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

Abstract

The present disclosure provides a substance element density distribution determination method and device, electronic equipment and storage medium, relates to physics and computer field, and especially relates to application of physics and computer technology in the field of radiotherapy. The substance element density distribution determination method comprises the following steps: obtaining sample data of a sample material, the sample material being consistent with the material type of a to-be-measured substance, and the sample data describing the element density distribution of the sample material; determining a distribution function of each of a plurality of elements based on the distribution characteristics of the plurality of elements in the sample material, the distribution function comprising a free parameter; determining a target value as the value of the free parameter from a plurality of preset values based on the association relationship between the sample data and the plurality of preset values; and determining the element density distribution of the to-be-measured substance by using the distribution function with the target value as the free parameter.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the fields of physics and computer, in particular to the application of physics and computer technology in the field of radio medical treatment, and more particularly to a method and device for determining the density distribution of a material element, an electronic device and a storage medium. BACKGROUND

[0002] Computed Tomography (CT) imaging technology has a wide range of applications in the field of medical diagnosis. Through CT imaging technology and image reconstruction technology, the internal structure of the object being detected can be presented. For example, based on the CT image, the density distribution of the material element can be obtained, and the element density distribution is of great significance in radio diagnosis and radiotherapy. SUMMARY

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

[0004] According to an aspect of the present disclosure, a method for determining the density distribution of a material element is provided, comprising: obtaining sample data of a sample material, the sample material being consistent with the material type of a to-be-detected substance, and the sample data describing the element density distribution of the sample material; determining a distribution function of each of a plurality of elements based on the distribution characteristics of the plurality of elements in the sample material, the distribution function including a free parameter; determining a target value from a 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; and determining the element density distribution of the to-be-detected substance by using the distribution function with the target value as the free parameter.

[0005] According to an embodiment of the present disclosure, determining a target value from a 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: generating a plurality of test data by using the distribution function with the plurality of preset values as the value of the free parameter respectively; determining 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 determining the target value from the plurality of preset values as the value of the free parameter based on the plurality of first similarities and the second similarity.

[0006] According to an embodiment of the present disclosure, determining a target value from a plurality of preset values as the value of the free parameter based on the plurality of first similarities and the second similarity comprises: determining a first probability function of the plurality of preset values, the probability function indicating the initial probability of the plurality of preset values being the target value; determining a second probability function of the plurality of preset values by using the probability function, the first similarity and the second similarity, the second probability function indicating the real probability of the plurality of preset values being the target value under the constraint of the sample data; and determining the target value from the plurality of preset values as the value of the free parameter based on the second probability function.

[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 respective corresponding second data satisfy a quotient of a distribution function, N is a positive integer; determining a first similarity between each of the 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 the 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 respective corresponding second data.

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

[0009] ;

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

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

[0012] According to an embodiment of the present disclosure, determining an element density distribution of a to-be-tested substance by using a distribution function with a target value as a free parameter includes: determining a test feature parameter of each of a plurality of to-be-tested objects of the to-be-tested substance based on a dual-energy CT image of the to-be-tested substance; determining an element mass of each of a plurality of elements in each of the plurality of to-be-tested objects by using the distribution function with the target value as the free parameter based on the test feature parameter; and determining a plurality of element density distributions of the to-be-tested substance according to a plurality of element masses corresponding to each of the plurality of to-be-tested objects.

[0013] According to another aspect of the present disclosure, a determination apparatus for an element density distribution of a substance is provided, including: an obtaining module configured to obtain sample data of a sample material, the sample material being consistent with a material type of a to-be-tested substance, the sample data describing an element density distribution of the sample material; a first determining module configured to determine a distribution function of each of a plurality of elements based on a distribution feature of the plurality of elements in the sample material, the distribution function including a free parameter; a second determining module configured to determine a target value as a value of the free parameter from a plurality of preset values based on an association between the sample data and the plurality of preset values; and determine the element density distribution of the to-be-tested substance by using the distribution function with the target value as the free parameter.

[0014] In another aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory connected with the at least one processor in communication; 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 perform the method for determining the density distribution of a substance element provided by the present disclosure.

[0015] According to another aspect of the embodiments of the present disclosure, a non-transitory computer readable storage medium having computer instructions stored therein is provided, wherein the computer instructions are used to make a computer perform the method for determining the density distribution of a substance element provided by the present disclosure.

[0016] It should be understood that the contents described in this part are not intended to identify the key or important features of the embodiments of the present disclosure, nor are they used to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0017] The accompanying drawings are used to better understand the present scheme, and do not constitute a limitation on the present disclosure. Among them:

[0018] Figure 1 is a flowchart of the method for determining the density distribution of a substance element according to the embodiments of the present disclosure;

[0019] Figure 2 is a scene principle diagram of the method for determining the density distribution of a substance element according to the embodiments of the present disclosure;

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

[0021] Figure 4 is a schematic block diagram of an example electronic device used to implement the embodiments of the present disclosure. DETAILED DESCRIPTION

[0022] Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, which include various details of the embodiments of the present disclosure to help understanding, and should be considered as merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Also, in order to be clear and concise, the description below omits the description of well-known functions and structures.

