Method, apparatus, device, and storage medium for determining particle dose

Abstract

The present disclosure provides a method, apparatus, device, and medium for determining particle dose, which are applied to the fields of radiation medicine and image processing. The method for determining particle dose includes: obtaining a first CT image and a second CT image of a tissue to be measured, where the energy of the first scanning ray corresponding to the first CT image is less than the energy of the second scanning ray corresponding to the second CT image, the tissue to be measured includes a plurality of objects to be measured, and the first CT image includes the first CT values of the plurality of objects to be measured respectively; determining the elemental density of each of the plurality of objects to be measured based on the first CT image and the second CT image; adjusting the first CT value of each of the plurality of objects to be measured based on the number of objects to be measured with the same first CT value in the first CT image and the second CT image to obtain the target CT value of each of the plurality of objects to be measured; simulating the Monte Carlo transport of particles in the tissue to be measured based on the target CT value and elemental density of each of the plurality of objects to be measured to obtain the Monte Carlo dose of the particles.

Description

Technical Field

[0001] The present disclosure relates to the fields of physics and computer science, and more particularly to the application of physics and computer technologies in the field of radiation medicine, and more specifically to a method, apparatus, device, medium, and program product for determining particle dose. Background Art

[0002] Computed Tomography (CT) imaging technology has been widely used in the field of medical diagnosis. Through CT imaging technology and image reconstruction technology, the internal structure of the detected object can be presented.

[0003] In the field of radiotherapy, the elemental density information of substances can be calculated from CT images, and based on the Monte Carlo method, the reaction process of particles in substances and the transport and energy deposition of particles in media can be simulated, so as to determine the radiotherapy effect. Summary of the Invention

[0004] In view of the above problems, the present disclosure provides a method, apparatus, device, medium, and program product for determining particle dose that improves computational efficiency.

[0005] According to a first aspect of the present disclosure, there is provided a method for determining particle dose, including: obtaining a first CT image and a second CT image of a tissue to be measured, where the energy of a first scanning ray corresponding to the first CT image is less than the energy of a second scanning ray corresponding to the second CT image, the tissue to be measured includes a plurality of objects to be measured, and the first CT image includes first CT values of the plurality of objects to be measured respectively; determining the elemental density of each of the plurality of objects to be measured based on the first CT image and the second CT image; adjusting the first CT value of each of the plurality of objects to be measured based on the number of objects to be measured having the same first CT value in the first CT image and the second CT image to obtain a target CT value of each of the plurality of objects to be measured; and simulating Monte Carlo transport of particles in the tissue to be measured based on the target CT values and elemental densities of the plurality of objects to be measured to obtain the Monte Carlo dose of the particles.

[0006] A second aspect of the present disclosure provides an apparatus for determining a particle dose, comprising: an acquisition module configured to acquire a first CT image and a second CT image of a tissue to be measured, wherein the energy of a first scanning ray corresponding to the first CT image is less than the energy of a second scanning ray corresponding to the second CT image, the tissue to be measured includes a plurality of objects to be measured, and the first CT image includes first CT values of the plurality of objects to be measured respectively; a determination module configured to determine the elemental density of each of the plurality of objects to be measured based on the first CT image and the second CT image; an adjustment module configured to adjust the first CT value of each of the plurality of objects to be measured based on the number of objects to be measured having the same first CT value in the first CT image and the second CT image, so as to obtain a target CT value of each of the plurality of objects to be measured; and a simulation module configured to simulate Monte Carlo transport of particles in the tissue to be measured based on the target CT values and elemental densities of the plurality of objects to be measured, so as to obtain the Monte Carlo dose of the particles.

[0007] A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; a memory configured to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors are caused to execute the above-mentioned method for determining a particle dose.

[0008] A fourth aspect of the present disclosure further provides a computer-readable storage medium, having stored thereon executable instructions, which when executed by a processor cause the processor to execute the above-mentioned method for determining a particle dose.

[0009] A fifth aspect of the present disclosure further provides a computer program product, comprising a computer program, which when executed by a processor implements the above-mentioned method for determining a particle dose. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, the above-mentioned content and other objects, features and advantages of the present disclosure will become clearer. In the drawings:

[0011] Figure 1 Schematically shows an application scenario diagram of the method for determining a particle dose according to an embodiment of the present disclosure;

[0012] Figure 2 Schematically shows a flowchart of the method for determining a particle dose according to an embodiment of the present disclosure;

[0013] Figure 3 Schematically shows a schematic diagram of determining a target CT value according to an embodiment of the present disclosure;

[0014] Figure 4 Schematically shows a structural block diagram of the apparatus for determining a particle dose according to an embodiment of the present disclosure; and

[0015] Figure 5 A block diagram of an electronic device suitable for implementing a method for determining a particle dose according to an embodiment of the present disclosure is schematically shown. Detailed implementation manners

[0016] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present disclosure. In the following detailed description, for the sake of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, obviously, one or more embodiments can also be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessarily confusing the concepts of the present disclosure.

[0017] The terms used herein are merely for describing specific embodiments and are not intended to limit the present disclosure. The terms "including", "comprising", etc. used herein indicate the presence of features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0018] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0019] In the case of using expressions such as "at least one of A, B, and C", generally, it should be interpreted according to the meaning commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include, but not be limited to, a system having only A, only B, only C, having A and B, having A and C, having B and C, and / or having A, B, and C, etc.).

[0020] In the technical solution of the present disclosure, the processing of collection, storage, use, processing, transmission, provision, disclosure, and application of the user's personal information involved all comply with the provisions of relevant laws and regulations, necessary confidentiality measures are taken, and public order and good customs are not violated. In the technical solution of the present disclosure, before obtaining or collecting the user's personal information, the authorization or consent of the user is obtained.

[0021] Figure 1 An application scenario diagram of a method for determining a particle dose according to an embodiment of the present disclosure is schematically shown.

