Dose determination apparatus and system based on monte carlo beam modeling

Abstract

The application provides a dose determination method, device and system based on Monte Carlo beam modeling, relates to the field of physics and computers, and in particular to the application of physics and computer technology in the field of radiotherapy. The method comprises the following steps: obtaining a plurality of target data, wherein the target data is obtained based on actual measurement and is used to describe the characteristics of a particle beam transmitted by a target device; performing inverse optimization on at least part of source parameters in an original model based on the plurality of target data to obtain target parameters and a target model containing the target parameters, wherein the source parameters are input data of the original model and are used to describe the initial state of the particle beam before entering the tissue material; and determining a target dose based on the target model.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of physics and computer, in particular to the application of physics and computer technology in the field of radiotherapy, and more particularly to a dose determination device and system based on Monte Carlo beam modeling. BACKGROUND

[0002] In the field of radiotherapy, it is usually necessary to describe the transport behavior of particle beams in tissues through pencil beam algorithm or Monte Carlo algorithm before actual treatment, so as to optimize treatment plan design based on the transport behavior of particle beams, and improve treatment effect and safety.

[0003] Compared with pencil beam algorithm, Monte Carlo algorithm can accurately describe the transport behavior of particles in tissues by simulating the motion trajectory and interaction process of a large number of particles through random sampling, and has higher calculation accuracy. However, the existing Monte Carlo beam modeling often relies on theoretical models and empirical parameters, and is modeled in an artificial configuration manner. On the one hand, the modeling process is relatively complex, and a lot of time and effort are needed, on the other hand, the modeling process is easily affected by the experience and skills of the modeler, and may result in inaccurate modeling. SUMMARY

[0004] The present application provides a dose determination device and system based on Monte Carlo beam modeling, which is used to at least partially solve one of the above technical problems.

[0005] According to a first aspect of the present application, a dose determination device based on Monte Carlo beam modeling is provided, comprising: an acquisition module configured to acquire a plurality of target data, wherein the target data is based on actual measurement and is used to describe the characteristics of particle beams transmitted by a target device; an inverse optimization module configured to perform inverse optimization on at least part of source parameters in an original model based on the plurality of target data, to obtain target parameters and a target model containing the target parameters, wherein the source parameters are input data of the original model and are used to describe the initial state of particle beams before entering tissue materials; and a determination module configured to determine a target dose based on the target model.

[0006] According to an embodiment of the present application, the acquisition of the plurality of target data comprises: approximating a plurality of devices in the target device as a virtual source, and determining the position of the virtual source, wherein the virtual source is located at a downstream position of the target device, and the target device is the last device in the target device that interacts with the particle beams or a range shifter; and extracting the plurality of target data from a pre-set database based on the virtual source.

[0007] According to an embodiment of the present application, the target data comprises an integral depth dose curve in water and a target beam spot size, and the extracting the plurality of target data from the preset database based on the position of the virtual source comprises: determining a beam spot size of the particle beam after passing through the target device as the target beam spot size.

[0008] According to an embodiment of the present application, the inverse optimization of at least part of the source parameters in the original model based on the plurality of target data to obtain the target parameters and the target model comprising the target parameters comprises: setting default values for the beam spot parameters and the energy parameters in the original model respectively to obtain a plurality of groups of source parameters; simulating a beam spot dose distribution of the particle beam under the current parameters based on the plurality of source parameters, wherein the beam spot dose distribution is used to reflect an energy deposition distribution of the particle beam; extracting a plurality of simulation data from the beam spot dose distribution, wherein the simulation data correspond to the target data; and optimizing the source parameters based on a difference relationship between the simulation data and the target data until the target parameters satisfying a preset condition are obtained.

[0009] According to an embodiment of the present application, the target parameters comprise at least one of a particle beam energy, an energy spread, a position distribution standard deviation and an angle distribution standard deviation.

[0010] According to an embodiment of the present application, the optimizing the source parameters based on the difference relationship between the simulation data and the target data until the target parameters satisfying the preset condition are obtained comprises: calculating the difference relationship between the simulation data and the target data based on an optimization function; selecting an optimal parameter from the plurality of groups of source parameters based on the difference relationship, and generating a plurality of groups of second parameters based on the optimal parameter; simulating a second beam spot dose distribution based on the plurality of groups of second parameters respectively; selecting a second optimal parameter from the plurality of groups of second parameters based on a difference relationship between a second simulation parameter in the second beam spot dose distribution and the target data; and determining the second optimal parameter as the target parameter in response to the second optimal parameter satisfying the preset condition.

[0011] According to an embodiment of the present application, the simulation data comprises a simulated integral depth dose curve in water and a simulated beam spot size, and the optimizing the source parameters based on the difference relationship between the simulation data and the target data until the target parameters satisfying the preset condition are obtained comprises: determining a first difference relationship between the simulated integral depth dose curve in water and the target integral depth dose curve in water; determining a second difference relationship between the simulated beam spot size and the target beam spot size; inversely optimizing a particle beam energy and an energy spread in the source parameters based on the first difference relationship; and inversely optimizing a position distribution standard deviation and an angle distribution standard deviation in the source parameters based on the second difference relationship.

[0012] According to an embodiment of the present application, the target dose is determined based on a target model, comprising: taking the target parameters as fixed input data, simulating the particle beam in the tissue material transportation process and the dose distribution based on the target parameters and the first reference information by the target model; wherein the first reference information is used to describe the spatial position relationship between the target device and the tissue material; and converting the dose distribution output by the target model into the energy value absorbed by the tissue material based on the second reference information to obtain the target dose.

