Dose distribution determination method and apparatus based on graphics processing unit, and electronic device

Abstract

Provided in the present invention are a dose distribution determination method and apparatus based on a graphics processing unit, and an electronic device, which can be applied in the technical field of medical radiation dose calculation. The method comprises: on the basis of imaging data of a target object and sampling data from the simulation of the interactions between each biological tissue and particles, constructing a digital phantom; on the basis of configuration parameters, using an accelerator gantry model to process a primary particle beam determined on the basis of a simulated particle source, so as to generate a target particle beam, wherein the shape of the target particle beam matches the shape of tumor tissue of the target object, and the configuration parameters comprise a shape parameter and a position parameter of a gantry collimator; using a physical action model to process the target particle beam and the digital phantom, so as to obtain a particle deposition energy distribution result which represents the transport of the target particle beam to the digital phantom; and on the basis of the particle deposition energy distribution result and an energy dose conversion factor, obtaining a dose distribution result.

Description

Methods, apparatus and electronic devices for determining dose distribution based on graphics processors Technical Field

[0001] This invention relates to the field of medical radiation dose calculation technology, and more specifically to a method, apparatus, and electronic device for determining dose distribution based on a graphics processor. Background Technology

[0002] In medical radiotherapy, accurately delivering prescribed doses to tumor cells while minimizing damage to surrounding healthy tissues is crucial. Traditional radiotherapy planning systems use model-based algorithms to calculate dose distributions, which have limitations in handling electron transport in heterogeneous regions, resulting in significant dose errors at tissue boundaries. The Monte Carlo method is considered the gold standard for dose calculation, but its low computational efficiency has limited its widespread clinical use. Calculating the dose distribution for a radiotherapy plan using a central processing unit (CPU) typically takes several hours or even a day. In recent years, the rapid development of GPU-based Monte Carlo dosing engines has brought a turning point to this problem. However, for time-critical applications such as online adaptive radiotherapy and iterative dose optimization, further improvements in dose calculation efficiency are needed. Summary of the Invention

[0003] In view of the above problems, the present invention provides a method, apparatus and electronic device for determining dose distribution based on a graphics processor, for rapidly 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. The dose distribution determination method is executed by a graphics processor and includes: constructing a digital phantom based on contrast data of a target object and sampling data simulating the interaction between various biological tissues and particles; 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 the shape of the target particle beam matches the shape of the tumor tissue of the target object, and the configuration parameters include the shape and position parameters of the head collimator; processing the target particle beam and the digital phantom using a physical interaction model to obtain a particle deposition energy distribution result characterizing the particle energy distribution generated by 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 in various digital tissues of the digital phantom; and obtaining a dose distribution result based on the particle deposition energy distribution result and an energy-dose conversion factor.

[0005] Optionally, the configuration parameters also include target shape parameters. The accelerator head model includes a jaw collimator and a multi-leaf grating collimator. Based on the configuration parameters, the accelerator head model processes the primary particle beam determined based on the simulated particle source to generate the target particle beam, which includes: modulating the primary particle beam using the jaw collimator to obtain a regular-shaped particle beam; and modulating the regular-shaped particle beam based on the target shape parameters using the multi-leaf grating collimator to obtain the target particle beam.

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

[0007] Optionally, there are multiple target threads, each of which is mapped to a set of simulated particles in an energy range. Each target thread is associated with at least one simulated particle in the set of simulated particles. The target threads of the graphics processor are used to start the simulated particle source based on the energy spectrum position parameters, energy spectrum energy range parameters, flux distribution parameters, and angular distribution parameters to obtain the primary particle beam.

[0008] Optionally, the method also includes: multiple simulated particles launched each time by the same target thread have the same particle type; the graphics processor allocates video memory for multiple types of data based on the data access frequency, wherein the multiple types of data include imaging data, sampling data simulating the interaction between various 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 interaction model includes multiple photon interaction sub-models and electron interaction sub-models, and the sampling data includes probability data of the reaction between particles and various tissues of the simulated biological organism and angular energy sampling probability data. Specifically, processing the target particle beam and the digital phantom using the physical interaction model to obtain the particle deposition energy distribution characteristic of the target particle beam transported to the digital phantom includes: transporting photons from the photon beam to various digital tissues within the digital phantom to generate reactive electrons; and determining the target interaction sub-model from multiple photon interaction sub-models based on the reaction probability data corresponding to each digital tissue. The process involves: processing the energy state information of photons based on the target interaction model and angular energy sampling probability data to obtain the photon deposition energy distribution result; transporting reactive electrons into a digital phantom and processing their energy state information based on the electron interaction model to obtain the reactive electron deposition energy distribution result; transporting initial electrons from the electron beam into the digital phantom and processing their energy state information based on the electron interaction model to obtain the initial electron deposition energy distribution result; and finally, obtaining the particle deposition energy distribution result based on the photon deposition energy distribution result, the reactive electron deposition energy distribution result, and the initial electron deposition energy distribution result.

