Model training method, substance element density distribution determination method, device, equipment and medium

Abstract

The invention provides a model training method, 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 model training method comprises the following steps: acquiring respective characteristic parameters of M scanning energy spectrums of computed tomography (CT) equipment; based on the M characteristic parameters and the element density distribution of the N sample materials, M sample CT images of the N sample materials under the M scanning energy spectrums are determined, and M * N sample CT images are obtained; constructing a sample data set based on respective M sample CT images and respective element density distribution of the N sample materials; the initial model is trained through the sample data set, a target model corresponding to the CT equipment is obtained, and the target model is used for determining element density distribution based on a CT image obtained through scanning of the CT equipment.

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 model training method, a method for determining the density distribution of material elements, a device, an electronic device and a storage medium. 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 model training method, a method for determining the density distribution of material elements, an apparatus, an electronic device, a storage medium, and a program product.

[0004] According to one aspect of the present disclosure, a model training method is provided, including: obtaining characteristic parameters of M scanning energy spectra of a computed tomography (CT) device; determining M sample CT images of N sample materials under the M scanning energy spectra based on the M characteristic parameters and the element density distribution of N sample materials to obtain M*N sample CT images; constructing a sample data set based on the M sample CT images of the N sample materials and their respective element density distributions; and training an initial model using the sample data set to obtain a target model corresponding to the CT device, the target model being used to determine the element density distribution based on the CT image obtained by scanning the CT device.

[0005] According to another aspect of the present disclosure, a method for determining the element density distribution of a substance is provided, comprising: using M scanning energy spectra of a CT device to respectively scan the substance to be tested to obtain M CT images; and using an element analysis model to determine the element density distribution of the substance to be tested based on the M CT images; wherein the element analysis model is a target model trained using a model training method as provided in an embodiment of the present disclosure.

[0006] According to another aspect of the present disclosure, a model training device is provided, including: an acquisition module, used to acquire characteristic parameters of each of M scanning energy spectra of a computer tomography (CT) device; a first determination module, used to determine, based on the M characteristic parameters and the element density distribution of the N sample materials, M sample CT images of each of the N sample materials under the M scanning energy spectra to obtain M*N sample CT images; a construction module, used to construct a sample data set based on the M sample CT images of each of the N sample materials and the respective element density distribution; and a training module, used to train an initial model using the sample data set to obtain a target model corresponding to the CT device, the target model being used to determine the element density distribution based on the CT image obtained by scanning the CT device.

[0007] According to another aspect of the present disclosure, a device for determining the element density distribution of a material is provided, comprising: a scanning module for scanning the material to be tested using M scanning energy spectra of a CT device to obtain M CT images; and a second determination module for determining the element density distribution of the material to be tested based on the M CT images using an element analysis model; wherein the element analysis model is a target model trained using a model training device as provided in an embodiment of the present disclosure.

[0008] 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 model training method and / or the method for determining the density distribution of material elements provided by the present disclosure.

[0009] 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 model training method and / or the method for determining the density distribution of material elements provided by the present disclosure.

[0010] 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

[0011] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0012] Figure 1 is a flowchart of a model training method according to an embodiment of the present disclosure;

[0013] Figure 2 is a schematic diagram of a scenario of a model training method according to an embodiment of the present disclosure;

[0014] Figure 3 is a schematic diagram of a target model according to an embodiment of the present disclosure;

[0015] Figure 4 is a flow chart of a method for determining the density distribution of material elements according to an embodiment of the present disclosure;

[0016] Figure 5 is a structural block diagram of a model training device according to an embodiment of the present disclosure;

[0017] Figure 6 is a structural block diagram of a device for determining material element density distribution according to an embodiment of the present disclosure; and

[0018] Figure 7 is a schematic block diagram of an example electronic device for implementing an embodiment of the present disclosure. DETAILED DESCRIPTION

[0019] 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.

[0020] 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.

[0021] 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.

[0022] Figure 1 It is a flowchart of the model training method according to an embodiment of the present disclosure.

[0023] like Figure 1 As shown, the model training method 100 of this embodiment may include operations S110 to S140.

[0024] In operation S110, characteristic parameters of M scanning energy spectra of a computed tomography (CT) device are obtained, where M is a positive integer.

[0025] In the embodiment of the present disclosure, the CT device scans the same tissue material based on multiple scanning energy spectra, and the obtained CT images may be different. The CT image includes the distribution of HU values ​​of the tissue material, and the HU value can reflect the interaction between matter and photons.