[0023] The terms used herein are merely used to describe specific embodiments, and are not intended to limit the present disclosure. The terms "include", "contain" and the like used herein indicate the existence of the 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 solutions of the present disclosure, the collection, storage, use, processing, transmission, provision, disclosure and application of data (such as, but not limited to, user personal information) comply with relevant laws and regulations, necessary security measures are taken, and the public order and good customs are not violated.

[0025] In the technical solutions of the present disclosure, “test” in “test characteristic parameter” and “sample” in “sample characteristic parameter” are only to distinguish the two kinds of “characteristic parameters”. For example, the “test characteristic parameter” is obtained by testing the to-be-tested substance in the process of implementing 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 will not be described again.

[0026] Figure 1 FIG. 1 is a flowchart of a method for determining the density distribution of a substance element according to an embodiment of the present disclosure.

[0027] As shown in FIG. 1, the method 100 for determining the density distribution of a substance element according to the embodiment of the present disclosure can include operations S110-S140. Figure 1

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

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

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

[0031] For example, if the effective atomic number Z eff is greater than 8.2, it is determined that the material type of the to-be-tested substance is bone tissue. If the effective atomic number Z eff is less than or equal to 8.2, it is determined that the material type of the to-be-tested substance is soft tissue. For example, the to-be-tested substance can be animal tissue. The bone tissue can be bone and the like, and the bone tissue has a large amount of calcium elements and phosphorus elements, and a certain amount of carbon elements, oxygen elements, hydrogen elements and nitrogen elements. The soft tissue includes a large amount of fat and protein and the like, and the soft tissue has a large amount of carbon elements, oxygen elements, hydrogen elements and nitrogen elements, and a small amount of calcium elements and phosphorus elements. The content of the calcium elements and the phosphorus elements in the soft tissue is usually less than 0.2%, and therefore the calcium elements and the phosphorus elements included in the soft tissue can be ignored.

[0032] ​Effective atomic number Z eff The effective atomic number Z eff The effective atomic number Z eff of the to-be-measured substance can be determined through a CT image.

[0033] The dual-energy CT image is formed based on two kinds of ray sources, and the dual-energy CT image includes two energy components different from each other. The HU values of the same substance under the irradiation of the two kinds of ray sources can be the same or different. When a plurality of to-be-measured substances are scanned based on a single ray source, the two to-be-measured substances have different element components but the same HU values under the energy spectrum. In this case, the two to-be-measured substances cannot be further distinguished in the CT image, which further leads to deviation of the diagnosis result. Therefore, by using the dual-energy CT, the two CT images of the two to-be-measured substances can be compared. When the HU values of the two to-be-measured substances under a certain energy spectrum are the same, the HU values under another energy spectrum are often different, so that the two to-be-measured substances can be further distinguished. In addition, according to the distribution of the HU values in the dual-energy CT image, the effective atomic number of the to-be-measured substance can be determined.

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

[0035] In the embodiments of the present disclosure, the sample data of the sample material can be obtained in advance. The sample data can include the effective atomic number Z eff of the sample material and the mass proportion of a plurality of elements in the sample material. Based on the effective atomic number Z eff of the sample material and the effective atomic number of the to-be-measured substance, it is determined whether the material type of the to-be-measured substance is consistent with that of the sample material.

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

[0037] In operation S120, based on the distribution characteristics of the plurality of elements in the sample material, a distribution function of each of the plurality of elements is determined.

[0038] In the embodiments of the present disclosure, the distribution characteristic represents a change rule of an element mass fraction of the sample material with respect to a change of the characteristic parameter of the sample material. The element mass fraction can be a 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 can be a plurality of sample materials of the same material type as the substance to be measured. Based on the sample data of each of the plurality of sample materials, a rule of change of the element mass fraction in the material of the material type with respect to the change of the characteristic parameter is determined.

[0040] For example, the characteristic parameter can further include a relative electron density ρ e The relative electron density ρ e distribution and the effective atomic number Z eff distribution of the substance to be measured can be determined based on the dual-energy CT image. The relative electron density ρ e distribution of the substance to be measured represents the distribution of the relative electron density ρ e inside the substance to be measured, and the effective atomic number Z eff distribution represents the distribution of the effective atomic number Z eff inside the substance to be measured. Based on the relative electron density ρ e of each of the plurality of objects to be measured, a three-dimensional matrix of the relative electron density ρ e can be formed.

[0041] According to the element composition, the element mass fraction, the relative electron density ρ e and the effective atomic number Z eff of each object in each sample material, a rule of change of the mass fraction of each element with respect to the relative electron density ρ e and the effective atomic number Z eff is determined. The sum of the carbon element mass fraction, the oxygen element mass fraction, the hydrogen element mass fraction, the nitrogen element mass fraction, the calcium element mass fraction and the phosphorus element mass fraction 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 change rule of the element mass fraction with respect to the change of the characteristic parameter. The distribution function includes a free parameter, for example, the free parameter can be a coefficient in the distribution function. By adjusting the value of the free parameter, the accuracy of the distribution function in describing the change rule of the element can be adjusted.