[0013] According to a second aspect of the present application, a dose determination system is provided, comprising: an interaction unit configured to capture user actions and trigger a target signal based on the user actions, wherein the target signal is used to select at least one target engine for performing a target action; a first engine based on a Monte Carlo algorithm calculation tool, the first engine being configured to determine the dose of the particle beam based on the method involved in the above device; a second engine based on a pencil beam algorithm calculation tool, the second engine being configured to determine the dose of the particle beam based on the pencil beam algorithm; and the first engine and the second engine sharing the target data of the same target device. BRIEF DESCRIPTION OF DRAWINGS

[0014] The above and other objects, features and advantages of the present application will become more apparent from the following description when taken in conjunction with the accompanying drawings, in which:

[0015] Figure 1 A flowchart of a dose determination method based on Monte Carlo beam modeling according to an embodiment of the present application is schematically shown;

[0016] Figure 2 A flowchart of extracting target data from a preset database according to an embodiment of the present application is schematically shown;

[0017] Figure 3 A flowchart of performing inverse optimization on at least part of the source parameters in the original model based on a plurality of target data to obtain the target parameters and the target model containing the target parameters according to an embodiment of the present application is schematically shown;

[0018] Figure 4 A plurality of IDD characteristic parameters in the target IDD curve according to an embodiment of the present application are schematically output;

[0019] Figure 5 A flowchart of optimizing the initial parameters based on the difference relationship between the simulation data and the measured data until the target parameters satisfying the preset condition are obtained according to an embodiment of the present application is schematically shown;

[0020] Figure 6 A schematic diagram of optimizing the initial parameters based on the difference relationship between the simulation data and the measured data until the target parameters satisfying the preset condition are obtained according to an embodiment of the present application is schematically shown.

[0021] Figure 7 A comparison of the integral depth dose curve generated by automatic Monte Carlo modeling based on an embodiment of the present application and measurement is schematically shown;

[0022] Figure 8 A comparison of the beam spot size generated by automatic Monte Carlo modeling based on an embodiment of the present application and measurement is schematically shown;

[0023] Figure 9 A comparison of the beam spot size through RS generated by automatic Monte Carlo modeling and measurement is schematically shown;

[0024] Figure 10 A comparison of the measured beam spot size in air and automatic Monte Carlo modeling at different depths from the isocenter is schematically shown;

[0025] Figure 11 A block diagram of a dose determination device based on Monte Carlo beam modeling according to an embodiment of the present application is schematically shown;

[0026] Figure 12 A block diagram of a dose determination system according to an embodiment of the present application is schematically shown;

[0027] Figure 13 A block diagram of an electronic device of a dose determination method based on Monte Carlo beam modeling according to an embodiment of the present application is schematically shown. DETAILED DESCRIPTION

[0028] To make the objects, technical solutions and advantages of the present application clearer, further detailed description will be made to the present application with reference to the embodiments and the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.

[0029] The terms used herein are only used to describe specific embodiments, and are not intended to limit the present application. The terms "comprise", "contain" and the like used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0030] In the present application, unless specifically defined otherwise and limited, the terms "mount", "connected", "connection", "fixed", and the like, should be construed as being broadly understood, for example, can be fixed connection, can also be detachable connection, or integrated; can be mechanical connection, or electrical connection or can communicate with each other; can be direct connection, or indirect connection through intermediate medium, can be the internal communication of two elements or the interaction relationship between two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0031] In the description of the present application, it should be understood that the orientation or positional relationship indicated by the terms "longitudinal", "length", "circumferential", "front", "back", "left", "right", "top", "bottom", "inner", "outer" and the like is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the subsystems or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application.

[0032] Throughout the drawings, the same elements are denoted by the same or similar reference numerals. When it may cause confusion in understanding the present application, the conventional structure or configuration will be omitted. And the shape, size, positional relationship of each component in the drawing does not reflect the true size, proportion and actual positional relationship. In addition, any reference symbol located between parentheses should not be construed as a limitation.

[0033] Similarly, in order to simplify the present application and help understand one or more of the various disclosed aspects, in the above description of the exemplary embodiments of the present application, various features of the present application are sometimes grouped together in a single embodiment, figure or description thereof. The description of the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In the description, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0034] In addition, the terms "first", "second" are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more features. In the description of the present application, the meaning of "multiple" is at least two, for example, two, three, etc., unless otherwise specifically limited.

[0035] The embodiment of the present application provides a dose determination method, device and system based on Monte Carlo beam modeling.

[0036] The existing Monte Carlo beam modeling needs to be completed manually. On the one hand, the aggregate model needs to be reconstructed in a third-party software, and the parameters of the source need to be established in other scientific research software or external scripts. After the external establishment is completed, the parameters of the source are manually configured to the Monte Carlo software for calculation, and the process is relatively cumbersome. On the other hand, the number of source parameters required for Monte Carlo calculation is large, and manual modeling has problems such as low efficiency, poor repeatability, inaccuracy and the like.

[0037] The embodiment of the present application provides a dose determination method based on Monte Carlo beam modeling, comprising: obtaining a plurality of target data, wherein the target data is based on actual measurement and is used to describe the characteristics of the particle beam transmitted by the target device; performing reverse optimization on at least part of the source parameters in the original model based on the plurality of target data to obtain target parameters and a target model containing the target parameters, wherein the source parameters are input data of the original model and are used to describe the initial state of the particle beam before entering the tissue material; determining a target dose based on the target model.

[0038] The embodiment of the present application obtains the target data measured actually to optimize the model parameters reversely, so that the target model can more accurately reflect the characteristics of the particle beam transmitted by the target device. This helps to more accurately simulate the transport process of the particle beam in the tissue, and then the target dose determined based on the target model is more accurate, and the effect and safety of the radiotherapy are improved. The method of reverse optimization is used to automatically determine the target parameters and the target model, which reduces the subjective errors and uncertainties that may be introduced in the manual modeling process. At the same time, the efficiency and automation degree of modeling are improved, and the dependence on professional modeling personnel is reduced. Moreover, the scheme can reverse optimize the original model according to the actual measurement data of different target devices to obtain a target model suitable for a specific device. It can be applied to various radiotherapy devices and has strong universality and adaptability.

[0039] Figure 1 A flowchart of the dose determination method based on Monte Carlo beam modeling according to the embodiment of the present application is schematically shown.

[0040] As Figure 1 shown, the dose determination method based on Monte Carlo beam modeling of the embodiment comprises operations S110-S130.