[0022] The basic physical principle of radiotherapy technology is to irradiate tumor tissues with rays. Through the interaction between the rays and the tissues, the energy carried by the rays is deposited at the cancer cells to kill them. Its fundamental goal is to kill as many cancer cells as possible (deliver the highest possible radiation dose to the tumor tissue) while protecting healthy tissues (deliver the lowest possible radiation dose to healthy tissues).

[0023] Proton radiotherapy is an emerging radiotherapy technology. Compared with traditional X-ray / electron beam radiotherapy, there will be a sudden dose spike (Bragg peak) in the proton dose curve. The proton dose is higher and sharper, which can greatly improve the killing efficiency of tumor tissues while achieving lower tissue damage. Compared with photons, the dose side effects of proton radiotherapy are lower.

[0024] As Figure 1 shown, the tissue to be treated is modeled to generate a simulated tissue 110 of particle deposition. A simulated particle stream 120 is incident from a specified position of the simulated tissue 110 and deposited at different positions of the simulated tissue 110. The dose situation of the particle source 120 in the simulated tissue 110 is calculated by the method for determining particle dose provided by the embodiments of the present disclosure.

[0025] A simulated tissue 110 of the tissue to be measured is constructed using the CT image of the tissue to be measured. The CT image of the tissue to be measured can describe the HU value distribution of the tissue to be measured, and the HU value reflects the interaction between the substance and photons.

[0026] However, the X-ray source of traditional CT equipment can only emit scanning rays of one energy spectrum, that is, single-energy CT. When scanning multiple tissue materials based on a single ray 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, it is impossible to further distinguish between two tissue materials in the CT image, which may lead to deviations in the diagnosis results.

[0027] For example, a dual-energy CT device has two energy spectra. Different energy spectra can be formed based on different ray sources, and the energy components included in multiple energy spectra can be different. When the CT device scans the same tissue material based on different energy spectra, the HU values may be the same or different.

[0028] Therefore, by using dual-energy CT, two CT images obtained by scanning two tissue materials respectively based on two energy spectra can be compared. When the HU values of two tissue materials are the same under a certain energy spectrum, the HU values under another energy spectrum tend to be different, so as to further distinguish between the two tissue materials.

[0029] Since the transport process of particles in the human body is very complex, it needs to be realized by Monte Carlo simulation methods. Common Monte Carlo simulation software includes FLUKA and Geant4, etc. Monte Carlo simulation software can calculate the dose distribution of particles (photons, protons, and heavy ions) based on the elemental density distribution in tissue materials. However, due to different logical structures, Monte Carlo simulation software usually can only read single-energy CT, which results in the inability to directly apply dual-energy CT images to Monte Carlo simulation software.

[0030] Since Monte Carlo simulation software can only read the CT images of single-energy CT among them, when the HU values of two materials are the same in the single-energy CT image, Monte Carlo simulation software cannot distinguish the two materials, resulting in deviations in the simulated particle transport process. In addition, Monte Carlo simulation software cannot read the elemental density distribution calculated based on dual-energy CT. Therefore, the current Monte Carlo simulation software cannot fully utilize the advantages of dual-energy CT elemental analysis.

[0031] Based on this, the present disclosure provides a method for determining particle dose, which uses the dual-energy CT image of the tissue to be measured to simulate the Monte Carlo transport of particles in the tissue to be measured, so as to statistically obtain the Monte Carlo dose of the particles.

[0032] Figure 2 Schematically shows a flowchart of the method for determining particle dose according to an embodiment of the present disclosure.

[0033] As Figure 2 shown, the method for determining particle dose in this embodiment includes operation S201 to operation S204.

[0034] In operation S210, a first CT image and a second CT image of the tissue to be measured are obtained.

[0035] In an embodiment of the present disclosure, the first CT image can be obtained by scanning the tissue to be measured by a CT device based on a first scanning ray, and the second CT image can be obtained by scanning the tissue to be measured by a CT device based on a second scanning ray. The energy of the first scanning ray corresponding to the first CT image is less than the energy of the second scanning ray corresponding to the second CT image. Therefore, the first CT image can be a low-energy CT image, and the second CT image can be a high-energy CT image.

[0036] In the embodiments of the present disclosure, the tissue to be measured includes a plurality of objects to be measured. The first CT image includes the first CT values of the respective plurality of objects to be measured, and the second CT image includes the second CT values of the respective plurality of objects to be measured. The tissue to be measured represented by the first CT image and the second CT image can be considered to be composed of a plurality of voxel blocks, and each voxel can be represented as an object to be measured. For example, the tissue to be measured can be a tumor region. The CT value is the HU (Hounsfield Unit) value used to represent the voxel, and the HU value is a measurement unit for measuring the density of local tissues or organs, which can reflect the degree of X-ray absorption of the tissues or organs.

[0037] In operation S220, based on the first CT image and the second CT image, determine the elemental density of each of the plurality of objects to be measured.

[0038] In the embodiments of the present disclosure, based on the distribution law between the dual-energy CT values and the elemental density of dual-energy CT, the elemental density of each object to be measured can be determined based on the dual-energy CT values of each object to be measured. The distribution law between the dual-energy CT values and the elemental density can be obtained based on the dual-energy CT values and the elemental density of known sample materials.

[0039] For example, the dual-energy CT values and the elemental density of the known sample materials are fitted to obtain a fitting function describing the distribution law. The fitting function describes the distribution law between the dual-energy CT values and the elemental density. Given the dual-energy CT value of the object to be measured and the fitting function, the elemental density of the object to be measured can be calculated.

[0040] For example, the known dual-energy CT values and the elemental density of the sample materials are input into a deep model, and the deep model is used to learn the data characteristics of the dual-energy CT values, the data characteristics of the elemental density, and the correlation characteristics between the dual-energy CT values and the elemental density, thereby realizing model training. After training, the deep model learns the distribution law between the dual-energy CT values and the elemental density. The dual-energy CT value of the object to be measured is input into the trained deep model, and the deep model can output the elemental density of the object to be measured.