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

[0044] In the embodiments of the present disclosure, the plurality of preset values can be a plurality of alternative values of the free parameter. The sample data includes an element mass proportion of the sample material, a relative electron density p e and an effective atomic number Z eff . Based on the distribution function, the variation law between the element mass proportion, the relative electron density p e distribution and the effective atomic number Z eff can be described. The accuracy of the distribution function with different preset values as the value of the free parameter in describing the variation law can be different. The correlation relationship can be the accuracy of the plurality of preset values in respectively describing the variation law between the element mass proportion, the relative electron density p e and the effective atomic number Z eff .

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

[0046] Among the plurality of preset values, the accuracy of the variation law between the element mass proportion, the relative electron density p e distribution and the effective atomic number Z eff described by the distribution function with the target value as the free parameter is the highest.

[0047] In the embodiments of the present disclosure, based on the relative electron density p e , the effective atomic number Z eff of the to-be-tested substance and the distribution function with the target value as the free parameter, the mass proportion of each element in the to-be-tested substance can be determined, and thus the element distribution in the to-be-tested substance can be determined.

[0048] For example, the to-be-tested substance is a bone tissue. Based on the distribution function for describing the distribution of carbon elements in the bone tissue and the relative electron density p e and the effective atomic number Z eff of the to-be-tested substance, the mass proportion of the carbon elements in the to-be-tested substance is determined.

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

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

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

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

[0053] In combination with Figure 2 the scenario shown in the figure. Figure 2 is a schematic diagram of the scenario of the method for determining the element density distribution of a substance according to an embodiment of the present disclosure.

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

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

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

[0057] For example, the to-be-tested substance 200 is divided into 5x3x3 to-be-tested objects. Based on the relative electron density p e and the effective atomic number Z eff of the to-be-tested object 201, the mass proportion of the carbon element inside the to-be-tested object 201 can be determined. For example, based on the mass proportion of the carbon element of the plurality of to-be-tested objects, a 5x3x3 three-dimensional matrix can be obtained, which can represent the distribution of the carbon element inside the to-be-tested substance 200. According to the mass proportion of the element and the volume of the to-be-tested object, the carbon element density distribution of the to-be-tested substance 200 can be calculated.

[0058] According to the embodiments of the present disclosure, the element distribution of the to-be-tested substance is determined based on the element mass of the plurality of to-be-tested objects of the to-be-tested substance. By adjusting the granularity of dividing the to-be-tested substance into the to-be-tested objects, the accuracy of the element distribution can be optimized. In addition, considering the material types of the to-be-tested objects, the distribution characteristics of each element in the bone tissue and the soft tissue are fully utilized, and the mass proportion of each element in the to-be-tested objects of each material type is calculated respectively, so as to improve the reliability of the calculation result.

[0059] In some embodiments, the accuracy of the distribution function is related to the sample data. A sample database is constructed based on the sample data, which can improve the richness of the sample data and thus improve the accuracy of the distribution function.

[0060] In the embodiments of the present disclosure, the 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 p e , the effective atomic number Z eff , and the mass proportion of a plurality of elements of the sample material. For example, the reference data can be obtained directly or obtained by pre-test. For example, the reference data of a plurality of animal tissue materials can be obtained from an open-source tissue material library, and the reference data includes the total density p, the relative electron density p e , the effective atomic number Z eff , and the mass proportion of H, C, N, O, P, and Ca of each animal tissue material.

[0061] For example, a sample database can be constructed based on the reference data of a plurality of sample materials.

[0062] For example, it is assumed that the relative electron density p e and the effective atomic number Z eff of the sample material are subject to Gaussian distribution. For a sample material, the true relative electron density p e and the true effective atomic number Z eff of the sample material are taken as the central values, and 10% of the central values are taken as the standard deviation, and 100 groups of relative electron density p e and effective atomic number Z eff are randomly generated. For each group of relative electron density p e and effective atomic number Z eff , 100 groups of mass proportions of a plurality of elements are randomly generated.

[0063] A group of relative electron density p e and effective atomic number Z effThe mass proportions of the plurality of elements can constitute a set of sample data. Based on the reference data of each sample material, 10000 sets of sample data can be generated. The standard deviation of the Gaussian distribution of the difference between the plurality of sets of sample data and the reference data is determined. For example, the smaller the standard deviation, the smaller the difference between the plurality of sets of sample data and the reference data.

[0064] In the embodiments of the present disclosure, the standard deviation can be determined based on actual data requirements, for example, 10% of the central value or 20% of the central value. In the process of constructing the sample database, the uncertainty of the element density proportion and the calculation of the relative electron density p e and the effective atomic number Z eff The uncertainty that may be generated in the process is fully considered, and a sufficient number of sample data are generated, thereby improving the accuracy of the values of the free parameters.

