Particle dose determination device

Abstract

The present disclosure provides a particle dose determination method and device, electronic equipment and storage medium, relates to physics and computer field, and particularly relates to application of physics and computer technology in the field of radiotherapy. The particle dose determination method comprises the following steps: respectively guiding particle beams to move through M groups of scanning parameters to obtain a plurality of lateral field dose distribution data; wherein each group of scanning parameters corresponds to a plurality of lateral field dose distribution data, M is a positive integer; a plurality of first field dose distribution data corresponding to each group of scanning parameters is calculated according to the plurality of lateral field dose distribution data; wherein the dimension of the first field dose distribution data is higher than that of the lateral field dose distribution data; a target model for determining a field center dose distribution is obtained based on the scanning parameters and the first field dose distribution data; target scanning parameters are input into the target model, and a target field center dose distribution output by the target model is used to determine a delivery dose.

Description

Technical Field

[0001] This disclosure relates to the fields of physics and computer science, specifically to the application of physics and computer technology in the field of radiomedicine, and more specifically to a particle dose determination device. Background Technology

[0002] In the field of radiotherapy, uniform scanning technology primarily uses magnets to control the beam's scanning motion, ensuring a uniform dose distribution within the target area. To guarantee the accuracy of the beam movement and the effectiveness of the treatment, a mathematical model is needed to predict the dose distribution within the target area before actual treatment. The treatment plan (such as beam parameters and delivery dose) is then adjusted based on this dose distribution to ensure that the dose is concentrated in the target area during treatment, avoiding damage to surrounding tissues.

[0003] Existing traditional modeling methods typically use Gaussian integrals to generate sigmoid functions to describe the radiation field dose distribution. During the modeling process, these methods usually assume that dose fluctuations in the central region are negligible, meaning the dose distribution at the center of the radiation field is flat. However, in reality, the dose at the center of the radiation field may fluctuate significantly (usually exceeding 5%) due to factors such as scanning speed variations. Therefore, the dose distribution at the center of the radiation field described by traditional modeling methods may deviate from reality, thus affecting the final treatment outcome. Summary of the Invention

[0004] This disclosure provides a particle dose determination method, apparatus, electronic device, and storage medium for at least partially solving one of the aforementioned technical problems.

[0005] According to a first aspect of this disclosure, a particle dose determination method is provided, comprising: guiding particle beam movement through M sets of scanning parameters to obtain multiple transverse field dose distribution data; wherein each set of scanning parameters corresponds to multiple transverse field dose distribution data, and M is a positive integer; calculating multiple first field dose distribution data corresponding to each set of scanning parameters based on the multiple transverse field dose distribution data; wherein the dimension of the first field dose distribution data is higher than the dimension of the transverse field dose distribution data; obtaining a target model for determining the field center dose distribution based on the scanning parameters and the first field dose distribution data; inputting the target scanning parameters into the target model, and determining the delivery dose based on the target field center dose distribution output by the target model.

[0006] According to embodiments of this disclosure, the particle beam is guided to move using M sets of scanning parameters to obtain N transverse field dose distribution data, including: guiding the particle beam to move within a preset depth range using M sets of scanning parameters, randomly measuring the transverse field dose distribution of the particle beam at N depth positions to obtain M*N transverse field dose distribution data; wherein each set of scanning parameters corresponds to N transverse field dose distributions, where N is a positive integer.

[0007] According to embodiments of this disclosure, multiple first field dose distribution data corresponding to each set of scanning parameters are calculated based on multiple lateral field dose distribution data, including: acquiring a depth dose curve of the particle beam; wherein the depth dose curve is used to describe the energy deposition of the particle beam in the depth direction; and calculating the depth dose curve with N lateral field dose distribution data under each set of scanning parameters to obtain X first field dose distribution data corresponding to multiple sets of scanning parameters, wherein X is a positive integer and X > N.

[0008] According to embodiments of this disclosure, the depth dose curve is calculated with N transverse field dose distribution data under each set of scanning parameters to obtain X first field dose distribution data corresponding to multiple sets of scanning parameters. This includes performing the following operations on the transverse field dose distribution data under each set of scanning parameters: multiplying the depth dose curve and multiple transverse field dose distribution data to obtain multiple first field dose distribution data at specific depths; and determining the first field dose distribution data at other depths besides the specific depths based on the first field dose distribution data at the specific depths.

[0009] According to an embodiment of this disclosure, a target model for determining the dose distribution at the center of the radiation field is obtained based on scanning parameters and first field dose distribution data, including: training an initial model based on scanning parameters and first field dose distribution data to obtain a first model, wherein the first model is used to predict the dose mask corresponding to different scanning parameters; and superimposing the first model with an objective function to obtain a target model for determining the dose distribution at the center of the radiation field, wherein the objective function is used to determine the transverse field dose distribution under ideal conditions.

[0010] According to an embodiment of this disclosure, training an initial model based on scanning parameters and first field dose distribution data to obtain a first model includes: determining multiple sets of scanning parameters as input data for the initial model, and determining multiple first field dose distribution data as output data for the initial model; using each distribution feature in the first field dose distribution data as prompt information; and training the initial model based on the prompt information, input data, and output data to obtain the first model.

[0011] According to embodiments of this disclosure, the scanning parameters include at least the energy of the particle beam, device performance parameters, and the range covered by the particle beam; at least some of the scanning parameters differ in each set.