[0010] Optionally, the electron interaction model is constructed based on soft collision functions, hard collision functions, and multiple scattering functions. Specifically, the reactive electrons are transported into the digital phantom, and the energy state information of the reactive electrons is processed according to the electron interaction model to obtain the reactive electron deposition energy distribution results. This includes: processing the energy state information of the reactive electrons according to the soft collision function to obtain the first electron deposition energy distribution result; processing the energy state information of the reactive electrons according to the hard collision function to obtain the second electron deposition energy distribution result; processing the state information of the reactive electrons according to the multiple scattering function to obtain the electron motion state result; and weighting the first and second electron deposition energy distribution results to obtain the final reactive electron deposition energy distribution result.

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

[0012] A second aspect of the present invention provides a graphics processor-based dose distribution determination apparatus, comprising:

[0013] The building module is used to construct digital phantoms based on the imaging data of the target object and the sampling data simulating the interaction between various biological tissues and particles;

[0014] The generation module is used to generate a target particle beam by processing the primary particle beam determined based on the simulated particle source using the accelerator head model based on configuration parameters. The shape of the target particle beam matches the shape of the tumor tissue of the target object. The configuration parameters include the shape and position parameters of the head collimator.

[0015] The processing module is used to process the target particle beam and the digital phantom using a physical action model to obtain the particle deposition energy distribution result characterizing the particle energy deposited by the target particle beam transported to the digital phantom. The particle deposition energy distribution result characterizes the energy deposited by the target particle beam in each digital tissue of the digital phantom.

[0016] The conversion module is used to obtain the dose distribution results based on the particle deposition energy distribution results and the energy dose conversion factor.

[0017] A third aspect of the present invention provides an electronic device comprising: one or more processors; and 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 cause the one or more processors to perform the above-described graphics processor-based dose distribution determination method.

[0018] According to the graphics processor-based dose distribution determination method, apparatus, and electronic device provided by the present invention, a target particle beam matching the shape of tumor tissue is obtained by configuring an accelerator head model based on configuration parameters; the target particle beam is transported to a digital phantom, and the particle deposition energy distribution result is obtained based on sampling data and a physical interaction model, thereby obtaining the dose distribution result. By using a simulated particle source and an accelerator head model to rapidly generate the target particle beam, thread divergence on the graphics processor is reduced, and computational efficiency is improved. Furthermore, by utilizing sampling data and a physical interaction model simulating the interaction between various biological tissues and particles, the accuracy and efficiency of the dose distribution result are further improved. Attached Figure Description

[0019] Figure 1 shows a flowchart of a graphics processor-based dose distribution determination method according to an embodiment of the present invention.

[0020] Figure 2 shows an example schematic diagram of a simulation system according to an embodiment of the present invention.

[0021] Figure 3 shows a flowchart of a simulation program according to an embodiment of the present invention.

[0022] Figure 4 shows an example schematic diagram of dose distribution results according to an embodiment of the present invention.

[0023] Figure 5 shows a structural block diagram of a graphics processor-based dose distribution determination device according to an embodiment of the present invention.

[0024] Figure 6 shows a block diagram of an electronic device suitable for implementing a graphics processor-based dose distribution determination method according to an embodiment of the present invention. Detailed Implementation

[0025] Hereinafter, 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 invention. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the invention for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concept of the invention.

[0026] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. The terms “comprising,” “including,” etc., as used herein indicate the presence of features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0027] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0028] When using expressions such as "at least one of A, B, and C", they should generally be interpreted in accordance with the meaning that is commonly understood by a person skilled in the art (e.g., "a system having at least one of A, B, and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B, and C, etc.).

[0029] In developing this invention, it was discovered that in medical radiotherapy, accurately delivering the prescribed dose to tumor cells while minimizing damage to surrounding normal tissues is crucial. Traditional radiotherapy planning systems use model-based algorithms to calculate dose distributions, which have limitations in handling electron transport in heterogeneous regions, resulting in significant dose errors at tissue boundaries. The Monte Carlo method is considered the gold standard for dose calculation, but its low computational efficiency has limited its widespread clinical use. Calculating the dose distribution for a radiotherapy plan using a central processing unit typically takes several hours or even a day. In recent years, the rapid development of graphics processor-based Monte Carlo dose calculation engines has brought a turning point to this problem. However, for time-critical applications such as online adaptive radiotherapy and iterative dose optimization, further improvements in dose calculation efficiency are needed.

[0030] In view of this, embodiments of the present invention provide a method, apparatus, and electronic device for determining dose distribution based on a graphics processor. The method includes: constructing a digital phantom based on imaging data of a target object and sampling data simulating the interaction between various biological tissues and particles; 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 the shape of the target particle beam matches the shape of the tumor tissue of the target object, and the configuration parameters include the shape and position parameters of the head collimator; processing the target particle beam and the digital phantom using a physical interaction model to obtain a particle deposition energy distribution result characterizing the particle deposition energy distribution generated by the target particle beam transported to the digital phantom; and obtaining the dose distribution result based on the particle deposition energy distribution result and the energy-dose conversion factor.

[0031] In the technical solution of this invention, the user information (including but not limited to user personal information, user image information, user device information, such as location information) and data (including but not limited to data used for analysis, stored data, and displayed data) involved are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of related data all comply with relevant laws, regulations, and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entry points for users to choose to authorize or refuse.

