A method, system, apparatus, and storage medium of ridge filter design

Abstract

This invention relates to the field of ridge filter design technology, and discloses a design method, system, device, and storage medium for ridge filters. The method includes acquiring first and second actual water depth dose distribution data for proton and heavy ion beams with multiple energies; performing Monte Carlo simulation on the proton and heavy ion beams to obtain a Monte Carlo beam model; constructing a geometric optimization model for the ridge filter using its geometric parameters as optimization variables, maximizing its performance index as the objective function, and using geometric parameter constraints as constraints; and solving the geometric optimization model using a particle swarm optimization algorithm to obtain the optimal geometric parameters. This invention can improve the parameter design accuracy of ridge filters, reduce the processing accuracy requirements of ridge filters, improve the flatness of the effective biological dose in the extended Bragg peak, and reduce the radiation risk from secondary neutrons.

Description

Technical Field

[0001] This invention relates to the field of ridge filter design technology, and in particular to a design method, system, device and storage medium for a ridge filter. Background Technology

[0002] The ridge filter is a core component in heavy ion radiotherapy systems that enables Bragg peak broadening and ensures uniform dose in the target area. The design parameters of the number of steps and the thickness of each step directly determine the radiotherapy effect.

[0003] However, existing ridge filter design methods have certain limitations: Firstly, ridge filters are typically made of heavy metals such as copper or tungsten. When high-energy protons or heavy ions (especially carbon ions) are transported within the ridge filter, they may undergo nuclear reactions with the heavy metal nuclei, generating a large number of secondary fragment particles and neutrons. Since neutrons are uncharged and have extremely strong penetrating power, they cannot be precisely controlled and can easily trigger secondary neutron radiation risks. Secondly, the widely used double scattering or beam oscillation method in conjunction with ridge filters to form the Spread Out Bragg Peak (SOBP) is based on a specific original Bragg curve. This results in ripples at the far end of the SOBP when using low-energy beams, potentially failing to meet the target area dose uniformity requirements. Furthermore, in terms of fabrication, controlling the weights of the Bragg curve components constituting the SOBP is difficult. A large number of ridge steps are typically required to cover the original Bragg curve with its extremely steep far-end descent. Due to the high mechanical precision requirements of the ridge steps, their fabrication may not meet the requirements, leading to inconsistencies between the target area delivered dose and the planned dose distribution. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a design method, system, device, and storage medium for a ridge filter.

[0005] In a first aspect, the present invention provides a design method for a ridge filter, the method comprising: Based on the water tank experiment and the aluminum plate penetration water tank experiment, the first and second actual water depth dose distribution data of proton and heavy ion beams with multiple energies were obtained. Using the first actual water depth dose distribution data and the second actual water depth dose distribution data as consistency constraints, Monte Carlo simulation was performed on the proton and heavy ion beam to obtain a Monte Carlo beam model. Using the geometric parameters of the ridge filter as optimization variables, maximizing the performance index of the ridge filter as the objective function, and the geometric parameter constraints of the ridge filter as constraints, a geometric optimization model of the ridge filter is constructed. The performance index is obtained by simulation calculation of the ridge filter model in a simulation environment, which is constructed based on the Monte Carlo beam model. The geometric optimization model is solved using the particle swarm optimization algorithm to obtain the optimal geometric parameters, and the optimal ridge filter model is generated based on the optimal geometric parameters.

[0006] Further, the step of performing Monte Carlo simulation on the proton and heavy ion beam using the first actual water depth dose distribution data and the second actual water depth dose distribution data as consistency constraints to obtain the Monte Carlo beam model includes: The proton and heavy ion beam was simulated in a water tank using a Monte Carlo simulation model to obtain the first simulated depth dose distribution data in the water. Based on the difference between the first simulated water depth dose distribution data and the first actual water depth dose distribution data, the model parameters of the Monte Carlo simulation model are adjusted to obtain the initial Monte Carlo beam model. The initial Monte Carlo beam model was used to simulate the proton and heavy ion beam penetrating a water tank through an aluminum plate, and the second simulated depth dose distribution in water was obtained. Based on the difference between the second simulated water depth dose distribution and the second actual water depth dose distribution, the initial Monte Carlo beam model is validated for consistency, and the Monte Carlo beam model is obtained.