[0041] The elemental density of the object to be measured can include the densities of carbon, nitrogen, oxygen, hydrogen, calcium, and phosphorus elements of the object to be measured. Based on the first dual-energy CT image and the second CT image, the density distribution of each element in the tissue to be measured can be obtained. The density distribution of each element can be represented by a three-dimensional matrix, and each value in the three-dimensional matrix can represent the elemental density of an object to be measured.

[0042] In operation S230, based on the number of objects to be measured having the same first CT value in the first CT image and the second CT image, adjust the first CT values of each of the plurality of objects to be measured to obtain the target CT values of each of the plurality of objects to be measured, and the plurality of target CT values are different from each other.

[0043] In dual-energy CT images, different objects to be measured may have the same first CT value and different second CT values. Therefore, if there is a corresponding relationship between the first CT value and the second CT value of the same object to be measured, there may be a corresponding relationship between one first CT value and multiple second CT values.

[0044] The span range of the CT values of the low-energy CT image is relatively large. Therefore, based on the first CT image, according to the number of objects to be measured with the same first CT value in the first CT image and the second CT image, the first CT values of each object to be measured in the first CT image are re-assigned to obtain the respective target CT values of each object to be measured. The target CT values of each object to be measured are different from each other, and the respective target CT values of each object to be measured constitute the target CT image of the tissue to be measured.

[0045] In the embodiments of the present disclosure, by assigning values to the respective first CT values based on the respective second CT values of the objects to be measured, the difference values between the second CT values of multiple objects to be measured can be fully utilized. By assigning values to the respective first CT values of the objects to be measured according to the number of objects to be measured with the same first CT value in the first CT image, the range of value assignment can be determined.

[0046] In operation S240, based on the respective target CT values and element densities of multiple objects to be measured, Monte Carlo transport of particles in the tissue to be measured is simulated to obtain the Monte Carlo dose of the particles.

[0047] For example, the particles can be protons, photons, heavy ions, etc. The transport of the particles is simulated by Monte Carlo simulation software. For example, the transport of the particles can be simulated by Monte Carlo simulation software such as Geant4, Gate, Fluka, Topas, etc. By establishing a matrix model for the tissue to be measured, the matrix model includes the position coordinate information, element information, and voxel density information (converted from HU values) of the voxel blocks, the transport process of the particles can be characterized, and the position information and energy information of the particles can be determined.

[0048] In the embodiments of the present disclosure, the first CT image and the second CT image are converted into a target CT image, realizing the conversion from dual-energy CT to single-energy CT. In the target CT image, the target CT values of each object to be measured are different from each other. Therefore, based on the target CT values, the objects to be measured with different element densities can be clearly distinguished.

[0049] Since the adjustment method of the first CT image is related to the number of objects to be measured with the same first CT value in the first CT image and the second CT image, the adjustment methods of multiple tissues to be measured may be different from each other, and the target CT images of multiple tissues to be measured are also different from each other.

[0050] After converting the first CT image and the second CT image into the target CT image, the Monte Carlo simulation software can read the target CT image. In addition, since the target CT images of multiple tissues to be measured are different from each other, the Monte Carlo simulation conversion can simulate different materials based on the target CT image, so as to distinguish the tissues to be measured with different components.

[0051] By implementing the present disclosure, the dual-energy CT image capable of distinguishing different elemental components can be converted into a mono-energy CT image, and the CT value characteristics of the dual-energy CT image are retained in the converted mono-energy CT image. Based on the mono-energy CT images respectively converted from multiple tissues to be measured, the Monte Carlo simulation software can assign different types of materials to the multiple tissues to be measured, and associate the different types of materials with their respective elemental densities, so as to obtain the respective simulated tissues of the multiple tissues to be measured. The Monte Carlo dose of particles can be statistically calculated based on the converted target CT image, making full use of the accuracy of the dual-energy CT in describing the tissues to be measured. Based on the first CT image, reassigning values to the first image according to the second CT image can retain the characteristic of a large span of CT value ranges in the first CT image, so as to improve the accuracy of the target CT image.

[0052] Figure 3 Schematically shows a schematic diagram of determining the target CT value according to an embodiment of the present disclosure.

[0053] As Figure 3 shown, based on the respective first CT values of all the objects to be measured in the tissue to be measured in the first CT image 310, the objects to be measured are divided into the first object to be measured 311 and the second object to be measured 312. For any first object to be measured 311, there is at least one object to be measured among all the objects to be measured that has the same first CT value as the first object to be measured 311. For any second object to be measured 312, the first CT values of all the objects to be measured are different from the first CT value of the second object to be measured.

[0054] In the embodiment of the present disclosure, based on the number of the first objects to be measured 311 in the first CT image 310, the adjustment range 330 is determined. For multiple first objects to be measured 331, based on the adjustment range 330 and the respective second CT values of the multiple first objects to be measured 311 in the second CT image 320, the respective first CT values of the multiple first objects to be measured 311 are adjusted to obtain the respective target CT values 340 of the multiple first objects to be measured. For multiple second objects to be measured 312, based on the adjustment range 330, the respective first CT values of the multiple second objects to be measured 312 are adjusted to obtain the respective target CT values 340 of the multiple second objects to be measured.

[0055] In the embodiments of the present disclosure, the adjustment range 330 and the quantity of the first object to be measured 311 may be positively correlated. When the quantity of the first object to be measured 311 is large, a relatively large adjustment range is required for the first CT value, so that the first CT value can be reassigned within a sufficient numerical range.

[0056] For the first object to be measured 311, the first CT value of the first object to be measured 311 is reassigned according to the second CT value of the first object to be measured 311. When the first CT values of two first objects to be measured 311 are the same and the second CT values are similar, the elemental compositions of these two first objects to be measured are similar, so the difference in the values assigned to these two first objects to be measured 311 is small. When the first CT values of two first objects to be measured 311 are the same and the second CT values are significantly different, the elemental compositions of these two first objects to be measured are also very different, so the difference in the values assigned to these two first objects to be measured 311 is large.

[0057] In the embodiments of the present disclosure, with reference to the numerical magnitudes of the second CT values of multiple first objects to be measured 311, the first CT values of the first objects to be measured 311 are sequentially assigned, so that the numerical magnitude relationship between the target CT values of the first objects to be measured 311 is similar to the numerical magnitude relationship between the second CT values.

