation of two radiation sources. After scanning multiple test substances based on a single radiation source, the elemental components of the two test substances are different but the HU values ​​under the energy spectrum are the same. In this case, the two test substances cannot be further distinguished in the CT image, which leads to deviations in the diagnostic results. Therefore, using dual-energy CT, the two CT images of the two test substances can be compared. When the HU values ​​of the two test substances are the same under a certain energy spectrum, the HU values ​​under another energy spectrum are often different, thereby further distinguishing the two test substances. In addition, based on the distribution of HU values ​​in the dual-energy CT image, the effective atomic number of the test substance can be determined.

[0034] For example, the substance to be tested can be a three-dimensional object, and the CT image of the substance to be tested is a three-dimensional CT image. The substance to be tested is divided into multiple objects to be tested, and each object to be tested is a three-dimensional sub-object. Each object to be tested can be considered as a point, and each object to be tested is composed of multiple elements. Each object to be tested has its own effective atomic number Z eff Based on the effective atomic number Z of multiple objects to be tested eff Can form effective atomic number Z eff A three-dimensional matrix.

[0035] In the embodiment of the present disclosure, the sample data of the sample material may be acquired in advance. The sample data may include the effective atomic number Z of the sample material. eff And the mass proportion of various elements in the sample material. Based on the effective atomic number Z of the sample material eff and the effective atomic number of the substance to be tested to determine whether the material type of the substance to be tested is consistent with that of the sample material.

[0036] For example, the multiple elements may include carbon, hydrogen, oxygen, nitrogen, phosphorus, and calcium. Based on the mass proportion of each element, the element density distribution of each element in the sample material may be calculated. The element density distribution includes the density distribution of each of the carbon, hydrogen, oxygen, nitrogen, phosphorus, and calcium elements in the sample material.

[0037] In operation S120 , distribution functions of the plurality of elements are determined based on distribution characteristics of the plurality of elements in the sample material.

[0038] In the disclosed embodiment, the distribution characteristics represent the changing rule of the element mass ratio of the sample material as the characteristic parameters of the sample material change. The element mass ratio can be the ratio of the mass of the element to the mass of the object to be measured. The element distribution characteristics of different material types can be different.

[0039] For example, the sample material may be a plurality of sample materials of the same material type as the substance to be tested, and based on the sample data of each of the plurality of sample materials, the law of how the mass proportion of each element in the material of the material type changes with the change of the characteristic parameter is determined.

[0040] For example, characteristic parameters may also include the relative electron density ρ e Based on the dual-energy CT image, the relative electron density ρ of the substance to be tested can be determined e Distribution and effective atomic number Z eff Distribution. The relative electron density of the substance to be measured is ρ e The distribution characterizes the relative electron density ρ inside the substance to be tested. e Distribution, effective atomic number Z eff The distribution characterizes the effective atomic number Z inside the substance to be tested eff Distribution. Based on the relative electron density ρ of multiple objects to be measured e The relative electron density ρ can be formed e A three-dimensional matrix.

[0041] According to the element composition, element mass ratio, relative electron density ρ of each object in each sample material e and effective atomic number Z eff , determine the mass proportion of each element as a function of the relative electron density ρ e and effective atomic number Z eff The sum of the mass percentage of carbon, oxygen, hydrogen, nitrogen, calcium and phosphorus in the sample material is greater than 99% and less than 100%.

[0042] In the embodiments of the present disclosure, the distribution function can describe the law of the change of the mass proportion of the element with the characteristic parameter. The distribution function includes free parameters, for example, the free parameters can be coefficients in the distribution function. By adjusting the value of the free parameters, the accuracy of the distribution function in describing the law of element change can be adjusted.

[0043] In operation S130 , a target value is determined as a value of a free parameter from among the plurality of preset values ​​based on an association relationship between the sample data and the plurality of preset values.

[0044] In the embodiment of the present disclosure, the plurality of preset values ​​may be a plurality of alternative values ​​of the free parameter. The sample data includes the element mass ratio of the sample material, the relative electron density ρ e and effective atomic number Z eff Based on the distribution function, we can describe the element mass ratio and relative electron density ρ e Distribution and effective atomic number Z eff The accuracy of describing the change law with different preset values ​​as the distribution function of the free parameter may be different. The correlation relationship can be that multiple preset values ​​each describe the element mass ratio, relative electron density ρ e and effective atomic number Z eff The accuracy of the changing rules.

[0045] In operation S140, the element density distribution of the substance to be tested is determined using a distribution function with a target value as a free parameter.

[0046] Among multiple preset values, the element mass ratio and relative electron density ρ described by the distribution function with the target value as the free parameter e Distribution and effective atomic number Z eff The accuracy of the change pattern between them is the highest.

[0047] In the embodiment of the present disclosure, based on the relative electron density ρ of the substance to be tested e , effective atomic number Z eff And the distribution function with the target value as the free parameter can determine the mass proportion of each element in the substance to be tested, thereby determining the element distribution in the substance to be tested.