[0007] Furthermore, the performance indicators include depth flatness, target area uniformity, and target area conformity. The depth flatness index is calculated based on the weighted biological dose depth distribution of the region of interest within the water phantom, and the weighted biological dose depth distribution is calculated based on a preset relative biological effect model. The target area uniformity index is calculated based on the weighted standard deviation of the target area dose, and the weights in the weighted standard deviation are determined by the volume of the volume element. The target conformity index is calculated based on the percentage of the target volume covered by the preset isodose line.

[0008] Furthermore, the step of solving the geometric optimization model using the particle swarm optimization algorithm to obtain the optimal geometric parameters includes: The geometric parameters of the ridge filter are encoded as particles to initialize the parameters of the particle swarm optimization algorithm. Using the aforementioned performance index as fitness, the population position is iteratively updated until the iteration stops, thus obtaining the optimal particle position. Based on the preset machining accuracy, the optimal particle position is discretized and mapped to obtain the optimal geometric parameters.

[0009] Furthermore, the step of iteratively updating the population position using the performance index as fitness includes: Based on the preset machining accuracy, the particle positions obtained from the current iteration calculation are discretized and mapped, and the fitness is calculated based on the mapped physical positions and the performance indicators. Based on the particle position calculated in the current iteration and the fitness, the particle position is iteratively updated.

[0010] Furthermore, after the step of iteratively updating the particle position based on the particle position calculated in the current iteration and the fitness, the method further includes: The particle swarm updated in each iteration is taken as the new population, and the positions of the particles in the new population are updated using a genetic algorithm to obtain the updated particle positions. The fitness is calculated based on the updated particle positions, and it is determined whether the iteration stopping condition is met. If not, the particle swarm optimization algorithm is used to iteratively calculate the updated particle positions; otherwise, the iteration is stopped.

[0011] Furthermore, the constraints include step thickness constraints and step number constraints.

[0012] Secondly, the present invention provides a design system for a ridge filter, the system comprising: The experimental data acquisition module is used to acquire first and second actual water depth dose distribution data of proton and heavy ion beams with multiple energies, based on water tank experiments and aluminum plate penetration water tank experiments. The simulation data acquisition module is used to perform Monte Carlo simulation on the proton and heavy ion beam using the first actual water depth dose distribution data and the second actual water depth dose distribution data as consistency constraints, so as to obtain a Monte Carlo beam model. The optimization model construction module is used to construct a geometric optimization model of the ridge filter with the geometric parameters of the ridge filter as optimization variables, the objective function being to maximize the performance index of the ridge filter, and the geometric parameter constraints of the ridge filter as constraints. The performance index is obtained by simulation calculation of the ridge filter model in a simulation environment, and the simulation environment is constructed based on the Monte Carlo beam model. The optimal parameter solving module is used to solve the geometric optimization model using the particle swarm optimization algorithm to obtain the optimal geometric parameters, and generate the optimal ridge filter model based on the optimal geometric parameters.

[0013] Thirdly, embodiments of the present invention also provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method.

[0014] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method.

[0015] This invention provides a design method, system, device, and storage medium for a ridge filter. Through dual calibration of Monte Carlo simulation, this invention effectively improves the accuracy of the beam model, providing accurate data support for subsequent parameter design and evaluation. By employing Monte Carlo particle transport simulation and particle swarm optimization, it enhances the optimization effect of geometric parameters. Furthermore, through discrete mapping of machining accuracy, it ensures engineering feasibility and avoids invalid simulation calculations. This invention can improve the parameter design accuracy of ridge filters, reduce the machining accuracy requirements of ridge filters, improve the flatness of the effective biological dose in the extended Bragg peak, and reduce the radiation risk from secondary neutrons. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the design method of the ridge filter in an embodiment of the present invention; Figure 2 This is a schematic diagram of the design system of the ridge filter in an embodiment of the present invention; Figure 3 This is an internal structural diagram of the computer device in an embodiment of the present invention.

