Beam model establishment method and device and computer readable storage medium

Abstract

The invention relates to a beam model establishment method and device and a computer readable storage medium. The method comprises the following steps: acquiring test data of the ray delivery equipment under a test condition, and determining an initial beam model of the ray delivery equipment and one or more types of to-be-adjusted model parameters in the initial beam model; and according to one or more of the test data corresponding to the to-be-adjusted model parameters, adjusting the to-be-adjusted model parameters to obtain a target beam model of the ray delivery device. According to the method, the data corresponding to the to-be-adjusted parameter items in the test data are adopted to correspondingly adjust the to-be-adjusted parameters based on different data types influenced by different types of to-be-adjusted model parameters in the initial beam model, so that the pertinence of parameter adjustment is improved, the modeling time is shortened, and the modeling efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of modeling algorithms, and in particular to a beam model building method, a beam model building device, and a computer-readable storage medium. Background Art

[0002] Typically, radiation delivery requires advance planning, and the accuracy of the plan depends greatly on the accuracy of beam modeling.

[0003] For example, to improve medical safety, doctors need to make a treatment plan in advance when performing surgery, radiotherapy and other treatment operations on patients. Taking radiotherapy as an example, before the patient undergoes radiotherapy, a radiotherapy plan can be made based on the patient and the radiotherapy equipment. In order to ensure the accuracy of the radiotherapy plan, it is necessary to model the beam of the radiation source of the accelerator in the radiotherapy equipment before making the radiotherapy plan to obtain a beam model that meets the dose distribution requirements.

[0004] However, the beam model involves many parameters, and multiple iterative calculations are required during the modeling process. Sometimes manual intervention is required to find the appropriate parameters, resulting in various problems such as time-consuming and low efficiency in the beam modeling process. Summary of the invention

[0005] Based on this, it is necessary to provide a beam model establishment method, device, and computer-readable storage medium to address the above technical issues.

[0006] In a first aspect, the present application provides a beam model establishment method, comprising:

[0007] obtaining test data of the radiation delivery device under test conditions;

[0008] determining an initial beam model of the radiation delivery device and one or more types of model parameters to be adjusted in the initial beam model; and

[0009] According to one or more parameters in the test data corresponding to the model parameters to be adjusted, the model parameters to be adjusted are adjusted to obtain a target beam model of the ray delivery device.

[0010] In one embodiment, adjusting the model parameters to be adjusted according to one or more parameters in the test data corresponding to the model parameters to be adjusted to obtain a target beam model of the radiation delivery device includes:

[0011] For one or more model parameters to be adjusted, the degree of conformity between a model curve calculated based on the parameter-adjusted initial beam model and a test curve based on test data is determined, and when the degree of conformity is lower than a threshold, the model parameters to be adjusted are re-iteratively adjusted.

[0012] In one embodiment, the data types of the test data and the model data calculated based on the initial beam model include depth dose distribution data and off-axis dose distribution data at a preset depth, and the model parameters to be adjusted include energy spectrum related parameters and field related parameters; according to one or more of the test data corresponding to the model parameters to be adjusted, the model parameters to be adjusted are adjusted to obtain a target beam model of the radiation delivery device, including:

[0013] Adjust the energy spectrum related parameters according to the depth dose distribution data in the test data and model data;

[0014] According to the off-axis dose distribution data at a preset depth in the test data and the model data, the field-related parameters are adjusted. In one embodiment, the depth dose distribution data includes a built-up distribution area and a drop-off distribution area;

[0015] According to the depth dose distribution data in the test data and model data, the energy spectrum related parameters are adjusted, including:

[0016] Adjusting the electron source size and electron source weight in the initial beam model according to the data of the built distribution area in the depth dose distribution data in the test data and the model data; and

[0017] The primary photon weight and the secondary photon weight in the initial beam model are adjusted according to the data of the corresponding drop distribution area in the depth dose distribution data in the test data and the model data.

[0018] In one of the embodiments, the off-axis dose distribution data at a preset depth includes a low dose distribution area, a penumbra distribution area, and a flattened distribution area;

[0019] According to the off-axis dose distribution data at the preset depth in the test dose distribution data and the model dose distribution data, the field-related parameters in the initial beam model are adjusted, including:

[0020] Adjusting the field external parameters in the initial beam model according to the data corresponding to the low-dose distribution area and the penumbra distribution area in the off-axis dose distribution data at the preset depth in the test data and the model data; and

[0021] The off-axis coefficient in the initial beam model is adjusted according to the data of the corresponding flattened distribution area in the off-axis dose distribution data at a preset depth in the test data and the model data.

[0022] In one embodiment, adjusting the model parameters to be adjusted according to one or more parameters in the test data corresponding to the model parameters to be adjusted to obtain a target beam model of the radiation delivery device includes:

[0023] Input the preset parameters into the parameter-adjusted initial beam model, and output reference dose distribution data;

[0024] Based on the reference dose distribution data, multiple control algorithms are used to determine the reference dose parameters of the target area under each control algorithm;

[0025] Obtaining test dose parameters of the target area determined by the test data under the corresponding control algorithm; and

[0026] The target beam model is determined according to each reference dose parameter and each test dose parameter.

[0027] In one embodiment, determining a target beam model according to each reference dose parameter and each test dose parameter includes:

[0028] Obtain the point dose deviation and surface dose pass rate between the reference dose parameters and the test dose parameters under each control algorithm;

[0029] When the point dose deviation and the surface dose pass rate under each control algorithm meet the preset dose requirements, the adjusted initial beam model is determined as the target beam model;

[0030] When the reference dose parameters do not meet the preset dose requirements, the parameters of the parameter-adjusted initial beam model are adjusted to obtain a target beam model.

[0031] In one embodiment, adjusting the parameters of the parameter-adjusted initial beam model to obtain a target beam model includes:

[0032] Determine a target control algorithm for calculating reference dose parameters for a target area;

[0033] Determining target model parameters corresponding to the target control algorithm according to the corresponding relationship between the control algorithm and the model parameters to be adjusted; and

[0034] The target model parameters in the parameter-adjusted initial beam model are adjusted to obtain a target beam model.

[0035] In a second aspect, the present application also provides a beam model establishment device, comprising:

[0036] A test data acquisition module, used to acquire test data of the radiation delivery device under test conditions;

[0037] An initial model determination module, used to determine an initial beam model of the radiation delivery device and one or more types of model parameters to be adjusted in the initial beam model;

[0038] The model parameter adjustment module is used to adjust the model parameters to be adjusted according to one or more parameters corresponding to the model parameters to be adjusted in the test data, so as to obtain the target beam model of the ray delivery device.

[0039] 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-mentioned model building methods when executing the computer program.

[0040] In a fourth aspect, the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of any of the above-mentioned model building methods are implemented.

[0041] In a fifth aspect, the present application also provides a computer program product, including a computer program, which implements the steps of any of the above-mentioned model building methods when executed by a processor.