[0012] According to a second aspect of this disclosure, a particle dose determination device is provided, comprising: a first obtaining module, configured to guide particle beam movement based on M sets of scanning parameters to obtain multiple transverse field dose distribution data; wherein each set of scanning parameters corresponds to multiple transverse field dose distribution data, and M is a positive integer; a calculation module, configured to calculate multiple first field dose distribution data corresponding to each set of scanning parameters based on the multiple transverse field dose distribution data; wherein the dimension of the first field dose distribution data is higher than the dimension of the transverse field dose distribution data; a second obtaining module, configured to obtain a target model for determining the field center dose distribution based on the scanning parameters and the first field dose distribution data; and a determining module, configured to input the target scanning parameters into the target model and determine the delivery dose based on the target field center dose distribution output by the target model. Attached Figure Description

[0013] The above and other objects, features, and advantages of this disclosure will become clearer from the following description of embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0014] Figure 1 The diagram schematically illustrates a system architecture of a particle dose determination method, apparatus, device, medium, and program product according to embodiments of the present disclosure.

[0015] Figure 2 A flowchart illustrating a particle dose determination method according to an embodiment of the present disclosure is shown schematically.

[0016] Figure 3 The schematic illustration shows the lateral field dose distribution obtained under different scanning parameters according to embodiments of the present disclosure;

[0017] Figure 4 The schematic illustration shows multiple transverse field dose distributions obtained under the same scanning parameters according to an embodiment of this disclosure;

[0018] Figure 5 This schematically illustrates a flowchart of calculating multiple first field dose distribution data corresponding to each set of scanning parameters based on multiple transverse field dose distribution data according to an embodiment of the present disclosure.

[0019] Figure 6 This schematically illustrates a flowchart of calculating the depth dose curve with N transverse field dose distribution data under each set of scanning parameters, according to an embodiment of the present disclosure, to obtain X first field dose distribution data corresponding to multiple sets of scanning parameters.

[0020] Figure 7 This schematically illustrates a flowchart of obtaining a target model for determining the dose distribution at the center of the radiation field based on scanning parameters and first field dose distribution data, according to an embodiment of the present disclosure.

[0021] Figure 8 This schematically illustrates a flowchart of training an initial model based on the scanning parameters and first field dose distribution data according to an embodiment of the present disclosure to obtain a first model;

[0022] Figure 9 The illustration shows a schematic diagram of obtaining a target model for determining the dose distribution at the center of the radiation field based on scanning parameters and first field dose distribution data according to an embodiment of the present disclosure.

[0023] Figure 10 The diagram schematically illustrates a comparison between the dose calculation results corresponding to the target field center distribution determined by the target model obtained according to the embodiments of this disclosure and the dose results of the conventional model.

[0024] Figure 11 A schematic block diagram of a particle dose determination apparatus according to an embodiment of the present disclosure is shown.

[0025] Figure 12 A block diagram schematically illustrates an electronic device suitable for implementing a particle dose determination method according to an embodiment of the present disclosure. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this disclosure clearer, the following detailed description is provided in conjunction with specific embodiments and accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without inventive effort are within the scope of protection of this disclosure.

[0027] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of a feature, step, operation, and / or component, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0028] In this disclosure, unless otherwise expressly specified and limited, the terms "installation," "connection," "linking," "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection, an electrical connection, or a connection that allows communication between them; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this disclosure according to the specific circumstances.

[0029] In the description of this disclosure, it should be understood that the terms "longitudinal", "length", "circumferential", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this disclosure and simplifying the description, and do not indicate or imply that the subsystem or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this disclosure.

[0030] Throughout the accompanying drawings, identical elements are represented by the same or similar reference numerals. Conventional structures or configurations have been omitted where they may cause confusion in understanding this disclosure. Furthermore, the shapes, dimensions, and positional relationships of the components in the drawings do not reflect actual size, scale, or actual positional relationships. Additionally, any reference symbols enclosed in parentheses should not be construed as limiting.

[0031] Similarly, to simplify this disclosure and aid in understanding one or more of the various aspects of the disclosure, in the above description of exemplary embodiments of the present disclosure, various features of the present disclosure are sometimes grouped together in a single embodiment, figure, or description thereof. The use of terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refers to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which is included in at least one embodiment or example of the present disclosure. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0032] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this disclosure, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0033] Embodiments of this disclosure provide a particle dose determination method, comprising: guiding the movement of a particle beam through multiple sets of scanning parameters to obtain multiple transverse field dose distribution data of the particle beam; wherein each set of scanning parameters corresponds to multiple transverse field dose distribution data; constructing an association model based on the multiple transverse field dose distribution data and the multiple sets of scanning parameters; wherein the association model is used to determine the fluctuation of the particle beam under specific conditions; determining the target field center dose distribution of the particle beam based on the association model; simulating the spatial distribution of the particle beam in the tissue to be tested based on the target field center dose distribution, and determining the particle delivery dose according to the spatial distribution.

[0034] Figure 1 A schematic diagram illustrating the system architecture of a particle dose determination method, apparatus, device, medium, and program product according to embodiments of this disclosure is provided. Figure 1 As shown, application scenario 100 according to this embodiment may include terminal devices 101, 102, and 103, network 104, and server 105. Network 104 is used as a medium to provide a communication link between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables, etc.

