Dose determination method and computing device

Abstract

The application is suitable for the medical technology field, and provides a dose determination method and a computing device, which comprises the following steps: determining whether radiation delivery for a target object comprises intrabody radiation and / or extracorporeal radiation; determining an intrabody radiation dose distribution when the radiation delivery comprises intrabody radiation, and determining an extracorporeal radiation dose distribution when the radiation delivery comprises extracorporeal radiation; and determining a target dose distribution based on the intrabody radiation dose distribution and / or the extracorporeal radiation dose distribution. The method can determine whether the radiation delivery for the target object comprises intrabody radiation and / or extracorporeal radiation, and then the intrabody radiation dose distribution and / or the extracorporeal radiation dose distribution can be calculated flexibly and pertinently according to the determination result, so that the computing efficiency is improved, and then the total absorbed dose under the superposition of intrabody radiation and extracorporeal radiation can be accurately obtained, and the determination accuracy of the dose is improved.

Description

Technical Field

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

[0002] In recent years, radiotherapy technology has been increasingly used in clinical practice. To ensure the efficacy of treatment for target patients and to assess the potential radiation hazards posed by clinical nuclear medicine diagnosis or treatment, it is necessary to accurately obtain the true dose distribution of radionuclides within the target patient's body. Currently, radiation delivery methods typically include external radiation (i.e., external radiation) and brachytherapy (i.e., internal radiation). External radiation refers to the radionuclide being outside the organism, exposing the organism to external radiation. Internal radiation refers to the radionuclide entering the organism, exposing the organism to internal radiation.

[0003] However, existing dose determination methods are not comprehensive enough to meet practical needs and cannot accurately determine the absorbed dose of tissues or organs in the target body under internal and / or external radiation, thus reducing the accuracy of dose determination. Summary of the Invention

[0004] This application provides a dose determination method and calculation device to address the technical problem that existing dose determination methods are not comprehensive enough, cannot meet actual needs, and cannot accurately determine the absorbed dose of tissues or organs in the target body under internal and / or external radiation, thereby reducing the accuracy of dose determination.

[0005] In a first aspect, embodiments of this application provide a dosage determination method, including:

[0006] Determine whether radiation delivery to the target object includes intra-object radiation and / or extra-object radiation;

[0007] Determine the internal radiation dose distribution when the radiation delivery includes radiation within the object, and determine the external radiation dose distribution when the radiation delivery includes radiation outside the object; and

[0008] The target dose distribution is determined based on the internal and / or external radiation dose distributions.

[0009] This application provides a dose determination method that determines whether radiation delivery to a target object includes internal and / or external radiation; determines the internal radiation dose distribution when radiation delivery includes internal radiation, and determines the external radiation dose distribution when radiation delivery includes external radiation; and determines the target dose distribution based on the internal and / or external radiation dose distributions. This method can determine whether radiation delivery to a target object includes internal and / or external radiation, and then flexibly and specifically calculate the internal and / or external radiation dose distributions based on the determination results, thereby improving computational efficiency and accurately obtaining the total absorbed dose under the superposition of internal and external radiation, thus improving the accuracy of dose determination.

[0010] In one possible implementation of the first aspect, determining the internal radiation dose distribution when radiation delivery includes internal radiation to the target includes:

[0011] When radiation delivery includes internal radiation, the initial dose of the radioactive material inside the target object is determined, as well as the first image of the target object after the radioactive material enters the interior, and the internal radiation dose distribution is determined based on the initial dose and the first image.

[0012] In the above embodiments, when radiation delivery includes radiation within the target object, the computing device considers the influence of the initial dose of radioactive material already present inside the target object on the final internal radiation dose distribution when calculating the internal radiation dose distribution, thereby improving the accuracy of the calculation of the final internal radiation dose distribution.

[0013] In one possible implementation of the first aspect, determining the internal radiation dose distribution based on the initial dose and the first image includes:

[0014] Determine the cumulative decay number of the radioactive material based on the initial dose or the first image;

[0015] The internal radiation dose distribution is determined based on the cumulative decay number.

[0016] In the above embodiments, the computing device can determine the decay status (i.e., cumulative decay number) of the radiopharmaceutical substance in the target body based on the first image or initial dose, and accurately calculate the internal radiation dose distribution of the target body under radiation based on the cumulative decay number, thereby improving the accuracy of determining the internal radiation dose distribution.

[0017] In one possible implementation of the first aspect, determining the cumulative decay number of the radioactive material based on the first image includes:

[0018] If there is no data on the radioactivity of the target's excrement at different times after the radioactive material enters the target's body, then the relationship between the radioactivity of the radioactive material in the target's body and time is determined based on the first image.

[0019] The cumulative decay number is determined based on the changing relationship.

[0020] In the above embodiments, when the computing device detects that there is no radioactivity measurement data of the excrement of the target object at different times, it can directly determine the relationship between the radioactivity of the radioactive material in the target object and time based on the first image, and calculate the cumulative decay number based on the relationship, thereby realizing the determination of the cumulative decay number in the absence of radioactivity measurement data of the excrement of the target object at different times.

[0021] In one embodiment of the first aspect, determining the cumulative decay number of a radioactive material based on an initial dose includes:

[0022] After a radioactive substance enters the body of a target, if there is data on the radioactivity of the target's excrement at different times, the current remaining radioactivity in the target's body can be calculated based on the initial dose and the radioactivity data of each excrement.

[0023] The cumulative decay number is calculated based on the current remaining radioactivity and the half-life of the radionuclides in the radioactive material.

[0024] In the above embodiments, when there is data on the radioactivity of the excrement of the target object at different times, the remaining radioactivity in the target object can be determined directly based on the data and the initial dose. Thus, the accurate cumulative decay number can be calculated based on the remaining radioactivity and the half-life of the radionuclides in the radioactive material, thereby improving the accuracy of the calculation of the cumulative decay number.

[0025] In one embodiment of the first aspect, determining the internal radiation dose distribution based on the cumulative decay number includes:

[0026] Determine the average absorbed dose per unit decay number;

[0027] The internal radiation dose distribution is calculated based on the cumulative decay number and the average absorbed dose.

[0028] In the above embodiments, the computing device can determine the average absorbed dose per unit decay number, and then directly and quickly calculate the absorbed dose under internal radiation based on the average absorbed dose and the cumulative decay number, thereby improving the computing efficiency.

[0029] In one embodiment of the first aspect, determining the internal radiation dose distribution based on the cumulative decay number includes:

[0030] Acquire the X-ray energy spectrum data corresponding to the radioactive material, as well as the photon reaction cross section data and electron reaction cross section data of the specified material.

[0031] Based on the first image, determine the distribution of radioactivity concentration and the physical density of tissues within the target object;

[0032] Sampling was performed based on the distribution of radioactivity concentration to obtain multiple first-sample particles;

[0033] Based on X-ray energy spectrum data, photon reaction cross section data, electron reaction cross section data, and physical density, parallel particle transport simulations were performed on multiple first-sampled particles to obtain the internal radiation dose distribution.

[0034] In the above embodiments, the computing device can realistically simulate the interactions that occur during the transport of radionuclides within the target object based on the Monte Carlo particle transport principle. These interactions include actual physical processes such as the photoelectric effect, Compton effect, electron-electron pair effect, Rayleigh scattering, bremsstrahlung, Mueller scattering, and multiple scattering, all based on photon and electron reaction cross-section data. Based on these physical densities, the device can directly track the trajectory of each particle within the target object, thereby improving the accuracy of determining the absorbed dose of the target object under internal radiation.

[0035] In one embodiment of the first aspect, when radiation delivery includes external radiation to the target, the external radiation dose distribution is determined according to the following manner:

[0036] Acquire a second image of the target object after the corresponding radioactive material emitted from outside the object enters the interior, and acquire the photon reaction cross section data and electron reaction cross section data of the set material.

[0037] The external radiation dose distribution was determined based on the second image, photon reaction cross section data, and electron reaction cross section data.

[0038] In the above embodiments, when radiation delivery includes external radiation to the target, the computing device can combine the second image of the radioactive material corresponding to the external radiation to the target after it enters the interior, the photon reaction cross section data, and the electron reaction cross section data when calculating the internal radiation dose distribution, thereby improving the accuracy of the final external radiation dose distribution calculation.

[0039] In one embodiment of the first aspect, determining the external radiation dose distribution based on a second image, photon reaction cross-section data, and electron reaction cross-section data includes:

[0040] Based on the grid image corresponding to the second image, determine the Hu value and physical density corresponding to each dose grid;

[0041] Based on the irradiation field corresponding to the external radiation of the target, determine the flux map corresponding to all dose grids under external radiation of the target;

[0042] Sampling is performed based on the beam model to obtain multiple second-sampled particles;

[0043] Based on the flux map, the Hu value and physical density corresponding to each dose grid, the photon reaction cross section data and the electronic reaction cross section data, parallel particle transport simulation is performed on multiple second sampled particles based on the Monte Carlo particle transport principle and CUDA multi-threading technology to obtain the second deposition energy corresponding to each dose grid.

[0044] Based on the cumulative decay number, the second deposition energy corresponding to each dose grid, the grid volume of each dose grid, and the number of second simulated particles, the second grid absorbed dose corresponding to each dose grid is calculated.

[0045] The external radiation dose distribution is calculated based on the number of dose grids and the absorbed dose of each second grid.

[0046] In the above embodiments, for external radiation, the computing device can also simulate the interactions that occur during particle transport in matter using the Monte Carlo particle transport principle, and determine the obtained physical density using the grid image corresponding to the second image. This allows for precise tracking of the transport trajectory of each particle in the matter, thereby improving the accuracy of determining the second deposition energy for each dose grid, and consequently improving the accuracy of determining the absorbed dose of the target object under external radiation. Simultaneously, during the particle transport simulation, the computing device can also utilize the parallel computing capabilities of CUDA's multi-threading technology within the GPU, with each thread tracking the trajectory of a single particle, saving computation time and improving computational efficiency.