[0032] It should be noted that the sequence numbers of the operations in the following methods are for descriptive purposes only and should not be considered as indicating the execution order of the operations. Unless explicitly stated otherwise, the method does not need to be executed in the exact order shown.

[0033] Figure 1 shows a flowchart of a graphics processor-based dose distribution determination method according to an embodiment of the present invention.

[0034] As shown in Figure 1, the method 100 includes operations S110 to S140.

[0035] In operation S110, a digital phantom is constructed based on the imaging data of the target object.

[0036] Optionally, the target object represents an object suffering from a tumor.

[0037] Optionally, the target object can be scanned using computed tomography (CT) to obtain contrast data, which is the CT image data of the target object.

[0038] Optionally, the acquired contrast data is stored in the Digital Imaging and Communications in Medicine (DICOM) format, and the contrast data is input into a graphics processing unit (GPU). Based on the mapping relationship between the CT values ​​and material density of the contrast data, the contrast data is converted into material density, and then a digital phantom is constructed.

[0039] In operation of S120, based on configuration parameters, the primary particle beam determined by the simulated particle source is processed using the accelerator head model to generate the target particle beam.

[0040] Optionally, simulated particle sources are used to simulate the emission of different types of primary particle beams from various particle sources. These sources include primary photon sources, scattered photon sources, and electron contamination sources. The positions of these various particle sources are initially set at the particle generation locations and further adjusted based on measurement data. The primary photon source is positioned at the target point, 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 collimators (Jaws). The primary and scattered photon sources emit primary photon beams, while the electron contamination source emits primary electron beams.

[0041] Optionally, the accelerator nose model is built based on the linear accelerator (Linac) model, and configuration parameters are used to configure the accelerator nose model. Configuration parameters include the shape and position parameters of the nose collimator.

[0042] Optionally, configuration parameters are set so that the image processor starts the simulated particle source to obtain a primary particle beam, and then the accelerator head model is used to process the primary particle beam to generate the target particle beam. The shape of the target particle beam matches the shape of the tumor tissue of the target object.

[0043] In operation S130, based on the sampling data of the interaction between various biological tissues and particles, the target particle beam and digital phantom are processed using a physical interaction model to obtain the particle deposition energy distribution results characterizing the transport of the target particle beam to the digital phantom.

[0044] Optionally, eleven common tissue materials are selected to simulate all biological tissues, including air, two types of lung tissue, five types of soft tissue, and three types of bone. The sampled data characterizes the probability of each tissue interacting with different particle types. The sampled data is obtained from the cross-section database on the open-source software package Geant4 and stored in tabular form. The sampled data includes interaction sampled data and angular energy sampled data.

[0045] Optionally, the cross-section database can be loaded into the graphics processor and a dose count array can be created, and all the sampling data required for the simulation can be passed into the image processor.

[0046] Optionally, a photon and electron physical interaction model is constructed. The physical interaction model includes multiple models. After the target particle beam is transported to the digital phantom and physical interaction occurs, energy is deposited. The physical interaction model is determined according to the type of the target particle beam and the sampling data corresponding to the type. The physical interaction model is used to calculate the particle deposition energy distribution of the target particle beam on the digital phantom.

[0047] Optionally, the particle deposition energy distribution results characterize the energy deposited by the target particle beam within each digital tissue of the digital phantom. The simulated digital tissues correspond one-to-one with biological tissues.

[0048] In operation S140, the dose distribution results are obtained based on the particle deposition energy distribution results and the energy dose conversion factor.

[0049] Optionally, the energy-dose conversion factor characterizes the mapping relationship between particle deposition energy and actual dose.

[0050] Optionally, the dose distribution result corresponding to the particle deposition energy distribution result can be determined based on the dose distribution result of the energy dose conversion factor and output in binary file format.

[0051] Optionally, by employing simulated particle sources and accelerator head models to rapidly generate target particle beams, thread divergence on the graphics processor is reduced, thus improving computational efficiency. Furthermore, by utilizing sampling data and physical interaction models simulating the interactions between various biological tissues and particles, the accuracy and efficiency of dose distribution results are further improved.

[0052] Optionally, the configuration parameters also include target shape parameters. The accelerator head model includes a jaw collimator and a multi-leaf grating collimator. Based on the configuration parameters, the accelerator head model processes the primary particle beam determined based on the simulated particle source to generate the target particle beam, which includes: modulating the primary particle beam using the jaw collimator to obtain a regular-shaped particle beam; and modulating the regular-shaped particle beam based on the target shape parameters using the multi-leaf grating collimator to obtain the target particle beam.

[0053] Optionally, the target shape parameters are all parameters of a multi-leaf collimator (MLC). The target shape parameters are used to modulate the shape of the particle beam, and can be determined based on the shape of the tumor tissue.

[0054] Optionally, a regular-shaped particle beam represents a square-shaped particle beam, the shape of which matches the shape of the tumor tissue of the target object.

[0055] Optionally, a novel accelerator head model can be obtained by combining a jaw collimator and a multi-leaf grating collimator, thereby obtaining a target particle beam that matches the shape of the tumor tissue of the target object.

[0056] Figure 2 shows an example schematic diagram of a simulation system according to an embodiment of the present invention.