[0042] The above-mentioned model building method, apparatus, computer equipment, storage medium and computer program product obtain test data of the radiation delivery device under test conditions, and determine the initial beam model of the radiation delivery device and one or more types of model parameters to be adjusted in the initial beam model; and adjust the model parameters to be adjusted according to one or more corresponding to the model parameters to be adjusted in the test data, so as to obtain the target beam model of the radiation delivery device. In the above-mentioned method, based on the different types of data affected by different types of model parameters to be adjusted in the initial beam model, the data corresponding to the parameter items to be adjusted in the test data are used to adjust the parameters to be adjusted accordingly, thereby improving the pertinence of parameter adjustment, thereby shortening the modeling time and improving the modeling efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is an internal structure diagram of a computer device in one embodiment;

[0044] Figure 2 A schematic diagram of a flow chart of a beam model establishment method in one embodiment;

[0045] Figure 3 A schematic diagram of a process for adjusting a model parameter to be adjusted in one embodiment;

[0046] Figure 4 A schematic diagram of a PDD curve in one embodiment;

[0047] Figure 5 A schematic diagram of a Profile X / Y curve in one embodiment;

[0048] Figure 6 A schematic diagram of a process for adjusting energy spectrum related parameters in one embodiment;

[0049] Figure 7 A schematic diagram of a process for adjusting radiation field related parameters in one embodiment;

[0050] Figure 8 A schematic diagram of a process for adjusting energy spectrum related parameters in another embodiment;

[0051] Fig. 9 A schematic diagram of a process for adjusting radiation field related parameters in one embodiment;

[0052] Fig.10 A schematic diagram of a flow chart of a model building method in another embodiment;

[0053] Fig.11 A schematic diagram of a process for determining a target beam model according to reference dose parameters and test dose parameters in one embodiment;

[0054] Fig.12 A schematic diagram of a flow chart of adjusting the parameters of a model to be adjusted in another embodiment;

[0055] Fig.13 A schematic diagram of a flow chart of a beam model establishment method in another embodiment;

[0056] Fig.14 A structural block diagram of a beam model building device in one embodiment. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0058] The model building method provided in the embodiment of the present application can be applied to Figure 1The computer device shown in the figure can be a terminal. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected by a system bus. Among them, 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 and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a model building method is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0059] 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 those shown in the figure, or combine certain components, or have a different arrangement of components.

[0060] In one embodiment, Figure 2 As shown, a beam model establishment method is provided, and the method is applied to Figure 1 The computer device in the example is used to illustrate, including the following steps:

[0061] S210. Acquire test data of the radiation delivery device under test conditions.

[0062] Here, the test conditions may be specific conditions used to form a control. For example, the test conditions may include one or more of the following items: the radiation delivery device emits radiation at a predetermined radiation energy; the radiation delivery device emits radiation at a predetermined beam size and / or shape; the radiation delivery device operates at a predetermined power; the radiation of the radiation delivery device forms a field of predetermined size and / or shape; the radiation delivery device emits radiation at a predetermined angle or range of motion; and the like. The test data may, for example, include one or more of test dose distribution data, test output dose data, test device status data, other types of test data, and the like. As an example, the test dose distribution data may include field size, measurement depth, curve type, and the like. The test data may be measured or calculated based on actual tests or simulated tests on a phantom (e.g., a water tank), an experimental animal, or a virtual model. Or as a non-limiting example, the process of acquiring test dose distribution data may include: after the accelerator in the radiation delivery device emits radiation of preset energy into the water tank, the radiation dose retained in the water in the water tank is measured to obtain the radiation dose distribution data as test dose distribution data (also referred to as "water tank test data" or "water tank data").

[0063] Exemplarily, the radiation may be a particle beam such as an X-ray, an electron beam or a proton beam.

[0064] Optionally, the computer device may be the radiation delivery device or included in the radiation delivery device, or may be a processing terminal independent of the radiation delivery device and communicatively connected with the radiation delivery device.

[0065] In the case where the computer device is a radiation delivery device or is included in the radiation delivery device, the computer device can read the test data stored in the radiation delivery device according to the reading requirement to obtain the test data when the radiation delivery device outputs a preset energy that matches the reading requirement. For example, the reading requirement includes a keyword, and the computer device can obtain the test data that matches the keyword in the reading requirement from a large number of test files by reading the keyword of the stored file.

[0066] S220: Determine an initial beam model of the radiation delivery device and one or more types of model parameters to be adjusted in the initial beam model.

[0067] Here, determining the initial beam model may include reading the initial beam model from a memory, receiving the initial beam model from an external device, or calculating the initial beam model through a computing device, etc. As a non-limiting example, the initial beam modeling may be performed using an automatic modeling program in a server. As another non-limiting example, the initial beam model may be called from a database storing one or more randomly generated beam models and / or predetermined template models. Here, the beam model may be a mathematical model including a plurality of model parameters, which is used to output corresponding model calculation results (e.g., dose distribution, machine parameters for radiation planning, etc.) according to given input parameters (e.g., given radiation radiation energy, given device power, given radiation delivery effect, etc.).

[0068] Optionally, the test condition parameters (e.g., parameters of preset energy) may be input into the initial beam model, and the model data may be outputted through the initial beam model. The initial beam model may be a beam model in which the model parameters are initial values. Exemplarily, the initial values ​​may be preset fixed values ​​or random values.

[0069] The model parameters to be adjusted are model parameters of a preset type in the initial beam model. For example, determining one or more types of model parameters to be adjusted may include setting the type of model parameters to be adjusted by a user or calculating the type of model parameters to be adjusted by a processor, etc.

[0070] S230 , adjusting the model parameters to be adjusted according to one or more parameters in the test data corresponding to the model parameters to be adjusted, to obtain a target beam model of the radiation delivery device.

[0071] Here, as an example, partially or completely, one or more of the test data corresponding to the model parameters to be adjusted may be of the same type as the corresponding model parameters to be adjusted, or may be of different types. One or more of the above test data may have a certain correspondence / association relationship with the corresponding model parameters to be adjusted, such as one of them affects the other, the two parties affect each other, one of them can be derived from the other, each can be derived from each other, and each is the same type of data. For example, the parameters to be adjusted may include the size of the field, the measurement depth, etc., and the one or more of the test data corresponding to the model parameters to be adjusted may also include the size of the field, the measurement depth, etc. For another example, the model parameters to be adjusted may include the electron pollution correlation coefficient associated with the shape of the built-up area (for example, the size of the electron source, the weight, etc.), the energy spectrum related parameters associated with the shape of the drop zone, etc., and the one or more of the test data corresponding to the model parameters to be adjusted may include the shape of the built-up area, the shape of the drop zone, etc.

[0072] As a non-limiting example, the test data may include test dose distribution data, and the test condition parameters may be input into the initial beam model to obtain the model dose distribution data. The test dose distribution data and the model dose distribution data include dose distribution data of different data types, and the data types of the dose distribution data affected by different types of model parameters to be adjusted in the initial beam model are different.

[0073] Optionally, the computer device may determine test dose distribution data and model dose distribution data of the same data type according to the data types of the test dose distribution data and the model dose distribution data, and based on the correspondence between the data types and the model parameter types, use the test dose distribution data and model dose distribution data of the same data type to adjust the corresponding type of model parameters to be adjusted in the initial beam model to obtain the target beam model of the radiation delivery device.

[0074] As a non-limiting example, for one or more model parameters to be adjusted (e.g., coefficients related to electron pollution, parameters related to energy spectrum), the degree of conformity between the model curve calculated based on the initial beam model after parameter adjustment and the test curve based on the test data can be determined, and when the degree of conformity is lower than a threshold, the model parameters to be adjusted are re-iteratively adjusted. The model curve and the test curve can be PDD curves, Profile X / Y curves, or other types of curves. The degree of conformity can be based on R 2 , SSE or other various standards as convergence criteria. The threshold can be set by the user or determined based on historical data or mathematical models.

[0075] In this embodiment, the computer device obtains test data of the radiation delivery device under test conditions, and determines the initial beam model of the radiation delivery device and one or more types of model parameters to be adjusted in the initial beam model; and adjusts the model parameters to be adjusted according to one or more corresponding to the model parameters to be adjusted in the test data, so as to obtain a target beam model of the radiation delivery device. In the above method, based on the different types of data affected by different types of model parameters to be adjusted in the initial beam model, the model parameters to be adjusted are adjusted accordingly using the data corresponding to the model parameters to be adjusted in the test data, thereby improving the pertinence of the model parameter adjustment, thereby shortening the modeling time and improving the modeling efficiency.

[0076] The data types of the test data and the model data calculated based on the initial beam model include depth dose distribution data and off-axis dose distribution data at a preset depth, and the model parameters to be adjusted include energy spectrum related parameters and field of view related parameters. Based on this, in one embodiment, if Figure 3 As shown, the above S230, adjusting the model parameters to be adjusted according to one or more of the test data corresponding to the model parameters to be adjusted, to obtain the target beam model of the radiation delivery device, includes:

[0077] S310 , adjusting energy spectrum related parameters according to the test data and the depth dose distribution data in the model data.