[0048] For example, if the substance to be tested is bone tissue, based on the distribution function used to describe the carbon element in the bone tissue and the relative electron density ρ of the substance to be tested, e and effective atomic number Z eff , determine the mass percentage of carbon element in the substance to be tested.

[0049] In some embodiments, based on the dual-energy CT image of the substance to be tested, the test characteristic parameters of each of the multiple test objects of the substance to be tested are determined. Based on the test characteristic parameters, the element mass of each of the multiple elements in each of the multiple test objects is determined using a distribution function with a target value as a free parameter; and the multiple element density distributions of the substance to be tested are determined according to the multiple element masses corresponding to each of the multiple test objects.

[0050] In the embodiment of the present disclosure, the substance to be tested is divided into a plurality of objects to be tested. Based on the dual-energy CT, the relative electron density ρ of each object to be tested can be determined. e and effective atomic number Z eff, so that the mass proportion of each element in each object to be tested can be determined based on the distribution function with the target value as the free parameter, and then the mass proportion distribution of each element in the substance to be tested can be determined.

[0051] For example, after obtaining the element mass percentage of each object to be tested, the distribution of the element mass inside the substance to be tested is determined based on the position of each object to be tested in the substance to be tested. For example, based on the carbon element mass percentage of multiple objects to be tested and the positions of multiple objects to be tested in the substance to be tested, a three-dimensional matrix formed by the carbon element mass percentage values ​​is obtained, and the carbon element mass distribution inside the substance to be tested can be determined through the three-dimensional matrix.

[0052] For example, the density distribution of multiple elements of the substance to be tested can be represented by multiple three-dimensional matrices, and each three-dimensional matrix represents the distribution of one element in the substance to be tested.

[0053] Combination Figure 2 The scenario shown is described. Figure 2 It is a scene principle diagram of the method for determining the density distribution of material elements according to an embodiment of the present disclosure.

[0054] like Figure 2 As shown, the substance to be tested 200 can be divided into a plurality of objects to be tested. For example, the substance to be tested 200 can be a cubic structure, and the substance to be tested 200 is evenly divided into a plurality of objects to be tested, and each object to be tested is also a cubic structure.

[0055] In the embodiment of the present disclosure, each object to be tested can be equivalent to a point inside the substance to be tested 200. According to the relative electron density ρ of the substance to be tested 200 e Distribution and effective atomic number Z eff The relative electron density ρ of each object to be measured can be determined by the distribution e and effective atomic number Z eff .

[0056] In the embodiment of the present disclosure, the element distribution inside the substance to be tested 200 can be determined by calculating the element mass ratio of each object to be tested.

[0057] For example, the substance to be tested 200 is divided into 5×3×3 objects to be tested. e and effective atomic number Z eff , the mass percentage of the carbon element inside the object to be tested 201 can be determined. For example, based on the mass percentage of the carbon element of multiple objects to be tested, a 5×3×3 three-dimensional matrix can be obtained, and the three-dimensional matrix can represent the distribution of the carbon element inside the substance to be tested 200. According to the mass percentage of the element and the volume of the object to be tested, the carbon element density distribution of the substance to be tested 200 can be calculated.

[0058] Through the disclosed embodiments, the element distribution of the substance to be tested is determined based on the element masses of multiple objects to be tested of the substance to be tested. The accuracy of the element distribution can be optimized by adjusting the particle size of the objects to be tested. In addition, the material type of the object to be tested is considered, the distribution characteristics of each element in the bone tissue and soft tissue are fully utilized, and the mass proportion of each element in the object to be tested of each material type is calculated respectively, thereby improving the reliability of the calculation results.

[0059] In some embodiments, the accuracy of the distribution function is related to the sample data. Building a sample database based on the sample data can improve the richness of the sample data, thereby improving the accuracy of the distribution function.

[0060] In the embodiment of the present disclosure, reference data of the sample material is obtained, and the reference data describes the characteristics of the sample material. The reference data can be the relative electron density ρ of the sample material e , effective atomic number Z eff and the mass proportions of multiple elements. For example, the reference data can be obtained from pre-tests or directly acquired known data. For example, reference data of multiple animal tissue materials can be obtained from an open source tissue material library. The reference data includes the total density ρ, relative electron density ρ of each animal tissue material. e , effective atomic number Z eff And the element mass proportions of H, C, N, O, P, and Ca.

[0061] For example, a sample database may be constructed based on reference data of a variety of sample materials.

[0062] For example, assuming that the relative electron density of the sample material is e and effective atomic number Z eff All obey Gaussian distribution. For a sample material, the real relative electron density ρ of the sample material e , and the true effective atomic number Z eff As the central value, and with 10% of the central value as the standard deviation, 100 groups of relative electron density ρ were randomly generated. e and effective atomic number Z eff For each randomly generated relative electron density ρ e and effective atomic number Z eff , randomly generate 100 sets of mass proportions of various elements.

