Method for simulating particle transport, radiotherapy system and related device

Abstract

The invention provides a particle transport simulation method, electronic equipment, a computer readable storage medium, a computer program product and a radiotherapy system, and is used for simulating the transport process of particles in a target energy modulator, the target energy modulator comprises a plurality of energy modulation sheets, and the method comprises the following steps: obtaining the center position of the target energy modulator; creating a virtual energy regulator according to the central position of the target energy regulator; the virtual energy regulator adopts a seamless structure, the central position of the virtual energy regulator is aligned with the central position of the target energy regulator, and the thickness of the virtual energy regulator is matched with the total thickness of the target energy regulator; wherein the total thickness is the sum of the thicknesses of all the energy adjusting sheets; and simulating the transportation process of the particles in the target energy regulator by simulating the transportation process of the particles in the virtual energy regulator. According to the method, the analog simulation calculation process of particle transportation in the target energy regulator is simplified and accelerated by creating the virtual energy regulator.

Description

[0001] This application is a divisional application of the Chinese application with application number 202310669377.5, application date June 7, 2023, and invention name “Method for simulating particle transport, radiotherapy system and related devices”. Technical Field

[0002] The present application relates to the technical field of radiotherapy, and in particular to a method for simulating particle transport, an electronic device, a computer-readable storage medium, a computer program product, and a radiotherapy system. Background Art

[0003] In order to calculate the dose distribution of protons in a proton therapy system using an energy modulator, it is necessary to simulate the energy, position, and secondary particle distribution of protons after passing through different combinations of energy modulators. However, due to the various combinations of energy modulators and the changes in the positions of energy modulators, it is impossible to use the pre-calculated results for simulation calculations. In addition, there are gaps between the energy modulator slices. When particles pass through these gaps, their motion deflection and range attenuation are different from those in the energy modulator material. Therefore, during the transport process, it is necessary to process each gap boundary, making the simulation of the entire transport process complicated and time-consuming.

[0004] Based on this, the present application provides a method for simulating particle transport, an electronic device, a computer-readable storage medium, a computer program product, and a radiotherapy system to improve related technologies. Summary of the invention

[0005] The purpose of this application is to provide a method, electronic device, computer-readable storage medium, computer program product and radiotherapy system for simulating particle transport, thereby simplifying and accelerating the simulation calculation process of particle transport in a target energy modulator by creating a virtual energy modulator.

[0006] The purpose of this application is achieved by the following technical solutions:

[0007] The present application provides a method for simulating particle transport, which is used to simulate the transport process of particles in a target energy regulator, wherein the target energy regulator includes a plurality of energy regulator slices, and the method includes:

[0008] According to the position and thickness of each energy regulating sheet, the center position of the target energy regulating device is obtained;

[0009] According to the center position of the target energy regulator, a virtual energy regulator is created; the virtual energy regulator adopts a seamless structure, and the center position of the virtual energy regulator is aligned with the center position of the target energy regulator, and the thickness of the virtual energy regulator matches the total thickness of the target energy regulator; wherein the total thickness is the sum of the thicknesses of all energy regulator sheets;

[0010] By simulating the transport process of particles in the virtual energy regulator, the transport process of particles in the target energy regulator is simulated;

[0011] According to the irradiation parameter set, a simulation information set after the particle passes through the virtual energy regulator is obtained; according to the simulation information set, simulated dose distribution information of the particle is obtained.

[0012] The beneficial effect of this technical solution is that it simplifies and accelerates the simulation and calculation process of particle transport in the target energy regulator by creating a virtual energy regulator. By creating a virtual energy regulator and simulating the transport process of particles in it, the transport of particles in the actual target energy regulator can be effectively simulated, which helps to accurately calculate the energy, position and secondary particle distribution of particles after passing through the target energy regulator, thereby providing accurate dose distribution calculation results; the virtual energy regulator adopts a seamless structure, eliminating the influence of the gap in the target energy regulator on the particle transport process. At the same time, the thickness of the virtual energy regulator matches the total thickness of the target energy regulator, ensuring the accuracy of the simulation. By performing particle transport simulation in the virtual energy regulator, additional processing of each gap boundary can be avoided, thereby simplifying the calculation process and reducing the calculation time; by accurately simulating the transport process of particles in the target energy regulator, the dose distribution of particles can be better understood, which helps to optimize the particle treatment plan, improve the treatment accuracy and effect, and minimize damage to healthy tissues. In summary, by simulating the particle transport process in the virtual energy regulator, the particle transport process in the target energy regulator is accurately simulated, the calculation process is simplified and the treatment accuracy and effect are improved. When simulating the particle transport process of the target energy regulator, the high efficiency and high accuracy of the calculation process can be taken into account.

[0013] In some possible implementations, obtaining the center position of the target energy regulator includes:

[0014] According to the position and thickness of each energy regulating sheet, the equivalent position and thickness ratio of each energy regulating sheet are calculated, wherein the thickness ratio of the energy regulating sheet is the ratio of the thickness of the energy regulating sheet to the total thickness;

[0015] The center position of the target energy regulator is calculated according to the equivalent position and thickness ratio of each energy regulator sheet.

[0016] The beneficial effects of this technical solution are: by calculating the equivalent position and thickness ratio of each energy modulation sheet, the proportion of each energy modulation sheet in the total thickness can be accurately determined, and based on these ratios, the center position of the target energy modulator can be calculated to ensure accuracy and reliability; this method takes into account the position and thickness of each energy modulation sheet, and calculates the center position of the target energy modulator according to their ratio. This comprehensive consideration of the characteristics of different energy modulation sheets can more accurately obtain the center position of the entire target energy modulator; by accurately obtaining the center position of the target energy modulator, it can be ensured that the subsequent creation of a virtual energy modulator and the simulation of the particle transport process have higher accuracy and reliability.

[0017] In some possible implementations, the process of calculating the equivalent position of each energy modulation piece includes:

[0018] According to the position of each energy adjustment sheet, the distance between each energy adjustment sheet and the isocenter plane is calculated;

[0019] According to the thickness of each energy modulation sheet and its distance from the isocenter plane, the equivalent position of each energy modulation sheet is calculated.

[0020] The beneficial effects of this technical solution are: by calculating the distance between each energy modulation sheet and the isocenter plane, the specific position of the energy modulation sheet in the target energy modulation device is taken into account, which makes the calculation result more accurate and can better reflect the structure and layout of the actual energy modulation device; the equivalent position of each energy modulation sheet is calculated in combination with the thickness of each energy modulation sheet and its distance from the isocenter plane. This comprehensive consideration of the size and position of the energy modulation sheet can better reflect its influence on the particle transport process, thereby improving the accuracy and reliability of the calculation; by considering the position, thickness and equivalent position of the energy modulation sheet, the transport process of particles in the virtual energy modulation device can be more accurately simulated, which will help to accurately calculate the energy, position and secondary particle distribution of the particles after passing through the target energy modulation device, thereby improving the accuracy of the simulation.

[0021] In some possible implementations, obtaining the center position of the target energy regulator includes:

[0022] Input the positions and thicknesses of all the energy regulator sheets into the center position model to obtain the center position of the target energy regulator;

[0023] The center position model is obtained by training a preset deep learning model using a training set.

[0024] The beneficial effects of this technical solution are: by inputting the positions and thicknesses of all energy modulators into the center position model, the pre-trained deep learning model (i.e., the center position model) can be used to obtain the center position of the target energy modulator. This method combines a large amount of training data and the learning ability of the model, and can accurately predict the center position of the target energy modulator; by adopting a deep learning-based model (i.e., the center position model) to obtain the center position of the target energy modulator, the dependence on manual calculation or simplified models can be reduced. The deep learning model can learn the complex relationship of the center position of the target energy modulator from a large amount of training data, and therefore can provide more accurate and reliable results; because the deep learning model has strong learning ability and adaptability, it can adapt to energy modulators of different types and complex structures, which makes the method widely applicable and able to cope with various energy modulator designs and combinations.

[0025] In some possible implementations, simulating the transport process of particles in the virtual energy regulator to simulate the transport process of particles in the target energy regulator includes:

[0026] Acquiring an incident position of a particle entering an incident surface of the virtual energy regulator;

[0027] The transport process of particles entering from the incident position of the incident surface of the virtual energy regulator and leaving from the exit surface of the virtual energy regulator is simulated to simulate the transport process of particles in the target energy regulator.

[0028] The beneficial effects of this technical solution are as follows: by determining the incident position of the particle entering the incident surface of the virtual energy modulator, the initial position and direction of the particle can be accurately simulated, which helps to accurately simulate the transport process of the particle in the virtual energy modulator and provide accurate starting conditions for subsequent simulations; by simulating the transport process of the particle entering from the incident position of the incident surface of the virtual energy modulator and leaving from the exit surface, the actual transport process of the particle in the target energy modulator can be simulated. This simulation takes into account the structure and characteristics of the virtual energy modulator, including the seamless structure and matching the thickness of the target energy modulator, so as to more accurately predict the energy, position and secondary particle distribution of the particle after passing through the target energy modulator; by simulating the transport process of the particle in the virtual energy modulator, accurate dose distribution calculation results can be provided, which helps to optimize the particle therapy plan, improve the accuracy of dose distribution and treatment effect, and minimize damage to healthy tissue.

[0029] In some possible implementations, obtaining the incident position of the particle entering the incident surface of the virtual energy regulator includes:

[0030] The incident position of the particle entering the incident surface of the virtual energy regulator is determined according to the distance between the center position of the virtual energy regulator and the isocenter plane, the thickness of the virtual energy regulator and the source model of the particle.