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

[0066] For example, the element correlation can represent the law of the mass proportion of the element changing with the relative electron density p e and the effective atomic number Z eff The element correlation can also represent the law of the mass proportion of one element changing with the mass proportion of another element.

[0067] In some embodiments, for the bone tissue material, by analyzing and fitting the relationship between the carbon element mass proportion, the oxygen element mass proportion, the relative electron density p e and the effective atomic number Z eff of the sample material, it can be determined that the sum of the mass proportions of the oxygen element and the carbon element is negatively correlated with the relative electron density p e and the effective atomic number Z eff The sum of the mass proportions of the oxygen element and the carbon element decreases with the increase of the relative electron density p e The sum of the mass proportions of the oxygen element and the carbon element decreases with the increase of the effective atomic number Z eff .

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

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

[0070] W C+O = (W C+O,r + W C+O,Z ) / 2 (1)

[0071] wherein W C+O represents the sum of the mass ratio of oxygen elements and the mass ratio of carbon elements, W C+O,r = f C+O (ρ e ) represents the change law between the sum of the mass ratio of oxygen elements and the mass ratio of carbon elements and the relative electron density ρ e , and W C+O,Z = f C+O (Z eff ) represents the change law between the sum of the mass ratio of oxygen elements and the mass ratio of carbon elements and the effective atomic number Z eff . f C+O (x) can be a second-order polynomial or a third-order polynomial, and x is inversely proportional to f C+O (x). The sum of the mass ratio of carbon elements and the mass ratio of oxygen elements can be the average of W C+O,r and W C+O,Z .

[0072] In the embodiments of the present disclosure, the carbon mass ratio function is constructed based on the arctangent function and the sample element mass of carbon elements.

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

[0074] For example, the carbon mass ratio function for bone tissue can be 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] wherein W C is the mass proportion 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 are free parameters of carbon element, and m and n are adjustment coefficients.

[0077] In the embodiments of the present disclosure, the formula (2) is fitted by the known sample mass proportion of carbon element, the sample relative electron density and the sample effective atomic number of the plurality of sample materials, and the values of a C , b C , c C , d C , m and n are calculated. The value of W C is limited in the range greater than 0 and less than 1 by adjusting the values of m and n.

[0078] In the embodiments of the present disclosure, since the value range of arctangent is -π / 2~π / 2, the value of the mass proportion of carbon element W C can be limited in a certain specific range based on arctangent, so as to avoid the mass proportion of carbon element W C being too high or too low, thereby improving the calculation accuracy. For example, since there is a certain error in the values of the relative electron density ρ e and the effective atomic number Z eff determined based on the dual-energy CT image, the error may be increased when the element mass proportion is further calculated based on the relative electron density ρ e and the effective atomic number Z eff , resulting in the sum of the mass proportions of the plurality of elements being greater than 100% or less than 0.

[0079] Through the embodiments of the present disclosure, the range of the mass proportion is constrained based on the arctangent function, so as to constrain the range of the sum of the mass proportions of the plurality of elements in a reasonable range, avoid the sum of the mass proportions of the plurality of elements being greater than 100% or less than 0, and improve the accuracy.

[0080] In the embodiments of the present disclosure, the oxygen mass proportion function is constructed according to the carbon-oxygen mass proportion function and the carbon mass proportion function. Since there is a negative correlation between the sum of the mass proportions of the carbon element and the oxygen element and the characteristic parameter, in the case of determining the mass proportion of the carbon element, the mass proportion of the oxygen element can be determined according to the sum of the mass proportions of the carbon element and the oxygen element W C+O and the mass proportion of the carbon element W CThe difference value is used to determine the mass proportion of oxygen element W O .

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

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

[0083] In some embodiments, based on the dual-energy CT image of the to-be-tested substance, the effective atomic number Z eff and the relative electron density p e of each to-be-tested object in the to-be-tested substance can be determined.

[0084] According to the carbon-oxygen mass proportion function, the effective atomic number Z eff and the relative electron density p e of each to-be-tested object, the sum of the element mass of oxygen element and the element mass of carbon element of each to-be-tested object can be determined. According to the carbon mass proportion function, the effective atomic number Z eff and the relative electron density p e of each to-be-tested object, the element mass of carbon element of each to-be-tested object can be determined. According to the sum of the element mass of carbon element and the element mass of oxygen element and the element mass of carbon element, the element mass of oxygen element of each to-be-tested object can be determined.

[0085] For example, by substituting the effective atomic number Z eff and the relative electron density p e of each to-be-tested object into formula (1) and formula (2), the sum of the mass proportion of oxygen element and the mass proportion of carbon element W C+O and the mass proportion of carbon element W C of each to-be-tested object can be obtained. By substituting the sum of the mass proportion of oxygen element and the mass proportion of carbon element W C+O and the mass proportion of carbon element W C into formula (3), the mass proportion of oxygen element W O can be determined.