[0063] A relative electron density ρ e and effective atomic number Z effAnd a set of mass proportions of multiple elements can constitute a set of sample data. Based on the reference data of each sample material, 10,000 sets of sample data can be generated. The difference between the multiple sets of sample data and the reference data is determined by the standard deviation of the Gaussian distribution. For example, the smaller the standard deviation, the smaller the difference between the multiple sets of sample data and the reference data.

[0064] In the embodiment of the present disclosure, the standard deviation can be determined based on actual data requirements, such as 10% of the center value or 20% of the center value. In the process of building the sample database, the uncertainty of the element density ratio and the calculation of the relative electron density ρ are fully considered. e and effective atomic number Z eff The uncertainty that may arise in the process generates enough sample data to improve the accuracy of the values ​​of the free parameters.

[0065] In the embodiment of the present disclosure, the effective atomic number Z in the multiple sets of sample data eff , the material type of the sample material described by each set of sample data can be used, and multiple animal tissue materials can be divided into bone tissue and soft tissue. For bone tissue materials and soft tissue materials, element distribution analysis is performed respectively to determine the distribution characteristics of multiple elements in different types of materials. Based on the distribution characteristics, the relevant information of multiple animal tissue materials is fitted to determine the distribution law of multiple elements in different tissue materials, thereby determining the element correlation. For example, the element correlation indicates the correlation between the characteristic parameter and the element mass of the element and indicates the correlation between multiple elements.

[0066] For example, element correlation can express the mass fraction of an element as a function of its relative electron density ρ e and effective atomic number Z eff The law of change, element correlation can also express the law of change of the mass proportion of one element as the mass proportion of another element changes.

[0067] In some embodiments, for bone tissue materials, by analyzing and fitting the carbon mass percentage, oxygen mass percentage, and relative electron density ρ e and effective atomic number Z eff The relationship between the sum of the mass proportions of oxygen and carbon and the relative electron density ρ can be determined. e and effective atomic number Z eff The sum of the mass proportions of oxygen and carbon increases with the relative electron density ρ e The sum of the mass proportions of oxygen and carbon decreases with the increase of the effective atomic number Z. eff decreases with the increase of .

[0068] In the embodiment of the present disclosure, a carbon-oxygen mass ratio function is constructed according to the negative correlation between the sum of the elemental mass of the oxygen element and the elemental mass of the carbon element and the characteristic parameter.

[0069] For example, the carbon-oxygen mass ratio function for bone tissue can be expressed as formula (1):

[0070] W C+O = (W C+O,r + W C+O,Z ) / twenty one)

[0071] Among them, W C+O W represents the sum of the mass proportion of oxygen and carbon, C+O,r =f C+O (ρ e ) represents the sum of the mass proportion of oxygen and carbon and the relative electron density ρ e The changing rules between C+O,Z =f C+O (Z eff ) represents the sum of the mass proportion of oxygen and carbon and the effective atomic number Z eff The changing rules between C+O (x) can be a second-order polynomial or a third-order polynomial. C+O The sum of the mass percentages of carbon and oxygen can be W C+O,r and W C+O,Z The average value of .

[0072] In the embodiment of the present disclosure, a carbon mass proportion function is constructed based on the inverse tangent function and the sample element mass of the carbon element.

[0073] For example, by fitting the mass proportion of carbon in a variety of bone tissue materials, a carbon mass proportion function is determined. The carbon mass proportion function is determined based on the inverse tangent function. For example, according to the sample relative electron density, sample effective atomic number and sample element mass of carbon of each of the multiple sample materials, under the constraint of the inverse tangent function, the free parameters corresponding to the carbon element are determined; and based on the free parameters, the carbon mass proportion function is constructed.

[0074] For example, the carbon mass ratio function for bone tissue can be expressed as formula (2):

[0075] W C = m + n*arctan(a C *ρ e + b C *Z eff + c C *ρ e *Z eff + dC ) (2)

[0076] Among them, W C is the mass percentage of carbon element, ρ e is the relative electron density, Z eff is the effective atomic number, a C , b C 、c C and d C is the free parameter of carbon element, and m and n are adjustment coefficients.

[0077] In the embodiment of the present disclosure, the formula (2) is fitted by using the known sample mass percentage of carbon element, sample relative electron density and sample effective atomic number of multiple sample materials to calculate a C , b C 、c C d C , m and n. By adjusting the values ​​of m and n, W C The value of is restricted to be in the range greater than 0 and less than 1.

[0078] In the embodiment of the present disclosure, since the value range of arctan is -π / 2~π / 2, the mass proportion of carbon element W can be calculated based on arctan. C The value of is limited to a certain range, so as to avoid the mass proportion of carbon element W C Too high or too low, thereby improving the calculation accuracy. For example, due to the relative electron density ρ determined based on dual-energy CT images e and effective atomic number Z eff There is a certain error in the value of e and effective atomic number Z eff Further calculation of the element mass percentage may increase the error, causing the sum of the mass percentages of multiple elements to be greater than 100% or less than 0.

[0079] Through the embodiments of the present disclosure, the range of the mass proportions is constrained based on the inverse tangent function, so that the range of the sum of the mass proportions of multiple elements can be constrained within a reasonable range, avoiding the situation where the sum of the mass proportions of multiple elements is greater than 100% or less than 0, thereby improving accuracy.

