Dose evaluation method

Abstract

The application relates to the technical field of medical devices, and discloses a dose evaluation method, wherein the method comprises the following steps: constructing an object model based on image data of a target object, the object model is divided into a plurality of voxels, each voxel comprises at least one cell element, the cell element has a nuclear target area, and component particles are distributed in at least one region inside and outside the cell element, the component particles are used for generating radiation particles; determining an equivalent nuclear energy probability density function based on a nuclear energy probability density function and a distribution number of the component particles in each region; determining a cell element survival rate based on the equivalent nuclear energy probability density function; and generating a dose evaluation result of the target object based on the cell element survival rate. The technical scheme can improve the accuracy of dose evaluation.

Description

Technical Field

[0001] This application relates to the field of medical device technology, and in particular to a dosage assessment method. Background Technology

[0002] In boron neutron capture therapy (BNCT), a dose assessment is required before injecting a targeted boron agent into the organism. Dosage can be expressed as physical deposition dose and bioeffective dose. Physical deposition dose refers to the dose of various types of radiation particles (such as alpha particles, etc.). 7 The total energy of ionizing radiation (Li particles, protons, and secondary electrons) deposited per unit mass of irradiated material. The bioeffective dose is the dose obtained by correcting for the physical deposition dose after considering the biological damaging capacity of radiation. The bioeffective dose is equal to the product of the relative biological effectiveness (RBE) parameter and the physical deposition dose.

[0003] Currently, some dose assessment techniques refer to particles that generate radiation, such as boron, nitrogen, hydrogen, and photons, as component particles. The dose of each component particle is multiplied by a fixed relative biological effect parameter and then weighted and summed to obtain the dose assessment result. These techniques analyze dose deposition at a macroscopic level and do not reflect dose differences at the microscopic level, resulting in inaccurate dose assessment results. Summary of the Invention

[0004] This application provides a dose assessment method, a dose assessment device, a computer device, and a readable storage medium, which can improve the accuracy of dose assessment.

[0005] To achieve the above objectives, the main technical solutions adopted in this application include: In a first aspect, embodiments of this application provide a dose assessment method, the method comprising: Based on the image data of the target object, an object model is constructed. The object model is divided into multiple voxels. Each voxel includes at least one cell element. The cell element has a nuclear target region. Component particles are distributed in at least one region inside and outside the cell element. The component particles are used to generate radiation particles. Based on the nuclear specific energy probability density function and distribution quantity of the component particles in each region, the equivalent nuclear specific energy probability density function is determined. The nuclear specific energy probability density function refers to the correspondence between the specific energy and probability density of the nuclear target region when a single radiation particle generated by the component particles in each region causes an ionization event in the nuclear target region. Based on the equivalent nuclear energy probability density function, the cell cell survival rate is determined, and based on the cell cell survival rate, the dose assessment result of the target object is generated.

[0006] Secondly, embodiments of this application provide a dose assessment device, the device comprising: The model building module is used to build an object model based on the image data of the target object. The object model is divided into multiple voxels, each voxel including at least one cell element, the cell element having a nuclear target region, and component particles distributed in at least one region inside and outside the cell element, the component particles being used to generate radiation particles. The parameter determination module is used to determine the equivalent nuclear specific energy probability density function based on the nuclear specific energy probability density function and the distribution quantity of the component particles in each region. The nuclear specific energy probability density function refers to the correspondence between the specific energy and probability density of the nuclear target region when a single radiation particle generated by the component particles in each region causes an ionization event in the nuclear target region. The evaluation module is used to determine the cell cell survival rate based on the equivalent nuclear energy probability density function, and to generate a dose evaluation result for the target object based on the cell cell survival rate.

[0007] Thirdly, embodiments of this application provide a computer device, including: A memory and a processor are communicatively connected, the memory storing computer instructions, and the processor executing the computer instructions to perform the dose assessment method as described in any of the preceding claims.

[0008] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer instructions that cause a computer to perform the dose assessment method as described in any of the preceding claims.

[0009] In some embodiments of this application, firstly, the application divides the cell element into internal and external regions and sets the nuclear specific energy probability density function of component particles according to the region. This reflects the energy differences and probability distribution differences of radiation particles generated in different regions deposited in the nuclear target region, i.e., it reflects regional differences. This makes the final cell element survival rate more consistent with the transport law of radiation particles, thereby improving the accuracy of dose assessment results. Secondly, the equivalent nuclear specific energy probability density function essentially reflects the probability distribution of the specific energy in the nuclear target region. Since the probability distribution is random, the specific energy in the nuclear target region between each cell element is random, thus reflecting the specific energy differences between different cell elements. This makes the cell element survival rate more consistent with the energy deposition law of radiation particles, thereby improving the accuracy of dose assessment results. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0011] Figure 1 A schematic flowchart illustrating a dose assessment method provided for some embodiments of this application; Figure 2 A schematic diagram illustrating the effect of boron concentration difference between two cell groups on cell survival curves provided in some embodiments of this application; Figure 3 A schematic diagram illustrating the effect of boron concentration difference between two cell groups on cell survival curves, provided in other embodiments of this application; Figure 4 Comparison charts with and without survival rate correction provided for some embodiments of this application; Figure 5 A schematic diagram of a three-dimensional matrix model provided in some embodiments of this application; Figure 6 This is a schematic diagram of the radiation particle energy deposition of a simulated photon; Figure 7 A schematic diagram of nitrogen radiation particle energy deposition obtained from the simulation; Figure 8 This is a schematic diagram of the simulated radiation particle energy deposition of hydrogen. Figure 9 A schematic diagram of the radiation particle energy deposition of boron obtained from the simulation; Figure 10 The domain specific energy spectrum corresponding to each component particle obtained from the simulation; Figure 11 The domain specific energy spectrum obtained from the simulation in both boron-containing and boron-free scenarios; Figure 12 The nuclear specific energy spectrum corresponding to each component particle obtained from the simulation; Figure 13 The nuclear ratio energy spectra obtained from simulations of boron-containing and boron-free scenarios; Figure 14 This application provides some embodiments of the relationship between bioeffective dose and depth calculated using different methods; Figure 15 This application provides some embodiments of the relationship between bioeffective dose and physically absorbed dose calculated using different methods; Figure 16This application provides some embodiments of the relationship between relative biological effects and depth calculated using different methods; Figure 17 This application provides some embodiments of the relationship between relative biological effects and physically absorbed doses calculated using different methods. Figure 18 This application provides some embodiments of the relationship between the specific release dose of targeted boron agents and cell viability. Figure 19 This application provides the relationship between the specific release dose and cell viability of some other targeted boron agents in some embodiments; Figure 20 The relationship between specific release dose and relative biological effect is provided for some embodiments of this application; Figure 21 The relationship between specific release dose and relative biological effect is provided for other embodiments of this application; Figure 22 A schematic diagram of the module of a dose assessment system provided for some embodiments of this application; Figure 23 for Figure 22 Data processing flowchart of the Monte Carlo transport model; Figure 24 A schematic diagram of a dose assessment device provided for some embodiments of this application; Figure 25 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0012] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0013] The principle of boron neutron capture therapy is: to capture boron neutrons carrying... 10 B-targeted boron agents are injected into biological tissues. 10 B-type neutrons specifically accumulate in the internal and external regions of tumor cells, such as the nucleus, cytoplasm, and extracellular space. Subsequently, an ultrathermal neutron beam is incident on the biological tissue. As the neutrons move within the tissue, they are probabilistically... 10 B captures, thus triggering 10 B(n,ɑ) 7 Li nuclear reaction. The nuclear reaction can release a large number of α particles with a range of only 4–10 μm and 7Li particles. This range is comparable to the diameter of tumor cells, allowing the energy carried by these two particles to be deposited within the tumor cells, achieving precise elimination of tumor cells while maximizing the protection of surrounding healthy tissue. Simultaneously, approximately 93.7% of the boron reacts with neutrons. 10 B(n,ɑ) 7 During lithium nuclei reaction, photons are produced. These photons interact with biological tissues, generating secondary electrons. When these secondary electrons pass through tumor cells, they deposit energy within the cells, thus eliminating the tumor. Additionally, during their movement, neutrons may also probabilistically react with nitrogen and hydrogen nuclei in the tissues. A neutron reacting with a nitrogen nucleus produces a monoenergetic proton. A neutron reacting with a hydrogen nucleus produces a recoil proton. Both monoenergetic and recoil protons, when passing through tumor cells, deposit energy within them, further contributing to tumor cell elimination.