[0078] The depth dose distribution data includes the dose values ​​of radiation at different depths, and the specific dose values ​​are related to the size of the radioactive energy spectrum. The energy spectrum related parameters are used to characterize the model parameters affected by the radiation energy spectrum in the initial beam model, for example, coefficients related to electron contamination, such as the size of the electron source, weight and other model parameters.

[0079] Optionally, the computer device may respectively obtain the depth dose distribution data in the test data and the depth dose distribution data in the model data, and compare the two to adjust the energy spectrum related parameters in the initial beam model.

[0080] The depth dose distribution data can be represented by a curve showing the dose changing with depth, namely the Percentage Depth Dose (PDD) curve. Figure 4 The curve shown is the PDD curve in the test data, that is, the PDD curve in the water tank test data.

[0081] Optionally, the computer device can respectively obtain the PDD curve in the test data and the PDD curve in the model data, and compare them to adjust the energy spectrum related parameters in the initial beam model according to the comparison results, and then change the model data so that the PDD curve in the model data matches the PDD curve in the test data.

[0082] S320, adjusting the radiation field related parameters according to the off-axis dose distribution data at a preset depth in the test data and the model data.

[0083] The off-axis dose distribution data at the preset depth includes the dose values ​​of the radiation at different off-axis distances at the preset depth, and the specific dose values ​​are related to the size of the radiation field. The field-related parameters are used to characterize the model parameters affected by the radiation field in the initial beam model, for example, field internal parameters, such as the off-axis coefficient; field external parameters, such as the primary source size, the secondary source size, or the multi-leaf grating leakage.

[0084] Optionally, the computer device may respectively obtain off-axis dose distribution data at a preset depth in the test data and off-axis dose distribution data at a corresponding depth in the model data, and compare the two to adjust the field-related parameters in the initial beam model.

[0085] The off-axis dose distribution data at a preset depth can be represented by a curve showing the dose changing with the off-axis distance, i.e., ProfileX / Y curve. Figure 5The curve shown is the Profile X / Y curve in the test data, that is, the Profile X / Y curve in the water tank test data.

[0086] Optionally, the computer device may respectively obtain the Profile X / Y curve in the test data and the Profile X / Y curve in the model data, and compare them to adjust the field-related parameters in the initial beam model according to the comparison results, and then change the model data so that the Profile X / Y curve in the model data matches the Profile X / Y curve in the test data.

[0087] Optionally, after the computer device completes the adjustment of the energy spectrum related parameters and the field related parameters in the initial model respectively, it adopts the adjusted energy spectrum related parameters that make the PDD curve in the model data match the PDD curve in the test data, and the adjusted field related parameters that make the Profile X / Y curve in the model data match the Profile X / Y curve in the test data as target parameter values ​​to replace the initial values ​​of the corresponding model parameters to be adjusted in the initial beam model, to obtain the initial beam model with adjusted parameters, and determines the target beam model based on the initial beam model with adjusted parameters, or uses the initial beam model with adjusted parameters as the target beam model.

[0088] In this embodiment, the computer device adjusts the energy spectrum related parameters according to the depth dose distribution data in the test data and the model data, and adjusts the field related parameters according to the off-axis dose distribution data at a preset depth in the test data and the model data, so as to obtain the target beam model. In the above method, the depth dose distribution data is limited by the radiation energy spectrum, and the energy spectrum related parameters are model parameters affected by the radiation energy spectrum. The off-axis dose distribution data is limited by the radiation field, and the field related parameters are model parameters affected by the radiation field. The energy spectrum related parameters are adjusted by using the depth dose distribution data, and the field related parameters are adjusted by using the off-axis dose distribution data, which improves the pertinence of the adjustment of the energy spectrum related parameters and the field related parameters, shortens the modeling time, and improves the modeling efficiency and accuracy.

[0089] In practical applications, the similarity between the depth dose distribution data can be used as a basis for adjusting the energy spectrum related parameters. Figure 6 As shown, the above S310, according to the test data and the depth dose distribution data in the model data, adjusts the energy spectrum related parameters in the initial beam model, including:

[0090] S610 , obtaining depth distribution similarity between the depth dose distribution data in the test data and the model data.

[0091] Among them, the similarity of depth distribution (i.e., conformity / matching degree) can be measured by the degree of fit between curves (R 2 ) or squared error (SSE). 2 The closer the value is to 1, the higher the similarity between the characterization curves, that is, the higher the matching degree; conversely, R 2 The further away the value is from 1, the lower the similarity between the characterization curves, that is, the worse the matching degree. The smaller the SSE value, the higher the similarity between the characterization curves, that is, the higher the matching degree; conversely, the larger the SSE value, the smaller the similarity between the characterization curves, that is, the worse the matching degree.

[0092] As an example, R 2 The calculation process of SSE can satisfy the following relationship:

[0093]

[0094]

[0095]

[0096]

[0097] Among them, y i are the measured values ​​in the test data, is the average value of the measured values, is the calculated value in the model data, SSR is the regression sum of squares, and SST is the total sum of squares.

[0098] Optionally, the computer device may respectively obtain a PDD curve in the test data and a PDD curve in the test data, and then obtain an R between the PDD curve in the test data and the PDD curve in the test data. 2 / SSE, as the depth distribution similarity.

[0099] S620, adjusting energy spectrum related parameters in the initial beam model until the depth distribution similarity meets a first preset condition.

[0100] Optionally, the computer device continuously adjusts the energy spectrum related parameters in the initial beam model to change the PDD curve output by the initial beam model, and then changes the above-mentioned depth distribution similarity until the depth distribution similarity meets the first preset condition, so as to determine that the PDD curve output by the initial beam model under the current model parameters matches the PDD curve in the test data.

[0101] Optionally, different types of model parameters to be adjusted have different corresponding first preset conditions. For energy spectrum related parameters, the first preset conditions include R 2Infinitely close to 1. The computer device can construct an objective function based on the PDD curve in the test data and the PDD curve in the test data, and the objective function is based on the R between the PDD curves. 2 The goal is to converge, so as to solve the energy spectrum related parameters that meet the convergence of the objective function, that is, meet the first preset condition.

[0102] In this embodiment, the computer device obtains the depth distribution similarity between the test data and the depth dose distribution data in the model data to adjust the energy spectrum related parameters in the initial beam model until the depth distribution similarity meets the first preset condition. In this method, the energy spectrum related parameters are adjusted based on the depth distribution similarity, and the depth distribution similarity can accurately reflect the matching degree between the depth dose distribution data in the test data and the model data, thereby improving the accuracy of adjusting the energy spectrum related parameters.

[0103] Similar to the above process, the similarity between the off-axis dose distribution data can be used as the basis for adjusting the field-related parameters. Figure 7 As shown, in the above S320, the field-related parameters in the initial beam model are adjusted according to the off-axis dose distribution data at a preset depth in the test data and the model data.

[0104] S710 , obtaining off-axis distribution similarity between off-axis dose distribution data at a preset depth in the test data and the model data.

[0105] Among them, the off-axis distribution similarity (i.e., conformity / matching degree) can be measured by the degree of fit between the curves (R 2 ) or squared error (SSE). 2 The closer the value is to 1, the higher the similarity between the characterization curves, that is, the higher the matching degree; conversely, R 2 The further away the value is from 1, the lower the similarity between the characterization curves, that is, the worse the matching degree. The smaller the SSE value, the higher the similarity between the characterization curves, that is, the higher the matching degree; conversely, the larger the SSE value, the smaller the similarity between the characterization curves, that is, the worse the matching degree.

[0106] Optionally, the computer device may respectively obtain the Profile X / Y curve at a preset depth in the test data and the Profile X / Y curve at a corresponding depth in the test data, and then obtain the R between the Profile X / Y curve in the test data and the Profile X / Y curve in the test data. 2 / SSE, as the off-axis distribution similarity.

[0107] S720: Input the adjusted energy spectrum related parameters into the initial beam model, and adjust the field related parameters in the initial beam model until the off-axis distribution similarity meets the second preset condition.