[0080] In the embodiment of the present disclosure, an oxygen mass proportion function is constructed according to the carbon-oxygen mass proportion function and the carbon mass proportion function. Due to the negative correlation between the sum of the mass proportion of the oxygen element and the mass proportion of the carbon element and the characteristic parameter, when determining the mass proportion of the carbon element, the sum of the mass proportion of the oxygen element and the mass proportion of the carbon element can be used as the basis. C+O The mass ratio of carbon to W CDifference, determine the mass proportion of oxygen element W O .

[0081] For example, the oxygen mass fraction function for bone tissue can be expressed as formula (3):

[0082] W O = W C+O -W C (3)

[0083] In some embodiments, the effective atomic number Z of each object to be tested in the material to be tested can be determined based on the dual-energy CT image of the material to be tested. eff and the relative electron density ρ e When the object to be measured is determined to be bone tissue, the carbon-oxygen mass proportion function and the carbon mass proportion function corresponding to the bone tissue are determined. For example, the carbon-oxygen mass proportion function may be as shown in formula (1), and the carbon mass proportion function may be as shown in formula (2).

[0084] According to the carbon-oxygen mass ratio function, the effective atomic number Z of each of the multiple objects to be tested eff and the relative electron density ρ e , determine the sum of the elemental mass of oxygen and the elemental mass of carbon of each of the multiple objects to be tested. According to the carbon mass proportion function, the effective atomic number Z of each of the multiple objects to be tested eff and the relative electron density ρ e , determine the elemental mass of the carbon element of each of the multiple objects to be measured. According to the sum of the elemental masses of the carbon element and the oxygen element and the elemental mass of the carbon element, determine the elemental mass of the oxygen element of each of the multiple objects to be measured.

[0085] For example, the effective atomic number Z of each of the multiple objects to be tested eff and the relative electron density ρ e Substituting into formula (1) and formula (2), we can get the sum of the mass proportion of oxygen element and the mass proportion of carbon element of each object to be measured, W C+O The mass ratio of carbon to W C The sum of the mass proportion of oxygen and the mass proportion of carbon is W C+O The mass ratio of carbon to W C Substituting into formula (3), we can determine the mass proportion of oxygen element W O .

[0086] In some embodiments, according to the mass proportion of carbon elements of each of the multiple objects to be tested and the respective positions of the multiple objects to be tested in the substance to be tested, a three-dimensional matrix of the mass proportion of carbon elements is constructed. The three-dimensional matrix of the mass proportion of carbon elements can represent the mass distribution of carbon elements in the substance to be tested. According to the mass proportion of carbon elements of each of the multiple objects to be tested and the volume and mass of each object to be tested, the carbon element density of each of the multiple objects to be tested can be determined, and according to the carbon element density of each of the multiple objects to be tested and the respective positions of the multiple objects to be tested in the substance to be tested, a three-dimensional matrix of carbon element density is constructed. The three-dimensional matrix of carbon element density can represent the density distribution of carbon elements in the substance to be tested.

[0087] For example, according to the mass proportion of oxygen elements of each of the multiple objects to be tested and the positions of each of the multiple objects to be tested in the substance to be tested, a three-dimensional matrix of oxygen element mass proportion is constructed. The three-dimensional matrix of oxygen element mass proportion can represent the mass distribution of oxygen elements in the substance to be tested. According to the mass proportion of oxygen elements of each of the multiple objects to be tested and the volume and mass of each object to be tested, the oxygen element density of each of the multiple objects to be tested can be determined, and according to the oxygen element density of each of the multiple objects to be tested and the positions of each of the multiple objects to be tested in the substance to be tested, a three-dimensional matrix of oxygen element density is constructed. The three-dimensional matrix of oxygen element density can represent the density distribution of oxygen elements in the substance to be tested.

[0088] In some embodiments, for bone tissue materials, by analyzing and fitting the sample data, the mass ratio of calcium element, the relative electron density ρ e and effective atomic number Z eff The relationship between the mass ratio of calcium and the relative electron density ρ can be determined e and effective atomic number Z eff The mass proportion of calcium increases with the relative electron density ρ e The mass proportion of calcium increases with the effective atomic number Z. eff increases with the increase of .

[0089] According to the positive correlation between the sample element mass of calcium and the sample characteristic parameters, a calcium mass proportion function is constructed. According to the positive correlation between the sample element mass of phosphorus and the sample characteristic parameters, a phosphorus mass proportion function is constructed.

[0090] For example, the calcium mass ratio function for bone tissue can be expressed as formula (4):

[0091] W Ca = (W Ca,r + W Ca,Z ) / twenty four)

[0092] Among them, W Ca Indicates the mass percentage of calcium, WCa,r =f Ca (ρ e ) represents the mass proportion of calcium and the relative electron density ρ e The changing rules between Ca,Z =f Ca (Z eff ) represents the mass ratio of calcium element and the effective atomic number Z eff The changing rules between Ca (x) can be a second-order polynomial or a third-order polynomial. Ca (x). The mass proportion of calcium can be W Ca,r and W Ca,Z The average value of .