[0047] In one embodiment of the first aspect, determining the target dose distribution based on the internal radiation dose distribution and / or external radiation dose distribution includes:

[0048] If a biological effect is detected within the target object, a first biological effect weight corresponding to radiation within the object and / or a second biological effect weight corresponding to radiation outside the object are determined based on the biological effect.

[0049] The target dose distribution is obtained by weighting and summing the internal and / or external radiation dose distributions based on the first biological effect weight and / or the second biological effect weight.

[0050] In the above embodiments, considering that the biological effects generated when the target subject undergoes radiotherapy will affect the absorbed dose under different radiotherapy delivery methods, the computing device can determine the weights corresponding to different radiotherapy delivery methods based on the generated biological effects, and perform weighted summation based on each weight to obtain a more accurate total dose, thereby further improving the accuracy of dose determination.

[0051] Secondly, embodiments of this application provide a dose determination device, comprising:

[0052] A radiation determination unit is used to determine whether radiation delivery to a target object includes radiation inside the object and / or radiation outside the object.

[0053] A first dose determination unit is configured to determine an internal radiation dose distribution when the radiation delivery includes internal radiation to the target, and to determine an external radiation dose distribution when the radiation delivery includes external radiation to the target; and

[0054] The second dose determination unit is used to determine the target dose distribution based on the internal radiation dose distribution and / or the external radiation dose distribution.

[0055] Thirdly, embodiments of this application provide a computing device, including a memory, a processor, and instructions stored in the memory and executable on the processor, characterized in that the processor executes the instructions to implement the dose determination method as described in any one of the first aspects above.

[0056] Fourthly, embodiments of this application provide a computer-readable storage medium storing instructions that, when executed by a processor, implement the dose determination method as described in any one of the first aspects above.

[0057] Fifthly, embodiments of this application provide a dosage determination method, including:

[0058] Determine the radiation source model for the near-field radiation device;

[0059] Based on the radiation source model, an internal radiation model corresponding to the radiation delivery performed by a near-field radiation device inside the target object is determined;

[0060] Based on the internal radiation model, the dose distribution formed inside the target object by radiation delivery is determined.

[0061] The dose determination method provided in this application, when the radiation inside the target is intracavitary radiation, allows the computing device to accurately calculate the dose distribution formed inside the human body by establishing a radiation source model of the intracavitary close-range irradiation device, thereby improving the accuracy of determining the dose distribution of the target object under intracavitary radiation.

[0062] In one embodiment of the fifth aspect, a radioactive source model is determined based on radionuclide information and / or structural information of the near-field radiation device.

[0063] In the above embodiments, the computing device can construct an accurate radioactive source model using the radionuclide information and / or structural information of the near-field radiation device.

[0064] In one implementation of the fifth aspect, an internal radiation model is determined based on a radiation source model and internal information of the target object.

[0065] In the above embodiments, the computing device can construct an accurate internal radiation model by using the radiation source model and the internal information of the target object.

[0066] In one embodiment of the fifth aspect, the internal information of the target object includes at least one of the following: structural information of voxels within the target object, material information, and physical density information.

[0067] In the above embodiments, the computing device can construct an accurate internal radiation model by using at least one of the structural information, material information, and physical density information of the voxels inside the target object.

[0068] In one embodiment of the fifth aspect, the internal information of the target object is determined based on a scanned image of the target object.

[0069] In the above embodiments, the computing device can obtain accurate internal information of the target object through a scanned image of the target object.

[0070] In one implementation of the fifth aspect, a GPU-based multi-threaded task parallel computing method is used to determine the internal information of the target object based on the image information of the scanned image.

[0071] In the above embodiments, the computing device can utilize the parallel computing capabilities of the multi-threading technology within the GPU to quickly determine the internal information of the target object, saving computing time and improving computing efficiency.

[0072] In one embodiment of the fifth aspect, the dose distribution is determined by simulating particle transport processes based on an internal radiation model.

[0073] In the above embodiments, the computing device can realistically simulate the interaction that occurs during the transport of radionuclides within the target object based on the simulated particle transport process, thereby improving the accuracy of determining the dose distribution formed inside the target object.

[0074] In one implementation of the fifth aspect, the radiation dose of one or more radiating particles is determined using the Monte Carlo algorithm, and the particle transport process is simulated based on the determined trajectory of the radiating particles.

[0075] In the above embodiments, the computing device can track the actual physical processes such as various reactions of radiated particles in the patient's body during intracavitary radiation based on the Monte Carlo particle transport principle, thereby improving the realism of the simulation of particle transport processes.

[0076] In one embodiment of the fifth aspect, determining the dose distribution formed within the target object by radiation delivery, based on an internal radiation model, includes:

[0077] Based on the internal radiation model, the dose grid is calculated and stored in the form of a sparse matrix;

[0078] Using the stored dose grid, a dose summation calculation is performed to determine the dose distribution.

[0079] In the above embodiments, the computing device can reduce the memory occupied in the device by using a sparse matrix.

[0080] Sixthly, embodiments of this application provide a dose determination device, comprising:

[0081] The first model determination unit is used to determine the radiation source model of the near-field radiation device;

[0082] The second model determination unit is used to determine the internal radiation model corresponding to the radiation delivery performed by the near-field radiation device inside the target object, based on the radiation source model.

[0083] The dose determination unit is used to determine the dose distribution formed inside the target object by radiation delivery based on the internal radiation model.

[0084] In a seventh aspect, embodiments of this application provide a computing device, including a memory, a processor, and instructions stored in the memory and executable on the processor, characterized in that the processor, when executing the instructions, implements the dose determination method as described in any one of the fifth aspects above.

[0085] Eighthly, embodiments of this application provide a computer-readable storage medium storing instructions that, when executed by a processor, implement the dose determination method as described in any one of the fifth aspects above. Attached Figure Description

[0086] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0087] Figure 1 This is a flowchart illustrating the implementation of a dosage determination method provided in an embodiment of this application;

[0088] Figure 2 This is a flowchart illustrating the implementation of a dosage determination method provided in another embodiment of this application;

[0089] Figure 3 This is a flowchart illustrating the implementation of a dosage determination method provided in another embodiment of this application;

[0090] Figure 4 This is a flowchart illustrating the implementation of a dosage determination method provided in another embodiment of this application;

[0091] Figure 5 This is a flowchart illustrating the implementation of a dosage determination method provided in another embodiment of this application;

[0092] Figure 6 This is a schematic diagram of the metabolic pathway of radionuclides in a radioactive material within a target body, provided in an embodiment of this application.

[0093] Figure 7 This is a flowchart illustrating the implementation of a dosage determination method provided in another embodiment of this application;

[0094] Figure 8 This is a flowchart illustrating the implementation of a dosage determination method provided in another embodiment of this application;

[0095] Figure 9 This is a flowchart illustrating the implementation of another dosage determination method provided in an embodiment of this application;

[0096] Figure 10 This is a schematic diagram of the structure of a dose determination device provided in an embodiment of this application;

[0097] Figure 11 This is a schematic diagram of the structure of a dose determination device provided in another embodiment of this application;

[0098] Figure 12 This is a schematic diagram of the structure of a computing device provided in an embodiment of this application;

[0099] Figure 13 This is a schematic diagram of the structure of a computing device provided in another embodiment of this application. Detailed Implementation

[0100] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0101] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0102] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0103] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0104] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0105] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0106] In practical applications, during clinical nuclear medicine diagnosis or treatment, patients often require oral or intravenous / arterial injection of a certain amount of radioactive material (such as F-18 contrast agents for imaging scans, or Y-90 microsphere radioactive embolization in liver cancer treatment). These radionuclides are selectively absorbed, metabolized, and spontaneously decay by human tissues. During their residence in the body, they irradiate not only the target organ or tissue but also adjacent organs or tissues. Furthermore, in the treatment of certain cancers (such as liver cancer, breast cancer, nasopharyngeal carcinoma, and pancreatic cancer), due to individual differences in the patient, the dose of radioactive material within the cancer cells may not meet clinical treatment requirements. To compensate for the insufficient dose, the patient may need to continue receiving external radiation from a linear accelerator, irradiating the lesion from a distance to kill cancer cells. Therefore, to assess the potential radiation hazard risk to the patient from clinical nuclear medicine diagnosis or treatment, it is necessary to accurately obtain the actual dose within the patient's body. Currently, radiation delivery methods typically include external radiation (i.e., long-range external radiation) and close-range irradiation (i.e., internal radiation). External radiation refers to the exposure of the organism to external radiation from outside the body by radioactive nuclides. Internal radiation refers to the entry of radioactive nuclides into a living organism, causing the organism to be irradiated by rays from within.

[0107] However, existing dose determination methods are not comprehensive enough to meet practical needs and cannot accurately determine the absorbed dose of tissues or organs in the target body under internal and / or external radiation, thus reducing the accuracy of dose determination.

[0108] Therefore, embodiments of this application provide a dose determination method to accurately determine the absorbed dose of tissues or organs in a target subject under internal and / or external radiation, thereby improving the accuracy of determining the absorbed dose in the target subject.

[0109] Please see Figure 1 , Figure 1 This is a flowchart illustrating the implementation of a dosage determination method according to an embodiment of this application. In this embodiment, the subject executing the dosage determination method is a computing device. The computing device includes, but is not limited to, devices such as laptops, desktop computers, and computers.

[0110] like Figure 1 As shown, a dose determination method provided in one embodiment of this application may include S101 to S103, which are described in detail below:

[0111] In S101, it is determined whether radiation delivery to the target object includes radiation inside the object and / or radiation outside the object.