[0108] Optionally, after obtaining the adjusted energy spectrum related parameters so that the depth distribution similarity satisfies the first preset condition, the computer device inputs the adjusted energy spectrum related parameters into the initial beam model to replace the initial values ​​of the corresponding energy spectrum related parameters, and then continuously adjusts the field related parameters in the initial beam model to change the Profile X / Y curve output by the initial beam model, thereby changing the above-mentioned off-axis distribution similarity until the off-axis distribution similarity satisfies the second preset condition, thereby determining that the Profile X / Y curve output by the initial beam model under the current model parameters matches the Profile X / Y curve in the test data.

[0109] Optionally, for the field-related parameters, the second preset condition includes minimum SSE. The computer device can construct an objective function based on the Profile X / Y curve in the test data and the Profile X / Y curve in the test data, and the objective function takes the SSE convergence between the Profile X / Y curves as a goal to solve the field-related parameters that meet the convergence of the objective function, that is, meet the second preset condition.

[0110] It should be noted that in the above model building method, whether in the process of adjusting the energy spectrum related parameters or the process of adjusting the field related parameters, the same model parameter to be adjusted can be optimized based on multiple test data of the same type, or multiple model parameters to be adjusted can be optimized based on multiple test data of different types. At this time, the objective function will be composed of multiple sub-objective functions f = (f1, f2, ..., f N-1 ,f N ). In the multi-parameter optimization process, there will be a sub-objective function that requires the maximum value and a sub-objective function that requires the minimum value. During optimization, it is necessary to find the Pareto optimal solution. The process of finding the Pareto optimal solution can be based on the weight coefficient change method. i Assign weight w i , where w i The corresponding f i The importance of the function in the multi-parameter optimization process is to linearly weight each sub-objective function, and the sum is At this time, using μ as the evaluation function of multi-parameter optimization, the multi-objective optimization problem can be transformed into a single-objective optimization problem for solution.

[0111] In this embodiment, the computer device obtains the off-axis distribution similarity between the off-axis dose distribution data at a preset depth in the test data and the model data, inputs the adjusted energy spectrum related parameters into the initial beam model, and adjusts the field related parameters in the initial beam model until the off-axis distribution similarity meets the second preset condition. In this method, the field related parameters are adjusted based on the off-axis distribution similarity, and the off-axis distribution similarity can accurately reflect the degree of matching between the off-axis dose distribution data in the test data and the model data, thereby improving the accuracy of adjusting the field related parameters, and when determining the field related parameters, the energy spectrum related parameters in the model parameters to be adjusted have been determined, reducing the number of parameters that need to be adjusted, simplifying the model iteration process, thereby saving modeling time and improving modeling efficiency.

[0112] The depth dose distribution data includes a built-up distribution area and a drop distribution area. Based on this, in one embodiment, Figure 8 As shown, the above S310, according to the test data and the depth dose distribution data in the model data, adjusts the energy spectrum related parameters, including:

[0113] S810. Adjust the electron source size and electron source weight in the initial beam model according to the data of the built distribution area corresponding to the depth dose distribution data in the test data and the model data.

[0114] The built-up distribution area is the area in the depth dose distribution data where the dose increases with increasing depth. Figure 4 As shown, the built distribution area is the area in the PDD curve where the dose increases from the preset dose point (20 in the figure) to the maximum dose point (100 in the figure) as the depth increases.

[0115] Optionally, the computer device can respectively obtain data of the built-up distribution area corresponding to the PDD curve in the test data, and data of the built-up distribution area corresponding to the PDD curve in the model data, to compare the similarity of data in the same area, and adjust the two energy spectrum-related parameters of the electron source size and the electron source weight in the initial beam model according to the comparison results, and then change the model data so that the curve shape of the built-up distribution area in the PDD curve in the model data approaches the curve shape of the built-up distribution area in the PDD curve in the test data, until the PDD curve in the model data matches the PDD curve in the test data.

[0116] S820. According to the data of the corresponding drop distribution area in the depth dose distribution data in the test data and the model data, the primary photon weight and the secondary photon weight in the initial beam model are adjusted.

[0117] The drop distribution area is the area in the depth dose distribution data where the dose decreases with increasing depth. Figure 4As shown, the drop distribution area is the area in the PDD curve where the dose decreases from the maximum dose point (100 in the figure) to the preset dose point (20 in the figure) as the depth increases.

[0118] Optionally, the computer device can respectively obtain data of the drop distribution area corresponding to the PDD curve in the test data and data of the drop distribution area corresponding to the PDD curve in the model data to compare the similarity of data in the same area, and adjust the two field-related parameters of the primary photon weight and the secondary photon weight in the initial beam model according to the comparison results, and then change the model data so that the curve shape of the drop distribution area in the PDD curve in the model data approaches the curve shape of the drop distribution area in the PDD curve in the test data until the PDD curve in the model data matches the PDD curve in the test data.

[0119] In this embodiment, the computer device adjusts the electron source size and the electron source weight in the initial beam model according to the data of the built distribution area corresponding to the test data and the model data in the depth dose distribution data, and adjusts the primary photon weight and the secondary photon weight in the initial beam model according to the data of the drop distribution area corresponding to the test data and the model data in the depth dose distribution data. In the above method, the depth dose distribution data is divided into finer granularities, and the electron source size and the electron source weight in the energy spectrum related parameters are adjusted by using the data of the built distribution area obtained by the division, and the primary photon weight and the secondary photon weight in the field related parameters are adjusted by using the data of the drop distribution area obtained by the division, so as to realize the precise adjustment of the specific model parameters to be adjusted based on the data after fine granular division, improve the accuracy of the parameter adjustment, and correspondingly improve the accuracy of the established target beam model.

[0120] The off-axis dose distribution data at the preset depth includes a low dose distribution area, a penumbra distribution area, and a flattened distribution area. Based on this, in one embodiment, Fig. 9 As shown, the above S320, according to the off-axis dose distribution data at a preset depth in the test data and the model data, adjusts the radiation field related parameters, including:

[0121] S910, adjusting the radiation field external parameters in the initial beam model according to the data corresponding to the low-dose distribution area and the penumbra distribution area in the off-axis dose distribution data at a preset depth in the test data and the model data.

[0122] The low-dose distribution area is the area in the off-axis dose distribution data where the dose is less than the preset minimum dose, and the penumbra distribution area is the area within the preset dose range. Figure 5As shown, the low-dose distribution area is the area in the Profile X / Y curve where the dose is less than 20% of the maximum dose point, and the penumbra distribution area is the area between the 20% maximum dose point and the 80% maximum dose point.

[0123] Optionally, the computer device can respectively obtain data corresponding to the low-dose distribution area and the penumbra distribution area in the Profile X / Y curve in the test data, and data corresponding to the low-dose distribution area and the penumbra distribution area in the Profile X / Y curve in the model data, so as to compare the similarity of data in the same area, and adjust the field external parameters in the initial beam model according to the comparison results, and then change the model data, so that the curve shape of the low-dose distribution area and the penumbra distribution area in the Profile X / Y curve in the model data is close to the curve shape of the low-dose distribution area and the penumbra distribution area in the Profile X / Y curve in the test data, until the Profile X / Y curve in the model data matches the Profile X / Y curve in the test data.

[0124] S920, adjusting the off-axis coefficient in the initial beam model according to the data of the flattened distribution area corresponding to the off-axis dose distribution data at a preset depth in the test data and the model data.

[0125] The flattened distribution area is the area in the off-axis dose distribution data where the dose is greater than the preset maximum dose. Figure 5 As shown, the flattened distribution area is the area in the Profile X / Y curve where the dose is greater than 90% of the maximum dose point.

[0126] Optionally, the computer device may respectively obtain data corresponding to the flattened distribution area in the Profile X / Y curve in the test data and data corresponding to the flattened distribution area in the Profile X / Y curve in the model data, so as to compare the similarity of the data in the same area, and adjust the off-axis coefficient in the initial beam model according to the comparison result, and then change the model data so that the curve shape of the flattened distribution area in the Profile X / Y curve in the model data is close to the curve shape of the flattened distribution area in the Profile X / Y curve in the test data, until the Profile X / Y curve in the model data matches the Profile X / Y curve in the test data.