[0031] The beneficial effects of this technical solution are as follows: by considering the thickness of the virtual energy regulator, the distance between the center position and the isocenter plane, the incident position of the particle entering the incident surface of the virtual energy regulator can be determined. This method can accurately consider the geometric shape, position and size of the virtual energy regulator to ensure that the initial position of the particle is closer to the actual simulation situation; by combining the source model of the particle, the incident position of the particle entering the incident surface of the virtual energy regulator can be further determined. The source model can provide information such as the initial position, direction and energy of the particle to help accurately locate the incident position, which helps to simulate the accurate transport process of the particle in the virtual energy regulator; by accurately determining the incident position of the particle entering the incident surface of the virtual energy regulator, the accuracy and reliability of the simulation can be improved, which helps to more accurately simulate the transport process of the particle in the target energy regulator, thereby providing accurate dose distribution calculation results.

[0032] In some possible implementations, the method further includes:

[0033] The simulation information set includes one or more of energy distribution information, emission position, range and secondary particle distribution information;

[0034] The irradiation parameter set includes one or more of particle type, particle beam diameter, radiation dose rate, irradiation area size, irradiation times and irradiation interval time.

[0035] The beneficial effects of this technical solution are as follows: by acquiring a set of simulated information generated during the transport process of particles in a virtual energy regulator, it may include energy distribution information, emission position, range, and secondary particle distribution, etc., which reflect the behavior and corresponding physical properties of the particles after passing through the virtual energy regulator; based on the acquired set of simulated information, the simulated dose distribution information of the particles may be further calculated and acquired, and by analyzing information such as energy distribution, emission position, range, and secondary particle distribution, the dose distribution of the particles after passing through the target energy regulator may be calculated, including the spatial distribution of the dose, the peak position of the dose, etc.; by acquiring the simulated dose distribution information of the particles, the accuracy and comprehensiveness of the dose calculation may be improved, and by considering factors such as the motion trajectory of the particles in the virtual energy regulator, energy deposition, and secondary particle generation, the dose distribution of the particles after passing through the target energy regulator may be more accurately simulated, providing an important basis for accurate treatment planning and dose optimization. In summary, by obtaining a set of simulated information after particles pass through a virtual energy regulator and calculating the simulated dose distribution of particles based on this information, the accuracy and comprehensiveness of dose calculation can be improved, which helps to optimize particle therapy plans, improve the accuracy of dose distribution and treatment effects, and provide reliable data support for the planning and evaluation of particle therapy.

[0036] In some possible implementations, the method further includes:

[0037] According to the simulated dose distribution information and the expected dose distribution information of the particles, the combination strategy of the target energy regulator is updated to re-simulate the transport process of the particles in the target energy regulator according to the updated combination strategy until the simulated dose distribution information of the particles matches the expected dose distribution information;

[0038] Wherein, the combination strategy includes one or more of the material, position and thickness of each energy regulating piece of the target energy regulator;

[0039] The transport process of the re-simulated particles in the target energy regulator includes:

[0040] Recreate the virtual energy regulator according to the updated combination strategy;

[0041] Simulate the transport of particles in a recreated virtual regulator.

[0042] The beneficial effects of this technical solution are as follows: by comparing the simulated dose distribution information of the particles with the expected dose distribution information, the effect of the current combination strategy can be evaluated. Based on the comparison result, the combination strategy of the target energizer can be updated to make the simulated dose distribution information and the expected dose distribution information more matched. Such an update can help optimize the treatment plan and improve the accuracy of the dose distribution and the treatment effect. According to the updated combination strategy, the transport process of the particles in the target energizer is re-simulated. By simulating the transport process of the particles in the target energizer, the influence of the updated combination strategy on the dose distribution can be evaluated and verified, which helps to ensure that the optimized combination strategy can achieve the expected dose distribution, thereby further improving the accuracy and effect of the treatment. By continuously updating the combination strategy and re-simulating the transport process of the particles, the treatment plan can be gradually optimized, which enables the treatment plan to better adapt to the patient's specific situation and achieve a more accurate dose distribution. At the same time, this also improves the degree of optimization of the treatment plan, making the treatment process safer and more effective. In summary, by updating the combination strategy based on the simulated dose distribution information and the expected dose distribution information and resimulating the particle transport process in the target energizer, the accuracy and optimization of the treatment plan can be improved, which helps to achieve the expected dose distribution, improve the treatment effect, and provide reliable data support for the planning and evaluation of particle therapy.

[0043] In some possible implementations, the transport process of particles in the virtual energy regulator can be simulated using various physical simulation methods, such as Monte Carlo simulation or diffusion theory, and calculations can be performed based on parameters such as particle energy, position, and interaction model.

[0044] The beneficial effect of this technical solution is that by recreating the virtual energy modulator, resimulating the particle transport process in the target energy modulator, and iteratively updating the combination strategy, the treatment plan is gradually optimized to achieve the desired dose distribution and treatment effect.

[0045] In some possible implementations, the particles are protons.

[0046] The beneficial effects of this technical solution are as follows: by creating a virtual energy regulator and simulating the transport process of protons therein, the transport of protons in an actual energy regulator (i.e., a target energy regulator) can be accurately simulated, which helps to accurately calculate the energy, position, and secondary particle distribution of protons after passing through the target energy regulator, thereby providing accurate dose distribution calculation results; by creating a seamless virtual energy regulator, the influence of gaps in the actual energy regulator on the proton transport process is eliminated, and the simulation calculation process of proton transport in the target energy regulator is simplified and accelerated. At the same time, the thickness of the virtual energy regulator matches the total thickness of the target energy regulator, ensuring the accuracy of the simulation, and by performing proton transport simulation in the virtual energy regulator, additional processing of each gap boundary is avoided, thereby simplifying the calculation process and reducing the calculation time; by accurately simulating the transport process of protons in the target energy regulator, the dose distribution of protons can be better understood, which helps to optimize the proton therapy plan, improve the treatment accuracy and effect, and minimize damage to healthy tissues.

[0047] In a second aspect, the present application provides an electronic device for simulating a particle transport process in a target energy regulator, wherein the target energy regulator includes a plurality of energy regulator slices, and the electronic device includes a memory and at least one processor, wherein the memory stores a computer program, and the at least one processor is configured to implement the following steps when executing the computer program:

[0048] According to the position and thickness of each energy regulating sheet, the center position of the target energy regulating device is obtained;

[0049] According to the center position of the target energy regulator, a virtual energy regulator is created; the virtual energy regulator adopts a seamless structure, and the center position of the virtual energy regulator is aligned with the center position of the target energy regulator, and the thickness of the virtual energy regulator matches the total thickness of the target energy regulator; wherein the total thickness is the sum of the thicknesses of all energy regulator sheets;

[0050] By simulating the transport process of particles in the virtual energy regulator, the transport process of particles in the target energy regulator is simulated;

[0051] According to the irradiation parameter set, a simulation information set after the particle passes through the virtual energy regulator is obtained; according to the simulation information set, simulated dose distribution information of the particle is obtained.

[0052] In a third aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by at least one processor, it implements the steps of any of the above methods or implements the functions of any of the above electronic devices.

[0053] In a fourth aspect, the present application provides a computer program product, which includes a computer program, and when the computer program is executed by at least one processor, it implements the steps of any of the above methods or implements the functions of any of the above electronic devices.

[0054] In a fifth aspect, the present application provides a radiotherapy system, comprising:

[0055] A device for simulating particle transport, used to match the simulated dose distribution information of the particles with the expected dose distribution information by using any of the above-mentioned methods for simulating particle transport;

[0056] The device for determining human body dose is used to determine the simulated dose as the dose used in radiotherapy after the simulated dose distribution information of the particles matches the expected dose distribution information. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The present application is further described below in conjunction with the accompanying drawings and specific implementation methods.

[0058] Figure 1 It is a flow chart of a method for simulating particle transport provided in an embodiment of the present application.

[0059] Figure 2 It is a schematic diagram of a flow chart for obtaining the center position of the target energy regulator provided in an embodiment of the present application.

[0060] Figure 3 It is a flow chart of another method for simulating particle transport provided in an embodiment of the present application.

[0061] Figure 4 It is a structural block diagram of an electronic device provided in an embodiment of the present application.

[0062] Figure 5 It is a structural diagram of a computer program product provided in an embodiment of the present application. DETAILED DESCRIPTION

[0063] The technical solution in the present application will be described below in conjunction with the drawings and specific implementation methods of the specification of the present application. It should be noted that, under the premise of no conflict, the various implementation methods or technical features described below can be arbitrarily combined to form a new implementation method.

[0064] In the embodiments of the present application, the words "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any implementation or design scheme described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other implementations or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0065] The first, second, etc. descriptions appearing in the embodiments of the present application are only used for illustration and distinction of the description objects. There is no order, nor does it indicate any special limitation on the quantity in the embodiments of the present application, and cannot constitute any limitation on the embodiments of the present application.

[0066] The following is a brief description of the technical field and related terms of the embodiments of the present application.

[0067] Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technology in computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a similar way to human intelligence. Artificial intelligence studies the design principles and implementation methods of various intelligent machines, so that machines have the functions of perception, reasoning and decision-making. Artificial intelligence technology is a comprehensive discipline that covers a wide range of fields, including both hardware-level technology and software-level technology. The basic technologies of artificial intelligence generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, and mechatronics. Artificial intelligence software technology mainly includes computer vision technology, speech processing technology, natural language processing technology, as well as machine learning / deep learning, autonomous driving, and smart transportation.