[0014] In this application, α particles, 7 Particles that deposit energy in tumor cells, such as Li particles, secondary electrons, recoil protons, and monoenergetic protons, are called radiating particles. Particles that produce radiating particles, such as boron, photons, hydrogen, and nitrogen, are called component particles. Different radiating particles can have different linear energy transfer (LET) capabilities. Linear energy transfer capability is an inherent property of radiating particles, used to describe the amount of energy deposited per unit length of movement of the radiating particle. Among the radiating particles mentioned above, α particles, ... 7 Li particles are high-LET particles with strong linear energy transfer capabilities and a strong ability to eliminate tumor cells. Secondary electrons are low-LET particles with weak linear energy transfer capabilities and a weak ability to eliminate tumor cells. Recoil protons and monoenergetic protons are medium-LET particles with moderate linear energy transfer capabilities, and their ability to eliminate tumor cells falls between that of high-LET and low-LET particles.

[0015] In summary, the energy sources for eliminating tumor cells in boron neutron capture therapy are quite complex. Dosage assessment is typically required before injecting targeted boron agents into a organism. Dosage refers to the energy deposited per unit mass of a substance.

[0016] Currently, some traditional dose assessment methods typically model biological tissues based on imaging data, dividing the model into multiple voxels. A voxel is a three-dimensional segmentation unit of the model, used to simulate a sub-tissue of the biological tissue. Then, based on methods such as Monte Carlo, the transport process of radiation particles in the biological tissue can be simulated in the model, and the physical deposition dose corresponding to each component particle in each voxel can be statistically obtained. The physical deposition dose corresponding to the component particle refers to the deposition dose of radiation particles generated by the component particle within each voxel. For example, α particles generated by boron... 7 The deposition dose of Li particles and the deposition dose of recoil protons generated by hydrogen in each voxel are calculated. Each component particle has its own corresponding relative biological effect parameter (RBE parameter). For example, the RBE parameter for boron is 2.2, and the RBE parameter for hydrogen is 1.1. Within any voxel, the physical deposition dose and relative biological effect parameter corresponding to each component particle are multiplied, and the results are weighted and fused to obtain the total deposition dose for the corresponding voxel.

[0017] The total deposition dose of multiple voxels constitutes the dose distribution of a biological tissue. Based on this dose distribution, it can be determined whether the total deposition dose within each voxel meets the dose requirement for eliminating tumor cells. However, in these techniques, analyzing the total deposition dose of a single voxel at a macroscopic level fails to reflect microscopic dose differences, resulting in inaccurate dose assessments. For example, due to the randomness of nuclear reactions, within the same voxel, some tumor cell nuclei may have lower deposition doses, while others may have higher doses. Even when the total deposition dose of the voxel meets the dose requirement for eliminating tumor cells, some tumor cells may still not be eliminated.

[0018] Other techniques construct MK (Microdosimetric Kinetic) models of tumor cell nuclei. In the MK model, the average specific energy of the cell nucleus is calculated, and the survival probability of a single tumor cell or the survival rate of multiple tumor cells is calculated based on this average specific energy. The average specific energy of the cell nucleus refers to the average ratio between the energy deposited within the cell nucleus and the mass of the cell nucleus. This statistical method based on average specific energy also fails to reflect the differences in specific energy between different cell nuclei.

[0019] Therefore, this application provides a dose assessment method that can improve the accuracy of dose assessment. The dose assessment method can be applied to electronic devices. Electronic devices may include, but are not limited to, tablet computers, desktop computers, laptop computers, etc. (See also...) Figure 1 This is a schematic flowchart of a dose assessment method provided in some embodiments of this application. Figure 1 In this context, dose assessment methods may include the following steps: Step S101: Based on the image data of the target object, construct an object model. The object model is divided into multiple voxels. Each voxel includes at least one cell element. The cell element has a nuclear target region. Component particles are distributed in at least one region inside and outside the cell element. The component particles are used to generate radiation particles.

[0020] Specifically, the target object can be a biological tissue including tumor cells. Imaging data can include, but is not limited to, X-ray computed tomography (CT) images and magnetic resonance imaging (MRI) images. Based on the image data, a three-dimensional object model of the target object can be constructed. Each voxel is used to simulate one sub-tissue of the target object. Cell elements are used to simulate cells within the target object, and the nuclear target region is used to simulate the cell nucleus of the target object.

[0021] Component particles include multiple sub-component particles, such as boron, photons, hydrogen, and nitrogen particles. Radiation particles include alpha particles. 7 Li particles, secondary electrons, recoil protons, monoenergetic protons, and other particles.

[0022] In this embodiment, the regions inside and outside the cell may include the nucleus region, the cytoplasm region, the cell membrane region, and the extracellular region. Component particles such as photons, hydrogen, and nitrogen can be uniformly distributed in each of these regions. Depending on the type of targeted boron agent, the amount of boron distributed in each region can differ. For example, for targeted boron agent 1, boron is only distributed in the cytoplasm and extracellular regions, not in the nucleus and cell membrane regions, and the amount of boron distributed in the cytoplasm and extracellular regions can be different. Similarly, for targeted boron agent 2, boron is only distributed in the cell membrane and extracellular regions, not in the nucleus and cytoplasm regions, and the amount of boron distributed in the cell membrane and extracellular regions can be different.

[0023] Step S102: Based on the nuclear specific energy probability density function and distribution quantity of component particles in each region, determine the equivalent nuclear specific energy probability density function. The nuclear specific energy probability density function refers to the correspondence between the specific energy and probability density of the nuclear target region when a single radiation particle generated by the component particles in each region causes an ionization event in the nuclear target region.

[0024] Specifically, an ionization event refers to the process by which radiating particles interact with matter in the nuclear target region, generating ion pairs and losing energy. The specific energy of the nuclear target region refers to the ratio of the deposited dose to the mass of the nuclear target region, i.e., the radiant energy absorbed by a unit mass of nuclear target region matter. Based on the correspondence between the specific energy of the nuclear target region and the probability density, the probability corresponding to different specific energy ranges can be determined. For example, the probability of a specific energy of the nuclear target region falling within the range of 2.2–2.3 Gy is 25%, and the probability of a specific energy of the nuclear target region falling within the range of 2.0–2.1 Gy is 10%.

[0025] The nuclear specific energy probability density function includes the subnuclear specific energy probability density functions of each subcomponent particle. For the aforementioned nuclear region, cytoplasm region, cell membrane region, and extracellular region, each subcomponent particle has its own corresponding subnuclear specific energy probability density function in each region. For example: The subnuclear specific energy probability density function corresponding to boron in the cell nucleus region refers to the first correspondence between the specific energy of the nuclear target region and the probability density when a single radiant particle from boron in the cell nucleus region passes through the nuclear target region and an ionization event with dose deposition occurs in the nuclear target region after the radiant particles are generated.

[0026] The subnuclear specific energy probability density function corresponding to nitrogen in the cell nucleus region refers to the second correspondence between the specific energy of the nuclear target region and the probability density when a single radiation particle of nitrogen in the cell nucleus region passes through the nuclear target region and an ionization event with dose deposition occurs in the nuclear target region after the radiation particles are generated.

[0027] The subnuclear specific energy probability density function corresponding to boron in the extracellular region refers to the third correspondence between the specific energy of the nuclear target region and the probability density when a single radiant particle from boron in the extracellular region passes through the nuclear target region and an ionization event with dose deposition occurs in the nuclear target region after the radiant particles are generated.

[0028] And so on.

[0029] For subcomponent particles that produce short-range radiative particles, the distance difference between different regions and the nuclear target region affects the energy deposited by the radiative particles in the nuclear target region. Therefore, the subnuclear specific energy probability density function of these subcomponent particles can be different in different regions. For example, α particles produced by boron... 7 Since Li particles are short-range radiative particles, the subnuclear specific energy probability density function of boron can be different in different regions.

[0030] For subcomponent particles that produce long-range radiating particles, because the radiating particles produced by these particles have a long range, the difference in distance between each region and the nuclear target region will not affect the energy deposited by the radiating particles in the nuclear target region. Therefore, the subnuclear specific energy probability density function of these subcomponent particles can be the same in each region. For example, since the monoenergetic protons produced by nitrogen are long-range radiating particles, the subnuclear specific energy probability density function of nitrogen can be the same in each region.

[0031] The subnuclear specific energy probability density function can be an inherent characteristic of each subcomponent particle. Before implementing the method of this application, the subnuclear specific energy probability density function of each subcomponent particle in each region can be obtained in advance based on the Monte Carlo simulation method, and the subnuclear specific energy probability density function can be preset in the electronic device that implements the method of this application.