[0026] Different scanning energy spectra may be formed based on different radiation sources, and the energy components included in the multiple scanning energy spectra may be different. When a CT device scans the same tissue material based on different scanning energy spectra, the HU values ​​may be the same or different.

[0027] For example, a dual-energy CT device has two scanning energy spectra. When multiple tissue materials are scanned based on a single radiation source, the elemental compositions of the multiple tissue materials are different but the HU values ​​under a single energy spectrum may be the same. In this case, the two tissue materials cannot be further distinguished in the CT image, which leads to deviations in the diagnostic results. Therefore, using dual-energy CT, two CT images obtained by scanning two tissue materials based on two energy spectra can be compared. When the HU values ​​of two tissue materials are the same under one energy spectrum, the HU values ​​under another energy spectrum are often different, thereby further distinguishing the two tissue materials.

[0028] In the disclosed embodiment, the characteristic parameter may describe the energy combination of multiple emission sources of the CT device. For example, the emission source may be composed of multiple sub-emission sources with different energies, and the energy range of photons emitted by the multiple sub-emission sources may be within 20-120 keV, and the energy of photons emitted by the multiple sub-emission sources forms a corresponding scanning energy spectrum.

[0029] For example, among the multiple emission sources, some emission sources emit 40keV photons with more energy, some emission sources emit 60keV photons with more energy, and some emission sources emit 80keV photons with more energy.

[0030] The photon energies emitted by multiple emission sources can be determined based on the characteristic parameters of the scanned energy spectrum.

[0031] In operation S120 , based on the M characteristic parameters and the element density distribution of the N sample materials, M sample CT images of the N sample materials under M scanning energy spectra are determined to obtain M*N sample CT images, where N is a positive integer.

[0032] In the embodiment of the present disclosure, the element density distribution of the sample material may be obtained in advance from a database. The element density distribution may describe the types of elements included in the sample material, the density of each element in the sample material, and the distribution of multiple elements in the sample material.

[0033] Based on the characteristic parameters, the composition of the photon energy emitted by the emission source corresponding to the scanned energy spectrum can be determined. Based on the element density distribution of the sample material, the absorption degree of the sample material to photons of different energies can be determined, thereby calculating the sample CT image of the sample material.

[0034] Since different CT devices are based on different scanning energy spectra, the CT images obtained by scanning the same material tissue are different. In addition, the known database only records the relevant parameters of the sample material, and cannot obtain the real tissue with the same characteristics as the sample material. Therefore, it is impossible to use the CT device to scan the real tissue of the sample material to determine the CT image of the sample material obtained based on the CT device scanning. Therefore, the present disclosure simulates the virtual scanning environment of the CT device based on the characteristic parameters, and simulates the virtual tissue of the sample material based on the element density distribution of the sample material. In this case, the virtual scanning environment is calculated to scan the virtual tissue to obtain the sample CT image.

[0035] In the disclosed embodiment, the sample CT image is calculated based on the characteristic parameters of the element density distribution and the scanning energy spectrum. Based on the characteristic parameters, the scanning environment of the CT device can be simulated to determine the photon energy emitted by the emission source. Based on the element density distribution, the element density at different positions in the sample material can be simulated, so as to calculate the absorption capacity of the photon energy at different positions in the sample material, and then calculate the HU value at different positions in the sample material based on the absorption capacity to obtain the sample CT image.

[0036] In operation S130 , a sample data set is constructed based on the respective M sample CT images of the N sample materials and the respective element density distributions.

[0037] In the disclosed embodiment, the sample CT image includes a plurality of HU values, and the element density distribution includes the element density of the region corresponding to each HU value. For example, each sample material includes a plurality of voxels. In the M sample CT images of each sample material, each voxel corresponds to M HU values. It can be understood that the M HU values ​​correspond to the element density of the voxel.

[0038] For example, the voxels described by the M HU values ​​determined under different scanning energy spectra represent the same element density. The sample data set constructed based on the sample CT image and element density includes the corresponding relationship between multiple groups of HU values ​​and element density.

[0039] In operation S140, an initial model is trained using the sample data set to obtain a target model corresponding to the CT device.

[0040] In the disclosed embodiment, the initial model may be a fully connected neural network. The fully connected neural network is trained using a sample data set, and the correlation between the HU value and the element density may be extracted using a fully connected operation, so that the trained target model may determine the element density distribution based on the CT image scanned by the CT device.