[0041] In operation S110, a plurality of target data is extracted, wherein the target data is based on actual measurement and is used to describe the characteristics of the particle beam transmitted by the target device.

[0042] In some embodiments, the preset database comprises actual measurement data reflecting certain characteristics of the particle beam during the particle beam treatment process based on the at least one device, which can include, for example, dose-related data, spatial distribution-related data, device performance-related data, and transmission characteristic-related data. The actual measurement data can reflect the actual situation during the particle beam treatment process and the characteristics of the particle beam. For example, the characteristics of energy deposition of the particle beam during interaction with tissue material and the initial characteristics of the particle beam after being emitted by the treatment device (such as the degree of divergence of the particle beam, the focusing effect, etc.).

[0043] For example, the target data can include, for example, the water-integral depth dose curve and the air spot size of the target device actually measured at different energies during historical treatment. The water-integral depth dose curve can reflect the energy deposition characteristics of the particle beam at different depths in water and can be used to verify the accuracy of the energy deposition of the model. The spot size can reflect the lateral diffusion characteristics of the particle beam and can be used to verify the spot shape of the model. The water-integral depth dose curve and the spot size are related to the energy of the particle beam, and obtaining the target data includes obtaining the water-integral depth dose curve and the spot size of the target device at different energies.

[0044] In operation S120, at least part of the source parameters in the original model are inversely optimized based on the plurality of target data to obtain target parameters and a target model comprising the target parameters, wherein the source parameters are input data of the original model and are used to describe the initial state of the particle beam before entering the tissue material.

[0045] In some embodiments, the source parameters refer to the initial state parameters of the particle beam before entering the treatment site of the patient, and the original model can be a model used to simulate the transport process of the particle beam in the tissue material and the dose distribution. The original model can simulate the motion trajectory of the particle beam based on the Monte Carlo algorithm.

[0046] For example, the original model can be a model that has not been trained and only contains the basic physical laws and model structure of the particle beam transmission. It can also be a model that has been trained and verified to a certain extent, has been put into practical application, but there are differences in historical application for the current quality plan, for example, a model in which the description of the transmission process of the particle beam in the air is accurate, and only the source parameters related to the current treatment plan need to be adjusted.

[0047] By inversely optimizing the source parameters based on the actual measurement data (i.e., target data), the accuracy of the source parameters can be effectively improved, and the particle beam characteristics and the actual state of the device can be more accurately reflected. For example, based on the target data corresponding to different energies, the source parameters are inversely optimized to obtain the most matched source parameters (i.e., target parameters) corresponding to each energy. Moreover, since the treatment positions of different patients are different, directly based on the source parameters set by artificial cannot consider the individual differences of patients, which is easy to lead to inaccurate dose distribution simulation. By combining the related measurement data in the historical treatment process of the patient, the source parameters can be more accurately adjusted to ensure the accuracy of the dose distribution simulation and the treatment effect. For another example, different treatment plans (such as treatment position, depth, shape, etc.) have different requirements for dose distribution, and accordingly, the requirements for the particle beam are also different. Based on the actual measurement data in the historical treatment plan, the source parameters are optimized, which can make the source parameters more suitable for the different dose distribution requirements of the treatment plan

[0048] The original model may include a plurality of source parameters as input data of the model for Monte Carlo simulation. For example, the source parameters may include original parameters such as particle beam, initial position distribution function type of particles, energy, and energy dispersion. The specific values of some source parameters do not affect the physical characteristics of the dose distribution of the Monte Carlo simulation, or these source parameters can be indirectly controlled by other means (such as head design). For these source parameters, the default values can be directly set according to experience. Another part of the source parameters will directly affect the accuracy and safety of the dose distribution of the dose distribution. For such source parameters, the present application proposes to inversely optimize these parameters based on the actual measurement data to accurately calibrate the parameters in the model, thereby improving the accuracy of the model.

[0049] The determination method of the source parameters of the original model and the values of the source parameters is shown in Table 1.

[0050] Table 1: Determination method of source parameters and values

[0051] Parameter Value Number of particles Default 900,000 Type of initial position distribution function of particles Default Gaussian Type of distribution function of angle between particle and incident direction Default Gaussian Energy Determined based on inverse optimization Energy spread (standard deviation of Gaussian distribution of energy) Determined based on inverse optimization Initial coordinates X, Y Default (0, 0) Standard deviation of position distribution X, Y Determined based on inverse optimization Standard deviation of distribution of angle with incident direction Determined based on inverse optimization Incident direction X, Y, Z Default (1, 0, 0), (0, 1, 0)

[0052] As shown in Table 1, the parameters (i.e., target parameters) that need to be inversely optimized based on the target data include energy, energy dispersion, position distribution standard deviation, and standard deviation of the angle distribution with the incident direction.

[0053] In some embodiments, the target data is used as an optimization target, and the simulation result is matched with the target data by adjusting parameters. For example, the inverse optimization process can include two steps: IDD matching and beam spot size matching. By simulating IDD curves under different energies and energy spreads, the IDD curves are compared with the corresponding measured data (i.e., target data) to optimize the energy parameters and energy spread parameters. For example, if the Bragg peak width of the simulated IDD is too large, the energy spread is reduced. In addition, by simulating the beam spot size under different position distribution standard deviations and angle distribution standard deviations, the simulated beam spot size is compared with the measured data to optimize the position and angle parameters. For example, if the beam spot size is too large, the position distribution standard deviation or the angle distribution standard deviation is reduced.

[0054] Based on the target data, at least part of the source parameters in the original model are inversely optimized to obtain target parameters suitable for the current treatment and a target model. The target parameters are used as input data of the target model for Monte Carlo simulation, which can effectively improve the accuracy of the simulation result and meet the dose distribution requirements of different treatment plans. For example, the source parameters of the original model can be inversely optimized according to the actual measured data of different treatment plans to ensure that the dose distribution is consistent with the treatment target.

[0055] In operation S130, the target dose is determined based on the output result of the target model.