[0032] When performing step S102, the sub-equivalent nuclear specific energy probability density function can be determined based on the subnuclear specific energy probability density function and the distribution quantity of the same sub-component particles in each region. Then, the sub-equivalent nuclear specific energy probability density functions of multiple sub-component particles are fused and calculated to obtain the equivalent nuclear specific energy probability density function. Specifically, the distribution quantity of the same sub-component particles in each region can be used as a weight to perform a weighted fusion calculation on all subnuclear specific energy probability density functions of the same sub-component particles to obtain the corresponding sub-equivalent nuclear specific energy probability density function of the sub-component particles. For example, taking boron as an example, the distribution quantity of boron in each region can be used as a weight. Then, based on expression (1), the subnuclear specific energy probability density functions of boron in each region are weighted and fused to obtain the sub-equivalent nuclear specific energy probability density function of boron.

[0033] (1) in, The probability density function representing the subequivalent nuclear specific energy of boron, This indicates the amount of boron distributed in the cell nucleus region. This indicates the amount of boron distributed in the cytoplasm. This indicates the amount of boron distributed in the cell membrane region. This indicates the amount of boron distributed in the extracellular region. This represents the subnuclear specific energy probability density function of boron in the cell nucleus region. This represents the subnuclear specific energy probability density function of boron in the cytoplasm region. This represents the subnuclear specific energy probability density function of boron in the cell membrane region. This represents the subnuclear specific energy probability density function of boron in the extracellular region.

[0034] It is understandable that for sub-component particles that generate long-range radiation particles, since the subnuclear specific energy probability density function of these particles is the same in each region, it can be seen from expression (1) that if the distribution quantity of these sub-component particles is the same in each region (i.e., uniformly distributed), then the subnuclear specific energy probability density function of these sub-component particles is the same as the sub-equivalent nuclear specific energy probability density function. In this embodiment, nitrogen, hydrogen, and photons generate long-range radiation particles, and these sub-component particles are uniformly distributed in each region. The subnuclear specific energy probability density function of these sub-component particles can be directly used as the sub-equivalent nuclear specific energy probability density function, without the need to perform the fusion calculation of expression (1). For boron, its distribution quantity in each region can be obtained through the human-computer interaction interface, and then the corresponding sub-equivalent nuclear specific energy probability density function can be obtained based on expression (1). Specifically, the distribution quantity of boron in each region can be determined by combining the characteristics of the targeted boron agent injected into the target object and the injection amount. For example, assuming that the targeted boron agent 1 is only distributed in the cytoplasm and extracellular regions, and the distribution ratio of boron in the cytoplasm and extracellular regions of the targeted boron agent has been obtained in advance based on the product instructions or experiments, then the amount of boron distributed in each region can be calculated by combining the injection amount of the targeted boron agent with its characteristics.

[0035] After obtaining the sub-equivalent nuclear specific energy probability density function of each sub-component particle, the sub-equivalent nuclear specific energy probability density function can be fused and calculated based on expression (2) to obtain the equivalent nuclear specific energy probability density function.

[0036] in, Let be the sub-equivalent nuclear specific energy probability density function for boron. Let be the subequivalent nuclear specific energy probability density function of hydrogen. Let be the sub-equivalent nuclear specific energy probability density function of nitrogen. Let be the subequivalent nuclear specific energy probability density function of the photon. Let be the equivalent nuclear specific energy probability density function. The weights of boron as a function. The function weights for hydrogen are... The weights of nitrogen are the function weights. The photon's functional weights are given. The equivalent nuclear specific energy probability density function can serve as a substitute for several sub-equivalent nuclear specific energy probability density functions.

[0037] In this embodiment, the deposition dose of each subcomponent particle at a single voxel can be obtained from the simulation results as the sub-deposition dose. Then, based on a preset conversion factor, the sub-deposition dose is converted into the sub-average specific energy. Finally, the function weight is obtained based on the first-order origin moment of the probability density function of the sub-average specific energy and the sub-equivalent nuclear specific energy. For example, assuming the sub-deposition dose of boron is... The conversion factor for boron is Then the sub-average specific energy of boron Finally, based on the expression The functional weights of boron can be determined. for The first-order origin moment.

[0038] Step S103: Based on the equivalent nuclear energy probability density function, determine the cell cell survival rate, and based on the cell cell survival rate, generate the dose assessment results for the target object.

[0039] Specifically, the survival of cellular cells is mainly determined by the degree of damage to the nuclear target region. For example, if two consecutive sites of severe damage occur within the nuclear target region, the survival probability of the cellular cells can be judged to be low.

[0040] Cellular survival rate can be used to generate dose assessment results. For example, a low cellular survival rate indicates that the dose distribution of the target object is reasonable and can achieve the cellular elimination target. A high cellular survival rate indicates that the dose distribution of the target object is unreasonable and cannot achieve the cellular elimination target.

[0041] It is understandable that the equivalent nuclear specific energy probability density function essentially reflects the probability distribution of the specific energy of the nuclear target region when a single ionization event occurs in the nuclear target region by a single radiating particle. Since the specific energy of the nuclear target region can reflect the degree of damage to the nuclear target region, and the probability distribution can reflect the random distribution characteristics of the specific energy of the nuclear target region, when determining the specific energy of the nuclear target region of different cell units based on the equivalent nuclear specific energy probability density function, the specific energy of the nuclear target region between each cell unit has randomness. In this way, the specific energy differences between different cell units can be reflected, making the cell unit survival rate more consistent with the transport and energy deposition laws of radiating particles, thereby improving the accuracy of dose assessment results.

[0042] In summary, in the technical solutions of some embodiments of this application, firstly, this application divides the cell cell into internal and external regions and sets the nuclear specific energy probability density function of component particles according to the region. This can reflect the energy differences and probability distribution differences of radiation particles generated in different regions deposited in the nuclear target region, that is, reflect regional differences. In this way, the final cell cell survival rate can be made more consistent with the transport law of radiation particles, thereby improving the accuracy of dose assessment results. Secondly, the equivalent nuclear specific energy probability density function essentially reflects the probability distribution of the specific energy in the nuclear target region. Since the probability distribution is random, the specific energy in the nuclear target region between each cell cell is random, which can reflect the specific energy differences between different cell cells, making the cell cell survival rate more consistent with the energy deposition law of radiation particles, thereby improving the accuracy of dose assessment results.

[0043] In some embodiments, determining cell viability based on the equivalent nuclear specific energy probability density function in step S103 includes: The transport process of radiative particles is simulated in the object model, and the total deposition dose of each voxel is obtained from the simulation results. The total deposition dose is then converted into the average specific energy of the nuclear target region within the voxel. Based on the average specific energy of the nuclear target region and the probability density function of the equivalent nuclear specific energy, a total specific energy probability density function is constructed. The total specific energy probability density function characterizes the correspondence between the total specific energy and the probability density of a single nuclear target region under the constraint of the average specific energy of the nuclear target region. Based on the total specific energy probability density function and the cell cell survival function, the cell cell survival rate is determined. The cell cell survival function characterizes the correlation between the survival probability of a single cell cell and the specific energy of the nuclear target region.

[0044] Specifically, based on the Monte Carlo method, the transport process of radiative particles is simulated in the object model. After the simulation, the total deposition dose is statistically obtained on a voxel-by-voxel basis. The total deposition dose is the macroscopic deposition dose. Based on the conversion factor obtained experimentally, the total deposition dose of each voxel can be converted into the average specific energy of the nuclear target region for the corresponding voxel. The average specific energy of the nuclear target region can be understood as the average total specific energy deposited in each nuclear target region after multiple ionization events.

[0045] The total specific energy probability density function can be understood as the probability distribution of the actual cumulative total specific energy of a single nuclear target region, centered on the average specific energy of the nuclear target region and represented by the equivalent nuclear specific energy probability density function as random specific energy fluctuations and probability fluctuations. For example, when the average specific energy of the nuclear target region is 2.3 Gy, the probability that the cumulative total specific energy of a single nuclear target region falls within the 2.2–2.3 Gy range is 60%, the probability that it falls within the 2.0–2.2 Gy range is 30%, and the probability that it falls within the 1.8–2.0 Gy range is 5%.

[0046] The cell survival function can be understood as the survival probability of a single cell at different specific energies in various nuclear target regions. For example, when the specific energy of the nuclear target region is in the range of 2.2 to 2.3 Gy, the survival probability of the cell is 50%, and when the specific energy of the nuclear target region is in the range of 2.5 to 3 Gy, the survival probability of the cell is 30%.