[0017] Figure label: 10. Experimental data acquisition module; 20. Simulation data acquisition module; 30. Optimization model construction module; 40. Optimal parameter solution module. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1 The first embodiment of the present invention proposes a design method for a ridge filter, including steps S10 to S40: Step S10: Based on the water tank experiment and the aluminum plate penetration water tank experiment, obtain the first actual water depth dose distribution data and the second actual water depth dose distribution data of proton and heavy ion beams with multiple energies. Step S20: Using the first actual water depth dose distribution data and the second actual water depth dose distribution data as consistency constraints, perform Monte Carlo simulation on the proton and heavy ion beam to obtain a Monte Carlo beam model; Step S30: Using the geometric parameters of the ridge filter as optimization variables, maximizing the performance index of the ridge filter as the objective function, and the geometric parameter constraints of the ridge filter as constraints, construct a geometric optimization model for the ridge filter. The performance index is obtained by simulation calculation of the ridge filter model in a simulation environment, and the simulation environment is constructed based on the Monte Carlo beam model. Step S40: The particle swarm optimization algorithm is used to solve the geometric optimization model to obtain the optimal geometric parameters, and the optimal ridge filter model is generated based on the optimal geometric parameters.

[0020] This embodiment obtains water depth dose distribution data of proton and heavy ion beams with different energies through a three-dimensional water tank experiment. Specifically, the water tank experimental platform is first used to measure the depth dose distribution curves of proton and heavy ion beams with 20 MeV / u, 50 MeV / u, 100 MeV / u, 200 MeV / u, 300 MeV / u, and 400 MeV / u in water. Then, interpolation calculations are performed on each measured curve to extract three characteristic depths, namely the depth corresponding to 50% of the maximum dose (D50), the depth corresponding to 70% of the maximum dose (D70), and the depth corresponding to 100% of the maximum dose (D100, i.e., the Bragg peak position).

[0021] In this embodiment, to reduce the secondary neutrons generated during the transport of heavy metals and heavy ions, a low atomic number material is used as the material for the ridge filter. Preferably, lightweight and easily processed aluminum is selected. To obtain the influence of the material on particle transport, in addition to the bare beam water tank experiment, this embodiment also conducts a material penetration water tank experiment. Taking aluminum as an example, based on the water tank experiment, an aluminum plate penetration water tank experiment is conducted, that is, measuring the depth dose distribution data in water after proton and heavy ion beams of different energies penetrate aluminum plates of different thicknesses. Preferably, the aluminum plate thickness is measured from 1 mm to 50 mm in increments of 1 mm. To simplify the experimental steps and improve experimental efficiency, one or two energies and thicknesses can be selected for the experimental measurement. The experimental steps are consistent with the water tank experiment steps, and the measurement results include the depth dose distribution in water and three characteristic depths. It should be noted that if other low atomic number materials are used as the material, the corresponding material needs to be used for the penetration experiment. The steps of the water tank experiment can refer to the conventional three-dimensional water tank experiment steps in the radiotherapy system, which will not be described in detail here.

[0022] Then, Monte Carlo simulations were performed on the proton and heavy ion beams of the same energy, and the measurement results from the water tank experiment were used as consistency verification parameters to generate a Monte Carlo beam model. The specific steps include: The proton and heavy ion beam was simulated in a water tank using a Monte Carlo simulation model to obtain the first simulated depth dose distribution data in the water. Based on the difference between the first simulated water depth dose distribution data and the first actual water depth dose distribution data, the model parameters of the Monte Carlo simulation model are adjusted to obtain the initial Monte Carlo beam model. The initial Monte Carlo beam model was used to simulate the proton and heavy ion beam penetrating a water tank through an aluminum plate, and the second simulated depth dose distribution in water was obtained. Based on the difference between the second simulated water depth dose distribution and the second actual water depth dose distribution, the initial Monte Carlo beam model is validated for consistency, and the Monte Carlo beam model is obtained.