[0057] As shown in Figure 2, the simulation system includes a simulated particle source, a Jaws collimator, and a multi-leaf grating collimator. The Jaws collimator is a clamp collimator. A primary photon source and a scattered photon source are established at the target and homogenizer positions, respectively. An electron contamination source is established above the Jaws collimator, and the particle distribution is altered by modifying the parameters of the simulated particle source. The simulated particle source is activated to obtain a primary particle beam. This primary particle beam passes through the Jaws collimator to obtain a regularly shaped particle beam, then passes through the multi-leaf grating collimator to obtain the target particle beam. Finally, the target particle beam is transported into the digital phantom to undergo physical interaction.

[0058] Optionally, 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 the energy spectrum position parameters, energy spectrum energy range parameters, flux distribution parameters, and angular distribution parameters to obtain the primary particle beam. 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 spatial distribution 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.

[0059] Optionally, simulated particle sources are used to simulate various types of particle sources emitting different types of primary particle beams. Particle sources include primary photon sources, scattered photon sources, and electron contamination sources. Primary photon sources, scattered photon sources, and electron contamination sources have the same energy spectrum, but their flux distribution and angular distribution differ.

[0060] Optionally, the parameters of the simulated particle source are analytical source parameters, including energy spectrum position parameters, energy spectrum range parameters, flux distribution parameters, and angular distribution parameters. The range of the energy range is defined according to the energy required by the specific radiotherapy plan. For example, in 6MV external beam radiotherapy, the energy range is 0 to 6.8 MeV, with each energy range in 0.2 MeV increments. The energy spectrum position parameters characterize the positional information of the simulated particle source.

[0061] Optionally, in each target thread of the graphics processor, a simulated particle source is started based on the energy spectrum position parameters, energy spectrum range parameters, flux distribution parameters, and angular distribution parameters to obtain a primary particle beam.

[0062] Optionally, there are multiple target threads, each mapped to a set of simulated particles within an energy range. Each target thread is associated with at least one simulated particle in the set. The process of using the target threads of the graphics processor to activate 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 includes: activating the set of simulated particles within the same energy range using multiple target threads corresponding to the same energy range, based on energy spectrum position parameters, energy spectrum energy range parameters, flux distribution parameters, and angular distribution parameters, to obtain a primary particle sub-bundle associated with the energy range; and determining the primary particle beam based on the primary particle sub-bundles associated with each of the multiple energy ranges.

[0063] Optionally, the image processor's operating parameters can be set, including random number seed parameters, simulated particle number parameters, and running thread parameters. The random number seed parameter determines the random number sequence in the simulation. The running thread parameters can set the thread bundle parameter (warp) and the target thread number parameter (thread). A thread bundle is the smallest unit of execution, and a thread bundle can include 32 parallel target threads.

[0064] Optionally, the simulated particle number parameters corresponding to different energy ranges can be determined based on the respective weights of different energy ranges in the energy spectrum corresponding to the simulated particle source.

[0065] Optionally, multiple target threads in a thread bundle are mapped to a set of simulated particles within an energy range. Using multiple target threads corresponding to the same energy range, based on energy spectrum position parameters, energy spectrum energy range parameters, flux distribution parameters, and angular distribution parameters, a set of simulated particles within the same energy range is initiated. The multiple target threads are then evenly distributed based on the number of simulated particles corresponding to this energy range, resulting in a primary particle sub-bundle associated with the energy range.

[0066] Optionally, a simulated particle set within the same energy range may contain multiple simulated particles, and each target thread may be assigned to one or more simulated particles within the simulated particle set. The number of simulated particles in the primary particle sub-bundle may be less than or equal to the number of simulated particles in the simulated particle set.

[0067] Optionally, the primary particle beam can be determined based on the primary particle sub-beams associated with each of the multiple energy ranges.

[0068] Optionally, the dose distribution determination method based on the graphics processor also includes: multiple simulated particles launched each time by the same target thread have the same particle type; the graphics processor allocates video memory for multiple types of data based on the data access frequency, wherein the multiple types of data include imaging data, sampling data simulating the interaction between various biological tissues and particles, configuration parameters, particle attribute data, and simulated particle source parameters.

[0069] Optionally, particle types include photons and electrons. Each target thread within the graphics processor is responsible for simulating only photons or electrons per simulation, ensuring synchronized operation at every moment as much as possible.

[0070] Optionally, during a simulation, photons and the digital phantom may interact physically to generate reactive electrons, which need to be transported back to the digital phantom in the next unified simulation. Simulations are performed on only one type of particle at a time, then switching to simulate another type until all particles have been simulated. Photon-electron separation simulations ensure synchronous simulation of particles of the same type.

[0071] Optional, multiple data types include imaging data, sampling data simulating the interaction between various biological tissues and particles, configuration parameters, particle attribute data, analytical parameters of simulated particle sources, and operating parameters of graphics processors.

[0072] Optionally, video memory can be allocated to various types of data based on the differences in memory access performance of different memory types on the graphics processor and the memory access frequency of the data required for simulation.

[0073] Optionally, the operating parameters of the graphics processor can be set to optimize its computing performance, reduce computing time, and improve computing accuracy.

