Dose distribution determination method and device based on graphics processor, and electronic equipment

Abstract

The invention provides a dose distribution determination method and device based on a graphics processor, and electronic equipment, which can be applied to the technical field of medical radiation dose calculation. The method comprises the following steps: based on radiography data of a target object, simulating sampling data of action of each tissue of a organism and particles, and constructing a digital phantom; based on the configuration parameters, the accelerator head model is used for processing a primary particle beam determined based on the simulation particle source to generate a target particle beam, the shape of the target particle beam is matched with the shape of the tumor tissue of the target object, and the configuration parameters comprise the upper limit parameter of the photon reaction action times; processing the target particle beam and the digital phantom by using a physical action model to obtain a particle deposition energy distribution result generated when the target particle beam is conveyed to the digital phantom; and obtaining a dose distribution result according to the particle deposition energy distribution result and the energy dose conversion factor.

Description

Technical Field

[0001] The present invention relates to the technical field of medical radiation dose calculation, and in particular to a dose distribution determination method, device and electronic equipment based on a graphics processor. Background Art

[0002] In medical radiotherapy, it is crucial to accurately deliver the prescribed dose to tumor cells while minimizing damage to surrounding normal tissues. Traditional radiotherapy planning systems use model-based algorithms to calculate dose distributions, which have certain limitations when dealing with electron transport in heterogeneous areas and produce large dose errors at tissue boundaries. The Monte Carlo method is considered the gold standard for dose calculation, but it is not widely used in clinical practice due to its low computational efficiency. It usually takes several hours or even a day to use a central processing unit to calculate the dose distribution in a radiotherapy plan. In recent years, the rapid development of Monte Carlo dose calculation engines based on graphics processors has brought a turning point to this problem. However, for application scenarios with higher time requirements such as online adaptive radiotherapy and dose iterative optimization, the efficiency of dose calculation needs to be further improved. Summary of the invention

[0003] In view of the above problems, the present invention provides a dose distribution determination method, device and electronic device based on a graphics processor for quickly calculating dose distribution.

[0004] According to a first aspect of the present invention, a dose distribution determination method based on a graphics processor is provided, and the dose distribution determination method is executed by a graphics processor, including: constructing a digital phantom based on the contrast data of the target object and the sampling data simulating the interaction between various biological tissues and particles; based on the configuration parameters, using the accelerator head model to process the primary particle beam determined based on the simulated particle source to generate a target particle beam, wherein the shape of the target particle beam matches the shape of the tumor tissue of the target object, and the configuration parameters include a parameter for the upper limit of the number of photon reaction actions; using the physical action model to process the target particle beam and the digital phantom to obtain a particle deposition energy distribution result that characterizes the target particle beam transported to the digital phantom, wherein the particle deposition energy distribution result characterizes the energy deposited by the target particle beam distributed in each digital tissue of the digital phantom; and obtaining a dose distribution result according to the particle deposition energy distribution result and the energy dose conversion factor.

[0005] Optionally, the configuration parameters also include photon reaction type parameters and target shape parameters, and the accelerator head model includes a jaw collimator and a multi-leaf grating collimator; wherein, based on the configuration parameters, the accelerator head model is used to process the primary particle beam determined based on the simulated particle source to generate the target particle beam, including: using the jaw collimator to modulate the primary particle beam to obtain a regular shape particle beam; using the multi-leaf grating collimator based on the photon reaction type parameters, the upper limit parameter of the number of photon reactions and the target shape parameters to modulate the regular shape particle beam to obtain the target particle beam.

[0006] Optionally, the primary particle beam is determined based on the following operations: using the target thread of the graphics processor, starting the simulated particle source based on energy spectrum position parameters, energy spectrum energy range parameters, flux distribution parameters, and angular distribution parameters to obtain the primary particle beam, wherein the energy spectrum energy range parameters are based on adjusting the weights of different energy ranges to match the measured dose curve, the flux distribution parameters are based on adjusting the spatial weights to match the lateral dose distribution curve, and the angular distribution is based on adjusting the parameters of the Gaussian function to match the lateral dose distribution curve.

[0007] Optionally, the target threads include multiple target threads, the multiple target threads have a mapping relationship with a simulation particle set in an energy range, and each of the multiple target threads is associated with at least one simulation particle in the simulation particle set; wherein, the target thread of the graphics processor is used to start the simulation particle source based on energy spectrum position parameters, energy spectrum energy range parameters, flux distribution parameters, and angular distribution parameters to obtain a primary particle beam.

[0008] Optionally, the method also includes: the particle types of multiple simulated particles started each time by the same target thread are the same; the graphics processor allocates video memory to multiple types of data based on data access frequency, wherein the multiple types of data include imaging data, sampling data simulating the interaction between biological tissues and particles, configuration parameters, particle attribute data, and simulated particle source parameters.

[0009] Optionally, the target particle beam includes a photon beam and an electron beam, the physical action model includes a plurality of photon action sub-models and electron action sub-models, and the sampling data includes reaction action probability data of the particles and various simulated biological tissues and angular energy sampling probability data; wherein, the target particle beam and the digital phantom are processed by the physical action model to obtain a particle deposition energy distribution result characterizing the target particle beam transported to the digital phantom, including: transporting photons in the photon beam to various digital tissues in the digital phantom to generate reaction electrons; and sampling the particle deposition energy distribution result from the plurality of photon action sub-models according to the reaction action probability data and angular energy sampling probability data corresponding to the various digital tissues. The target action sub-model is determined in the sub-model; the energy state information of the photons is processed according to the target action sub-model to obtain the photon deposition energy distribution result; the reaction electrons are transported to the digital phantom, and the energy state information of the reaction electrons is processed according to the electron action sub-model to obtain the reaction electron deposition energy distribution result; the initial electrons in the electron beam are transported to the digital phantom, and the energy state information of the initial electrons is processed according to the electron action sub-model to obtain the initial electron deposition energy distribution result; the particle deposition energy distribution result is obtained according to the photon deposition energy distribution result, the reaction electron deposition energy distribution result and the initial electron deposition energy distribution result.