[0112] In practical applications, before administering radiotherapy to a target subject, in order to improve the accuracy of subsequent determination of the absorbed dose within the target body, the computing device can determine the radiation delivery method for the target subject. These radiation delivery methods include, but are not limited to: intra-target radiation only, extra-target radiation only, and simultaneous intra-target and extra-target radiation.

[0113] It should be noted that when the radiation delivery method is to use only internal radiation, the computing device can only calculate the internal radiation dose distribution; when the radiotherapy radiation is to use only external radiation, the computing device can only calculate the external radiation dose distribution; when the radiation delivery method uses both internal and external radiation, the computing device can calculate the internal and external radiation dose distributions separately, and finally couple and superimpose the internal and external radiation dose distributions.

[0114] Among them, the aforementioned internal radiation of the target includes, but is not limited to, administration of a certain amount of radioactive material via oral administration, intravenous / arterial injection, or release of radioactive material into the target body through an internal radiation source such as a close-range radiation device.

[0115] In some possible embodiments, the computing device's confirmation of the radiation delivery method for the target object may include receiving treatment information for the target object input by relevant personnel. This treatment information describes the radiation delivery method for the target object.

[0116] In other possible embodiments, the computing device is provided with multiple preset controls, each corresponding to a radiation delivery method. Therefore, when the computing device detects that one of the preset controls is clicked, it can determine that the radiation delivery method of the target object is the radiation delivery method corresponding to that preset control.

[0117] In S102, the internal radiation dose distribution is determined when radiation delivery includes radiation within the target, and the external radiation dose distribution is determined when radiation delivery includes radiation outside the target.

[0118] In this embodiment of the application, when the radiation delivery method of the target object includes internal radiation, it indicates that internal radiation dose calculation is required. Therefore, the calculation device can determine the internal radiation dose distribution.

[0119] In one embodiment of this application, the computing device can specifically be configured as follows: Figure 2 Steps S201 to S202, which determine the internal radiation dose distribution, are described in detail below:

[0120] In S201, when radiation delivery includes radiation inside the target object, the initial dose of radioactive material inside the target object and the first image of the target object after the radioactive material enters the interior are determined.

[0121] In S202, the internal radiation dose distribution is determined based on the initial dose and the first image.

[0122] In this embodiment, when the computing device detects that the radiation delivery method of the target object includes internal radiation, it indicates that internal radiation dose calculation is required. Therefore, the computing device can obtain the initial dose of the radioactive material in the target object and obtain the first image of the target object after the radioactive material enters the body.

[0123] It should be noted that since imaging examination methods include computed tomography (CT) and single-photon emission computed tomography (SPECT), the aforementioned first image may include CT images and SPECT images.

[0124] In this embodiment, the initial dose may be the dose of radioactive material injected or ingested by the target subject during this treatment.

[0125] In some possible embodiments, since the target subject may have been injected with a radioactive contrast agent before this treatment, in order to improve the accuracy of the internal radiation dose calculation, the target subject needs to undergo a CT or SPECT scan before this treatment, i.e. before the injection or oral administration of the radioactive material. Afterwards, the computing device can acquire the CT or SPECT image of the target subject before this treatment and determine the background radiation in the target subject's body based on the CT or SPECT image before this treatment, i.e., the content of radionuclides present in the target subject's body before this treatment.

[0126] Based on this, the computing device can determine the initial dose of radioactive material in the target's body at this time, according to the content of radionuclides present in the target's body before the treatment and the dose of radioactive material injected or ingested by the target during the treatment.

[0127] The computing device can then determine the internal radiation dose distribution based on the initial dose and the first image.

[0128] In some possible embodiments, the computing device determines the internal radiation dose distribution at the current moment based on the initial dose and the first image obtained from the current scan.

[0129] In other possible embodiments, the computing device may also predict the internal radiation dose distribution at future times based on the initial dose and the first image obtained from the current scan. Here, "future times" refers to times after the current time.

[0130] In some other possible embodiments, the computing device may also predict the internal radiation dose distribution at the current moment based on the initial dose and the first image obtained from the historical time scan. Here, the historical time refers to the time before the current moment.

[0131] In some other possible embodiments, the computing device may also predict the distribution of internal radiation dose at future times based on the initial dose and the first image obtained from the current scan.

[0132] In one embodiment of this application, the computing device can specifically be configured as follows: Figure 3 Steps S301 to S302 shown implement step S202, as detailed below:

[0133] In S301, the cumulative decay number of the radioactive material is determined based on the initial dose or the first image.

[0134] In practical applications, since the radioactivity of excreted waste (such as urine or feces) can be measured at different times after a radioactive substance enters the body of a target, the computing device, after obtaining the first image and initial dose, can determine whether it can acquire the excreted waste radioactivity measurement data obtained by measuring the excreted waste radioactivity of the target at different times. The excreted waste radioactivity measurement data can be the dose of the radionuclide excreted from the body by the radioactive substance.

[0135] In one embodiment of this application, when there is no data on the radioactivity of the target object's excrement at different times, the computing device can specifically use, as follows: Figure 4 The cumulative decay number is calculated in steps S401 to S402, as detailed below:

[0136] In S401, after the radioactive material enters the target body, if there is no data on the radioactivity of the target body's excrement at different times, the relationship between the radioactivity of the radioactive material in the target body and time is determined based on the first image.

[0137] In S402, the cumulative decay number is determined based on the change relationship.

[0138] In this embodiment, if there is no data on the radioactivity of the excrement of the target object at different times after the radioactive material enters the target object's body, it means that the computing device cannot calculate the cumulative decay number of the radioactive material in the target object's body based on the aforementioned data on the radioactivity of the excrement. Therefore, the computing device can determine the relationship between the radioactivity of the radioactive material in the target object's body and the change over time based on the first image.

[0139] It should be noted that the current time specifically refers to the moment when the computing device executes step S102, that is, the moment when the internal radiation dose calculation begins.

[0140] In this embodiment, the computing device can process the aforementioned change relationship based on a set method to obtain the cumulative decay number. The set method includes, but is not limited to: data modeling based on the change relationship, experimental simulation based on the change relationship, and calculation by importing the change relationship into a biological dynamic model in a reference text.

[0141] It should be noted that the reference text may be ICRP Report No. 128.

[0142] In some possible embodiments, when the calculation method involves importing the change relationship into a biological dynamic model in the reference text, the computing device can determine the initial radioactivity of the radioactive material upon entering the target body, and the current radioactivity at the current moment, based on the change relationship. The computing device can then import the initial and current radioactivity into the biological dynamic model in the reference text to calculate the cumulative decay number.

[0143] In this embodiment, the biological dynamic model is as follows:

[0144]

[0145] in, Let A0 represent the cumulative decay number, Fs represent the initial radioactivity, and As(t) / A0 represent the proportion of radionuclides in organ or tissue S at a certain time, i.e., the ratio of radioactivity at time t to initial radioactivity. ai represents the fraction of component Ti in Fs with a biological half-life (Σai=1), aj represents the fraction of component Tj in Fs with a biological half-life (Σaj=1), n ​​represents the amount of excreted components, m represents the amount of uptake components, and T... i,eff T represents the effective half-life of the excreted substance. j,eff Indicates the effective half-life of the ingested food.

[0146] It should be noted that the above-mentioned effective half-lives can be calculated based on the corresponding biological half-life and physical half-life:

[0147]

[0148] Among them, T i,eff T represents the effective half-life of the excreted substance. j,eff T represents the effective half-life of the ingested food. i Indicates with T i,eff The corresponding biological half-life, T j Indicates with T j,eff The corresponding biological half-life, T p It indicates the physical half-life.

[0149] Integrating the above biological dynamics model over time to infinity yields the equation corresponding to the normalized cumulative decay number:

[0150]

[0151] Assuming immediate uptake in the target organ, the equation corresponding to the normalized cumulative decay number above can be simplified to:

[0152]

[0153] Integrating the simplified equation above, we get:

[0154]

[0155] Based on this, the computing device can input the initial radioactivity, current radioactivity, biological half-life (the fraction of Ti component in Fs), and excreted effective half-life into the simplified equation above to calculate the cumulative decay number.

[0156] In another embodiment of this application, when there is data on the radioactivity of the excrement of a target object at different times, the computing device can specifically perform calculations as follows: Figure 5 The cumulative decay number is calculated in steps S501 to S502, as detailed below:

[0157] In S501, after a radioactive substance enters the body of a target, if there are radioactivity measurement data of the target's excrement at different times, the current remaining radioactivity in the target's body is calculated based on the initial dose and the radioactivity measurement data of each excrement.

[0158] In S502, the cumulative decay number is calculated based on the current remaining radioactivity and the half-life of the radionuclides in the radioactive material.

[0159] In this embodiment, after the radioactive material enters the target body, if there is data on the radioactivity of the target body's excrement at different times, the computing device can calculate the cumulative decay number of the radioactive material in the target body based on the aforementioned excrement radioactivity measurement data. Therefore, the computing device can calculate the current remaining radioactivity in the target body based on the initial dose and the radioactivity measurement data of each excrement. Then, the computing device can input the current remaining radioactivity and the half-life of the radionuclides in the radioactive material into a preset formula to calculate the cumulative decay number.

[0160] Please see Figure 6 , Figure 6 This is a schematic diagram of the metabolic pathway of radionuclides in a radioactive material within a target body, provided in an embodiment of this application.

[0161] Please refer to Table 1, which shows the amount of excretion and the amount of residual radioactivity (i.e., residual radioactivity) of the target object at different times according to an embodiment of this application.

[0162] Table 1:

[0163]

[0164] In this embodiment, the preset formula is as follows:

[0165]

[0166] in, This represents the cumulative decay number at time n. λ represents the remaining radioactivity at time i, and λ represents the half-life of the radionuclide.