[0127] In this embodiment, the computer device adjusts the field external parameters in the initial beam model according to the data corresponding to the low-dose distribution area and the penumbra distribution area in the off-axis dose distribution data at a preset depth in the test data and the model data, and adjusts the off-axis coefficient in the initial beam model according to the data corresponding to the flattened distribution area in the off-axis dose distribution data at a preset depth in the test data and the model data. In the above method, the off-axis dose distribution data is divided into finer granularities, and the data of the low-dose distribution area and the penumbra distribution area obtained by the division are used to adjust the field external parameters in the field-related parameters, and the data of the flattened distribution area obtained by the division are used to adjust the off-axis coefficient in the field-related parameters, so as to achieve precise adjustment of specific model parameters to be adjusted based on the data after fine-grained division, improve the accuracy of parameter adjustment, and correspondingly improve the accuracy of the established target beam model.

[0128] In order to improve the accuracy of the target beam model, in one embodiment, as Fig.10 As shown, the above S230, adjusting the model parameters to be adjusted according to one or more of the test data corresponding to the model parameters to be adjusted, to obtain the target beam model of the radiation delivery device, includes:

[0129] S1010, inputting preset parameters into the parameter-adjusted initial beam model, and outputting reference dose distribution data.

[0130] The preset parameter is the size of the preset energy input into the initial beam model.

[0131] Optionally, after obtaining the parameter-adjusted initial beam model, the computer device may perform quality verification on the parameter-adjusted initial beam model based on test data to obtain a target beam model that meets quality requirements (ie, delivery standards).

[0132] Optionally, the computer device may input preset parameters equal to the energy of the input initial beam model into the parameter-adjusted initial beam model, and the parameter-adjusted initial beam model performs calculations based on current model parameters to output dose distribution data as reference dose distribution data.

[0133] S1020. Based on the reference dose distribution data, multiple control algorithms are used to determine the reference dose parameters of the target area under each control algorithm.

[0134] Different control algorithms are used to characterize different model verification plans. Usually, the target beam model needs to pass a batch of model verification plans. The reference dose parameters are used to characterize the dose distribution characteristics of the target area, which can include point dose and surface dose. The surface dose can be determined based on the point dose.

[0135] Optionally, the computer device may obtain a plurality of pre-stored control algorithms to determine the reference dose parameters of the target area under each control algorithm using each control algorithm according to the reference dose distribution data.

[0136] S1030, obtaining test dose parameters of the target area determined by the test data under the corresponding control algorithm.

[0137] Optionally, the computer device may adopt each control algorithm according to the test data to determine the test dose parameters of the target area under each control algorithm.

[0138] S1040. Determine a target beam model according to each reference dose parameter and each test dose parameter.

[0139] Optionally, after obtaining the reference dose parameters and the test dose parameters of the target area under each control algorithm respectively, the computer device may compare the reference dose parameters and the test dose parameters under the same control algorithm, so as to verify the parameter-adjusted initial beam model according to the comparison result, and determine the parameter-adjusted initial beam model as the target beam model if the verification passes; otherwise, if the verification fails, further adjust the parameters of the parameter-adjusted initial beam model until the verification passes and the target beam model is obtained.

[0140] Optionally, in the case of executing multiple control algorithms, if the reference dose parameters obtained under all control algorithms are compared with the test dose parameters, and the comparison results are used to indicate that the initial beam model with adjusted parameters has passed the verification, the computer device can determine that the initial beam model with adjusted parameters has passed the verification; conversely, if there is at least one control algorithm under which the reference dose parameters are compared with the test dose parameters, and the comparison results are used to indicate that the initial beam model with adjusted parameters has not passed the verification, the computer device can determine that the initial beam model with adjusted parameters has not passed the verification.

[0141] Optionally, before executing the above S1040, the computer device may also perform data verification on the reference dose distribution data for obtaining the current reference dose parameters to reduce the impact of data fluctuations on model establishment. The computer device may obtain historical reference dose distribution data, determine historical reference dose parameters of the historical reference dose distribution data, and compare the historical reference dose parameters with the current reference dose parameters to determine the parameter deviation between the two. When the parameter deviation is less than the deviation threshold, the current reference dose parameters are determined to be stable, and the above S1040 is executed; conversely, when the parameter deviation is greater than the deviation threshold, new reference dose distribution data is obtained, and corresponding new reference dose parameters are determined to compare the new reference dose parameters with the new historical reference dose parameters until the obtained new parameter deviation is less than the deviation threshold.

[0142] In this embodiment, the computer device inputs the preset parameters into the parameter-adjusted initial beam model, outputs reference dose distribution data, and uses a plurality of control algorithms to determine the reference dose parameters of the target area under each control algorithm based on the reference dose distribution data, and obtains the test dose parameters of the target area determined by the test data under the corresponding control algorithm, and then determines the target beam model according to each reference dose parameter and each test dose parameter. In the above method, the preset parameters are input into the parameter-adjusted initial beam model to obtain the reference dose distribution data, so as to actually apply the parameter-adjusted initial beam model, and then the parameter-adjusted initial beam model is quality verified according to the reference dose parameters and test dose parameters under different control algorithms, so as to obtain a target beam model that meets the quality requirements and improve the accuracy of the target beam model.

[0143] The parameter-adjusted initial beam model can be directly used as the target beam model, or the parameter can be further adjusted to obtain the target beam model. Fig.11 As shown, the above S1040, determining the target beam model according to each reference dose parameter and each test dose parameter, includes:

[0144] S1110 , obtaining the point dose deviation and the surface dose passing rate between the reference dose parameters and the test dose parameters under each control algorithm.

[0145] Among them, point dose deviation and surface dose pass rate are used to characterize the deviation between reference dose parameters and test dose parameters.

[0146] Optionally, for reference dose parameters and test dose parameters under different control algorithms, the computer device can obtain the reference dose parameters and test dose parameters under each control algorithm to calculate the point dose deviation and surface dose pass rate between the reference dose parameters and test dose parameters under each control algorithm.

[0147] S1120: When the point dose deviation and the surface dose pass rate under each control algorithm meet the preset dose requirement, the adjusted initial beam model is determined as the target beam model.

[0148] The preset dose requirement includes a point dose deviation threshold and a surface dose pass rate threshold.

[0149] Optionally, the computer device may compare the point dose deviation obtained under each control algorithm with the point dose deviation threshold, and compare the surface dose pass rate obtained under each control algorithm with the surface dose pass rate threshold, so as to determine whether the preset dose requirements are met based on the comparison results, and when the point dose deviation and the surface dose pass rate under each control algorithm meet the preset dose requirements, directly determine the adjusted initial beam model as the target beam model.

[0150] Optionally, when the point dose deviation under each control algorithm is less than the point dose deviation threshold, and the surface dose pass rate under each control algorithm is greater than the surface dose pass rate threshold, the computer device can determine that the point dose deviation and the surface dose pass rate under each control algorithm both meet the preset dose requirements; when there is at least one control algorithm with a point dose deviation greater than or equal to the point dose deviation threshold, the computer device can determine that the point dose deviation under the control algorithm does not meet the preset dose requirement; when there is at least one control algorithm with a surface dose pass rate less than or equal to the surface dose pass rate threshold, the computer device can determine that the surface dose pass rate under the control algorithm does not meet the preset dose requirement.

[0151] S1130: When the reference dose parameters do not meet the preset dose requirement, the parameters of the parameter-adjusted initial beam model are adjusted to obtain a target beam model.

[0152] Optionally, when the reference dose parameters do not meet the preset dose requirements, the computer device may determine the model parameters to be adjusted that need to be further adjusted based on the control algorithm corresponding to the failure to meet the preset dose requirements, and adjust the model parameters to be adjusted accordingly until the reference dose parameters meet the preset dose requirements, thereby obtaining the target beam model.