[0023] In this embodiment, a water tank simulation of the proton and heavy ion beams at the aforementioned energies is first performed using a Monte Carlo simulation model. This model, generated using the Monte Carlo program FLUKA, simulates the depth dose distribution data in water, i.e., the simulated depth dose distribution curve. Then, the first actual depth dose distribution data (i.e., the measured curve) generated from the water tank experiment is normalized to the first simulated depth dose distribution data (i.e., the simulated curve) generated from the water tank simulation. The positional differences between the simulated and measured curves at D50, D70, and D100 are calculated. Based on these differences, the model parameters in the Monte Carlo simulation model, such as the beam energy, dispersion, and average ionization energy, are iteratively adjusted until the positional differences between the simulated and measured curves at D50, D70, and D100 are all within a preset threshold, for example, within 0.1 mm. The simulated energy and corresponding simulated data that best approximate the measured curve are selected and recorded. By measuring and simulating the aforementioned energies, the correspondence between the measured and simulated energies is found, and a Monte Carlo beam model of the proton and heavy ion beam is established. The model was validated using the measurement results of a 290 MeV / u proton carbon ion beam, and the initial parameters of the Monte Carlo beam model were finally determined, thus obtaining the initial Monte Carlo beam model.

[0024] Then, the initial Monte Carlo beam model was used to simulate the proton and heavy ion beam penetrating a water tank through an aluminum plate, obtaining the corresponding simulation curves and characteristic depths. The simulation curves were then compared with the measured curves obtained from the aluminum plate penetration experiment to verify consistency. If the difference between the two curves at the three characteristic depths was within a preset threshold, the verification was successful, and the initial Monte Carlo beam model was used as the final Monte Carlo beam model. If the difference was not within the preset threshold, the model parameters of the initial Monte Carlo beam model were fine-tuned until the threshold constraints of the consistency verification were met, thus obtaining the final Monte Carlo beam model. This embodiment, through parameter adjustment and consistency verification based on the difference, can effectively improve the rationality of the Monte Carlo beam model, providing accurate data support for the subsequent parameter design of the ridge filter, thereby improving the accuracy of the parameter design results.

[0025] This embodiment obtains the geometric parameters of the ridge filter by constructing and solving a geometric optimization model. The model uses the geometric parameters of the ridge filter as optimization variables, maximizes the performance index of the ridge filter as the objective function, and uses the geometric parameters as constraints. A particle swarm optimization algorithm is used to iteratively solve the model to obtain the optimal geometric parameters. It should be noted that the ridge filter structure in this embodiment adopts a typical existing ridge filter structure, and the optimal geometric parameters of the ridge filter structure are obtained through parameter optimization.

[0026] Specifically, the number of steps in the ridge filter and the thickness of each step are used as optimization variables, and maximizing the performance index of the ridge filter is taken as the objective function. The performance index can be characterized by one or more indicators such as SOBP flatness (i.e., the uniformity of dose distribution within the target area of ​​the broadened Bragg peak generated by the ridge filter), range modulation accuracy, and dose output stability. The performance index is obtained by constructing a simulation model of the ridge filter and a water phantom to simulate particle transport, and the index is calculated based on the simulation results.

[0027] Specifically, the Monte Carlo program FLUKA constructs simulation models of the ridge filter and the water phantom. The geometric parameters of the ridge filter are determined through each iteration of the geometric simulation model. The repeating structure of the ridge filter is defined using lattice and rot-defini cards. The lattice card defines the periodically repeating geometric structure, while the rot-defini card defines the rotation, translation, or transformation relationships of the coordinate system. The parameters of the water phantom simulation model are consistent with those of the Monte Carlo beam model. In essence, this embodiment calculates the performance indicators of the ridge filter by substituting the solution parameters into the simulation model and using the results of Monte Carlo particle transport simulations during each iteration.

[0028] The constraints of the geometric optimization model are determined based on the relevant constraints of the geometric parameters of the ridge filter, including the constraints on the number of steps and the thickness of steps. For example, the constraint range of the number of steps, the constraint range of the thickness of a single step, and the constraint range of the thickness of all steps. Preferably, considering the operability of machining and 3D printing, the constraint of increasing step thickness can also be added. The design of increasing step thickness can reduce the machining difficulty.