[0010] Optionally, the electron interaction sub-model is constructed based on the electron transport function, the inelastic collision function and the multiple scattering function; wherein, the reaction electrons are transported into the digital phantom, and the energy state information of the reaction electrons is processed according to the electron interaction sub-model to obtain the reaction electron deposition energy distribution result, including: processing the energy state information of the reaction electrons according to the electron transport function to obtain a first electron deposition energy distribution result; processing the energy state information of the reaction electrons according to the inelastic collision function to obtain a second electron deposition energy distribution result; processing the energy state information of the reaction electrons according to the multiple scattering function to obtain a third electron deposition energy distribution result; weighting the first electron deposition energy distribution result, the second electron deposition energy distribution result and the third electron deposition energy distribution result to obtain a reaction electron deposition energy distribution result.

[0011] Optionally, the multiple photon effect sub-models include at least one of the following: a photoelectric effect sub-model, a Compton scattering sub-model and an electron pair generation sub-model.

[0012] A second aspect of the present invention provides a device for determining dose distribution based on a graphics processor, comprising:

[0013] A construction module is used to construct a digital phantom based on the contrast data of the target object and the sampling data simulating the interaction between various biological tissues and particles;

[0014] A generation module, for generating a target particle beam by processing a primary particle beam determined based on a simulated particle source using an accelerator head model based on configuration parameters, wherein a shape of the target particle beam matches a tumor tissue shape of a target object, and the configuration parameters include an upper limit parameter of photon reaction times;

[0015] A processing module is used to process the target particle beam and the digital phantom using the physical action model to obtain a particle deposition energy distribution result representing the target particle beam transported to the digital phantom, wherein the particle deposition energy distribution result represents the energy deposited by the target particle beam distributed in each digital tissue of the digital phantom;

[0016] The conversion module is used to obtain the dose distribution result according to the particle deposition energy distribution result and the energy dose conversion factor.

[0017] The third aspect of the present invention provides an electronic device, comprising: one or more processors; a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors execute the above-mentioned graphics processor-based dose distribution determination method.

[0018] According to the method, device and electronic device for determining dose distribution based on a graphics processor provided by the present invention, a target particle beam matching the shape of tumor tissue is obtained by configuring the accelerator head model based on configuration parameters; the target particle beam is transported to a digital phantom, and based on sampling data and a physical action model, a particle deposition energy distribution result is obtained, thereby obtaining a dose distribution result. Since the accelerator head model sets an upper limit parameter for the number of photon reaction actions and a target shape parameter, thread divergence on the graphics processor is reduced and computational efficiency is improved. By using sampling data and a physical action model that simulates the interaction between various biological tissues and particles, the accuracy and efficiency of the dose distribution results are further improved, while minimizing damage to surrounding normal tissues. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The above and other objects, features and advantages of the present invention will become more apparent through the following description of the embodiments of the present invention with reference to the accompanying drawings.

[0020] Figure 1 A flowchart of a method for determining dose distribution based on a graphics processor according to an embodiment of the present invention is shown.

[0021] Figure 2 An exemplary schematic diagram of a simulation system according to an embodiment of the present invention is shown.

[0022] Figure 3 A flow chart of a simulation program according to an embodiment of the present invention is shown.

[0023] Figure 4An exemplary schematic diagram showing a dose distribution result according to an embodiment of the present invention is shown.

[0024] Figure 5 A structural block diagram of a device for determining dose distribution based on a graphics processor according to an embodiment of the present invention is shown.

[0025] Figure 6 A block diagram of an electronic device suitable for implementing a dose distribution determination method based on a graphics processor according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0026] Below, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present invention. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of embodiments of the present invention. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of concepts of the present invention.

[0027] The terms used herein are only for describing specific embodiments and are not intended to limit the present invention. The terms "comprise", "include", etc. used herein indicate the existence of features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0028] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0029] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0030] In the process of implementing the present invention, it is found that in medical radiotherapy, it is crucial to accurately deliver the prescribed dose to tumor cells while minimizing damage to surrounding normal tissues. Traditional radiotherapy planning systems use model-based algorithms to calculate dose distributions. These algorithms have certain limitations when dealing with electron transport in heterogeneous areas and will produce large dose errors at tissue boundaries. The Monte Carlo method is considered to be the gold standard for dose calculation, but it is not widely used in clinical practice due to its low computational efficiency. It usually takes several hours or even a day to use a central processing unit to calculate the dose distribution in a radiotherapy plan. In recent years, the rapid development of Monte Carlo dose calculation engines based on graphics processors has brought a turning point to this problem. However, for application scenarios with higher time requirements such as online adaptive radiotherapy and dose iterative optimization, the efficiency of dose calculation needs to be further improved.

[0031] In view of this, an embodiment of the present invention provides a method, device and electronic device for determining dose distribution based on a graphics processor. The method includes: constructing a digital phantom based on the contrast data of the target object and the sampling data of simulating the interaction between various biological tissues and particles; based on the configuration parameters, using the accelerator head model to process the primary particle beam determined based on the simulated particle source to generate a target particle beam, wherein the shape of the target particle beam matches the shape of the tumor tissue of the target object, and the configuration parameters include the upper limit parameter of the number of photon reaction actions; using the physical action model to process the target particle beam and the digital phantom to obtain a particle deposition energy distribution result that characterizes the target particle beam transported to the digital phantom; and obtaining a dose distribution result based on the particle deposition energy distribution result and the energy dose conversion factor.

[0032] In the technical solution of the present invention, the user information (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0033] It should be noted that the sequence numbers of the operations in the following method are only used as representations of the operations for the purpose of description, and should not be regarded as representing the execution order of the operations. Unless explicitly stated, the method does not need to be executed completely in the order shown.

[0034] Figure 1 A flowchart of a method for determining dose distribution based on a graphics processor according to an embodiment of the present invention is shown.