[0167] In S302, the internal radiation dose distribution is determined based on the cumulative decay number.

[0168] In this embodiment, after obtaining the cumulative decay number, the computing device can calculate the internal radiation dose distribution of the target object under internal radiation, i.e., the internal radiation dose, based on the cumulative decay number.

[0169] In one embodiment of this application, in order to improve the calculation efficiency of the internal radiation dose distribution of a target object under radiation within the object, the computing device can specifically calculate the internal radiation dose distribution according to the following steps, detailed below:

[0170] Determine the average absorbed dose per unit decay number;

[0171] The internal radiation dose distribution is calculated based on the cumulative decay number and the average absorbed dose.

[0172] In one embodiment of this application, the internal radiation dose distribution can be calculated based on the cumulative decay number, combined with data that can reflect the physical properties of the radiation-receiving area of ​​the object, such as a first image and a digital model of the object.

[0173] In another embodiment of this application, after obtaining the cumulative decay number, the computing device can also simulate the average absorbed dose per unit decay number based on the Monte Carlo algorithm.

[0174] In practical applications, the Monte Carlo (MC) algorithm, also known as the statistical simulation method or statistical experiment method, is a numerical simulation method that takes probabilistic phenomena as the research object. It is also a calculation method that uses sampling surveys to obtain statistical values ​​to estimate unknown characteristic quantities.

[0175] In this embodiment, Monte Carlo simulation constructs a mathematical model to simulate the propagation paths and interactions of radiation particles (such as photons, electrons, heavy ions, protons, etc.) corresponding to radioactive materials within the material, including processes such as scattering and absorption. By simulating a large number of particle events, the average absorbed dose per unit decay number can be calculated under specific conditions.

[0176] Based on this, the computing device can multiply the cumulative decay number and the average absorbed dose generated by the unit decay number, and determine the product as the internal radiation dose distribution of the target object under internal radiation.

[0177] In another embodiment of this application, in order to improve the accuracy of calculating the internal radiation dose distribution of the target object under internal radiation, the computing device can specifically be configured as follows: Figure 7 The internal radiation dose distribution is calculated using steps S601 to S604, as detailed below:

[0178] In S601, the X-ray energy spectrum data corresponding to the radioactive material is acquired, as well as the photon reaction cross section data and electron reaction cross section data of the specified material are acquired.

[0179] In S602, based on the first image, the distribution of radioactivity concentration and the physical density of tissues within the target object are determined.

[0180] In S603, sampling is performed based on the distribution of radioactive concentration to obtain multiple first sampled particles.

[0181] In S604, based on X-ray energy spectrum data, photon reaction cross section data, electron reaction cross section data, and physical density, parallel particle transport simulations are performed on multiple first-sampled particles to obtain the internal radiation dose distribution.

[0182] In some possible embodiments, the computing device can perform parallel particle transport simulations on multiple first sampled particles based on X-ray energy spectrum data, photon reaction cross section data, electron reaction cross section data, and physical density, using Monte Carlo particle transport principles and CUDA multithreading technology, to obtain the first deposition energy corresponding to each dose grid. Based on the cumulative decay number, the first deposition energy corresponding to each dose grid, the grid volume of each dose grid, and the number of first simulated particles, the first grid absorbed dose corresponding to each dose grid is calculated. Then, based on the number of dose grids and the absorbed dose of each first grid, the internal radiation dose distribution is calculated.

[0183] In this embodiment, the computing device can read the X-ray energy spectrum data of the radionuclides of the radioactive material injected into the target object from a database connected to it wirelessly, as well as the photon reaction cross-section data (such as Compton effect, photoelectric effect, and electron-electron pair effect) and electron reaction cross-section data (such as Rayleigh scattering and bremsstrahlung) of the selected material. The selected material can be determined according to actual needs and is not limited here.

[0184] In some possible embodiments, in order to improve computational efficiency, the computing device can copy the above-mentioned X-ray energy spectrum data, photon reaction cross section data and electron reaction cross section data from the central processing unit (CPU) to the graphics processing unit (GPU), and execute steps S602 to S603 in the GPU.

[0185] Subsequently, the computing device can determine the distribution of radioactivity concentration and the physical density of tissues within the target object based on the tomographic images, in order to determine the metabolic kinetics of the target object.

[0186] Specifically, the computing device can determine the distribution of radioactive sources within the target object by utilizing the radioactivity concentration distribution obtained from the tomographic scan image. This means treating each pixel in the tomographic image as a point source and using the pixel distribution as the distribution of radioactive sources for subsequent Monte Carlo particle transport simulations. Simultaneously, the computing device can align the tomographic scan image with a dose grid and update the HU values ​​in the grid image to the corresponding dose grid using trilinear interpolation, thus obtaining the physical density distribution of the tissue within the dose grid. Here, the HU value is a unit of CT value used to represent the density of human tissue.

[0187] In some possible embodiments, since the dimensionality of the tomographic scan image is relatively large, processing the distribution and density distribution of the radiation source requires multiple iterations. Therefore, in order to save computation time and improve computational efficiency, the computing device can use CUDA multithreading technology on the GPU to perform parallel computation of the above determination process.

[0188] Subsequently, the computing device can perform sampling based on the determined radioactivity concentration distribution to obtain multiple first sampled particles. Simultaneously, the computing device can determine the first particle information for each of the multiple first sampled particles. This first particle information includes, but is not limited to, the particle's initial position ri, direction of motion Ωi, particle energy Ei, and type.

[0189] It should be noted that the particle energy Ei and type mentioned above can be obtained by sampling the X-ray energy spectrum data of radionuclides.

[0190] Subsequently, the computing device can perform parallel particle transport simulations on multiple first-sampled particles based on X-ray energy spectrum data, photon reaction cross section data, electron reaction cross section data, and physical density to obtain the internal radiation dose distribution.

[0191] Specifically, the computing device can perform parallel particle transport simulations on multiple first-sampled particles based on the Monte Carlo particle transport principle, using X-ray energy spectrum data, photon reaction cross-section data, electron reaction cross-section data, and physical density. This involves using random sampling methods and combining X-ray energy spectrum data, photon reaction cross-section data, and electron reaction cross-section data to realistically simulate the interactions that occur during the transport of radionuclides within the target object, such as the photoelectric effect, Compton effect, electron-electron pair effect, Rayleigh scattering, bremsstrahlung, Mueller scattering, and multiple scattering. Based on the physical density within the dose grid, the device directly tracks the trajectory of each particle within the target object. Simultaneously, during the particle transport simulation, CUDA multithreading technology is used to track the trajectory of a single first-sampled particle using a single thread, recording the reactions and energy losses of that first-sampled particle within different dose grids, thus obtaining the first deposited energy corresponding to each dose grid.

[0192] It should be noted that in the above particle transport simulation, it is necessary to frequently obtain the physical density of the dose grid where each first sampled particle is currently located and write the energy deposited by each first sampled particle into each dose grid. This process requires repeated reading and writing of the GPU's global memory, which will hinder the program's running speed. Therefore, in order to improve efficiency, the computing device removes redundant density grid accesses and only retrieves density information after crossing grids, storing it in temporary variables. These temporary variables are used to record multiple dose depositions of electrons within the same grid, thereby achieving merged access. In addition, in the photon transport process, in order to improve transport efficiency, the computing device can divide the entire transport modulus into several large regions. Based on the small grids contained within each large grid, the local maximum reaction cross section is obtained, and the sampling step size using this cross section is longer.

[0193] Finally, once the energy of all the first sampled particles has been transported, meaning the current energy of all the first sampled particles is below a preset threshold, the computing device can calculate the energy loss within each CUDA thread, thereby obtaining the first deposition energy corresponding to each dose grid. The preset threshold can be set according to actual needs and is not limited here.

[0194] In this embodiment, after obtaining the first deposition energy corresponding to each dose grid, the computing device can calculate the first grid absorbed dose corresponding to each dose grid based on the cumulative decay number, the first deposition energy corresponding to each dose grid, the grid volume of each dose grid, and the first simulated particle number. Specifically, the first simulated particle number refers to the number of the first sampled particles.

[0195] Specifically, the computing device can calculate the first grid absorbed dose corresponding to each dose grid according to the following formula:

[0196]

[0197] Where i represents the location marker of the i-th grid, and D i E represents the absorbed dose of the first grid corresponding to the i-th dose grid. i V represents the first deposition energy corresponding to the i-th dose grid. i Let A represent the grid volume of the i-th dose grid, A~ represent the cumulative decay number, N represent the number of the first simulated particles, and n represent the number of particles generated in each decay.

[0198] Based on this, the computing device can calculate the final internal radiation dose distribution based on the number of dose grids and the absorbed dose of each first grid.

[0199] In other possible embodiments, the computing device may also use other methods to perform parallel particle transport simulations on multiple first sampled particles to obtain the internal radiation dose distribution. For example, it may use a semi-random simulation numerical method (a method that combines Monte Carlo methods and deterministic methods) to simulate the transport process of multiple first sampled particles to determine the spatial distribution and motion state of multiple first sampled particles, and based on this, determine the internal radiation dose distribution by combining particle energy, particle number, etc.

[0200] In this embodiment of the application, when the radiation delivery method of the target object includes external radiation, it indicates that external radiation dose calculation is required. Therefore, the calculation device can determine the external radiation dose distribution.

[0201] In some possible embodiments, when the radiation delivery method of the target object includes external radiation, the computing device can calculate the external radiation dose distribution of the target object under external radiation according to the existing external radiation dose calculation method, which will not be elaborated here.

