Modeling method and device and storage medium

Abstract

The invention relates to a modeling method and device and a storage medium. The method comprises the following steps: determining source particle distribution and leakage particle distribution, determining a first particle ratio between target scattering particles and source particles according to the source particle distribution and the leakage particle distribution, and further determining the target scattering particles according to at least one of the leakage particle distribution, angle distribution and energy distribution of the target scattering particles and the first particle ratio. And sampling to determine particle motion parameters of the target scattering particles so as to determine a distribution model of the target scattering particles based on the particle motion parameters of the target scattering particles. By adopting the method, the modeling accuracy can be improved.

Description

Technical Field

[0001] The present application relates to the field of radiation delivery technology, and in particular to a modeling method, device and storage medium for radiation delivery modeling. Background Art

[0002] In the process of planning radiation delivery (eg, radiotherapy, radiation processing, radiation detection, etc.), radiation dose calculation is an important step that determines the quality of radiation delivery planning.

[0003] Dose calculation relies on modeling particles in radiation delivery devices. Currently, Monte Carlo algorithms are commonly used to simulate particle motion within radiation delivery devices. However, current modeling methods suffer from low accuracy. Summary of the Invention

[0004] Based on this, it is necessary to provide a modeling method, device and storage medium that can improve the accuracy in response to the above technical problems.

[0005] In a first aspect, the present application provides a modeling method, comprising:

[0006] Determine the source particle distribution and the leakage particle distribution;

[0007] determining a first particle ratio between target scattered particles and source particles based on the source particle distribution and the leakage particle distribution;

[0008] Sampling and determining particle motion parameters of the target scattered particles based on the leakage particle distribution, at least one of the angular distribution and the energy distribution of the target scattered particles, and the first particle ratio; and

[0009] A distribution model of the target scattering particles is determined based on the particle motion parameters of the target scattering particles.

[0010] In one embodiment, the particle motion parameter includes at least one of an initial position, a motion direction, and a particle energy.

[0011] In one embodiment, sampling and determining the particle motion parameters of the target scattered particles based on the leakage particle distribution, at least one of the angular distribution and the energy distribution of the target scattered particles, and the first particle ratio includes:

[0012] Determine the sampling number of target scattered particles according to the first particle ratio and the total flux of source particles;

[0013] At least one of the initial position, movement direction and particle energy of the target scattered particles is determined based on at least one of the leakage particle distribution, angle distribution and energy distribution and the sampling quantity.

[0014] In one embodiment, determining at least one of an initial position, a moving direction, and a particle energy of a target scattered particle based on at least one of a leakage particle distribution, an angle distribution, and an energy distribution and a sampling number includes:

[0015] Based on the sampling number, the leakage particle distribution is sampled to obtain the initial position of the target scattered particles;

[0016] Based on the sampling number, each possible motion direction in the angle distribution is sampled to obtain the motion direction of the target scattered particles; and

[0017] Based on the sampling number, each possible particle energy of the energy distribution is sampled to obtain the particle energy of the target scattered particle.

[0018] In one embodiment, determining a first particle ratio between target scattered particles and source particles based on the source particle distribution and the leakage particle distribution includes:

[0019] Integrate the leakage particle distribution to obtain the total flux of leakage particles;

[0020] Integrate the source particle distribution to obtain the total flux of source particles;

[0021] The first particle ratio is determined based on the total flux of leakage particles and the total flux of source particles.

[0022] In one embodiment, determining a first particle ratio based on a total flux of leakage particles and a total flux of source particles comprises:

[0023] determining a second particle ratio between the total flux of leakage particles and the total flux of source particles;

[0024] The first particle ratio is determined according to the second particle ratio and the preset ratio.

[0025] In one embodiment, the flux value in the leakage particle distribution is correlated with the sampling probability.

[0026] In one embodiment, determining the source particle distribution and the leakage particle distribution includes:

[0027] determining collimator parameters; and

[0028] According to the collimator parameters, the source particle distribution and the leakage particle distribution are determined.

[0029] In a second aspect, the present application further provides a modeling device, comprising:

[0030] A first determination module is used to determine the source particle distribution and the leakage particle distribution;

[0031] a second determining module, configured to determine a first particle ratio between target scattered particles and source particles based on the source particle distribution and the leakage particle distribution;

[0032] a sampling module for sampling and determining particle motion parameters of target scattered particles based on the leakage particle distribution, at least one of the angular distribution and the energy distribution of the target scattered particles, and the first particle ratio;

[0033] The third determining module is configured to determine a distribution model of the target scattered particles based on the particle motion parameters of the target scattered particles.

[0034] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any of the above methods when executing the computer program.

[0035] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of any of the above methods when executed by a processor.

[0036] In a fifth aspect, the present application also provides a computer program product, comprising a computer program, which implements the steps of any of the above methods when executed by a processor.

[0037] The above-described modeling method, device, and storage medium first determine the source particle distribution and the leakage particle distribution. Based on the source and leakage particle distributions, a first particle ratio between target scattered particles and source particles is determined. Then, based on the leakage particle distribution, at least one of the angular distribution and energy distribution of the target scattered particles, and the first particle ratio, the particle motion parameters of the target scattered particles are sampled and determined. Based on the particle motion parameters of the target scattered particles, a distribution model of the target scattered particles can be determined. On the one hand, current modeling methods such as Monte Carlo algorithms do not consider the scattering effect of the collimator on the particles, resulting in low accuracy. However, the method provided in this embodiment, by determining the distribution model of the target scattered particles, considers the scattering effect of the collimator on the particles, thereby improving modeling accuracy. On the other hand, current modeling methods such as Monte Carlo algorithms simulate the particle transport process through a full modeling approach, resulting in a large number of simulation events and long computation time, leading to low modeling efficiency. This embodiment, however, directly samples the particle motion parameters of the target scattered particles, eliminating the need to simulate the particle transport process within the collimator, reducing computation time and thus improving modeling efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0039] Figure 1 This is an application environment diagram of the modeling method in the embodiment of the present application;