[0041] For example, the fully connected neural network can extract the features of the HU value and the features of the element density, and perform a fully connected operation on the extracted features to determine the corresponding relationship between the HU value and the element density.

[0042] In the target model, HU value is input data and element density is output data. Therefore, by inputting the CT image of the material to be tested into the target model, the element density of different areas in the material to be tested can be output, thereby determining the element density distribution.

[0043] Combination Figure 2 The scenario shown is described. Figure 2 It is a scenario principle diagram of the model training method according to an embodiment of the present disclosure.

[0044] like Figure 2 As shown, the sample material 200 can be divided into a plurality of objects. For example, the sample material 200 can be a cubic structure, and the sample material 200 is evenly divided into a plurality of objects, each of which is also a cubic structure.

[0045] In the embodiment of the present disclosure, each object may be a voxel of the sample material 200, and each object may be equivalent to a point inside the sample material 200. According to the element densities of the multiple objects, the element density distribution of the sample material 200 may be determined.

[0046] For example, the sample material 200 is divided into 5×3×3 objects. The element density distribution may include the density distribution of carbon, hydrogen, oxygen, nitrogen, phosphorus, and calcium. For example, the carbon element density distribution of the sample material 200 may be represented by a 5×3×3 three-dimensional matrix.

[0047] Based on the characteristic parameters of the multiple scanning energy spectra and the element density distribution of the sample material 200, the HU values ​​of the 30 objects can be calculated. Based on the M scanning energy spectra, M HU values ​​of each object can be calculated. The M HU values ​​of each object correspond to the same element density.

[0048] For example, based on the M scan energy spectra, M HU values ​​of the object 201 can be calculated. The M HU values ​​of the object 201 are associated with the element density of the object 201. Based on the 30 sets of associated HU values ​​and element densities of the sample material 200, a sample data set can be constructed.

[0049] Through the embodiments of the present disclosure, a sample data set for a CT device can be constructed based on the element density distribution of the sample material and the energy spectrum characteristic parameters of the CT device. The initial model is trained using the data pairs of HU values ​​and element densities included in the sample data set, so that the initial model can learn the correlation between the HU value and the element density to obtain a target model. After learning the correlation between the HU value and the element density under the scanning energy spectrum constraints of the CT device, the target model can output the element density distribution of the substance to be tested corresponding to the CT image obtained by scanning the CT device based on the CT image. In addition, the model training method provided by the present disclosure can be implemented based on a fully connected neural network, which requires fewer hardware resources and computing resources for model training, and can reduce resource overhead.

[0050] In some embodiments, the characteristic parameters of the scanning energy spectrum of the CT device can be determined by a known standard phantom. For example, a reference material is scanned using M scanning energy spectrum distributions of the CT device to obtain M scanning data of the reference material; and the M scanning data are respectively compared with the reference data of the reference material obtained in advance to obtain M characteristic parameters of the M scanning energy spectra.

[0051] In the disclosed embodiment, the reference material may be a known standard phantom. For example, the element density distribution of the reference material, the total material density, and the CT image obtained based on the known scanning energy spectrum are all known.

[0052] For example, the reference material may be a Gammex phantom and a CIRS phantom. The Gammex phantom is scanned by a CT device based on M scanning energy spectra to obtain M CT images of the Gammex phantom as scanning data. The absorption degree of each voxel in the Gammex phantom to a specific photon energy can be determined based on the known CT image, element density distribution and total material density of the Gammex phantom. Based on the absorption degree and the scanning data, the photon energy emitted by the emission source of the CT device is calculated, thereby determining the characteristic energy spectrum of the CT device.

[0053] For example, the absorption capacity of a voxel of a certain material in the Gammex phantom for radiation with a specific energy can be considered to be fixed. Based on the known CT image of the Gammex phantom, the corresponding relationship between the HU value and the photon energy of the energy spectrum is determined. Based on this corresponding relationship, the photon energy of the energy spectrum is calculated based on the HU value in the scan data, so that the characteristic parameters of the M scan energy spectra can be determined based on the M CT images in the scan data.

[0054] In some embodiments, a method for calculating M*N sample CT images obtained by a CT device scanning N sample materials may include: using M characteristic parameters and the element density distribution of the N sample materials to determine the energy absorption capacity of the N sample materials for M scanning energy spectra respectively; and based on the energy absorption capacity, determining M groups of CT values ​​of the N sample materials respectively, the M groups of CT values ​​forming M CT images.