[0086] In some embodiments, a carbon mass fraction three-dimensional matrix is constructed according to the mass fraction of carbon element of each of the plurality of objects to be measured and the position of each of the plurality of objects to be measured in the substance to be measured. The carbon mass fraction three-dimensional matrix can represent the mass distribution of carbon element in the substance to be measured. According to the mass fraction of carbon element of each of the plurality of objects to be measured and the volume and mass of each of the plurality of objects to be measured, the carbon element density of each of the plurality of objects to be measured can be determined. According to the carbon element density of each of the plurality of objects to be measured and the position of each of the plurality of objects to be measured in the substance to be measured, a carbon element density three-dimensional matrix is constructed. The carbon element density three-dimensional matrix can represent the density distribution of carbon element in the substance to be measured.

[0087] For example, an oxygen mass fraction three-dimensional matrix is constructed according to the mass fraction of oxygen element of each of the plurality of objects to be measured and the position of each of the plurality of objects to be measured in the substance to be measured. The oxygen mass fraction three-dimensional matrix can represent the mass distribution of oxygen element in the substance to be measured. According to the mass fraction of oxygen element of each of the plurality of objects to be measured and the volume and mass of each of the plurality of objects to be measured, the oxygen element density of each of the plurality of objects to be measured can be determined. According to the oxygen element density of each of the plurality of objects to be measured and the position of each of the plurality of objects to be measured in the substance to be measured, an oxygen element density three-dimensional matrix is constructed. The oxygen element density three-dimensional matrix can represent the density distribution of oxygen element in the substance to be measured.

[0088] In some embodiments, for bone tissue material, by analyzing the relationship between the mass fraction of calcium element, the relative electron density p e and the effective atomic number Z eff in the sample data, it can be determined that the mass fraction of calcium element is positively correlated with the relative electron density p e and the effective atomic number Z eff . The mass fraction of calcium element increases with the increase of the relative electron density p e , and the mass fraction of calcium element increases with the increase of the effective atomic number Z eff .

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

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

[0091] W Ca = (W Ca,r + W Ca ) / 2 (4)

[0092] wherein W e represents the mass fraction of calcium element, WCa,r =f Ca (ρ e ) represents the change rule between the mass proportion of calcium element and the relative electron density p e , W Ca,Z =f Ca (Z eff ) represents the change rule between the mass proportion of calcium element and the effective atomic number Z eff , f Ca (x) can be a second-order polynomial or a third-order polynomial, and x is proportional to f Ca (x). The mass proportion of calcium element can be the average of W Ca,r and W Ca,Z .

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

[0094] For example, the phosphorus mass proportion function of bone tissue can be formula (5):

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

[0096] Wherein, W P represents the mass proportion of phosphorus element, W P,r =f P (ρ e ) represents the change rule between the mass proportion of phosphorus element and the relative electron density p e , W P,Z =f P (Z eff ) represents the change rule between the mass proportion of phosphorus element and the effective atomic number Z eff , f P (x) can be a second-order polynomial or a third-order polynomial, and x is proportional to f P (x). The mass proportion of phosphorus element can be the average of W P,r and W P,Z .

[0097] In the embodiments of the present disclosure, the calculation method of hydrogen element mass proportion is the same as that of carbon element, and under the constraint of arctangent function, the hydrogen mass proportion function is constructed according to the sample element mass of hydrogen element.

[0098] For example, the hydrogen mass proportion function of soft tissue can be formula (6):

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

[0100] wherein, W H is the mass proportion of hydrogen element, p e is the relative electron density, Z eff is the effective atomic number, a H , b H , c H and d H are free parameters of hydrogen element, and m and n are adjustment coefficients.

[0101] In the embodiments of the present disclosure, the formula (6) is fitted by the known sample mass proportion of hydrogen element, sample relative electron density and sample effective atomic number of the plurality of sample materials, to calculate the values of a H , b H , c H , d H , m and n. The values of m and n are adjusted to limit the value of W H within a reasonable range.

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

[0103] For example, the nitrogen mass proportion function of bone tissue can be formula (7):

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

[0105] wherein, W P represents the mass proportion of phosphorus element.

[0106] In the embodiments of the present disclosure, based on the formula (1) to formula (7), the mass proportions of calcium element, phosphorus element, carbon element, oxygen element, hydrogen element and nitrogen element in the bone tissue material can be calculated, so as to determine the density distribution of calcium element, phosphorus element, carbon element, oxygen element, hydrogen element and nitrogen element in the measured substance.

[0107] In some embodiments, for the soft tissue material, the mass ratio of oxygen element and carbon element are negatively correlated with each other. The mass ratio of carbon element decreases with the increase of oxygen element, and thus the oxygen mass ratio function for soft tissue can be constructed.

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

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

[0110] wherein W O represents the mass ratio of oxygen element, f(x) can be a second-order polynomial or a third-order polynomial, and x and f(x) are negatively correlated. The mass ratio of phosphorus element can be W P,r and the average value of W P,Z

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

[0112] Since the content of phosphorus element and calcium element in soft tissue is extremely small and can be ignored, the nitrogen mass ratio function for soft tissue can be formula (9):

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

[0114] In the embodiments of the present disclosure, based on formula (2), formula (6), formula (8) and formula (9), the mass ratios of carbon element, oxygen element, hydrogen element and nitrogen element in soft tissue material can be calculated, and the density distribution of carbon element, oxygen element, hydrogen element and nitrogen element in the measured substance can be determined.