[0056] In some embodiments, the target parameters are used as fixed input data of the model, and the target model simulates the particle beam transport process in the tissue material and the dose distribution based on the target parameters and first reference information; wherein the first reference information is used to describe the spatial position relationship between the target device and the tissue material. The dose distribution output by the target model is converted into an energy value absorbed by the tissue material based on the second reference information to obtain the target dose.

[0057] In some embodiments, the first reference information can be the actual measured head geometry data, which can include head geometry data for describing the calibration geometry parameters, including: isocenter, source-axis distance, set of range shifter, etc.

[0058] By reading the calibration geometry parameters of the header file, a geometry model consistent with the clinical calibration is constructed, wherein the geometry model is a model used to describe the physical space structure in Monte Carlo simulation, including the treatment head, the tissue material geometry shape, the spatial position relationship, etc. The geometry model provides a physical space framework for the target model to simulate the particle beam transport and energy deposition, so as to ensure that the simulation environment of the target model is consistent with the clinical calibration or the actual treatment.

[0059] Exemplarily, the target model is based on the geometry model, and performs MC simulation on each scanning point based on the target parameters, records the particle transport and energy deposition process, and calculates the MC simulation dose of each scanning point based on the simulation result, i.e., the dose distribution.

[0060] After obtaining the dose distribution, the target model simulation dose distribution result is associated with the dose unit (Gy) and the monitor unit (MU) through the second reference information, the MC simulation particle number is converted into the dose unit, and the target dose is obtained. The dose unit (Gy) is used to measure the degree of absorption of ionizing radiation energy by a substance, and the monitor unit (MU) is a parameter used to measure the particle beam output in a radiotherapy device. The second reference information may be, for example, a proportionality coefficient in a dose calibration file, which is determined by absolute dose calibration in actual measurement data. Through the proportionality coefficient in the dose calibration file, the particle beam output related to MU in the simulation is converted into an actual dose value (Gy). Then, according to the dose deposition distribution obtained by simulation, the energy deposition value in each voxel is converted into the corresponding dose value, so as to obtain the target dose distribution result (i.e., the target dose) in the unit of Gy.

[0061] The dose determination method provided by the embodiment of the present application performs inverse optimization on the source parameters through the actually measured target data, so as to ensure that the model output is highly consistent with the real physical process, and improve the accuracy of the relative dose distribution calculation of the target model. Compared with the prior art which may depend on empirical parameters or theoretical assumptions, the present application automatically drives the optimization of the source parameters through the actually measured data, can effectively reduce the subjectivity and uncertainty of artificial intervention, improve the modeling efficiency of the model and the accuracy of the Monte Carlo simulation, and provide accurate dose support for particle beam therapy, so as to effectively improve the accuracy of dose calculation.

[0062] Figure 2 A flowchart for obtaining a plurality of target data according to the embodiment of the present application is schematically shown.

[0063] As shown in Figure 2 The embodiment of obtaining a plurality of target data includes operation S210 to operation S220.

[0064] In operation S210, a plurality of devices in the target device are approximated as a virtual source, and the position of the virtual source is determined, wherein the virtual source is located at a downstream position of the target device, and the target device is the last device in the target device that interacts with the particle beam or the range shifter.

[0065] In operation S220, a plurality of target data are extracted from a preset database based on the position of the virtual source.

[0066] In some embodiments, the selection of the virtual source placement position is highly related to the modeling process and the computational efficiency in Monte Carlo modeling, which is an abstract concept used in Monte Carlo modeling to simplify the complex accelerator or treatment head geometry. The virtual source is used as an equivalent particle source emission source, which can be used to simulate the final characteristics of the particle beam after passing through all the accelerator devices by parameterized modeling. The target device can be, for example, an accelerator head, which contains a large number of complex devices. If each device is modeled, the computational complexity and time will be significantly increased.

[0067] In some embodiments, in order to simplify the Monte Carlo modeling process and avoid simulating all the devices in the head, the present application proposes to place the virtual source at a downstream position of the target device, i.e. in the modeling process, only the particles emitted from the virtual source need to be tracked, and the transport process of the particles in the accelerator does not need to be simulated, which effectively reduces the amount of calculation while keeping the dose calculation accuracy unchanged.

[0068] In some embodiments, the target device is the head part of the radiotherapy device used to generate or shape the particle beam. The design and configuration of the head will vary depending on the type of treatment and the model of the device. The head includes devices such as primary collimator, scattering foil, multi-leaf collimator, range shifter (RS), etc.

[0069] The selection of the target device can be determined based on the composition of the head. For example, the last device in the target device that interacts with the particle beam is selected as the target device, and the virtual source is placed downstream of all the devices in the target device. If the head includes a range shifter, the range shifter is selected as the target device, and the virtual source is placed downstream of the range shifter. The range shifter changes the range of the particle beam by introducing material, thereby adjusting the position of the Bragg peak and changing the lateral distribution of the particle beam, thereby affecting the dose distribution downstream.

[0070] Placing the virtual source downstream of the range shifter can ensure that the parameters of the virtual source include all the effects of the range shifter on the particle beam, thereby more accurately simulating the dose distribution of the particle beam in the tissue material and improving the dose calculation accuracy.

[0071] In some embodiments, the pre-set database contains measured data such as beam spot size, divergence angle, water integral depth dose curve, head geometry (isocenter, source axis distance, range shifter geometry, etc.) under different device configurations (such as different energies, different RS settings), based on the setting of the virtual source, the measured data corresponding to the virtual source is extracted from the database, thereby quickly determining the target data corresponding to the current virtual source. For example, the target data can include the water integral depth dose curve and the target beam spot size, which is the beam spot size of the particle beam after passing through the target device. For example, the target device is the RS, and the target beam spot is stored as the air beam spot size data after passing through the RS.

[0072] In some embodiments, the target database can be queried according to the configuration of the target device (e.g., energy, RS type, etc.) to extract the target data (IDD curve and beam spot size) corresponding to the current virtual source as the input parameters of the virtual source.