[0058] For the second object to be measured 312, since the first CT values of multiple second objects to be measured 312 are different from each other, in order to unify the span ranges of the target CT values of the first object to be measured 311 and the second object to be measured 312, the first CT values of the second object to be measured 312 are adjusted with the same adjustment range 330. The target CT values of the second object to be measured 312 after the amplitude adjustment are also different from each other, so there is no need to reassign values with reference to the second CT values of the second object to be measured 312.

[0059] In the embodiments of the present disclosure, the value of the adjustment range 330 may be determined based on the quantity of the first objects to be measured 311 with the same first CT value.

[0060] For example, among multiple first objects to be measured 311, the first CT values of the first objects to be measured 311 are not necessarily exactly the same. For example, there may be at least one first CT value corresponding to multiple first objects to be measured.

[0061] For example, based on the first CT image, the quantity of the objects to be measured corresponding to each first CT value is determined. In the case where it is determined that the quantities of the objects to be measured corresponding to multiple first CT values are multiple, the maximum value among the quantities of the objects to be measured corresponding to multiple first CT values is used as the adjustment range.

[0062] For example, among multiple first objects to be measured 311, the first CT values of M first objects to be measured 311 are all 1000, the first CT values of N first objects to be measured 311 are all 900, and the first CT values of Q first objects to be measured 311 are all 800. When M is the maximum value among M, N, and Q, M is used as the adjustment range 330.

[0063] When there are multiple first CT values corresponding to multiple objects to be measured (there are multiple "one-to-many" situations), using the maximum value among the numbers of objects to be measured as the adjustment range can meet the assignment ranges required for these multiple "one-to-many" situations. For the situation where the number of objects to be measured corresponding to the first CT value is the largest, the assignment range required for this first CT value is the largest. Since the number of objects to be measured in other "one-to-many" situations is less than or equal to the maximum value, the assignment range for the first CT value in other "one-to-many" situations is less than or equal to the assignment range required for the first CT value with the largest number of objects to be measured. When the assignment range requirement for the first CT value with the largest number of objects to be measured can be met, the assignment range requirements for other "one-to-many" situations can all be met.

[0064] In the embodiments of the present disclosure, the adjustment method for the first CT value of the first object to be measured 311 may include: based on the adjustment range, multiplying the first CT values of the multiple first objects to be measured 311 respectively to obtain the multiplied values of the multiple first objects to be measured 311; respectively sorting the second CT values of the multiple first objects to be measured 311 with the same first CT value in ascending order to obtain at least one ascending sequence; and based on at least one ascending sequence, sequentially assigning the multiplied values of the multiple first objects to be measured 311 with the same first CT value to obtain the target CT values of the multiple first objects to be measured 311.

[0065] Multiple first objects to be measured with the same first CT value can be defined as an object group. For the first CT value of the first object to be measured in each object group, it is multiplied based on the adjustment range. Then, based on the ascending order of the second CT values of the first objects to be measured in each object group, the multiplied values are sequentially assigned in ascending order, so that the ascending order of the target CT values of the multiple first objects to be measured is consistent with the ascending order of the corresponding multiple second CT values.

[0066] For example, the first CT values of 4 first objects to be measured in the first object group are all 900, and the second CT values are 810, 820, 830, and 850 respectively. The first CT values of 5 first objects to be measured in the second object group are all 1000, and the second CT values are 900, 920, 930, 940, and 950 respectively. If the adjustment range is 5, then the first CT values of the first objects to be measured in the first object group after multiplication are 4500, and the first CT values of the first objects to be measured in the second object group after multiplication are 4000.

[0067] According to the magnitude order of the second CT values of the 4 first objects to be measured in the first object group, assign a multiplication value of 4500 to the first object to be measured with a second CT value of 810, assign a multiplication value of 4501 to the first object to be measured with a second CT value of 820, assign a multiplication value of 4502 to the first object to be measured with a second CT value of 830, and assign a multiplication value of 4503 to the first object to be measured with a second CT value of 850.

[0068] According to the ascending order of the second CT values of the 5 first objects to be measured in the second object group, assign a multiplication value of 5000 to the first object to be measured with a second CT value of 900, assign a multiplication value of 5001 to the first object to be measured with a second CT value of 920, assign a multiplication value of 5002 to the first object to be measured with a second CT value of 930, assign a multiplication value of 5003 to the first object to be measured with a second CT value of 940, and assign a multiplication value of 5004 to the first object to be measured with a second CT value of 950.

[0069] That is, the target CT values of the 4 first objects to be measured in the first object group are 4500, 4501, 4502, and 4503 respectively. The target CT values of the 5 first objects to be measured in the second object group are 5000, 5001, 5002, 5003, and 5004 respectively.

[0070] For the second objects to be measured, when the first CT values of multiple second objects to be measured are 899, 901, 999, and 1001 respectively, the target CT values of the multiple second objects to be measured are 4495, 4505, 4995, and 5005 respectively.

[0071] In the embodiments of the present disclosure, after reassigning the first CT value of each object to be measured in the object to be measured, the target CT value of each object to be measured is obtained. The target CT values of all objects to be measured are not equal to each other. The assignment method is related to the number of first CT values and the second CT value in the object to be measured that are the same, so the assignment method of each object to be measured is different, and the obtained target CT images are also different.

[0072] In the embodiments of the present disclosure, when the number of first objects to be measured included in the object group is too large, the object group can be divided into multiple material groups, each material group includes multiple first objects to be measured, and each material group including multiple first objects to be measured is regarded as an independent object, and the multiple first objects to be measured included in each material group are assigned values, so that the target CT values of the multiple first objects to be measured included in each material group are the same.