[0029] In a preferred embodiment, depth flatness, target uniformity, and target conformity indices are used to characterize the performance of the ridge filter. The weighted sum of these three indices is used as the performance index. These indices are calculated by using FLUKA simulation of integral depth-dose curves and the deposition dose in each voxel within the water phantom. Specifically, the depth flatness index characterizes the flatness of the depth-dose distribution in the region of interest (ROI) within the water phantom, which is a pre-defined region. Preferably, under specific energy particle incident conditions, the peak position of the Bragg peak is calculated based on the incident energy, and the proximal and distal boundaries are determined based on the desired SOBP width. These boundaries are then converted into dose percentage boundaries on the depth-dose curve, thereby defining the SOBP region, i.e., the ROI.

[0030] In this embodiment, the depth flatness index is calculated by integrating the second derivative of the weighted biological dose depth distribution in the region of interest within the water phantom, and its formula is expressed as: In the formula, Indicators representing depth flatness and denoted by , respectively, represent the starting and ending depths of the region of interest within the water model. D(t) represents the weighted biological dose depth distribution, which is the product of the simulated depth dose distribution of the region of interest and the relative biological effect weights.

[0031] The relative biological effect (RBE) is the ratio of photon to proton or heavy ion physical doses required to produce the same radiation biological effect. It is a key parameter for quantitatively assessing the biological advantage of particles in proton and heavy ion radiotherapy. Commonly used biological models for RBE calculation include the micro-dose kinetic model (MKM) and the local effect model (LEM). These biological models can convert physical dose to RBE. The specific calculation steps can be found in the conversion steps of the selected biological model, and will not be elaborated here.

[0032] The target homogeneity index characterizes the uniformity of dose distribution within the target area. It can be calculated based on the weighted standard deviation of the target dose, and its expression is: In the formula, Indicators representing target area uniformity Representing the The dose size of a volume element, This represents the average dose level of the planned irradiation target area. Representing the The volume size of each volume element This represents the total volume of the planned irradiation target area.

[0033] The target volume covered by the isodose line, characterized by the target conformity index, can be calculated based on the proportion of the target volume covered by the preset isodose line. Its expression is: In the formula, Indicates target adaptability index, The target volume covered by the 95% isodose line. This refers to the planned target area size.

[0034] The three indicators mentioned above are calculated by using the integral depth-dose curve obtained from Monte Carlo simulation and the deposition dose in each voxel within the water model. The results are then weighted and summed according to preset weights to obtain the performance index of the ridge filter under the current geometric parameters.

[0035] For the above geometric optimization model, this embodiment uses the particle swarm optimization algorithm to solve the model. The specific steps include: The geometric parameters of the ridge filter are encoded as particles to initialize the parameters of the particle swarm optimization algorithm. Using the aforementioned performance index as fitness, the population position is iteratively updated until the iteration stops, thus obtaining the optimal particle position. Based on the preset machining accuracy, the optimal particle position is discretized and mapped to obtain the optimal geometric parameters.

[0036] In this embodiment, the geometric parameters of the ridge filter are first encoded as particles. The vector value of each particle is a feasible solution to the problem. Each particle in the population is a set of D-dimensional vectors, which is the same as the search space dimension of the problem, i.e., the number of steps and the thickness of the steps of the ridge filter. Then, the parameters of the particle swarm optimization algorithm are initialized, including the population size, particle dimension, particle inertia, learning factor, local optimum, global optimum learning weight, and algorithm cutoff condition.

[0037] The performance index calculation formula is used as the fitness function of the particle swarm optimization algorithm. The result of each iteration is used as the input geometric parameters for the ridge filter geometric model. Fitness is calculated based on the results of Monte Carlo particle transport simulations, and the population position is updated according to the fitness value. When a preset iteration stopping condition is met, such as the fitness function or the number of iterations meeting a preset condition, the optimal geometric parameters are output. The specific iterative solution steps can be found in the standard solution steps of the particle swarm optimization algorithm, and will not be elaborated here.