[0073] In some embodiments, the IDD curve is used to describe the change of the dose of the particle beam in the water phantom with the depth, and the target beam spot size is the transverse size of the particle beam downstream of the virtual source. It should be noted that if the accelerator configuration (e.g., energy, RS type) changes or the treatment plan changes, the database needs to be queried again to update the virtual source parameters (i.e., target data).

[0074] Figure 3 A flowchart of the process of inversely optimizing at least part of the source parameters in the original model based on multiple target data to obtain the target parameters and the target model containing the target parameters according to an embodiment of the present application is schematically shown.

[0075] As shown in Figure 3 The process of inversely optimizing at least part of the source parameters in the original model based on multiple target data to obtain the target parameters and the target model containing the target parameters according to the embodiment includes operations S310-S340.

[0076] In operation S310, default values are respectively set for the beam spot parameters and the energy parameters in the original model to obtain multiple groups of source parameters.

[0077] In some embodiments, the beam spot parameters can include the position distribution standard deviation and the angle distribution standard deviation, and the energy parameters can include the energy of the particle beam and the energy spread (the width of the energy distribution). Default values are respectively set for the beam spot parameters and the energy parameters in the original model, and multiple groups of initial parameters (i.e., source parameters) are generated by combining different default values or fine-tuning around the default values.

[0078] In operation S320, the beam spot dose distribution of the particle beam under the current parameters is simulated based on the multiple initial parameters, wherein the beam spot dose distribution is used to reflect the energy deposition distribution of the particle beam.

[0079] In some embodiments, the transmission process of the particle beam in the water phantom is simulated based on the multiple groups of initial parameters respectively, the energy deposition distribution is recorded, and the beam spot dose distribution is generated. The beam spot dose distribution can reflect the energy deposition distribution of the particle beam.

[0080] In operation S330, multiple simulation data are extracted from the beam spot dose distribution, wherein the simulation data correspond to the measurement data.

[0081] In some embodiments, the simulation data may, for example, include a simulated IDD curve and a simulated beam spot size.

[0082] In operation S340, the initial parameters are optimized based on the difference relationship between the simulation data and the measurement data until the target parameters satisfying the preset condition are obtained.

[0083] In some embodiments, the extracted simulation IDD curve and the simulation beam spot size are compared with the IDD curve and the beam spot size in the target measurement data, respectively obtaining the difference relationship between the simulation data and the measurement data. An iterative optimization algorithm is used to adjust the initial parameters to minimize the difference between the simulation data and the measurement data. The adjustment direction of the initial parameters can be determined based on the difference relationship, for example, if the Bragg peak position of the simulation IDD is shallower than the measured value, the particle beam energy is increased. If the simulation beam spot size is larger than the measured value, the position distribution standard deviation or the angle distribution standard deviation is reduced. The new parameter set is used to re-simulate the beam spot dose distribution, the simulation data is extracted from the newly simulated beam spot dose distribution, and the simulation data is compared with the measurement data. If the difference value between the simulation data and the measurement data satisfies the preset condition (i.e. the difference value is less than the preset threshold), the optimization is stopped, and the multiple parameters in the parameter set are determined as the target parameters. If the difference value between the simulation data and the measurement data does not satisfy the preset condition, the optimization is continued until the preset condition is satisfied. In some embodiments, the initial parameters are optimized based on the difference relationship between the simulation data and the measurement data, and the method further comprises: determining the difference relationship between the simulation data and the measurement data.

[0084] In an embodiment of the present application, determining the difference relationship between the simulation data and the measurement data can include operations S341a-S342a. In operation S341a, a first difference relationship between the simulation water depth integral dose curve and the target water depth integral dose curve is determined. In operation S342a, the position distribution standard deviation and the angle distribution standard deviation in the initial parameters are inversely optimized based on the first difference relationship.

[0085] In some embodiments, multiple IDD feature parameters are extracted from the target IDD curve to obtain multiple target IDD feature parameters S mes .

[0086] Figure 4 The multiple IDD feature parameters in the target IDD curve of the embodiment of the present application are schematically output.

[0087] As Figure 4 shown, for example, the depth at 80% of the Bragg peak rising region, the depth at 80% of the Bragg peak falling region, the depth at 20% of the Bragg peak falling region, the depth of the Bragg peak half-height width, the depth of the peak value, the depth at 50% of the Bragg peak rising region can be extracted to obtain the target IDD feature parameter S i,meas , S R80_p , S R80_D, S R20_D , S FWHM , S peak , S R50_P . Extract the feature parameters corresponding to the target IDD feature from the simulated IDD curve, denoted as the simulated IDD feature parameter S i,calc .

[0088] Based on the optimization function, the difference values between the simulated IDD feature parameters and the target IDD feature parameters are calculated, and based on the optimization function and the plurality of difference values, the first difference relationship between the simulated IDD curve and the target IDD curve is determined, wherein the first optimization function can be represented as:

[0089] .

[0090] The energy and energy dispersion of the particle beam directly affect the shape and feature parameters of the IDD curve, and by minimizing the optimization parameters, the particle beam energy and energy dispersion in the initial parameters are optimized.

[0091] In another embodiment of the present application, determining the difference relationship between the simulation data and the measurement data can include operations S341b~operation S342b.

[0092] In operation S341b, a second difference relationship between the simulated beam spot size and the target beam spot size is determined. In operation S342b, the particle beam energy and energy dispersion in the initial parameters are inversely optimized based on the second difference relationship.

[0093] In some embodiments, the target beam spot size at different distances in different directions is obtained respectively. For example, the measured beam spot size in the X and Y directions at 5cm, 10cm, 15cm, 20cm, and 25cm from the center is obtained.

[0094] The beam spot size of the particle beam in the X direction and the Y direction at different distances under the current parameters is calculated respectively, and for each measurement position i, the sum of the variances of the measured values and the simulated values in the X direction and the Y direction is calculated, and the second difference relationship between the simulated beam spot size and the target beam spot size is determined based on the second optimization function. Wherein the second optimization function can be represented as:

[0095] .

