Boron powder transportation simulation and neutron-boron powder transportation coupling simulation method and medium

Abstract

The invention provides a boron powder transportation simulation and neutron-boron powder transportation coupling simulation method and a medium. The method comprises the following steps: setting a plurality of voxel grids and boron powder transportation parameters; performing off-line coding on each voxel grid by using the boron medicine transportation parameters to obtain a corresponding boron medicine transportation simulation model, wherein the boron medicine transportation simulation model is used for predicting the boron medicine outflow rate and the boron medicine concentration in the voxel grid based on the radiation intensity, the boron medicine inflow rate and the radiation irradiation time; acquiring a CT image of the target object, and matching the voxel grid according to the CT image to acquire a voxel model of the target object; and obtaining the boron drug concentration in the voxel model based on the boron drug transportation simulation models of all voxel grids in the voxel model. According to the method, the boron medicine transportation simulation model of various voxel grids can be generated offline, the voxel grids are directly coupled based on the individual condition of the patient, the boron medicine concentration distribution condition in the body of the patient is rapidly obtained, and the non-uniformity of boron medicine transportation is fully considered.

Description

Technical Field

[0001] This application belongs to the field of radiation technology and relates to a coupled simulation method and medium for boron drug transportation simulation and neutron-boron drug transportation simulation. Background Art

[0002] Boron neutron capture therapy (BNCT), as a highly potential cancer treatment method, has attracted much attention in recent years with the development of accelerators. Its principle is based on the high capture cross-section characteristics of 10B for thermal neutrons. When a boron-containing compound (boron drug) is specifically taken up by tumor cells, a thermal neutron beam is introduced for irradiation. After 10B captures a thermal neutron, a nuclear reaction occurs, generating high-energy α particles and 7Li atomic nuclei. The ranges of these particles are extremely short, only within a few micrometers, which means they will almost only damage the tumor cells containing 10B and have minimal impact on surrounding normal tissues. This unique treatment mechanism provides a highly precise way for cancer treatment and is expected to improve the tumor treatment effect while significantly reducing the side effects on normal tissues.

[0003] The treatment planning system (Treatment Planning System, abbreviated as TPS) is a key link in the entire boron neutron capture therapy process. In BNCT-TPS, accurate dose calculation is the core link to ensure the treatment effect and safety. For example, through accurate dose calculation, it can be ensured that the tumor tissue can receive sufficient treatment dose to completely kill tumor cells, while controlling the dose of normal tissues within a safe range to avoid serious complications caused by excessive dose. However, in actual BNCT treatment, there are dose deficit problems caused by various factors, which seriously affect the further improvement of the treatment effect. Therefore, how to generate an accurate BNCT treatment plan considering multiple factors is a technical problem that needs to be solved urgently by those skilled in the art. Summary of the Invention

[0004] The purpose of this application is to provide a coupled simulation method and medium for boron drug transportation simulation and neutron-boron drug transportation simulation to solve the technical problem of how to generate an accurate BNCT treatment plan considering multiple factors.

[0005] In a first aspect, this application provides a boron drug transportation simulation method, which is applicable to a BNCT treatment planning system. The method includes: Setting multiple voxel grids and boron drug transportation parameters, where the voxel grids correspond to various human tissue materials; For each of the voxel grids, offline encoding is performed using the boron drug transport parameters to obtain a corresponding boron drug transport simulation model, which is used to predict the boron drug outflow rate and the boron drug concentration in the voxel grid based on the radiation intensity, the boron drug inflow rate, and the radiation exposure time; Obtain a CT image of the target object, and match at least one of the voxel grids according to the CT image to obtain a voxel model of the target object; Based on the boron drug transport simulation models of all the voxel grids in the voxel model, obtain the boron drug concentration in the voxel model.

[0006] In some embodiments of the first aspect of the present application, performing offline encoding on each of the voxel grids using the boron drug transport parameters includes: Construct a control equation for the voxel grid, which is established based on the computational fluid dynamics equation and the convection-diffusion-reaction equation; For each of the voxel grids, different radiation intensities, boron drug inflow rates, and radiation exposure times are given, and the control equation is solved in combination with the boron drug transport parameters to obtain the corresponding boron drug outflow rate and the boron drug concentration in the voxel grid; Based on the given radiation intensity, boron drug inflow rate, radiation exposure time, and the obtained boron drug outflow rate and boron drug concentration in the voxel grid, offline encode the boron drug transport simulation model of each voxel grid.

[0007] In some embodiments of the first aspect of the present application, the method includes: Define the boron drug inflow rate and the boron drug outflow rate in a two-dimensional phase space, which includes a three-dimensional space phase space and a time phase space; In the three-dimensional space phase space, calculate the three-dimensional space distribution of the boron drug inflow rate based on quadrature discretization, so as to solve the control equation in combination with the boron drug transport parameters, and obtain the three-dimensional space distribution of the corresponding boron drug outflow rate and the three-dimensional space distribution of the boron drug concentration; In the time phase space, calculate the time distribution of the boron drug inflow rate based on the asymptotic state approximation, so as to solve the control equation in combination with the boron drug transport parameters, and obtain the time distribution of the corresponding boron drug outflow rate and the time distribution of the boron drug concentration.

[0008] In some embodiments of the first aspect of the present application, the boron drug transport parameters include the characteristic diffusion coefficient of the boron drug, the characteristic flow rate of the body fluid, and the free boron conversion rate.

[0009] In a second aspect, the present application provides a coupled simulation method for neutron-boron drug transport, which is applicable to a BNCT treatment planning system, and the method includes: Set multiple voxel grids and boron drug transport parameters, and perform offline encoding on each of the voxel grids using the boron drug transport parameters to obtain corresponding boron drug transport simulation models; the voxel grids correspond to various human tissue materials; the boron drug transport simulation models are used to predict the boron drug outflow rate and the boron drug concentration within the voxel grids based on the radiation intensity, the boron drug inflow rate, and the radiation exposure time; Perform offline encoding on each of the voxel grids to obtain corresponding physical encoding layers, and the physical encoding layers are used to predict the corresponding neutron outflow and neutron flux distribution based on the neutron incident flux and the boron drug concentration; Obtain the CT image of the target object, and match at least one of the voxel grids according to the CT image to obtain the voxel model of the target object; Perform two-way coupling based on the physical encoding layers and the boron drug transport simulation models of all the voxel grids in the voxel model to obtain the neutron flux distribution and the boron drug concentration in the voxel model.

[0010] In some embodiments of the second aspect of the present application, performing offline encoding on each of the voxel grids to obtain the corresponding physical encoding layer includes: Customize the neutron incident flux and the neutron outflow in the eight-dimensional phase space; the eight-dimensional phase space includes a time phase space, an energy phase space, a three-dimensional angular phase space, and a three-dimensional space phase space; Given different neutron incident fluxes and boron drug concentrations for each of the voxel grids to correspondingly obtain the neutron outflow and the neutron flux distribution in the eight-dimensional phase space; Offline encode the physical encoding layer of each of the voxel grids based on the given neutron incident flux and boron drug concentration and the corresponding neutron outflow and the neutron flux distribution.

[0011] In some embodiments of the second aspect of the present application, the method includes: Combined with the definition of the eight-dimensional phase space of the neutron incident flux and the neutron outflow, perform spatial integration on the voxel grid to characterize the neutron transport process within the voxel grid as the relationship characteristics of the neutron incident flux and the neutron outflow.