[0047] In some embodiments, cell viability can be determined based on expression (3).

[0048] in, Indicates cell survival rate. Represents the cell survival function. This represents the total specific energy probability density function. Indicates the average specific energy of the nuclear target area. is a variable representing the specific energy of the nuclear target region.

[0049] In the above embodiments, the cell survival rate is determined based on the correlation between the survival probability of a single cell and the specific energy of the nuclear target region, and the correspondence between the total specific energy of a single nuclear target region and the probability density. This fully considers the randomness of cell survival under different specific energies of nuclear target regions and the randomness of the total specific energy of each nuclear target region. The cell survival rate obtained can be adapted to the actual transport process of radiated particles, thereby improving the accuracy of dose assessment.

[0050] In some embodiments, the above-mentioned construction of the total specific energy probability density function based on the average specific energy of the nuclear target region and the equivalent nuclear specific energy probability density function includes: Based on the average specific energy and equivalent specific energy probability density function of the nuclear target area, an event probability function and a cumulative specific energy probability density function are constructed. The event probability function characterizes the correspondence between the number of ionization events and the probability of occurrence of the events in a single nuclear target area, while the cumulative specific energy probability density function characterizes the correspondence between the number of ionization events and the specific energy probability distribution in a single nuclear target area. Based on the cumulative specific energy probability density function and the event probability function, the total specific energy probability density function is constructed.

[0051] Specifically, the event probability function can be understood as the probability of each ionization event occurring. For example, the probability of 3 ionization events is 20%, the probability of 4 ionization events is 30%, and the probability of 5 ionization events is 35%.

[0052] The cumulative specific energy probability density function can be understood as the specific energy probability distribution corresponding to each number of ionization events. For example, when the number of ionization events is 3, the probability of the specific energy of the nuclear target region being in the 1.2–1.3 Gy range is 30%, the probability of being in the 1.4–1.5 Gy range is 25%, and the probability of being in the 1.6–1.7 Gy range is 20%; when the number of ionization events is 4, the probability of the specific energy of the nuclear target region being in the 1.2–1.3 Gy range is 20%, the probability of being in the 1.4–1.5 Gy range is 30%, and the probability of being in the 1.6–1.7 Gy range is 23%; when the number of ionization events is 5, the probability of the specific energy of the nuclear target region being in the 1.2–1.3 Gy range is 15%, the probability of being in the 1.4–1.5 Gy range is 35%, and the probability of being in the 1.6–1.7 Gy range is 30%.

[0053] In the above embodiments, the total specific energy probability density function is constructed based on the cumulative specific energy probability density function and the event probability function. This allows the total specific energy probability density function to simultaneously reflect the randomness of the number of ionization events and the specific energy randomness of the nuclear target region corresponding to different numbers of ionization events. This effectively reflects the specific energy randomness of different nuclear target regions and conforms to the actual specific energy deposition law of the nuclear target region.

[0054] In some embodiments, a cumulative specific energy probability density function is constructed based on the average specific energy of the nuclear target region and the equivalent nuclear specific energy probability density function, including: Based on the number of ionization events, a convolution operation is performed on the equivalent nuclear specific energy probability density function to obtain the cumulative specific energy probability density function.

[0055] Specifically, the cumulative specific energy probability density function can be constructed based on expression (4).

[0056] (4) in, This represents the cumulative specific energy probability density function. This represents the probability density function of the equivalent kernel specific energy. This represents the relationship between the total specific energy accumulated in a single nuclear target region and the probability density after the first k-1 ionization events. Through multi-layer convolution, the relationship between the total specific energy accumulated in a single nuclear target region and the probability density after k ionization events can be obtained.

[0057] In the above embodiments, by using layer-by-layer convolution, the correspondence between the nuclear target region specific energy and probability density of a single ionization event can be preserved. At the same time, the influence of multiple ionization events is superimposed, so that the final cumulative specific energy probability density function conforms to the randomness characteristics in the actual scenario.

[0058] In some embodiments, an event probability function is constructed based on the average specific energy of the nuclear target region and the equivalent nuclear specific energy probability density function, including: The first-order origin moment of the equivalent nuclear specific energy probability density function is taken as the frequency-averaged specific energy. Based on the average specific energy of the nuclear target region and the average specific energy of the frequency, the average number of ionization events occurring in a single nuclear target region is determined, and the event probability function is constructed using the average number of ionization events as a characteristic parameter.

[0059] Specifically, the first-order moment at the origin represents the average nuclear specific energy under a single ionization event. Based on expression (5), the average number of ionization events can be determined.

[0060] in, Indicates the average number of ionization events. It represents the frequency-average specific energy (i.e., the average nuclear specific energy under a single ionization event). It represents the average specific energy of the nuclear target region, that is, the average total specific energy deposited in each nuclear target region after multiple ionization events.

[0061] Using the average number of ionization events as a feature parameter, an event probability function can be constructed. In the event probability function, k is a variable representing the number of ionization events. The average number of ionization events... The distribution of the probability function determines the probability of each ionization event, and thus the probability of the number of events. For example, the probability of the average number of ionization events... When the value is 5, the probability of the number of ionization events k being 4 is 30%, the probability of the number of ionization events k being 5 is 50%, and the probability of the number of ionization events k being 3 is 10%. The average number of ionization events... When the value is 4, the probability of ionization event number k being 4 is 70%, the probability of ionization event number k being 5 is 10%, and the probability of ionization event number k being 3 is 10%.

[0062] Based on the above description, the total specific energy probability density function can be expressed as in expression (6).

[0063] in, This represents the total specific energy probability density function. Let be the event probability function. Let be the cumulative specific energy probability density function.

[0064] Furthermore, based on the above expression (4), it can be seen that multiple convolutions are required in the construction of the cumulative specific energy probability density function, resulting in a large computational load. Therefore, some embodiments of this application provide one solution: during the convolution operation, a Fourier transform is performed on the equivalent kernel specific energy probability density function to obtain the frequency domain feature function. Then, according to the number of ionization events, multiple exponentiation operations are performed on the frequency domain feature function. Finally, an inverse Fourier transform is performed on the result of the multiple exponentiation operations to obtain the cumulative specific energy probability density function, as shown in expression (7).

[0065] (7) in, For Fourier transform operators, For inverse Fourier transform operators, Let k be the equivalent nuclear specific energy probability density function, and k represent the number of ionization events. This represents the cumulative specific energy probability density function.

[0066] In the above embodiments, converting k-th time-domain convolution into k-th power operations in the frequency domain can greatly reduce the amount of computation when constructing the cumulative specific energy probability density function, thereby improving computational efficiency.

[0067] In some embodiments, the total deposition dose includes sub-deposition doses corresponding to each sub-component particle. The conversion of the total deposition dose to the average specific energy of the nuclear target region within the voxel includes: By using the conversion factor associated with each subcomponent particle, the subdeposition dose corresponding to the respective subcomponent particle is converted to obtain multiple sub-average specific energies. The total conversion factor is determined based on the sum of the total deposition dose and the sub-average specific energy. The total deposition dose was converted using the total conversion factor to obtain the average specific energy of the nuclear target area.

[0068] Specifically, the formula for calculating the total conversion factor is shown in expression (8).

[0069] Where K is the total conversion factor. , , , The conversion factors are boron, hydrogen, nitrogen, and photons, in that order. , , , The sub-deposition doses are, in order, boron, hydrogen, nitrogen, and photons. , , , The sub-average specific energies are boron, hydrogen, nitrogen, and photons, in that order, with D representing the total deposition dose. The above... , , , These are preset parameters obtained based on experimental data, where, , , The default preset value can be 1.