[0153] In this embodiment, the computer device obtains the point dose deviation and the surface dose pass rate between the reference dose parameters and the test dose parameters under each control algorithm, so that when the point dose deviation and the surface dose pass rate under each control algorithm meet the preset dose requirements, the adjusted initial beam model is determined to be the target beam model, and when the reference dose parameters do not meet the preset dose requirements, the parameter-adjusted initial beam model is adjusted to obtain the target beam model. In the above method, the point dose deviation and the surface dose pass rate can accurately reflect the data deviation between the reference dose parameters and the test dose parameters, and as the data basis for verifying the parameter-adjusted initial beam model, it can accurately determine whether it is necessary to continue to adjust the model parameters in the parameter-adjusted initial beam model, thereby improving the accuracy of the obtained target beam model.

[0154] Different control algorithms affect different model parameters to be adjusted. Therefore, in one embodiment, Fig.12As shown, in the above S1130, the parameters of the initial beam model after parameter adjustment are adjusted to obtain the target beam model, including:

[0155] S1210. Determine a target control algorithm for calculating reference dose parameters of a target area.

[0156] Optionally, when the reference dose parameter does not meet the preset dose requirement, the computer device obtains the control algorithm of the reference dose parameter and uses it as the target control algorithm. For example, when the computer device uses three control algorithms A, B, and C to perform quality verification on the parameter-adjusted initial beam model, if the reference dose parameter obtained based on the control algorithm A does not meet the preset dose requirement, the computer device determines that the control algorithm A is the target control algorithm.

[0157] S1220. Determine target model parameters corresponding to the target control algorithm according to the corresponding relationship between the control algorithm and the model parameters to be adjusted.

[0158] Among them, the corresponding relationship between the control algorithm and the model parameters to be adjusted is a priori relationship determined based on big data.

[0159] Optionally, after obtaining the target control algorithm, the computer device determines the model parameters to be adjusted corresponding to the target control algorithm according to the corresponding relationship between the control algorithm and the model parameters to be adjusted, and uses them as the target model parameters.

[0160] S1230 , adjusting target model parameters in the parameter-adjusted initial beam model to obtain a target beam model.

[0161] Optionally, after determining the target model parameters, the computer device adjusts the target model parameters to change the reference dose distribution data so that the point dose deviation and the surface dose pass rate under each control algorithm determined based on the changed reference dose distribution data meet the preset dose requirements, so as to determine the target beam model from the initial beam model under the current model parameters.

[0162] In this embodiment, the computer device determines the target control algorithm for calculating the reference dose parameters of the target area, and determines the target model parameters corresponding to the target control algorithm according to the corresponding relationship between the control algorithm and the model parameters to be adjusted, and then adjusts the target model parameters in the initial beam model after parameter adjustment to obtain the target beam model. In the above method, the prior influence relationship between the control algorithm and the model parameters is used to accurately determine the target model parameters corresponding to the target control algorithm when the reference dose parameters do not meet the preset dose requirements, and then the target model parameters are adjusted in a targeted manner without adjusting other model parameters to be adjusted, so as to achieve fine-tuning of the parameters of the initial beam model after parameter adjustment, reduce the amount of parameter adjustment data, and improve the parameter adjustment efficiency.

[0163] In order to facilitate the understanding of those skilled in the art, Fig.13 Taking the beam model establishment method provided in this application as an example, an overall introduction is given.

[0164] After the medical accelerator emits radiation of preset energy to the water tank, the radiation dose retained in the water in the water tank is measured to obtain water tank data. The computer device obtains the water tank data, completes the water tank data acquisition, and preprocesses the water tank data, such as classifying and saving the water tank data according to different data types. The computer device inputs the preset parameters equal to the preset energy into the initial beam model, obtains the model data calculated by the initial beam model under the initial values ​​of each model parameter to be adjusted (i.e., beam model parameter), and adjusts the corresponding model parameters to be adjusted in the initial beam model according to the model data and the data of different data types in the water tank data, so as to realize the automatic adjustment of the beam parameters until all the model parameters to be adjusted converge.

[0165] After the above-mentioned automatic adjustment of beam parameters, the parameter-adjusted initial beam model can be obtained. The computer device can further perform batch automatic calculation of the quality assurance (QA) plan (i.e., model verification plan) according to the parameter-adjusted initial beam model to obtain the reference dose parameters determined based on the model data. The QA measurement device detects the aforementioned irradiated water tank to obtain the QA data (i.e., measured dose parameters) under different QA plans. The computer device obtains the QA data, completes QA data acquisition, compares the reference dose parameters and the QA data to determine the QA results, and determines whether the parameter-adjusted initial beam model meets the delivery standards according to the QA results, and performs automatic post-processing. Among them, the automatic post-processing includes: if the QA result is that the parameter-adjusted initial beam model meets the delivery standards, the beam model parameters are saved and the parameter-adjusted initial beam model is used as the target beam model; conversely, if the QA result is that the parameter-adjusted initial beam model does not meet the delivery standards, the beam parameter automatic adjustment is returned until the QA result meets the delivery standards and the target beam model is obtained.

[0166] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed 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 part of the steps or stages in other steps.

[0167] Based on the same inventive concept, the embodiment of the present application also provides a model building device for implementing the model building method involved above. The implementation solution provided by the device to solve the problem is similar to the implementation solution recorded in the above method, so the specific limitations in one or more model building device embodiments provided below can refer to the limitations of the model building method above, and will not be repeated here.

[0168] In one embodiment, Fig.14 As shown, a model building device is provided, including: a test data acquisition module 1401, an initial model determination module 1402 and a model parameter adjustment module 1403, wherein:

[0169] The test data acquisition module 1401 is used to acquire test data of the radiation delivery device under test conditions;

[0170] The initial model determination module 1402 is used to determine an initial beam model of the radiation delivery device and one or more types of model parameters to be adjusted in the initial beam model;

[0171] The model parameter adjustment module 1403 is used to adjust the model parameters to be adjusted according to one or more parameters in the test data corresponding to the model parameters to be adjusted, so as to obtain a target beam model of the radiation delivery device.

[0172] In one embodiment, the model parameter adjustment module 1403 is further used to:

[0173] For one or more model parameters to be adjusted, the degree of conformity between a model curve calculated based on the parameter-adjusted initial beam model and a test curve based on test data is determined, and when the degree of conformity is lower than a threshold, the model parameters to be adjusted are re-iteratively adjusted.

[0174] In one embodiment, the data types of the test data and the model data calculated based on the initial beam model include depth dose distribution data and off-axis dose distribution data at a preset depth, and the model parameters to be adjusted include energy spectrum related parameters and field related parameters; the model parameter adjustment module 1403 includes:

[0175] The energy spectrum submodule is used to adjust the energy spectrum related parameters according to the depth dose distribution data in the test data and model data;

[0176] The radiation field submodule is used to adjust radiation field related parameters according to the off-axis dose distribution data at a preset depth in the test data and model data.

[0177] In one embodiment, the depth dose distribution data includes a built-up distribution area and a drop distribution area; the energy spectrum submodule includes:

[0178] A built-up adjustment unit is used to adjust the electron source size and the electron source weight in the initial beam model according to the data of the built-up distribution area corresponding to the depth dose distribution data in the test data and the model data;

[0179] The drop adjustment unit is used to adjust the primary photon weight and the secondary photon weight in the initial beam model according to the data of the corresponding drop distribution area in the depth dose distribution data in the test data and the model data.

[0180] In one of the embodiments, the off-axis dose distribution data at a preset depth includes a low dose distribution area, a penumbra distribution area, and a flattened distribution area;

[0181] The field submodules include:

[0182] An external parameter adjustment unit is used to adjust the field external parameters in the initial beam model according to the data corresponding to the low-dose distribution area and the penumbra distribution area in the off-axis dose distribution data at a preset depth in the test data and the model data;

[0183] The off-axis adjustment unit is used to adjust the off-axis coefficient in the initial beam model according to the data of the corresponding flattened distribution area in the off-axis dose distribution data at a preset depth in the test data and the model data.