[0012] In some embodiments of the second aspect of the present application, performing two-way coupling based on the physical encoding layers and the boron drug transport simulation models of all the voxel grids in the voxel model includes: Take the boron drug concentration output by the boron drug transport simulation model as an input of the physical encoding layer to perform online coupling on the physical encoding layers of all the voxel grids to obtain the neutron flux distribution in the voxel model; Take the neutron flux distribution output by the physical encoding layer as an input to the boron drug transport simulation model, and perform online coupling on the boron drug transport simulation models of all the voxel grids to obtain the boron drug concentration in the voxel model.

[0013] In some embodiments of the second aspect of the present application, the method includes: Set a voxel grid in the voxel model as the initial grid, and give a radiation source information and a boron drug administration plan to the initial grid. The radiation source information includes the radiation intensity of a neutron source and a radiation exposure time, and the boron drug administration method includes a boron drug inflow rate and a boron drug concentration; Input the radiation intensity of the neutron source, the boron drug inflow rate, and the radiation exposure time into the boron drug transport simulation model of the initial grid to obtain the corresponding boron drug outflow rate and boron drug concentration; Determine the neutron incident flux entering the initial grid based on the radiation intensity of the neutron source, and input the boron drug concentration and the neutron incident flux into the physical encoding layer of the initial grid to obtain the corresponding neutron outgoing flux and neutron flux distribution; Take the neutron flux distribution of the initial grid, the boron drug outflow rate of the initial grid, and the radiation exposure time as inputs to the next voxel grid, and based on the boron drug transport simulation model of the next voxel grid, obtain the corresponding boron drug outflow rate and boron drug concentration. Repeat this process until all the voxel grids are traversed to obtain the boron drug concentration in the voxel model; Take the neutron outgoing flux of the initial grid and the boron drug concentration of the initial grid as inputs to the next voxel grid, and based on the physical encoding layer of the next voxel grid, predict the corresponding neutron outgoing flux and neutron flux distribution. Repeat this process until all the voxel grids are traversed to obtain the neutron flux distribution in the voxel model.

[0014] In a third aspect, the present application provides a computer storage medium, and when the computer program is executed by a processor, it implements the method as described above.

[0015] As described above, the boron drug transport simulation and neutron-boron drug transport coupling simulation method and medium provided by the present application have the following beneficial effects: First, the present application innovatively proposes a boron drug transport simulation method under radiation conditions. Aiming at the complex mechanism of the boron drug transport process, in the phase space of "three-dimensional space - time", a boron drug transport simulation model is constructed to specifically characterize the non-uniformity of boron drug transport in voxel grids under radiation conditions. Based on the actual situation of the target object, each voxel grid is coupled to establish a personalized boron drug transport simulation mechanism, and then the boron drug concentration in the target object can be quickly obtained.

[0016] Secondly, the present application innovatively proposes a dynamic neutron transport simulation method. In view of the complex mechanism of the neutron transport process, in the eight-dimensional phase space of "time - energy - three-dimensional space - three-dimensional angle coupling", a physical coding layer that can specifically characterize the non-uniformity of voxel grid particle transport is proposed, so as to quickly obtain the neutron flux distribution in the body based on the actual situation of the target object.

[0017] Finally, the present application further proposes a transport coupling method considering multiple factors of neutrons and boron drugs. On the basis of fully analyzing the non-uniformity of particle transport and the non-uniformity of boron drug transport, the two-way coupling mechanism between them is further realized, and then a more accurate BNCT treatment plan can be generated under the premise of considering multiple factors, thereby improving the BNCT dose evaluation accuracy and safety performance, and accelerating the clinical promotion of BNCT.

[0018] In addition, the present application innovatively proposes an off-line coding - on-line coupling technical solution, which can perform off-line coding on each voxel grid without considering the personal situation of the patient, obtain the boron drug transport simulation model and physical coding layer at the voxel grid level, and directly perform on-line coupling and two-way coupling based on the individual diagnosis and treatment needs of different patients, so as to directly and quickly predict the boron drug concentration and neutron flux distribution in the patient's body, and then generate a BNCT treatment plan. The present application does not need to collect a large number of sample case data, nor does it need to perform a large number of pre-calculations and model trainings repeatedly based on individual diagnosis and treatment needs, which greatly improves the timeliness of generating a BNCT treatment plan based on the boron drug concentration and neutron flux distribution. Description of the Drawings

[0019] Figure 1 It shows a schematic flow chart of the boron drug transport simulation method provided by the embodiment of the present application.

[0020] Figure 2 It shows a schematic flow chart of the boron drug transport simulation method provided by the embodiment of the present application.

[0021] Figure 3 It shows a schematic diagram of the voxel grid provided by the embodiment of the present application.

[0022] Figure 4 It shows a schematic flow chart of the boron drug transport simulation method provided by the embodiment of the present application.

[0023] Figure 5 It shows a schematic distribution diagram of N quadrature points in the three-dimensional angle phase space provided by the embodiment of the present application.

[0024] Figure 6 It shows a schematic principle diagram of obtaining the boron drug transport simulation model described in the embodiment of the present application.

[0025] Figure 7 It shows a schematic flowchart of the coupled simulation method for neutron-boron drug transportation provided by an embodiment of the present application.

[0026] Figure 8 It shows a schematic flowchart of the coupled simulation method for neutron-boron drug transportation provided by an embodiment of the present application.

[0027] Figure 9 It shows a schematic diagram of the relationship between the three-dimensional angular phase space and the three-dimensional spatial phase space provided by an embodiment of the present application.

[0028] Figure 10 It shows a schematic diagram of the principle for obtaining the physical coding layer provided by an embodiment of the present application. Detailed implementation manners

[0029] The following uses specific specific examples to illustrate the implementation manners of the present application. Those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in this specification. The present application can also be implemented or applied through other different specific implementation manners. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0030] It should be noted that in the following description, reference is made to the accompanying drawings, which describe several embodiments of the present application. It should be understood that other embodiments can also be used, and mechanical composition, structure, electrical, and operational changes can be made without departing from the spirit and scope of the present application. The following detailed description should not be considered restrictive, and the scope of the embodiments of the present application is only defined by the claims of the published patent. The terms used herein are only for describing specific embodiments and are not intended to limit the present application. Spatially related terms, such as "upper", "lower", "left", "right", "below", "beneath", "lower part", "above", "upper part", etc., can be used in the text to facilitate the description of the relationship between one element or feature shown in the figure and another element or feature.

[0031] Furthermore, as used herein, the singular forms "a", "an", and "the" are also intended to include the plural forms unless the context indicates otherwise. It should be further understood that the terms "comprise", "include" indicate the presence of the described features, operations, elements, components, items, types, and / or groups, but do not exclude the presence, appearance, or addition of one or more other features, operations, elements, components, items, types, and / or groups. The terms "or" and "and / or" used herein are interpreted as inclusive, or meaning either one or any combination.

[0032] During the BNCT treatment process, accurate dose calculation is a core step to ensure the treatment effect and safety. However, in actual BNCT treatments, there are dose deficit problems caused by various factors, which seriously affect the further improvement of the treatment effect. Among them, "inhomogeneous particle transport" is an important factor. When neutrons are transported in human tissues, considering factors such as the complexity of tissue composition and structure, the particle transport process is affected by independent variables such as three-dimensional space, three-dimensional angle, energy, and time, and the scattering and absorption of neutrons will change. For example, in tissues with higher density such as bone, the scattering probability of neutrons increases, resulting in an inhomogeneous distribution of neutron flux, so that the neutron doses received by different parts of the tumor tissue are different, and some tumor regions may not receive sufficient treatment doses.