[0070] In some embodiments, due to factors such as the irradiation duration and incident direction of the neutron beam, the number of some sub-component particles and their relative biological effect parameters may differ at different depths of the target object or in different sub-regions within the same voxel, leading to differences in deposition dose. For example, consider the first and second regions of the same voxel. Assuming the first region includes a first cell unit and the second region includes a second cell unit, with a higher boron particle concentration in the first region and a lower boron particle concentration in the second region, the deposition dose of the first cell unit will be greater than that of the second region. If the average specific energy of the nuclear target area is calculated uniformly based on the total deposition dose, it may not reflect the differences in deposition dose between regions, thus reducing the accuracy of the dose assessment results. For another example, assuming the first and second regions have the same amount of boron, but the cell unit activity in the first region is higher, absorbing more boron, while the cell unit activity in the second region is lower, absorbing less boron, this will also lead to a significant difference in deposition dose between the first and second regions. Therefore, the method of this application further includes: One of the subcomponent particles is taken as the target subcomponent particle, and the cell element is divided into multiple cell element groups. The particle concentration of the target subcomponent particle is different in different cell element groups. Based on the concentration ratio of each cell tuple, the conversion factor associated with the target subcomponent particles is corrected to obtain multiple corrected conversion factors. The concentration ratio refers to the multiple of the concentration of the target subcomponent particles in the cell tuple relative to the average concentration of the target subcomponent particles. Based on multiple modified conversion factors, several different total conversion factors are determined; Multiple average specific energies of nuclear target regions were obtained by using multiple different total conversion factors. Based on the average specific energy of each nuclear target region, the cell survival reference rate was determined. The cell survival rate is obtained by fusing multiple cell survival reference rates.

[0071] Specifically, the target subcomponent particles are those that differ in quantity or absorption among different cellular tuples, such as boron. For ease of understanding, we will use boron as the target subcomponent particle and illustrate with an example of two cellular tuples.

[0072] In the presence of two cell groups, the concentration ratio can be determined based on a preset dispersion parameter δ. The dispersion parameter δ is a preset parameter obtained from experimental data and is used to characterize the difference in boron particle concentration between the two cell groups. Specifically, the boron particle concentration in the first cell group can be 1-δ, and the boron particle concentration in the second cell group can be 1+δ. The average boron particle concentration is 1. The concentration ratio k(x1) of the first cell group = 1-δ, and the concentration ratio k(x2) of the second cell group = 1+δ. For example, with δ = 0.4, the boron particle concentration in the first cell group can be 0.6, and the boron particle concentration in the second cell group can be 1.4, with an average boron particle concentration of 1. The concentration ratio of the first cell group is 0.6, and the concentration ratio of the second cell group is 1.4.

[0073] Based on the concentration fold k(x1) of the first cell tuple, the boron-related conversion factor After making corrections, the first corrected conversion factor can be obtained. Based on the concentration ratio k(x2) of the second cell tuple, the boron-related conversion factor After making corrections, a second corrected conversion factor can be obtained. .

[0074] Based on the corrected conversion factor By combining expression (9), we can obtain the first total conversion factor K1.

[0075] Based on the corrected conversion factor By combining expression (10), we can obtain the second total conversion factor K2.

[0076] The average specific energy of the nuclear target area can be obtained by converting the total deposition dose D based on the total conversion factor K1. By converting the total deposition dose D based on the total conversion factor K2, the average specific energy of the nuclear target area can be obtained. .

[0077] Based on the average specific energy of the nuclear target area , Based on the above expressions (3) to (6), multiple cell viability rates can be determined as cell viability reference rates. For example, based on the average specific energy of the nuclear target region... The cell survival reference rate can be obtained. Based on the average specific energy of the nuclear target area The cell survival reference rate can be obtained. .

[0078] Using the occurrence probabilities of the two cell tuples as weights, and based on expression (11), the cell survival reference rate is calculated. , After weighted fusion calculation, the result can be used as the cell survival rate.

[0079] (11) in, This represents the probability of the i-th cell tuple appearing. This represents the cell survival reference rate corresponding to the i-th cell tuple. To calculate the cell viability.

[0080] For continuously distributed cell tuples, the above expression (11) can be transformed into the following expression: In the above embodiments, by considering the particle concentration differences between different cell tuples, the particle concentration distribution in each voxel can be made closer to the actual particle concentration distribution of the target object. By fusing and calculating the cell survival reference rates corresponding to multiple cell tuples, the accuracy of cell survival rates can be guaranteed, thereby improving the accuracy of dose assessment.

[0081] See also Figure 2 and Figure 3 . Figure 2 This is a schematic diagram illustrating the effect of boron concentration differences between two cell groups on cell survival curves, provided in some embodiments of this application. Figure 3This is a schematic diagram illustrating the effect of boron concentration differences between two cell groups on cell survival curves, provided for other embodiments of this application. Figure 2 and Figure 3 It can be seen that, under the same dispersion parameter δ, the higher the specific kinetic energy, the greater the total deposited dose, and the lower the cell survival rate. Under the same specific kinetic energy, the larger the dispersion parameter δ, the higher the cell survival rate, indicating that even with a large total deposited dose, some cells will still remain viable due to dose differences between different cell groups. Here, the specific kinetic energy refers to the sum of the initial kinetic energies of all charged particles released by indirect ionizing radiation per unit mass of voxel. Indirect ionizing radiation refers to the interaction between uncharged radiation particles (such as neutrons) and matter to produce secondary charged particles, which then induce ionization.

[0082] In some embodiments, the cell survival function is constructed based on the following method: Based on the domain specific energy probability density function and distribution quantity of component particles in each region, multiple equivalent domain specific energy probability density functions are constructed. The domain specific energy probability density function characterizes the correspondence between the micro-domain specific energy and probability density when a single radiation particle generated by the component particles in each region undergoes an ionization event in the micro-domain. The micro-domain refers to the detection area located in the nuclear target region. Component particles have multiple domain specific energy probability density functions in each region, and the micro-domain radius corresponding to different domain specific energy probability density functions is different. Based on the specific energy probability density function of each equivalent domain, multiple sets of function parameters are generated; A cell survival function is constructed based on the function parameters that minimize error.

[0083] Specifically, the relevant principles of the domain specific energy probability density function are similar to those of the nuclear specific energy probability density function, as detailed in the relevant descriptions of expressions (1) and (2) above, which will not be repeated here. The difference between the domain specific energy probability density function and the nuclear specific energy probability density function is that the nuclear specific energy probability density function characterizes the correspondence between the specific energy and probability density of the nuclear target region, while the domain specific energy probability density function characterizes the correspondence between the specific energy and probability density of the micro-domain. In addition, according to the micro-domain radius, the domain specific energy probability density function corresponding to different micro-domain radii can be pre-set. For example, by changing the micro-domain radius from 0.1μm to 0.3μm with a step size of 0.01μm, the domain specific energy probability density function corresponding to different micro-domain radii can be pre-set.

[0084] Function parameters can include , , ,in, denoted as the sensitivity coefficient of the first term under the low LET limit, it represents the intrinsic probability of directly causing lethal damage in the sensitive domain of the nuclear target region per unit specific energy when the linear energy transfer (i.e., LET) tends to 0 and there is almost no sublethal damage crosslinking within a single ionization event. It is used to quantitatively describe the probability that two sublethal injuries generated within the same sensitive domain, after being paired and misrepaired, combine to form a lethal injury. It represents the average specific energy of the equivalent dose and can be calculated by expression (12).

[0085] in, , Let be the domain specific energy probability density function corresponding to boron. Indicates the specific energy of the micro-domain. This represents the dose-average specific energy corresponding to boron; , Let be the domain specific energy probability density function corresponding to hydrogen. This represents the dose-average specific energy corresponding to hydrogen; , Let be the domain specific energy probability density function corresponding to nitrogen. This represents the dose-average specific energy corresponding to nitrogen; , Let be the domain specific energy probability density function corresponding to the photon. This represents the average dose-to-energy ratio corresponding to a photon.

[0086] In this embodiment, the cell survival function As shown in expression (13).

[0087] (13) When determining the function parameters, multiple sets of function parameters can be set separately for the domain specific energy probability density function of each micro-domain radius. For example: For a micro-domain radius of 1, the specific energy probability density function corresponding to the micro-domain radius of 1 can be used to calculate... Then set multiple groups , Parameters, such as ( , ), ( , ), and so on.

[0088] For a micro-domain radius of 2, the specific energy probability density function corresponding to the micro-domain radius of 2 can be used to calculate... Then set multiple groups , Parameters, such as ( , ), ( , ), and so on.

[0089] Then, fix the radius of the micro-domain and the corresponding micro-domain radius. Iterate through each group under the corresponding micro-domain radius , Parameters. After traversal, change the micro-domain radius and its corresponding value. Continue traversing each group under the corresponding micro-domain radius. , Parameters. And so on.

[0090] When iterating through any set of function parameters, you can substitute those parameters into the cell survival function. and using the cell survival function The theoretical cell survival rate is calculated at different experimental points (e.g., different dose points), and the actual cell survival rate at the corresponding experimental points is detected experimentally. In this way, multiple sets of theoretical and actual cell survival rates corresponding to the same set of function parameters can be obtained. Based on these multiple sets of theoretical and actual cell survival rates, the theoretical cell survival rate and the experimentally obtained actual cell survival rate can be compared based on expression (14) to calculate the chi-square value corresponding to the set of function parameters. .