[0093] In the embodiment of the present disclosure, the mass ratio characteristics of phosphorus are similar to those of calcium, and the phosphorus mass ratio function can be obtained by fitting parameters using the same method.

[0094] For example, the phosphorus mass ratio function for bone tissue can be expressed as formula (5):

[0095] W P = (W P,r + W P,Z ) / 2 (5)

[0096] Among them, W P Indicates the mass percentage of phosphorus, W P,r =f P (ρ e ) represents the mass proportion of phosphorus and the relative electron density ρ e The changing rules between P,Z =f P (Z eff ) represents the mass ratio of phosphorus and the effective atomic number Z eff The changing rules between P (x) can be a second-order polynomial or a third-order polynomial. P (x). The mass percentage of phosphorus can be W P,r and W P,Z The average value of .

[0097] In the disclosed embodiment, the calculation method of the mass percentage of the hydrogen element is the same as that of the carbon element. Under the constraint of the inverse tangent function, a hydrogen mass percentage function is constructed according to the sample element mass of the hydrogen element.

[0098] For example, the hydrogen mass fraction function for soft tissue can be expressed as formula (6):

[0099] W H = m + n*arctan(a H*ρ e + b H *Z eff + c H *ρ e *Z eff + d H ) (6)

[0100] Among them, W H is the mass percentage of hydrogen, ρ e is the relative electron density, Z eff is the effective atomic number, a H , b H 、c H and d H is the free parameter of hydrogen element, and m and n are adjustment coefficients.

[0101] In the embodiment of the present disclosure, the known sample mass percentage of hydrogen element, sample relative electron density and sample effective atomic number of multiple sample materials are used to fit formula (6) to calculate a H , b H 、c H d H , m and n. By adjusting the values ​​of m and n, W H The value of is limited to a reasonable range.

[0102] In the embodiment of the present disclosure, the mass proportion of the nitrogen element can be determined by subtracting 100% from the proportions of the above five elements.

[0103] For example, the nitrogen mass ratio function for bone tissue can be expressed as formula (7):

[0104] W N = 1 - W H -W C -W O -W P -W Ca (7)

[0105] Among them, W P Represents the mass percentage of phosphorus.

[0106] In the embodiment of the present disclosure, based on formula (1) to formula (7), the mass proportions of calcium, phosphorus, carbon, oxygen, hydrogen and nitrogen in the bone tissue material can be calculated, thereby determining the density distribution of calcium, phosphorus, carbon, oxygen, hydrogen and nitrogen in the substance to be tested.

[0107] In some embodiments, for soft tissue materials, the mass proportions of oxygen and carbon are negatively correlated. The mass proportion of carbon decreases as the oxygen increases, thereby constructing an oxygen mass proportion function for soft tissue.

[0108] For example, the oxygen mass fraction function for soft tissue can be expressed as formula (8):

[0109] W O = f(W C ) (8)

[0110] Among them, W O represents the mass proportion of oxygen, f(x) can be a second-order polynomial or a third-order polynomial, and x is negatively proportional to f(x). The mass proportion of phosphorus can be W P,r and W P,Z The average

[0111] The mass ratio of carbon and hydrogen in soft tissue is calculated in the same way as in bone tissue. The mass ratio of carbon in soft tissue can be calculated based on formula (2). The mass ratio of hydrogen in soft tissue can be calculated based on formula (6).

[0112] Since the phosphorus and calcium contents in soft tissues are extremely small and can be ignored, the nitrogen mass ratio function for soft tissues can be expressed as formula (9):

[0113] W N = 1 - W H -W C -W O (9)

[0114] In the embodiment of the present disclosure, based on formula (2), formula (6), formula (8) and formula (9), the mass proportions of carbon, oxygen, hydrogen and nitrogen in the soft tissue material can be calculated, thereby determining the density distribution of carbon, oxygen, hydrogen and nitrogen in the substance to be tested.

[0115] Through the embodiments of the present disclosure, based on a relatively simple function parameterization method, the algorithm is easy to implement and saves computing resources. The characteristics of the element proportions in bone tissue and soft tissue are fully utilized to determine the mass proportion of each element and improve the reliability of the determination method. In addition, the arctan function is used to fit and determine the element proportions, which improves the accuracy and robustness of the calculation.

[0116] In the embodiments of the present disclosure, the distribution function of the carbon element of the bone tissue material can refer to formula (2), the distribution function of the oxygen element of the bone tissue material can refer to formula (3), the distribution function of the calcium element of the bone tissue material can refer to formula (4), the distribution function of the phosphorus element of the bone tissue material can refer to formula (5), the distribution function of the hydrogen element of the bone tissue material can refer to formula (6), and the distribution function of the nitrogen element of the bone tissue material can refer to formula (7). The distribution function of the oxygen element of the soft tissue material can refer to formula (8), the distribution function of the carbon element of the soft tissue material can refer to formula (2), the distribution function of the hydrogen element of the soft tissue material can refer to formula (6), and the distribution function of the nitrogen element of the soft tissue material can refer to formula (9).