[0035] like Figure 1 As shown, the method 100 includes operations S110 to S140.

[0036] In operation S110 , a digital phantom is constructed based on the angiography data of the target object.

[0037] Optionally, the target subject represents a subject suffering from a neoplastic disease.

[0038] Optionally, computed tomography (CT) is used to scan the target object to obtain contrast data, where the contrast data is CT image data of the target object.

[0039] Optionally, the acquired contrast data is stored in the Digital Imaging and Communications in Medicine (DICOM) format, the contrast data is input into a graphics processing unit (GPU), the contrast data is converted into material density according to a mapping relationship between the CT value of the contrast data and the material density, and a digital phantom is constructed.

[0040] In operation S120 , based on the configuration parameters, a primary particle beam determined based on the simulated particle source is processed using an accelerator head model to generate a target particle beam.

[0041] Optionally, a simulated particle source is used to simulate multiple types of particle sources emitting different types of primary particle beams. Particle sources include primary photon sources, scattered photon sources, and electron contamination sources. The positions of the multiple types of particle sources are initially set at the positions where the particles are generated and are further adjusted based on the measurement data. The primary photon source is positioned at the target, the scattered photon source is located at the geometric midpoint between the primary collimator and the flattening filter, and the electron contamination source is above the jaw collimator (Jaws). The primary photon source and the scattered photon source emit primary photon beams, and the electron contamination source emits a primary electron beam.

[0042] Optionally, the accelerator head model is built based on the linear accelerator model (Linear Accelerator, Linac), and the configuration parameters are used to configure the accelerator head model. The configuration parameters include the photon reaction number upper limit parameter. The photon reaction number upper limit parameter is used to constrain the maximum number of photon reactions, thereby accelerating the simulation process. For example, when the primary particle beam is a primary photon beam, the photon reaction number upper limit parameter is set to 5.

[0043] Optionally, configuration parameters are set, the image processor starts the simulated particle source to obtain a primary particle beam, and then uses the accelerator head model to process the primary particle beam to generate a target particle beam. The shape of the target particle beam matches the shape of the tumor tissue of the target object.

[0044] In operation S130, based on the sampled data of the simulated interaction between various biological tissues and particles, the target particle beam and the digital phantom are processed using a physical interaction model to obtain a particle deposition energy distribution result representing the target particle beam being transported to the digital phantom.

[0045] Optionally, eleven common tissue materials are selected to simulate all biological tissues, including air, two lung tissues, five soft tissues and three bones. The sampling data represents the probability data of the interaction between the simulated biological tissues and different particle types. The sampling data is obtained from the cross-section database on the open source software package Geant4 and stored in the form of a table. The sampling data includes interaction sampling data and angle energy sampling data.

[0046] Optionally, a cross-section database is loaded in a graphics processor and a dose count array is created, and all sampling data required for the simulation are passed to the image processor.

[0047] Optionally, a physical action model under 20 MV is constructed, the physical action model includes multiple ones, the target particle beam is transported to the digital phantom, energy is deposited after physical action occurs, the physical action model is determined according to the type of the target particle beam and the sampling data corresponding to the type, and the particle deposition energy distribution result of the target particle beam on the digital phantom is calculated using the physical action model.

[0048] Optionally, the particle deposition energy distribution result represents the energy deposited by the target particle beam distribution in each digital tissue of the digital phantom. The simulated digital tissue corresponds to the biological tissue one by one.

[0049] In operation S140 , a dose distribution result is obtained according to the particle deposition energy distribution result and the energy dose conversion factor.

[0050] Optionally, the energy-dose conversion factor represents the mapping relationship between the particle deposition energy and the actual dose.

[0051] Optionally, a dose distribution result corresponding to the particle deposition energy distribution result is determined according to the energy dose conversion factor dose distribution result and output in a binary file format.

[0052] Optionally, the accelerator head model sets the upper limit parameters of the number of photon reaction actions and the target shape parameters, thereby reducing the thread divergence on the graphics processor and improving the calculation efficiency. The sampling data and physical action model that simulate the interaction between biological tissues and particles are used to further improve the accuracy and efficiency of the dose distribution results, while minimizing the damage to the surrounding normal tissues.

[0053] Optionally, the configuration parameters also include photon reaction type parameters and target shape parameters, and the accelerator head model includes a jaw collimator and a multi-leaf grating collimator; wherein, based on the configuration parameters, the accelerator head model is used to process the primary particle beam determined based on the simulated particle source to generate the target particle beam, including: using the jaw collimator to modulate the primary particle beam to obtain a regular shape particle beam; using the multi-leaf grating collimator based on the photon reaction type parameters, the upper limit parameter of the number of photon reactions and the target shape parameters to modulate the regular shape particle beam to obtain the target particle beam.

[0054] Optionally, the photon reaction type parameter and the target shape parameter are parameters of a multileaf collimator (MLC). The photon reaction type parameter is used to constrain the photon reaction type. For example, the photon reaction type may be a Compton effect, and the electrons generated by the Compton effect are absorbed in the multileaf collimator. The target shape parameter is used to modulate the shape of the particle beam, and the target shape parameter may be determined based on the shape of the tumor tissue.

[0055] Optionally, the regular-shaped particle beam represents a particle beam in a square shape, and the shape of the target particle beam matches the shape of a tumor tissue of the target object.

[0056] Optionally, a novel accelerator head model is obtained by combining a jaw collimator and a multi-leaf grating collimator, and then a target particle beam is obtained. The target particle beam matches the shape of the tumor tissue of the target object, which can minimize the damage to the surrounding normal tissue. Compared with the existing all-photon transport simulation, the setting of the photon reaction action type parameters results in a photon flux change of less than 1% on the surface of the target object, so it is considered to have a negligible effect on the dose, thereby improving the efficiency of the dose calculation.

[0057] Figure 2 An exemplary schematic diagram of a simulation system according to an embodiment of the present invention is shown.