[0184] In one embodiment, the target submodule includes:

[0185] A reference unit, used for inputting preset parameters into the parameter-adjusted initial beam model and outputting reference dose distribution data;

[0186] An algorithm unit, used for determining a reference dose parameter of a target area under each control algorithm by using a plurality of control algorithms based on the reference dose distribution data;

[0187] A parameter unit, used to obtain the test dose parameters of the target area determined by the test data under the corresponding control algorithm;

[0188] The target unit is used to determine the target beam model according to each reference dose parameter and each test dose parameter.

[0189] In one embodiment, the target unit includes:

[0190] Deviation subunit, used to obtain the point dose deviation and surface dose pass rate between the reference dose parameters and the test dose parameters under each control algorithm;

[0191] The first target subunit is used to determine the adjusted initial beam model as the target beam model when the point dose deviation and the surface dose pass rate under each control algorithm meet the preset dose requirements;

[0192] The second target subunit is used to adjust the parameters of the parameter-adjusted initial beam model to obtain a target beam model when the reference dose parameters do not meet the preset dose requirements.

[0193] In one embodiment, the second target subunit is used to:

[0194] Determine a target control algorithm for calculating reference dose parameters of a target area; determine target model parameters corresponding to the target control algorithm according to a corresponding relationship between the control algorithm and the model parameters to be adjusted; adjust the target model parameters in the parameter-adjusted initial beam model to obtain a target beam model.

[0195] Each module in the above model building device can be implemented in whole or in part by 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 a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0196] In one 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:

[0197] Acquire test data of a radiation delivery device under test conditions; determine an initial beam model of the radiation delivery device and one or more types of model parameters to be adjusted in the initial beam model; and adjust the model parameters to be adjusted according to one or more of the test data corresponding to the model parameters to be adjusted to obtain a target beam model of the radiation delivery device.

[0198] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0199] For one or more model parameters to be adjusted, the degree of conformity between a model curve calculated based on the parameter-adjusted initial beam model and a test curve based on test data is determined, and when the degree of conformity is lower than a threshold, the model parameters to be adjusted are re-iteratively adjusted.

[0200] In one embodiment, the data types of the test data and the model data calculated based on the initial beam model include depth dose distribution data and off-axis dose distribution data at a preset depth, and the model parameters to be adjusted include energy spectrum related parameters and field related parameters; when the processor executes the computer program, the following steps are also implemented:

[0201] The energy spectrum related parameters are adjusted according to the depth dose distribution data in the test data and the model data; and the field related parameters are adjusted according to the off-axis dose distribution data at a preset depth in the test data and the model data.

[0202] In one embodiment, the depth dose distribution data includes a built-up distribution area and a drop-off distribution area; when the processor executes the computer program, the following steps are also implemented:

[0203] The electron source size and the electron source weight in the initial beam model are adjusted according to the data of the built-up distribution area corresponding to the depth dose distribution data in the test data and the model data; and the primary photon weight and the secondary photon weight in the initial beam model are adjusted according to the data of the dropped distribution area corresponding to the depth dose distribution data in the test data and the model data.

[0204] In one embodiment, the off-axis dose distribution data at a preset depth includes a low dose distribution area, a penumbra distribution area, and a flattened distribution area; when the processor executes the computer program, the following steps are also implemented:

[0205] The field external parameters in the initial beam model are adjusted according to the data corresponding to the low-dose distribution area and the penumbra distribution area in the off-axis dose distribution data at a preset depth in the test data and the model data; and the off-axis coefficient in the initial beam model is adjusted according to the data corresponding to the flattened distribution area in the off-axis dose distribution data at a preset depth in the test data and the model data.

[0206] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0207] The preset parameters are input into the parameter-adjusted initial beam model, and reference dose distribution data is output; based on the reference dose distribution data, a plurality of control algorithms are used to determine the reference dose parameters of the target area under each control algorithm; the test dose parameters of the target area determined by the test data under the corresponding control algorithm are obtained; and the target beam model is determined according to each reference dose parameter and each test dose parameter.

[0208] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0209] The point dose deviation and the surface dose pass rate between the reference dose parameters and the test dose parameters under each control algorithm are obtained; when the point dose deviation and the surface dose pass rate under each control algorithm meet the preset dose requirements, the adjusted initial beam model is determined as the target beam model; and when the reference dose parameters do not meet the preset dose requirements, the parameters of the parameter-adjusted initial beam model are adjusted to obtain the target beam model.

[0210] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0211] Determine a target control algorithm for calculating reference dose parameters of a target area; determine target model parameters corresponding to the target control algorithm according to a corresponding relationship between the control algorithm and the model parameters; and adjust the target model parameters in the parameter-adjusted initial beam model to obtain a target beam model.

[0212] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0213] Acquire test data of a radiation delivery device under test conditions; determine an initial beam model of the radiation delivery device and one or more types of model parameters to be adjusted in the initial beam model; and adjust the model parameters to be adjusted according to one or more of the test data corresponding to the model parameters to be adjusted to obtain a target beam model of the radiation delivery device.

[0214] In one embodiment, the computer program, when executed by a processor, implements the following steps:

[0215] For one or more model parameters to be adjusted, the degree of conformity between a model curve calculated based on the parameter-adjusted initial beam model and a test curve based on test data is determined, and when the degree of conformity is lower than a threshold, the model parameters to be adjusted are re-iteratively adjusted.

[0216] In one embodiment, the data types of the test data and the model data calculated based on the initial beam model include depth dose distribution data and off-axis dose distribution data at a preset depth, and the model parameters to be adjusted include energy spectrum related parameters and field related parameters; when the computer program is executed by the processor, the following steps are also implemented:

[0217] The energy spectrum related parameters are adjusted according to the depth dose distribution data in the test data and the model data; and the field related parameters are adjusted according to the off-axis dose distribution data at a preset depth in the test data and the model data.

[0218] In one embodiment, the depth dose distribution data includes a built-up distribution area and a drop-off distribution area; when the computer program is executed by the processor, the following steps are also implemented:

[0219] The electron source size and the electron source weight in the initial beam model are adjusted according to the data of the built-up distribution area corresponding to the depth dose distribution data in the test data and the model data; and the primary photon weight and the secondary photon weight in the initial beam model are adjusted according to the data of the dropped distribution area corresponding to the depth dose distribution data in the test data and the model data.

[0220] In one embodiment, the off-axis dose distribution data at a preset depth includes a low dose distribution area, a penumbra distribution area, and a flattened distribution area; when the computer program is executed by the processor, the following steps are also implemented:

[0221] The field external parameters in the initial beam model are adjusted according to the data corresponding to the low-dose distribution area and the penumbra distribution area in the off-axis dose distribution data at a preset depth in the test data and the model data; and the off-axis coefficient in the initial beam model is adjusted according to the data corresponding to the flattened distribution area in the off-axis dose distribution data at a preset depth in the test data and the model data.

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

[0223] The preset parameters are input into the parameter-adjusted initial beam model, and reference dose distribution data is output; a plurality of control algorithms are used to determine the reference dose parameters of the target area under each control algorithm according to the reference dose distribution data; the test dose parameters of the target area determined by the test data under the corresponding control algorithm are obtained; and the target beam model is determined according to each reference dose parameter and each test dose parameter.

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

[0225] The point dose deviation and the surface dose pass rate between the reference dose parameters and the test dose parameters under each control algorithm are obtained; when the point dose deviation and the surface dose pass rate under each control algorithm meet the preset dose requirements, the adjusted initial beam model is determined as the target beam model; and when the reference dose parameters do not meet the preset dose requirements, the parameters of the parameter-adjusted initial beam model are adjusted to obtain the target beam model.

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

[0227] Determine a target control algorithm for calculating reference dose parameters of a target area; determine target model parameters corresponding to the target control algorithm according to a corresponding relationship between the control algorithm and the model parameters; and adjust the target model parameters in the parameter-adjusted initial beam model to obtain a target beam model.