[0035] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as physics simulation applications, shopping applications, web browser applications, search applications, instant messaging tools, email clients, social media platform software, etc. (for example only).

[0036] Terminal devices 101, 102, and 103 can be various electronic devices with displays and web browsing capabilities, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0037] Server 105 can be a server that provides various services, such as a backend management server that supports websites browsed by users using terminal devices 101, 102, and 103 (for example only). The backend management server can analyze and process data such as received user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal devices.

[0038] It should be noted that the particle dose determination method provided in this embodiment can generally be executed by server 105. Correspondingly, the particle dose determination device provided in this embodiment can generally be located in server 105. The particle dose determination method provided in this embodiment can also be executed by a server or server cluster that is different from server 105 and capable of communicating with terminal devices 101, 102, 103 and / or server 105. Correspondingly, the particle dose determination device provided in this embodiment can also be located in a server or server cluster that is different from server 105 and capable of communicating with terminal devices 101, 102, 103 and / or server 105.

[0039] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0040] The following will be based on Figure 1 The described scene, through Figures 2-10 The particle dose determination method of the disclosed embodiments will be described in detail.

[0041] Figure 2 A flowchart illustrating a particle dose determination method according to an embodiment of the present disclosure is shown schematically.

[0042] like Figure 2 As shown, the particle dose determination method of this embodiment includes operations S210 to S240.

[0043] In operation S210, the particle beam is guided to move by M sets of scanning parameters to obtain multiple transverse field dose distribution data; where each set of scanning parameters corresponds to multiple transverse field dose distribution data, and M is a positive integer.

[0044] In some embodiments, a magnetic control system guides the particle beam to move along a predetermined path within the target area, and measures the lateral distribution of the particle beam within the target area in real time to obtain multiple lateral field dose distribution data. The lateral field dose distribution data is used to describe the dose distribution in a plane (lateral section) perpendicular to the beam direction, such as how the dose diffuses or attenuates from the center outward in a certain lateral section. The uniformity of the lateral field dose distribution directly affects the accuracy of dose coverage within the target area.

[0045] The scanning parameter combination includes multiple different field parameters set according to treatment needs, and at least one field parameter is different in different scanning parameter combinations. Field parameters may include, for example, beam energy (which determines the penetration depth), device (such as ridge filter) performance parameters, particle beam coverage, scanning magnet speed (which affects residence time), etc.

[0046] For example, the transverse dose distribution can be measured in the transverse plane under each set of scanning parameters using tools such as a matrix ionization chamber and radiation film. The measured transverse dose distribution can effectively record the dynamic fluctuations in actual operation of the equipment, including real effects such as uneven residence time caused by magnet speed limitation and periodic fluctuations caused by scanning waveform distortion.

[0047] In operation S220, the first field dose distribution data corresponding to each set of scanning parameters is determined based on multiple transverse field dose distribution data.

[0048] In some embodiments, the lateral field dose distribution data is two-dimensional data. Besides scanning parameters affecting the lateral field dose distribution, changes in penetration depth also influence it. The corresponding first field dose distribution data is calculated from the lateral field dose distribution data, wherein the first field dose distribution data is three-dimensional data.

[0049] In the initial stage of particle beam penetration (such as skin or superficial tissue), the energy is high, and lateral scattering (caused by nuclear scattering and multiple Coulomb scattering) is small, resulting in a concentrated lateral dose distribution with sharp edges. As the particle beam gradually loses energy, the lateral scattering effect intensifies, and the beam broadening in the lateral direction (called "beam broadening") becomes increasingly apparent, leading to a wider lateral dose distribution with blurred edges. When the particle beam energy is almost exhausted, lateral scattering reaches its maximum, resulting in a significant broadening of the lateral dose distribution.

[0050] Therefore, in order to more accurately reflect the deposition process of the beam in the target tissue, this disclosure proposes to convert the measured dose distribution data of multiple lateral fields into three-dimensional first field dose distribution data. The first field dose distribution data integrates the dose information of depth and lateral (X / Y), which can more realistically reflect the deposition process of the particle beam in the target tissue, thereby improving the accuracy of subsequent particle dose determination.

[0051] In operation S230, a target model for determining particle dose is obtained based on the scanning parameters and the dose distribution data of the first field.

[0052] In some embodiments, the target model is used to determine the dose distribution at the center of the radiation field under target conditions.

[0053] The target model can be obtained, for example, by superimposing the first model with a traditional model. The first model is trained based on scanning parameters and dose distribution data from the first radiation field. By analyzing the correlation between the scanning parameters and the actual dose distribution of the radiation field, a two-dimensional correction coefficient matrix (i.e., a dose mask) is generated. This two-dimensional correction coefficient matrix can reflect the dose fluctuations caused by the actual scanning parameters. The correction coefficient at each location can, for example, represent the ratio of the actual dose to the theoretical value at that location.

[0054] The first model can predict dose fluctuations under target scan parameters based on input scan parameters and output a dose mask suitable for the current scan parameters. The dose mask reflects the impact of scan parameters on dose distribution. For example, the dose mask can be a two-dimensional correction coefficient matrix composed of multiple two-dimensional correction coefficients. Each two-dimensional correction coefficient reflects the difference between the actual lateral field dose distribution and the ideal field dose distribution at a specific depth. Different scan parameters correspond to different masks to ensure the target model can adapt to parameter changes. For example, during high-speed scanning, the mask automatically incorporates corrections for edge dose attenuation caused by shortened residence time.