[0074] Optionally, the target particle beam includes a photon beam and an electron beam, the physical interaction model includes multiple photon interaction sub-models and electron interaction sub-models, and the sampling data includes probability data of the reaction between particles and various tissues of the simulated organism and angular energy sampling probability data; among them, based on the sampling data of the interaction between various tissues of the simulated organism and particles, the target particle beam and digital phantom are processed using the physical interaction model to obtain the particle deposition energy distribution results characterizing the transport of the target particle beam to the digital phantom, including the following operations.

[0075] Photons from the photon beam are transported to various digital structures within the digital phantom to generate reactive electrons. Based on the reaction probability data corresponding to each digital structure, a target interaction sub-model is determined from multiple photon interaction sub-models. The energy state information of the photons is processed according to the target interaction sub-model and angular energy sampling probability data to obtain the photon deposition energy distribution result. Reactive electrons are transported into the digital phantom, and their energy state information is processed according to the electron interaction sub-model to obtain the reactive electron deposition energy distribution result. Initial electrons from the electron beam are transported into the digital phantom, and their energy state information is processed according to the electron interaction sub-model to obtain the initial electron deposition energy distribution result. Based on the photon deposition energy distribution result, the reactive electron deposition energy distribution result, and the initial electron deposition energy distribution result, the particle deposition energy distribution result is obtained.

[0076] Optionally, the photon beam includes multiple photons, and the electron beam includes multiple electrons. The photon interaction model is used to calculate the particle deposition energy distribution generated by the photon beam transported to the digital phantom, and the electron interaction model is used to calculate the particle deposition energy distribution generated by the electron beam transported to the digital phantom.

[0077] Optionally, each digital tissue within the digital phantom absorbs photons from the photon beam, generating reactive electrons. Based on the probability data of the reaction interactions between photons and each digital tissue, a target interaction sub-model is determined from multiple photon interaction sub-models. For example, if the photons undergo Compton scattering, the target interaction sub-model can be a Compton scattering sub-model.

[0078] Optionally, the energy state information of photons and reactive electrons can be processed according to the target interaction model to obtain the photon deposition energy distribution results. The photon deposition energy distribution results characterize the energy distribution of photons deposited in each digital tissue of the digital phantom.

[0079] Optionally, each digital tissue within the digital phantom absorbs reactive electrons, and the energy distribution of the reactive electron deposition characterizes the energy distribution of the reactive electrons deposited within each digital tissue of the digital phantom.

[0080] Optionally, each digital tissue within the digital phantom absorbs initial electrons, and the initial electron deposition energy distribution results characterize the energy distribution of the initial electrons deposited in each digital tissue of the digital phantom.

[0081] Optionally, the particle deposition energy distribution result is obtained based on the energy distribution of photons, reactive electrons, and initial electrons deposited in each digital tissue of the digital phantom. The particle deposition energy distribution result characterizes the total energy distribution deposited in each digital tissue.

[0082] Optionally, the electron interaction model is constructed based on soft collision functions, hard collision functions, and multiple scattering functions. Specifically, the reactive electrons are transported into the digital phantom, and the energy state information of the reactive electrons is processed according to the electron interaction model to obtain the reactive electron deposition energy distribution results. This includes: processing the energy state information of the reactive electrons according to the soft collision function to obtain the first electron deposition energy distribution result; processing the energy state information of the reactive electrons according to the hard collision function to obtain the second electron deposition energy distribution result; processing the state information of the reactive electrons according to the multiple scattering function to obtain the electron motion state result; and weighting the first and second electron deposition energy distribution results to obtain the final reactive electron deposition energy distribution result.

[0083] Optionally, the energy state information of the reaction electrons can be processed using a soft collision function to obtain the energy distribution of the first electron deposition. The soft collision function employs a condensation history method, which can uniformly simulate interactions with deposition energies below a predetermined threshold. The deposition energy is approximated using continuous slowing. The predetermined threshold is the same as the electron cutoff energy.

[0084] Optionally, the energy state information of the reaction electrons can be processed using hard collision functions to obtain the energy distribution result of the second electron deposition. Hard collision functions include inelastic scattering functions and bremsstrahlung functions.

[0085] Optionally, the state information of the reaction electrons can be processed using a multiple scattering function to obtain the state results of electron motion. A multiple scattering function is constructed based on multiple scattering theory.

[0086] Optionally, the multiple photon interaction sub-models include at least one of the following: photoelectric effect interaction sub-model, Compton scattering interaction sub-model, and electron pair generation interaction sub-model.

[0087] Optional, the interaction between photons and digital structures includes three types: photoelectric effect, Compton scattering, and electron pair generation.

[0088] Optionally, in the case of photoelectric effect interaction, the target interaction model is determined to be the photoelectric effect interaction model, which models the interaction without generating reactive electrons and depositing energy locally; in the case of Compton scattering interaction, the target interaction model is determined to be the Compton scattering interaction model, which constructs the interaction model based on the Klein-Nishina cross section; in the case of electron pair generation interaction, the target interaction model is determined to be the electron pair generation interaction model, where the reactive electrons and positrons generated by the electron pair generation interaction are assumed to have the same energy and initial orientation.