[0202] In other possible embodiments, when the radiation delivery method to the target object includes external radiation, in order to improve the accuracy of calculating the external radiation dose, the computing device can also use methods such as... Figure 8 Steps S701 to S702 shown obtain the external radiation dose distribution of the target object under external radiation, which is described in detail below:

[0203] In S701, a second image is acquired after the target object emits radioactive material corresponding to the object outside the object and then enters the interior, as well as photon reaction cross-section data and electron reaction cross-section data of the set material.

[0204] In S702, the external radiation dose distribution is determined based on the second image, photon reaction cross section data, and electron reaction cross section data.

[0205] It should be noted that since imaging examination methods include computed tomography (CT) and single-photon emission computed tomography (SPECT), the aforementioned second image may include CT images and SPECT images.

[0206] In this embodiment, the computing device can read photon reaction cross-section data (such as Compton effect, photoelectric effect, and electron-electron pair effect) and electron reaction cross-section data (such as Rayleigh scattering and bremsstrahlung) of a specified material from a database with which it is wirelessly connected. The specified material can be determined according to actual needs and is not limited here.

[0207] In some possible embodiments, in order to improve computational efficiency, the computing device can copy the above-mentioned photonic reaction cross section data and electronic reaction cross section data from the CPU to the GPU, and execute step S702 in the GPU.

[0208] In this embodiment, after obtaining the second image, photon reaction cross section data, and electron reaction cross section data, the computing device can use the Monte Carlo algorithm to perform particle transport simulation based on the second image, photon reaction cross section data, and electron reaction cross section data, thereby determining the external radiation dose distribution.

[0209] In one embodiment of this application, in order to improve the accuracy of calculating the external radiation dose distribution, the computing device can specifically implement step S702 according to the following steps, detailed below:

[0210] Based on the grid image corresponding to the second image, the Hu value and physical density corresponding to each dose grid are determined.

[0211] Based on the irradiation field corresponding to the external radiation of the target, the flux map corresponding to all dose grids under external radiation of the target is determined.

[0212] Sampling was performed based on the beam model to obtain multiple second-sampled particles.

[0213] Based on flux maps, Hu values ​​and physical densities corresponding to each dose grid, photon reaction cross-section data, and electron reaction cross-section data, parallel particle transport simulations were performed on multiple second-sampled particles using Monte Carlo particle transport principles and CUDA multi-threading technology to obtain the second deposition energy corresponding to each dose grid.

[0214] Based on the cumulative decay number, the second deposition energy corresponding to each dose grid, the grid volume of each dose grid, and the second simulated particle number, the second grid absorbed dose corresponding to each dose grid is calculated.

[0215] The external radiation dose distribution is calculated based on the number of dose grids and the absorbed dose of each second grid.

[0216] In this embodiment, the computing device can determine the Hu value and physical density corresponding to each dose grid based on the grid image corresponding to the tomographic scan image.

[0217] Specifically, the computing device can align the second corresponding grid image with the dose grid, and use trilinear interpolation to convert each HU value in the grid image to a new HU value in the dose grid, and calculate the physical density of each dose grid based on the new HU value.

[0218] In some possible embodiments, since the second image has a relatively large dimension, the density distribution obtained by processing needs to be processed multiple times. Therefore, in order to save computation time and improve computational efficiency, the computing device can use CUDA multithreading technology on the GPU to perform parallel computation on the above determination process.

[0219] Then, the computing device can determine the flux map corresponding to all dose grids under external radiation based on the irradiation field corresponding to the external radiation of the object.

[0220] Specifically, the computing device can use CUDA multi-threaded computing to calculate the flux map corresponding to the multi-leave collimators (MLC), the flux map corresponding to the groove effect, and the flux map corresponding to the leaf tip effect based on the above irradiation field. Then, the computing device can superimpose and sum the various flux maps obtained by all CUDA threads to obtain the complete flux map, that is, the flux map corresponding to all dose grids.

[0221] During electrodeposition, electric field lines tend to be denser at the edges and tips of the workpiece or electrode; this phenomenon is called the tip effect or edge effect.

[0222] In practical applications, the irradiation field refers to the area irradiated by radiation during radiotherapy.

[0223] Since the rays used in radiotherapy are delivered at multiple beam angles and multiple beam intensities, a flux map refers to the intensity distribution at each point (x, y) in a coordinate system composed of all dose grids.

[0224] Subsequently, the computing device can perform sampling based on the beam model of the linear accelerator to obtain multiple second-sampled particles. Simultaneously, the computing device can determine the second-particle information for each of the multiple second-sampled particles. This second-particle information includes, but is not limited to, the particle's initial position rj, direction of motion Ωj, and particle energy Ej.

[0225] Subsequently, the computing device can perform parallel particle transport simulations on multiple second-sampled particles based on the Monte Carlo particle transport principle, using the aforementioned flux map, Hu values ​​and physical densities corresponding to each dose grid, photon reaction cross-section data, and electron reaction cross-section data. This involves using random sampling methods, combined with flux map, photon reaction cross-section data, and electron reaction cross-section data, to realistically simulate the interactions occurring during the transport of radionuclides within the target object, such as the photoelectric effect, Compton effect, electron-electron pair effect, Rayleigh scattering, bremsstrahlung, Mueller scattering, and multiple scattering. Based on the Hu values ​​and physical densities corresponding to each dose grid, the device directly tracks the trajectory of each particle within the target object. Simultaneously, during the particle transport simulation, CUDA multithreading technology is used to use a single thread to track the trajectory of one second-sampled particle, recording the reactions and energy losses of that particle within different dose grids, thus obtaining the second-deposited energy corresponding to each dose grid.

[0226] It should be noted that in the above particle transport simulation, it is necessary to frequently obtain the physical density of the dose grid where each second sampled particle is currently located and write the energy deposited by each second sampled particle into each dose grid. This process requires repeated reading and writing of the GPU's global memory, which will hinder the program's running speed. Therefore, in order to improve efficiency, the computing device removes redundant density grid accesses and only retrieves density information after crossing grids, storing it in temporary variables. These temporary variables are used to record multiple dose depositions of electrons within the same grid, thereby achieving merged access. In addition, in the photon transport process, in order to improve transport efficiency, the computing device can divide the entire transport modulus into several large regions. Based on the small grids contained within each large grid, the local maximum reaction cross section is obtained, and the sampling step size using this cross section is also longer.

[0227] Finally, once all the energy of the second sampled particles has been transported, meaning the current energy of all the second sampled particles is below a preset threshold, the computing device can calculate the energy loss within each CUDA thread, thereby obtaining the second deposition energy corresponding to each dose grid. The preset threshold can be set according to actual needs and is not limited here.

[0228] In this embodiment, after obtaining the second deposition energy corresponding to each dose grid, the computing device can calculate the second grid absorbed dose corresponding to each dose grid based on the cumulative decay number, the second deposition energy corresponding to each dose grid, the grid volume of each dose grid, and the second simulated particle number. Specifically, the second simulated particle number refers to the number of second sampled particles.

[0229] Specifically, the computing device can calculate the absorbed dose of the second grid corresponding to each dose grid according to the following formula:

[0230]

[0231] Where j represents the location marker of the j-th grid, and D j E represents the absorbed dose of the second grid corresponding to the j-th dose grid. j V represents the second deposition energy corresponding to the j-th dose grid. j Let A represent the grid volume of the j-th dose grid, A represent the cumulative decay number, P represent the number of particles in the second simulation, and p represent the number of particles generated in each decay.

[0232] In S103, the target dose distribution of the target object is calculated based on the internal radiation dose distribution and / or external radiation dose distribution.

[0233] In this embodiment of the application, when the radiation delivery method for the target object only includes radiation within the object, the computing device can directly determine the internal radiation dose distribution as the target dose distribution.

[0234] When the radiation delivery method for the target object only includes external radiation, the computing device can directly determine the external radiation dose distribution as the target dose distribution.

[0235] When the radiation delivery method for a target object includes internal radiation and external radiation, the computing device can sum the internal radiation dose distribution and the external radiation dose distribution, and determine the sum as the target dose distribution of the target object.

[0236] In one embodiment of this application, since the target object may experience biological effects during radiotherapy, and these biological effects can affect the absorbed dose under different radiation delivery methods, in order to further improve the accuracy of calculating the final absorbed dose of the target object, the computing device can specifically calculate the target dose distribution within the target object according to the following steps, detailed below:

[0237] If a biological effect is detected within the target object, the weight of the first biological effect corresponding to radiation within the object and / or the weight of the second biological effect corresponding to radiation outside the object are determined based on the biological effect.

[0238] The target dose distribution is obtained by weighting and summing the internal and / or external radiation dose distributions based on the first biological effect weight and / or the second biological effect weight.

[0239] In practical applications, biological effects can be classified according to the object on which they occur: somatic effects and genetic effects; according to the pattern of their occurrence: non-stochastic effects and stochastic effects; and according to the time of their occurrence: short-term effects and long-term effects.

[0240] It should be noted that the computing device can detect whether a biological effect is produced in the target object based on existing radiometric technology and radioimmunoassay.

[0241] In this embodiment, when the computing device detects a biological effect (i.e., a radiobiological effect) generated within the target object, it can determine a first biological effect weight corresponding to radiation within the object and / or a second biological effect weight corresponding to radiation outside the object based on the generated biological effect.

[0242] In practical applications, the weights corresponding to radiobiological effects mainly include radiation weighting factors and tissue weighting factors.

[0243] The radiation weighting factor (WR) is a factor used in radiobiology to characterize the differences in biological efficacy among different types of radiation. It is determined based on the degree of biological effect produced by a specific type of radiation and a reference type of radiation (usually X-rays or gamma rays) under the same irradiation conditions. The radiation weighting factor considers the type of radiation, the manner of energy deposition, and the interaction between radiation and matter, thus reflecting the degree of influence of different types of radiation on organisms. This factor is used to correct for absorbed dose when calculating equivalent doses to ensure an accurate reflection of the actual effects of radiation on organisms.