[0055] The uniformity of the lateral radiation distribution directly affects the accuracy of target area dose coverage. If the model is inaccurate, it may result in insufficient dose at the target area edge or excessive dose to surrounding healthy tissue. The target model generates dose masks corresponding to different scanning parameters based on the actual measured lateral radiation field dose distribution data, which can effectively replace the fixed assumptions of traditional models (such as Gaussian distribution) and enable the model to dynamically adapt to parameter changes.

[0056] Traditional models are based on the conventional sigmoid function, which outputs the ideal dose distribution at the center of the radiation field. These models assume a flat dose (without fluctuations) at the center and that the dose at the edges decays at a fixed gradient (e.g., Gaussian diffusion), failing to reflect the dynamic errors that occur during actual scanning.

[0057] By using the dose mask output by the first model, the ideal field center dose distribution output by the traditional S-shaped function can be dynamically corrected, resulting in a target field center dose distribution that better reflects the actual situation.

[0058] In operation S240, the target scanning parameters are input into the target model, and the delivery dose is determined based on the target field center dose distribution output by the target model.

[0059] In some embodiments, target scanning parameters are input into the target model to obtain the target field center dose distribution output by the target model, and the beam parameters and delivery dose are determined based on the target field center dose distribution.

[0060] The dose distribution at the center of the target radiation field can be visually displayed to show the dose coverage in the target area, providing a basis for clinical decision-making. Beam parameters and delivered dose are adjusted based on the dose at the center of the target radiation field to ensure the final dose is achieved.

[0061] In this process, the dose distribution at the center of the target field is the benchmark for determining beam parameters and delivered dose. If the dose distribution at the center of the field cannot accurately reflect the actual dose deposition (e.g., the planned value deviates too much from the actual value), it will lead to inaccurate final delivered dose, resulting in range deviation, destruction of dose uniformity, and other issues.

[0062] The particle dose delivery method provided in this disclosure constructs a target model for determining the dose distribution at the center of the radiation field based on actually measured transverse radiation field dose distribution data. This target model, by superimposing a first model trained on the measured transverse radiation field dose data with a conventional model, results in a final dose distribution at the center of the radiation field that is dynamically corrected based on a dose mask and includes dynamic fluctuations. Compared to the dose distribution at the center of the radiation field determined by the conventional model, the dose distribution at the center of the radiation field determined by the target model of this disclosure more closely reflects the dose fluctuations during actual treatment, thereby effectively improving the accuracy of model parameter and dose delivery calculations and enhancing treatment efficacy.

[0063] In some embodiments, the multiple transverse field dose distribution data obtained in this disclosure, in addition to the transverse field dose distribution data corresponding to multiple sets of scanning parameters, also include multiple transverse field dose distribution parameters at different depths for each set of scanning parameters.

[0064] Figure 3 The diagram illustrates the transverse field dose distribution obtained under different scanning parameters according to embodiments of the present disclosure. Figure 4 The illustration schematically shows multiple transverse field dose distributions obtained under the same scanning parameters according to an embodiment of this disclosure.

[0065] In some embodiments, scanning parameters may include, for example, beam energy, scanning velocity, beam size, etc., which determine the beam's penetration capability and lateral diffusion range. When charged particles (such as carbon ions) enter the target tissue, they interact with the atomic nuclei and electrons in the tissue, resulting in energy loss (ionization loss) and path deflection (scattering). The scattering effect increases as the particle energy decreases, and the energy loss determines the position of the Bragg peak. Therefore, as depth increases, particle energy decreases, which may lead to increased lateral scattering, thereby affecting the lateral field dose distribution.

[0066] like Figure 3 As shown, the embodiments of this disclosure guide the movement of the particle beam through multiple sets of scanning parameters to obtain multiple transverse field dose distribution data, including: guiding the movement of the particle beam based on M different sets of scanning parameters to obtain transverse field dose distribution data corresponding to M sets of scanning parameters.

[0067] See Figure 3 , Figure 3 a represents the lateral dose distribution under scanning parameters of 400 MeV / u energy, RF100 ridge filter, and F20 field size; Figure 3 b represents the lateral dose distribution under scanning parameters of 400 MeV / u energy, RF90 ridge filter, and F10 field size; Figure 3b represents the lateral dose distribution under scanning parameters of energy 330 MeV / u, RF40 ridge filter, and field size F20.

[0068] In some embodiments, operation S210, which guides the particle beam to move through multiple sets of scanning parameters to obtain multiple transverse field dose distribution data of the particle beam, further includes: guiding the particle beam to move within a preset depth range through M sets of scanning parameters, randomly measuring the transverse field dose distribution of the particle beam at N depth positions to obtain M*N transverse field dose distribution data; wherein each set of scanning parameters corresponds to N transverse field dose distributions, where N is a positive integer.

[0069] like Figure 4 As shown, for each set of scanning parameters, the dose distribution in the transverse plane is measured at multiple random depths to obtain the transverse radiation field dose distribution at N different depths corresponding to that set of scanning parameters.

[0070] Figure 5 The flowchart illustrates a process for calculating multiple first field dose distribution data corresponding to each set of scanning parameters based on multiple transverse field dose distribution data according to an embodiment of the present disclosure.