[0055] In the disclosed embodiment, each sample material can be divided into a plurality of voxels, and the absorption capacity of each voxel to photon energy is determined by the density and element distribution of the voxel. For example, if the density of calcium and phosphorus in a voxel is high, the voxel may be bone tissue, which has a strong absorption capacity to photon energy and a correspondingly large HU value. If the density of carbon, oxygen, hydrogen and nitrogen in a voxel is high, the voxel may be soft tissue, which has a weak absorption capacity to photon energy and a correspondingly small HU value.

[0056] Based on the absorption capacity of each voxel in the sample material to photon energy and the corresponding relationship between the HU value and the photon energy of the energy spectrum, the HU value of each voxel in the sample material is calculated, and the CT image of the sample material can be reconstructed based on the HU value.

[0057] Each voxel in the sample material has different absorption capabilities for different photon energies, so different HU values ​​can be calculated for each voxel for the scan energy spectrum described by different characteristic parameters. For M characteristic parameters, M HU values ​​for each voxel can be calculated.

[0058] In some embodiments, the method for constructing a sample data set may include performing the following operations for each sample material among N sample materials: determining M CT values ​​of each of a plurality of sample objects in the sample material based on M sample CT images; obtaining M element densities corresponding to the M CT values ​​based on element density distribution; determining sample data of each of the plurality of sample objects based on the M CT values ​​and the M element densities; and constructing a sample data set for the sample material based on the sample data.

[0059] In the embodiment of the present disclosure, the sample object may be a voxel in the sample material, and the CT value is the HU value of each voxel in the CT image. The HU values ​​of multiple voxels in the sample material may be represented in the form of a three-dimensional matrix, and the element density distribution may also be the element density of each voxel represented in the form of a three-dimensional matrix. The HU value and the element density of the same voxel in the three-dimensional matrix have the same coordinates. In the three-dimensional matrix of HU values ​​of multiple M sample CT images, the M HU values ​​of the same coordinate all correspond to the element density of the coordinate in the three-dimensional matrix of element density distribution.

[0060] The M HU values ​​of the same coordinate and the corresponding element density constitute a data pair about the voxel. The data pair can be used to describe the data of the correlation between the HU value and the element density of the voxel. The data pairs of each voxel in the N sample materials form a sample data set. The sample data set describes the correlation between the HU value and the element density of each voxel.

[0061] In some embodiments, data enhancement can also be performed on the sample data of each sample material to improve the accuracy of the sample data. For example, within a preset range, multiple groups of enhanced element densities are generated based on M element densities, each group of enhanced element densities includes M enhanced element densities, and the difference between the M element densities and each group of enhanced element densities is within a preset range; and based on the M CT values ​​of each of the multiple sample objects and the multiple groups of enhanced element densities, the sample data of each of the multiple sample objects is determined.

[0062] In the embodiment of the present disclosure, the accuracy of the element density distribution output by the target model is related to the quality of the sample data. Enhancing the sample data can improve the richness of the sample data, thereby improving the quality of the target model.

[0063] For example, due to the error in element density and the error and uncertainty in HU value, sample enhancement can be performed on the element density of each voxel in the sample material so that the M HU values ​​of each voxel can correspond to multiple element densities to increase the data pairs used to characterize the correlation between the HU value and the element density of each voxel, so as to reduce the error of the data pairs before data enhancement.

[0064] For example, assuming that the element density of the voxel of the sample material obeys a Gaussian distribution. For a sample material, 100 sets of element density are randomly generated with the element density based on the voxel as the center value and 10% of the center value as the standard deviation, and each set of element density includes the densities of multiple elements. Based on the 100 sets of element density and the M HU values ​​of the voxel, 100 data pairs are constructed, so that the original set of data pairs can be enhanced to 100 data pairs.

[0065] For example, data enhancement can be performed on the overall density of the sample material, and the overall density of the sample material and the CT image can be constructed as a data pair. The data pair of the overall density of the sample material and the CT image can describe the correlation between the overall density of the sample material and the CT image (all HU values), and can also reflect the influence of the correlation between the HU values ​​of adjacent voxels on the overall density.

[0066] For example, for a sample material, 100 population densities are randomly generated with the population density based on the sample material as the center value and 10% of the center value as the standard deviation. Based on the 100 population densities and M sample CT images of the sample material, 100 data pairs are constructed, so that the original set of data pairs can be enhanced to 100 data pairs.