[0244] The tissue weighting factor (WT) is used to account for the varying sensitivities of different organs or tissues to the stochastic effects of radiation. This factor corrects for the equivalent dose to ensure that the corrected equivalent dose accurately reflects the level of danger posed by the irradiated tissue or organ after absorbing radiation. Each WT is less than 1, with higher WTs for more sensitive tissues. The sum of all tissue weighting factors is 1. This factor was introduced to more accurately assess the potential impact of radiation on specific organs or tissues, thereby providing more precise information in radiation protection and risk management.

[0245] Therefore, in this embodiment, both the first biological effect weight and the second biological effect weight are radiation weight factors.

[0246] It should be noted that when the radiation delivery method for the target object only involves radiation within the object, the computing device may determine only the weight of the first biological effect.

[0247] When the radiation delivery method for the target object only involves radiation outside the target object, the computing device can determine only the weight of the second biological effect.

[0248] When the radiation delivery method for a target object includes intra-target radiation and extra-target radiation, the computing device can determine the first biological effect weight and the second biological effect weight.

[0249] The computing device can then perform a weighted summation of the internal and external radiation dose distributions based on the first biological effect weight and / or the second biological effect weight, thereby obtaining the final target dose distribution.

[0250] Specifically, the computing device can calculate the target dose distribution according to the following formula:

[0251] D total =w in D in +w out D out ;

[0252] Among them, D total Indicates the target dose distribution, w in w represents the weight of the first biological effect. out D represents the weight of the second biological effect. in D represents the internal radiation dose distribution. out This indicates the distribution of external radiation dose.

[0253] It should be noted that when the radiation delivery method for the target object only includes radiation within the object, the computing device can directly determine the target dose distribution by multiplying the first biological effect weight by the internal radiation dose distribution.

[0254] When the radiation delivery method for the target object only includes external radiation, the computing device can directly determine the target dose distribution by multiplying the second biological effect weight by the external radiation dose distribution.

[0255] As can be seen from the above, the dose determination method provided in this application determines whether radiation delivery to a target object includes internal and / or external radiation; determines the internal radiation dose distribution when radiation delivery includes internal radiation, and determines the external radiation dose distribution when radiation delivery includes external radiation; and determines the target dose distribution based on the internal and / or external radiation dose distributions. This method can determine whether radiation delivery to a target object includes internal and / or external radiation, and then flexibly and specifically calculate the internal and / or external radiation dose distributions based on the determination results, thereby improving calculation efficiency and accurately obtaining the total absorbed dose under the superposition of internal and external radiation, thus improving the accuracy of dose determination.

[0256] In practical applications, intracavitary radiotherapy utilizes the natural cavities within human tissues to deliver the radiation source stepwise to the treatment site. The residence time at different locations is adjusted according to clinical needs to optimize dose distribution and achieve clinical therapeutic objectives. The dosimetric advantages of intracavitary radiotherapy lie in the higher dose within the treatment target area and the lower dose received by surrounding normal tissues. It also features a shorter treatment time, allowing for continuous or fractionated irradiation. Intracavitary radiotherapy can be used as a standalone treatment or as a supplement to external beam radiotherapy. Accurately calculating the radiation dose to the tumor target area and organs at risk during intracavitary radiotherapy is crucial for improving the accuracy and precision of target area design and reducing the dose to surrounding normal tissues, thus enhancing the efficacy of intracavitary radiotherapy.

[0257] Since intracavitary radiation is administered via a different route than conventional intracavitary radiation (such as oral or injectable), this application provides a dose determination method for intracavitary radiation in order to improve the accuracy of determining the dose distribution under intracavitary radiation.

[0258] Please see Figure 9 , Figure 9 This is a flowchart illustrating another dosage determination method provided in this application. In this embodiment, the subject executing the dosage determination method is a computing device. The computing device includes, but is not limited to, laptops, desktop computers, and other computers.

[0259] like Figure 9 As shown, a dose determination method provided in one embodiment of this application may include S801 to S803, which are detailed below:

[0260] In S801, the radiation source model of the near-field radiation device is determined.

[0261] In one embodiment of this application, the computing device can determine a radioactive source model based on the radionuclide information and / or structural information of the near-field radiation device. Specifically, the computing device sets the basic information corresponding to the radioactive source model based on the structural information of the near-field radiation device, and determines the decay data related to the radioactive source of the near-field radiation device, such as decay energy spectrum and decay type, based on the radionuclide information, thereby obtaining the final radioactive source model. The basic information includes, but is not limited to, the type of radioactive source (e.g., 192Ir, 125I, etc.), radioactive source activity, half-life, density, etc.

[0262] In S802, based on the radiation source model, an internal radiation model corresponding to the radiation delivery performed by the near-field radiation device inside the target object is determined.

[0263] In this embodiment, after obtaining the radiation source model, the computing device can determine an internal radiation model corresponding to the radiation delivery performed by the brachytherapy device inside the target object based on the radiation source model. When the radiation therapy of the target object includes intracavitary radiation, the computing device can construct a radiation source model corresponding to the brachytherapy device used at this time, i.e., the brachytherapy device located inside the target object.

[0264] Specifically, the computing device can determine an internal radiation model based on a radiation source model and internal information of the target object. This internal information includes, but is not limited to, one or more of the following: structural information, material information, and physical density information of voxels within the target object. For example, the radiation source model can be directly combined with the internal information of the target object to determine the predicted radiation state of the radiation source within the target object, serving as the internal radiation model. As another example, a new combined model can be established based on the internal information of the target object and the corresponding information from the radiation source model (this new combined model can reflect or embody the comprehensive interaction between the target object's interior and the radiation source), serving as the internal radiation model. However, this is not the only option; various other modeling methods can also be used to establish this internal radiation model.

[0265] In S803, the dose distribution formed inside the target object by radiation delivery is determined based on the internal radiation model.

[0266] In this embodiment, once the internal radiation model is determined, the movement or distribution of radiated particles within the target object can be determined, thereby determining the dose distribution. As an example, a computing device can obtain the dose distribution formed within the target object by simulating the particle transport process based on the aforementioned internal radiation model.

[0267] It should be noted that when the target patient's radiotherapy includes intracavitary radiation, the target patient can be placed in a supine position on a carbon fiber simulation positioning bed. After the implantation of a brachytherapy device (such as an applicator), the target patient needs to maintain a natural body position, and relevant personnel can inject intravenous contrast agents into the target patient.

[0268] In one implementation of this application, a computing device can determine the internal information of a target object by scanning an image of the target object.

[0269] In this embodiment, the scanned images include, but are not limited to, CT images or magnetic resonance (MR) images.

[0270] In one embodiment of this application, in order to improve processing efficiency, the computing device can utilize a GPU-based multi-threaded task parallel computing method to determine the internal information of the target object based on the image information of the scanned image.

[0271] In this embodiment, the computing device can sample the internal radiation model to obtain multiple sampled particles. Simultaneously, the computing device can determine the particle information of each sampled particle. This particle information includes, but is not limited to, the particle's initial position ri, direction of motion Ωi, particle energy Ei, and type.

[0272] It should be noted that the particle energy Ei and type mentioned above can be obtained by sampling the radiation energy spectrum data of the radionuclides corresponding to the near-field radiation device. This radiation energy spectrum data can be obtained from a nuclide database wirelessly connected to a computing device.

[0273] The particle's direction of motion Ωi is mainly obtained by sampling the particle's structure (point source or line source) within the near-field radiation device, while the particle's initial position ri is mainly obtained by sampling using the following function:

[0274]

[0275] Here, a0, a1, a2, a3, a4, a5, and a6 are different coefficients for different point or line sources. The coefficients for most near-field radiation devices can be obtained directly from various existing literature. For certain specially designed radiation source structures, they can be simulated using EGS4 first, and then obtained by fitting the phase space results.

[0276] It should be noted that during sampling, multiple new positions can be randomly generated along each particle direction; at multiple new positions, the same particle information and the same trajectory data can be used, and the CUDA multi-threading technology based on the GPU can be used to read the information of each particle separately to realize the parallel simulation of the particle transport process.

[0277] The computing device can then combine the aforementioned multiple sampled particles to simulate the particle transport process in order to obtain the dose distribution formed inside the target object.

[0278] In another embodiment of this application, the computing device can use a Monte Carlo algorithm to determine the radiation dose of one or more radiating particles, and simulate the particle transport process based on the determined trajectory of the radiating particles. The radiating particles include, but are not limited to, photons and electrons.

[0279] Specifically, the computing device can perform parallel particle transport simulation of the aforementioned radiation particles based on the Monte Carlo particle transport principle. That is, by using random sampling methods, it can realistically simulate the interactions that occur during the transport of radionuclides within the target object, such as the photoelectric effect, Compton effect, electron-electron pair effect, Rayleigh scattering, bremsstrahlung, Mueller scattering, and multiple scattering, thereby determining the radiation dose of one or more radiation particles and tracking the trajectory of each radiation particle within the target object to obtain the dose distribution formed inside the target object.

[0280] It should be noted that particle transport simulation is mainly divided into photon transport and electron transport.

[0281] Photon transport primarily considers the photoelectric effect, Compton effect, and electron-electron pair effect. In photon transport, a random number generator is used to generate a random number whose range falls within the macroscopic cross-sectional range corresponding to a specific reaction type. If the random number falls within this range, the radioactive source particle undergoes that type of reaction. Secondary particles generated by the reaction, including photons, electrons, or positrons, are stored in a secondary particle stack, awaiting further transport simulation. To improve transport efficiency, the particle transport step size is sampled based on particle energy and compared with the distance from the particle to the grid boundary to determine if a reaction has occurred within the grid. To further reduce the number of transported photons, each photon is split into n sub-photons, each with a weight of 1 / n. When a Compton reaction occurs, a Russian roulette game is played with the scattered photon using a probability of 1 / n. If the photon survives, its parameters are stored. The next sub-photon (i = i + 1) repeats the above steps. When a subsequent photon has the same trajectory as a previous photon, the previously saved historical data is used directly as the trajectory of that photon.