[0071] like Figure 5 As shown, this embodiment calculates multiple first field dose distribution data corresponding to each set of scanning parameters based on multiple transverse field dose distribution data, including operations S510 to S520.

[0072] In operation S510, the depth dose curve of the particle beam is obtained; wherein, the depth dose curve is used to describe the energy deposition of the particle beam in the depth direction.

[0073] In some embodiments, the depth-dose profile is used to describe the change in dose along the beam direction and can determine the location of maximum energy deposition and peak width of particles at a specific depth. For example, when the depth is z, the dose value of the particle beam is Ddepth(z).

[0074] In operation S520, the depth dose curve is calculated with the dose distribution data of N transverse fields under each set of scanning parameters to obtain X first field dose distribution data corresponding to multiple sets of scanning parameters, where X is a positive integer and X>N.

[0075] In some embodiments, the lateral field dose distribution is used to describe the dose diffusion pattern in the lateral plane (xy plane) at a certain depth z, and the lateral dose distribution is a two-dimensional distribution.

[0076] By multiplying the depth dose curve layer by layer with the transverse field dose distribution data, the three-dimensional first field dose distribution is obtained. This first field dose distribution data reflects the dose diffusion pattern at a specific location within the three-dimensional space of the particle beam. To further enrich the first field dose distribution data, in addition to calculating the corresponding first field dose distribution data based on the actual measured transverse field dose distribution data, the difference between multiple calculated field dose distribution data can be performed to obtain even more first field dose distribution data. This reduces the measurement workload while enriching the first field dose distribution data, thereby improving the accuracy of the target model.

[0077] In some embodiments, the correlation model can determine the fluctuation of the particle beam and the correction coefficient based on the input scanning parameters.

[0078] For example, the correction coefficients corresponding to each spatial position under each set of scanning parameters can be determined by the ratio of the measured dose (i.e., the dose distribution data of the second field) to the theoretical dose, and the dose mask corresponding to the set of scanning parameters can be obtained based on the correction coefficients. The dose mask can be, for example, a two-dimensional correction coefficient matrix, where each element in the dose mask is the correction coefficient for the corresponding spatial position.

[0079] The correction factor can be used to adjust the dose distribution at the center of the field calculated under ideal conditions, so that the fluctuation of the dose distribution at the center of the field under ideal conditions is more in line with the actual situation, thereby improving the accuracy of the dose distribution of the target field.

[0080] Figure 6 The flowchart illustrates an embodiment of the present disclosure, showing how a depth dose curve is calculated using N transverse field dose distribution data under each set of scanning parameters to obtain X first field dose distribution data corresponding to multiple sets of scanning parameters.

[0081] like Figure 6 As shown, in this embodiment, the depth dose curve is calculated with the dose distribution data of N transverse fields under each set of scanning parameters to obtain X first field dose distribution data corresponding to multiple sets of scanning parameters. Operations S610 to S620 are performed on each set of scanning parameters to obtain X first field dose distribution data corresponding to that set of scanning parameters.

[0082] In operation S610, the depth dose curve and multiple transverse field dose distribution data are multiplied to obtain the first field dose distribution data at multiple specific depths.

[0083] In operation S620, based on the first field dose distribution data at a specific depth, the first field dose distribution data at other depths besides the specific depth are determined.

[0084] In some embodiments, for each set of reference data at a measured depth z1, the detected transverse field dose distribution data is multiplied by the dose value at the corresponding depth in the depth dose curve to obtain a three-dimensional dose depth slice corresponding to that depth, i.e., the first field dose distribution data.

[0085] For any unmeasured depth z2 in the set of reference data, determine the upper and lower adjacent measurement points of depth z2, and determine the first field dose distribution data corresponding to the unmeasured depth z2 based on the first field dose distribution of the upper and lower adjacent measurement points.

[0086] For example, the measured depth z a The corresponding first field dose distribution data D a (x,y,z) is:

[0087] D a (x,y,z)=D a (z)×L a (x,y,z a )

[0088] Among them, D a (z) represents the depth z in the depth dose curve. a The corresponding dose value, L a (x,y,z) represents the depth z a Measured dose distribution data for the transverse radiation field.

[0089] For an unmeasured depth zc, determine the measurement points that are adjacent to depth zc both vertically and horizontally as zc. a and z b (Assume z) c =18cm, between z a =15cm and z b =between 18cm), then z c The corresponding first field dose distribution data D c (x,y,z) is:

[0090]

[0091] in, For difference weights,

[0092] This embodiment combines the depth-dose curve with the measured lateral field dose distribution through product calculation to generate discrete depth three-dimensional dose data (i.e., first field dose distribution data). Then, it fills in the dose distribution at unmeasured depths using interpolation data, effectively reducing experimental complexity while maintaining accuracy. On one hand, by combining the measured depth with the lateral dose characteristics, the true field dose distribution is restored. On the other hand, by using interpolation methods to fill in the unmeasured depth distribution, different treatment situations can be flexibly addressed, achieving dynamic adaptation to different scanning parameters and equipment. Based on this, the amount of data can be expanded, data errors reduced, and uncertainties in the data improved, thereby optimizing the correlation between two data points in a data pair and improving the quality of the target model.

[0093] Figure 7 The flowchart illustrates a process for obtaining a target model for determining the dose distribution at the center of the radiation field based on scanning parameters and first field dose distribution data, according to an embodiment of the present disclosure.