[0038] Considering the precision requirements of machining, this embodiment adds a discrete mapping step based on machining precision to the conventional particle swarm optimization solution steps. The fitness of the mapped particles is calculated, and the specific steps include: Based on the preset machining accuracy, the particle positions obtained from the current iteration calculation are discretized and mapped, and the fitness is calculated based on the mapped physical positions and the performance indicators. Based on the particle position calculated in the current iteration and the fitness, the particle position is iteratively updated.

[0039] In this embodiment, in the fitness calculation step of the particle swarm optimization algorithm, the particle positions obtained from the current iteration are first discretized and mapped according to a preset machining precision. Then, the fitness is calculated based on the discretized positions. Preferably, the formula for the discretization mapping can be expressed as: X real =round(X / p)×p, where X represents the particle position obtained in the current iteration, X real This represents the physical location after discrete mapping, p represents the preset machining accuracy, and round(*) represents the rounding function.

[0040] The discrete mapping in this embodiment can be understood as data preprocessing during fitness calculation. This discrete mapping is only used in the fitness evaluation stage. In the next iteration, the particle positions used are still the unmapped particle positions. By combining continuous space search with discrete mapping evaluation in this embodiment, a balance can be effectively achieved between the mathematical continuity of the algorithm (ensuring convergence) and the physical discreteness of the engineering (ensuring manufacturability), thereby meeting the current requirements for machining accuracy. Furthermore, after the iteration ends and before the optimal parameters are output, the optimal particle positions can be mapped to the optimal geometric parameters that meet the requirements of machining accuracy through discrete mapping, thereby achieving the optimal design of the ridge filter.

[0041] In a preferred embodiment, to avoid the particle swarm optimization algorithm getting trapped in local optima, this embodiment introduces a genetic algorithm based on the particle swarm optimization algorithm. The specific steps include: The particle swarm updated in each iteration is taken as the new population, and the positions of the particles in the new population are updated using a genetic algorithm to obtain the updated particle positions. The fitness is calculated based on the updated particle positions, and it is determined whether the iteration stopping condition is met. If not, the particle swarm optimization algorithm is used to iteratively calculate the updated particle positions; otherwise, the iteration is stopped.

[0042] In this embodiment, the particle swarm updated in each iteration is used as a new population. A genetic algorithm is employed to perform genetic operations such as encoding, selection, mutation, and crossover on the new population to update particle positions. Then, the fitness is calculated based on the updated particle positions, and it is determined whether the iteration stopping condition is met. If the condition is met, the iteration stops; otherwise, the particle swarm optimization iteration continues. That is, this embodiment introduces a genetic algorithm in each optimization iteration, using a hybrid particle swarm and genetic algorithm to avoid local optima.

[0043] Preferably, to reduce computational overhead, conditional triggering can be used. For example, the global fitness can be used to determine whether the particle swarm optimization algorithm has gotten stuck in a local optimum. If so, a genetic algorithm can be introduced for one or more iterations, generally not exceeding three times; or the genetic algorithm can be introduced when the particle swarm iteration exceeds a preset number. The specific hybrid strategy can be flexibly set according to actual needs, and is not specifically limited here.

[0044] This embodiment provides a design method for a ridge filter. Through dual calibration of Monte Carlo simulation, the accuracy of the beam model is effectively improved, providing accurate data support for subsequent parameter design and evaluation. Monte Carlo particle transport simulation and particle swarm optimization improve the optimization effect of geometric parameters, and discrete mapping of machining accuracy ensures engineering feasibility and avoids invalid simulation calculations. This embodiment can improve the parameter design accuracy of the ridge filter, reduce the machining accuracy requirements of the ridge filter, improve the flatness of the effective biological dose in the extended Bragg peak, and reduce the radiation risk from secondary neutrons.