[0067] For example, data enhancement can be performed on the element density distribution of the sample material, and the element density distribution and the CT image can be constructed as a data pair. The element density distribution and the CT image can be constructed as a data pair to describe the correlation between the overall element density distribution of the sample material and the CT image, and can also reflect the influence of the correlation between the element density of adjacent voxels on the HU value.

[0068] For example, for a sample material, 100 element density distributions are randomly generated with the element density distribution based on the sample material as the center value and 10% of the center value as the standard deviation. Based on the 100 element density distributions and M sample CT images of the sample material, 100 data pairs are constructed, so that the original set of data pairs can be enhanced to 100 data pairs.

[0069] In the embodiment of the present disclosure, data enhancement can be performed on the overall density, element density distribution, and element density of a single voxel of the sample material, and a data pair can be constructed based on at least one of the enhanced overall density, the enhanced element density distribution, and the enhanced element density of a single voxel and the HU value of the CT image or the single voxel.

[0070] Based on this, data augmentation operations can expand the amount of data, reduce data errors, and improve the uncertainty in the data, thereby optimizing the correlation between the two data in the data pair, and increasing the robustness of model training against CT image noise and uncertainty in the element density distribution of tissue materials. In addition, constructing data pairs based on overall density and element density distribution can fully consider the correlation and mutual influence between voxels, avoiding the data pairs constructed based on a single voxel ignoring the correlation between data, thereby improving the quality of the target model.

[0071] In some embodiments, using a sample data set to train an initial model to obtain a target model corresponding to the CT device may include: determining a sample CT image in the sample data set as input data of the initial model, and determining an element density distribution in the sample data set as output data of the initial model; using distribution characteristics of multiple elements in the element density distribution as prompt information; and training the initial model based on the prompt information, the input data, and the output data to obtain the target model.

[0072] In the embodiment of the present disclosure, the sample CT image is used as the input data of the model, and the element density of the sample material is used as the output data of the model, so that the model can calculate and output the element density distribution of the material to be tested based on the CT image of the material to be tested.

[0073] For example, M HU values ​​of a single voxel can be used as input data, and the element density of a single voxel can be used as output data, so that the model can calculate and output the element density of each voxel based on the HU value of each voxel in the material to be tested, thereby determining the element density distribution of the material to be tested.

[0074] The initial model can learn the correlation between the HU value and the element density of a single voxel described by the data pair in the sample data set, the influence of the element density between adjacent voxels on the HU value, the influence of the overall element density distribution of the sample material on the HU value of a single voxel, and the influence of the overall density of the sample material on the HU value of a single voxel.

[0075] In the disclosed embodiment, the distribution characteristics of each of the multiple elements in the element density distribution may be the law of how the HU value changes with the element density. For example, for different elements, the trend of how the element density changes with the HU value is different. Based on the distribution characteristics of each element as prompt information, the accuracy of the element density of each element output by the target model based on the HU value can be improved.

[0076] For example, based on the HU value of each voxel in the sample material, the relative electron density ρ of each voxel can be calculated e and effective atomic number Z eff , to determine the relative electron density ρ of multiple voxels e , effective atomic number Z eff and the variation law between element density.

[0077] For example, the voxel-based relative electron density ρ e and effective atomic number Z eff It can be determined whether a voxel belongs to bone tissue or soft tissue. 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.

[0078] For example, for bone tissue, the sum of the mass proportions of oxygen and carbon is respectively related to the relative electron density ρ e and effective atomic number Z eff There is a negative correlation between the mass ratio of calcium and the relative electron density ρ e and effective atomic number Z eff There is a positive correlation between the mass proportion of phosphorus and the relative electron density ρ e and effective atomic number Z eff The mass proportion of carbon and the mass proportion of hydrogen increase with the relative electron density ρ e and effective atomic number Z eff The change of can satisfy the constraints of the inverse tangent function. The mass proportion of nitrogen can be determined by subtracting 100% from the sum of the mass proportions of calcium, phosphorus, carbon, oxygen and hydrogen. The sum of the mass proportions of carbon, oxygen, hydrogen, nitrogen, calcium and phosphorus is greater than 99% and less than 100%.

[0079] For soft tissues, the mass percentages of oxygen and carbon are negatively correlated. The mass percentage of carbon decreases as the oxygen content increases.

[0080] Based on the mass proportions of calcium, phosphorus, carbon, oxygen, hydrogen and nitrogen, the density distribution of calcium, phosphorus, carbon, oxygen, hydrogen and nitrogen can be determined.