[0094] like Figure 7 As shown, this embodiment obtains a target model for determining the dose distribution at the center of the radiation field based on scanning parameters and first field dose distribution data, including operations S710 to S720.

[0095] In operation S710, an initial model is trained based on the scanning parameters and the dose distribution data of the first field to obtain the first model, which is used to predict the dose mask corresponding to different scanning parameters.

[0096] In operation S720, the first model is superimposed with the objective function to obtain the target model for determining the particle dose, wherein the objective function is used to determine the dose distribution of the lateral field under ideal conditions.

[0097] In some embodiments, the target model includes a first module and a second module, wherein the first module is determined based on operation S610. The first module is used to determine the dose mask corresponding to the input target scanning parameters. The dose mask can be, for example, a two-dimensional correction coefficient matrix composed of multiple two-dimensional correction coefficients, wherein each two-dimensional correction coefficient reflects the difference between the actual lateral field dose distribution and the ideal field dose distribution at a certain depth.

[0098] The second module is used to determine the ideal lateral field dose distribution based on the objective function. The objective function can be, for example, a traditional sigmoid function.

[0099] For example, when the target scanning parameters are input into the target model, the ideal transverse field dose distribution is first determined according to the objective function in the second module. Then, the first module determines the dose template according to the target scanning parameters. The dose distribution of the ideal transverse field in the second module is dynamically adjusted by the correction coefficient in the dose mask to obtain the non-flat field center dose distribution (i.e., the target field center dose distribution).

[0100] The objective function, as a function that has been validated over a long period, can accurately describe the basic shape of the lateral dose distribution under ideal conditions (such as edge gradient attenuation). It has high computational efficiency and strong stability. The target model obtained by superimposing the first model with the objective function can utilize the existing model framework to introduce dynamic corrections, thereby improving the flexibility of the target model and the accuracy of the final output dose distribution at the center of the target field. By using the dose template output by the first model, the dose fluctuations caused by scanning parameters are quantified, directly correcting the idealized assumptions of the traditional function and improving the accuracy of the dose distribution at the center of the target field.

[0101] Compared to directly generating a new model, superimposing the objective function and the first model allows for independent optimization and flexible adaptation of the objective model. For example, the dose mask focuses on correcting dynamic errors (such as magnet velocity fluctuations), while the traditional model handles the basic morphology. After decoupling, the first model can be optimized independently (e.g., updating the mask does not require modifying the sigmoid function). Furthermore, specific masks can be dynamically generated for different devices or parameter combinations, eliminating the need to rebuild a complete model for each scene. This effectively improves the flexibility of the objective model, allowing for dynamic generation of specific templates for different devices or parameter combinations without requiring a complete model reconstruction for each scene.

[0102] Figure 8 The flowchart illustrating the process of training an initial model based on the scanning parameters and first field dose distribution data according to an embodiment of the present disclosure to obtain a first model is shown.

[0103] like Figure 8 As shown, in this embodiment, the initial model is trained based on the scanning parameters and the dose distribution data of the first field to obtain the first model, which includes operations S810 to S830.

[0104] In operation S810, multiple sets of scanning parameters are determined as input data for the initial model, and multiple first field dose distribution data are determined as output data for the initial model.

[0105] In operation S820, the distribution characteristics of each dose distribution data in the first radiation field are used as prompt information.

[0106] When operating the S830, an initial model is trained based on prompts, input data, and output data to obtain the first model.

[0107] In some embodiments, multiple sets of scanning parameters are used as input data for the model, and the calculated first field dose distribution is used as output data for the model. The initial model is trained so that the trained first model can calculate the corresponding particle beam correction coefficient based on the target scanning parameters.

[0108] The first model is obtained by training an initial model based on scanning parameters and first field dose distribution data. The initial model can be, for example, a convolutional neural network model or a fully connected model capable of processing spatial data. Taking a fully connected model as an example, the correlation between scanning parameters and the three-dimensional field dose distribution can be extracted using fully connected operations. This allows the trained first model to determine the dose mask under target conditions based on target scanning parameters (such as energy, waveform, field size, etc.). The dose mask reflects the influence of scanning parameters on the dose distribution. The dose mask can be, for example, a two-dimensional correction coefficient matrix used to correct the ideal dose center distribution output by the second model to obtain the target dose center distribution.

[0109] Figure 9 The illustration shows a schematic diagram of obtaining a target model for determining the dose distribution at the center of the radiation field based on scanning parameters and first field dose distribution data according to an embodiment of the present disclosure. Figure 10 The diagram schematically illustrates a comparison between the dose calculation results corresponding to the target field center distribution determined by the target model obtained according to the embodiments of this disclosure and the dose results of the conventional model.

[0110] See Figure 9 , Figure 10 Compared to traditional models, the target model based on the embodiments of this disclosure (i.e. Figure 9 , Figure 10 The dose calculation results determined by the new model in this disclosure are more consistent with the actual measured values, that is, the particle dose determined based on the embodiments of this disclosure is more accurate.

[0111] Based on the above-described particle dose determination method, this disclosure also provides a particle dose determination apparatus. The following will be combined with... Figure 11 The device is described in detail.

[0112] Figure 11 A schematic block diagram of a particle dose determination apparatus according to an embodiment of the present disclosure is shown.