[0073] In an embodiment of the present disclosure, when the number of first objects to be measured included in the object group is too large, the adjustment method for the first CT value of the first object to be measured 311 may include: respectively sorting the second CT values of multiple first objects to be measured in the object group in ascending order to obtain at least one ascending sequence; based on the at least one ascending sequence, dividing the multiple first objects to be measured in the object group into multiple material groups; based on the adjustment range, multiplying the first CT values of the multiple material groups to obtain the multiplied values of each of the multiple material groups; and sequentially assigning the multiplied values of each of the multiple material groups to obtain the target CT values of each of the multiple first objects to be measured, where the target CT values of the multiple first objects to be measured included in each material group are the same.

[0074] Based on the ascending order of the second CT values of the first objects to be measured in each object group, divide the multiple first objects to be measured included in each object group into multiple material groups, and the number of first objects to be measured included in each material group may be the same. For example, referring to the ascending order of the second CT values, divide every three first objects to be measured in the object group into one material group.

[0075] The adjustment range may be determined based on the number of material groups in each object group. For example, the adjustment range may be consistent with the maximum value of the number of material groups in multiple object groups.

[0076] For example, for multiple object groups, multiple material groups may be divided respectively with the same granularity, and the number of first objects to be measured included in the divided material groups is the same. At this time, take the maximum value of the number of material groups in multiple object groups as the adjustment range. For example, multiple object groups are respectively divided into 5 material groups, 4 material groups, and 3 material groups, and each material group includes 4 first objects to be measured. At this time, the adjustment range is 5.

[0077] For example, multiple object groups may be divided into the same number of material groups, and the number of first objects to be measured included in each material group may be different or the same. At this time, take the number of material groups into which each object group is divided as the adjustment range. For example, multiple object groups are all divided into 4 material groups, and the material groups of multiple object groups may respectively include 3, 4, and 5 first objects to be measured. At this time, the adjustment range is 4.

[0078] Based on the second CT values of the first objects to be measured in each material group, multiple material groups may also be sorted in ascending order based on the second CT values. Based on the ascending arrangement of multiple material groups, sequentially assign values to the first objects to be measured included in multiple material groups from small to large, so that the ascending order of the target CT values of the first objects to be measured included in multiple material groups is consistent with the ascending arrangement of multiple material groups.

[0079] In the embodiments of the present disclosure, based on the target CT values and elemental densities of multiple objects to be measured, a conversion relationship between CT value and elemental density is determined; based on the target CT value and the conversion relationship, a simulated tissue corresponding to the tissue to be measured is simulated; and in the simulated tissue, a Monte Carlo transport process of particles is simulated to obtain the Monte Carlo dose of the particles.

[0080] The Monte Carlo simulation software can define material types based on elemental density, and then assign the predefined material types corresponding to the CT values to the voxels of the tissue to be measured based on the conversion relationship between the CT value and the elemental density.

[0081] The conversion relationship between CT value and elemental density can describe the conversion relationship between the target CT value and the elemental density of the object to be measured. The Monte Carlo simulation software can pre-learn the conversion relationship between the target CT value and the elemental density, and after reading the target CT value of each object to be measured, assign the corresponding type of material to the object to be measured, so that the Monte Carlo simulation software simulates the elemental density of the object to be measured.

[0082] After all the objects to be measured of the tissue to be measured are assigned the corresponding types of materials, the Monte Carlo simulation software simulates and obtains the simulated tissue of the tissue to be measured, thereby simulating the elemental density distribution of the tissue to be measured. Using the elemental density distribution of the tissue to be measured, the Monte Carlo simulation software can simulate the transport process of particles inside the tissue to be measured, and thus can statistically obtain the particle dose distribution.

[0083] In the embodiments of the present disclosure, when the object group is divided into multiple material groups, the Monte Carlo dose statistical method of particles can include: determining the average elemental density of each of the multiple material groups based on the elemental densities of the multiple first objects to be measured included in the multiple material groups; and simulating the Monte Carlo transport of particles in the tissue to be measured based on the target CT values of the multiple first objects to be measured and the average elemental density of the material group to which they belong, and the target CT values and elemental densities of the multiple second objects to be measured, to obtain the Monte Carlo dose of the particles.

[0084] When the object group is divided into multiple material groups, the target CT values of the multiple first objects to be measured included in the material group are the same. In order to determine the conversion relationship between the target CT value and the elemental density, it is necessary to uniformly process the elemental densities of the multiple first objects to be measured included in the material group. For example, the multiple first objects to be measured included in the material group are uniformly defined as a new material, and the average value of the elemental densities of the multiple first objects to be measured included in the material group is used as the elemental density of the new material, and the target CT value of the multiple first objects to be measured included in the material group is used as the target CT value of the new material. It can be understood that the multiple first objects to be measured included in the material group are all considered to be the same material.

[0085] The following uses an embodiment to schematically illustrate the particle determination method of the present disclosure.

[0086] For example, the tissue to be measured has dual-energy CT images of 80 kV and 150 kV. Among them, in the CT image of 80 kV, the HU values of voxels 1, 2, 3, and 4 are all equal to 1000. The first CT values of voxels 1, 2, 3, and 4 can be denoted as HUL1 = HUL2 = HUL3 = HUL4 = 1000. In the CT image of 150 kV, the HU values of voxels 1, 2, 3, and 4 are HUH1 = 770, HUH2 = 780, HUH3 = 790, and HUH4 = 800 respectively. Voxels 1, 2, 3, and 4 form an object group.

[0087] To simplify the calculation, based on the respective HUH values of voxels 1, 2, 3, and 4, the voxels 1, 2, 3, and 4 of the object group are evenly divided into two material groups. According to the ascending order of the second CT values of voxels 1, 2, 3, and 4, two voxels are divided into a group in sequence, that is, voxels 1 and 2 are divided into material group 1, and voxels 3 and 4 are divided into material group 2.