[0058] like Figure 2As shown, the simulation system includes a simulated particle source, a Jaws collimator and a multi-leaf grating collimator. The Jaws collimator is a jaw collimator. A primary photon source and a scattered photon source are established at the target and the homogenizer positions, respectively, an electron contamination source is established above the Jaws collimator, and the particle distribution is changed by modifying the parameters of the simulated particle source. The simulated particle source is started to obtain a primary particle beam, the primary particle beam passes through the Jaws collimator to obtain a regular shape particle beam, and then passes through the multi-leaf grating collimator to obtain a target particle beam, and finally the target particle beam is transported to the digital phantom for physical action.

[0059] Optionally, the primary particle beam is determined based on the following operations: using the target thread of the graphics processor, starting the simulated particle source based on energy spectrum position parameters, energy spectrum energy range parameters, flux distribution parameters, and angular distribution parameters to obtain the primary particle beam, wherein the energy spectrum energy range parameters are obtained by adjusting the weights of different energy ranges to match the measured dose distribution curve, the flux distribution parameters are obtained by adjusting the depth to match the measured dose distribution curve, and the angular distribution parameters are obtained by adjusting the parameters of the Gaussian function to match the measured dose distribution curve.

[0060] Optionally, the simulated particle source is used to simulate multiple types of particle sources emitting different types of primary particle beams. The particle source includes a primary photon source, a scattered photon source, and an electron pollution source. The primary photon source, the scattered photon source, and the electron pollution source have the same energy spectrum, but their flux distribution and angular distribution are different.

[0061] Optionally, the parameters of the simulated particle source are analytical source parameters, which include energy spectrum position parameters, energy spectrum energy interval parameters, flux distribution parameters, angular distribution parameters, etc. The energy interval ranges from 0 to 6.8 MeV, with each 0.2 MeV being an energy interval. The energy spectrum position parameters characterize the position information of the simulated particle source.

[0062] Optionally, in each target thread of the graphics processor, a simulated particle source is started based on energy spectrum position parameters, energy spectrum energy interval parameters, flux distribution parameters, and angular distribution parameters to obtain a primary particle beam.

[0063] Optionally, the target threads include multiple target threads, the multiple target threads have a mapping relationship with a simulation particle set of an energy interval, and the multiple target threads are each associated with at least one simulation particle in the simulation particle set; wherein, using the target thread of the graphics processor to start the simulation particle source based on energy spectrum position parameters, energy spectrum energy interval parameters, flux distribution parameters, and angular distribution parameters to obtain a primary particle beam includes: using multiple target threads corresponding to the same energy interval to start the simulation particle set with the same energy interval based on energy spectrum position parameters, energy spectrum energy interval parameters, flux distribution parameters, and angular distribution parameters to obtain primary particle beamlets associated with the energy interval; and determining the primary particle beam based on the primary particle beamlets associated with each of the multiple energy intervals.

[0064] Optionally, set the operating parameters of the image processor, including random number seed parameters, simulation particle number parameters, operating thread parameters, etc. The random number seed parameters determine the random number sequence in the simulation, and the operating thread parameters can set the thread warp parameters (warp) and the target thread number parameters (thread). A thread warp is the smallest execution unit, and a thread warp can include 32 parallel target threads.

[0065] Optionally, the simulated particle quantity parameters corresponding to different energy intervals may be determined according to the weights of different energy intervals in the energy spectrum corresponding to the simulated particle source.

[0066] Optionally, multiple target threads in a thread bundle have a mapping relationship with a set of simulated particles in an energy interval. Using multiple target threads corresponding to the same energy interval, based on energy spectrum position parameters, energy spectrum energy interval parameters, flux distribution parameters, and angular distribution parameters, the set of simulated particles in the same energy interval is started, and multiple target threads are evenly distributed based on the simulated particle quantity parameters corresponding to the energy interval to obtain primary particle sub-beams related to the energy interval.

[0067] Optionally, the simulation particle set in the same energy interval contains multiple simulation particles, and each target thread can be assigned to one or more simulation particles in the simulation particle set. The number of simulation particles in the primary particle beamlet is less than or equal to the number of simulation particles in the simulation particle set.

[0068] Optionally, the primary particle beam is determined based on primary particle beamlets respectively associated with a plurality of energy intervals.

[0069] Optionally, a mapping relationship between multiple target threads in a thread bundle and a set of simulated particles in an energy range can ensure that each thread bundle simulates particles with the same energy, reduce thread divergence, ensure that multiple target threads run synchronously to the greatest extent, and improve the computing efficiency of the image processor.

[0070] Optionally, the dose distribution determination method based on the graphics processor also includes: the particle types of multiple simulated particles started each time by the same target thread are the same; the graphics processor allocates video memory to multiple types of data based on data access frequency, wherein the multiple types of data include contrast data, sampling data simulating the interaction between biological tissues and particles, configuration parameters, particle attribute data, and simulated particle source parameters.

[0071] Optionally, the particle type includes photon type and electron type. Each target thread in the GPU is only responsible for simulating photons or electrons in a single simulation, and tries to ensure synchronous operation at every moment.

[0072] Optionally, during a simulation, photons and digital phantoms will generate reaction electrons after physical interaction, which need to be transferred to the digital phantom in the next unified simulation. Simulate only one type of particle each time and then switch to simulating another type of particle until all particles are simulated. Photon-electron separation simulation ensures synchronous simulation of particles of the same type.

[0073] Optionally, the multiple types of data include imaging data, sampling data for simulating the interaction between biological tissues and particles, configuration parameters, particle attribute data, analytical parameters of simulated particle sources, operating parameters of a graphics processor, and the like.

[0074] Optionally, based on the memory access performance differences of different memories on the graphics processor and the memory access frequency of the data required for the simulation, video memory is allocated for multiple types of data. Variables that are frequently accessed but occupy a small space, such as photon attribute data, will be stored in registers; some read-only data and occupy a small space, such as the analytical parameters of the simulated particle source, will be stored in constant memory; some read-only but large data variables, such as sampling data and imaging data for simulating the interaction between biological tissues and particles, are stored in global memory. The access latency of global memory is the highest, so the operating parameters that are much larger than the actual number of threads will be set to offset some of the latency.