[0033] In addition, "inhomogeneous boron drug transport" cannot be ignored either. The distribution of boron drugs in the body is not an ideal uniform state. Factors such as the blood vessel distribution and cell metabolic activity inside the tumor tissue will affect the uptake and distribution of boron drugs. Some tumor regions may have a low boron drug concentration due to insufficient blood perfusion and other reasons, and cannot effectively undergo capture reactions with neutrons, resulting in dose deficits. And BNCT has the characteristics of two-way neutron-boron drug targeting, and the neutron targeting will affect the distribution of boron drugs. These factors will seriously affect the treatment effect of BNCT.

[0034] Currently, in order to accurately adapt the treatment plan to the real-time situation of patients, the clinical strategy of "integration of diagnosis and treatment" has been increasingly emphasized. This strategy emphasizes the close combination of the diagnosis and treatment processes. For this reason, in the existing technology, artificial intelligence methods are used to quickly obtain the neutron flux distribution in the patient's body based on data-driven, so as to generate a BNCT treatment plan. However, this method only analyzes one cause of inhomogeneous particle transport and does not consider the factor of inhomogeneous boron drug transport, thus affecting the accuracy of the generation of the BNCT treatment plan. In addition, the generalization ability of this type of method is not strong. When it is necessary to improve the analysis accuracy and generalization ability, a large amount of sample data needs to be prepared to train the model. But this means that if the real patient situation is very different from the cases in the sample set, a more accurate analysis result cannot be obtained through the existing model, and a large amount of pre-calculation and model training need to be carried out again, which also greatly affects the timeliness of the generation of the BNCT treatment plan.

[0035] To at least solve the above technical problems, the present application provides a method and medium for simulating boron drug transportation and coupling simulation of neutron-boron drug transportation, which can quickly simulate the process of boron drug transportation in a target object. On the basis of considering the boron drug distribution, the neutron transportation factor is further combined to realize the coupling simulation method of neutron-boron drug transportation, so as to quickly predict the boron drug concentration and neutron flux distribution in different target objects, thereby providing a basis for accurately calculating the BNCT dose and improving the accuracy and timeliness of BNCT treatment plan generation.

[0036] Figure 1 The flowchart of the boron drug transportation simulation method provided by an embodiment of the present application is shown. The neutron transportation simulation method provided by the present application is applicable to the BNCT treatment planning system to quickly generate a BNCT treatment plan. As Figure 1 shown, the neutron transportation simulation method includes steps S1 to S3.

[0037] S1. Set multiple voxel grids and boron drug transportation parameters, and the voxel grids correspond to various human tissue materials.

[0038] In some embodiments, various human tissue materials are constructed based on the database of human tissue material elements published by the International Commission on Radiation Units and Measurements (ICRU), and each type of human tissue material is set as a voxel grid. For example, if there are N types of human tissue material grids, then N voxel grids are correspondingly set.

[0039] In some embodiments, the boron drug transportation parameters include the characteristic diffusion coefficient of the boron drug, the characteristic flow rate of body fluid, and the free boron conversion rate. By the given boron drug transportation parameters, it can effectively help to establish a boron drug transportation simulation model for the voxel grid.

[0040] Among them, the characteristic diffusion coefficient of the boron drug is a physical quantity that measures the diffusion ability of the boron drug. It is affected by the properties of the drug molecule such as size, shape, and polarity, and is also related to the internal environment such as the viscosity and temperature of tissue fluid. The characteristic flow rate of body fluid refers to the speed of body fluid circulating and flowing in the body, reflecting the flow of body fluid in pipelines such as blood vessels and lymphatic vessels. The free boron conversion rate refers to the rate at which boron elements are converted from one chemical form to another in the body, reflecting the metabolic process and chemical reaction rate of boron elements in the body.

[0041] It should be noted that the values of the boron drug transportation parameters are determined by those skilled in the art, and the present application is not limited thereto.

[0042] S2. Use the boron drug transport parameters to perform offline encoding on each of the voxel grids to obtain corresponding boron drug transport simulation models, which are used to predict the boron drug outflow rate and the boron drug concentration in the voxel grid based on the radiation intensity, boron drug inflow rate, and radiation exposure time.

[0043] In order to quickly obtain the boron drug transport process in the target object and obtain the boron drug concentration distribution, the present application performs offline encoding on each voxel grid to obtain a corresponding boron drug transport simulation model, so as to predict the boron drug outflow rate and the boron drug concentration in the voxel grid based on the given radiation intensity, boron drug inflow rate, and radiation exposure time. In the process of establishing the boron drug transport simulation model, the present application considers the boron drug transport mechanism under radiation conditions, making the simulation of the boron drug transport process more accurate. Figure 2 Shown is a schematic flow chart of the boron drug transport simulation method provided by an embodiment of the present application. As Figure 2 shown, performing offline encoding on each of the voxel grids using the boron drug transport parameters includes steps S21 to S23.

[0044] S21. Construct a control equation for the voxel grid, which is established based on the computational fluid dynamics equation and the convection-diffusion-reaction equation.

[0045] Figure 3 Shown is a schematic diagram of the voxel grid provided by an embodiment of the present application. As Figure 3 shown, when the boron drug enters the voxel grid, it will be transported within the voxel grid. Among them, when free boron reacts with biomolecules in the voxel grid, bound boron is formed, that is, free boron and bound boron will mutually transform during the transport process of the boron drug in the voxel grid. Further, in the embodiment of the present application, it is considered that the transformation between free boron and bound boron is in dynamic equilibrium, that is, the change in boron drug concentration caused by the conversion of bound boron into free boron is not considered.

[0046] Based on this, the present application combines the computational fluid dynamics model (CFD) method with the convection / diffusion / reaction model (CDR) to construct the control equation of the voxel grid, so as to characterize the transport process of the boron drug in the voxel grid under radiation conditions.

[0047] Specifically, the control equation of the voxel grid under radiation conditions is shown in Equation (1).

[0048]

[0049] where r is the three-dimensional space and t is the time, is the free boron concentration (mol / m3 ), the first term on the left side of the equation represents the relationship between the free boron concentration and time (t), with the unit of (mol / m3·s).

[0050] where D is the characteristic diffusion coefficient of the boron drug (m 2 / s), reflecting the diffusion ability of the reactive boron drug in the medium. is the Laplace operator of the free boron concentration (mol / m 5 ), and its product with D has the unit of mol / (m 3 ·s).

[0051] where is the free boron concentration gradient (mol / m 4 ), and v is the characteristic flow rate of the body fluid. The product of the two has the unit of mol / (m 3 ·s).

[0052] where k is the free boron conversion rate, representing the conversion ratio of free boron per unit time, with the unit of mol / (m 3 ·s).

[0053] where has the unit of mol / (m 3 ·s), is the neutron flux; is the absorption cross section of boron. The dimensions of each term in the formula are all N L −3 T −1 .

[0054] As shown in Equation (1), the left side represents the change of boron concentration with time. The first term on the right side is the diffusion term, used to characterize the transfer of the boron drug from high concentration to low concentration in the voxel grid. The second term is the convection term, used to characterize the change of boron drug concentration caused by the flow of the boron drug with the body fluid. The third term is the elimination term, used to characterize the change of boron drug concentration caused by the body metabolism. The fourth term is the radiation consumption term. In fact, by constructing the control equation of the voxel grid, the transportation process of the boron drug in the voxel grid affected by diffusion, convection, elimination and radiation consumption can be characterized, providing a solid foundation for the establishment of the simulation model of boron drug transportation under radiation conditions.