[0040] Figure 2 This is a flow chart of the modeling method in the embodiment of the present application;

[0041] Figure 3 Schematic diagram of particles in the examples of this application;

[0042] Figure 4 This is a schematic diagram of a process for determining particle motion parameters in an embodiment of the present application;

[0043] Figure 5 This is a schematic diagram of another process for determining particle motion parameters in an embodiment of the present application;

[0044] Figure 6 This is a schematic diagram of a process for determining a first particle ratio in an embodiment of the present application;

[0045] Figure 7 This is a schematic diagram of another process for determining the first particle ratio in an embodiment of the present application;

[0046] Figure 8 This is a schematic diagram of a process for determining particle distribution in an embodiment of the present application;

[0047] Figure 9 A schematic diagram of the principle of a modeling method in an embodiment of the present application;

[0048] Figure 10 A schematic diagram of a process of a modeling method in an embodiment of the present application;

[0049] Figure 11 This is a structural block diagram of the modeling and adjustment device in an embodiment of the present application. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0051] Figure 1 This is an application environment diagram of the modeling method in the embodiment of this application. Figure 1A computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 1 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store relevant data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a modeling method is implemented.

[0052] Those skilled in the art will understand that Figure 1 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0053] This embodiment illustrates this method using a server as an example. It is understood that this method can also be applied to a terminal, or to a system comprising a terminal and a server, and implemented through interaction between the terminal and the server. The terminal may be, but is not limited to, various personal computers, laptops, smartphones, and tablet computers. The server may be implemented as a standalone server or a server cluster consisting of multiple servers.

[0054] Figure 2 This is a flow chart of the modeling method in the embodiment of the present application. In an exemplary embodiment, Figure 2 As shown, a modeling method is provided to Figure 1 The computer device in is used as an example for description, including the following S201 to S203.

[0055] S201, determining source particle distribution and leakage particle distribution.

[0056] In order to more clearly introduce the modeling method of this embodiment, first combine Figure 3 Provide explanation. Figure 3 Schematic diagram of particles in the embodiment of this application, such as Figure 3As shown, the emission source 301 emits particles, most of which will be blocked by the collimator 302 and cannot pass through the collimator 302; some particles will pass through the opening between the two collimators 302; and a small number of particles will pass through the collimator 302. The above particles that are not blocked are collectively referred to as source particles. Figure 3 Taking position 304 in the image as an example, the particles that reach position 304 are also source particles. It can be seen that source particles include particles that pass through the opening and particles that pass through the collimator. Among the source particles, the particles that pass through collimator 302 are leakage particles 303. In other words, leakage particles 303 are the source particles excluding the particles that pass through the opening.

[0057] Here, radiation source 301 refers to the radiation source in a radiation delivery device. Radiation delivery devices may include, but are not limited to, various devices that deliver radiation, such as treatment heads and radiation detection probes. Collimator 302 may include one or more of a primary collimator, a secondary collimator, and a multi-leaf collimator.

[0058] Please continue to refer to Figure 3 When the leakage particles 303 pass through the collimator, some of the particles are directly transmitted from the collimator 302 without angular deflection. These particles are called transmitted particles 306. Another part of the particles is scattered by the collimator 302. These particles are called scattered particles 305. The scattered particles 305 can irradiate the outside of the collimator 302, thereby increasing the dose outside the radiation field. The applicant wants to explain that although Figure 3 The figure shows the situation where the scattering particles 305 change their movement direction at the lower end surface of the collimator 302 (i.e., scattering occurs at the lower end surface of the collimator 302), but it is only used to schematically illustrate the distribution of the particles. Scattering may occur in various parts of the collimator 302, for example, on other end surfaces of the collimator 302, inside the collimator 302, etc.

[0059] It is understandable that the smaller the opening, the fewer particles passing through the opening, the more leakage particles 302, and the more scattered particles 305. Therefore, the applicant realizes that the influence of the scattered particles 305 on the dose distribution cannot be ignored.

[0060] In this embodiment, the source particle distribution and the leakage particle distribution can first be obtained by a computer device. There are no limitations on the method for obtaining the source particle distribution and the leakage particle distribution. For example, the source particle distribution and the leakage particle distribution can be read from a storage device, directly measured using a measuring device such as a detector, or calculated based on various data such as machine parameters and dose measurement values.

[0061] Please combine Figure 3The source particle distribution indicates the distribution of source particles as a percentage of particles emitted by emission source 301. The leakage particle distribution indicates the distribution of leakage particles 303 as a percentage of particles emitted by emission source 301. The source particle distribution and the leakage particle distribution can be in any form, including but not limited to, a flux diagram, a histogram, a distribution curve, or a mathematical function model. The source particle distribution and the leakage particle distribution can be in the same or different forms.

[0062] In one embodiment, the source particle distribution includes a source particle flux map, and the leakage particle distribution includes a leakage particle distribution map. Taking the leakage particle flux map as an example, assuming the collimator coverage is 40 cm × 40 cm, the collimator coverage can be divided into a 900 × 900 two-dimensional grid. Each grid uniquely identifies a position in the collimator. For example, grid 1 represents the position corresponding to the first row and first column, and grid 2 represents the position corresponding to the first row and second column.