[0089] Optionally, a dosage treatment plan is developed for a target patient with cervical cancer. The dosage treatment plan consists of 7 uniformly distributed beams, each with 10 control points, and a prescribed dose of 50 Gy. A simulation software package is built to simulate the particle transport process in the digital phantom. The simulation software package includes building a physical interaction model, an accelerator head model, and a simulated particle source; optimizing the graphics processor performance based on the parameters in the configuration file; and running the simulation program.

[0090] Figure 3 shows a flowchart of a simulation program according to an embodiment of the present invention.

[0091] As shown in Figure 3, the simulation program running process 300 includes steps S310 to S390.

[0092] In step S310, the program initializes and loads data.

[0093] Optionally, based on the parameter information in the configuration file, the required imaging data, sampling data simulating the interaction between various biological tissues and particles, configuration parameters, particle attribute data, and simulation particle source parameters can be read into the program.

[0094] In step S320, video memory is allocated for the multiple types of data and the multiple types of data are passed to the image processor.

[0095] In step S330, the target particle beam transport procedure is invoked.

[0096] In step S340, the photon distribution is modulated.

[0097] In step S350, photon / electron simulation.

[0098] In step S360, move to the next interaction point.

[0099] In step S370, interactions occur and energy is deposited.

[0100] In step S380, check if the required number of simulated particles has been reached. If yes, proceed to step S390; otherwise, proceed to step S360.

[0101] Optionally, the energy deposited by the particles within the digital phantom is recorded in a counter. After all particles have been simulated, the particle deposition energy distribution results are returned to the image processor.

[0102] In step 390, the dose distribution result is obtained based on the energy dose conversion factor.

[0103] Figure 4 shows an example schematic diagram of dose distribution results according to an embodiment of the present invention.

[0104] As shown in Figure 4, the dose distribution results were obtained using a graphics processor-based dose distribution determination method. A dose exceeding 50 Gy was used on tumor cells in cervical tissue, while a dose lower than the dose distribution corresponding to the tissue was used on other normal tissues to minimize damage to normal tissues.

[0105] Based on the above-described graphics processor-based dose distribution determination method, this invention also provides a graphics processor-based dose distribution determination device. The device will be described in detail below with reference to Figure 5.

[0106] Figure 5 shows a structural block diagram of a graphics processor-based dose distribution determination device according to an embodiment of the present invention.

[0107] As shown in Figure 5, the graphics processor-based dose distribution determination device 500 of this embodiment includes a construction module 510, a generation module 520, a processing module 530, and a conversion module 540.

[0108] The construction module 510 is used to construct a digital phantom based on the imaging 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 repeated here.

[0109] The generation module 520 is used to generate a target particle beam based on a primary particle beam determined by a simulated particle source, using a model of the accelerator head based on configuration parameters. The shape of the target particle beam matches the shape of the tumor tissue of the target object. The configuration parameters include the shape and position parameters of the head collimator. In one embodiment, the generation module 520 can be used to perform the operation S120 described above, which will not be repeated here.

[0110] The processing module 530 is used to process the target particle beam and digital phantom using a physical interaction model based on the sampling data of the interaction between simulated biological tissues and particles, to obtain the particle deposition energy distribution result characterizing the energy deposited by the target particle beam transported to the digital phantom. The particle deposition energy distribution result characterizes the energy deposited by the target particle beam 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.

[0111] The conversion module 540 is used to obtain the dose distribution result based on 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 repeated here.

[0112] Optionally, the generation module 520 includes a first generation submodule and a second generation submodule.

[0113] The first generation submodule is used to modulate the primary particle beam using a jaw collimator to obtain a particle beam with a regular shape.

[0114] The second generation submodule is used to modulate a regular-shaped particle beam using a multi-leaf grating collimator based on the photon reaction type parameter, the upper limit parameter of the number of photon reaction actions, and the target shape parameter, to obtain the target particle beam.

[0115] Optionally, the generation module 520 may also include a third generation submodule and a fourth generation submodule.

[0116] The third generation submodule is used to utilize multiple target threads corresponding to the same energy range, based on energy spectrum position parameters, energy spectrum energy range parameters, flux distribution parameters, and angular distribution parameters, to start a set of simulated particles in the same energy range and obtain primary particles related to the energy range.

[0117] The fourth generation submodule is used to determine the primary particle beam based on the primary particle sub-beams associated with multiple energy ranges.

[0118] Optionally, the graphics processor-based dose distribution determination device 500 may further include a first optimization module and a second optimization module.

[0119] The first optimization module is used to ensure that the particle type is the same for multiple simulation particles started each time by the same target thread.

[0120] The second optimization module is used by the graphics processor to allocate video memory for multiple types of data based on the data access frequency. These multiple types of data include imaging data, sampling data simulating the interaction between various biological tissues and particles, configuration parameters, particle attribute data, and simulated particle source parameters.

[0121] 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.

[0122] The first processing submodule is used to transport photons from the photon beam to various digital tissues within the digital phantom to generate reactive electrons.

[0123] The second processing submodule is used to determine the target interaction sub-model from multiple photon interaction sub-models based on the reaction probability data and angular energy sampling probability data corresponding to each digital organization.

[0124] The third processing submodule is used to process the energy state information of photons according to the target action sub-model to obtain the photon deposition energy distribution results.