[0055] S22. For each of the voxel grids, different radiation intensities, boron drug inflow rates and radiation exposure times are given, and the control equation is solved in combination with the boron drug transportation parameters to obtain the corresponding boron drug outflow rate and the boron drug concentration in the voxel grid.

[0056] In some embodiments, in order to enable the boron drug transport simulation model based on the voxel grid to respond quickly to a given radiation intensity, the boron drug inflow rate, and the radiation exposure time, so as to predict the corresponding boron drug outflow rate and the boron drug concentration within the voxel grid, the present application will process Equation (1) to characterize the transport process of the boron drug within the voxel grid as the relationship characteristics between the boron drug inflow rate and the boron drug outflow rate. Among them, the boron drug inflow rate characterizes the boron drug concentration entering the voxel grid from the surface, and the boron drug outflow rate characterizes the boron drug concentration diffusing out of the voxel grid from the surface.

[0057] Specifically, in the two-dimensional phase space, the boron drug inflow rate is defined as:

[0058] And the boron drug outflow rate is defined as:

[0059] Among them, the units of the boron drug inflow rate and the boron drug outflow rate are mol / (m 2 ·s), and the units of the two terms on the right side of the equation are also both mol / (m 2 ·s), and the dimension is NL -2 T -1 . And the negative sign in the formula indicates that the diffusion direction of the boron drug is opposite to the concentration gradient direction.

[0060] Among them, n is the unit normal vector on the surface of the voxel grid, and the normal direction points to the outside of the voxel grid.

[0061] Then, by integrating Equation (1) spatially within the internal region m of a certain voxel grid in combination with the above definitions, Equation (2) can be obtained:

[0062] Then, according to Green's second formula, Equation (3) can be obtained:

[0063] Among them, the negative sign on the right side of Equation (3) also indicates that the diffusion direction of the boron drug is opposite to the concentration gradient direction.

[0064] Then, according to Gauss's formula, Equation (4) is obtained:

[0065] Finally, the control equation of the voxel grid is shown as Equation (5):

[0066] Among them, the units of the left side of the equation and the first and second terms on the right side are all mol / (m 3· s), after triple integration in space, the unit is mol / s and the dimension is NT -1 .

[0067] Then, it can be seen from Equation (5) that the boron drug transportation process in each voxel grid can actually be characterized by the relationship between the boron drug inflow rate and the boron drug outflow rate in that voxel grid. That is, when simulating the boron drug transportation process under radiation conditions based on Equation (5), in fact, can be used to characterize the boron drug transportation process, and based on this, the boron drug concentration can be obtained. By simulating the boron drug transportation process based on Equation (5) in the two-dimensional phase space, the spatio-temporal distribution of the boron drug can be comprehensively considered to determine the boron drug concentration and further determine the boron drug outflow rate.

[0068] S23. Offline encode the boron drug transportation simulation model of each voxel grid based on the given radiation intensity, the boron drug inflow rate, the radiation irradiation time, and the obtained boron drug outflow rate and the boron drug concentration in the voxel grid.

[0069] As described above, it can be seen from Equation (5) that the boron drug transportation process in the voxel grid can be characterized by the relationship between the boron drug inflow rate and the boron drug outflow rate in that voxel grid. Then, at this time, different radiation intensities, boron drug inflow rates, and radiation irradiation times are given to each voxel grid, and combined with the preset values of the boron drug transportation parameters, an algebraic equation system can be formed based on Equation (5) to solve the corresponding boron drug outflow rate and the boron drug concentration in the voxel grid. At this time, a large number of the above given input data and the corresponding obtained input data can be used to train the pre-trained model to obtain the boron drug transportation simulation model, so that the voxel grid can quickly respond to the given radiation intensity, boron drug inflow rate, and radiation irradiation time based on this model to predict the corresponding boron drug outflow rate and the boron drug concentration in the voxel grid. And the construction of the boron drug transportation simulation model can be explained based on Equation (5), reflecting that the non-uniformity of boron drug transportation is fully considered in the construction process, and the interpretability is relatively strong.

[0070] Among them, the pre-trained model is a neural network model, and this application does not impose any restrictions on the model type.

[0071] Figure 4 It shows a schematic flow chart of the boron drug transportation simulation method provided by the embodiment of the present application. As Figure 4 shown, different radiation intensities, boron drug inflow rates, and radiation irradiation times are given to each voxel grid, and the control equation is solved in combination with the boron drug transportation parameters to obtain the corresponding boron drug outflow rate and the boron drug concentration in the voxel grid, including steps S221 to S223.

[0072] S221. Define the influx rate and efflux rate of the boron drug in the two-dimensional phase space, where the two-dimensional phase space includes a three-dimensional spatial phase space and a time phase space.

[0073] As mentioned above, customize the influx rate and efflux rate of the boron drug in the two-dimensional phase space to reflect the spatio-temporal distribution changes of the boron drug concentration during transportation.

[0074] S222. In the three-dimensional spatial phase space, calculate the three-dimensional spatial distribution of the influx rate of the boron drug based on quadrature discretization, so as to solve the control equation in combination with the boron drug transport parameters to obtain the three-dimensional spatial distribution of the corresponding efflux rate of the boron drug and the three-dimensional spatial distribution of the boron drug concentration.

[0075] In some embodiments, S N quadrature points can be used for discrete calculation to obtain the integral value of the three-dimensional angular phase space, that is, the three-dimensional spatial distribution of the boron drug concentration within the voxel grid. As Figure 5 shown, N classical quadrature points are selected in the three-dimensional angular phase space to obtain the distribution function values of the boron drug in each discrete angular direction, and the integral values of the distribution function values in all discrete angular directions are integrated using Equation (6) to approximately obtain the integral value of the three-dimensional angular phase space, that is, the three-dimensional spatial distribution of the boron drug concentration.

[0076]

[0077] Based on this, the control equation (5) can be solved by combining the preset boron drug transport parameter values with the given input radiation intensity and radiation exposure time to obtain the three-dimensional spatial distribution of the corresponding efflux rate of the boron drug and the three-dimensional spatial distribution of the boron drug concentration.

[0078] S223. In the time phase space, calculate the time distribution of the influx rate of the boron drug based on the asymptotic state approximation, so as to solve the control equation in combination with the boron drug transport parameters to obtain the time distribution of the corresponding efflux rate of the boron drug and the time distribution of the boron drug concentration.

[0079] In some embodiments, in the time phase space, the time distribution of the influx rate of the boron drug is discretely calculated based on the asymptotic state approximation to correspondingly obtain the time distribution of the boron drug concentration. That is, in the time dimension, the time derivative term of the equation is processed using the asymptotic state approximation to obtain the change of the boron drug concentration with time in the time phase space. Among them, the asymptotic state approximation processing of the time derivative term usually refers to the processing method of the time derivative part when discretizing or approximately processing a time-dependent differential equation. In some embodiments, the time distribution of the influx rate of the boron drug is usually discretely calculated at intervals of 1% of the treatment time. For example, the forward Euler format can be used for discrete calculation.