[0075] Optionally, allocating video memory to multiple types of data based on data access frequency improves memory access performance of the graphics processor.

[0076] Optionally, the performance optimization of the graphics processor also includes inverse sampling optimization of the sampled data, where the inverse sampling optimization process allows direct sampling during the target particle beam transport process, thereby eliminating the divergence problem associated with the random rejection sampling process.

[0077] Optionally, operating parameters are set for the graphics processor to optimize the computing performance of the graphics processor, reduce computing time, and improve computing accuracy.

[0078] Optionally, the target particle beam includes a photon beam and an electron beam, the physical action model includes multiple photon action sub-models and electron action sub-models, and the sampling data includes reaction action probability data between particles and simulated biological tissues and angle energy sampling probability data; wherein, based on the sampling data of the interaction between simulated biological tissues and particles, the physical action model is used to process the target particle beam and the digital phantom to obtain a particle deposition energy distribution result characterizing the target particle beam transported to the digital phantom, including the following operations.

[0079] The photons in the photon beam are transported to each digital tissue in the digital phantom to generate reaction electrons; the target action sub-model is determined from multiple photon action sub-models according to the reaction action probability data and the angle energy sampling probability data corresponding to each digital tissue; the energy state information of the photons is processed according to the target action sub-model to obtain the photon deposition energy distribution result; the reaction electrons are transported to the digital phantom, and the energy state information of the reaction electrons is processed according to the electron action sub-model to obtain the reaction electron deposition energy distribution result; the initial electrons in the electron beam are transported to the digital phantom, and the energy state information of the initial electrons is processed according to the electron action sub-model to obtain the initial electron deposition energy distribution result; the particle deposition energy distribution result is obtained according to the photon deposition energy distribution result, the reaction electron deposition energy distribution result and the initial electron deposition energy distribution result.

[0080] Optionally, the photon beam includes multiple photons, and the electron beam includes multiple electrons. The photon action sub-model is used to calculate the particle deposition energy distribution result generated by the photon beam being transported to the digital phantom, and the electron action sub-model is used to calculate the particle deposition energy distribution result generated by the electron beam being transported to the digital phantom.

[0081] Optionally, each digital tissue in the digital phantom absorbs photons in the photon beam and generates reaction electrons, and the target action sub-model is determined from multiple photon action sub-models according to the reaction action probability data and angle energy sampling probability data corresponding to the photons and the digital tissues. For example, if the photons undergo Compton scattering, the target action sub-model may be a Compton scattering action sub-model.

[0082] Optionally, the energy state information of photons and reaction electrons is processed according to the target actor model to obtain a photon deposition energy distribution result. The photon deposition energy distribution result represents the energy distribution of photons deposited in each digital tissue of the digital phantom.

[0083] Optionally, each digital tissue in the digital phantom absorbs the reaction electrons, and the reaction electron deposition energy distribution result represents the energy distribution of the reaction electrons deposited in each digital tissue in the digital phantom.

[0084] Optionally, each digital tissue in the digital phantom absorbs the initial electrons, and the initial electron deposition energy distribution result represents the energy distribution of the initial electrons deposited in each digital tissue in the digital phantom.

[0085] Optionally, a particle deposition energy distribution result is obtained according to the energy distribution of photons, reaction electrons and initial electrons deposited in each digital tissue of the digital phantom, and the particle deposition energy distribution result represents the total energy distribution deposited in each digital tissue.

[0086] Optionally, the electron interaction sub-model is constructed based on the electron transport function, the inelastic collision function and the multiple scattering function; wherein, the reaction electrons are transported into the digital phantom, and the energy state information of the reaction electrons is processed according to the electron interaction sub-model to obtain the reaction electron deposition energy distribution result, including: processing the energy state information of the reaction electrons according to the electron transport function to obtain a first electron deposition energy distribution result; processing the energy state information of the reaction electrons according to the inelastic collision function to obtain a second electron deposition energy distribution result; processing the energy state information of the reaction electrons according to the multiple scattering function to obtain a third electron deposition energy distribution result; weighting the first electron deposition energy distribution result, the second electron deposition energy distribution result and the third electron deposition energy distribution result to obtain a reaction electron deposition energy distribution result.

[0087] Optionally, the energy state information of the reaction electrons is processed using an electron transport function to obtain a first electron deposition energy distribution result. The electron transport function uses a condensation history function, and the electron transport function can discretely simulate interactions with deposition energies exceeding a predetermined threshold, and the deposition energies below the predetermined threshold are processed by continuous slowing down approximation. The predetermined threshold is the same as the electron cutoff energy.

[0088] Optionally, the energy state information of the reaction electrons is processed using an inelastic collision function to obtain the second electron deposition energy distribution result. Møller scattering is used to model the free electron approximation and construct an inelastic collision function.

[0089] Optionally, the energy state information of the reaction electrons is processed using a multiple scattering function to obtain a third electron deposition energy distribution result. Based on multiple scattering theory modeling, a multiple scattering function is constructed.

[0090] Optionally, the multiple photon effect sub-models include at least one of the following: a photoelectric effect sub-model, a Compton scattering sub-model and an electron pair generation sub-model.

[0091] Optionally, the interaction of photons with digital tissues includes three types: photoelectric effect, Compton scattering and electron pair generation.

[0092] Optionally, in the case of a photoelectric effect interaction, the target actor model is determined to be a photoelectric effect actor model, and the photoelectric effect actor model is used to model the absence of reaction electrons and local deposition of energy; in the case of a Compton scattering interaction, the target actor model is determined to be a Compton scattering actor model, and the Compton scattering actor model constructs an interaction model based on the Klein-Nishina cross section; in the case of an electron pair generation interaction, the target actor model is determined to be an electron pair generation actor model, and the reaction electrons and positrons generated by the electron pair generation interaction are assumed to have the same energy and initial direction.