[0125] 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 electronic interaction sub-model, and obtain the energy distribution result of the reaction electron deposition.

[0126] 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 interaction sub-model, and obtain the initial electron deposition energy distribution result.

[0127] The sixth processing submodule is used to obtain the particle deposition energy distribution results based on the photon deposition energy distribution results, the reactive electron deposition energy distribution results, and the initial electron deposition energy distribution results.

[0128] Optionally, the fourth processing submodule includes a first processing unit, a second processing unit, a third processing unit, and a fourth processing unit.

[0129] The first processing unit is used to process the energy state information of the reaction electrons according to the soft collision function to obtain the energy distribution result of the first electron deposition.

[0130] The second processing unit is used to process the energy state information of the reaction electrons according to the hard collision function to obtain the energy distribution result of the second electron deposition.

[0131] The third processing unit is used to process the state information of the reaction electrons according to the multiple scattering function to obtain the state results of the electron motion.

[0132] The fourth processing unit is used to weight the first electron deposition energy distribution result and the second electron deposition energy distribution result to obtain the reaction electron deposition energy distribution result.

[0133] Optionally, any plurality of modules among the building module 510, generating module 520, processing module 530, and conversion module 540 may be combined into one module, or any one of these modules may be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of other modules and implemented in one module. Optionally, at least one of the building module 510, generating module 520, processing module 530, and conversion module 540 may be at least partially implemented as hardware circuitry, 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-package, an application-specific integrated circuit (ASIC), or any other reasonable means of integrating or packaging circuitry, or implemented in software, hardware, or firmware, or in any appropriate combination of any of these three implementation methods. Alternatively, at least one of the building module 510, generating module 520, processing module 530, and conversion module 540 may be at least partially implemented as a computer program module, which, when run, can perform corresponding functions.

[0134] Figure 6 shows a block diagram of an electronic device suitable for implementing a graphics processor-based dose distribution determination method according to an embodiment of the present invention.

[0135] The electronic device shown in Figure 6 is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0136] As shown in FIG6, 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 portion 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 an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 601 may also include 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.

[0137] RAM 603 stores various programs and data required for the operation of electronic device 600. Processor 601, ROM 602, and RAM 603 are interconnected via bus 604. Processor 601 executes various operations of the method flow according to embodiments of the present invention by executing programs in ROM 602 and / or RAM 603. It should be noted that programs may also be stored in one or more memories other than ROM 602 and RAM 603. Processor 601 may also execute various operations of the method flow according to embodiments of the present invention by executing programs stored in one or more memories.

[0138] Optionally, the electronic device 600 may also include an input / output (I / O) interface 605, which is also connected to the bus 604. The electronic device 600 may also include one or more of the following components connected to the input / output (I / O) interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, modem, etc. The communication section 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 disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 610 as needed so that computer programs read from it can be installed into the storage section 608 as needed.

[0139] Optionally, the method flow according to embodiments of the present invention can be implemented as a computer software program. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable storage medium, the computer program containing program code for performing the method shown in the flowchart. In such embodiments, the computer program can be downloaded and installed from a network via communication section 609, and / or installed from removable medium 611. When the computer program is executed by processor 601, it performs the functions defined in the system of embodiments of the present invention. Optionally, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0140] The present invention also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the graphics processor-based dose distribution determination method according to embodiments of the present invention.

[0141] Optionally, the computer-readable storage medium can be a non-volatile computer-readable storage medium. Examples include, but are not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0142] For example, optionally, the computer-readable storage medium may include ROM 602 and / or RAM 603 and / or one or more memories other than ROM 602 and RAM 603 as described above.

[0143] Embodiments of the present invention also include a computer program product comprising a computer program containing program code for performing the methods provided in the embodiments of the present invention. When the computer program product is run on an electronic device, the program code is used to enable the electronic device to implement the graphics processor-based dose distribution determination method provided in the embodiments of the present invention.

[0144] When the computer program is executed by the processor 601, it performs the functions defined in the system / apparatus of this embodiment of the invention. Optionally, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0145] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and downloaded and installed via the communication section 609, and / or installed from the removable medium 611. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0146] Optionally, program code for executing the computer programs provided in the embodiments of the present invention can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0147] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions. Those skilled in the art will understand that the features described in the various embodiments of the present invention can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in the present invention. In particular, the features described in the various embodiments of the present invention can be combined and / or combined in various ways without departing from the spirit and teachings of the present invention. All such combinations and / or pairings fall within the scope of this invention.

[0148] The embodiments of the present invention have been described above. However, these embodiments are merely illustrative and not intended to limit the scope of the invention. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of the invention, and all such substitutions and modifications should fall within the scope of the invention.

Claims

1. A graphics processor based dose distribution determination method, characterized by, The dose distribution determination method is executed by a graphics processor, and the method includes: Constructing a digital phantom based on the imaging data of the target object; Based on configuration parameters, a target particle beam is generated by processing a primary particle beam determined based on a simulated particle source using an accelerator head model. The shape of the target particle beam matches the shape of the tumor tissue of the target object. The configuration parameters include the shape and position parameters of the head collimator. Based on the sampling data of the interaction between various biological tissues and particles, the target particle beam and the digital phantom are processed using a physical interaction model to obtain the particle deposition energy distribution result characterizing the energy deposited by the target particle beam transported to the digital phantom. The particle deposition energy distribution result characterizes the energy deposited by the target particle beam in each digital tissue of the digital phantom. The dose distribution results are obtained based on the particle deposition energy distribution results and the energy dose conversion factor.