[0068] Machine Learning (ML) is a multi-disciplinary subject involving probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and other disciplines. A computer program can learn from experience E given a certain type of task T and performance metric P. If its performance in task T can be measured by P, it will improve with experience E. Machine learning specifically studies how computers simulate or implement human learning behavior to acquire new knowledge or skills and reorganize existing knowledge structures to continuously improve their performance. Machine learning is the core of artificial intelligence and the fundamental way to make computers intelligent. Its applications are in all areas of artificial intelligence.

[0069] Deep learning is a special type of machine learning that achieves tremendous power and flexibility by learning to represent the world using nested conceptual hierarchies, where each concept is defined in relation to simpler concepts, and more abstract representations are computed in less abstract ways. Machine learning and deep learning typically include artificial neural networks, belief networks, reinforcement learning, transfer learning, inductive learning, and demonstration learning.

[0070] Proton therapy is a radiotherapy technique that uses high-energy proton beams to precisely treat tumors. Compared with traditional X-ray radiotherapy, proton therapy can better control the delivery of radiation doses, reduce damage to normal tissues, and improve treatment effectiveness.

[0071] The proton therapy system is a medical device used for proton therapy, which consists of two parts: the accelerator system and the treatment system. The accelerator system includes the injector system, low-energy transmission system, main accelerator system, high-energy beam transmission system and auxiliary electrical system, and the treatment system includes the fixed beam treatment system, rotating beam treatment system and treatment planning system.

[0072] The principle of proton therapy is to use the physical properties of protons, that is, after entering the human body, the proton beam will reach the maximum dose (Bragg peak) at a certain depth, and then decrease sharply until it stops. This characteristic enables the proton beam to release the maximum dose in the tumor, while reducing the dose deposition in the normal tissue behind the tumor, thereby reducing the side effects caused by treatment. Proton therapy is suitable for many types of tumors, including pediatric tumors, cranial tumors, head and neck tumors, chest tumors, abdominal tumors, bone and soft tissue tumors, etc. Proton therapy is particularly suitable for tumors around critical organs or those that are sensitive to radiation. Compared with traditional radiotherapy, proton therapy can better protect normal tissues and organs and reduce the side effects caused by treatment. Especially for pediatric patients, proton therapy can reduce long-term treatment sequelae and reduce the risk of secondary tumors. Some studies have shown that proton therapy can provide therapeutic effects comparable to traditional radiotherapy in some cases, while reducing adverse reactions.

[0073] Due to its excellent dose distribution, proton therapy can significantly reduce radiation damage to other tissues. However, during tumor irradiation, energy is deposited near the tumor in the access channel. This effect may cause adverse side effects, such as skin irritation. In addition, the higher skin toxicity of proton irradiation than photon irradiation may be related to higher skin doses or skin equivalent doses. Skin dose refers to the dose equivalent value of irradiation at a depth of 0.07 mm from the body surface measured by a personal dosimeter (refer to the Encyclopedia of China). The skin equivalent dose refers to the product of the average absorbed dose produced by a specific ionizing radiation in the skin and the radiation weighting factor of that radiation (refer to "Method for Estimating Skin Dose Caused by Ionizing Radiation", GBZ / T244-2017).

[0074] The patient's skin is affected by the proton field dose gradient caused by the linear energy transfer of primary protons and secondary particles generated during proton therapy. The deposited dose may damage the skin. The deposited dose mainly consists of two parts: one part comes from the short-range electron accumulation downstream of the air-patient contact, and the other part comes from the nuclear accumulation. Among them, short-range electrons will significantly affect the skin dose because they mainly store energy locally.

[0075] Relevant proton beam therapy centers are implementing proton pencil beam scanning (PBS) technology. Compared with passively scattered protons, the pencil beam scanning method allows for a more conformal dose to be delivered to the tumor and reduces the dose of neutrons. However, due to various technical limitations, current pencil beam scanning systems have a minimum proton energy limit. In order to apply pencil beam scanning technology to tumors located near the minimum range, an energy modulator (Range Shifter, RS, also known as energy modulator, range shifter, etc.) is required to reduce the beam energy. The energy modulator is one or more uniform material plates (or sheet structures), such as ABS, Lexan, Lucite, polyethylene, polystyrene and wax.

[0076] The energy modulator is a special device in the treatment head that can adjust the energy to treat shallow tumors, and the energy modulation process will produce a large number of secondary particles (i.e., secondary particles). Air, as a medium between RS and the skin, has a low blocking force, which greatly increases the chance of these secondary particles entering the human body. The Monte Carlo method can analyze the magnitude of electron and proton accumulation at different energies, as well as the effect of the air gap on the magnitude of the two effects. Related studies have studied the effect of the distance between the treatment head and the source model on the skin dose when examining the skin dose changes caused by RS. In fact, proton therapy systems generally provide RS of different thicknesses to meet the clinical energy modulation needs of different types of tumors. The thickness of the RS will be combined with parameters such as the air gap and material to affect changes in skin dose and isocenter beam size.

[0077] In the field of radiotherapy technology, the Monte Carlo method has become an irreplaceable method due to its ability to handle complex problems (complex geometry, complex arrangement of radiation sources, etc.). The Monte Carlo method can accurately model the physical processes involved in the radiotherapy process, using fewer approximations.

[0078] In order to accurately calculate the dose distribution of protons in a proton therapy system using an energy modulator, it is necessary to accurately simulate the energy, position, and secondary particle distribution of protons after passing through different combinations of energy modulators. However, due to the existence of multiple combinations and changes in the position of the energy modulator, it is impossible to use the pre-calculated results for simulation calculations. In addition, there are gaps between the energy modulator slices. When particles pass through these gaps, their motion deflection and range attenuation are different from those in the energy modulator material. Therefore, during the transport process, each gap boundary needs to be processed, making the simulation of the entire transport process complicated and time-consuming.

[0079] In order to optimize and accelerate the simulation calculation of the proton transport process in the energy modulator, the present application provides a method for simulating particle transport, an electronic device, a computer-readable storage medium, a computer program product and a radiotherapy system to improve related technologies.

[0080] The solutions provided in the embodiments of the present application involve technologies such as radiotherapy and simulation, which are specifically described by the following embodiments. It should be noted that the description order of the following embodiments is not intended to limit the preferred order of the embodiments.

[0081] (Methods for simulating particle transport)

[0082] See also Figure 1 , Figure 1 It is a flow chart of a method for simulating particle transport provided in an embodiment of the present application.

[0083] The present application provides a method for simulating particle transport, which is used to simulate the transport process of particles in a target energy regulator, wherein the target energy regulator includes a plurality of energy regulator slices, and the method includes:

[0084] Step S101: obtaining the center position of the target energy regulator;

[0085] Step S102: Create a virtual energy regulator according to the center position of the target energy regulator; the virtual energy regulator adopts a seamless structure, and the center position of the virtual energy regulator is aligned with the center position of the target energy regulator, and the thickness of the virtual energy regulator matches the total thickness of the target energy regulator; wherein the total thickness is the sum of the thicknesses of all energy regulator sheets; as an example, the thickness of the virtual energy regulator matches the total thickness of the target energy regulator, which may be that the thickness of the virtual energy regulator is equal to the total thickness of the target energy regulator.

[0086] Step S103: simulating the transport process of particles in the target energy regulator by simulating the transport process of particles in the virtual energy regulator.

[0087] An energy modulator is a device used in radiotherapy to adjust the energy and range of particles (such as protons or heavy ions) to achieve the desired treatment depth. It can be composed of one or more energy modulators, each of which has the same or different materials and thicknesses. As an example, an energy modulator can be composed of multiple ABS sheets of different thicknesses. By selecting different combinations of ABS sheets, the energy and range of the proton beam can be adjusted to adapt to different treatment depths. In other words, the same energy modulator can be suitable for different treatment depths for the same patient, and the same energy modulator can be suitable for different treatment depths for different patients.

[0088] Particle transport is the process by which particles propagate and interact with each other in a medium. In radiation therapy, particle transport refers to the process by which protons or other particles are transported and interact with each other in the modulator or in human tissue. As an example, when protons pass through the modulator, they interact with the nuclei in the modulator, changing their direction and energy and releasing secondary particles. Particle transport simulation can help predict the energy deposition and dose distribution of protons during treatment.

[0089] The virtual energy regulator is a computational model created based on the structure and parameters of the actual energy regulator (i.e., the target energy regulator) to simulate the transport process of particles in the target energy regulator. It adopts a seamless structure, is aligned with the center position of the actual energy regulator, and has a total thickness that matches the target energy regulator. As an example, a virtual energy regulator model is created based on the composition and geometric characteristics of the actual energy regulator through computer software or simulation algorithms. This model can be used to simulate the transport process of protons in the target energy regulator to predict the energy deposition and dose distribution of protons.

[0090] The total thickness refers to the sum of the thicknesses of all the energy modulation plates in the target energy modulation device, which represents the total thickness of the material that the proton needs to pass through to pass through the energy modulation device. As an example, a target energy modulation device is composed of three energy modulation plates with thicknesses of 2 mm, 4 mm, and 3 mm respectively. The total thickness is 2 mm + 4 mm + 3 mm = 9 mm.

[0091] As another example, the thickness of the virtual energy regulator matches the total thickness of the target energy regulator may be that the absolute value of the difference between the thickness of the virtual energy regulator and the total thickness of the target energy regulator is less than a preset difference. As yet another example, the thickness of the virtual energy regulator matches the total thickness of the target energy regulator may be that the ratio of the absolute value of the difference between the thickness of the virtual energy regulator and the total thickness of the target energy regulator to the total thickness is less than a preset ratio.