[0045] Please see Figure 2 Based on the same inventive concept, the second embodiment of the present invention proposes a design system for a ridge filter, comprising: The experimental data acquisition module 10 is used to acquire first and second actual water depth dose distribution data of proton and heavy ion beams with multiple energies based on the water tank experiment and the aluminum plate penetration water tank experiment. The simulation data acquisition module 20 is used to perform Monte Carlo simulation on the proton and heavy ion beam using the first actual water depth dose distribution data and the second actual water depth dose distribution data as consistency constraints, and obtain a Monte Carlo beam model. The optimization model construction module 30 is used to construct a geometric optimization model of the ridge filter with the geometric parameters of the ridge filter as optimization variables, the objective function being to maximize the performance index of the ridge filter, and the geometric parameter constraints of the ridge filter as constraints. The performance index is obtained by simulation calculation of the ridge filter model in a simulation environment, and the simulation environment is constructed based on the Monte Carlo beam model. The optimal parameter solving module 40 is used to solve the geometric optimization model using the particle swarm optimization algorithm to obtain the optimal geometric parameters, and generate the optimal ridge filter model based on the optimal geometric parameters.

[0046] The technical features and effects of the ridge filter design system proposed in this embodiment of the invention are the same as those of the method proposed in this embodiment of the invention, and will not be repeated here. Each module in the above-mentioned ridge filter design system can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in a computer device in hardware form, or it can be stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0047] Furthermore, embodiments of the present invention also propose a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method.

[0048] Please see Figure 3 The diagram illustrates the internal structure of a computer device in one embodiment, which may specifically be a terminal or a server. The computer device includes a processor, memory, network interface, display, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a ridge filter design method. The display screen may be a liquid crystal display (LCD) or an e-ink display. The input devices may be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0049] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computing devices may include more or fewer components than those shown in the figure, or combine certain components, or have the same component arrangement.

[0050] Furthermore, embodiments of the present invention also propose a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described method.

[0051] In summary, the present invention proposes a design method, system, device, and storage medium for a ridge filter. The method involves obtaining first and second actual water depth dose distribution data for proton and heavy ion beams with multiple energies based on water tank experiments and aluminum plate penetration experiments. Using the first and second actual water depth dose distribution data as consistency constraints, Monte Carlo simulation is performed on the proton and heavy ion beams to obtain a Monte Carlo beam model. A geometric optimization model of the ridge filter is constructed using its geometric parameters as optimization variables, maximizing its performance index as the objective function, and the geometric parameter constraints as constraints. The performance index is obtained through simulation calculations of the ridge filter model in a simulation environment built based on the Monte Carlo beam model. A particle swarm optimization algorithm is used to solve the geometric optimization model to obtain the optimal geometric parameters, and an optimal ridge filter model is generated based on these optimal geometric parameters. This invention effectively improves the accuracy of the beam model through dual calibration of Monte Carlo simulation, providing accurate data support for subsequent parameter design and evaluation. It enhances the optimization effect of geometric parameters through Monte Carlo particle transport simulation and particle swarm iteration optimization, and ensures engineering feasibility and avoids invalid simulation calculations through discrete mapping of machining accuracy. This invention can improve the parameter design accuracy of ridge filters, reduce the machining accuracy requirements of ridge filters, improve the flatness of the effective biological dose in the extended Bragg peak, and reduce the radiation risk from secondary neutrons.

[0052] The various embodiments in this specification are described in a progressive manner. For directly identical or similar parts of the embodiments, refer to each other. Each embodiment focuses on its differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. It should be noted that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0053] The embodiments described above are merely preferred embodiments of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various improvements and substitutions without departing from the technical principles of this invention, and these improvements and substitutions should also be considered within the scope of protection of this application. Therefore, the scope of protection of this patent application should be determined by the scope of the claims.

Claims

1. A method of designing a ridge filter, characterized by, include: Based on the water tank experiment and the aluminum plate penetration water tank experiment, the first and second actual water depth dose distribution data of proton and heavy ion beams with multiple energies were obtained. Using the first actual water depth dose distribution data and the second actual water depth dose distribution data as consistency constraints, Monte Carlo simulation was performed on the proton and heavy ion beam to obtain a Monte Carlo beam model. Using the geometric parameters of the ridge filter as optimization variables, maximizing the performance index of the ridge filter as the objective function, and the geometric parameter constraints of the ridge filter as constraints, a geometric optimization model of the ridge filter is constructed. The performance index is obtained by simulation calculation of the ridge filter model in a simulation environment, which is constructed based on the Monte Carlo beam model. The geometric optimization model is solved using the particle swarm optimization algorithm to obtain the optimal geometric parameters, and the optimal ridge filter model is generated based on the optimal geometric parameters.