[0113] like Figure 11 As shown, the particle dose determination device 1100 of this embodiment includes a first acquisition module 1110, a calculation module 1120, a second acquisition module 1130, and a determination module 1140.

[0114] The first acquisition module 1110 is used to guide the particle beam movement based on M sets of scanning parameters to obtain multiple lateral field dose distribution data; wherein each set of scanning parameters corresponds to multiple lateral field dose distribution data, and M is a positive integer. In one embodiment, the first acquisition module 1110 can be used to perform the operation S210 described above, which will not be repeated here.

[0115] The calculation module 1120 is used to calculate multiple first field dose distribution data corresponding to each set of scanning parameters based on multiple lateral field dose distribution data; wherein the dimension of the first field dose distribution data is higher than the dimension of the lateral field dose distribution data. In one embodiment, the calculation module 1120 can be used to perform the operation S220 described above, which will not be repeated here.

[0116] The second acquisition module 1130 is used to obtain a target model for determining the dose distribution at the center of the radiation field based on the scanning parameters and the first radiation field dose distribution data. In one embodiment, the second acquisition module 1130 can be used to perform the operation S230 described above, which will not be repeated here.

[0117] The determination module 1140 is used to input target scanning parameters into the target model and determine the delivery dose based on the target field center dose distribution output by the target model. In one embodiment, the determination module 1140 can be used to perform the operation S240 described above, which will not be repeated here.

[0118] According to an embodiment of this disclosure, the first obtaining module 1110 guides the particle beam to move through M sets of scanning parameters to obtain N transverse field dose distribution data, including: guiding the particle beam to move within a preset depth range through M sets of scanning parameters, randomly measuring the transverse field dose distribution of the particle beam at N depth positions, and obtaining M*N transverse field dose distribution data; wherein, each set of scanning parameters corresponds to N transverse field dose distributions, where N is a positive integer.

[0119] According to an embodiment of this disclosure, the calculation module 1120 calculates multiple first field dose distribution data corresponding to each set of scanning parameters based on multiple lateral field dose distribution data, including: acquiring the depth dose curve of the particle beam; wherein the depth dose curve is used to describe the energy deposition of the particle beam in the depth direction; and calculating the depth dose curve with N lateral field dose distribution data under each set of scanning parameters to obtain X first field dose distribution data corresponding to multiple sets of scanning parameters, wherein X is a positive integer and X > N.

[0120] According to an embodiment of this disclosure, the calculation module 1120 calculates the depth dose curve with N transverse field dose distribution data under each set of scanning parameters to obtain X first field dose distribution data corresponding to multiple sets of scanning parameters. This includes performing the following operations on the transverse field dose distribution data under each set of scanning parameters: multiplying the depth dose curve and multiple transverse field dose distribution data to obtain multiple first field dose distribution data at specific depths; and determining the first field dose distribution data at other depths besides the specific depths based on the first field dose distribution data at the specific depths.

[0121] According to an embodiment of this disclosure, the second obtaining module 1130 obtains a target model for determining the dose distribution at the center of the radiation field based on the scanning parameters and the first radiation field dose distribution data, including: training an initial model based on the scanning parameters and the first radiation field dose distribution data to obtain a first model, wherein the first model is used to predict the dose mask corresponding to different scanning parameters; and superimposing the first model with an objective function to obtain a target model for determining the dose distribution at the center of the radiation field, wherein the objective function is used to determine the transverse radiation field dose distribution under ideal conditions.

[0122] According to an embodiment of this disclosure, the second obtaining module 1130 trains an initial model based on scanning parameters and first field dose distribution data to obtain a first model, including: determining multiple sets of scanning parameters as input data for the initial model, and determining multiple first field dose distribution data as output data for the initial model; using each distribution feature in the first field dose distribution data as prompt information; and training the initial model based on the prompt information, input data, and output data to obtain the first model.

[0123] According to embodiments of this disclosure, any plurality of modules among the first obtaining module 1110, the calculation module 1120, the second obtaining module 1130, and the determining module 1140 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. According to embodiments of this disclosure, at least one of the first obtaining module 1110, the calculation module 1120, the second obtaining module 1130, and the determining module 1140 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 one of the three implementation methods or a suitable combination of any of them. Alternatively, at least one of the first obtaining module 1110, the calculation module 1120, the second obtaining module 1130, and the determining module 1140 may be at least partially implemented as a computer program module, which can perform corresponding functions when the computer program module is run.

[0124] Figure 12 A block diagram schematically illustrates an electronic device suitable for implementing a particle dose determination method according to an embodiment of the present disclosure.

[0125] like Figure 12 As shown, an electronic device 1200 according to an embodiment of the present disclosure includes a processor 1201, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1202 or a program loaded from a storage portion 1208 into a random access memory (RAM) 1203. The processor 1201 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 1201 may also include onboard memory for caching purposes. The processor 1201 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 disclosure.

[0126] RAM 1203 stores various programs and data required for the operation of electronic device 1200. Processor 1201, ROM 1202, and RAM 1203 are interconnected via bus 1204. Processor 1201 performs various operations of the method flow according to embodiments of the present disclosure by executing programs in ROM 1202 and / or RAM 1203. It should be noted that the programs may also be stored in one or more memories other than ROM 1202 and RAM 1203. Processor 1201 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in said one or more memories.