[0092] Therefore, the simulation and calculation process of particle transport in the target energy regulator is simplified and accelerated by creating a virtual energy regulator. By creating a virtual energy regulator and simulating the particle transport process in it, the particle transport in the actual target energy regulator can be effectively simulated, which helps to accurately calculate the energy, position and secondary particle distribution of the particles after passing through the target energy regulator, thereby providing accurate dose distribution calculation results; the virtual energy regulator adopts a seamless structure, eliminating the influence of the gap in the target energy regulator on the particle transport process. At the same time, the thickness of the virtual energy regulator matches the total thickness of the target energy regulator, ensuring the accuracy of the simulation. By performing particle transport simulation in the virtual energy regulator, additional processing of each gap boundary can be avoided, thereby simplifying the calculation process and reducing the calculation time; by accurately simulating the particle transport process in the target energy regulator, the particle dose distribution can be better understood, which helps to optimize the particle therapy plan, improve the treatment accuracy and effect, and minimize damage to healthy tissues. In summary, by simulating the particle transport process in the virtual energy regulator, the particle transport process in the target energy regulator is accurately simulated, the calculation process is simplified and the treatment accuracy and effect are improved. When simulating the particle transport process of the target energy regulator, the high efficiency and high accuracy of the calculation process can be taken into account.

[0093] See also Figure 2 , Figure 2 It is a schematic diagram of a flow chart for obtaining the center position of the target energy regulator provided in an embodiment of the present application.

[0094] In some embodiments, obtaining the center position of the target energy regulator (ie, step S101) includes:

[0095] Step S201: Calculating the equivalent position and thickness ratio of each energy regulating sheet according to the position and thickness of each energy regulating sheet, wherein the thickness ratio of the energy regulating sheet is the ratio of the thickness of the energy regulating sheet to the total thickness;

[0096] Step S202: Calculate the center position of the target energy regulator according to the equivalent position and thickness ratio of each energy regulator sheet.

[0097] The material and thickness of the energy regulating sheet are not limited in the embodiment of the present application, and the multiple energy regulating sheets of the target energy regulating device may have the same or different materials and thicknesses. The material of each energy regulating sheet may be ABS, Lexan, Lucite, polyethylene, polystyrene, wax, etc.

[0098] The energy modulator is a part of the energy modulator, which is used to adjust the energy and range of the particle beam. Each energy modulator has a specific position and thickness. As an example, an energy modulator consists of three energy modulators, with the beam center axis as the Y axis, the coordinates of the exit surfaces of the three energy modulators on the Y axis (in millimeters) are 10, 20 and 30 respectively, and the thicknesses of the three energy modulators are 3 mm, 4 mm and 5 mm respectively.

[0099] The equivalent position is the value representing the center position of the energy modulation sheet in the target energy modulation device, calculated based on the position and thickness of the energy modulation sheet, rather than the position of the incident surface and the exit surface of the energy modulation sheet. The equivalent position is calculated based on the relative position and total thickness of the energy modulation sheet. As an example, the total thickness is 12 mm, the Y coordinate of the first energy modulation sheet is 10, and the thickness is 3 mm. Calculate its equivalent position and get its Y coordinate as 10+1.5 mm (i.e. 11.5 mm). Similarly, the equivalent positions of the second and third energy modulation sheets can be calculated, expressed in Y coordinates, which are 20+2 mm (i.e. 22 mm) and 30+2.5 mm (i.e. 32.5 mm), respectively.

[0100] The thickness ratio refers to the ratio of the thickness of each energy regulating plate to the total thickness of the target energy regulating device. It indicates the relative contribution of each energy regulating plate to the entire energy regulating device structure. By calculating the thickness ratio, it can be obtained that the thickness ratio of the first energy regulating plate to the total thickness is 3 / 12. Similarly, the thickness ratios of the second and third energy regulating plates are calculated to be 4 / 12 and 5 / 12 respectively.

[0101] The center position of the target energy regulator is the center point of the entire energy regulator structure. It is calculated based on the equivalent position and thickness ratio of each energy regulator sheet. Assume that the equivalent positions of the three energy regulator sheets are 11.5, 22, and 32.5, respectively, and the thickness ratios are 3 / 12, 4 / 12, and 5 / 12, respectively. Calculate the center position pos of the target energy regulator, and its Y coordinate Y pos =11.5×(3 / 12)+22×(4 / 12)+32.5×(5 / 12)=23.75 mm.

[0102] Therefore, by calculating the equivalent position and thickness ratio of each energy modulation sheet, the proportion of each energy modulation sheet in the total thickness can be accurately determined. Based on these ratios, the center position of the target energy modulator can be calculated to ensure accuracy and reliability. This method takes into account the position and thickness of each energy modulation sheet, and calculates the center position of the target energy modulator according to their ratio. This comprehensive consideration of the characteristics of different energy modulation sheets can more accurately obtain the center position of the entire target energy modulator. By accurately obtaining the center position of the target energy modulator, it can be ensured that the subsequent creation of the virtual energy modulator and the simulation of the particle transport process have higher accuracy and reliability.

[0103] In some embodiments, the process of calculating the equivalent position of each energy modulation patch includes:

[0104] According to the position of each energy adjustment sheet, the distance between each energy adjustment sheet and the isocenter plane is calculated;

[0105] According to the thickness of each energy modulation sheet and its distance from the isocenter plane, the equivalent position of each energy modulation sheet is calculated.

[0106] Medical particle accelerators are designed according to the isocenter principle. Theoretically, within the entire angular range of the machine's operation, the machine's three rotation axes (rotation axis of the rotating gantry, revolution axis of the treatment head, and revolution axis of the treatment bed) should intersect at one point (for example, a point inside the patient's body), which is called the isocenter (ISO). The isocenter plane is a plane that passes through the isocenter and is perpendicular to the beam. Among them, the revolution axis of the treatment head is also called the beam center axis. As an example, with the beam center axis as the Y axis, the isocenter plane is the XOZ plane.

[0107] The distance between the energy modulation sheet and the isocenter plane refers to the vertical distance between the energy modulation sheet (the surface close to the isocenter plane, i.e., the exit surface) and the isocenter plane. Assuming that the unit of the Y axis is millimeter, the Y coordinate of an energy modulation sheet (i.e., the Y coordinate of the exit surface of the energy modulation sheet) is 20, and the isocenter plane is the XOZ plane, then the distance between the energy modulation sheet and the isocenter plane is 20 mm. As an example, the thickness of an energy modulation sheet is 4 mm, and its distance from the isocenter plane is 20 mm. Then the equivalent position of the energy modulation sheet can be expressed by the Y coordinate of its center position as: 20+(4 / 2)=20+2=22 mm.

[0108] In the above embodiment, the equivalent position is calculated based on the thickness of the energy modulation sheet and the distance between the energy modulation sheet and the isocenter plane, and is used to represent the position of the energy modulation sheet in the target energy modulator.

[0109] Therefore, by calculating the distance between each energy modulation sheet and the isocenter plane, the specific position of the energy modulation sheet in the target energy modulation device is taken into account, which makes the calculation result more accurate and can better reflect the structure and layout of the actual energy modulation device; the equivalent position of each energy modulation sheet is calculated in combination with the thickness of each energy modulation sheet and its distance from the isocenter plane. This comprehensive consideration of the size and position of the energy modulation sheet can better reflect its influence on the particle transport process, thereby improving the accuracy and reliability of the calculation; by considering the position, thickness and equivalent position of the energy modulation sheet, the transport process of particles in the virtual energy modulation device can be more accurately simulated, which will help to accurately calculate the energy, position and secondary particle distribution of the particles after passing through the target energy modulation device, thereby improving the accuracy of the simulation.

[0110] In some embodiments, obtaining the center position of the target energy regulator includes:

[0111] Input the positions and thicknesses of all the energy regulator sheets into the center position model to obtain the center position of the target energy regulator;

[0112] The center position model is obtained by training a preset deep learning model using a training set.

[0113] The center position model is a model obtained by training a preset deep learning model using a training set. It is used to predict the center position of the target energy regulator based on information such as the position and thickness of the energy regulator. For example, during the training process, a large amount of energy regulator sample data is used, including parameters such as their position and thickness, as well as the corresponding center position information. A preset deep learning model is trained with these data to obtain a center position model that can predict the center position of the target energy regulator based on the given energy regulator information. In practical applications, after obtaining parameters such as the position and thickness of the target energy regulator, these parameters can be input into the trained center position model to obtain the center position of the target energy regulator.

[0114] In the field of radiotherapy, there are many choices for deep learning models used to train the center location model, depending on the complexity of the problem and the available dataset. In practical applications, examples of deep learning models that can be selected are as follows:

[0115] Convolutional Neural Network (CNN): CNN is widely used in the field of image processing and can be used to process information such as the position and thickness of the modulator to predict the center position of the target modulator. For example, a CNN model with multiple convolutional layers and pooling layers can be designed to extract features from the position and thickness of the modulator and predict the center position.

[0116] Recurrent Neural Network (RNN): RNN is suitable for processing sequence data and can be used to model the position and thickness sequence of the energy modulation slice and predict the center position. For example, RNN variants such as Long Short-Term Memory (LSTM) or Gated Recurrent Unit (GRU) can be used.

[0117] Transfer Learning Models: Transfer learning is a technique that uses pre-trained models that have been trained on large-scale data to solve new tasks. Pre-trained image recognition models such as VGGNet, ResNet, or Inception can be used to process the position and thickness data of the regulator and predict the center position through fine-tuning.

[0118] Therefore, by inputting the position and thickness of all energy modulators into the center position model, the pre-trained deep learning model (i.e., the center position model) can be used to obtain the center position of the target energy modulator. This method combines a large amount of training data and the learning ability of the model, and can accurately predict the center position of the target energy modulator; by adopting a deep learning-based model (i.e., the center position model) to obtain the center position of the target energy modulator, the dependence on manual calculation or simplified models can be reduced. The deep learning model can learn the complex relationship of the center position of the target energy modulator from a large amount of training data, and therefore can provide more accurate and reliable results; since the deep learning model has strong learning ability and adaptability, it can adapt to energy modulators of different types and complex structures, which makes this method widely applicable and able to cope with various energy modulator designs and combinations.