[0096] By minimizing the value of the second optimization function F itness2 , the position distribution standard deviation and the angle distribution standard deviation can be gradually adjusted, so that the simulated beam spot size simulated based on the parameters is closer to the actual measured target beam spot size.

[0097] Figure 5A flowchart is shown schematically to illustrate the optimization of initial parameters based on the difference between simulated data and measured data until the target parameters satisfying the preset condition are obtained according to an embodiment of the present application. Figure 6 A schematic diagram is shown to illustrate the principle of the optimization of initial parameters based on the difference between simulated data and measured data until the target parameters satisfying the preset condition are obtained according to an embodiment of the present application.

[0098] As shown in the figure, the optimization of initial parameters based on the difference between simulated data and measured data until the target parameters satisfying the preset condition are obtained in the embodiment includes operations S510-S540. Figure 5

[0099] In operation S510, the difference between simulated data and measured data is calculated based on an optimization function.

[0100] In some embodiments, a plurality of groups of initial parameters are provided, each group of initial parameters including particle beam energy, energy dispersion, position distribution standard deviation and angle distribution standard deviation, and the values of at least some parameters in different groups of initial parameters are different.

[0101] For example, an initial population can be created by randomly generating a certain number of individuals within a reasonable search range, and for each individual in the initial population, a fitness value is calculated, which is used to measure the degree of excellence of the individual in solving the problem, and the higher the fitness value, the closer the individual is to the optimal solution of the problem. The fitness of the individuals in the population is adjusted to change the numerical value or distribution range of the fitness value of each individual, improve the fairness and effectiveness of the subsequent operations such as selecting parent individuals, and the like. See Figure 6 , wherein the population is a group of initial parameters in the present application, and the individuals in the population are initial parameters.

[0102] For each group of initial parameters, the dose deposition distribution corresponding to the initial parameter group is calculated using Monte Carlo simulation, and the simulated data (i.e., simulated IDD curve and simulated beam spot size) is extracted based on the dose deposition distribution. The difference between the simulated data and the measured data is quantified using the optimization function to obtain the difference between the simulated data and the measured data, i.e., the optimization function value. For the calculation of the difference between the simulated function and the measured data based on the optimization function, please refer to operations S341a and S341b, which will not be repeated here.

[0103] In operation S520, the optimal parameter group is selected from the plurality of groups of initial parameters based on the difference, and a plurality of groups of second parameters are generated based on the optimal parameter group.

[0104] In some embodiments, the optimal parameter group is selected from the plurality of groups of initial parameters according to the optimization function value, i.e., the parameter group with the smallest optimization function value (corresponding to Figure 6 ​the optimal parameter set, and generate a plurality of second parameter sets based on the optimal parameter set. For example, the second parameter sets are generated by exchanging at least one parameter value in the optimal parameter set with the corresponding parameter value in any initial parameter set. Alternatively, the second parameter sets are generated by randomly modifying at least part of the parameters in the optimal parameter set. The second parameter sets can be generated by both of the above ways respectively. Figure 6 the process of selecting the parent, generating the cross and mutation offspring).

[0105] At operation S530, a second beam spot dose distribution is simulated based on the plurality of second parameter sets respectively.

[0106] At operation S540, a second optimal parameter is selected from the plurality of second parameter sets based on the difference between the second simulation parameter in the second beam spot dose distribution and the measured parameter.

[0107] At operation S550, the second optimal parameter is determined as the target parameter in response to the second optimal parameter satisfying a preset condition.

[0108] In some embodiments, for each second parameter set, a beam spot dose distribution corresponding to the second parameter set is simulated, and an optimization function value corresponding to each second parameter set is calculated according to the beam spot dose distribution, and a second optimal parameter is selected from the plurality of second parameter sets according to the optimization function value.

[0109] It is determined whether the second optimal parameter satisfies a preset condition (e.g., the optimization function value is less than a specific threshold value), and if the second optimal parameter satisfies the preset condition, the second optimal parameter is determined as the target parameter, and if the second optimal parameter does not satisfy the preset condition, operations S420 to S440 are repeated (i.e., a third parameter is generated based on the second optimal parameter, and the iteration optimization is continued) until a parameter satisfying the preset condition is obtained as the target parameter.

[0110] Figure 7~Figure 10 A comparison diagram of particle beam related data and measured data based on Monte Carlo beam modeling simulation according to an embodiment of the present application is schematically shown.

[0111] Figure 7 A comparison diagram of integral depth dose curve generated by automatic Monte Carlo modeling and measured based on an embodiment of the present application is schematically shown. Figure 8 A comparison diagram of beam spot size generated by automatic Monte Carlo modeling and measured based on an embodiment of the present application is schematically shown. Figure 9 A comparison diagram of beam spot size passing through RS generated by automatic Monte Carlo modeling and measured is schematically shown. Figure 10 A comparison example of measured beam spot size in air and Monte Carlo fitting at different depths away from the isocenter is schematically shown.

[0112] Referring to Figure 7~Figure 10The particle beam motion track modeled by the Monte Carlo beam modeling according to the embodiment of the present application has high similarity with the actually measured data in both the lateral direction and the depth direction, that is, the precision of the particle beam simulation can be effectively improved based on the method of the embodiment of the present application, thereby improving the accuracy of the dose determination. Based on the particle dose determination method, the present application further provides a dose determination device based on Monte Carlo beam modeling. The following will be described in combination with the accompanying drawings Figure 11 The device is described in detail.

[0113] Figure 11 The structure block diagram of the dose determination device based on Monte Carlo beam modeling according to the embodiment of the present application is schematically shown.

[0114] As shown in Figure 11 The particle dose determination device 1000 of the embodiment includes an acquisition module 1010, a reverse optimization module 1020 and a determination module 1030.

[0115] The acquisition module 1010 is configured to acquire a plurality of target data, wherein the target data is based on actually measured data for describing the characteristics of the particle beam transmitted by the target device. In an embodiment, the acquisition module 1010 can be configured to perform the operation S110 described above, and details are not described herein.