[0115] Through the embodiments of the present disclosure, based on the relatively simple function parameterization method, the algorithm is easy to implement and saves computing resources. The characteristics of the element ratio in bone tissue and soft tissue are fully utilized to determine the mass ratio of each element, and the reliability of the determination method is improved. In addition, the arctan function is used to fit and determine the element ratio, 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 embodiments of the present disclosure, based on the correlation between the sample data of the bone tissue material in the plurality of sets of sample data and the plurality of preset values, the value of the free parameter in the plurality of distribution functions of the bone tissue material is determined. Based on the correlation between the sample data of the soft tissue material in the plurality of sets of sample data and the plurality of preset values, the value of the free parameter in the plurality of distribution functions of the soft tissue material is determined.

[0118] For example, a plurality of test data are generated by using the distribution function with the plurality of preset values as the values of the free parameters respectively; a first similarity between each of the plurality of test data and the sample data is determined; a second similarity between the plurality of test data and the sample data is determined; and based on the plurality of first similarities and the second similarity, a target value is determined from the plurality of preset values as the value of the free parameter.

[0119] In the embodiments of the present disclosure, the test data can be calculated based on the preset value and the distribution function. The sample data can be known real data. Based on the difference between the test data and the real data, the first similarity of the test data and the sample data with respect to the preset value is determined.

[0120] For example, the smaller the difference between the test data and the real data, the greater the first similarity of the test data and the sample data with respect to the preset value. By separately comparing the plurality of test data with the real data, the first similarity under the constraint of each preset value 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 of the preset value as the target value.

[0121] By jointly comparing the plurality of test data with the real data, the second similarity under the constraint of the plurality of preset values can be determined, thereby reflecting the overall accuracy of the plurality of preset values. Based on the second similarity and the first similarity, the target value can be determined, which can avoid errors caused by single comparison and eliminate extremely high first similarity or extremely low first similarity due to particularity or uncertainty.

[0122] In some embodiments, the sample data can include N first data and N second data corresponding to the N first data, the N first data and the corresponding second data satisfying a quotient of a distribution function, N being a positive integer.

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

[0124] For example, the first data can be an effective atomic number Z eff and a relative electron density p e of a sample object in a sample material, and the second data is an element mass proportion of the sample object. The test data can be an element mass proportion calculated based on a preset value, a distribution function, an effective atomic number Z eff and a relative electron density p e . For example, a set of preset values are taken as values of free parameters in a distribution function of the element mass proportion of carbon described by formula (2), and an effective atomic number Z eff and a relative electron density p e代入 are taken as formula (2), and the element mass proportion of carbon calculated is the test data.

[0125] In the plurality of groups of sample data, an effective atomic number Z eff and a relative electron density p e correspond to a true data of an element mass proportion of carbon in each group. Based on a difference between the test data and the true data, a first similarity of the test data and the sample data with respect to the set of preset values is determined.

[0126] For example, the plurality of preset values can include a plurality of sets of preset values. For formula (2), each set of preset values includes values of a C , b C , c C and d C .

[0127] For example, based on the plurality of sets of preset values, a plurality of test data of the element mass proportion of carbon is calculated. By comparing each test data with the true data respectively, a first similarity under each set of preset values can be determined, so as to reflect an 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 of the set of preset values as a target value.

[0128] In some embodiments, determining the target value as the value of the free parameter from the plurality of preset values based on the plurality of first similarities and the second similarity includes: determining a first probability function of the plurality of preset values, the first probability function indicating an initial probability of the plurality of preset values being the target value; determining 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 a real probability of the plurality of preset values being the target value under the constraint of the sample data; and determining the target value as the value of the free parameter from the plurality of preset values based on the second probability function.

[0129] In embodiments of the present disclosure, the initial probability can satisfy a Gaussian distribution, the initial probability can be a probability of each preset value being the target value without conditional constraint, and the initial probability can be a relatively wide probability distribution. For example, the value of the free parameter in the distribution function can be assumed to follow a Gaussian distribution with a center value of 0 and a standard deviation of 100.

[0130] In embodiments 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, W Ca,r =f Ca (ρ e ) in the distribution function of the calcium element in the bone tissue described in formula (4). W Ca,r =f Ca (ρ e ) can be a cubic polynomial, and thus W Ca,r =f Ca (ρ e ) can be represented as formula (10):

[0132] (10)

[0133] where θ is a free parameter in W =f (ρ ), and the value range of θ is the plurality of preset values. The error represents an error term or the influence of a variable of random sampling noise, and the error

[0134] follows a Gaussian distribution.

[0135] (11)

[0136] where s is the first similarity, and ρ is the i th relative electron density in the sample data. Let f be the mass percentage of the i-th element, and f be the distribution function. The standard deviation is denoted as .