[0063] Each network corresponds to a flux value between 0 and 1, which represents the ratio of the number of particles leaking out of the source to the number of particles emitted by the source in that network area. For example, if the flux value corresponding to grid 1 is 0, it means that at the location corresponding to grid 1, all particles emitted by the source are blocked by the collimator. If the flux value corresponding to grid 1 is 1, it means that at the location corresponding to grid 1, all particles emitted by the source are not blocked by the collimator. If the flux value corresponding to grid 1 is 0.1, it means that at the location corresponding to grid 1, nine out of ten particles emitted by the source are blocked by the collimator, and one out of ten particles are not blocked by the collimator.

[0064] It should be noted that the above only illustrates an optional form of the leakage particle flux map, and this embodiment is not limited thereto. The source particle flux map is similar and will not be described in detail here.

[0065] S202 : Determine a first particle ratio between target scattered particles and source particles according to the source particle distribution and the leakage particle distribution.

[0066] In this embodiment, after determining the source particle distribution and the leakage particle distribution, the computer device can determine a first particle ratio based on the source particle distribution and the leakage particle distribution. The first particle ratio refers to the ratio between target scattered particles and source particles. Target scattered particles refer to the scattered particles used for modeling among the leakage particles, and may be some or all of the scattering particles corresponding to the leakage particles.

[0067] Optionally, the computer device may determine a first particle ratio between target scattered particles and source particles based on the source particle distribution and the leakage particle distribution, as well as a first preset relationship between different source particle distributions, leakage particle distributions, and the particle ratio between scattered particles and source particles. The first preset relationship may be obtained in various ways, such as, for example, by calculation based on physical principles, by statistical determination based on historical measurement data, by determination based on user experience, and so forth.

[0068] For example, assuming that the source particle distribution is in range A and the leakage particle distribution is in range B, and in the first preset relationship, when the source particles are distributed in range A and the leakage particles are distributed in range B, the ratio between the scattered particles and the source particles is in range C, then the computer device can take any value from range C as the first particle ratio.

[0069] S203 , sampling and determining particle motion parameters of the target scattered particles based on the leakage particle distribution, at least one of the angular distribution and the energy distribution of the target scattered particles, and the first particle ratio.

[0070] Because the leaked particles reflect within the collimator, different scattered particles will have different motion directions and energies after exiting the collimator. Therefore, in this embodiment, the computer device needs to sample and determine the particle motion parameters of the target scattered particles based on the leaked particle distribution, at least one of the angular distribution and energy distribution of the target scattered particles, and the first particle ratio.

[0071] The angular distribution indicates the possible angular distribution of the target scattered particles after exiting the collimator, including multiple possible motion directions. The angular distribution can be discrete or continuous, for example, a distribution function corresponding to 0 to 180 degrees, where 0 to 180 degrees indicates each possible motion direction.

[0072] The energy distribution indicates the possible energy of the target scattered particles after exiting the collimator. It includes multiple possible particle energies. Similarly, the energy distribution can be discrete or continuous. For example, the energy distribution is a distribution function corresponding to E1 and E2, where E1 and E2 are used to indicate the possible particle energies, E1 and E2 are two different values, and E2>E1.

[0073] The angular distribution or energy distribution of the target scattered particles may be a distribution obtained by the computer device from other electronic devices, or a distribution fitted by the computer device using a preset fitting method, or a distribution determined by the computer device in response to a user's input operation. This embodiment is not limited to this.

[0074] Particle motion parameters indicate the motion of target scattered particles after exiting the collimator. In one exemplary embodiment, the particle motion parameters may include at least one of initial position, motion direction, and particle energy. Specifically, the computer device can sample and determine at least one of the initial position, motion direction, and particle energy of the target scattered particles based on at least one of the leakage particle distribution, angular distribution, and energy distribution, as well as the first particle ratio.

[0075] At least one of the leakage particle distribution, the angular distribution and the energy distribution of the target scattered particles includes one or more of the leakage particle distribution, the angular distribution and the energy distribution of the target scattered particles.

[0076] It can be understood that if the computer device determines the initial position of the target scattered particles based on the leakage particle distribution and the first particle ratio, it can sample and determine the movement direction of the target scattered particles based on the angle distribution and the first particle ratio; if the computer device determines the particle energy of the target scattered particles based on the energy distribution and the first particle ratio.

[0077] In the above description, since the particle motion parameters include at least one of the initial position, the motion direction and the particle energy, the flexibility of determining the particle motion parameters is improved.

[0078] It should be noted that, in some embodiments, part or all of the particle motion parameters may be determined by sampling, and part of the particle motion parameters may also be determined by reading parameters pre-stored in a computer device or by experimental measurement.

[0079] For example, the computer device may sample and determine the particle motion parameters of the target scattered particles according to the leakage particle distribution and the first particle ratio, and determine the particle energy of the target scattered particles by reading pre-stored parameters.

[0080] S204 : Determine a distribution model of the target scattering particles based on the particle motion parameters of the target scattering particles.

[0081] In this embodiment, after the computer device determines the particle motion parameters of the target scattered particles, that is, determines the motion of the target scattered particles after being emitted from the collimator, the computer device can then determine a distribution model of the target scattered particles based on the particle motion parameters of the target scattered particles. The computer device can determine the distribution model of the target scattered particles based on the particle motion parameters of the target scattered particles using various algorithms, such as a Monte Carlo algorithm, a differential convolution integral algorithm, or a pencil beam algorithm. For example, the computer device can fit the particle motion parameters of the target scattered particles using a preset fitting function based on the Monte Carlo algorithm to obtain the distribution model of the target scattered particles. As a non-limiting example, the distribution model of the target scattered particles can illustrate, for example, the distribution and / or motion of the target scattered particles within a space (e.g., near the beam outlet of a radiation delivery device, within the radiation delivery device, etc.).