[0228] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0229] Acquire test data of a radiation delivery device under test conditions; determine an initial beam model of the radiation delivery device and one or more types of model parameters to be adjusted in the initial beam model; and adjust the model parameters to be adjusted according to one or more of the test data corresponding to the model parameters to be adjusted to obtain a target beam model of the radiation delivery device.

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

[0231] For one or more model parameters to be adjusted, the degree of conformity between a model curve calculated based on the parameter-adjusted initial beam model and a test curve based on test data is determined, and when the degree of conformity is lower than a threshold, the model parameters to be adjusted are re-iteratively adjusted.

[0232] In one embodiment, the data types of the test data and the model data calculated based on the initial beam model include depth dose distribution data and off-axis dose distribution data at a preset depth, and the model parameters to be adjusted include energy spectrum related parameters and field related parameters; when the computer program is executed by the processor, the following steps are also implemented:

[0233] The energy spectrum related parameters are adjusted according to the depth dose distribution data in the test data and the model data; and the field related parameters are adjusted according to the off-axis dose distribution data at a preset depth in the test data and the model data.

[0234] In one embodiment, the depth dose distribution data includes a built-up distribution area and a drop-off distribution area; when the computer program is executed by the processor, the following steps are also implemented:

[0235] The electron source size and the electron source weight in the initial beam model are adjusted according to the data of the corresponding built-up distribution area in the depth dose distribution data in the test data and the model data; and the primary photon weight and the secondary photon weight in the initial beam model are adjusted according to the data of the corresponding drop distribution area in the depth dose distribution data in the test data and the model data.

[0236] In one embodiment, the off-axis dose distribution data at a preset depth includes a low dose distribution area, a penumbra distribution area, and a flattened distribution area; when the computer program is executed by the processor, the following steps are also implemented:

[0237] The field external parameters in the initial beam model are adjusted according to the data corresponding to the low-dose distribution area and the penumbra distribution area in the off-axis dose distribution data at a preset depth in the test data and the model data; and the off-axis coefficient in the initial beam model is adjusted according to the data corresponding to the flattened distribution area in the off-axis dose distribution data at a preset depth in the test data and the model data.

[0238] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0239] The preset parameters are input into the parameter-adjusted initial beam model, and reference dose distribution data is output; based on the reference dose distribution data, a plurality of control algorithms are used to determine the reference dose parameters of the target area under each control algorithm; the test dose parameters of the target area determined by the test dose distribution data under the corresponding control algorithm are obtained; and the target beam model is determined according to each reference dose parameter and each test dose parameter.

[0240] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0241] The point dose deviation and the surface dose pass rate between the reference dose parameters and the test dose parameters under each control algorithm are obtained; when the point dose deviation and the surface dose pass rate under each control algorithm meet the preset dose requirements, the adjusted initial beam model is determined as the target beam model; and when the reference dose parameters do not meet the preset dose requirements, the parameters of the parameter-adjusted initial beam model are adjusted to obtain the target beam model.

[0242] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0243] Determine a target control algorithm for calculating reference dose parameters of a target area; determine target model parameters corresponding to the target control algorithm according to a corresponding relationship between the control algorithm and the model parameters to be adjusted; and adjust the target model parameters in the parameter-adjusted initial beam model to obtain a target beam model.

[0244] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed 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 the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an 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 each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0245] The technical features of the above embodiments may 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.

[0246] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A beam model establishment method, characterized in that: include: obtaining test data of the radiation delivery device under test conditions; determining an initial beam model of the radiation delivery device and one or more types of model parameters to be adjusted in the initial beam model; and According to one or more of the test data corresponding to the model parameters to be adjusted, the model parameters to be adjusted are adjusted to obtain a target beam model of the radiation delivery device.

2. The beam model establishment method according to claim 1, characterized in that: The step of adjusting the model parameters to be adjusted according to one or more of the test data corresponding to the model parameters to be adjusted to obtain a target beam model of the radiation delivery device comprises: For one or more model parameters to be adjusted, the degree of conformity between a model curve calculated based on the parameter-adjusted initial beam model and a test curve based on the test data is determined, and when the degree of conformity is lower than a threshold, the model parameters to be adjusted are re-iteratively adjusted.

3. The beam model establishment method according to claim 1, characterized in that: The data types of the test data and the model data calculated based on the initial beam model include depth dose distribution data and off-axis dose distribution data at a preset depth, the model parameters to be adjusted include energy spectrum related parameters and field related parameters, and The step of adjusting the model parameters to be adjusted according to one or more of the test data corresponding to the model parameters to be adjusted to obtain a target beam model of the radiation delivery device comprises: Adjusting the energy spectrum related parameters according to the test data and the depth dose distribution data in the model data; and The field-related parameters are adjusted according to the off-axis dose distribution data at the preset depth in the test data and the model data.

4. The beam model establishment method according to claim 3, characterized in that: The depth dose distribution data includes a built-up distribution area and a drop distribution area; and The adjusting the energy spectrum related parameters according to the test data and the depth dose distribution data in the model data includes: Adjusting the electron source size and the electron source weight in the initial beam model according to the test data and the data corresponding to the built distribution area in the depth dose distribution data in the model data; and The primary photon weight and the secondary photon weight in the initial beam model are adjusted according to the data corresponding to the drop distribution area in the test data and the depth dose distribution data in the model data.

5. The beam model establishment method according to claim 3, characterized in that: The off-axis dose distribution data at the preset depth includes a low dose distribution area, a penumbra distribution area and a flattened distribution area; The step of adjusting the radiation field related parameters according to the off-axis dose distribution data at a preset depth in the test data and the model data includes: Adjusting the field external parameters in the initial beam model according to the test data and the data corresponding to the low-dose distribution area and the penumbra distribution area in the off-axis dose distribution data at a preset depth in the model data; and The off-axis coefficient in the initial beam model is adjusted according to the test data and the data of the corresponding flattened distribution area in the off-axis dose distribution data at a preset depth in the model data.

6. The beam model establishment method according to claim 1, characterized in that: The step of adjusting the model parameters to be adjusted according to one or more of the test data corresponding to the model parameters to be adjusted to obtain a target beam model of the radiation delivery device comprises: Input the preset parameters into the parameter-adjusted initial beam model, and output reference dose distribution data; Based on the reference dose distribution data, a plurality of control algorithms are used to determine a reference dose parameter of the target area under each control algorithm; Acquiring test dose parameters of the target area determined by the test data under a corresponding control algorithm; and The target beam model is determined according to each of the reference dose parameters and each of the test dose parameters.

7. The beam model establishment method according to claim 6, characterized in that: The step of determining the target beam model according to each of the reference dose parameters and each of the test dose parameters includes: Obtaining the point dose deviation and the surface dose passing rate between the reference dose parameters and the test dose parameters under each of the control algorithms; When the point dose deviation and the surface dose pass rate under each of the control algorithms meet the preset dose requirements, determining the adjusted initial beam model as the target beam model; and In the case that the reference dose parameter does not meet the preset dose requirement, the parameter-adjusted initial beam model is subjected to parameter adjustment to obtain the target beam model.

8. The beam model establishment method according to claim 7, characterized in that: The step of adjusting the parameters of the parameter-adjusted initial beam model to obtain the target beam model includes: Determining a target control algorithm for calculating reference dose parameters for the target area; Determining target model parameters corresponding to the target control algorithm according to the corresponding relationship between the control algorithm and the model parameters to be adjusted; and The target model parameters in the parameter-adjusted initial beam model are adjusted to obtain the target beam model.

9. A beam model building device, characterized in that: include: A test data acquisition module, used to acquire test data of the radiation delivery device under test conditions; An initial model determination module, used to determine an initial beam model of the radiation delivery device and one or more types of model parameters to be adjusted in the initial beam model; as well as The model parameter adjustment module is used to adjust the model parameters to be adjusted according to one or more of the test data corresponding to the model parameters to be adjusted, so as to obtain a target beam model of the radiation delivery device.

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

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