[0282] In some possible embodiments, during particle transport simulation, the computing device can utilize CUDA multithreading technology to use a single thread to track the trajectory of a single radiating particle and record the reactions and radiation doses lost by the radiating particle in different dose grids, thereby obtaining the deposited energy corresponding to each dose grid.

[0283] It should be noted that during the particle transport simulation described above, any thread in the CUDA multithreading can access and modify the trajectory data corresponding to the particle energy of the radiation particle in the GPU's shared memory for that thread. Any thread among the multiple CUDA threads can query the corresponding particle trajectory data index table based on its own particle energy to determine the address of each particle trajectory data in the GPU's global memory; it can also retrieve the particle trajectory data corresponding to its own particle energy from the GPU's global memory based on that address. In this way, each CUDA thread can simulate the transport process of different particles in parallel without interference, improving the computational efficiency of Monte Carlo particle transport simulation and reducing the time required for dose calculation.

[0284] In this embodiment, once all radiation particle energy has been transported, i.e., all current radiant particle energies are below a preset threshold, the computing device can calculate the radiation dose within each CUDA thread and, based on the initial energy of each radiation particle and the aforementioned radiation dose, calculate the deposited energy corresponding to each dose grid. The preset threshold can be set according to actual needs and is not limited here.

[0285] In one embodiment of this application, in order to more realistically simulate the actual physical processes such as the various reactions of radiation particles within the target body during intracavitary radiation, and thus obtain more accurate deposition energy, the computing device may further include the following steps before calculating the deposition energy corresponding to each dose grid, as detailed below:

[0286] Acquire positioning images of the target object after implantation of a brachytherapy device; the positioning images are obtained from a pre-set simulation positioning device;

[0287] The location image is delineated using the point-plotting method to determine the area of ​​the near-field radiation device in the location image;

[0288] The location image after delineation is marked based on the area of ​​the near-field radiation device to obtain a target image containing dose reference points.

[0289] Based on the target image, determine the Hu value and physical density corresponding to each dose grid.

[0290] In this embodiment, after the target object is implanted with a brachytherapy device, relevant personnel can use a preset simulation positioning device to perform intracavitary brachytherapy positioning scans to obtain positioning images, and then send the positioning images to a computing device.

[0291] Based on this, the computing device can obtain a location image of the target object after the implantation of a close-range radiation device.

[0292] Subsequently, the computing device can use a point-plotting method to delineate the brachytherapy device layer by layer on the localization image, thus determining the brachytherapy device region in the localization image. Then, for each tumor slice in the localization image, the computing device can mark a point on the tumor edge in four directions to the left and right of the brachytherapy device region as a dose reference point, thereby obtaining a target image containing the dose reference points. In the treatment planning system, the dose distribution in the target area and normal tissue is represented as a relative distribution with dose normalization to a specific point within the target area; this point is called the target area dose normalization point.

[0293] Next, the computing device updates the HU value of the target image into the corresponding dose grid using trilinear interpolation based on the electronic conversion density table specified by the relevant personnel and the preset dose grid size, thus obtaining the HU value and physical density corresponding to each dose grid.

[0294] In some possible embodiments, since the target image of the target object has a large number of grids, the computing device can utilize the parallel computing function of CUDA multithreading technology to simultaneously process HU interpolation and density conversion within different voxels based on different grid index numbers, so as to improve the algorithm's running efficiency.

[0295] Based on this, the computing device can specifically calculate the deposition energy corresponding to each dose grid according to the following steps:

[0296] At the Hu value and physical density corresponding to each dose grid, parallel particle transport simulations were performed on multiple sampled particles based on the Monte Carlo particle transport principle and CUDA multithreading technology to obtain the deposition energy corresponding to each dose grid.

[0297] In this embodiment, the computing device can perform parallel particle transport simulation on multiple sampled particles based on the Monte Carlo particle transport principle under the Hu value and physical density corresponding to each dose grid. That is, by using a random sampling method and combining the Hu value and physical density corresponding to each dose grid, it can realistically simulate the interactions that occur during the transport of radionuclides in the target object, such as the photoelectric effect, Compton effect, electron-electron pair effect, Rayleigh scattering, bremsstrahlung, Mueller and multiple scattering, and other actual physical processes. Based on the physical density within the dose grid, it can directly track the trajectory of each particle in the target object, thereby obtaining the deposition energy corresponding to each dose grid.

[0298] After obtaining the deposition energy corresponding to each dose grid, the computing device can store it in the global memory of the GPU in the form of a three-dimensional matrix, and accumulate the deposition energy calculated by different threads into the same dose grid, thereby obtaining the deposition energy distribution within the same dose grid.

[0299] Subsequently, the computing device can calculate the absorbed dose of each dose grid in the deposition energy distribution within the same dose grid, based on the cumulative decay number, the deposition energy corresponding to each dose grid in the deposition energy distribution within the same dose grid, the grid volume of each dose grid in the deposition energy distribution within the same dose grid, and the number of simulated particles. The number of simulated particles specifically refers to the number of radiated particles.

[0300] It should be noted that the above cumulative decay number can be calculated based on, for example... Figures 2 to 3 The calculations were obtained from their respective corresponding embodiments.

[0301] Specifically, the computing device can calculate the grid absorbed dose corresponding to each dose grid in the deposition energy distribution within the same dose grid according to the following formula:

[0302]

[0303] Where i represents the location marker of the i-th grid, and D i E represents the absorbed dose of the first grid corresponding to the i-th dose grid. i V represents the first deposition energy corresponding to the i-th dose grid. i This represents the grid volume of the i-th dose grid. This represents the cumulative decay number, N represents the number of particles in the first simulation, and n represents the number of particles produced in each decay.

[0304] Based on this, the computing device can sum the absorbed doses of each of the above grids and determine the sum as the dose distribution formed inside the target object.

[0305] In one embodiment of this application, the computing device may further determine the dose distribution formed within the target object according to the following steps:

[0306] Based on the internal radiation model, the dose grid is calculated and stored in the form of a sparse matrix;

[0307] Using the stored dose grid, a dose summation calculation is performed to determine the dose distribution.

[0308] In this embodiment, the computing device can specifically perform parallel particle transport simulation on each radiating particle in the internal radiation model based on the Monte Carlo particle transport principle. This allows it to record the reactions and radiation dose losses of each radiating particle in different dose grids, thereby obtaining the deposited energy corresponding to each dose grid, i.e., calculating the dose network.

[0309] In this embodiment, since the GPU needs to store multiple dose grids, which requires a large amount of additional video memory, in order to reduce video memory usage, the computing device can use CUDA's cusparseXpruneDense2csr() function to store the dose grids in sparse matrix format, and after the dose calculation is completed, convert the sparse matrix back to a dense matrix to output the dose grids.

[0310] Then, the computing device can use the stored dose grid to perform dose summation calculation, that is, to calculate the grid absorbed dose corresponding to each dose grid for the deposition energy corresponding to each dose grid, and to sum the absorbed doses of each grid to determine the final dose distribution.

[0311] As can be seen from the above, the dose determination method provided in this application embodiment can accurately calculate the dose distribution formed inside the human body by establishing a radiation source model of the intracavitary irradiation device when the radiation inside the object is intracavitary radiation, thereby improving the accuracy of determining the dose distribution of the target object under intracavitary radiation.

[0312] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0313] Corresponding to the above text, such as Figures 1 to 8 A dosage determination method as described in the corresponding embodiment, Figure 10 A schematic diagram of a dose determination device according to an embodiment of this application is shown. For ease of explanation, only the parts relevant to the embodiment of this application are shown. (Refer to...) Figure 10 The dose determination device 100 includes: a radiation determination unit 11, a first dose determination unit 12, and a second dose determination unit 13. Wherein:

[0314] The radiation determination unit 11 is used to determine whether radiation delivery to the target object includes radiation inside the object and / or radiation outside the object.

[0315] The first dose determination unit 12 is used to determine the internal radiation dose distribution when the radiation delivery includes internal radiation to the object, and to determine the external radiation dose distribution when the radiation delivery includes external radiation to the object.

[0316] The second dose determination unit 13 is used to determine the target dose distribution based on the internal radiation dose distribution and / or the external radiation dose distribution.

[0317] In one embodiment of this application, the first dose determination unit 12 specifically includes a third dose determination unit.

[0318] The third dose determination unit is used to determine the initial dose of radioactive material inside the target object and the first image of the target object after the radioactive material enters the object during radiation delivery, including radiation inside the object, and to determine the internal radiation dose distribution based on the initial dose and the first image.

[0319] In one embodiment of this application, the third dose determination unit specifically includes:

[0320] The first cumulative decay number determination unit is used to determine the cumulative decay number of a radioactive material based on an initial dose or a first image.

[0321] The fourth dose determination unit is used to determine the internal radiation dose distribution based on the cumulative decay number.

[0322] In one embodiment of this application, the first cumulative decay number determination unit specifically includes: a relationship determination unit and a second cumulative decay number determination unit. Wherein:

[0323] The relationship determination unit is used to determine the relationship between the radioactivity of the radioactive material in the target body and time, based on the first image, if there is no data on the radioactivity activity of the target body's excrement at different times after the radioactive material enters the target body.

[0324] The second cumulative decay number determination unit is used to determine the cumulative decay number based on the change relationship.