[0082] Further optionally, the computer device can add a distribution model of target scattered particles to the virtual source model corresponding to the radiation delivery device. The virtual source model can be, for example, a model obtained by modeling particles in the radiation delivery device other than the target scattered particles using various algorithms, such as Monte Carlo, differential convolution integral, and pencil beam algorithms. Based on this, the simulation of scattered particles can be added to the existing virtual source model to obtain a distribution model of particles in the radiation delivery device. This can more accurately simulate the actual distribution of particles emitted from the radiation delivery device, thereby more realistically simulating the actual dose distribution, particularly in low-dose areas outside the radiation field.

[0083] In the above modeling method, the source particle distribution and the leakage particle distribution are first determined. Based on the source and leakage particle distributions, a first particle ratio between the target scattered particles and the source particles is determined. Then, based on the leakage particle distribution, at least one of the angular distribution and energy distribution of the target scattered particles, and the first particle ratio, the particle motion parameters of the target scattered particles are sampled and determined. Based on the particle motion parameters of the target scattered particles, a distribution model of the target scattered particles can be determined. On the one hand, current modeling methods such as the Monte Carlo algorithm do not consider the scattering effect of the collimator on the particles, resulting in low accuracy. However, the method provided in this embodiment, by determining the distribution model of the target scattered particles, considers the scattering effect of the collimator on the particles, improving modeling accuracy. On the other hand, current modeling methods such as the Monte Carlo algorithm simulate the particle transport process through a full modeling approach, resulting in a large number of simulation events and long computation time, leading to low modeling efficiency. This embodiment, however, directly samples the particle motion parameters of the target scattered particles, eliminating the need to simulate the particle transport process within the collimator, reducing computation time and thus improving modeling efficiency.

[0084] Figure 4 This is a flow chart of determining particle motion parameters in an embodiment of the present application. In an exemplary embodiment, Figure 4 As shown, S203 includes S401 to S402.

[0085] S401 : Determine the sampling quantity of target scattered particles according to the first particle ratio and the total flux of source particles.

[0086] In this embodiment, the computer device can determine the total flux of source particles, where the total flux of source particles is used to indicate the total number of particles emitted from the emission source and not blocked by the collimator.

[0087] Optionally, the computer device may receive the total flux of source particles sent by other electronic devices, the computer device may determine the total flux of source particles in response to an input operation, or the computer device may determine the total flux of source particles based on a virtual source model of the treatment head.

[0088] Furthermore, the computer device can determine the number of target scattered particles to be sampled based on the first particle ratio and the total flux of the source particles. Alternatively, the computer device can directly use the product of the first particle ratio and the total flux of the source particles as the number of target scattered particles to be sampled.

[0089] S402: Determine at least one of an initial position, a moving direction, and a particle energy of the target scattered particles based on at least one of the leakage particle distribution, the angle distribution, and the energy distribution, and the number of samples.

[0090] In this embodiment, after the computer device determines the sampling number, it can determine at least one of the initial position, movement direction and particle energy of the target scattered particles based on at least one of the leakage particle distribution, angle distribution, energy distribution and the sampling number.

[0091] Likewise, at least one of the leakage particle distribution, the angular distribution, and the energy distribution includes one or more of the leakage particle distribution, the angular distribution, and the energy distribution.

[0092] For example, the computer device can sample the leakage particle distribution based on the sampling number to obtain the initial position of the target scattered particles, or can sample the leakage particle distribution and angle distribution respectively based on the sampling number to obtain the initial position and movement direction of the target scattered particles.

[0093] Since this embodiment can determine the sampling number of target scattered particles based on the first particle ratio and the total flux of source particles, and determine at least one of the initial position, movement direction, and particle energy of the target scattered particles based on at least one of the leakage particle distribution, angular distribution, energy distribution, and sampling number, it is unnecessary to simulate the transport process of the scattered particles within the collimator, thereby improving the efficiency of determining the particle motion parameters.

[0094] Figure 5 This is another flow chart of determining particle motion parameters in an embodiment of the present application. In an exemplary embodiment, Figure 5 As shown, S402 includes S501 to S503.

[0095] S501: Based on the sampling quantity, the leakage particle distribution is sampled to obtain the initial position of the target scattered particle.

[0096] In this embodiment, the scattered particles are the part of the leaked particles that are scattered by the collimator. Therefore, the initial positions of the scattered particles and the initial positions of the leaked particles are the same. Therefore, the initial positions of the target scattered particles can be obtained by sampling the leaked particle distribution.

[0097] Assuming that the sampling number is N, where N is an integer greater than 0, the computer device samples the leakage particle distribution based on the sampling number to obtain N initial positions corresponding to the N target scattering particles.

[0098] Taking the leakage particle distribution including the leakage particle flux map as an example, the computer device can extract the positions corresponding to N networks from the 900×900 networks as the N initial positions corresponding to the N target scattering particles. For example, the computer device uses the position corresponding to network 2 as the initial position of target scattering particle 1, the position corresponding to network 50 as the initial position of target scattering particle 2, and so on, using the position corresponding to network 65 as the initial position of target scattering particle N.

[0099] S502: Based on the sampling quantity, each possible motion direction in the angle distribution is sampled to obtain the motion direction of the target scattered particles.

[0100] Similarly, the computer device samples each possible direction of motion in the angle distribution based on the sampling quantity, and obtains N directions of motion corresponding to N target scattering particles.

[0101] Exemplarily, 0 to 180° includes all possible motion directions. The computer device can extract 53° from 0 to 180° as the motion direction of target scattering particle 1, extract 0° from 0 to 180° as the motion direction of target scattering particle 2, and so on, extract 30° from 0 to 180° as the motion direction of target scattering particle N.