[0119] In some embodiments, simulating the transport process of particles in the target energy regulator by simulating the transport process of particles in the virtual energy regulator includes:

[0120] Acquiring an incident position of a particle entering an incident surface of the virtual energy regulator;

[0121] The transport process of particles entering from the incident position of the incident surface of the virtual energy regulator and leaving from the exit surface of the virtual energy regulator is simulated to simulate the transport process of particles in the target energy regulator.

[0122] The virtual energy regulator is a virtual device (or simulation device) that is used to replace the actual target energy regulator for simulation. It adopts a seamless structure and has a thickness and geometry that matches the target energy regulator. The purpose of the virtual energy regulator is to simulate the transport process of particles in the target energy regulator.

[0123] The virtual energy regulator has an incident surface and an exit surface. The incident surface is the surface where the particles enter the virtual energy regulator, and the exit surface is the surface where the particles leave the virtual energy regulator. As an example, with the isocenter as the origin and the beam center axis as the Y axis, the incident surface is the upper surface of the virtual energy regulator in the Y axis direction, and the exit surface is the lower surface of the virtual energy regulator in the Y axis direction.

[0124] The incident position is the specific position where the particle enters the incident surface of the virtual regulator. This position can be determined according to the source model of the particle, the geometry of the virtual regulator and the simulation requirements.

[0125] The transport process refers to the movement of particles in the virtual energy regulator (or target energy regulator). In the simulation, particles enter from the incident position of the incident surface of the virtual energy regulator, then are transported through the inside of the virtual energy regulator, and finally leave from the exit surface. By simulating the transport process, the actual transport of particles in the target energy regulator can be simulated.

[0126] For example, suppose there is a virtual modulator whose geometry and thickness exactly match the target modulator. The particle's incident position into the virtual modulator's incident surface may be at the center of the incident surface, such as (0,29.75,0). The particle will then be transported inside the virtual modulator, passing through layers of different materials and thicknesses. Finally, the particle leaves the virtual modulator's exit surface and continues into a subsequent treatment device or subsequent simulation analysis.

[0127] By simulating the particle transport process in the virtual energy modulator, relevant information after the particles pass through the target energy modulator, such as energy deposition distribution, emission position and secondary particle distribution, can be predicted, which helps to optimize radiotherapy plans and dose calculations.

[0128] Thus, by determining the incident position of the particle entering the incident surface of the virtual energy modulator, the initial position and direction of the particle can be accurately simulated, which helps to accurately simulate the transport process of the particle in the virtual energy modulator and provide accurate starting conditions for subsequent simulations; by simulating the transport process of the particle entering from the incident position of the incident surface of the virtual energy modulator and leaving from the exit surface, the actual transport process of the particle in the target energy modulator can be simulated. This simulation takes into account the structure and characteristics of the virtual energy modulator, including the seamless structure and matching the thickness of the target energy modulator, so as to more accurately predict the energy, position and secondary particle distribution of the particle after passing through the target energy modulator; by simulating the transport process of the particle in the virtual energy modulator, accurate dose distribution calculation results can be provided, which helps to optimize the particle therapy plan, improve the accuracy of dose distribution and treatment effect, and minimize damage to healthy tissue.

[0129] In some embodiments, obtaining the incident position of the particle entering the incident surface of the virtual energy regulator includes:

[0130] The incident position of the particle entering the incident surface of the virtual energy regulator is determined according to the distance between the center position of the virtual energy regulator and the isocenter plane, the thickness of the virtual energy regulator and the source model of the particle.

[0131] The incident position represents the starting point of the particle entering the virtual regulator. The position can be described using a coordinate system (such as Cartesian coordinates or polar coordinates) and is determined based on the geometry of the virtual regulator, the center position, the source model of the particle, and other parameters.

[0132] The center position of the virtual regulator refers to the geometric center or axis position of the virtual regulator. It is usually aligned with the center position of the target regulator to ensure the accuracy and consistency of the simulation.

[0133] The source is a particle spatial distribution formed on a plane adjacent to the secondary collimator and parallel to the isocenter plane after the rays pass through the primary collimator and ionization chamber. The isocenter plane refers to a plane (such as the XOZ plane) passing through the isocenter point in the patient's body and perpendicular to the central axis of the beam (such as the Y axis). The information of the particles that constitute the source includes the position, energy, direction, type, and weight of the particles. For example, in proton therapy, the energy of the incident proton can be 70 to 230 MeV.

[0134] The embodiments of the present application do not limit the method for generating the source. The source may be a particle spatial distribution formed by rays generated after an electron beam generated by an electron linear accelerator hits a target, or may be other ray sources, such as a particle spatial distribution formed by rays generated by cobalt 60. Alternatively, the source may be a particle spatial distribution formed by protons.

[0135] In some embodiments, a Monte Carlo program can be used to simulate the process of electron beams hitting a target to generate photons, and the generated photons then pass through a primary collimator, a homogenizer, an ionization chamber, a mirror and other devices to form particle information of the source plane position. The source contains one or more particles, which can be uncharged particles or charged particles. For example, uncharged particles may include at least one of photons and neutrons, and charged particles may include at least one of positrons, negative electrons, protons, and heavy ions. The types of particles contained in different sources may be the same or different.

[0136] In the field of radiotherapy, the particle source model is a mathematical model used to describe the initial characteristics and initial state of the particle. It defines the parameters such as the particle's initial position, speed, energy and direction to simulate the particle's entry into the system and transport process.

[0137] As an example, in proton therapy, the source model used to simulate the proton (beam) can adopt the Gaussian model. In this model, the initial position and velocity of the proton obey the Gaussian distribution, thereby achieving the randomness modeling of the particle. Specifically, the Gaussian model can control the initial position and velocity distribution of the proton by setting the mean and standard deviation, thereby simulating the initial state of the proton in real conditions.

[0138] As another example, in electron beam therapy, the source model used to simulate the electron beam can adopt methods such as uniform distribution or Monte Carlo simulation. The uniform distribution source model distributes the electron particles uniformly in a region, while the Monte Carlo simulation simulates the starting position and velocity distribution of the electrons based on random sampling.

[0139] The selection of source models is usually based on the characteristics of the treatment equipment, treatment requirements, and computational complexity. By properly selecting and adjusting the parameters of the source model, the initial state of the particles can be simulated and further used to calculate and optimize radiotherapy plans, calculate dose distribution, and evaluate treatment effects.

[0140] For example, suppose there is a virtual energy regulator whose geometric shape is a sheet structure. The distance between its upper surface and the machine head is h1, and the distance between the isocenter plane and the machine head is h2. Assuming that the position of a particle reaching the isocenter plane when it is not blocked by the energy regulator is (x, z), then when the virtual energy regulator is present, the position at which it reaches the upper surface of the energy regulator is (x*h1 / h2, z*h1 / h2), and the particle starts to be transported from this position on the upper surface of the virtual energy regulator.

[0141] Therefore, by considering the thickness of the virtual energy regulator, the distance between the center position and the isocenter plane, the incident position of the particle entering the incident surface of the virtual energy regulator can be determined. This method can accurately consider the geometric shape, position and size of the virtual energy regulator to ensure that the initial position of the particle is closer to the actual simulation situation; by combining the source model of the particle, the incident position of the particle entering the incident surface of the virtual energy regulator can be further determined. The source model can provide information such as the initial position, direction and energy of the particle to help accurately locate the incident position, which helps to simulate the accurate transport process of the particle in the virtual energy regulator; by accurately determining the incident position of the particle entering the incident surface of the virtual energy regulator, the accuracy and reliability of the simulation can be improved, which helps to more accurately simulate the transport process of the particle in the target energy regulator, thereby providing accurate dose distribution calculation results.

[0142] In some embodiments, the method further comprises:

[0143] Acquire a set of simulation information after the particle passes through the virtual energy regulator, wherein the set of simulation information includes one or more of energy distribution information, emission position, range, and secondary particle distribution information;

[0144] According to the simulation information set, simulated dose distribution information of particles is obtained.

[0145] The secondary particles produced by the proton therapy system in the human body include secondary electrons produced by ionization, which are also the main component of direct DNA damage in proton radiotherapy. In addition, there are neutrons, alpha particles and isotopes of various elements produced by nuclear reactions. Positrons are also produced, mainly from the β+ decay of radioactive isotopes of oxygen, carbon and nitrogen.

[0146] The simulation information set is the information about particle behavior collected after the particle transport simulation in the virtual energy regulator. This information can include energy distribution information, emission position, range, and secondary particle distribution.

[0147] The energy distribution information refers to the energy distribution of particles after they pass through the virtual energy regulator. The energy distribution information describes the energy deposition of particles at different positions and is used to analyze and evaluate the dose distribution of particles.

[0148] The exit position refers to the position where the particle leaves the lower surface of the virtual energy regulator (i.e., the exit surface). The exit position is used to indicate the movement trajectory of the particle and the final position after passing through the energy regulator.

[0149] Range is used to indicate the penetration ability of particles after passing through the virtual energy regulator.

[0150] Secondary particle distribution information refers to the distribution of secondary particles generated by the interaction between particles and the modulator material after passing through the virtual modulator. Secondary particle distribution information can be used to evaluate the dose contribution of secondary particles and the effect of radiation therapy.