[0127] According to embodiments of this disclosure, the electronic device 1200 may further include an input / output (I / O) interface 1205, which is also connected to the bus 1204. The electronic device 1200 may 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, mouse, etc.; an output section 1207 including a cathode ray tube (CRT), 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, 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 needed. A removable medium 1211, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 1210 as needed so that computer programs read from it can be installed into the storage section 1208 as needed.

[0128] This disclosure 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 that, when executed, implement the method according to the embodiments of this disclosure.

[0129] According to embodiments of this disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, such as including, but 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 disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this disclosure, the computer-readable storage medium may include ROM 1202 and / or RAM 1203 and / or one or more memories other than ROM 1202 and RAM 1203 described above.

[0130] Embodiments of this disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code is used to cause the computer system to implement the particle dose determination method provided in the embodiments of this disclosure.

[0131] When the computer program is executed by the processor 1201, it performs the functions defined in the system / apparatus of this disclosure embodiments. According to embodiments of this disclosure, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0132] 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 may be downloaded and installed via the communication section 1209, and / or installed from the removable medium 1211. 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.

[0133] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 1209, and / or installed from the removable medium 1211. When the computer program is executed by the processor 1201, it performs the functions defined in the system of this disclosure embodiment. According to embodiments of this disclosure, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0134] According to embodiments of this disclosure, program code for executing the computer programs provided in embodiments of this disclosure 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 execute entirely on a user's computing device, partially on a 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).

[0135] 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 this disclosure. 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.

[0136] Those skilled in the art will understand that the features described in the various embodiments of this disclosure can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this disclosure. In particular, the features described in the various embodiments of this disclosure can be combined and / or combined in various ways without departing from the spirit and teachings of this disclosure. All such combinations and / or combinations fall within the scope of this disclosure.

[0137] The embodiments of this disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of this disclosure. 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 this disclosure, and all such substitutions and modifications should fall within the scope of this disclosure.

Claims

1. A particle dosage determination device, characterized in that, include: The first acquisition module is used to guide the particle beam movement based on M sets of scanning parameters to obtain multiple transverse field dose distribution data; wherein each set of scanning parameters corresponds to multiple transverse field dose distribution data, and M is a positive integer; The calculation module is used to calculate multiple first field dose distribution data corresponding to each set of scanning parameters based on the multiple lateral field dose distribution data; wherein the dimension of the first field dose distribution data is higher than the dimension of the lateral field dose distribution data. The second acquisition module is used to obtain a target model for determining the dose distribution at the center of the radiation field based on the scanning parameters and the first radiation field dose distribution data. The determination module is used to input target scanning parameters into the target model and determine the delivery dose based on the target field center dose distribution output by the target model. The step of guiding the particle beam movement based on M sets of scanning parameters to obtain multiple lateral field dose distribution data includes: guiding the particle beam to move within a preset depth range using M sets of scanning parameters, randomly measuring the lateral field dose distribution of the particle beam at N depth positions, and obtaining M*N lateral field dose distribution data; wherein each set of scanning parameters corresponds to N lateral field dose distributions, where N is a positive integer; The multiple first field dose distribution data are obtained by calculating the depth dose curve with the N transverse field dose distribution data under each set of scanning parameters; The step of calculating multiple first field dose distribution data corresponding to each set of scanning parameters based on the multiple lateral field dose distribution data includes: performing the following operations on the lateral field dose distribution data under each set of scanning parameters: multiplying the depth dose curve and the multiple lateral field dose distribution data respectively to obtain multiple first field dose distribution data at specific depths; and determining the first field dose distribution data at other depths besides the specific depths based on the first field dose distribution data at the specific depths.

2. The particle dosage determination device according to claim 1, characterized in that, The step of calculating multiple first field dose distribution data corresponding to each set of scanning parameters based on the multiple transverse field dose distribution data includes: Obtain the depth-dose curve of the particle beam; wherein the depth-dose curve is used to describe the energy deposition of the particle beam in the depth direction; The depth dose curve is calculated with the dose distribution data of N transverse fields under each set of scanning parameters to obtain X first field dose distribution data corresponding to multiple sets of scanning parameters, where X is a positive integer and X>N.

3. The particle dosage determination device according to claim 1, characterized in that, The step of obtaining a target model for determining the dose distribution at the center of the radiation field based on the scanning parameters and the first radiation field dose distribution data includes: An initial model is trained based on the scanning parameters and the dose distribution data of the first field to obtain a first model, wherein the first model is used to predict the dose mask corresponding to different scanning parameters; The first model is superimposed with the objective function to obtain a target model for determining the dose distribution at the center of the firing field, wherein the objective function is used to determine the dose distribution of the lateral firing field under ideal conditions.

4. The particle dosage determination device according to claim 1, characterized in that, The step of training an initial model based on the scanning parameters and the first radiation field dose distribution data to obtain a first model includes: Multiple sets of scanning parameters are determined as input data for the initial model, and the multiple first field dose distribution data are determined as output data for the initial model; The distribution characteristics in the first radiation field dose distribution data are used as prompt information; The initial model is trained based on the prompt information, the input data, and the output data to obtain the first model.

5. The particle dosage determination device according to claim 1, characterized in that, The scanning parameters include at least the energy of the particle beam, device performance parameters, and the range covered by the particle beam; at least some of the scanning parameters in each set are different.

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