[0093] Optionally, a dose treatment plan is formulated for a target subject with cervical cancer. The dose treatment plan consists of 7 uniformly distributed beams, each beam has 10 control points, and the prescription dose is 50 Gy. A simulation software package is constructed to simulate the transport process of particles in a digital phantom. The simulation software package includes constructing a physical action model, an accelerator head model, a simulated particle source, etc.; optimizing the performance of the graphics processor based on the parameters in the configuration file; and running the simulation program.

[0094] Figure 3 A flow chart of a simulation program according to an embodiment of the present invention is shown.

[0095] like Figure 3 As shown, the simulation program running process 300 includes steps S310~S390.

[0096] In step S310, the program is initialized and data is loaded.

[0097] Optionally, according to the parameter information in the configuration file, the required imaging data, sampling data for simulating the interaction between various biological tissues and particles, configuration parameters, particle attribute data, simulation particle source parameters, etc. are read into the program.

[0098] In step S320, video memory is allocated for the multi-type data and the multi-type data is transferred to the image processor.

[0099] In step S330, the target particle beam transport program is called.

[0100] In step S340, the photon distribution is modulated.

[0101] In step S350, photon / electron simulation.

[0102] In step S360, move to the next interaction point.

[0103] In step S370, interaction is performed and energy is deposited.

[0104] In step S380, check whether the required number of particles to be simulated has been reached. If so, jump to step 390; otherwise, jump to step S360.

[0105] Optionally, the energy deposited by the particles in the digital phantom is recorded in a counter. After all particles are simulated, the particle deposition energy distribution results are returned to the image processor.

[0106] In step 390, a dose distribution result is obtained based on the energy dose conversion factor.

[0107] Figure 4 An exemplary schematic diagram showing a dose distribution result according to an embodiment of the present invention is shown.

[0108] like Figure 4 As shown, the dose distribution result is obtained by using the dose distribution determination method based on a graphics processor. A dose of more than 50 Gy is used for tumor cells in cervical tissue, and a dose lower than the dose distribution result corresponding to the tissue is used in other normal tissues to minimize damage to normal tissues.

[0109] Based on the above-mentioned method for determining dose distribution based on a graphics processor, the present invention also provides a device for determining dose distribution based on a graphics processor. Figure 5 The device is described in detail.

[0110] Figure 5 A structural block diagram of a device for determining dose distribution based on a graphics processor according to an embodiment of the present invention is shown.

[0111] like Figure 5 As shown, the device 500 for determining dose distribution based on a graphics processor of this embodiment includes a construction module 510 , a generation module 520 , a processing module 530 and a conversion module 540 .

[0112] The construction module 510 is used to construct a digital phantom based on the angiography data of the target object. In one embodiment, the construction module 510 can be used to perform the operation S110 described above, which will not be described in detail here.

[0113] The generation module 520 is used to generate a target particle beam based on the configuration parameters and to process the primary particle beam determined based on the simulated particle source using the accelerator head model, wherein the shape of the target particle beam matches the shape of the tumor tissue of the target object, and the configuration parameters include a parameter for the upper limit of the number of photon reaction actions. In one embodiment, the generation module 520 can be used to perform the operation S120 described above, which will not be described in detail here.

[0114] The processing module 530 is used to process the target particle beam and the digital phantom using the physical interaction model based on the sampled data of the simulated biological tissues and particle interactions, and obtain a particle deposition energy distribution result representing the target particle beam transported to the digital phantom, wherein the particle deposition energy distribution result represents the energy deposited by the target particle beam distributed in each digital tissue of the digital phantom. In one embodiment, the processing module 530 can be used to perform the operation S130 described above, which will not be repeated here.

[0115] The conversion module 540 is used to obtain a dose distribution result according to the particle deposition energy distribution result and the energy dose conversion factor. In one embodiment, the conversion module 540 can be used to perform the operation S140 described above, which will not be described in detail here.

[0116] Optionally, the generation module 520 includes a first generation submodule and a second generation submodule.

[0117] The first generation submodule is used to modulate the primary particle beam using a jaw collimator to obtain a particle beam of regular shape.

[0118] The second generation submodule is used to modulate the regular shape particle beam using a multi-leaf grating collimator based on the photon reaction type parameter, the photon reaction number upper limit parameter and the target shape parameter to obtain the target particle beam.

[0119] Optionally, the generation module 520 further includes a third generation submodule and a fourth generation submodule.

[0120] The third generation submodule is used to use multiple target threads corresponding to the same energy interval to start the simulation particle set with the same energy interval based on energy spectrum position parameters, energy spectrum energy interval parameters, flux distribution parameters, and angular distribution parameters to obtain primary particles related to the energy interval.

[0121] The fourth generation submodule is used to determine the primary particle beam based on the primary particle beamlets respectively associated with the plurality of energy intervals.

[0122] Optionally, the device 500 for determining dose distribution based on a graphics processor further includes a first optimization module and a second optimization module.

[0123] The first optimization module is used for the same target thread to start multiple simulation particles each time with the same particle type.

[0124] The second optimization module is used for the graphics processor to allocate video memory to multiple types of data based on data access frequency, wherein the multiple types of data include imaging data, sampling data simulating the interaction between biological tissues and particles, configuration parameters, particle attribute data, and simulation particle source parameters.

[0125] Optionally, the processing module 530 includes a first processing submodule, a second processing submodule, a third processing submodule, a fourth processing submodule, a fifth processing submodule and a sixth processing submodule.

[0126] The first processing submodule is used to transport the photons in the photon beam to each digital tissue in the digital phantom to generate reaction electrons.

[0127] The second processing submodule is used to determine the target action submodel from multiple photon action submodels according to the reaction action probability data and angle energy sampling probability data corresponding to each digital tissue.