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

[0138]

[0139] in, First similarity, D represents the sample data. Let i be the i-th relative electron density in the sample data. The mass percentage of the i-th calcium element Let be the distribution function of the mass percentage of calcium with respect to relative electron density, N represent the number of bone tissue materials in the sample database, and i range from 1 to N.

[0140] The value is calculated based on preset values ​​and a distribution function to obtain the test data. Taking a third-order polynomial as an example, It can be represented as:

[0141]

[0142] In this embodiment of the disclosure, the first similarity corresponds to a probability distribution function that, given a preset value for the free parameter, the similarity is based on known feature parameters (relative electron density ρ) in the sample data. e and effective atomic number Z eff The probability of the corresponding element's quality percentage in the sample data is calculated. The probability can be reflected by the difference between the real data and the test data.

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

[0144] (12)

[0145] in, The second probability function, Let be the first probability function. This is the probability distribution function obtained from the first similarity transformation. This is the probability distribution function obtained from the second similarity transformation. This means that, given all preset values ​​for the free parameter, the known characteristic parameters (relative electron density ρ) in the sample data are used. e and effective atomic number Z eff The probability of the mass percentage of the corresponding element in the sample data is calculated. The value can also be the second probability function. The normalization constant.

[0146] Second probability function This means that, assuming the elemental mass percentage is the true value in the sample data, based on known characteristic parameters (relative electron density ρ) in the sample data... e and effective atomic number Z eff ) Calculate the probability that the free parameter value is the preset value.

[0147] by Let's take an example.

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

[0149] Second probability function Indicates: Substitute the known relative electron density from the sample data into... Substitute the element mass proportions from the sample data into In this case, the values ​​of the free parameters θ0, θ1, θ2, and θ3 are the probabilities of being preset values.

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

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

[0152] When 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 embodiments of the present disclosure, in the case of determining the value of the free parameter, the effective atomic number Z of the to-be-tested substance is determined eff and the relative electron density p e代入 In the distribution function, the element mass distribution of the to-be-tested substance is determined, so as to determine the element density distribution of the to-be-tested substance.

[0154] Based on the method for determining the element density distribution of a substance provided in the present disclosure, the present disclosure further provides a device for determining the element density distribution of a substance, which will be described in detail below. Figure 3 The device will be described in detail.

[0155] Figure 3 is a structural block diagram of the device for determining the element density distribution of a substance according to the embodiments of the present disclosure.

[0156] As Figure 3 shown, the device 300 for determining the element density distribution of a substance according to the embodiments of the present disclosure can 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 configured to acquire sample data of a sample material, the sample material being consistent with the material type of the to-be-tested substance, and the sample data describing the element density distribution of the sample material. In an embodiment, the acquisition module 310 can be configured to perform the operation S110 described above, and thus no further description is given herein.

[0158] The first determination module 320 is configured to determine a distribution function of each of a plurality of elements in the sample material based on the distribution characteristics of the plurality of elements, the distribution function including a free parameter. In an embodiment, the first determination module 320 can be configured to perform the operation S120 described above, and thus no further description is given herein.

[0159] The second determination module 330 is configured to determine a target value as the value of the free parameter from a plurality of preset values based on the association relationship between the sample data and the plurality of preset values. In an embodiment, the second determination module 330 can be configured to perform the operation S130 described above, and thus no further description is given herein.

[0160] The third determination module 340 is configured to determine the element density distribution of the to-be-tested substance by using the distribution function with the target value as the free parameter. In an embodiment, the third determination module 340 can be configured to perform the operation S140 described above, and thus no further description is given herein.

[0161] It should be noted that, in the technical solutions of the present disclosure, the collection, storage, use, processing, transmission, provision, disclosure, and application of user personal information all comply with relevant laws and regulations, necessary security measures are taken, and the public order and good customs are not violated. In the technical solutions of the present disclosure, the authorization or consent of the user is obtained before the user's personal information is acquired or collected.

[0162] According to embodiments 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 methods of embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present disclosure described and / or claimed in this document.

[0164] As shown in Figure 4 The electronic device 400 includes a computing unit 401 that can perform various appropriate actions and processes in accordance with 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. Various programs and data required for the operation of the electronic device 400 can also be stored in the RAM 403. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other through a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0165] Various 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, a speaker, etc.; the storage unit 408, such as a magnetic 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 can be various general and / or special purpose 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 specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 401 performs various methods and processes described above, such as the determination method of the density distribution of the material element. For example, in some embodiments, the determination method of the density distribution of the material element can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 408. In some embodiments, part or all of the computer program can 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 determination method of the density distribution of the material element described above can be performed. Alternatively, in other embodiments, the computing unit 401 can be configured to perform the determination method of the density distribution of the material element by any other appropriate means, such as by means of firmware.