[0080] Based on this, the equation (5) can be solved by combining the preset boron drug transportation parameter values with the given input radiation intensity and radiation exposure time, so as to obtain the time distribution of the corresponding boron drug outflow rate and the time distribution of the boron drug concentration.

[0081] Figure 6 shows a schematic diagram of the principle of obtaining the boron drug transportation simulation model in an embodiment of the present application. As Figure 6 shown, for each voxel grid, different radiation intensities, boron drug inflow rates, and radiation exposure times are input, and the transportation process of the boron drug in the voxel grid is simulated based on the control equation under radiation conditions to obtain the corresponding boron drug outflow rate and boron drug concentration. Then, the model is trained according to the given input and the corresponding output, and finally the required boron drug transportation simulation model is obtained, so as to be able to quickly predict the corresponding boron drug outflow rate and boron drug concentration based on the given radiation intensity, boron drug inflow rate, and radiation exposure time.

[0082] S3. Obtain the CT image of the target object, and match at least one of the voxel grids according to the CT image to obtain the voxel model of the target object.

[0083] S4. Obtain the boron drug concentration in the voxel model based on the boron drug transportation simulation models of all voxel grids in the voxel model.

[0084] Specifically, when the boron drug transportation simulation model is constructed for each voxel grid, the voxel model of the target object can be obtained based on the specific information of the target object, and the boron drug transportation simulation models of all voxel grids in the voxel model are coupled online to obtain the boron drug concentration in the voxel model.

[0085] In some embodiments, obtaining the CT image of the target object and matching at least one of the voxel grids according to the CT image to obtain the voxel model of the target object includes: obtaining the CT image of the target object, determining the cancerous region in the CT image, and thereby matching the corresponding at least one voxel grid, and obtaining the voxel model of the target object through the matched voxel grid.

[0086] Furthermore, the cancerous region can actually be composed of multiple voxel grids, that is, the voxel grid is a finer division unit. For example, according to the CT image, the cancerous region can be determined to be the brain of the target object, and the brain is composed of multiple tissue materials, so this region matches multiple voxel grids. In some other embodiments, a certain cancerous region may be precise enough, and in this case, one voxel grid is matched. In addition, the voxel model of the target object can also include the voxel grids corresponding to the cancerous region and the voxel grids of the normal region. That is, all voxel grids are completely matched according to various human tissue materials of the target object to form the voxel model.

[0087] For example, if step S1 sets N voxel grids and the voxel model of a certain target object is composed of M voxel grids (M is not greater than N), then the boron drug transport simulation models of the M voxel grids can be coupled. By predicting the corresponding boron drug outflow rate and boron drug concentration through the boron drug transport simulation models of the M voxel grids respectively, the boron drug concentration in the voxel model of the target object can actually be obtained based on the boron drug concentration in all voxel grids.

[0088] In some embodiments, obtaining the boron drug concentration in the voxel model based on the boron drug transport simulation models of all voxel grids in the voxel model includes steps S41 to S43.

[0089] S41. Set a voxel grid in the voxel model as the initial grid, and give a radiation intensity, a boron drug inflow rate, and a radiation exposure time to the initial grid.

[0090] S42. Predict the corresponding boron drug outflow rate and boron drug concentration based on the boron drug transport simulation model of the initial grid.

[0091] S43. Use the boron drug outflow rate of the initial grid as the boron drug inflow rate of the next voxel grid, and predict the corresponding boron drug outflow rate and boron drug concentration based on the boron drug transport simulation model of the next voxel grid until all the voxel grids are traversed.

[0092] For example, the voxel model of a certain target object is composed of M voxel grids, and a boron drug outflow rate J set in the given treatment plan is given. in1 And set a voxel grid M1 as the initial grid. Input J in1 into M1, and predict the corresponding boron drug outflow rate J out1 and the boron drug concentration in M1 through the boron drug transport simulation model of M1. Use the boron drug outflow rate J out1 of M1 as the boron drug inflow rate of the next voxel grid M2, and predict the corresponding boron drug outflow rate J out2 and the boron drug concentration in M2 through the boron drug transport simulation model of M2, and so on until all the voxel grids are traversed to obtain the boron drug concentration in all voxel grids, that is, the boron drug concentration in the voxel grid.

[0093] Therefore, without the need to know any information of the patient in advance, the present application can offline generate the boron drug transport simulation models of voxel grids, thereby quickly simulating the transport process of boron drug in voxel grids, and finally obtaining the boron drug outflow rate and the boron drug concentration in voxel grids. And based on the boron drug transport simulation models of voxel grids, the voxel grids can be quickly coupled according to the individual conditions of the target object, so as to quickly obtain the boron drug distribution in the target object while considering the non-uniformity of boron drug transport.

[0094] An embodiment of the present application also provides a coupled simulation method for neutron-boron drug transportation, and the method is applicable to a BNCT treatment planning system. Figure 7 It is shown as a schematic flow chart of the coupled simulation method for neutron-boron drug transportation provided by an embodiment of the present application. As Figure 7 shown, the method includes steps S5 to S8.

[0095] S5. Set a plurality of voxel grids and boron drug transportation parameters, and perform offline encoding on each of the voxel grids using the boron drug transportation parameters to obtain corresponding boron drug transportation simulation models.

[0096] As described above, set a plurality of voxel grids, and obtain the boron drug transportation simulation models of each voxel grid with reference to the foregoing embodiments. Among them, the voxel grids correspond to various human tissue materials; the boron drug transportation simulation models are used to predict the boron drug outflow rate and the boron drug concentration in the voxel grid based on the radiation intensity, the boron drug inflow rate, and the radiation exposure time.

[0097] S6. Perform offline encoding on each of the voxel grids to obtain corresponding physical encoding layers, and the physical encoding layers are used to predict the corresponding neutron outflow and neutron flux distribution based on the neutron incident flux and the boron drug concentration.

[0098] Specifically, offline encoding means that without knowing any information of the patient in advance, directly construct physical encoding layers for each of the voxel grids through a large amount of general data, and through the physical encoding layers, when a neutron incident flux and a boron drug concentration are given to the voxel grid, the transportation process of neutrons in the voxel grid can be quickly simulated, so as to respond and predict the corresponding neutron outflow and neutron flux distribution. Among them, the neutron flux distribution is actually the distribution of the neutron flux in different phase space dimensions, representing the complete state of the neutron flux in the phase space.

[0099] As Figure 8 shown, performing offline encoding on each of the voxel grids to obtain corresponding physical encoding layers includes steps S61 to S63.

[0100] S61. Customize the neutron incident flux and the neutron outflow in an eight-dimensional phase space; the eight-dimensional phase space includes a time phase space, an energy phase space, a three-dimensional angular phase space, and a three-dimensional space phase space.

[0101] Specifically, in the eight-dimensional phase space, define the neutron incident flux as , and the neutron incident flux is located on the surface of the voxel grid and the included angle between the neutron movement direction and the normal direction is greater than 90 degrees, and define the neutron outflow as , and the neutron emission flux is located on the surface of the voxel grid and the angle between the neutron movement direction and the normal direction is less than 90 degrees. In addition, the internal region of the voxel grid is denoted as m, and the surface is denoted as S.

[0102] When the neutron incident flux enters the voxel grid, actually, the transportation process of the neutron beam in the voxel grid can be represented by the dynamic particle transport equation, as shown in Equation (7):

[0103] where t is time; r is three-dimensional space; is the three-dimensional angle; E is the particle energy.