[0117] In the embodiment of the present disclosure, based on the association between the sample data of the bone tissue material in the multiple sets of sample data and the multiple preset values, the values ​​of the free parameters in the multiple distribution functions of the bone tissue material are determined. Based on the association between the sample data of the soft tissue material in the multiple sets of sample data and the multiple preset values, the values ​​of the free parameters in the multiple distribution functions of the soft tissue material are determined.

[0118] For example, multiple preset values ​​are used as the values ​​of free parameters, and a distribution function is used to generate multiple test data; a first similarity between each of the multiple test data and the sample data is determined; a second similarity between the multiple test data and the sample data is determined; and based on the multiple first similarities and the second similarities, a target value is determined from the multiple preset values ​​as the value of the free parameter.

[0119] In the disclosed embodiment, the test data may be calculated based on a preset value and a distribution function. The sample data may be known real data. Based on the difference between the test data and the real data, a first similarity between the test data and the sample data with respect to the preset value may be determined.

[0120] For example, the smaller the difference between the test data and the real data, the greater the first similarity between the test data and the sample data with respect to the preset value. By comparing multiple test data with the real data separately, the first similarity under each preset value constraint can be determined, thereby reflecting the accuracy of the preset value. For example, the higher the first similarity, the higher the accuracy of the preset value, and the higher the probability that the preset value is the target value.

[0121] By jointly comparing multiple test data with real data, the second similarity under the constraints of multiple preset values ​​can be determined, thereby reflecting the overall accuracy of multiple preset values. Determining the target value based on the second similarity and the first similarity can avoid the error caused by a single comparison and eliminate the extremely high first similarity or extremely low first similarity due to particularity or uncertainty.

[0122] In some embodiments, the sample data may include N first data and N second data corresponding to the N first data, the N first data and the corresponding second data satisfy a divisor of a distribution function, and N is a positive integer.

[0123] In an embodiment of the present disclosure, test data related to each of N first data is obtained from multiple test data to obtain N groups of test data; and based on the difference between each of the N groups of test data and the corresponding second data, the first similarity between each of the multiple test data and the sample data is determined.

[0124] For example, the first data may be the effective atomic number Z of a sample object in the sample material. eff and the relative electron density ρ e , the second data is the element mass percentage of the sample object. The test data can be based on the preset value, distribution function, effective atomic number Z eff and the relative electron density ρ e For example, a set of preset values ​​is used as the value of the free parameter in the distribution function of the carbon mass fraction described by formula (2), and the effective atomic number Z is eff and the relative electron density ρ e代入 Formula (2) calculates the mass percentage of carbon element as the test data.

[0125] In multiple sets of sample data, each set of effective atomic number Z eff and the relative electron density ρ e Corresponding to the real data of the mass percentage of a carbon element, based on the difference between the test data and the real data, a first similarity between the test data and the sample data with respect to the set of preset values ​​can be determined.

[0126] For example, the plurality of preset values ​​may include a plurality of groups of preset values. For formula (2), each group of preset values ​​includes a C , b C 、c C and d C The value of .

[0127] For example, based on multiple sets of preset values, multiple carbon element test data are calculated. Each test data is compared with the real data, and the first similarity under each set of preset value constraints can be determined, thereby reflecting the accuracy of the set of preset values. For example, the higher the first similarity, the higher the accuracy of the set of preset values, and the higher the probability that the set of preset values ​​is used as the target value.

[0128] In some embodiments, based on multiple first similarities and second similarities, a target value is determined from multiple preset values ​​as the value of a free parameter, including: determining a first probability function of the multiple preset values, the first probability function indicating an initial probability that the multiple preset values ​​are the target value; using the first probability function, the first similarity and the second similarity to determine a second probability function of the multiple preset values, the second probability function indicating the true probability that the multiple preset values ​​are the target value under the constraints of the sample data; and based on the second probability function, determining the target value from the multiple preset values ​​as the value of the free parameter.

[0129] In the disclosed embodiment, the initial probability may satisfy a Gaussian distribution, the initial probability may be the probability of each preset value being a target value without conditional constraints, and the initial probability may be a relatively broad probability distribution. For example, the values ​​of the free parameters in the distribution function may all be assumed to obey a Gaussian distribution with a central value of 0 and a standard deviation of 100.

[0130] In the embodiment of the present disclosure, the first similarity can be converted into a probability distribution function about the preset value and the sample data. The second similarity can be converted into a probability distribution function about the sample data.

[0131] For example, the distribution function of calcium in bone tissue described by formula (4) is W Ca,r =f Ca (ρ e ) as an example. Ca,r =f Ca (ρ e ) can be a cubic polynomial, so W Ca,r =f Ca (ρ e ) can be expressed as formula (10):

[0132] (10)

[0133] Among them, θ is The free parameter in θ has a range of preset values. The error term or variable that represents random sampling noise, error Follows a Gaussian distribution.