[0102] S503: Based on the sampling quantity, each possible particle energy of the energy distribution is sampled to obtain the particle energy of the target scattered particle.

[0103] Similarly, the computer equipment samples each possible particle energy in the energy distribution based on the sampling quantity, and obtains N particle energies corresponding to N target scattered particles.

[0104] Exemplarily, E1 to E2 include various possible particle energies, and the computer device can extract a value from E1 to E2 as the particle energy of target scattering particle 1, extract a value from E1 to E2 as the particle energy of target scattering particle 2, and so on.

[0105] It should be noted that random sampling can be performed in S501 to S503, or sampling can be performed using methods including but not limited to stratified sampling, quota sampling, etc.

[0106] This embodiment can sample the leakage particle distribution based on the sampling number to obtain the initial position of the target scattered particle, and can sample each possible motion direction in the angle distribution based on the sampling number to obtain the motion direction of the target scattered particle, and can sample each possible particle energy in the energy distribution based on the sampling number to obtain the particle energy of the target scattered particle. Therefore, the particle motion parameters of the target scattering particle can be directly sampled, thereby improving modeling efficiency.

[0107] In an exemplary embodiment, optionally, the flux value in the leakage particle distribution is correlated with the sampling probability.

[0108] When it is necessary to sample the initial position of the target scattering particles, since sampling is performed using the leakage particle distribution, and the flux value in the leakage particle distribution can reflect the flux of the leakage particles in the corresponding position, sampling using the leakage particle distribution makes the flux value correlated with the sampling probability, which can make the sampling process closer to the actual situation and improve the sampling accuracy.

[0109] Alternatively, the flux value in the leakage particle distribution is positively correlated with the sampling probability. In other words, the larger the flux value in the leakage particle distribution, the more likely that location is to be sampled as the initial location of the target scattered particle. Since a larger flux value in the leakage particle distribution indicates a greater flux of leakage particles at that location, sampling at locations with a high number of leakage particles helps improve the reliability and accuracy of the resulting particle motion parameters.

[0110] Figure 6 This is a flow chart of determining the first particle ratio in an embodiment of the present application. In an exemplary embodiment, as Figure 5 As shown, S202 includes S501 to S503.

[0111] S601: Integrate the leakage particle distribution to obtain the total flux of the leakage particles.

[0112] S602: Integrate the source particle distribution to obtain the total flux of the source particles.

[0113] In this embodiment, since the leakage particle distribution and the source particle distribution can respectively indicate the distribution of leakage particles and source particles, the total flux of leakage particles can be determined by integrating the leakage particle distribution, and the total flux of source particles can be obtained by integrating the source particle distribution.

[0114] S603: Determine a first particle ratio according to the total flux of the leakage particles and the total flux of the source particles.

[0115] In this embodiment, after the computer device determines the total flux of the leakage particles and the total flux of the source particles, it can determine the first particle ratio according to the total flux of the leakage particles and the total flux of the source particles.

[0116] Optionally, the computer device may determine the first particle ratio based on the total flux of the leakage particles and the total flux of the source particles and a second preset relationship. The second preset relationship includes different relationships between the total flux of the leakage particles, the total flux of the source particles, and the first particle ratio. For example, assuming that the total flux of the leakage particles is M and the total flux of the source particles is N, and in the second preset relationship, when the total flux of the leakage particles is M and the total flux of the source particles is N, the first particle ratio between the scattered particles and the source particles is K, then the computer device determines the first particle ratio to be K. M, N, and K are all numbers greater than 0, and N>M.

[0117] Since this embodiment can integrate the leakage particle distribution to obtain the total flux of leakage particles, and integrate the source particle distribution to obtain the total flux of source particles, the first particle ratio can be determined efficiently and accurately based on the total flux of leakage particles and the total flux of source particles.

[0118] Figure 7 This is another flow chart of determining the first particle ratio in an embodiment of the present application. In an exemplary embodiment, as Figure 7 As shown, S603 includes S701 to S702.

[0119] S701 , determining a second particle ratio between a total flux of leakage particles and a total flux of source particles.

[0120] In this embodiment, assuming that the total flux of leakage particles is M and the total flux of source particles is N, the computer device can use M / N as the second particle ratio. Of course, the computer device can also obtain the second particle ratio by rounding M / N.

[0121] S702 : Determine a first particle ratio according to the second particle ratio and a preset ratio, wherein the preset ratio is a relationship between the first particle ratio and the second particle ratio.

[0122] In this embodiment, the preset ratio may be a parameter pre-stored in a computer device. The preset ratio is the relationship between the first particle ratio and the second particle ratio. For example, the preset ratio includes the ratio between scattered particles and transmitted particles in the leakage particles. The preset ratio may also include the ratio between the ratio of leakage particles to source particles and the ratio of transmitted particles to source particles.

[0123] The computer then determines the first particle ratio based on the second particle ratio and the preset ratio. For example, if the computer determines the second particle ratio to be M / N and it is known that the number of scattered particles in the leakage particles is twice that of transmitted particles, the computer can determine the first particle ratio to be 3M / 2N.

[0124] In this embodiment, since the preset ratio is the relationship between the first particle ratio and the second particle ratio, after determining the second particle ratio between the total flux of the leakage particles and the total flux of the source particles, the first particle ratio can be determined based on the second particle ratio and the preset ratio.

[0125] Figure 8 This is a flow chart of determining particle distribution in an embodiment of the present application. In an exemplary embodiment, as shown in FIG. Figure 8 As shown, S201 includes S801 to S802.

[0126] S801 , determining collimator parameters, where the collimator parameters include the size and position of a collimator of a radiation delivery device.