[0151] The simulated dose distribution information is the dose distribution of particles after passing through the virtual energy regulator, which is calculated based on the simulated information set. The simulated dose distribution information describes the dose deposition of particles at different positions and is used to evaluate the effect and safety of the radiotherapy plan. As an example, the simulated dose distribution information includes dose spatial distribution information, dose peak position, etc.

[0152] For example, suppose that in the simulation process of proton therapy, a virtual energy regulator is used to simulate the transport process of protons. After the simulation, the energy distribution information, emission position and secondary particle distribution information of the protons are collected. Based on these sets of simulation information, the simulated dose distribution information of the protons can be calculated. The simulated dose distribution information describes the dose deposition of protons at different positions in the virtual energy regulator. For example, at the center of the isocenter plane (i.e., the isocenter point), the dose deposition of protons may be higher, while at the edge it may be lower. By analyzing the simulated dose distribution information, the dose distribution of protons in the target tissue can be evaluated, the effectiveness and safety of the treatment plan can be evaluated, and the treatment plan can be optimized to ensure that the target area obtains sufficient dose coverage while minimizing the dose impact on surrounding healthy tissues.

[0153] Therefore, by obtaining the simulated information set generated by the transport process of particles in the virtual energy regulator, it can include energy distribution information, emission position, range and secondary particle distribution, etc., which reflect the behavior and corresponding physical properties of the particles after passing through the virtual energy regulator; based on the obtained simulation information set, the simulated dose distribution information of the particles can be further calculated and obtained, and by analyzing the energy distribution, emission position, range and secondary particle distribution information, the dose distribution of the particles after passing through the target energy regulator can be calculated, including the spatial distribution of the dose, the peak position of the dose, etc.; by obtaining the simulated dose distribution information of the particles, the accuracy and comprehensiveness of the dose calculation can be improved, and by considering factors such as the motion trajectory of the particles in the virtual energy regulator, energy deposition and secondary particle generation, the dose distribution of the particles after passing through the target energy regulator can be more accurately simulated, providing an important basis for accurate treatment planning and dose optimization. In summary, by obtaining a set of simulated information after particles pass through a virtual energy regulator and calculating the simulated dose distribution of particles based on this information, the accuracy and comprehensiveness of dose calculation can be improved, which helps to optimize particle therapy plans, improve the accuracy of dose distribution and treatment effects, and provide reliable data support for the planning and evaluation of particle therapy.

[0154] For example, by simulating the transport process of particles in the virtual energy regulator, the simulated dose distribution information after the particles pass through the target energy regulator can be obtained. The simulated dose distribution information can be represented by a dose distribution matrix or dose map, where each matrix element or pixel represents the dose value at the corresponding position point. These results can be presented in graphical or numerical form to show the dose distribution of protons after passing through the target energy regulator. It should be noted that the actual simulated dose distribution results will be affected by many factors, including the incident energy of the protons, the incident position, the material, thickness and geometry of the energy regulator, and the patient's anatomical structure.

[0155] In some embodiments, the step of obtaining a set of simulated information after particles pass through the virtual energy regulator includes:

[0156] According to the irradiation parameter set, a simulation information set after the particle passes through the virtual energy regulator is obtained; wherein the irradiation parameter set includes one or more of particle type, particle beam diameter, radiation dose rate, irradiation area size, number of irradiations and irradiation interval time.

[0157] The irradiation parameter set is used to describe various parameter sets during radiotherapy, including particle type (such as protons), particle beam diameter, radiation dose rate, irradiation area size, irradiation number and irradiation interval time, etc. These parameters are used to control the dose distribution and irradiation plan of the treatment.

[0158] Particle type refers to the type of particles used for radiotherapy. Common particle types include protons, heavy ions (such as carbon ions), electrons, etc. Different particles have different physical properties and therapeutic effects.

[0159] The particle beam diameter refers to the diameter or cross-sectional size of the particle beam. It represents the size of the particle beam within the irradiation area and can be measured in millimeters. The size of the particle beam diameter affects the spatial resolution of the irradiation area and the accuracy of the dose distribution.

[0160] The radiation dose rate is the radiation dose delivered to the patient per unit time. It can be expressed in Gray / second (Gy / s) or milligray / minute (mGy / min). The radiation dose rate determines the radiation dose delivered to the patient per unit time during treatment.

[0161] The irradiation area size refers to the size of the area irradiated by the particle beam on the patient's body surface. It represents the range and size of the treatment area and can be measured in square centimeters. The irradiation area size determines the distribution range of the radiation energy during the treatment process.

[0162] The number of irradiation times N is used to indicate that the radiation irradiation is divided into N independent dose distribution processes, where N is a positive integer. Each irradiation process corresponds to the irradiation of one or more particle beams.

[0163] The irradiation interval refers to the time interval between two irradiations. It indicates the interval between two adjacent irradiations to allow the patient's tissue to recover and the radiation effect to occur gradually. The choice of the irradiation interval is based on the treatment plan and the patient's tolerance.

[0164] For example, suppose a patient receives proton therapy, and the irradiation parameter set includes: proton as particle type, particle beam diameter of 10 mm, radiation dose rate of 2 Gray / minute, irradiation area size of 20 square centimeters, irradiation number of times of 5 times, and irradiation interval of 1 hour. These irradiation parameters will be used to plan and control the treatment process to achieve the desired dose.

[0165] In some embodiments, the method further comprises:

[0166] According to the simulated dose distribution information and the expected dose distribution information of the particles, the combination strategy of the target energy regulator is updated to re-simulate the transport process of the particles in the target energy regulator according to the updated combination strategy until the simulated dose distribution information of the particles matches the expected dose distribution information;

[0167] The combination strategy includes one or more of the material, position and thickness of each energy regulator sheet of the target energy regulator.

[0168] The expected dose distribution information refers to the expected dose distribution of the target area determined according to the treatment plan and clinical requirements. The expected dose distribution information describes the ideal dose distribution that is expected to be achieved in the target area, and is used to guide the treatment plan and evaluate the treatment effect. As an example, the expected dose distribution information can be the dose distribution information measured by the water phantom after the particles pass through the target energy modulator.

[0169] The combination strategy refers to the strategy for selecting and adjusting the energy modulator in the target energy modulator. The combination strategy involves selecting the appropriate energy modulator material, position, and thickness to adjust the particle transport process in the target energy modulator so that the simulated dose distribution information matches the expected dose distribution information. Energy modulators of different materials have different effects on particle scattering and energy deposition. Therefore, considering the material of the energy modulator in the combination strategy can more accurately simulate the particle transport process in the target energy modulator.

[0170] For example, assume that the target energy modulator is an energy modulation system with multiple energy modulators. Through simulation calculation, the simulated dose distribution information of the particles after passing through the target energy modulator is obtained. Then, according to the clinical requirements and the expected dose distribution information in the treatment plan, the combination strategy of the target energy modulator can be updated manually or intelligently. For example, according to the difference between the simulated dose distribution information and the expected dose distribution information, the doctor can replace the material of some energy modulators, adjust their positions or change their thickness, and then re-simulate the particle transport process in the target energy modulator through simulation tools (such as Monte Carlo programs). Of course, when simulating the particle transport process in the target energy modulator, the simulation calculation process can be simplified by recreating a virtual energy modulator. By continuously adjusting the combination strategy and re-simulating until the simulated dose distribution information matches the expected dose distribution information, the expected treatment effect can be achieved.

[0171] Therefore, by comparing the simulated dose distribution information of the particles with the expected dose distribution information, the effect of the current combination strategy can be evaluated. Based on the comparison results, the combination strategy of the target energy modulator can be updated to make the simulated dose distribution information and the expected dose distribution information more matched. Such an update can help optimize the treatment plan and improve the accuracy of the dose distribution and the treatment effect. According to the updated combination strategy, the transport process of the particles in the target energy modulator is re-simulated. By simulating the transport process of the particles in the target energy modulator, the impact of the updated combination strategy on the dose distribution can be evaluated and verified, which helps to ensure that the optimized combination strategy can achieve the expected dose distribution, thereby further improving the accuracy and effect of the treatment. By continuously updating the combination strategy and resimulating the transport process of the particles, the treatment plan can be gradually optimized, which enables the treatment plan to better adapt to the patient's specific conditions and achieve a more accurate dose distribution. At the same time, this also improves the degree of optimization of the treatment plan, making the treatment process safer and more effective. In summary, by updating the combination strategy based on the simulated dose distribution information and the expected dose distribution information and resimulating the particle transport process in the target energizer, the accuracy and optimization of the treatment plan can be improved, which helps to achieve the expected dose distribution, improve the treatment effect, and provide reliable data support for the planning and evaluation of particle therapy.

[0172] In some embodiments, re-simulating the transport process of particles in the target energy regulator includes:

[0173] Recreate the virtual energy regulator according to the updated combination strategy;

[0174] Simulate the transport of particles in the recreated virtual energy regulator;

[0175] Record the new set of simulation information after the particles pass through the recreated virtual regulator;

[0176] Based on the recorded new set of simulation information, new simulated dose distribution information of the particles is calculated and obtained, and then compared with the expected dose distribution information;

[0177] If the new simulated dose distribution information does not match the expected dose distribution information, the combination strategy is updated again and the simulation is performed again until the simulated dose distribution information of the particles matches the expected dose distribution information.

[0178] In some embodiments, the transport process of particles in the virtual energy regulator can be simulated using various physical simulation methods, such as Monte Carlo simulation or diffusion theory, and calculations can be performed based on parameters such as the energy, position, and interaction model of the particles.

[0179] Therefore, by recreating the virtual energy modulator, resimulating the particle transport process in the target energy modulator, and iteratively updating the combination strategy, the treatment plan is gradually optimized to achieve the desired dose distribution and treatment effect.