[0104] where the first term on the left side of the equation represents the change rate of the neutron flux with time, divided by the neutron velocity v. It reflects the degree of change of the neutron fluence with a specific energy E and moving in the direction Ω per unit time and per unit volume, considering the time accumulation effect caused by the neutron moving at the velocity v. Among them, is the neutron flux , v is the neutron velocity (cm·s −1 ).

[0105] where the second term on the left side of the equation is the convection term, which represents the change of neutrons in space due to their own movement (moving in the direction Ω at the velocity v), that is, the influence of the space transport process on the neutron flux. The unit of is

[0106] where the third term on the left side of the equation represents the collision removal term in is the total cross section (cm -1 ), which represents the comprehensive probability of neutrons leaving the original movement direction and energy state due to various interactions with the medium (such as absorption, scattering, etc.). Multiplying it by the neutron flux represents the reduction of the number of neutrons with a specific energy and direction per unit volume due to various interactions such as collisions.

[0107] where the first term on the right side of the equation is the scattering production term, and its integral is carried out for all possible incident energies E' and incident directions Ω'. Among them, is the scattering cross section , which represents the probability that neutrons in a unit volume are scattered from the energy E ' and direction Ω' to the energy E and direction Ω. Multiplying it by the neutron flux and then integrating and summing can obtain the number of neutrons generated with the current energy and direction due to the scattering effect.

[0108] where the second term on the right side of the equation is the external source term , i.e., the BNCT neutron source, represents the number of neutrons generated per unit volume at position r, energy E, direction Ω, and time t.

[0109] The first term on the left side of Equation (7) is the rate of change of neutrons with time, the second term is the neutron leakage rate, and the third term is the neutron absorption rate; the right side of the equal sign is the neutron production rate, where the first term is the neutron source; the second term is the scattered neutron production rate. In fact, Equation (7) represents the dynamic transport process of neutrons. From Equation (7), it can be seen that the neutron flux is affected by neutron leakage, interaction loss with the boron drug (total cross section and scattering cross section), scattering, and the source term. Solving Equation (7) can simulate the neutron transport process and obtain the neutron flux distribution and neutron outgoing flux accordingly.

[0110] Then, combining the above eight-dimensional phase space definitions of the neutron incident flux and neutron outgoing flux, spatial integration is performed on the voxel grid to characterize the neutron transport process within the voxel grid as the relationship characteristics of the neutron incident flux and the neutron outgoing flux. That is, by combining the above definitions, spatial integration is performed on Equation (7) within the internal region m of a certain voxel grid, and Equation (8) can be obtained:

[0111] That is, from Equation (8), it can be seen that the dynamic transport process of neutrons within each voxel grid can actually be represented as the relationship characteristics of the neutron incident flux and neutron outgoing flux in that voxel grid, that is .

[0112] Among them, Figure 9 It shows a schematic diagram of the relationship between the three-dimensional angular phase space and the three-dimensional spatial phase space provided by the embodiments of the present application. By simulating the neutron transport process within the voxel grid based on Equation (8) in the eight-dimensional phase space, it is possible to comprehensively consider the influence of various variables that the dynamic transport process of neutrons within the voxel grid will actually be affected by, so as to obtain the neutron flux distribution and neutron outgoing flux corresponding to the neutron incident flux and boron drug concentration in the eight-dimensional phase space, enabling a more accurate simulation of the neutron transport process within the voxel grid.

[0113] S62. Different neutron incident fluxes and boron drug concentrations are given to each of the voxel grids to correspondingly obtain the neutron outgoing flux and the neutron flux distribution in the eight-dimensional phase space.

[0114] S63. Based on the given neutron incident flux and boron drug concentration and the corresponding neutron outgoing flux and neutron flux distribution, offline code the physical coding layer of each voxel grid.

[0115] Specifically, giving different neutron incident fluxes and boron drug concentrations to each of the voxel grids to correspondingly obtain the neutron outgoing fluxes and the neutron flux distributions in the eight-dimensional phase space includes: simulating the neutron transport process in the voxel grid based on the eight-dimensional phase space distribution of the neutron incident flux and the two-dimensional phase space distribution of the boron drug concentration, so as to obtain the eight-dimensional phase space distribution of the neutron outgoing flux and the eight-dimensional phase space distribution of the neutron flux; wherein, the neutron transport process is characterized by the relationship characteristics between the neutron incident flux and the neutron outgoing flux.

[0116] As described above, it can be seen from Equation (8) that the dynamic neutron transport process in the voxel grid can be characterized by the relationship characteristics between the neutron incident flux and the corresponding neutron outgoing flux. At this time, the Monte Carlo method can be used to simulate the neutron transport process in the voxel grid in the eight-dimensional phase space based on the spatio-temporal distribution of the neutron incident flux at the boron drug concentration, so as to obtain the eight-dimensional phase space distribution of the neutron outgoing flux and the eight-dimensional phase space distribution of the neutron flux. And it can be seen from Equation (8) that the neutron transport process can be characterized by the relationship characteristics between the neutron incident flux and the neutron outgoing flux. At this time, a large number of neutron outgoing fluxes and neutron flux distributions can be obtained by using the Monte Carlo method, and the input-output prediction model of the voxel grid can be trained by using the given neutron incident flux, boron drug concentration and the corresponding neutron outgoing flux and neutron flux distributions, that is, a physical coding layer is constructed, so as to enable the voxel grid to quickly respond to the given neutron incident flux to predict the corresponding neutron outgoing flux and neutron flux distributions. And the construction process of the physical coding layer, that is, the neutron transport process in each voxel grid can be explained based on Equation (8), and the interpretability is relatively strong.

[0117] Wherein, the input-output prediction model is a neural network model, and this application does not impose any restrictions on the model type.

[0118] In some embodiments, in the time phase space, the time distribution of the neutron incident flux is discretely calculated based on the asymptotic state approximation to correspondingly obtain the time distribution of the neutron outgoing flux and the time distribution of the neutron flux. That is to say, in the time dimension, the asymptotic state approximation is used to process the time derivative term of the equation to obtain the change of the neutron flux with time in the time phase space, that is, to obtain the time distribution of the neutron flux and the time distribution of the neutron outgoing flux. Among them, the asymptotic state approximation to process the time derivative term of the equation usually refers to the processing method of the time derivative part when discretizing or approximating a time-dependent differential equation. In some embodiments, usually with 1% of the treatment time as the time interval, the time variable of the neutron incident flux is discretely calculated. For example, the forward Euler scheme can be used for discrete calculation.

[0119] In some embodiments, in the energy phase space, the energy distribution of the neutron incident flux is discretely calculated based on the group approximation to correspondingly obtain the energy distribution of the neutron outgoing flux and the energy distribution of the neutron flux. That is, in the energy dimension, the "group approximation" method is used for discrete processing, that is, it is considered that the neutron flux does not change within a certain energy range, and thus the energy distribution of the neutron outgoing flux does not change either.

[0120] In some implementations, in the three-dimensional angular phase space, the three-dimensional angular distribution of the neutron incident flux is discretely calculated based on quadrature discretization to correspondingly obtain the three-dimensional angular distribution of the neutron outgoing flux and the three-dimensional angular distribution of the neutron flux.

[0121] In some embodiments, in the three-dimensional spatial phase space, the three-dimensional spatial distribution of the neutron incident flux is discretely calculated respectively based on quadrature discretization to correspondingly obtain the three-dimensional spatial distribution of the neutron outgoing flux and the three-dimensional spatial distribution of the neutron flux.