[0091] (14) in, This represents the theoretical cell survival rate corresponding to the i-th experimental point. This represents the actual cell viability corresponding to the i-th experimental point. This represents the error in the survival rate of the i-th actual cell element.

[0092] By performing the above operation on each set of function parameters, the chi-square value of each set of function parameters can be obtained. Then, the chi-square value can be... The smallest set of function parameters is taken as the function parameters with the smallest error, and the micro-domain radius corresponding to the function parameters with the smallest error is taken as the target micro-domain radius. The domain specific energy probability density function corresponding to the target micro-domain radius is taken as the domain specific energy probability density function actually used in the object model.

[0093] In some embodiments, the cell element survival function includes an equivalent linear energy parameter characterizing the linear energy transfer capability of radiating particles, i.e., in expression (13). After constructing the cell-based survival function, the method of this application further includes: Based on the saturation correction coefficient, the equivalent linear energy parameters are corrected to constrain the maximum linear energy transfer capability that radiating particles can achieve.

[0094] Specifically, when the dose is highly concentrated in the microdomain, damage to cytokines accumulates significantly. At this point, if the dose is further increased, the efficiency of cytokine elimination will no longer increase linearly. Continuing to increase this level is inconsistent with actual patterns, therefore a saturation correction coefficient is used to adjust it. Corrections are made to ensure the accuracy of dose assessment.

[0095] In some embodiments, the irradiation duration of the target object affects the survival rate of cellular cells. For example, suppose that two injuries need to occur simultaneously in the nuclear target region to eliminate cellular cells. If, with an increased irradiation time, a long interval occurs between the first injury and the second injury in the nuclear target region, the cellular cell may self-repair the first injury during this interval. As a result, by the time the second injury occurs, the first injury has already been repaired, and thus, because the two injuries cannot occur simultaneously, the cellular cell cannot be eliminated.

[0096] Therefore, after constructing the cell-based survival function, the method of this application further includes: A survival rate correction parameter has been added to the cell cell survival function. This parameter is used to correct the cell cell survival rate based on the duration of radiation particle action. The longer the duration of radiation particle action, the higher the cell cell survival rate.

[0097] The cell survival function after adding the survival rate correction parameter is shown in expression (15).

[0098] (15) in, The survival rate correction parameter is shown in expression (16).

[0099] (16) Where T represents the irradiation duration, It represents the first-order rate constant for cell damage repair and spontaneous transformation.

[0100] In the above embodiments, the accuracy of cell survival rate can be improved by adding a survival rate correction parameter to the cell survival function.

[0101] See also Figure 4 The above is a comparison chart showing the difference between survival rate correction and non-survival rate correction for some embodiments of this application. Figure 4In the diagram, the blue diagonal line represents the survival curve after injection of the first targeted boron agent without survival rate correction; the black diagonal line represents the survival curve after injection of the first targeted boron agent with survival rate correction; the green diagonal line represents the survival curve after injection of the second targeted boron agent without survival rate correction; and the red diagonal line represents the survival curve after injection of the second targeted boron agent with survival rate correction. Figure 4 It can be seen that, after considering the survival rate correction, the cell cell survival rate is higher at the same dose.

[0102] In some embodiments, determining cell viability based on the total specific energy probability density function and the cell viability function includes: Using the total deposition dose as the expansion center point, the survival probability function is expanded by a second-order Taylor polynomial to obtain the expansion function; Cellular viability is determined based on the expansion function and the equivalent nuclear specific energy probability density function.

[0103] Specifically, the function expansion can be shown in expression (17).

[0104] (17) in, , .

[0105] The expansion function can be used as an approximate expression of expression (13). By solving expression (17), an approximate value of cell survival rate can be obtained, while greatly reducing the difficulty of solving.

[0106] In some embodiments, the domain specific energy probability density function corresponding to each sub-component particle in expression (12) is taken as the sub-domain specific energy probability density function. The subnuclear specific energy probability density function and the sub-domain specific energy probability density function of this application are obtained based on the following method: Construct a three-dimensional matrix model composed of multiple cellular elements; In the three-dimensional matrix model, the radiation particle generation process and radiation particle transport process of various sub-component particles in each region are simulated respectively. Based on the simulation results, the subnuclear specific energy probability density function and the subdomain specific energy probability density function are obtained.

[0107] For details, please refer to the following: Figure 5 This is a schematic diagram of a three-dimensional matrix model provided in some embodiments of this application. Figure 5In this model, the three-dimensional matrix is ​​an 11×11×11 lattice structure cell element matrix, containing 1331 cell elements. The cell elements and their nuclear target regions are assumed to be spheres with radii of 5 μm and 3 μm, respectively. The distance between the cell element center and its nuclear target region is fixed at 1.5 μm, corresponding to the average value calculated under the assumption that the nuclear target region is randomly located in the cytoplasm. For ease of calculation, the density within each cell element is set to 1 g / cm³, and the material is a tissue-equivalent material with mass fractions of H (10.1%), C (11.1%), N (2.6%), and O (76.2%).

[0108] Four sub-particles are defined: boron, nitrogen, photons, and hydrogen. 93.7% boron produces 1.47 MeV alpha particles and 0.84 MeV photons. 7 Li particles and 0.478 MeV of photons, 6.3% boron produces 1.78 MeV of alpha particles and 1.01 MeV of photons. 7 Li particles. Nitrogen produces monoenergetic protons at 0.54 MeV. Hydrogen produces recoil protons. Simultaneously, monoenergetic photons at 0.662 MeV are defined. Alpha particles and... 7 Li particles are produced in opposite directions and generate different energies under different conditions.

[0109] Based on the Monte Carlo method, the radiative particle generation and transport processes of various subcomponent particles in each region are simulated in a three-dimensional matrix model. After the simulation, the subnuclear specific energy probability density functions and subdomain specific energy probability density functions corresponding to each subcomponent particle can be obtained.

[0110] Compared to some techniques that construct single-cell models and simulate the radiation particle generation and transport processes of various sub-component particles in each region, this application constructs a three-dimensional matrix model composed of multiple cell elements, which can reflect the interrelationships between cells, making the simulation process more consistent with the actual particle transport process, thereby improving the accuracy of the simulation results.

[0111] Combination Figures 6 to 9 . Figure 6 This is a schematic diagram of the radiation particle energy deposition of a simulated photon. Figure 7 This is a schematic diagram of the simulated nitrogen radiation particle energy deposition. Figure 8 This is a schematic diagram of the simulated hydrogen radiation particle energy deposition. Figure 9 This is a schematic diagram of the simulated radiation particle energy deposition of boron. From... Figures 6 to 9 It can be seen that the radiation particle energy of boron and nitrogen is highly localized, the radiation particle energy of hydrogen is diffusely distributed, and the radiation particle dose of photons is uniformly distributed.

[0112] See also Figure 10 and Figure 11 . Figure 10 It is the domain specific energy spectrum corresponding to each component particle obtained from the simulation. Figure 11 These are the domain specific energy spectra obtained from simulations of boron-containing and boron-free scenarios. From Figure 10 It can be seen that the domain ratio spectra of particles with different components are completely separated, indicating that the radiation particle energy deposition of particles with different components has significant differences. From Figure 11 It can be seen that in the presence of boron, there is a high-energy region for the specific energy, while in the absence of boron, the high-energy region for the specific energy disappears.

[0113] See also Figure 12 and Figure 13 . Figure 12 It is the nuclear specific energy spectrum corresponding to each component particle obtained from the simulation. Figure 13 It is the nuclear ratio energy spectrum obtained from simulations of boron-containing and boron-free scenarios. Figure 12 and Figure 10 similar, Figure 13 and Figure 11 Similarly, I will not elaborate further here.

[0114] See also Figure 14 This provides the relationship between bioeffective dose and depth calculated using different methods in some embodiments of this application. Figure 14 It can be seen that there are significant differences in the bioeffective dose and depth obtained based on different methods.

[0115] See also Figure 15 This provides the relationship between the bioeffective dose and the physically absorbed dose calculated using different methods, as shown in some embodiments of this application. Figure 15 It can be seen that the conversion coefficient between physical absorbed dose and biological effective dose differs based on different conversion methods.

[0116] See also Figure 16 This provides the relationship between relative biological effects and depth calculated using different methods in some embodiments of this application. Figure 16 It can be seen that the relative biological effects obtained based on different methods correspond to different depths.