[0180] In some embodiments, the particles are protons.

[0181] Therefore, by creating a virtual energy regulator and simulating the transport process of protons therein, the transport of protons in an actual energy regulator (i.e., a target energy regulator) can be accurately simulated, which helps to accurately calculate the energy, position, and secondary particle distribution of protons after passing through the target energy regulator, thereby providing accurate dose distribution calculation results; by creating a seamless virtual energy regulator, the influence of gaps in the actual energy regulator on the proton transport process is eliminated, and the simulation calculation process of proton transport in the target energy regulator is simplified and accelerated. At the same time, the thickness of the virtual energy regulator matches the total thickness of the target energy regulator, ensuring the accuracy of the simulation. By performing proton transport simulation in the virtual energy regulator, additional processing of each gap boundary is avoided, thereby simplifying the calculation process and reducing the calculation time; by accurately simulating the transport process of protons in the target energy regulator, the dose distribution of protons can be better understood, which helps to optimize the proton therapy plan, improve the treatment accuracy and effect, and minimize damage to healthy tissues.

[0182] See also Figure 3 , Figure 3 It is a flow chart of another method for simulating particle transport provided in an embodiment of the present application.

[0183] In a specific application scenario, the embodiment of the present application further provides a method for simulating particle transport, which is used to simulate the transport process of particles in a target energy regulator, wherein the particles are protons, and the target energy regulator includes a plurality of energy regulator sheets. The method includes:

[0184] Step S301: according to the position and thickness of each energy regulating sheet, the equivalent position of each energy regulating sheet is calculated, and the thickness ratio of each energy regulating sheet is calculated, where the thickness ratio of the energy regulating sheet is the ratio of the thickness of the energy regulating sheet to the total thickness;

[0185] Step S302: Calculating the center position of the target energy regulator according to the equivalent position and thickness ratio of each energy regulator sheet;

[0186] Step S303: creating a virtual energy regulator according to the center position of the target energy regulator; the virtual energy regulator adopts a seamless structure, and the center position of the virtual energy regulator is aligned with the center position of the target energy regulator, and the thickness of the virtual energy regulator matches the total thickness of the target energy regulator; wherein the total thickness is the sum of the thicknesses of all energy regulator sheets;

[0187] Step S304: determining the incident position of the particle entering the incident surface of the virtual energy regulator according to the distance between the center position of the virtual energy regulator and the isocenter plane, the thickness of the virtual energy regulator and the source model of the particle;

[0188] Step S305: simulating a transport process of particles entering from the incident position of the incident surface of the virtual energy regulator and leaving from the exit surface of the virtual energy regulator, so as to simulate a transport process of particles in the target energy regulator;

[0189] Step S306: Acquire a set of simulation information after the particle passes through the virtual energy regulator, wherein the set of simulation information includes one or more of energy distribution information, emission position, range, and secondary particle distribution information;

[0190] Step S307: acquiring simulated dose distribution information of particles according to the simulation information set;

[0191] Step S308: According to the simulated dose distribution information and the expected dose distribution information of the particles, the combination strategy of the target energy regulator is updated to re-simulate the transport process of the particles in the target energy regulator according to the updated combination strategy until the simulated dose distribution information of the particles matches the expected dose distribution information; wherein the combination strategy includes one or more of the material, position and thickness of each energy regulator sheet of the target energy regulator. As an example, until the simulated dose distribution information of the particles matches the expected dose distribution information can be that the simulated dose distribution information of the particles is the same as the expected dose distribution information.

[0192] The process of calculating the equivalent position of each energy regulating piece includes:

[0193] According to the position of each energy adjustment sheet, the distance between each energy adjustment sheet and the isocenter plane is calculated;

[0194] According to the thickness of each energy modulation sheet and its distance from the isocenter plane, the equivalent position of each energy modulation sheet is calculated.

[0195] In the method of simulating particle transport, the case of non-uniform energy modulation sheets can also be considered. That is, assuming that some or all of the energy modulation sheets adopt non-uniform energy modulation sheets, the thickness ratio of each energy modulation sheet can further consider the specific thickness of each energy modulation sheet at the central axis of the beam to more accurately calculate the equivalent position and thickness ratio. In other words, the geometric shape of the energy modulation sheet can adopt wedge-shaped, prism-shaped, truncated cone-shaped and other shapes in addition to the sheet structure (including round sheet, square sheet, irregular sheet, etc.).

[0196] (Radiation Therapy System)

[0197] The present application also provides a radiotherapy system, including:

[0198] A device for simulating particle transport, used to match the simulated dose distribution information of the particles with the expected dose distribution information by using any of the above-mentioned methods for simulating particle transport;

[0199] The device for determining human body dose is used to determine the simulated dose as the dose used in radiotherapy after the simulated dose distribution information of the particles matches the expected dose distribution information.

[0200] The advantage of this is that by using a device that simulates particle transport and matching the simulated dose distribution information of the particles with the expected dose distribution information, the dose used in the radiotherapy plan can be determined more accurately, which helps to ensure that the applied radiation dose is consistent with the expected dose of the treatment target and improves the accuracy of the treatment plan; by simulating particle transport and determining the simulated dose distribution, a personalized radiotherapy plan can be customized according to the patient's specific situation and treatment goals. Each patient may have different anatomical structures and lesion characteristics. By matching the simulated dose and the expected dose, a treatment plan suitable for the patient's individual situation can be formulated according to his or her unique needs; by determining the simulated dose distribution, dose optimization can be performed so that the radiation dose in the treatment is consistent with the expected dose. The radiation dose is more accurate and appropriate, which helps to minimize the radiation exposure of normal tissues and reduce the risk of side effects and complications caused by treatment; using the method of simulating particle transport, the treatment process of multiple possible treatment plans can be simulated in advance, and each treatment plan corresponds to a different particle transport process and dose distribution, etc., so as to evaluate the treatment effect, side effects, etc. of each treatment plan, so that doctors and patients can choose the treatment plan with the best treatment effect and the lowest side effects, or take into account both good treatment effects and low side effects. The simulation process and simulation results of the above-mentioned multiple treatment plans can be open and transparent to patients, increase patients' autonomous selectivity, help enhance mutual trust between doctors and patients, and improve doctor-patient relationships. In summary, the use of this radiotherapy system can improve the accuracy of treatment plans, formulate personalized treatment plans, achieve dose optimization and reduce side effects, thereby improving the effect and safety of radiotherapy.

[0201] (Electronic equipment)

[0202] The embodiment of the present application also provides an electronic device, the specific embodiment of which is consistent with the embodiment recorded in the above method embodiment and the technical effects achieved, and some contents will not be repeated here.

[0203] The electronic device is used to simulate the transport process of particles in a target energy regulator, the target energy regulator includes a plurality of energy regulator slices, the electronic device includes a memory and at least one processor, the memory stores a computer program, and the at least one processor is configured to implement the following steps when executing the computer program:

[0204] Obtaining the center position of the target energy regulator;

[0205] According to the center position of the target energy regulator, a virtual energy regulator is created; the virtual energy regulator adopts a seamless structure, and the center position of the virtual energy regulator is aligned with the center position of the target energy regulator, and the thickness of the virtual energy regulator matches the total thickness of the target energy regulator; wherein the total thickness is the sum of the thicknesses of all energy regulator sheets;

[0206] The transport process of particles in the virtual energy regulator is simulated to simulate the transport process of particles in the target energy regulator.

[0207] In some embodiments, the at least one processor is configured to acquire the center position of the target energy regulator in the following manner when executing the computer program:

[0208] According to the position and thickness of each energy regulating sheet, the equivalent position and thickness ratio of each energy regulating sheet are calculated, wherein the thickness ratio of the energy regulating sheet is the ratio of the thickness of the energy regulating sheet to the total thickness;

[0209] The center position of the target energy regulator is calculated according to the equivalent position and thickness ratio of each energy regulator sheet.

[0210] In some embodiments, the at least one processor is configured to calculate the equivalent position of each energy modulation piece in the following manner when executing the computer program:

[0211] According to the position of each energy adjustment sheet, the distance between each energy adjustment sheet and the isocenter plane is calculated;

[0212] According to the thickness of each energy modulation sheet and its distance from the isocenter plane, the equivalent position of each energy modulation sheet is calculated.

[0213] In some embodiments, the at least one processor is configured to acquire the center position of the target energy regulator in the following manner when executing the computer program:

[0214] Input the positions and thicknesses of all the energy regulator sheets into the center position model to obtain the center position of the target energy regulator;

[0215] The center position model is obtained by training a preset deep learning model using a training set.

[0216] In some embodiments, the at least one processor is configured to execute the computer program to simulate the transport process of particles in the target energy regulator by simulating the transport process of particles in the virtual energy regulator in the following manner:

[0217] Acquiring an incident position of a particle entering an incident surface of the virtual energy regulator;

[0218] The transport process of particles entering from the incident position of the incident surface of the virtual energy regulator and leaving from the exit surface of the virtual energy regulator is simulated to simulate the transport process of particles in the target energy regulator.

[0219] In some embodiments, the at least one processor is configured to acquire the incident position of the particle entering the incident surface of the virtual energy regulator in the following manner when executing the computer program:

[0220] The incident position of the particle entering the incident surface of the virtual energy regulator is determined according to the distance between the center position of the virtual energy regulator and the isocenter plane, the thickness of the virtual energy regulator and the source model of the particle.

[0221] In some embodiments, the at least one processor is configured to further implement the following steps when executing the computer program:

[0222] Acquire a set of simulation information after the particle passes through the virtual energy regulator, wherein the set of simulation information includes one or more of energy distribution information, emission position, range, and secondary particle distribution information;

[0223] According to the simulation information set, simulated dose distribution information of particles is obtained.