[0122] Among them, for the three-dimensional angular phase space, SN quadrature points can be used for discrete calculation to obtain the integral value of the three-dimensional angular phase space, that is, the three-dimensional angular distribution of the neutron flux within the voxel grid, and the three-dimensional angular distribution of the neutron outgoing flux is simulated using Monte Carlo. In some embodiments, as Figure 5 shown, N classical quadrature points are selected in the three-dimensional angular phase space to obtain the distribution function values of neutrons in each discrete angular direction, and the integral value of the three-dimensional angular phase space, that is, the angular distribution of the neutron flux, can be approximately obtained by integrating the distribution function values in all discrete angular directions using Equation (6).

[0123] Among them, for the three-dimensional spatial phase space, discrete calculation is performed according to the quadrature points of the classical multi-dimensional plane, and the integral value of the three-dimensional spatial phase space, that is, the three-dimensional spatial distribution of the neutron flux, can be approximately obtained by solving Equation (6) in the form of weighted summation, and the three-dimensional spatial distribution of the neutron outgoing flux is simulated using Monte Carlo.

[0124] Figure 10 shows a schematic diagram of the principle of obtaining the physical coding layer in an embodiment of the present application. As Figure 10 shown, for each voxel grid, different boron drug concentrations and neutron incident fluxes are input, and the transportation process of neutrons based on the boron drug distribution within the voxel grid is simulated by the Monte Carlo method to obtain the corresponding neutron outgoing flux and neutron flux distribution. Based on this, the model is trained according to the given input and corresponding output, and finally the required physical coding layer is obtained, so as to realize the ability to quickly predict the corresponding neutron outgoing flux and neutron flux distribution based on the given neutron outgoing flux and boron drug concentration.

[0125] S7. Obtain the CT image of the target object, and match at least one of the voxel grids according to the CT image to obtain the voxel model of the target object.

[0126] As described above, when the boron drug transport simulation model and the physical encoding layer are constructed for each voxel grid, the voxel model of the target object can be obtained based on the specific information of the target object. In some embodiments, obtaining the CT image of the target object and matching at least one of the voxel grids according to the CT image to obtain the voxel model of the target object includes: obtaining the CT image of the target object, determining the cancerous region in the CT image, thereby matching the corresponding at least one voxel grid, and obtaining the voxel model of the target object through the matched voxel grid.

[0127] Further, the cancerous region can actually be composed of multiple voxel grids, that is, the voxel grid is a more microscopic division unit. For example, according to the CT image, the cancerous region can be determined to be the brain of the target object, and the brain is composed of multiple tissue materials, then this region matches multiple voxel grids. In some other embodiments, a certain cancerous region may be precise enough, and in this case, one voxel grid is matched. In addition, the voxel model of the target object can also include the voxel grids corresponding to the cancerous region and the voxel grids of the normal region. That is, all voxel grids are completely matched according to various human tissue materials of the target object to form a voxel model.

[0128] S8. Perform bidirectional coupling based on the physical encoding layer and the boron drug transport simulation model of all voxel grids in the voxel model to obtain the neutron flux distribution and boron drug concentration in the voxel model.

[0129] As described above, in fact, without the need to know any information of the patient in advance, this application offline generates the physical encoding layer and the boron drug transport simulation method corresponding to each voxel grid. At this time, the corresponding voxel model is obtained according to the actual situation of different target objects. And the physical encoding layers of all voxel grids in the voxel model are coupled to predict the neutron flux distribution in the target object's body. Similarly, this application also couples the boron drug transport simulation models of all voxel grids in the voxel model to predict the boron drug concentration in the target object's body.

[0130] Further, in order to simultaneously consider the neutron transport and boron drug transport processes in the target object's body and consider the characteristics of neutron-boron drug bidirectional targeting of BNCT, in fact, during the coupling of the physical encoding layer and the coupling of the boron drug transport simulation model, a bidirectional coupling relationship at the voxel grid level is also formed between the two, so as to fully reflect the characteristics that BNCT treatment is actually affected by the combined action of the two.

[0131] Specifically, the bidirectional coupling based on the physical encoding layer of all voxel grids in the voxel model and the boron drug transport simulation model includes: using the boron drug concentration output by the boron drug transport simulation model as an input to the physical encoding layer to online couple the physical encoding layers of all the voxel grids, and obtaining the neutron flux distribution in the voxel model; using the neutron flux distribution output by the physical encoding layer as an input to the boron drug transport simulation model to online couple the boron drug transport simulation models of all the voxel grids, and obtaining the boron drug concentration in the voxel model.

[0132] In some embodiments, the bidirectional coupling based on the physical encoding layer of all voxel grids in the voxel model and the boron drug transport simulation model includes: (1) Setting a voxel grid in the voxel model as an initial grid, and giving the initial grid a radiation source information and a boron drug administration plan. The radiation source information includes the radiation intensity of a neutron source and a radiation exposure time, and the boron drug administration method includes a boron drug inflow rate and a boron drug concentration.

[0133] (2) Inputting the radiation intensity of the neutron source, the boron drug inflow rate, and the radiation exposure time into the boron drug transport simulation model of the initial grid to obtain the corresponding boron drug outflow rate and boron drug concentration.

[0134] (3) Determining the neutron incident flux entering the initial grid based on the radiation intensity of the neutron source, and inputting the boron drug concentration and the neutron incident flux into the physical encoding layer of the initial grid to obtain the corresponding neutron outgoing flux and neutron flux distribution. Among them, determining the neutron incident flux entering the initial grid based on the radiation intensity of the neutron source also needs to be determined according to factors such as the type of neutron source and the environment. Those skilled in the art can determine the neutron incident flux by themselves according to the radiation intensity of the neutron source and in combination with other information. This application does not limit this.

[0135] (4) Using the neutron flux distribution of the initial grid, the boron drug outflow rate of the initial grid, and the radiation exposure time as inputs to the next voxel grid, and obtaining the corresponding boron drug outflow rate and boron drug concentration based on the boron drug transport simulation model of the next voxel grid, and so on until all the voxel grids are traversed to obtain the boron drug concentration in the voxel model.

[0136] (5) Using the neutron outgoing flux of the initial grid and the boron drug concentration of the initial grid as inputs to the next voxel grid, and predicting the corresponding neutron outgoing flux and neutron flux distribution based on the physical encoding layer of the next voxel grid, and so on until all the voxel grids are traversed to obtain the neutron flux distribution in the voxel model.

[0137] It should be noted that there is no fixed execution order between (2) and (3), and there is also no fixed execution order between (5) and (6).

[0138] In fact, the process of bidirectional coupling can be considered that during the coupling process of the physical coding layer of the voxel grid, the output of the boron drug transport simulation model needs to be considered, and during the coupling process of the boron drug transport simulation model of the voxel grid, the output of the physical coding layer also needs to be considered. This exactly conforms to the characteristics of BNCT with neutron-boron drug bidirectional targeting, making the BNCT treatment plan generated based on this method have higher accuracy.

[0139] The protection scope of the neutron transport simulation method described in the embodiments of this application is not limited to the execution order of the steps listed in this embodiment. Any solution achieved by adding or subtracting steps of the prior art and replacing steps according to the principle of this application is included in the protection scope of this application.

[0140] The embodiments of this application also provide a computer-readable storage medium, on which a computer program is stored. When the program is called by a processor, it implements the boron drug transport simulation method and / or the coupling simulation method of neutron-boron drug transport provided by this application.