[0081] In the embodiment of the present disclosure, based on the mass ratio of each element in the voxel with the relative electron density ρ e and effective atomic number Z eff The initial model learns the law of how the density of each element in the voxel changes with the HU value, and determines the law of how the mass proportion of each element in the voxel changes with the HU value, and then determines the law of how the element density of each element in the voxel changes with the HU value. The initial model learns the law of how the element density changes with the HU value, so that the target model can improve the accuracy of calculating the density of each element in the voxel based on the HU value based on this law of change.

[0082] For example, after the target model calculates the element density of multiple voxels based on the HU values ​​of multiple voxels, the element density of each voxel can be optimized and adjusted based on the change rule described in the prompt information. The target model can also assist in calculating the element density of elements that are difficult to calculate based on the HU value based on the change rule described in the prompt information and the element density that has been calculated based on the HU value.

[0083] Combination Figure 3 The training process is illustrated as follows: Figure 3It is a schematic diagram of the principle of the target model according to an embodiment of the present disclosure.

[0084] In the embodiment of the present disclosure, a CT device is used to scan the material to be tested based on M scanning energy spectra to obtain M CT images. Based on the M CT images, M HU values ​​HU1, HU2, ..., HU of each voxel in the material to be tested are determined. M .

[0085] The input data 301 of the target model 303 is M HU values ​​HU1, HU2, ..., HU M , the output data 302 is the element density ρ of each voxel j , j can be H, C, N, O, P and Ca, representing the element density of hydrogen, carbon, nitrogen, oxygen, phosphorus and calcium, respectively.

[0086] In the embodiment of the present disclosure, the CT device for determining the CT image of the substance to be tested is the same CT device as the CT device for determining the sample CT image of the sample material. The target model 303 is trained based on the sample CT image simulated by the characteristic parameters of the CT device, and the target model 303 also needs to perform element density distribution calculation based on the CT image scanned by the CT device. Therefore, the target model 303 is associated with the CT device. When the CT device is changed, the initial model needs to be retrained to obtain a new target model.

[0087] In the embodiment of the present disclosure, the target model 303 is trained based on M CT images of each sample material. In order to ensure the calculation accuracy of the target model 303, M CT images obtained by scanning M scanning energy spectra based on the CT device are required as input data 301.

[0088] Figure 4 It is a flowchart of a method for determining the density distribution of material elements according to an embodiment of the present disclosure.

[0089] like Figure 4 As shown, the method 400 for determining the material element density distribution of this embodiment may include operations S410 to S420.

[0090] In operation S410, the material to be tested is scanned respectively using M scanning energy spectra of the CT device to obtain M CT images.

[0091] In operation S420, the element density distribution of the substance to be tested is determined based on the M CT images using an element analysis model.

[0092] In the embodiment of the present disclosure, the element analysis model is a target model trained using the model training method 100 provided in the embodiment of the present disclosure.

[0093] For example, the HU values ​​included in the M CT images are output to the element analysis model, and the element analysis model can output the element density distribution of the material to be tested.

[0094] In the embodiment of the present disclosure, the process of determining the element density distribution of the substance to be tested based on M CT images through the element analysis model is similar to the training process described above, and will not be repeated here for the sake of simplicity.

[0095] Based on the model training method provided by the present disclosure, the present disclosure also provides a model training device. Figure 5 The device is described in detail.

[0096] Figure 5 It is a structural block diagram of a model training device according to an embodiment of the present disclosure.

[0097] like Figure 5 As shown, the model training device 500 of this embodiment may include an acquisition module 510, a first determination module 520, a construction module 530 and a training module 540.

[0098] The acquisition module 510 is used to acquire characteristic parameters of each of the M scanning energy spectra of the computed tomography (CT) device. In one embodiment, the acquisition module 510 can be used to perform the operation S110 described above, which will not be described in detail herein.

[0099] The first determination module 520 is used to determine M sample CT images of the N sample materials under M scanning energy spectra based on the M characteristic parameters and the element density distribution of the N sample materials, and obtain M*N sample CT images. In one embodiment, the first determination module 520 is used to perform the operation S120 described above, which will not be repeated here.

[0100] The construction module 530 is used to construct a sample data set based on the M sample CT images and element density distributions of the N sample materials. In one embodiment, the construction module 530 can be used to perform the operation S130 described above, which will not be described in detail here.