[0134] The first similarity is converted into a probability distribution function about the preset value and the sample data, which can be expressed as formula (11):

[0135] (11)

[0136] in, is the first similarity, is the i-th relative electron density in the sample data, is the mass proportion of the i-th element, f is the distribution function, is the standard deviation.

[0137] For example, the first similarity for calcium in bone tissue can be expressed as:

[0138]

[0139] in, The first similarity, D represents the sample data, is the i-th relative electron density in the sample data, is the mass proportion of the i-th calcium element is the distribution function of the mass proportion of calcium element with respect to relative electron density, N represents the number of bone tissue materials in the sample database, and the value range of i is 1~N.

[0140] The value of is the test data calculated based on the preset value and distribution function. Taking the third-order polynomial as an example, It can be expressed as:

[0141]

[0142] In the embodiment of the present disclosure, the first similarity corresponds to a probability distribution function, which indicates that, when the value of the free parameter is determined to be a preset value, based on the known characteristic parameters (relative electron density ρ e and effective atomic number Z eff ) calculates the probability of the corresponding element mass proportion in the sample data. The probability can be reflected by the difference between the real data and the test data.

[0143] In the embodiment of the present disclosure, the second probability function can be expressed as formula (12):

[0144] (12)

[0145] in, is the second probability function, is the first probability function, is the probability distribution function obtained by the first similarity transformation. is the probability distribution function obtained by the second similarity transformation. It means that when the free parameter values ​​are determined to be all preset values, based on the known characteristic parameters in the sample data (relative electron density ρ e and effective atomic number Z eff ) calculates the probability of the corresponding element mass proportion in the sample data. The value of can also be the second probability function The normalization constant of .

[0146] The second probability function It means that when the mass ratio of the element is determined to be the true value in the sample data, based on the known characteristic parameters in the sample data (relative electron density ρ e and effective atomic number Z eff ) calculates the probability that the value of the free parameter is the preset value.

[0147] by Take this as an example.

[0148] The first similarity corresponds to the probability distribution function Representation: A set of preset values ​​are used as the values ​​of the free parameters θ0, θ1, θ2, and θ3, based on the known relative electron density in the sample data Calculated is the relative electron density in the sample data The corresponding element mass ratio probability.

[0149] The second probability function Representation: Substitute the known relative electron density in the sample data into , substitute the element mass ratio in the sample data into In this case, the values ​​of the free parameters θ0, θ1, θ2, and θ3 are the probabilities of the preset values.

[0150] Probability distribution function corresponding to the second similarity Representation: A set of preset values ​​are used as the values ​​of the free parameters θ0, θ1, θ2, and θ3, based on all known relative electron densities in the sample data The test data calculated separately These multiple test data is the relative electron density in the sample data The corresponding element mass ratio probability.

[0151] In the embodiment of the present disclosure, the second probability function It can also follow a Gaussian distribution. The second probability function It can be understood as the updated first probability function For example, the first probability function The central value of can be 0 and the standard deviation is 10. The second probability function The central value is 0.3 and the standard deviation is 0.1. The preset value indicated by the central value can be used as the target value.

[0152] In the case where the distribution function includes multiple free parameters, the target value includes the values ​​of the multiple free parameters in the distribution function.

[0153] In the embodiment of the present disclosure, when the value of the free parameter is determined, the effective atomic number Z of the substance to be tested is eff and the relative electron density ρ e代入 In the distribution function, the element mass distribution of the substance to be measured is determined, thereby determining the element density distribution of the substance to be measured.

[0154] Based on the method for determining the density distribution of material elements provided in the present disclosure, the present disclosure also provides a device for determining the density distribution of material elements. Figure 3 The device is described in detail.

[0155] Figure 3 It is a structural block diagram of a device for determining the density distribution of material elements according to an embodiment of the present disclosure.

[0156] like Figure 3 As shown, the device 300 for determining the material element density distribution of this embodiment may include an acquisition module 310 , a first determination module 320 , a second determination module 330 and a third determination module 340 .

[0157] The acquisition module 310 is used to acquire sample data of a sample material, the sample material is consistent with the material type of the substance to be tested, and the sample data describes the element density distribution of the sample material. In one embodiment, the acquisition module 310 can be used to perform the operation S110 described above, which will not be repeated here.

[0158] The first determination module 320 is used to determine the distribution functions of the multiple elements based on the distribution characteristics of the multiple elements in the sample material, and the distribution functions include free parameters. In one embodiment, the first determination module 320 is used to perform the operation S120 described above, which will not be repeated here.

[0159] The second determination module 330 is used to determine the target value as the value of the free parameter from the plurality of preset values ​​based on the association between the sample data and the plurality of preset values. In one embodiment, the second determination module 330 can be used to perform the operation S130 described above, which will not be described in detail here.

[0160] The third determination module 340 is used to determine the element density distribution of the substance to be tested by using a distribution function with a target value as a free parameter. In one embodiment, the third determination module 340 can be used to perform the operation S140 described above, which will not be described in detail here.