[0088] Using the dual-energy CT element analysis algorithm, with (1000, 770), (1000, 780), (1000, 790), and (1000, 800) as inputs respectively, the element density ρ of voxels 1, 2, 3, and 4 can be obtained (the unit of ρ is g / cm 3 )

[0089] Voxel 1: ρ(H)1 = 0.07, ρ(C)1 = 0.31, ρ(N)1 = 0.07, ρ(O)1 = 0.67, ρ(P)1 = 0.13, ρ(Ca)1 = 0.31;

[0090] Voxel 2: ρ(H)1 = 0.06, ρ(C)1 = 0.3, ρ(N)1 = 0.07, ρ(O)1 = 0.68, ρ(P)1 = 0.14, ρ(Ca)1 = 0.32;

[0091] Voxel 3: ρ(H)1 = 0.05, ρ(C)1 = 0.29, ρ(N)1 = 0.07, ρ(O)1 = 0.69, ρ(P)1 = 0.15, ρ(Ca)1 = 0.33;

[0092] Voxel 4: ρ(H)1 = 0.04, ρ(C)1 = 0.28, ρ(N)1 = 0.07, ρ(O)1 = 0.70, ρ(P)1 = 0.16, ρ(Ca)1 = 0.34.

[0093] Based on the elemental densities of voxel 1, voxel 2, voxel 3, and voxel 4 respectively, the elemental density of each material group can be calculated, that is:

[0094] Material group 1: ρ’(H)1 = 0.065, ρ’(C)1 = 0.305, ρ’(N)1 = 0.07, ρ’(O)1 = 0.675, ρ’(P)1 = 0.135, ρ’(Ca)1 = 0.315;

[0095] Material group 2: ρ’(H)1 = 0.055, ρ’(C)1 = 0.285, ρ’(N)1 = 0.07, ρ’(O)1 = 0.695, ρ’(P)1 = 0.155, ρ’(Ca)1 = 0.335.

[0096] Since the object group is divided into two material groups, the adjustment range can be 2. Based on the adjustment range and the second CT value of the voxels in the material group, the first CT value and the second CT value of voxel 1, voxel 2, voxel 3, and voxel 4 are converted into target CT values.

[0097] For example, the sum of the second CT values of voxel 1 and voxel 2 in material group 1 is less than the sum of the second CT values of voxel 3 and voxel 4 in material group 2. Therefore, the target CT of material group 1 can be 2 + 1000 + 0 = 2000, and the target CT value of material group 2 can be 2 + 1000 + 1 = 2001.

[0098] In the Monte Carlo simulation software, voxel points with an HU value of 2000 are assigned to voxel 1 and voxel, and voxel points with an HU value of 2001 are assigned to voxel 3 and voxel 4. The Monte Carlo simulation software also reads the conversion relationship between the target CT values and the elemental densities of material group 1 and material group 2 respectively to obtain the simulated tissue of the tissue to be measured and simulate the Monte Carlo transport of particles.

[0099] Based on the above method for determining the particle dose, the present disclosure also provides a device for determining the particle dose. The following will be combined with Figure 4 to describe the device in detail.

[0100] Figure 4 Schematically shows a structural block diagram of a device for determining the particle dose according to an embodiment of the present disclosure.

[0101] As Figure 4 shown, the device 400 for determining the particle dose according to this embodiment includes an acquisition module 410, a determination module 420, an adjustment module 430, and a simulation module 440.

[0102] The acquisition module 410 is configured to acquire a first CT image and a second CT image of a tissue to be measured. The energy of a first scanning ray corresponding to the first CT image is less than the energy of a second scanning ray corresponding to the second CT image. The tissue to be measured includes a plurality of objects to be measured, and the first CT image includes first CT values of the plurality of objects to be measured respectively. In one embodiment, the acquisition module 410 may be configured to perform the operation S201 described above, which will not be elaborated herein.

[0103] The determination module 420 is configured to determine the elemental density of each of the plurality of objects to be measured based on the first CT image and the second CT image. In one embodiment, the determination module 420 may be configured to perform the operation S202 described above, which will not be elaborated herein.

[0104] The adjustment module 430 is configured to adjust the first CT value of each of the plurality of objects to be measured based on the number of objects to be measured having the same first CT value in the first CT image and the second CT image, so as to obtain a target CT value of each of the plurality of objects to be measured. In one embodiment, the adjustment module 430 may be configured to perform the operation S203 described above, which will not be elaborated herein.

[0105] The simulation module 440 is configured to simulate Monte Carlo transport of particles in the tissue to be measured based on the target CT values and elemental densities of the plurality of objects to be measured, so as to obtain the Monte Carlo dose of the particles. In one embodiment, the simulation module 440 may be configured to perform the operation S204 described above, which will not be elaborated herein.

[0106] According to an embodiment of the present disclosure, the adjustment module 430 is further configured to determine an adjustment range based on the number of objects to be measured having the same first CT value in the first CT image; for a plurality of first objects to be measured having the same first CT value, adjust the first CT value of each of the plurality of first objects to be measured based on the adjustment range and the second CT values of the plurality of first objects to be measured in the second CT image, so as to obtain a target CT value of each of the plurality of first objects to be measured; and for a plurality of second objects to be measured having different first CT values, adjust the first CT value of each of the plurality of second objects to be measured based on the adjustment range, so as to obtain a target CT value of each of the plurality of second objects to be measured.

[0107] According to an embodiment of the present disclosure, the adjustment module 430 is further configured to determine the number of objects to be measured corresponding to each first CT value based on the first CT image; and use the maximum value among the numbers of objects to be measured corresponding to each first CT value as the adjustment range.

[0108] According to an embodiment of the present disclosure, the adjustment module 430 is further configured to multiply the first CT value of each of the plurality of first objects to be measured based on the adjustment range, so as to obtain a multiplied value of each of the plurality of first objects to be measured;

[0109] Ascendingly sort the second CT values of multiple first objects to be measured having the same first CT value respectively, to obtain at least one ascending sequence; and

[0110] Based on at least one ascending sequence, sequentially assign doubling values to multiple first objects to be measured having the same first CT value respectively, to obtain the target CT value of each of the multiple first objects to be measured.