[0127] In this embodiment, the radiation delivery device may include, but is not limited to, a treatment head. The collimator may include one or more of a primary collimator (also referred to as a primary collimator), a secondary collimator, and a multi-leaf collimator. As non-limiting examples, the secondary collimator may include, but is not limited to, a tungsten gate, and the multi-leaf collimator may include, but is not limited to, a multi-leaf collimator.

[0128] The computer device can determine collimator parameters in response to user input. The computer device can also receive collimator parameters sent by other electronic devices. The collimator parameters can also be pre-stored in the computer device and can be retrieved when the source particle distribution and the leakage particle distribution need to be determined.

[0129] For example, the computer device may determine the size and position of the multi-leaf grating and the tungsten gate.

[0130] S802: Determine source particle distribution and leakage particle distribution according to collimator parameters.

[0131] Furthermore, the computer device can determine the source particle distribution and the leakage particle distribution based on the collimator parameters. Alternatively, the computer device can use the collimator parameters to perform projection based on the direction and distance between the emission source and the collimator to determine the source particle distribution and the leakage particle distribution.

[0132] In this embodiment, the collimator parameters may include the size and position of the collimator of the radiation delivery device. Therefore, based on the collimator parameters, a more accurate source particle distribution and leakage particle distribution can be determined to improve the accuracy of modeling.

[0133] In order to more clearly introduce the modeling method of this application, Figure 9 and Figure 10 Provide explanation. Figure 9 This is a schematic diagram of the principle of an exemplary modeling method in an embodiment of the present application, such as Figure 9 As shown, in this embodiment, a source particle distribution and a leakage particle distribution are generated based on the collimator parameters. The first particle ratio between the target scattered particles and the source particles is calculated based on the source and leakage particle distributions. Subsequently, based on the first particle ratio, the initial position, motion direction, and particle energy of the target scattered particles are sampled. Ultimately, based on the initial position, motion direction, and particle energy of the target scattered particles, the distribution model of the target scattered particles is determined.

[0134] Figure 10 This is a schematic diagram of a process of a modeling method in an embodiment of the present application, such as Figure 10 As shown, the computer device can execute the method according to the following process.

[0135] S1001, determining collimator parameters, wherein the collimator parameters include the size and position of the collimator of the radiation delivery device.

[0136] S1002: Determine source particle distribution and leakage particle distribution according to collimator parameters.

[0137] S1003: Integrate the leakage particle distribution to obtain the total flux of the leakage particles.

[0138] S1004: Integrate the source particle distribution to obtain the total flux of the source particles.

[0139] S1005 , determining a second particle ratio between the total flux of leakage particles and the total flux of source particles.

[0140] S1006: Determine the first particle ratio according to the second particle ratio and the preset ratio, wherein the preset ratio is the relationship between the first particle ratio and the second particle ratio.

[0141] S1007: Determine the sampling quantity of target scattered particles according to the first particle ratio and the total flux of source particles.

[0142] S1008: Based on the sampling quantity, the leakage particle distribution is sampled to obtain the initial position of the target scattered particle.

[0143] S1009 , sampling each possible movement direction in the angular distribution of the target scattered particles based on the sampling quantity to obtain the movement direction of the target scattered particles.

[0144] S1010: Based on the sampling number, each possible particle energy in the energy distribution of the target scattered particles is sampled to obtain the particle energy of the target scattered particles. In the above sampling, the flux value in the leakage particle distribution is correlated with the sampling probability.

[0145] S1011 : Determine a distribution model of the target scattering particles based on the particle motion parameters of the target scattering particles.

[0146] S1001 to S1011 may refer to the above embodiment and will not be described in detail here.

[0147] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0148] Based on the same inventive concept, the present application also provides a modeling device for implementing the aforementioned modeling method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more modeling device embodiments provided below can be found in the above-mentioned limitations of the modeling method and will not be repeated here.

[0149] Figure 11This is a structural block diagram of the modeling and adjustment device in the embodiment of the present application. In an exemplary embodiment, Figure 11 As shown, a modeling device is provided, including: a first determination module 1101, a second determination module 1102, a sampling module 1103 and a third determination module 1104, wherein:

[0150] The first determining module 1101 is used to determine the source particle distribution and the leakage particle distribution.

[0151] The second determining module 1102 is configured to determine a first particle ratio between target scattered particles and source particles according to the source particle distribution and the leakage particle distribution.

[0152] The sampling module 1103 is configured to sample and determine the particle motion parameters of the target scattered particles according to the leakage particle distribution, at least one of the angular distribution and the energy distribution of the target scattered particles, and the first particle ratio.

[0153] The third determining module 1104 is configured to determine a distribution model of the target scattered particles based on the particle motion parameters of the target scattered particles.

[0154] In the above-described modeling device, the source particle distribution and the leakage particle distribution are first determined. Based on the source and leakage particle distributions, a first particle ratio between the target scattered particles and the source particles is determined. Then, based on the leakage particle distribution, at least one of the angular distribution and energy distribution of the target scattered particles, and the first particle ratio, the particle motion parameters of the target scattered particles are sampled and determined. Based on the particle motion parameters of the target scattered particles, a distribution model of the target scattered particles can be determined. On the one hand, current modeling methods such as the Monte Carlo algorithm do not consider the scattering effect of the collimator on the particles, resulting in low accuracy. However, the device provided in this embodiment, by determining the distribution model of the target scattered particles, considers the scattering effect of the collimator on the particles, improving modeling accuracy. On the other hand, current modeling methods such as the Monte Carlo algorithm simulate the particle transport process through a full modeling approach, resulting in a large number of simulation events and long computation time, leading to low modeling efficiency. This embodiment, however, directly samples the particle motion parameters of the target scattered particles, eliminating the need to simulate the particle transport process within the collimator, reducing computation time and thus improving modeling efficiency.