[0101] The training module 540 is used to train the initial model using the sample data set to obtain a target model corresponding to the CT device, and the target model is used to determine the element density distribution based on the CT image scanned by the CT device. In one embodiment, the training module 540 can be used to perform the operation S140 described above, which will not be repeated here.

[0102] According to an embodiment of the present disclosure, the acquisition module 510 acquires the characteristic parameters of each of M scanning energy spectra of a computed tomography (CT) device, including: scanning a reference material using the M scanning energy spectrum distribution of the CT device to obtain M scanning data of the reference material; and comparing the M scanning data with the reference data of the reference material acquired in advance to obtain M characteristic parameters of the M scanning energy spectra.

[0103] According to an embodiment of the present disclosure, the construction module 530 is used to construct a sample data set based on M sample CT images and element density distributions of N sample materials, including: for each sample material in the N sample materials, the following operations are performed: based on the M sample CT images, M CT values ​​of multiple sample objects in the sample material are determined; based on the element density distribution, M element densities corresponding to the M CT values ​​are obtained; based on the M CT values ​​and the M element densities, sample data of multiple sample objects are determined; and based on the sample data, a sample data set of the sample material is constructed.

[0104] According to an embodiment of the present disclosure, the construction module 530 is used to determine the sample data of each of the multiple sample objects based on M CT values ​​and element densities, including: within a preset range, generating multiple groups of enhanced element densities based on the M element densities, each group of enhanced element densities including M enhanced element densities, and the difference between the M element densities and each group of enhanced element densities is within a preset range; and determining the sample data of each of the multiple sample objects based on the M CT values ​​of each of the multiple sample objects and the multiple groups of enhanced element densities.

[0105] According to an embodiment of the present disclosure, the first determination module 520 is used to determine M sample CT images of N sample materials under M scanning energy spectra based on M characteristic parameters and the element density distribution of N sample materials to obtain M*N sample CT images, including: using the M characteristic parameters and the element density distribution of N sample materials to determine the energy absorption capacity of the N sample materials for the M scanning energy spectra; and based on the energy absorption capacity, determine M groups of CT values ​​of the N sample materials, and the M groups of CT values ​​form M CT images.

[0106] According to an embodiment of the present disclosure, the training module 540 is used to train an initial model using a sample data set to obtain a target model corresponding to the CT device, and the target model is used to determine the element density distribution based on the CT image obtained by scanning the CT device, including: determining the sample CT image in the sample data set as the input data of the initial model, and determining the element density distribution in the sample data set as the output data of the initial model; using the distribution characteristics of multiple elements in the element density distribution as prompt information; and training the initial model based on the prompt information, the input data and the output data to obtain the target model.

[0107] Figure 6 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.

[0108] like Figure 6 As shown, the device 600 for determining the material element density distribution of this embodiment may include a scanning module 610 and a second determining module 620 .

[0109] The scanning module 610 is configured to respectively scan the material to be tested using M scanning energy spectra of the CT device to obtain M CT images. In one embodiment, the scanning module 610 can be used to perform the operation S410 described above, which will not be described in detail herein.

[0110] The second determination module 620 is configured to determine the element density distribution of the substance to be tested based on the M CT images using the element analysis model. In one embodiment, the second determination module 620 is used to perform the operation S420 described above, which will not be described in detail herein.

[0111] In the embodiment of the present disclosure, the element analysis model is a target model obtained by training using, for example, the model training device 500 provided in the embodiment of the present disclosure.

[0112] 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.

[0113] 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.

[0114] Figure 7 A schematic block diagram of an example electronic device 700 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.

[0115] like Figure 7As shown, the electronic device 700 includes a computing unit 701, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 702 or a computer program loaded from a storage unit 708 into a random access memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the electronic device 700 can also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0116] Multiple components in the electronic device 700 are connected to the I / O interface 705, including: an input unit 706, such as a keyboard, a mouse, etc.; an output unit 707, such as various types of displays, speakers, etc.; a storage unit 708, such as a disk, an optical disk, etc.; and a communication unit 709, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 709 allows the electronic device 700 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0117] The computing unit 701 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 701 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 701 performs the various methods and processes described above, such as a determination method and / or a model training method for material element density distribution. For example, in some embodiments, the determination method and / or the model training method for material element density distribution may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 708. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 700 via the ROM 702 and / or the communication unit 709. When the computer program is loaded into the RAM 703 and executed by the computing unit 701, one or more steps of the determination method and / or the model training method for material element density distribution described above may be performed. Alternatively, in other embodiments, the computing unit 701 may be configured to execute the material element density distribution determination method and / or model training in any other appropriate manner (eg, by means of firmware).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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 model training method, comprising: Obtaining characteristic parameters of M scanning energy spectra of a computed tomography (CT) device, where M is a positive integer; Based on the M characteristic parameters and the element density distribution of the N sample materials, determine M sample CT images of the N sample materials under the M scanning energy spectra to obtain M*N sample CT images, where N is a positive integer; constructing a sample data set based on the M sample CT images of the N sample materials and the element density distribution of each; as well as The sample data set is used to train an initial model to obtain a target model corresponding to the CT device, and the target model is used to determine element density distribution based on a CT image scanned by the CT device.