2. The design method of a ridge filter according to claim 1, wherein The step of performing Monte Carlo simulation on the proton and heavy ion beam to obtain a Monte Carlo beam model, using the first and second actual water depth dose distribution data as consistency constraints, includes: The proton and heavy ion beam was simulated in a water tank using a Monte Carlo simulation model to obtain the first simulated depth dose distribution data in the water. Based on the difference between the first simulated water depth dose distribution data and the first actual water depth dose distribution data, the model parameters of the Monte Carlo simulation model are adjusted to obtain the initial Monte Carlo beam model. The initial Monte Carlo beam model was used to simulate the proton and heavy ion beam penetrating a water tank through an aluminum plate, and the second simulated depth dose distribution in water was obtained. Based on the difference between the second simulated water depth dose distribution and the second actual water depth dose distribution, the initial Monte Carlo beam model is validated for consistency, and the Monte Carlo beam model is obtained.

3. The design method of the ridge filter according to claim 1, characterized in that, The performance indicators include depth flatness, target area uniformity, and target area conformity. The depth flatness index is calculated based on the weighted biological dose depth distribution of the region of interest within the water phantom, and the weighted biological dose depth distribution is calculated based on a preset relative biological effect model. The target area uniformity index is calculated based on the weighted standard deviation of the target area dose, and the weights in the weighted standard deviation are determined by the volume of the volume element. The target conformity index is calculated based on the percentage of the target volume covered by the preset isodose line.

4. The design method of the ridge filter according to claim 3, characterized in that, The step of solving the geometric optimization model using the particle swarm optimization algorithm to obtain the optimal geometric parameters includes: The geometric parameters of the ridge filter are encoded as particles to initialize the parameters of the particle swarm optimization algorithm. Using the aforementioned performance index as fitness, the population position is iteratively updated until the iteration stops, thus obtaining the optimal particle position. Based on the preset machining accuracy, the optimal particle position is discretized and mapped to obtain the optimal geometric parameters.

5. The design method of the ridge filter according to claim 4, characterized in that, The step of iteratively updating the population position using the performance index as fitness includes: Based on the preset machining accuracy, the particle positions obtained from the current iteration calculation are discretized and mapped, and the fitness is calculated based on the mapped physical positions and the performance indicators. Based on the particle position calculated in the current iteration and the fitness, the particle position is iteratively updated.

6. The design method of the ridge filter according to claim 5, characterized in that, After the step of iteratively updating the particle position based on the particle position calculated in the current iteration and the fitness, the method further includes: The particle swarm updated in each iteration is taken as the new population, and the positions of the particles in the new population are updated using a genetic algorithm to obtain the updated particle positions. The fitness is calculated based on the updated particle positions, and it is determined whether the iteration stopping condition is met. If not, the particle swarm algorithm is used to iteratively calculate the updated particle positions; otherwise, the iteration is stopped.

7. The design method of the ridge filter according to claim 1, characterized in that, The constraints include step thickness constraints and step number constraints.

8. A design system for a ridge-shaped filter, characterized in that, include: The experimental data acquisition module is used to acquire first and second actual water depth dose distribution data of proton and heavy ion beams with multiple energies, based on water tank experiments and aluminum plate penetration water tank experiments. The simulation data acquisition module is used to perform Monte Carlo simulation on the proton and heavy ion beam using the first actual water depth dose distribution data and the second actual water depth dose distribution data as consistency constraints, so as to obtain a Monte Carlo beam model. The optimization model construction module is used to construct a geometric optimization model of the ridge filter with the geometric parameters of the ridge filter as optimization variables, the objective function being to maximize the performance index of the ridge filter, and the geometric parameter constraints of the ridge filter as constraints. The performance index is obtained by simulation calculation of the ridge filter model in a simulation environment, and the simulation environment is constructed based on the Monte Carlo beam model. The optimal parameter solving module is used to solve the geometric optimization model using the particle swarm optimization algorithm to obtain the optimal geometric parameters, and generate the optimal ridge filter model based on the optimal geometric parameters.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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