[0155] Optionally, the particle motion parameters include at least one of an initial position, a motion direction, and particle energy.

[0156] Optionally, the sampling module 1103 includes:

[0157] The first determining unit is configured to determine a sampling quantity of target scattered particles according to the first particle ratio and the total flux of source particles.

[0158] The second determining unit is configured to determine at least one of an initial position, a moving direction, and a particle energy of the target scattered particles based on at least one of the leakage particle distribution, the angle distribution, the energy distribution, and the sampling quantity.

[0159] Optionally, the second determining unit includes:

[0160] The first sampling subunit is used to sample the leakage particle distribution based on the sampling quantity to obtain the initial position of the target scattered particle.

[0161] The second sampling subunit is used to sample each possible motion direction in the angle distribution based on the sampling quantity to obtain the motion direction of the target scattered particles.

[0162] The third sampling subunit is used to sample each possible particle energy of the energy distribution based on the sampling quantity to obtain the particle energy of the target scattered particle.

[0163] Optionally, the second determining module 1102 includes:

[0164] The first integration unit is used to integrate the leakage particle distribution to obtain the total flux of the leakage particles.

[0165] The second integration unit is used to integrate the source particle distribution to obtain the total flux of the source particles.

[0166] The third determining unit is configured to determine a first particle ratio according to a total flux of leakage particles and a total flux of source particles.

[0167] Optionally, the third determining unit includes:

[0168] The first determining subunit is configured to determine a second particle ratio between a total flux of leakage particles and a total flux of source particles.

[0169] The second determining subunit is configured to determine the first particle ratio according to the second particle ratio and a preset ratio, wherein the preset ratio is a relationship between the first particle ratio and the second particle ratio.

[0170] Optionally, the flux value in the leakage particle distribution is correlated with the sampling probability.

[0171] Optionally, the first determining module 1101 includes:

[0172] The fourth determining unit is configured to determine collimator parameters, where the collimator parameters include a size and a position of a collimator of the radiation delivery device.

[0173] The fifth determining unit is configured to determine the source particle distribution and the leakage particle distribution according to the collimator parameters.

[0174] Each module in the above-mentioned modeling device can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0175] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0176] Determine the source particle distribution and the leakage particle distribution;

[0177] determining a first particle ratio between target scattered particles and source particles based on the source particle distribution and the leakage particle distribution;

[0178] determining, by sampling, particle motion parameters of the target scattered particles based on the leakage particle distribution, at least one of the angular distribution and the energy distribution of the target scattered particles, and the first particle ratio; and

[0179] A distribution model of the target scattering particles is determined based on the particle motion parameters of the target scattering particles.

[0180] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0181] The particle motion parameters include at least one of an initial position, a motion direction, and particle energy.

[0182] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0183] The sampling number of the target scattered particles is determined according to the first particle ratio and the total flux of the source particles; and the initial position, movement direction and particle energy of the target scattered particles are determined based on at least one of the leakage particle distribution, the angular distribution and the energy distribution and the sampling number.

[0184] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0185] Based on the sampling number, the leakage particle distribution is sampled to obtain the initial position of the target scattered particle; based on the sampling number, each possible movement direction in the angle distribution is sampled to obtain the movement direction of the target scattered particle; based on the sampling number, each possible particle energy in the energy distribution is sampled to obtain the particle energy of the target scattered particle.

[0186] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0187] Integrating the leakage particle distribution to obtain the total flux of the leakage particles; integrating the source particle distribution to obtain the total flux of the source particles; and determining the first particle ratio according to the total flux of the leakage particles and the total flux of the source particles.

[0188] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0189] Determine a second particle ratio between the total flux of the leakage particles and the total flux of the source particles; and determine the first particle ratio based on the second particle ratio and a preset ratio, wherein the preset ratio is a relationship between the first particle ratio and the second particle ratio.

[0190] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0191] The flux value in the leakage particle distribution is correlated with the sampling probability.

[0192] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0193] Collimator parameters are determined, including the size and position of a collimator of a radiation delivery device; and the source particle distribution and the leakage particle distribution are determined based on the collimator parameters.

[0194] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0195] Determine the source particle distribution and the leakage particle distribution;

[0196] determining a first particle ratio between target scattered particles and source particles based on the source particle distribution and the leakage particle distribution;

[0197] determining, by sampling, particle motion parameters of the target scattered particles based on the leakage particle distribution, at least one of the angular distribution and the energy distribution of the target scattered particles, and the first particle ratio; and

[0198] A distribution model of the target scattering particles is determined based on the particle motion parameters of the target scattering particles.

[0199] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0200] The particle motion parameters include at least one of an initial position, a motion direction, and particle energy.

[0201] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0202] The sampling number of the target scattered particles is determined according to the first particle ratio and the total flux of the source particles; and the initial position, movement direction and particle energy of the target scattered particles are determined based on at least one of the leakage particle distribution, the angular distribution and the energy distribution and the sampling number.

[0203] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0204] Based on the sampling number, the leakage particle distribution is sampled to obtain the initial position of the target scattered particle; based on the sampling number, each possible movement direction in the angle distribution is sampled to obtain the movement direction of the target scattered particle; based on the sampling number, each possible particle energy in the energy distribution is sampled to obtain the particle energy of the target scattered particle.

[0205] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0206] Integrating the leakage particle distribution to obtain the total flux of the leakage particles; integrating the source particle distribution to obtain the total flux of the source particles; and determining the first particle ratio according to the total flux of the leakage particles and the total flux of the source particles.

[0207] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0208] Determine a second particle ratio between the total flux of the leakage particles and the total flux of the source particles; and determine the first particle ratio based on the second particle ratio and a preset ratio, wherein the preset ratio is a relationship between the first particle ratio and the second particle ratio.