2. The method according to claim 1, wherein: The step of obtaining characteristic parameters of M scanning energy spectra of a computer tomography (CT) device comprises: Scanning a reference material using the M scanning energy spectrum distributions of the CT device to obtain M scanning data of the reference material; and The M scanning data are respectively compared with the reference data of the reference material acquired in advance to obtain M characteristic parameters of the M scanning energy spectra.

3. The method according to claim 1, wherein: The constructing a sample data set based on the M sample CT images and the element density distributions of the N sample materials respectively includes: For each of the N sample materials, do the following: Based on the M sample CT images, determining M CT values ​​of each of a plurality of sample objects in the sample material; Based on the element density distribution, obtaining M element densities corresponding to the M CT values; Determining sample data of each of the plurality of sample objects based on the M CT values ​​and the M element densities; and Based on the sample data, a sample data set of the sample material is constructed.

4. The method according to claim 3, wherein: The determining, based on the M CT values ​​and the element density, the sample data of each of the plurality of sample objects comprises: generating a plurality of groups of enhancement element densities based on the M element densities within a preset range, each group of enhancement element densities including M enhancement element densities, and a difference between the M element densities and each group of enhancement element densities being within the preset range; and Based on the M CT values ​​of each of the plurality of sample objects and the plurality of sets of enhancement element densities, sample data of each of the plurality of sample objects are determined.

5. The method according to claim 1, wherein: The determining, based on the M characteristic parameters and the element density distribution of the N sample materials, M sample CT images of the N sample materials under the M scanning energy spectra to obtain M*N sample CT images includes: Determining the energy absorption capacity of each of the N sample materials for the M scanning energy spectra by using the M characteristic parameters and the element density distributions of the N sample materials; and Based on the energy absorption capabilities, M groups of CT values ​​are determined for each of the N sample materials, and the M groups of CT values ​​form M CT images.

6. The method according to claim 1, wherein: The using the sample data set to train the initial model to obtain the target model corresponding to the CT device includes: Determining a sample CT image in the sample data set as input data of the initial model, and determining an element density distribution in the sample data set as output data of the initial model; using distribution characteristics of each of the plurality of elements in the element density distribution as prompt information; and Based on the prompt information, the input data and the output data, the initial model is trained to obtain the target model.

7. A method for determining the density distribution of a material element, comprising: Scanning the material to be tested respectively using the M scanning energy spectra of the CT device to obtain M CT images, where M is a positive integer; as well as Determining the element density distribution of the substance to be tested based on the M CT images using an element analysis model; Wherein, the element analysis model is a target model trained using the method described in any one of claims 1-6.

8. A model training device, comprising: An acquisition module, used for acquiring characteristic parameters of M scanning energy spectra of a computed tomography (CT) device, where M is a positive integer; A first determination module is used to determine, based on the M characteristic parameters and the element density distribution of the N sample materials, the M sample CT images of the N sample materials under the M scanning energy spectra to obtain M*N sample CT images, where N is a positive integer; A construction module, configured to construct a sample data set based on the M sample CT images and the element density distributions of the respective N sample materials; as well as A training module is used to train an initial model using the sample data set to obtain a target model corresponding to the CT device, wherein the target model is used to determine element density distribution based on a CT image scanned by the CT device.

9. A device for determining the density distribution of material elements, comprising: A scanning module, used to respectively scan the material to be tested using the M scanning energy spectra of the CT device to obtain M CT images; as well as A second determination module is used to determine the element density distribution of the substance to be tested based on the M CT images using an element analysis model; Wherein, the element analysis model is a target model trained using the device as described in claim 8.

10. 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.

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