[0111] According to an embodiment of the present disclosure, the adjustment module 430 is further configured to ascendingly sort the second CT values of multiple first objects to be measured having the same first CT value respectively, to obtain at least one ascending sequence; based on at least one ascending sequence, divide multiple first objects to be measured having the same first CT value into multiple material groups; based on the adjustment range, multiply the first CT values of the multiple material groups to obtain the doubling values of each of the multiple material groups; and sequentially assign the doubling values of each of the multiple material groups to obtain the target CT value of each of the multiple first objects to be measured, wherein the target CT values of multiple first objects to be measured included in each material group are the same.

[0112] According to an embodiment of the present disclosure, the simulation module 440 is further configured to determine the average element density of each of the multiple material groups based on the element densities of multiple first objects to be measured included in the multiple material groups; and simulate the Monte Carlo transport of particles in the tissue to be measured based on the target CT values of the multiple first objects to be measured, the average element density of the material group to which they belong, the target CT values of the multiple second objects to be measured, and the element densities, to obtain the Monte Carlo dose of the particles.

[0113] According to an embodiment of the present disclosure, the simulation module 440 is further configured to determine the conversion relationship between CT value and element density based on the target CT values and element densities of the multiple objects to be measured; simulate the simulated tissue corresponding to the tissue to be measured based on the target CT value and the conversion relationship; and simulate the Monte Carlo transport process of particles in the simulated tissue to obtain the Monte Carlo dose of the particles.

[0114] According to an embodiment of the present disclosure, any plurality of modules among the acquisition module 410, the determination module 420, the adjustment module 430, and the simulation module 440 may be combined and implemented in one module, or any one of them may be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules may be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the acquisition module 410, the determination module 420, the adjustment module 430, and the simulation module 440 may be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on chip, a system on substrate, a system on package, an application specific integrated circuit (ASIC), or any other reasonable manner that can integrate or package circuits, etc., implemented by hardware or firmware, or implemented in any one or a suitable combination of the three implementation manners of software, hardware, and firmware. Alternatively, at least one of the acquisition module 410, the determination module 420, the adjustment module 430, and the simulation module 440 may be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding functions may be executed.

[0115] Figure 5 A block diagram of an electronic device suitable for implementing the method for determining particle dose according to an embodiment of the present disclosure is schematically shown.

[0116] As Figure 5 shown, the electronic device 500 according to an embodiment of the present disclosure includes a processor 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage section 508 into a random access memory (RAM) 503. The processor 501 may include, for example, a general microprocessor (such as a CPU), an instruction set processor, and / or a related chipset, and / or a dedicated microprocessor (such as an application specific integrated circuit (ASIC)), etc. The processor 501 may also include on-board memory for caching purposes. The processor 501 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0117] In the RAM 503, various programs and data required for the operation of the electronic device 500 are stored. The processor 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. The processor 501 performs various operations of the method flow according to the embodiments of the present disclosure by executing the programs in the ROM 502 and / or the RAM 503. It should be noted that the programs can also be stored in one or more memories other than the ROM 502 and the RAM 503. The processor 501 can also perform various operations of the method flow according to the embodiments of the present disclosure by executing the programs stored in one or more memories.

[0118] According to an embodiment of the present disclosure, the electronic device 500 may further include an input / output (I / O) interface 505, and the input / output (I / O) interface 505 is also connected to the bus 504. The electronic device 500 may further include one or more of the following components connected to the I / O interface 505: an input part 506 including a keyboard, a mouse, etc.; an output part 507 including, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker, etc.; a storage part 508 including a hard disk, etc.; and a communication part 509 including a network interface card such as a LAN card, a modem, etc. The communication part 509 performs communication processing via a network such as the Internet. A driver 510 is also connected to the I / O interface 505 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the driver 510 as needed so that a computer program read from it can be installed into the storage part 508 as needed.

[0119] The present disclosure also provides a computer-readable storage medium, which may be included in the device / device / system described in the above embodiments; or may exist separately without being assembled into the device / device / system. The above computer-readable storage medium carries one or more programs, and when the one or more programs are executed, the method according to the embodiments of the present disclosure is implemented.

[0120] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example, but is not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In the present disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program, and the program may be used by or in combination with an instruction execution system, apparatus, or device. For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the above-described ROM 502 and / or RAM 503 and / or one or more memories other than ROM 502 and RAM 503.

[0121] An embodiment of the present disclosure further includes a computer program product, which includes a computer program that contains program code for executing the method shown in the flowchart. When the computer program product runs in a computer system, the program code is used to enable the computer system to implement the method for determining the particle dose provided by the embodiment of the present disclosure.

[0122] When the computer program is executed by the processor 501, it executes the above functions defined in the system / apparatus of the embodiment of the present disclosure. According to an embodiment of the present disclosure, the above-described systems, apparatuses, modules, units, etc. may be implemented by computer program modules.

[0123] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices and magnetic storage devices. In another embodiment, the computer program may also be transmitted and distributed in the form of a signal on a network medium and be downloaded and installed through the communication part 509, and / or be installed from the removable medium 511. The program code included in the computer program may be transmitted by any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0124] In such an embodiment, the computer program may be downloaded and installed from the network through the communication part 509, and / or be installed from the removable medium 511. When the computer program is executed by the processor 501, it executes the above functions defined in the system of the embodiment of the present disclosure. According to an embodiment of the present disclosure, the above-described systems, devices, apparatuses, modules, units, etc. may be implemented by computer program modules.

[0125] According to embodiments of the present disclosure, program code for executing the computer programs provided by the embodiments of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. The programming languages include, but are not limited to, such as Java, C++, Python, the "C" language, or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (for example, by using an Internet service provider to connect through the Internet).

[0126] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram can represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks can occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, and the combination of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0127] Those skilled in the art can understand that the features recited in the various embodiments and / or claims of the present disclosure can be combined or combined in various ways, even if such combinations or combinations are not explicitly recited in the present disclosure. In particular, without departing from the spirit and teachings of the present disclosure, the features recited in the various embodiments and / or claims of the present disclosure can be combined and combined in various ways. All such combinations and / or combinations fall within the scope of the present disclosure.