[0161] It should be noted that the collection, storage, use, processing, transmission, provision, disclosure and application of user personal information involved in the technical solution of this disclosure are in compliance with the provisions of relevant laws and regulations, necessary confidentiality measures have been taken, and they do not violate public order and good morals. In the technical solution of this disclosure, the user's authorization or consent is obtained before obtaining or collecting user personal information.

[0162] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0163] Figure 4 A schematic block diagram of an example electronic device 400 that can be used to implement the method of an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0164] like Figure 4 As shown, the electronic device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the electronic device 400 can also be stored. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0165] Multiple components in the electronic device 400 are connected to the I / O interface 405, including: an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a disk, an optical disk, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows the electronic device 400 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0166] The computing unit 401 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 401 performs the various methods and processes described above, such as a method for determining the density distribution of material elements. For example, in some embodiments, the method for determining the density distribution of material elements may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 400 via the ROM 402 and / or the communication unit 409. When the computer program is loaded into the RAM 403 and executed by the computing unit 401, one or more steps of the method for determining the density distribution of material elements described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to execute the method for determining the material element density distribution in any other appropriate manner (eg, by means of firmware).

[0167] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0168] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0169] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0170] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0171] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0172] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. Among them, the server may be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server may also be a server of a distributed system, or a server combined with a blockchain.

[0173] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0174] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for determining the density distribution of a material element, comprising: Acquire sample data of a sample material, the sample material being consistent with the material type of the substance to be tested, the sample data describing the element density distribution of the sample material; Determining distribution functions of each of the plurality of elements based on distribution characteristics of the plurality of elements in the sample material, wherein the distribution functions include free parameters; Based on the association relationship between the sample data and a plurality of preset values, determining a target value from the plurality of preset values ​​as the value of the free parameter; as well as The element density distribution of the substance to be tested is determined using the distribution function with the target value as a free parameter.

2. The method according to claim 1, wherein: The step of determining a target value from the plurality of preset values ​​as the value of the free parameter based on the association relationship between the sample data and the plurality of preset values ​​comprises: Using the plurality of preset values ​​as the values ​​of the free parameters respectively and utilizing the distribution function to generate a plurality of test data; Determine a first similarity between each of the plurality of test data and the sample data; determining a second similarity between the plurality of test data and the sample data; and Based on the first similarities and the second similarities, the target value is determined from the preset values ​​as the value of the free parameter.

3. The method according to claim 2, wherein: The step of determining the target value as the value of the free parameter from the plurality of preset values ​​based on the plurality of the first similarities and the second similarities includes: determining a first probability function of the plurality of preset values, the first probability function indicating initial probabilities of the plurality of preset values ​​being the target value; Determine a second probability function of the plurality of preset values ​​using the first probability function, the first similarity, and the second similarity, the second probability function indicating true probabilities of the plurality of preset values ​​being the target values ​​under constraints of the sample data; and Based on the second probability function, the target value is determined from the plurality of preset values ​​as the value of the free parameter.

4. The method according to claim 2, wherein: The sample data includes N first data and N second data corresponding to the N first data, the N first data and the second data corresponding to each other satisfy a divisor of the distribution function, and N is a positive integer; The determining a first similarity between each of the plurality of test data and the sample data comprises: Acquire test data related to each of the N first data from the plurality of test data to obtain N groups of test data; as well as Based on the difference between each of the N groups of test data and the corresponding second data, a first similarity between each of the plurality of test data and the sample data is determined.

5. The method according to claim 4, wherein: The first similarity is: ； in, is the first similarity, is the i-th first data, is the i-th second data, is a preset value, f is the distribution function, is the standard deviation.

6. The method according to claim 1, wherein: The step of obtaining sample data of a plurality of sample materials comprises: obtaining reference data of the sample material, the reference data describing characteristics of the sample material; and The sample data is generated based on reference data within a preset range, and the difference between the sample data and the reference data is within the preset range.

7. The method according to claim 1, wherein: The step of determining the element density distribution of the substance to be tested by using the distribution function with the target value as a free parameter comprises: Determining the test characteristic parameters of each of the plurality of test objects of the test material based on the dual-energy CT image of the test material; Based on the test characteristic parameters, determining the element mass of each of the plurality of elements in each of the plurality of objects to be tested using the distribution function with the target value as a free parameter; and The density distribution of multiple elements of the substance to be tested is determined according to the masses of multiple elements corresponding to the multiple objects to be tested.

8. A device for determining the density distribution of material elements, comprising: An acquisition module, used to acquire sample data of a sample material, the sample material being consistent with the material type of the substance to be tested, and the sample data describing the element density distribution of the sample material; A first determination module, configured to determine a distribution function of each of the plurality of elements based on distribution characteristics of the plurality of elements in the sample material, wherein the distribution function includes a free parameter; A second determination module, configured to determine a target value as a value of the free parameter from among the plurality of preset values ​​based on an association relationship between the sample data and the plurality of preset values; as well as The third determination module is used to determine the element density distribution of the substance to be tested by using the distribution function with the target value as a free parameter.

9. An electronic device, comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-7.

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