[0117] See also Figure 17 This provides the relationship between relative biological effects and physically absorbed doses calculated using different methods, as shown in some embodiments of this application. Figure 17 It can be seen that the correspondence between the physical absorbed dose and the relative biological effect is different based on different methods.

[0118] from Figures 14 to 17It can be seen that the choice of calculation method or calculation model is very important in the dose assessment process. At the same time, it can also be seen that survival rate correction and equivalent linear energy parameter correction are important for dose assessment.

[0119] See also Figure 18 and Figure 19 . Figure 18 The relationship between the specific release dose and cell survival rate of some targeted boron agents provided in some embodiments of this application. Figure 19 This application provides for the relationship between the specific release dose and cell viability of some other targeted boron agents according to certain embodiments. From Figure 18 and Figure 19 It can be seen that, at the same dosage, different targeted boron agents have different cell elimination capabilities.

[0120] See also Figure 20 and Figure 21 . Figure 20 The relationship between specific release dose and relative biological effect is provided for some embodiments of this application. Figure 21 The relationship between specific dose and relative biological effect is provided for other embodiments of this application. Specifically, Figure 20 and Figure 21 The targeted boron agents differ. Based on different targeted boron agents, the specific release dose and relative biological effect can have different relationships.

[0121] See also Figure 22 and Figure 23 . Figure 22 This is a schematic diagram of the modules of a dose assessment system provided in some embodiments of this application. Figure 23 for Figure 22 The data processing flowchart of the Monte Carlo transport model. Figure 22 The dose assessment system includes a Monte Carlo transport model, a microdositology model, and a post-processing module. Before running the Monte Carlo transport model and the microdositology model, offline simulations based on experiments and a three-dimensional cell element matrix can be performed to pre-obtain offline parameters such as the subnuclear specific energy probability density function, subdomain specific energy probability density function, conversion factor, and dispersion δ between different cell elements for each subcomponent particle. After obtaining the offline parameters, they can be pre-set in the microdositology model.

[0122] See Figure 23During operation, the Monte Carlo transport model acquires the CT file of the target object, processes it using a program, converts the CT file into the input format required by the geometric model, and then inputs the CT file into the geometric model. The geometric model models the target object based on the CT, resulting in an object model. Based on the object model, boron concentration, and radiation source, Monte Carlo calculations are performed, outputting the physical absorbed dose of each voxel, i.e., the sub-deposition dose corresponding to each of the aforementioned sub-component particles. The microdosimetry model, based on the sub-deposition dose output by the Monte Carlo transport model and combined with preset discrete parameters, executes the dose assessment method of this application, simultaneously outputting indicators such as bioeffective dose and cell viability. The post-processing module can display the data through histograms, dose-depth curves, etc.

[0123] See also Figure 24 This is a schematic diagram of a dose assessment device provided in some embodiments of this application. The dose assessment device includes: The model building module 241 is used to build an object model based on the image data of the target object. The object model is divided into multiple voxels. Each voxel includes at least one cell element. The cell element has a nuclear target region. Component particles are distributed in at least one region inside and outside the cell element. The component particles are used to generate radiation particles. The parameter determination module 242 is used to determine the equivalent nuclear specific energy probability density function based on the nuclear specific energy probability density function and the distribution quantity of component particles in each region. The nuclear specific energy probability density function refers to the correspondence between the specific energy and probability density of the nuclear target region when a single radiation particle generated by the component particles in each region causes an ionization event in the nuclear target region. Evaluation module 243 is used to determine cell cell viability based on the equivalent nuclear energy probability density function, and to generate dose evaluation results for the target object based on the cell cell viability.

[0124] In some embodiments, the component particles include multiple sub-component particles, and the nuclear specific energy probability density function includes the sub-nuclear specific energy probability density function of each sub-component particle. The parameter determination module 242 is specifically used for: Based on the subnuclear specific energy probability density function and the number of distributions of particles of the same subcomponent in each region, the subequivalent nuclear specific energy probability density function is determined. The sub-equivalent nuclear specific energy probability density function of multiple sub-component particles is fused and calculated to obtain the equivalent nuclear specific energy probability density function.

[0125] In some embodiments, the parameter determination module 242 is specifically used for: The transport process of radiative particles is simulated in the object model, and the total deposition dose of each voxel is obtained from the simulation results. The total deposition dose is then converted into the average specific energy of the nuclear target region within the voxel. Based on the average specific energy of the nuclear target region and the probability density function of the equivalent nuclear specific energy, a total specific energy probability density function is constructed. The total specific energy probability density function characterizes the correspondence between the total specific energy and the probability density of a single nuclear target region under the constraint of the average specific energy of the nuclear target region. Based on the total specific energy probability density function and the cell cell survival function, the cell cell survival rate is determined. The cell cell survival function characterizes the correlation between the survival probability of a single cell cell and the specific energy of the nuclear target region.

[0126] In some embodiments, the parameter determination module 242 is specifically used for: Based on the average specific energy and equivalent specific energy probability density function of the nuclear target area, an event probability function and a cumulative specific energy probability density function are constructed. The event probability function characterizes the correspondence between the number of ionization events and the probability of occurrence of the events in a single nuclear target area, while the cumulative specific energy probability density function characterizes the correspondence between the number of ionization events and the specific energy probability distribution in a single nuclear target area. Based on the cumulative specific energy probability density function and the event probability function, the total specific energy probability density function is constructed.

[0127] In some embodiments, the parameter determination module 242 is specifically used for: The first-order origin moment of the equivalent nuclear specific energy probability density function is taken as the frequency-averaged specific energy. Based on the average specific energy of the nuclear target region and the average specific energy of the frequency, the average number of ionization events occurring in a single nuclear target region is determined, and the event probability function is constructed using the average number of ionization events as a characteristic parameter.

[0128] In some embodiments, the parameter determination module 242 is specifically used for: Based on the number of ionization events, a convolution operation is performed on the equivalent nuclear specific energy probability density function to obtain the cumulative specific energy probability density function.

[0129] In some embodiments, the parameter determination module 242 is specifically used for: During the convolution operation, a Fourier transform is performed on the equivalent kernel density probability density function to obtain the frequency domain feature function; Perform a power operation on the frequency domain characteristic function according to the number of ionization events; Perform an inverse Fourier transform on the result of the exponentiation operation to obtain the cumulative specific energy probability density function.

[0130] In some embodiments, the evaluation module 243 is specifically used for: Using the total deposition dose as the expansion center point, the survival probability function is expanded by a second-order Taylor polynomial to obtain the expansion function; Cellular viability is determined based on the expansion function and the equivalent nuclear specific energy probability density function.

[0131] In some embodiments, the parameter determination module 242 constructs the cell survival function based on the following method: Based on the domain specific energy probability density function and distribution quantity of component particles in each region, multiple equivalent domain specific energy probability density functions are constructed. The domain specific energy probability density function characterizes the correspondence between the micro-domain specific energy and probability density when a single radiation particle generated by the component particles in each region undergoes an ionization event in the micro-domain. The micro-domain refers to the detection area located in the nuclear target region. Component particles have multiple domain specific energy probability density functions in each region, and the micro-domain radius corresponding to different domain specific energy probability density functions is different. Based on the specific energy probability density function of each equivalent domain, multiple sets of function parameters are generated; A cell survival function is constructed based on the function parameters that minimize error.

[0132] In some embodiments, the cell element survival function includes an equivalent linear energy parameter characterizing the linear energy transfer capability of radiating particles; After constructing the cell survival function, the parameter determination module 242 is also used for: Based on the saturation correction coefficient, the equivalent linear energy parameters are corrected to constrain the maximum linear energy transfer capability that radiating particles can achieve.

[0133] In some embodiments, after constructing the cell survival function, the parameter determination module 242 is further configured to: A survival rate correction parameter has been added to the cell cell survival function. This parameter is used to correct the cell cell survival rate based on the duration of radiation particle action. The longer the duration of radiation particle action, the higher the cell cell survival rate.

[0134] In some embodiments, the parameter determination module 242 is used to obtain the subnuclear specific energy probability density function and the domain specific energy probability density function based on the following method: Construct a three-dimensional matrix model composed of multiple cellular elements; In the three-dimensional matrix model, the radiation particle generation process and radiation particle transport process of various sub-component particles in each region are simulated respectively. Based on the simulation results, the subnuclear specific energy probability density function and the domain specific energy probability density function are obtained.