[0116] The reverse optimization module 1020 is configured to perform reverse optimization on at least part of the source parameters in the original model based on the plurality of target data to obtain the target parameters and the target model containing the target parameters, wherein the source parameters are input data of the original model, used to describe the initial state of the particle beam before entering the tissue material. In an embodiment, the reverse optimization module 1020 can be configured to perform the operation S120 described above, and details are not described herein.

[0117] The determination module 1030 is configured to determine the target dose based on the target model. In an embodiment, the determination module 1030 can be configured to perform the operation S130 described above, and details are not described herein.

[0118] According to an embodiment of the present application, any of the modules 1010, 1020, 1030 can be combined in one module, or any of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of the modules can be combined with at least part of the functions of the other modules, and implemented in one module. According to an embodiment of the present application, at least one of the modules 1010, 1020, 1030 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 chip, a system on board, a system on package, an application specific integrated circuit (ASIC), or any other reasonable manner of integrating or packaging a circuit, etc. in hardware or firmware, or implemented in any one of software, hardware and firmware, or in a proper combination of any of them. Alternatively, at least one of the modules 1010, 1020, 1030 can be at least partially implemented as a computer program module that can perform the corresponding functions when the computer program module is run.

[0119] Figure 12 A structural block diagram of a dose determination system according to an embodiment of the present application is schematically shown.

[0120] As shown in Figure 12 the dose determination apparatus 1100 of the embodiment includes an interaction unit 1110, a first engine 1120, and a second engine 1130.

[0121] The interaction unit 1110 is configured to capture a user action and trigger a target signal based on the user action, where the target signal is used to select at least one target engine for performing a target action.

[0122] The first engine 1120 is a calculation tool based on a Monte Carlo algorithm, and is configured to determine a dose of a particle beam based on the method of the above embodiment.

[0123] The second engine 1130 is a calculation tool based on a pencil beam algorithm, and is configured to determine a dose of a particle beam based on the pencil beam algorithm.

[0124] In some embodiments, the first engine 1120 and the second engine 1130 share a preset database, and a user can select to determine a dose of a particle beam based on the first engine 1120 or the second engine 1130 by himself / herself based on the interaction unit 1110. For example, the user can select a target option on the interaction unit 1110 to realize Monte Carlo beam flow automatic modeling as shown in the embodiment of the present application, and determine a dose based on the generated modeling.

[0125] Figure 13A block diagram of an electronic device according to an embodiment of the present application is shown schematically.

[0126] As shown in Figure 13 The electronic device 1200 according to an embodiment of the present application includes a processor 1201, which can perform various appropriate actions and processes according to programs stored in a read only memory (ROM) 1202 or loaded from a storage section 1208 into a random access memory (RAM) 1203. The processor 1201 can include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor, and / or a related chip set, and / or a dedicated microprocessor (e.g., an application specific integrated circuit (ASIC)), and so on. The processor 1201 can also include an on-board memory for cache use. The processor 1201 can include a single processing unit or multiple processing units to perform the various actions of the method processes according to embodiments of the present application.

[0127] In the RAM 1203, various programs and data required for the operation of the electronic device 1200 are stored. The processor 1201, the ROM 1202, and the RAM 1203 are connected to each other via a bus 1204. The processor 1201 performs various operations of the method processes according to embodiments of the present application by executing the programs in the ROM 1202 and / or the RAM 1203. Note that the programs can also be stored in one or more memories other than the ROM 1202 and the RAM 1203. The processor 1201 can also perform various operations of the method processes according to embodiments of the present application by executing the programs stored in the one or more memories.

[0128] According to an embodiment of the present application, the electronic device 1200 can also include an input / output (I / O) interface 1205, which is also connected to the bus 1204. The electronic device 1200 can also include one or more of the following components connected to the input / output (I / O) interface 1205: an input section 1206 including a keyboard, a mouse, etc.; an output section 1207 including a display such as a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 1208 including a hard disk, etc.; and a communication section 1209 including a network interface card such as a LAN card, a modem, etc. The communication section 1209 performs communication processing via a network such as the Internet. A drive 1210 is also connected to the input / output (I / O) interface 1205 as necessary. A removable recording medium 1211 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is attached to the drive 1210 as necessary, so that a computer program read therefrom is installed into the storage section 1208 as necessary.

[0129] The application further provides a computer readable storage medium, which can be included in the device / apparatus / system described in the above embodiments, or can exist independently without being assembled into the device / apparatus / system. The computer readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of the application.

[0130] According to the embodiments of the application, the computer readable storage medium can be a non-volatile computer readable storage medium, which can include, but is not limited to, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this application, a computer readable storage medium can be any tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. For example, in the embodiments of the application, a computer readable storage medium can include one or more of the ROM 1202 and / or the RAM 1203 described above, and / or one or more memories other than the ROM 1202 and the RAM 1203.

[0131] The embodiments of the application also include a computer program product, which includes a computer program containing program codes for executing the method shown in the flow chart. When the computer program product is run in a computer system, the program codes are used to make the computer system implement the dose determination method based on Monte Carlo beam modeling provided by the embodiments of the application.

[0132] The above functions defined in the system / apparatus of the embodiments of the application are performed when the computer program is executed by the processor 1201. According to the embodiments of the application, the system, apparatus, module, unit, etc. described above can be implemented by computer program modules.

[0133] In one embodiment, the computer program can rely on a tangible storage medium such as an optical storage device, a magnetic storage device, etc. In another embodiment, the computer program can also be transmitted, distributed, and downloaded in the form of a signal on a network medium, and be downloaded and installed through the communication part 1209, and / or installed from the detachable medium 1211. The program codes contained in the computer program can be transmitted by any suitable network medium, including but not limited to wireless, wired, etc., or any suitable combination of the foregoing.

[0134] In such embodiments, the computer program can be downloaded and installed from the network through the communication part 1209, and / or installed from the detachable medium 1211. When the computer program is executed by the processor 1201, the above-described functions defined in the system of the embodiments of the present application are executed. According to the embodiments of the present application, the system, the apparatus, the device, the module, the unit, and the like described above can be realized by the computer program modules.