[0128] The third processing submodule is used to process the energy state information of the photons according to the target action submodel to obtain the photon deposition energy distribution result.

[0129] The fourth processing submodule is used to transport the reaction electrons into the digital phantom, process the energy state information of the reaction electrons according to the electron action submodel, and obtain the reaction electron deposition energy distribution result.

[0130] The fifth processing submodule is used to transport the initial electrons in the electron beam into the digital phantom, process the energy state information of the initial electrons according to the electron action submodel, and obtain the initial electron deposition energy distribution result.

[0131] The sixth processing submodule is used to obtain a particle deposition energy distribution result according to the photon deposition energy distribution result, the reaction electron deposition energy distribution result and the initial electron deposition energy distribution result.

[0132] Optionally, the fourth processing submodule includes a first processing unit, a second processing unit, a third processing unit and a fourth processing unit.

[0133] The first processing unit is used to process the energy state information of the reaction electrons according to the electron transport function to obtain a first electron deposition energy distribution result.

[0134] The second processing unit is used to process the energy state information of the reaction electrons according to the inelastic collision function to obtain a second electron deposition energy distribution result.

[0135] The third processing unit is used to process the energy state information of the reaction electrons according to the multiple scattering function to obtain a third electron deposition energy distribution result.

[0136] The fourth processing unit is used to weight the first electron deposition energy distribution result, the second electron deposition energy distribution result and the third electron deposition energy distribution result to obtain a reaction electron deposition energy distribution result.

[0137] Optionally, any multiple modules among the construction module 510, the generation module 520, the processing module 530 and the conversion module 540 can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. Optionally, at least one of the construction module 510, the generation module 520, the processing module 530 and the conversion module 540 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware and firmware or in any appropriate combination of any of them. Alternatively, at least one of the construction module 510, the generation module 520, the processing module 530 and the conversion module 540 can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding function can be executed.

[0138] Figure 6 A block diagram of an electronic device suitable for implementing a dose distribution determination method based on a graphics processor according to an embodiment of the present invention is shown.

[0139] Figure 6 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0140] like Figure 6 As shown, a computer electronic device 600 according to an embodiment of the present invention includes a processor 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage part 608 into a random access memory (RAM) 603. The processor 601 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 601 may also include an onboard memory for caching purposes. The processor 601 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present invention.

[0141] In RAM 603, various programs and data required for the operation of electronic device 600 are stored. Processor 601, ROM 602 and RAM 603 are connected to each other via bus 604. Processor 601 performs various operations of the method flow according to the embodiment of the present invention by executing the programs in ROM 602 and / or RAM 603. It should be noted that the program can also be stored in one or more memories other than ROM 602 and RAM 603. Processor 601 can also perform various operations of the method flow according to the embodiment of the present invention by executing the programs stored in one or more memories.

[0142] Optionally, the electronic device 600 may further include an input / output (I / O) interface 605, which is also connected to the bus 604. The electronic device 600 may further include one or more of the following components connected to the input / output (I / O) interface 605: an input portion 606 including a keyboard, a mouse, etc.; an output portion 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 608 including a hard disk, etc.; and a communication portion 609 including a network interface card such as a LAN card, a modem, etc. The communication portion 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the input / output (I / O) interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed, so that a computer program read therefrom is installed into the storage portion 608 as needed.

[0143] Optionally, the method flow according to the embodiment of the present invention can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable storage medium, and the computer program contains a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 609, and / or installed from the removable medium 611. When the computer program is executed by the processor 601, the above-mentioned functions defined in the system of the embodiment of the present invention are executed. Optionally, the system, equipment, device, module, unit, etc. described above can be implemented by a computer program module.

[0144] The present invention also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiment; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the dose distribution determination method based on the graphics processor according to the embodiment of the present invention is implemented.

[0145] Optionally, the computer-readable storage medium may be a non-volatile computer-readable storage medium. For example, it may include, but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0146] For example, optionally, the computer-readable storage medium may include the ROM 602 and / or the RAM 603 described above and / or one or more memories other than the ROM 602 and the RAM 603 .

[0147] An embodiment of the present invention also includes a computer program product, which includes a computer program, which contains program code for executing the method provided by the embodiment of the present invention. When the computer program product runs on an electronic device, the program code is used to enable the electronic device to implement the dose distribution determination method based on a graphics processor provided by the embodiment of the present invention.

[0148] When the computer program is executed by the processor 601, the above functions defined in the system / device of the embodiment of the present invention are performed. Optionally, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0149] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 609, and / or installed from a removable medium 611. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0150] Optionally, the program code for executing the computer program provided by the embodiment of the present invention can be written in any combination of one or more programming languages, specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, such as Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).

[0151] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions. It can be understood by those skilled in the art that the features recorded in the various embodiments of the present invention can be combined and / or combined in various ways, even if such a combination or combination is not explicitly recorded in the present invention. In particular, without departing from the spirit and teachings of the present invention, the features recorded in the various embodiments of the present invention can be combined and / or combined in various ways. All these combinations and / or combinations fall within the scope of the present invention.

[0152] The embodiments of the present invention are described above. However, these embodiments are only for the purpose of illustration, and are not intended to limit the scope of the present invention. Although each embodiment is described above, it does not mean that the measures in each embodiment cannot be used in combination advantageously. Without departing from the scope of the present invention, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present invention.

Claims

1. A method for determining dose distribution based on a graphics processor, characterized in that: The dose distribution determination method is executed by a graphics processor, and the method includes: constructing a digital phantom based on the angiographic data of the target object; Based on configuration parameters, using an accelerator head model to process a primary particle beam determined based on a simulated particle source to generate a target particle beam, wherein a shape of the target particle beam matches a tumor tissue shape of the target object, and the configuration parameters include an upper limit parameter of the number of photon reaction actions; Based on the sampled data of the simulated biological tissues and particle interactions, the target particle beam and the digital phantom are processed using a physical interaction model to obtain a particle deposition energy distribution result representing the target particle beam transported to the digital phantom, wherein the particle deposition energy distribution result represents the energy deposited by the target particle beam distributed in each digital tissue of the digital phantom; A dose distribution result is obtained according to the particle deposition energy distribution result and the energy dose conversion factor.