[0141] Among them, a computer-readable storage medium can be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium can be, for example (but not limited to), an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD), memory stick, floppy disk, mechanical coding device.

[0142] The computer-readable program characterized here can be downloaded from the computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device through a network, such as the Internet, local area network, wide area network, and / or wireless network. The network adapter or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0143] The above embodiments are only illustrative of the principles and effects of the present application and are not intended to limit the present application. Any person familiar with this technology can modify or change the above embodiments without departing from the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by those with ordinary knowledge in the technical field without departing from the spirit and technical idea disclosed in the present application should still be covered by the claims of the present application.

Claims

1. A boron drug transportation simulation method, characterized in that, The method is applicable to a BNCT treatment planning system, and the method includes: Setting a plurality of voxel grids and boron drug transport parameters, where the voxel grids correspond to various human tissue materials; Offline encoding each of the voxel grids using the boron drug transport parameters to obtain a corresponding boron drug transport simulation model, which is used to predict the boron drug outflow rate and the boron drug concentration in the voxel grid based on the radiation intensity, the boron drug inflow rate, and the radiation irradiation time; Obtaining a CT image of a target object, and matching at least one of the voxel grids according to the CT image to obtain a voxel model of the target object; Obtaining the boron drug concentration in the voxel model based on the boron drug transport simulation models of all the voxel grids in the voxel model.

2. The boron drug transportation simulation method according to claim 1, wherein Offline encoding each of the voxel grids using the boron drug transport parameters includes: Constructing a control equation for the voxel grid, which is established based on the computational fluid dynamics equation and the convection-diffusion-reaction equation; Giving different radiation intensities, boron drug inflow rates, and radiation irradiation times to each of the voxel grids, and solving the control equation in combination with the boron drug transport parameters to obtain the corresponding boron drug outflow rate and the boron drug concentration in the voxel grid; Offline encoding the boron drug transport simulation model of each voxel grid based on the given radiation intensity, boron drug inflow rate, radiation irradiation time, and the obtained boron drug outflow rate and boron drug concentration in the voxel grid.

3. The boron drug transportation simulation method according to claim 2, wherein The method includes: Defining the boron drug inflow rate and the boron drug outflow rate in a two-dimensional phase space, where the two-dimensional phase space includes a three-dimensional space phase space and a time phase space; In the three-dimensional space phase space, calculating the three-dimensional space distribution of the boron drug inflow rate based on quadrature discretization, and solving the control equation in combination with the boron drug transport parameters to obtain the three-dimensional space distribution of the corresponding boron drug outflow rate and the three-dimensional space distribution of the boron drug concentration; In the time phase space, calculating the time distribution of the boron drug inflow rate based on the asymptotic state approximation, and solving the control equation in combination with the boron drug transport parameters to obtain the time distribution of the corresponding boron drug outflow rate and the time distribution of the boron drug concentration.

4. The boron drug transportation simulation method according to claim 1, characterized in that The boron drug transport parameters include the characteristic diffusion coefficient of the boron drug, the characteristic flow rate of body fluid, and the free boron conversion rate.

5. A coupled simulation method for neutron-boron drug transportation, characterized in that, The method is applicable to a BNCT treatment planning system, and the method includes: Setting a plurality of voxel grids and boron drug transport parameters, and offline encoding each of the voxel grids using the boron drug transport parameters to obtain a corresponding boron drug transport simulation model; the voxel grids correspond to various human tissue materials; the boron drug transport simulation model is used to predict the boron drug outflow rate and the boron drug concentration in the voxel grid based on the radiation intensity, the boron drug inflow rate, and the radiation irradiation time; Offline encoding each of the voxel grids to obtain a corresponding physical encoding layer, which is used to predict the corresponding neutron outflow and neutron flux distribution based on the neutron incident flux and the boron drug concentration. Obtain the CT image of the target object, and match at least one of the voxel grids according to the CT image to obtain the voxel model of the target object; Based on the physical encoding layer of all voxel grids in the voxel model and the boron drug transport simulation model, perform two-way coupling to obtain the neutron flux distribution and boron drug concentration in the voxel model.

6. The coupled simulation method for neutron-boron drug transportation according to claim 5, characterized in that Performing offline encoding on each of the voxel grids to obtain the corresponding physical encoding layer includes: Customize the neutron incident flux and the neutron outgoing flux in the eight-dimensional phase space; the eight-dimensional phase space includes a time phase space, an energy phase space, a three-dimensional angular phase space, and a three-dimensional spatial phase space; Given different neutron incident fluxes and boron drug concentrations for each of the voxel grids to correspondingly obtain the neutron outgoing flux and the neutron flux distribution in the eight-dimensional phase space; Based on the given neutron incident flux, boron drug concentration, and the corresponding neutron outgoing flux and neutron flux distribution, offline encode the physical encoding layer of each voxel grid.

7. The coupled simulation method for neutron-boron drug transportation according to claim 6, wherein The method includes: Combined with the definition of the eight-dimensional phase space of the neutron incident flux and the neutron outgoing flux, perform spatial integration on the voxel grid to characterize the neutron transport process in the voxel grid as the relationship characteristics of the neutron incident flux and the neutron outgoing flux.

8. The coupled simulation method for neutron-boron drug transportation according to claim 5, wherein Based on the physical encoding layer of all voxel grids in the voxel model and the boron drug transport simulation model, performing two-way coupling includes: Use the boron drug concentration output by the boron drug transport simulation model as an input to the physical encoding layer to perform online coupling on the physical encoding layers of all voxel grids to obtain the neutron flux distribution in the voxel model; Use the neutron flux distribution output by the physical encoding layer as an input to the boron drug transport simulation model to perform online coupling on the boron drug transport simulation models of all voxel grids to obtain the boron drug concentration in the voxel model.

9. The coupled simulation method for neutron-boron drug transportation according to claim 8, wherein The method includes: Set a voxel grid in the voxel model as the initial grid, and give a radiation source information and a boron drug administration plan to the initial grid. The radiation source information includes the radiation intensity of a neutron source and a radiation exposure time. The boron drug administration method includes a boron drug inflow rate and a boron drug concentration; Input the radiation intensity of the neutron source, the boron drug inflow rate, and the radiation exposure time into the boron drug transport simulation model of the initial grid to obtain the corresponding boron drug outflow rate and boron drug concentration; Determine the neutron incident flux entering the initial grid based on the radiation intensity of the neutron source, and input the boron drug concentration and the neutron incident flux into the physical encoding layer of the initial grid to obtain the corresponding neutron outgoing flux and neutron flux distribution; Use the neutron flux distribution of the initial grid, the boron drug outflow rate of the initial grid, and the radiation exposure time as the input of the next voxel grid, and based on the boron drug transport simulation model of the next voxel grid, obtain the corresponding boron drug outflow rate and boron drug concentration, and so on until all voxel grids are traversed to obtain the boron drug concentration in the voxel model; Take the neutron efflux of the initial grid and the boron drug concentration of the initial grid as the input of the next voxel grid, and predict the corresponding neutron efflux and neutron flux distribution based on the physical coding layer of the next voxel grid, and so on until all the voxel grids are traversed to obtain the neutron flux distribution in the voxel model.

10. A computer storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the boron drug transport simulation method according to any one of claims 1 to 4 or the coupled simulation method of neutron-boron drug transport according to any one of claims 5 to 9.

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