[0135] According to the embodiments of the present application, the program code for executing the computer program provided by the embodiments of the present application can be written in any combination of one or more programming languages, and specifically, these computer programs can be implemented using high-level procedural and / or object-oriented programming language, and / or assembly / machine language. The programming language includes, but is not limited to, such as Java, C++, python, "C" language, or similar programming language. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In the case involving a remote computing device, the remote computing device can be connected to the user computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, connected through the Internet by using an Internet service provider).

[0136] The flowcharts and the block diagrams in the drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flowcharts or the block diagrams can represent a module, a segment, or a portion of code, which contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the drawings. For example, two blocks noted in succession can in fact be executed substantially concurrently or in the reverse order, depending on the functionality involved. It should also be noted that each block in the flowcharts or the block diagrams, and combinations of blocks in the flowcharts or the block diagrams, can be implemented by dedicated hardware-based systems that perform the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0137] Those skilled in the art can understand that the features described in various embodiments of the present application can be combined and / or integrated in various combinations and / or integrations, even if such combinations or integrations are not explicitly described in the present application. In particular, the features described in various embodiments of the present application can be combined and / or integrated in various combinations and / or integrations without departing from the spirit and teachings of the present application. All such combinations and / or integrations fall within the scope of the present application.

[0138] The above described embodiments of the application have been described. However, these embodiments are merely meant to be illustrative of the present application and are not meant to limit the scope of the present application. Although each of the above described embodiments has been described separately, this does not mean that measures from the various embodiments cannot be used advantageously in combination. Numerous alternatives and modifications will be apparent to those skilled in the art without departing from the scope of the present application, which is defined in the following claims.

Claims

1. A device for dose determination based on Monte Carlo beam modeling, characterized in that, The method comprises the following steps: an acquisition module is configured to acquire a plurality of target data, wherein the target data is obtained based on actual measurement and is used to describe characteristics of a particle beam transmitted by a target device; a reverse optimization module is configured to perform reverse optimization on at least part of source parameters in an original model based on the plurality of target data, to obtain target parameters and a target model containing the target parameters, wherein the source parameters are input data of the original model and are used to describe an initial state of the particle beam before entering a tissue material; a determination module is configured to determine a target dose based on the target model.

2. The dose setting device according to claim 1, characterized in that The acquisition of the plurality of target data comprises the following steps: a plurality of devices in the target device are approximated as a virtual source, and a position of the virtual source is determined, wherein the virtual source is located at a downstream position of a target device, and the target device is the last device in the target device that interacts with the particle beam or a range shifter; a plurality of target data is extracted from a preset database based on the position of the virtual source.

3. The dose setting device according to claim 2, characterized in that The target data comprises an integral depth dose curve in water and a target beam spot size, and the extraction of the plurality of target data from the preset database based on the position of the virtual source comprises the following steps: a beam spot size of the particle beam after passing through the target device is determined as the target beam spot size.

4. The dose setting device according to claim 1, characterized in that The reverse optimization on at least part of the source parameters in the original model based on the plurality of target data to obtain the target parameters and the target model containing the target parameters comprises the following steps: default values are respectively set for beam spot parameters and energy parameters in the original model, to obtain a plurality of groups of source parameters; a beam spot dose distribution of the particle beam under current parameters is simulated based on the plurality of groups of source parameters, wherein the beam spot dose distribution is used to reflect an energy deposition distribution of the particle beam; a plurality of simulation data is extracted from the beam spot dose distribution, wherein the simulation data corresponds to the target data; the source parameters are optimized based on a difference relationship between the simulation data and the target data, until the target parameters satisfying a preset condition are obtained.

5. The dose determination apparatus according to claim 1, wherein the target parameters comprise at least one of a particle beam energy, an energy spread, a position distribution standard deviation, and an angle distribution standard deviation.

6. The dose setting device according to claim 4, characterized in that The optimization of the source parameters based on the difference relationship between the simulation data and the target data, until the target parameters satisfying the preset condition are obtained, comprises the following steps: a difference relationship between the simulation data and the target data is calculated based on an optimization function; an optimal parameter is selected from the plurality of groups of source parameters based on the difference relationship, and a plurality of groups of second parameters are generated based on the optimal parameter; second beam spot dose distributions are simulated based on the plurality of groups of second parameters respectively; a second optimal parameter is selected from the plurality of groups of second parameters based on a difference relationship between second simulation parameters in the second beam spot dose distributions and the target data; in response to the second optimal parameter satisfying the preset condition, the second optimal parameter is determined as the target parameter.

7. The dose setting device according to claim 4, characterized in that The simulation data includes a simulated water depth dose curve and a simulated beam spot size, and the source parameters are optimized based on a difference relationship between the simulation data and the target data until target parameters meeting preset conditions are obtained, including: determining a first difference relationship between the simulated water depth dose curve and the target water depth dose curve; determining a second difference relationship between the simulated beam spot size and the target beam spot size; optimizing particle beam energy and energy dispersion in the source parameters in reverse based on the first difference relationship; optimizing position distribution standard deviation and angle distribution standard deviation in the source parameters in reverse based on the second difference relationship.

8. The dose determination apparatus according to claim 1, wherein the target dose is determined based on the target model, including: using the target parameters as fixed input data, simulating the transport process of the particle beam in the tissue material and the dose distribution based on the target parameters and first reference information by the target model; wherein the first reference information is used to describe the spatial position relationship between the target device and the tissue material; converting the dose distribution output by the target model into an energy value absorbed by the tissue material based on second reference information to obtain the target dose.

9. A dose determination system, including: an interaction unit configured to capture user actions and trigger a target signal based on the user actions, wherein the target signal is used to select at least one target engine for performing a target action; a first engine based on a Monte Carlo algorithm calculation tool, the first engine being configured to determine the dose of the particle beam based on the method involved in any one of the apparatuses of claims 1-8; a second engine based on a pencil beam algorithm calculation tool, the second engine being configured to determine the dose of the particle beam based on the pencil beam algorithm; the first engine and the second engine share the target data of the same target device.

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