[0224] In some embodiments, the at least one processor is configured to further implement the following steps when executing the computer program:

[0225] According to the simulated dose distribution information and the expected dose distribution information of the particles, the combination strategy of the target energy regulator is updated to re-simulate the transport process of the particles in the target energy regulator according to the updated combination strategy until the simulated dose distribution information of the particles matches the expected dose distribution information;

[0226] The combination strategy includes one or more of the material, position and thickness of each energy regulator sheet of the target energy regulator.

[0227] In some embodiments, the particles are protons.

[0228] See also Figure 4 , Figure 4 It is a structural block diagram of an electronic device 10 provided in an embodiment of the present application.

[0229] The electronic device 10 may include, for example, at least one memory 11 , at least one processor 12 , and a bus 13 connecting different platform systems.

[0230] The memory 11 may include a (computer) readable medium in the form of a volatile memory, such as a random access memory (RAM) 111 and / or a cache memory 112, and may further include a read-only memory (ROM) 113. The memory 11 also stores a computer program, which can be executed by the processor 12, so that the processor 12 implements the steps of any of the above methods. The memory 11 may also include a utility 114 having at least one program module 115, such program module 115 includes but is not limited to: an operating system, one or more application programs, other program modules and program data, each of which or some combination thereof may include the implementation of a network environment.

[0231] Accordingly, the processor 12 may execute the above-mentioned computer program and may execute the utility 114. The processor 12 may be implemented by one or more application specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field programmable gate arrays (FPGAs), or other electronic components.

[0232] The bus 13 may be a local bus representing one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or any of a variety of bus architectures.

[0233] The electronic device 10 may also communicate with one or more external devices such as a keyboard, a pointing device, a Bluetooth device, etc., and may also communicate with one or more devices that can interact with the electronic device 10, and / or communicate with any device (such as a router, a modem, etc.) that enables the electronic device 10 to communicate with one or more other computing devices. Such communication may be performed through an input / output interface 14. In addition, the electronic device 10 may also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN) and / or a public network, such as the Internet) through a network adapter 15. The network adapter 15 may communicate with other modules of the electronic device 10 through the bus 13. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 10 in actual applications, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0234] (Computer readable storage medium)

[0235] The embodiment of the present application also provides a computer-readable storage medium, the specific embodiment of which is consistent with the embodiment recorded in the above method embodiment and the technical effects achieved, and some contents will not be repeated here.

[0236] The computer-readable storage medium stores a computer program, and when the computer program is executed by at least one processor, the steps of any of the above methods or the functions of any of the above electronic devices are implemented.

[0237] Computer readable medium can be a computer readable signal medium or a computer readable storage medium. In an embodiment of the present application, a computer readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it. Computer readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of computer readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0238] The computer-readable storage medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable storage medium may also be any computer-readable medium that can send, propagate, or transmit a program for use by an instruction execution system, an apparatus, or a device or used in combination with it. The program code contained on the computer-readable storage medium may be transmitted with any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above. The program code for performing the operation of the present invention may be written in any combination of one or more programming languages, including Java, C++, Python, C#, JavaScript, PHP, Ruby, Swift, Go, Kotlin, etc. The program code may be executed entirely on a user computing device, partially on a user device, as a separate software package, partially on a user device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0239] (Computer Program Product)

[0240] The embodiment of the present application also provides a computer program product, the specific embodiments of which are consistent with the embodiments recorded in the above method embodiments and the technical effects achieved, and some contents will not be repeated here.

[0241] The present application provides a computer program product, which includes a computer program. When the computer program is executed by at least one processor, the steps of any of the above methods or the functions of any of the above electronic devices are implemented.

[0242] See also Figure 5 , Figure 5 It is a structural diagram of a computer program product provided in an embodiment of the present application.

[0243] The computer program product is used to implement the steps of any of the above methods or to implement the functions of any of the above electronic devices. The computer program product may be a portable compact disk read-only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the computer program product of the present invention is not limited thereto, and the computer program product may be any combination of one or more computer-readable media.

[0244] This application is explained from the perspectives of purpose of use, effectiveness, progress and novelty, and has met the functional enhancement and usage requirements emphasized by the Patent Law. The above description and drawings of this application are only the preferred embodiments of this application, and are not intended to limit this application. Therefore, all structures, devices, features, etc. that are similar or identical to this application, that is, all equivalent replacements or modifications made in accordance with the scope of the patent application of this application, should fall within the scope of protection of the patent application of this application.

Claims

1. A method for simulating particle transport, characterized in that: For simulating the transport process of particles in a target energy regulator, the target energy regulator includes a plurality of energy regulator slices, and the method includes: According to the position and thickness of each energy regulating sheet, the center position of the target energy regulating device is obtained; According to the center position of the target energy regulator, a virtual energy regulator is created; the virtual energy regulator adopts a seamless structure, and the center position of the virtual energy regulator is aligned with the center position of the target energy regulator, and the thickness of the virtual energy regulator matches the total thickness of the target energy regulator; By simulating the transport process of particles in the virtual energy regulator, the transport process of particles in the target energy regulator is simulated; According to the irradiation parameter set, a simulation information set after the particle passes through the virtual energy regulator is obtained; according to the simulation information set, simulated dose distribution information of the particle is obtained.

2. The method for simulating particle transport according to claim 1, characterized in that: The obtaining of the center position of the target energy regulator includes: According to the position and thickness of each energy regulating sheet, the equivalent position and thickness ratio of each energy regulating sheet are calculated, wherein the thickness ratio of the energy regulating sheet is the ratio of the thickness of the energy regulating sheet to the total thickness; The center position of the target energy regulator is calculated according to the equivalent position and thickness ratio of each energy regulator sheet.

3. The method for simulating particle transport according to claim 2, characterized in that: The process of calculating the equivalent position of each energy regulator includes: According to the position of each energy adjustment sheet, the distance between each energy adjustment sheet and the isocenter plane is calculated; According to the thickness of each energy modulation sheet and its distance from the isocenter plane, the equivalent position of each energy modulation sheet is calculated.

4. The method for simulating particle transport according to claim 1, characterized in that: The obtaining of the center position of the target energy regulator includes: Input the positions and thicknesses of all the energy regulator sheets into the center position model to obtain the center position of the target energy regulator; The center position model is obtained by training a preset deep learning model using a training set.

5. The method for simulating particle transport according to claim 1, characterized in that: The simulating the transport process of particles in the target energy regulator by simulating the transport process of particles in the virtual energy regulator includes: Acquiring an incident position of a particle entering an incident surface of the virtual energy regulator; The transport process of particles entering from the incident position of the incident surface of the virtual energy regulator and leaving from the exit surface of the virtual energy regulator is simulated to simulate the transport process of particles in the target energy regulator.

6. The method for simulating particle transport according to claim 5, characterized in that: The obtaining of the incident position of the particle entering the incident surface of the virtual energy regulator comprises: The incident position of the particle entering the incident surface of the virtual energy regulator is determined according to the distance between the center position of the virtual energy regulator and the isocenter plane, the thickness of the virtual energy regulator and the source model of the particle.

7. The method for simulating particle transport according to claim 1, characterized in that: The simulation information set includes one or more of energy distribution information, emission position, range and secondary particle distribution information; The irradiation parameter set includes one or more of particle type, particle beam diameter, radiation dose rate, irradiation area size, irradiation times and irradiation interval time.

8. The method for simulating particle transport according to claim 7, characterized in that: The method further comprises: According to the simulated dose distribution information and the expected dose distribution information of the particles, the combination strategy of the target energy regulator is updated to re-simulate the transport process of the particles in the target energy regulator according to the updated combination strategy until the simulated dose distribution information of the particles matches the expected dose distribution information; Wherein, the combination strategy includes one or more of the material, position and thickness of each energy regulating piece of the target energy regulator; The transport process of the re-simulated particles in the target energy regulator includes: Recreate the virtual energy regulator according to the updated combination strategy; Simulate the transport of particles in a recreated virtual regulator.

9. The method for simulating particle transport according to claim 1, characterized in that: The particles are protons.

10. An electronic device, characterized in that: The electronic device is used to simulate the transport process of particles in a target energy regulator, wherein the target energy regulator includes a plurality of energy regulator chips, and the electronic device includes a memory and at least one processor, wherein the memory stores a computer program, and the at least one processor is configured to implement the following steps when executing the computer program: According to the position and thickness of each energy regulating sheet, the center position of the target energy regulating device is obtained; According to the center position of the target energy regulator, a virtual energy regulator is created; the virtual energy regulator adopts a seamless structure, and the center position of the virtual energy regulator is aligned with the center position of the target energy regulator, and the thickness of the virtual energy regulator matches the total thickness of the target energy regulator; wherein the total thickness is the sum of the thicknesses of all energy regulator sheets; By simulating the transport process of particles in the virtual energy regulator, the transport process of particles in the target energy regulator is simulated; According to the irradiation parameter set, a simulation information set after the particle passes through the virtual energy regulator is obtained; according to the simulation information set, simulated dose distribution information of the particle is obtained.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by at least one processor, the steps of the method according to any one of claims 1 to 9 or the functions of the electronic device according to claim 10 are implemented.

12. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by at least one processor, the steps of the method according to any one of claims 1 to 9 or the functions of the electronic device according to claim 10 are implemented.

13. A radiotherapy system, characterized in that: include: A device for simulating particle transport, used to match simulated dose distribution information of particles with expected dose distribution information by using the method for simulating particle transport according to any one of claims 1 to 9; The device for determining human body dose is used to determine the simulated dose as the dose used in radiotherapy after the simulated dose distribution information of the particles matches the expected dose distribution information.

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