[0128] The embodiments of the present disclosure have been described above. However, these embodiments are merely for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments have been described separately above, this does not mean that the measures in each embodiment cannot be used advantageously in combination. The scope of the present disclosure is defined by the appended claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art can make various substitutions and modifications, and all such substitutions and modifications should fall within the scope of the present disclosure.

Claims

1. A method for determining a particle dose, comprising: Acquire a first CT image and a second CT image of a tissue to be tested, wherein the energy of a first scanning ray corresponding to the first CT image is less than the energy of a second scanning ray corresponding to the second CT image, the tissue to be tested includes a plurality of objects to be tested, and the first CT image includes first CT values ​​of the plurality of objects to be tested; Determining element density of each of the plurality of objects to be tested based on the first CT image and the second CT image; Based on the number of objects to be tested having the same first CT value in the first CT image and the second CT image, adjusting the first CT values ​​of the plurality of objects to be tested to obtain target CT values ​​of the plurality of objects to be tested; as well as Based on the target CT values ​​and the element densities of the multiple objects to be tested, the Monte Carlo transport of particles in the tissue to be tested is simulated to obtain the Monte Carlo dose of the particles.

2. The determination method according to claim 1, wherein: The adjusting the first CT values ​​of the plurality of objects to be tested based on the number of objects to be tested having the same first CT value in the first CT image and the second CT image to obtain the target CT values ​​of the plurality of objects to be tested comprises: determining an adjustment amplitude based on the number of objects to be tested having the same first CT value in the first CT image; For a plurality of first objects to be tested having the same first CT value, adjusting the first CT value of each of the plurality of first objects to be tested based on the adjustment amplitude and the second CT value of each of the plurality of first objects to be tested in the second CT image to obtain a target CT value of each of the plurality of first objects to be tested; and For a plurality of second objects to be tested having different first CT values, the first CT values ​​of the plurality of second objects to be tested are adjusted based on the adjustment amplitude to obtain target CT values ​​of the plurality of second objects to be tested.

3. The determination method according to claim 2, wherein: The step of determining the adjustment range based on the number of the objects to be tested having the same first CT value in the first CT image includes: Based on the first CT image, determining the number of objects to be tested corresponding to each first CT value; and The maximum value of the number of objects to be tested corresponding to each first CT value is used as the adjustment amplitude.

4. The determination method according to claim 2, wherein: The adjusting the first CT values ​​of the plurality of first objects to be tested based on the adjustment amplitude and the second CT values ​​of the plurality of first objects to be tested in the second CT image to obtain the target CT values ​​of the plurality of first objects to be tested comprises: Based on the adjustment amplitude, the first CT value of each of the plurality of first objects to be tested is multiplied to obtain a multiplied value of each of the plurality of first objects to be tested; sorting the second CT values ​​of the plurality of first test objects having the same first CT value in ascending order to obtain at least one ascending sequence; and Based on the at least one ascending sequence, the multiplication values ​​of the plurality of first objects to be tested having the same first CT value are assigned in sequence to obtain the target CT values ​​of the plurality of first objects to be tested.

5. The determination method according to claim 2, wherein: The adjusting the first CT values ​​of the plurality of first objects to be tested based on the adjustment amplitude and the second CT values ​​of the plurality of first objects to be tested in the second CT image to obtain the target CT values ​​of the plurality of first objects to be tested comprises: sorting the second CT values ​​of the plurality of first test objects having the same first CT value in ascending order to obtain at least one ascending sequence; Based on the at least one ascending sequence, dividing the plurality of first objects to be tested having the same first CT value into a plurality of material groups; Based on the adjustment amplitude, multiplying the first CT values ​​of the plurality of material groups to obtain respective multiplied values ​​of the plurality of material groups; and The multiplication values ​​of the plurality of material groups are assigned in sequence to obtain the target CT values ​​of the plurality of first objects to be tested, wherein the target CT values ​​of the plurality of first objects to be tested included in each of the material groups are the same.

6. The determination method according to claim 5, wherein: The step of simulating the Monte Carlo transport of particles in the tissue to be tested based on the target CT values ​​and the element densities of the multiple objects to be tested to obtain the Monte Carlo dose of the particles includes: Determining an average element density of each of the plurality of material groups based on the element density of each of the plurality of first objects to be measured included in the plurality of material groups; and Based on the target CT values ​​of each of the multiple first objects to be tested and the average element density of the material group to which they belong, and the target CT values ​​and element density of each of the multiple second objects to be tested, Monte Carlo transport of particles in the test tissue is simulated to obtain a Monte Carlo dose of the particles.

7. The determination method according to claim 1, wherein: The step of simulating the Monte Carlo transport of particles in the tissue to be tested based on the target CT values ​​and the element densities of the multiple objects to be tested to obtain the Monte Carlo dose of the particles includes: Determining a conversion relationship between CT value and element density based on the target CT values ​​of each of the plurality of objects to be tested and the element density; Based on the target CT value and the conversion relationship, simulating a simulated tissue corresponding to the tissue to be tested; and In the simulated tissue, the Monte Carlo transport process of the particles is simulated to obtain the Monte Carlo dose of the particles.

8. A device for determining a particle dose, comprising: an acquisition module, configured to acquire a first CT image and a second CT image of a tissue to be tested, wherein the energy of a first scanning ray corresponding to the first CT image is less than the energy of a second scanning ray corresponding to the second CT image, the tissue to be tested includes a plurality of objects to be tested, and the first CT image includes first CT values ​​of the plurality of objects to be tested; A determination module, configured to determine element densities of each of the plurality of objects to be tested based on the first CT image and the second CT image; an adjustment module, configured to adjust the first CT values ​​of the plurality of objects to be tested based on the number of objects to be tested having the same first CT value in the first CT image and the second CT image, so as to obtain target CT values ​​of the plurality of objects to be tested; as well as The simulation module is used to simulate the Monte Carlo transport of particles in the tissue to be tested based on the target CT value and the element density of each of the multiple objects to be tested, so as to obtain the Monte Carlo dose of the particles.

9. An electronic device, comprising: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, causes the processor to execute the method according to any one of claims 1 to 7.

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