2. The method of claim 1, wherein, 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. The step of 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 includes: The primary particle beam is modulated using the clamp collimator to obtain a particle beam with a regular shape; The target particle beam is obtained by modulating the regular-shaped particle beam based on the target shape parameters using the multi-leaf grating collimator.

3. The method of claim 2, wherein, The primary particle beam is determined based on the following operation: Using the target thread of the graphics processor, a simulated particle source is started based on the energy spectrum position parameters, energy spectrum energy range parameters, flux distribution parameters, and angular distribution parameters to obtain the primary particle beam. 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 spatial distribution to match the measured dose distribution curve. The angular distribution parameters are obtained by adjusting the parameters of the Gaussian function to match the measured dose distribution curve.

4. The method of claim 3, wherein, The target threads include multiple ones, and the multiple target threads have a mapping relationship with a set of simulated particles in an energy range. Each of the multiple target threads is associated with at least one simulated particle in the set of simulated particles. The step of using the target thread of the graphics processor to start the simulated particle source based on the energy spectrum position parameters, energy spectrum energy range parameters, flux distribution parameters, and angular distribution parameters to obtain the primary particle beam includes: Using multiple target threads corresponding to the same energy range, based on the energy spectrum position parameters, the energy spectrum energy range parameters, the flux distribution parameters, and the angular distribution parameters, a set of simulated particles corresponding to the same energy range is initiated to obtain a primary particle sub-bundle related to the energy range; and based on the primary particle sub-bundles related to each of the multiple energy ranges, the primary particle beam is determined.

5. The method of claim 3, wherein, The method further includes: The particle types of multiple simulated particles launched 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. These multiple types of data include the imaging data, the sampling data of the interaction between simulated biological tissues and particles, the configuration parameters, particle attribute data, and simulated particle source parameters.

6. The method of claim 1, wherein, The target particle beam includes a photon beam and an electron beam; the physical interaction model includes multiple photon interaction sub-models and electron interaction models; and the sampling data includes probability data of the reaction between particles and simulated biological tissues and probability data of angular energy sampling. The sampling data based on simulated interactions between biological tissues and particles, processed using a physical interaction model to obtain the target particle beam and the digital phantom, and the resulting particle deposition energy distribution characterizing the transport of the target particle beam to the digital phantom, includes: Photons in the photon beam are transported to various digital structures within the digital phantom to generate reactive electrons; Based on the reaction probability data corresponding to each of the digital organizations, a target action sub-model is determined from a plurality of photon action sub-models; The energy state information of the photons is processed based on the target interaction sub-model and the angle energy sampling probability data to obtain the photon deposition energy distribution result; The reactive electrons are transported into the digital phantom, and the energy state information of the reactive electrons is processed according to the electronic interaction model to obtain the energy distribution result of the reactive electron deposition. The initial electrons in the electron beam are transported into the digital phantom, and the energy state information of the initial electrons is processed according to the electron interaction model to obtain the initial electron deposition energy distribution result. The particle deposition energy distribution result is obtained based on the photon deposition energy distribution result, the reactive electron deposition energy distribution result, and the initial electron deposition energy distribution result.

7. The method of claim 6, wherein, The electron interaction model is constructed based on soft collision function, hard collision function and multiple scattering function; The step of transporting the reactive electrons into the digital phantom and processing the energy state information of the reactive electrons according to the electronic interaction model to obtain the reactive electron deposition energy distribution result includes: The energy state information of the reaction electrons is processed according to the soft collision function to obtain the energy distribution result of the first electron deposition. The energy state information of the reaction electrons is processed according to the hard collision function to obtain the energy distribution result of the second electron deposition. The state information of the reaction electrons is processed according to the multiple scattering function to obtain the state result of the electron motion. The first electron deposition energy distribution result and the second electron deposition energy distribution result are weighted to obtain the reaction electron deposition energy distribution result.

8. The method of claim 1, wherein, The plurality of said photon interaction sub-models include at least one of the following: Photoelectric effect interaction model, Compton scattering interaction model, and electron pair generation interaction model.

9. A graphics processor-based dose distribution determination apparatus, characterized by, The device includes: The building block is used to construct digital phantoms based on the imaging data of the target object; The generation module is used 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. The shape of the target particle beam matches the shape of the tumor tissue of the target object. The configuration parameters include the shape and position parameters of the head collimator. The processing module is used to process the target particle beam and the digital phantom based on the sampling data of the interaction between simulated biological tissues and particles, using a physical interaction model, to obtain a particle deposition energy distribution result characterizing the energy deposited by 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 in each digital tissue of the digital phantom. The conversion module is used to obtain the dose distribution result based on the particle deposition energy distribution result and the energy dose conversion factor.

10. An electronic device, comprising: include: One or more processors; Memory, used to store one or more instructions. When the one or more instructions are executed by the one or more processors, the one or more processors cause the one or more processors to implement the method of any one of claims 1 to 8.

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