[0135] In some embodiments, the total deposition dose includes the sub-deposition dose corresponding to each sub-component particle; the parameter determination module 242 is specifically used for: By using the conversion factor associated with each subcomponent particle, the subdeposition dose corresponding to the respective subcomponent particle is converted to obtain multiple sub-average specific energies. The total conversion factor is determined based on the sum of the total deposition dose and the sub-average specific energy. The total deposition dose was converted using the total conversion factor to obtain the average specific energy of the nuclear target area.

[0136] In some embodiments, the parameter determination module 242 is specifically used for: One of the subcomponent particles is taken as the target subcomponent particle, and the cell element is divided into multiple cell element groups. The particle concentration of the target subcomponent particle is different in different cell element groups. Based on the concentration ratio of each cell tuple, the conversion factor associated with the target subcomponent particles is corrected to obtain multiple corrected conversion factors. The concentration ratio refers to the multiple of the concentration of the target subcomponent particles in the cell tuple relative to the average concentration of the target subcomponent particles. Based on multiple modified conversion factors, several different total conversion factors are determined; Multiple average specific energies of nuclear target regions were obtained by using multiple different total conversion factors. Based on the average specific energy of each nuclear target region, the cell survival reference rate was determined. The cell survival rate is obtained by fusing multiple cell survival reference rates.

[0137] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0138] In this embodiment, the dose assessment device is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0139] Please see Figure 25 , Figure 25 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application, such as... Figure 25 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 25 Take a processor 10 as an example.

[0140] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GPA), or any combination thereof.

[0141] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0142] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0143] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0144] This application provides a computer program product including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the method of any embodiment of this application.

[0145] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A dose assessment method, characterized in that, The method includes: Based on the image data of the target object, an object model is constructed. The object model is divided into multiple voxels. Each voxel includes at least one cell element. The cell element has a nuclear target region. Component particles are distributed in at least one region inside and outside the cell element. The component particles are used to generate radiation particles. Based on the nuclear specific energy probability density function and distribution quantity of the component particles in each region, the equivalent nuclear specific energy probability density function is determined. The nuclear specific energy probability density function refers to the correspondence between the specific energy and probability density of the nuclear target region when a single radiation particle generated by the component particles in each region causes an ionization event in the nuclear target region. The transport process of the radiated particles is simulated in the object model, and the total deposition dose of each voxel is obtained from the simulation results, and the total deposition dose is converted into the average specific energy of the nuclear target region within the voxel; Based on the average specific energy of the nuclear target region and the equivalent specific energy probability density function, an event probability function and a cumulative specific energy probability density function are constructed. The event probability function characterizes the correspondence between the number of ionization events and the probability of occurrence of the events in a single nuclear target region, and the cumulative specific energy probability density function characterizes the correspondence between the number of ionization events and the specific energy probability distribution in a single nuclear target region. Based on the cumulative specific energy probability density function and the event probability function, a total specific energy probability density function is constructed. The total specific energy probability density function characterizes the correspondence between the total specific energy and the probability density of a single nuclear target region under the constraint of the average specific energy of the nuclear target region. Based on the total specific energy probability density function and the cell cell survival function, the cell cell survival rate is determined, and based on the cell cell survival rate, the dose assessment result of the target object is generated. The cell cell survival function characterizes the correlation between the survival probability of a single cell cell and the specific energy of the nuclear target region.

2. The method according to claim 1, characterized in that, Based on the average specific energy of the nuclear target region and the equivalent nuclear specific energy probability density function, an event probability function is constructed, including: The first-order origin moment of the equivalent nuclear specific energy probability density function is used as the frequency-averaged specific energy. Based on the average specific energy of the nuclear target region and the average specific energy of the frequency, the average number of ionization events occurring in a single nuclear target region is determined, and the event probability function is constructed using the average number of ionization events as a feature parameter.

3. The method according to claim 1, characterized in that, Based on the average specific energy of the nuclear target region and the equivalent nuclear specific energy probability density function, a cumulative specific energy probability density function is constructed, including: The equivalent nuclear specific energy probability density function is convolved according to the number of ionization events to obtain the cumulative specific energy probability density function.

4. The method according to claim 3, characterized in that, The step of performing a convolution operation on the equivalent nuclear specific energy probability density function according to the number of ionization events to obtain the cumulative specific energy probability density function includes: During the convolution operation, the equivalent kernel density probability density function is subjected to a Fourier transform to obtain the frequency domain feature function; The frequency domain feature function is subjected to a power operation based on the number of ionization events. The cumulative specific energy probability density function is obtained by performing an inverse Fourier transform on the result of the exponentiation operation.

5. The method according to claim 1, characterized in that, The determination of the cell viability rate based on the total specific energy probability density function and the cell viability function includes: Using the total deposition dose as the expansion center point, the survival probability function is expanded using a second-order Taylor polynomial to obtain the expanded function; The cell cell survival rate is determined based on the expansion function and the equivalent nuclear specific energy probability density function.

6. The method according to claim 1, characterized in that, The cell survival function was constructed based on the following method: Based on the domain specific energy probability density function and distribution quantity of the component particles in each region, multiple equivalent domain specific energy probability density functions are constructed. The domain specific energy probability density function characterizes the correspondence between the micro-domain specific energy and probability density when a single radiation particle generated by the component particles in each region undergoes an ionization event in the micro-domain. The micro-domain refers to the detection area located in the nuclear target region. The component particles have multiple domain specific energy probability density functions in each region, and the micro-domain radius corresponding to different domain specific energy probability density functions is different. Based on the specific energy probability density functions of each of the aforementioned equivalent domains, multiple sets of function parameters are generated; The cell survival function is constructed based on the function parameters that minimize error.

7. The method according to claim 6, characterized in that, The cell element survival function includes an equivalent linear energy parameter characterizing the linear energy transfer capability of radiating particles; After constructing the cell survival function, the method further includes: Based on the saturation correction coefficient, the equivalent linear energy parameters are corrected to constrain the maximum linear energy transfer capability that the radiating particles can achieve.

8. The method according to claim 6, characterized in that, After constructing the cell survival function, the method further includes: A survival rate correction parameter is added to the cell cell survival function. The survival rate correction parameter is used to correct the cell cell survival rate based on the duration of the radiation particle's action. The longer the duration of the radiation particle's action, the higher the cell cell survival rate.

9. The method according to claim 6, characterized in that, The component particles include multiple sub-component particles, and the nuclear specific energy probability density function includes the sub-nuclear specific energy probability density function of each of the sub-component particles; The determination of the equivalent nuclear specific energy probability density function based on the nuclear specific energy probability density function and distribution quantity of the component particles in each region includes: Based on the subnuclear specific energy probability density function and the number of distributions of the same sub-component particles in each region, the sub-equivalent nuclear specific energy probability density function is determined. The equivalent nuclear specific energy probability density function is obtained by fusing the sub-equivalent nuclear specific energy probability density functions of multiple sub-component particles.

10. The method according to claim 9, characterized in that, The subnuclear specific energy probability density function and the domain specific energy probability density function are obtained based on the following method: Construct a three-dimensional matrix model composed of multiple cellular elements; In the three-dimensional matrix model, the radiation particle generation process and radiation particle transport process of various sub-component particles in each region are simulated respectively. Based on the simulation results, the subnuclear specific energy probability density function and the domain specific energy probability density function are obtained.

11. The method according to claim 9, characterized in that, The total deposition dose includes sub-deposition doses corresponding to each sub-component particle; converting the total deposition dose into the average specific energy of the nuclear target region within the voxel includes: By using the conversion factor associated with each subcomponent particle, the subdeposition dose corresponding to the respective subcomponent particle is converted to obtain multiple sub-average specific energies. The total conversion factor is determined based on the sum of the total deposition dose and the sub-average specific energy. The total deposition dose is converted using the total conversion factor to obtain the average specific energy of the nuclear target area.

12. The method according to claim 11, characterized in that, The method further includes: One of the subcomponent particles is taken as the target subcomponent particle, and the cell element is divided into multiple cell element groups. In different cell element groups, the particle concentration of the target subcomponent particle is different. Based on the concentration ratio of each cell tuple, the conversion factor associated with the target subcomponent particles is corrected to obtain multiple corrected conversion factors. The concentration ratio refers to the multiple of the concentration of the target subcomponent particles in the cell tuple relative to the average concentration of the target subcomponent particles. Based on the aforementioned modified conversion factors, several different total conversion factors are determined; Using the various different total conversion factors, the average specific energy of multiple nuclear target regions was obtained; Based on the average specific energy of each nuclear target region, the cell survival reference rate was determined. The cell survival rate is obtained by fusing and calculating multiple cell survival reference rates.

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