[0209] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0210] The flux value in the leakage particle distribution is correlated with the sampling probability.

[0211] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0212] Collimator parameters are determined, including the size and position of a collimator of a radiation delivery device; and the source particle distribution and the leakage particle distribution are determined based on the collimator parameters.

[0213] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0214] Determine the source particle distribution and the leakage particle distribution;

[0215] determining a first particle ratio between target scattered particles and source particles based on the source particle distribution and the leakage particle distribution;

[0216] determining, by sampling, particle motion parameters of the target scattered particles based on the leakage particle distribution, at least one of the angular distribution and the energy distribution of the target scattered particles, and the first particle ratio; and

[0217] A distribution model of the target scattering particles is determined based on the particle motion parameters of the target scattering particles.

[0218] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0219] The particle motion parameters include at least one of an initial position, a motion direction, and particle energy.

[0220] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0221] The sampling number of the target scattered particles is determined according to the first particle ratio and the total flux of the source particles; and the initial position, movement direction and particle energy of the target scattered particles are determined based on at least one of the leakage particle distribution, the angular distribution and the energy distribution and the sampling number.

[0222] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0223] Based on the sampling number, the leakage particle distribution is sampled to obtain the initial position of the target scattered particle; based on the sampling number, each possible movement direction in the angle distribution is sampled to obtain the movement direction of the target scattered particle; based on the sampling number, each possible particle energy in the energy distribution is sampled to obtain the particle energy of the target scattered particle.

[0224] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0225] Integrating the leakage particle distribution to obtain the total flux of the leakage particles; integrating the source particle distribution to obtain the total flux of the source particles; and determining the first particle ratio according to the total flux of the leakage particles and the total flux of the source particles.

[0226] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0227] Determine a second particle ratio between the total flux of the leakage particles and the total flux of the source particles; and determine the first particle ratio based on the second particle ratio and a preset ratio, wherein the preset ratio is a relationship between the first particle ratio and the second particle ratio.

[0228] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0229] The flux value in the leakage particle distribution is correlated with the sampling probability.

[0230] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0231] Collimator parameters are determined, including the size and position of a collimator of a radiation delivery device; and the source particle distribution and the leakage particle distribution are determined based on the collimator parameters.

[0232] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, and the like.

[0233] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0234] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A modeling method for radiation delivery modeling, characterized in that: The modeling method comprises: Determine the source particle distribution and the leakage particle distribution; determining a first particle ratio between target scattered particles and source particles according to the source particle distribution and the leakage particle distribution; Determine, by sampling, particle motion parameters of the target scattered particles according to the leakage particle distribution, at least one of the angular distribution and the energy distribution of the target scattered particles, and the first particle ratio; and Based on the particle motion parameters of the target scattering particles, a distribution model of the target scattering particles is determined.

2. The modeling method according to claim 1, characterized in that: The particle motion parameters include at least one of an initial position, a motion direction and a particle energy.

3. The modeling method according to claim 2, characterized in that: The step of sampling and determining the particle motion parameters of the target scattered particles according to the leakage particle distribution, at least one of the angular distribution and the energy distribution of the target scattered particles, and the first particle ratio comprises: determining a sampling number of the target scattered particles according to the first particle ratio and the total flux of the source particles; At least one of the initial position, movement direction and particle energy of the target scattered particles is determined based on at least one of the leakage particle distribution, the angle distribution, the energy distribution and the sampling quantity.

4. The modeling method according to claim 3, characterized in that: The determining at least one of the initial position, the moving direction and the particle energy of the target scattered particles based on at least one of the leakage particle distribution, the angle distribution and the energy distribution and the sampling quantity comprises: Based on the sampling quantity, sampling the leakage particle distribution to obtain the initial position of the target scattered particle; Based on the sampling quantity, sampling each possible movement direction in the angle distribution to obtain the movement direction of the target scattered particles; and Based on the sampling quantity, each possible particle energy of the energy distribution is sampled to obtain the particle energy of the target scattered particle.

5. The modeling method according to any one of claims 1 to 4, characterized in that: The step of determining a first particle ratio between target scattered particles and source particles according to the source particle distribution and the leakage particle distribution comprises: Integrating the leakage particle distribution to obtain the total flux of the leakage particles; Integrating the source particle distribution to obtain a total flux of the source particles; The first particle ratio is determined according to the total flux of the leakage particles and the total flux of the source particles.

6. The modeling method according to claim 5, characterized in that: The determining the first particle ratio according to the total flux of the leakage particles and the total flux of the source particles comprises: determining a second particle ratio between the total flux of the leakage particles and the total flux of the source particles; The first particle ratio is determined according to the second particle ratio and a preset ratio.

7. The modeling method according to claim 3, characterized in that: The flux value in the leakage particle distribution is correlated with the sampling probability.

8. The modeling method according to claim 1, characterized in that: The determining of the source particle distribution and the leakage particle distribution comprises: determining collimator parameters; and The source particle distribution and the leakage particle distribution are determined according to the collimator parameters.

9. A modeling device for radiation delivery modeling, characterized in that: The modeling device comprises: A first determination module is used to determine source particle distribution and leakage particle distribution; A second determination module, configured to determine a first particle ratio between target scattered particles and source particles according to the source particle distribution and the leakage particle distribution; A sampling module, configured to sample and determine the particle motion parameters of the target scattered particles according to the leakage particle distribution, at least one of the angular distribution and the energy distribution of the target scattered particles, and the first particle ratio; The third determination module is used to determine the distribution model of the target scattering particles based on the particle motion parameters of the target scattering particles.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by a processor, the steps of the modeling method according to any one of claims 1 to 8 are implemented.

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