2. The method according to claim 1, characterized in that The configuration parameters also include photon reaction action type parameters and target shape parameters, and the accelerator head model includes a jaw collimator and a multi-leaf grating collimator; Wherein, based on the configuration parameters, using the accelerator head model to process the primary particle beam determined based on the simulated particle source to generate the target particle beam includes: Modulating the primary particle beam using the jaw collimator to obtain a particle beam of regular shape; The multileaf grating collimator is used to modulate the regular-shaped particle beam based on the photon reaction type parameter, the photon reaction number upper limit parameter and the target shape parameter to obtain the target particle beam.

3. The method according to claim 2, characterized in that The primary particle beam is determined based on the following operations: Using the target thread of the graphics processor, a simulated particle source is started based on an energy spectrum position parameter, an energy spectrum energy interval parameter, a flux distribution parameter, and an angular distribution parameter to obtain the primary particle beam, wherein the energy spectrum energy interval parameter is obtained by adjusting the weights of different energy intervals to match a measured dose distribution curve, the flux distribution parameter is obtained by adjusting the depth to match the measured dose distribution curve, and the angular distribution parameter is obtained by adjusting the parameters of a Gaussian function to match the measured dose distribution curve.

4. The method according to claim 3, characterized in that The target threads include a plurality of target threads, the plurality of target threads have a mapping relationship with a set of simulation particles in an energy interval, and each of the plurality of target threads is associated with at least one simulation particle in the set of simulation particles; The step of using the target thread of the graphics processor to start the simulated particle source based on the energy spectrum position parameter, the energy spectrum energy interval parameter, the flux distribution parameter, and the angular distribution parameter to obtain the primary particle beam includes: Using multiple target threads corresponding to the same energy interval, based on the energy spectrum position parameter, the energy spectrum energy interval parameter, the flux distribution parameter, and the angular distribution parameter, a set of simulation particles corresponding to the same energy interval is started to obtain primary particle beamlets related to the energy interval; and based on the primary particle beamlets related to each of the multiple energy intervals, the primary particle beam is determined.

5. The method according to claim 3, characterized in that: The method further comprises: The particle types of the plurality of simulation particles started each time by the same target thread are the same; The graphics processor allocates video memory to multiple types of data based on data access frequency, wherein the multiple types of data include the imaging data, the sampling data of the simulated biological tissues and particles, the configuration parameters, particle attribute data, and simulated particle source parameters.

6. The method according to claim 1, characterized in that The target particle beam includes a photon beam and an electron beam, the physical action model includes a plurality of photon action sub-models and electron action sub-models, and the sampling data includes reaction action probability data between particles and simulated biological tissues and angle energy sampling probability data; The method of processing the target particle beam and the digital phantom using a physical action model based on the sampled data simulating the interaction between various biological tissues and particles to obtain a particle deposition energy distribution result representing the target particle beam transported to the digital phantom includes: transporting photons in the photon beam to each digital tissue in the digital phantom to generate reaction electrons; Determine a target action sub-model from a plurality of photon action sub-models according to the reaction action probability data and the angle energy sampling probability data corresponding to each digital tissue; Processing the energy state information of the photons according to the target actor model to obtain a photon deposition energy distribution result; The reaction electrons are transported into the digital phantom, and the energy state information of the reaction electrons is processed according to the electron action sub-model to obtain the reaction electron deposition energy distribution result; transporting the initial electrons in the electron beam into the digital phantom, processing the energy state information of the initial electrons according to the electron action sub-model, and obtaining the initial electron deposition energy distribution result; The particle deposition energy distribution result is obtained according to the photon deposition energy distribution result, the reaction electron deposition energy distribution result and the initial electron deposition energy distribution result.

7. The method according to claim 6, characterized in that The electron interaction submodel is constructed based on electron transport function, inelastic collision function and multiple scattering function; The step of transporting the reaction electrons into the digital phantom and processing the energy state information of the reaction electrons according to the electron action sub-model to obtain the reaction electron deposition energy distribution result comprises: Processing the energy state information of the reaction electrons according to the electron transport function to obtain a first electron deposition energy distribution result; Processing the energy state information of the reaction electrons according to the inelastic collision function to obtain a second electron deposition energy distribution result; Processing the energy state information of the reaction electrons according to the multiple scattering function to obtain a third electron deposition energy distribution result; The first electron deposition energy distribution result, the second electron deposition energy distribution result and the third electron deposition energy distribution result are weighted to obtain the reaction electron deposition energy distribution result.

8. The method according to claim 1, characterized in that The plurality of photon action sub-models include at least one of the following: Photoelectric effect actor model, Compton scattering actor model and electron pair production actor model.

9. A device for determining dose distribution based on a graphics processor, characterized in that: The device comprises: A construction module, for constructing a digital phantom based on the angiographic data of the target object; A generating module, configured to generate a target particle beam by processing a primary particle beam determined based on a simulated particle source using an accelerator head model based on configuration parameters, wherein a shape of the target particle beam matches a tumor tissue shape of the target object, and the configuration parameters include an upper limit parameter of the number of photon reaction actions; A processing module, for processing the target particle beam and the digital phantom using a physical action model based on sampled data of the simulated interaction between various biological tissues and particles, to obtain a particle deposition energy distribution result representing the target particle beam transported to the digital phantom, wherein the particle deposition energy distribution result represents the energy deposited by the target particle beam distributed in various digital tissues of the digital phantom; The conversion module is used to obtain a dose distribution result according to the particle deposition energy distribution result and the energy dose conversion factor.

10. An electronic device, characterized in that: include: one or more processors; a memory for storing one or more instructions, Wherein, when the one or more instructions are executed by the one or more processors, the one or more processors are enabled to implement the method according to any one of claims 1 to 8.

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