[0167] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a complex programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0168] Program code for carrying out methods of the present disclosure can be written in any combination of one or more programming languages. The program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, produces a means for implementing the functions / acts specified in the flowcharts and / or block diagrams. The program code can be executed entirely on a machine, partially on a machine, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0169] In the context of this disclosure, a machine-readable medium can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include but is not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium will include one or more lines of electrical connections, portable computer disks, hard disk drives, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), optical fibers, portable compact disc read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0170] To provide for interaction with a user, the systems and techniques described here 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 a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, 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, speech, or tactile input.

[0171] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can 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] The computer system can include clients and servers. This relationship can be. The servers can be cloud servers, also known as cloud computing servers or cloud hosts, which are mainframe products in the cloud computing service system to solve the defects of large management difficulty and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or simply "VPS"). The servers can also be servers of a distributed system, or servers combined with a blockchain.

[0173] It should be understood that the various forms of flow shown above can be reordered, steps added or deleted. For example, the steps described in the present disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions of the present disclosure can be achieved, which are not limited herein.

[0174] The above detailed description does not constitute a limitation on the protection scope of the present disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for determining the elemental density distribution of a substance, comprising: Acquire sample data of a sample material, wherein the sample material is of the same type as the substance to be tested, and the sample data describes the elemental density distribution of the sample material; Based on the distribution characteristics of multiple elements in the sample material, the distribution functions of each element are determined, and the distribution functions include free parameters; wherein, the values ​​of the free parameters are related to the accuracy of the distribution functions in describing the variation law of the elements; different elements have different distribution characteristics in different types of materials; Based on the correlation between the sample data and multiple preset values, a target value is determined from the multiple preset values ​​as the value of the free parameter; the correlation is used to describe the accuracy of the variation pattern corresponding to each preset value; and The elemental density distribution of the substance to be tested is determined using the distribution function with the target value as a free parameter. The step of determining the elemental density distribution of the test substance using the distribution function with the target value as a free parameter includes: determining the test feature parameters of each of the multiple test objects of the test substance based on the dual-energy CT image of the test substance; determining the elemental mass of each of the multiple test objects in each test object based on the test feature parameters using the distribution function with the target value as a free parameter; and determining the multiple elemental density distribution of the test substance according to the multiple elemental masses corresponding to each of the multiple test objects.

2. The method according to claim 1, wherein, The step of determining the target value as the value of the free parameter from the multiple preset values ​​based on the correlation between the sample data and multiple preset values ​​includes: Using the preset values ​​as the values ​​of the free parameters, and utilizing the distribution function, multiple test data are generated; Determine the first similarity between each of the plurality of test data and the sample data; Determine the second similarity between the plurality of test data and the sample data; and Based on multiple first similarities and second similarities, the target value is determined from the multiple 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 a plurality of first similarities and second similarities includes: A first probability function is used to determine the plurality of preset values, wherein the first probability function indicates the initial probability of the plurality of preset values ​​being the target value; Using the first probability function, the first similarity, and the second similarity, a second probability function is used to determine the plurality of preset values, wherein the second probability function indicates the true probability that the plurality of preset values ​​are the target value under the 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 points and N second data points corresponding to the N first data points. The N first data points and their respective corresponding second data points satisfy the divisors of the distribution function, where N is a positive integer. Determining the first similarity between each of the plurality of test data and the sample data includes: From the plurality of test data, obtain test data related to each of the N first data to obtain N sets of test data; as well as Based on the differences between each of the N sets of test data and the corresponding second data, a first similarity between each of the multiple test data and the sample data is determined.

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

6. The method according to claim 1, wherein, Obtain sample data from multiple sample materials, including: Obtain reference data for the sample material, wherein the reference data describes the characteristics of the sample material; and Within a preset range, the sample data is generated based on reference data, and the difference between the sample data and the reference data lies within the preset range.

7. An apparatus for determining the elemental density distribution of a substance, comprising: The acquisition module is used to acquire sample data of sample materials, wherein the sample materials are of the same material type as the substance to be tested, and the sample data describes the element density distribution of the sample materials. The first determining module is used to determine the distribution function of each of the multiple elements based on the distribution characteristics of the multiple elements in the sample material. The distribution function includes free parameters. The value of the free parameters is related to the accuracy of the distribution function in describing the variation law of the elements. Different elements have different distribution characteristics in different types of materials. The second determining module is used to determine a target value as the value of the free parameter from the multiple preset values ​​based on the correlation between the sample data and multiple preset values; the correlation is used to describe the accuracy of the change pattern corresponding to each preset value; and The third determining module is used to determine the elemental density distribution of the substance to be tested using the distribution function with the target value as a free parameter. The step of determining the elemental density distribution of the test substance using the distribution function with the target value as a free parameter includes: determining the test feature parameters of each of the multiple test objects of the test substance based on the dual-energy CT image of the test substance; determining the elemental mass of each of the multiple test objects in each test object based on the test feature parameters using the distribution function with the target value as a free parameter; and determining the multiple elemental density distribution of the test substance according to the multiple elemental masses corresponding to each of the multiple test objects.

8. 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 to enable the at least one processor to perform the method of any one of claims 1-6.

9. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-6.

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