[0325] In one embodiment of this application, the first cumulative decay number determination unit specifically includes: a first calculation unit and a second calculation unit. Wherein:

[0326] The first calculation unit is used to calculate the current remaining radioactivity in the target body after the radioactive material enters the target body, based on the initial dose and the radioactivity measurement data of each excrement at different times, if there is radioactivity measurement data of the target body at different times.

[0327] The second calculation unit is used to calculate the cumulative decay number based on the current remaining radioactivity and the half-life of the radionuclides in the radioactive material.

[0328] In one embodiment of this application, the fourth dose determination unit specifically includes: an average dose determination unit and a third calculation unit. Wherein:

[0329] The average dose determination unit is used to determine the average absorbed dose per unit decay number.

[0330] The third calculation unit is used to calculate the internal radiation dose distribution based on the cumulative decay number and the average absorbed dose.

[0331] In one embodiment of this application, the fourth dose determination unit specifically includes: a first acquisition unit, a first density determination unit, a first processing unit, and a first simulation unit. Wherein:

[0332] The first acquisition unit is used to acquire the radiation energy spectrum data corresponding to the radioactive material, as well as the photon reaction cross section data and electron reaction cross section data of the specified material.

[0333] The first density determination unit is used to determine the distribution of radioactivity concentration and the physical density of tissues within the target object based on the first image.

[0334] The first processing unit is used to perform sampling based on the radioactivity concentration distribution to obtain multiple first sampled particles.

[0335] The first simulation unit is used to perform parallel particle transport simulations on multiple first sampled particles based on X-ray energy spectrum data, photon reaction cross section data, electron reaction cross section data, and physical density, to obtain the internal radiation dose distribution.

[0336] In one embodiment of this application, the first dose determination unit 12 specifically includes: a second acquisition unit and a fifth dose determination unit. Wherein:

[0337] The second acquisition unit is used to acquire a second image of the target object after the corresponding radioactive material emitted from outside the object enters the interior, as well as to acquire the photon reaction cross-section data and electron reaction cross-section data of the set material.

[0338] The fifth dose determination unit is used to determine the external radiation dose distribution based on the second image, photon reaction cross section data, and electron reaction cross section data.

[0339] In one embodiment of this application, the fifth dose determination unit specifically includes: a second density determination unit, a flux map determination unit, a second processing unit, a second simulation unit, a fourth calculation unit, and a fifth calculation unit. Wherein:

[0340] The second density determination unit is used to determine the Hu value and physical density of each dose grid based on the grid image corresponding to the second image.

[0341] The flux map determination unit is used to determine the flux map corresponding to all dose grids under external radiation based on the irradiation field corresponding to the external radiation of the object.

[0342] The second processing unit is used to perform sampling based on the beam model to obtain multiple second sampled particles.

[0343] The second simulation unit is used to perform parallel particle transport simulations on multiple second-sampled particles based on the Monte Carlo particle transport principle and CUDA multi-threading technology, under flux maps, Hu values ​​and physical densities corresponding to each dose grid, photon reaction cross-section data, and electron reaction cross-section data, to obtain the second deposition energy corresponding to each dose grid.

[0344] The fourth calculation unit is used to calculate the second grid absorbed dose for each dose grid based on the cumulative decay number, the second deposition energy corresponding to each dose grid, the grid volume of each dose grid, and the second simulated particle number.

[0345] The fifth calculation unit is used to calculate the external radiation dose distribution based on the number of dose grids and the absorbed dose of each second grid.

[0346] In one embodiment of this application, the second dose determination unit 13 specifically includes a weight determination unit and a summation unit.

[0347] in:

[0348] The weight determination unit is used to determine the first biological effect weight corresponding to radiation inside the target object and / or the second biological effect weight corresponding to radiation outside the target object based on the biological effect if a biological effect is detected inside the target object.

[0349] The summation unit is used to perform weighted summation of the internal radiation dose distribution and / or external radiation dose distribution based on the first biological effect weight and / or the second biological effect weight, to obtain the target dose distribution.

[0350] Corresponding to the above text, such as Figure 9 A dosage determination method as described in the corresponding embodiment, Figure 11 A schematic diagram of a dose determination device according to another embodiment of this application is shown. For ease of explanation, only the parts relevant to the embodiment of this application are shown. (Refer to...) Figure 11 The dosage determination device 200 includes: a first model determination unit 21, a second model determination unit 22, and a dosage determination unit 23. Wherein:

[0351] The construction unit 21 is used to construct a radiation source model corresponding to a close-range radiation device located within the target object when the radiation delivery method of the target object includes intracavitary radiation.

[0352] The first model determination unit 21 is used to determine the radiation source model of the near-field radiation device.

[0353] The second model determination unit 22 is used to determine, based on the radiation source model, the internal radiation model corresponding to the radiation delivery performed by the near-field radiation device inside the target object.

[0354] The dose determination unit 23 is used to determine the dose distribution formed inside the target object by radiation delivery based on the internal radiation model.

[0355] In one embodiment of this application, the first model determination unit 21 is specifically used to determine a radioactive source model based on the radionuclide information and / or structural information of the near-field radiation device.

[0356] In one embodiment of this application, the second model determination unit 22 is specifically used to determine the internal radiation model based on the radiation source model and the internal information of the target object.

[0357] In one embodiment of this application, the internal information of the target object includes at least one of the following: structural information, material information, and physical density information of the voxels inside the target object.

[0358] In one embodiment of this application, the internal information of the target object is determined based on a scanned image of the target object.

[0359] In one embodiment of this application, the second model determination unit 22 is specifically used to determine the internal information of the target object based on the image information of the scanned image using a GPU-based multi-threaded task parallel computing method.

[0360] In one embodiment of this application, the dose determination unit 23 is specifically used to determine the dose distribution based on an internal radiation model by simulating the particle transport process.

[0361] In one embodiment of this application, the dose determination unit 23 is specifically used to determine the radiation dose of one or more radiating particles using the Monte Carlo algorithm, and to simulate the particle transport process based on the determined motion trajectory of the radiating particles.

[0362] In one embodiment of this application, the dose determination unit 23 specifically includes: a calculation unit and an execution unit. Wherein:

[0363] The computational unit is used to calculate the dose grid based on the internal radiation model and store the dose grid in the form of a sparse matrix.

[0364] The execution unit is used to perform dose summation calculations using the stored dose grid to determine the dose distribution.

[0365] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0366] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0367] Figure 12 This is a schematic diagram of the structure of a computing device provided in one embodiment of this application. Figure 12 As shown, the computing device 3 in this embodiment includes: at least one processor 30 ( Figure 12 (Only one is shown) a processor, a memory 31, and a computer program 32 stored in the memory 31 and executable on the at least one processor 30, wherein the processor 30 executes the computer program 32 to perform the above-described... Figures 1 to 8 The steps in each dosage determination method embodiment.

[0368] The computing device may include, but is not limited to, a processor 30 and a memory 31. Those skilled in the art will understand that... Figure 12 This is merely an example of computing device 3 and does not constitute a limitation on computing device 3. It may include more or fewer components than shown in the figure, or combine certain components, or different components, such as input / output devices, network access devices, etc.

[0369] The processor 30 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0370] In some embodiments, the memory 31 may be an internal storage unit of the computing device 3, such as the RAM of the computing device 3. In other embodiments, the memory 31 may be an external storage device of the computing device 3, such as a plug-in hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computing device 3. Furthermore, the memory 31 may include both internal and external storage units of the computing device 3. The memory 31 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory 31 can also be used to temporarily store data that has been output or will be output.

[0371] This application also provides a computer-readable storage medium storing instructions that, when executed by a processor, can implement the above-described functions. Figures 1 to 8 The steps in the corresponding method embodiments.

[0372] Figure 13 This is a schematic diagram of the structure of a computing device provided in another embodiment of this application. (See attached diagram.) Figure 13 As shown, the computing device 4 in this embodiment includes: at least one processor 40 ( Figure 13 (Only one is shown) a processor, a memory 41, and a computer program 42 stored in the memory 41 and executable on the at least one processor 40, wherein the processor 40 executes the computer program 42 to perform the above-described... Figure 9 The steps in the corresponding dosage determination method embodiment.

[0373] The computing device may include, but is not limited to, a processor 40 and a memory 41. Those skilled in the art will understand that... Figure 13 This is merely an example of computing device 4 and does not constitute a limitation on computing device 4. It may include more or fewer components than shown in the figure, or combine certain components, or different components, such as input / output devices, network access devices, etc.

[0374] The processor 40 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0375] In some embodiments, the memory 41 may be an internal storage unit of the computing device 4, such as the RAM of the computing device 4. In other embodiments, the memory 41 may be an external storage device of the computing device 4, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the computing device 4. Furthermore, the memory 41 may include both internal and external storage units of the computing device 4. The memory 41 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory 41 can also be used to temporarily store data that has been output or will be output.

[0376] This application also provides a computer-readable storage medium storing instructions that, when executed by a processor, can achieve the above-described functionality. Figure 9 The steps in the corresponding method embodiments.

[0377] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0378] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for determining dosage, characterized in that, include: Determine whether radiation delivery to the target object includes intra-object radiation and / or extra-object radiation; Determine the internal radiation dose distribution when the radiation delivery includes radiation within the object, and determine the external radiation dose distribution when the radiation delivery includes radiation outside the object; and The target dose distribution is determined based on the internal radiation dose distribution and / or the external radiation dose distribution.

2. The dosage determination method according to claim 1, characterized in that, Determining the internal radiation dose distribution when the radiation delivery includes internal radiation to the object includes: When the radiation delivery includes internal radiation to the target object, an initial dose of radioactive material inside the target object and a first image of the target object after the radioactive material enters the object are determined, and the internal radiation dose distribution is determined based on the initial dose and the first image.

3. The dosage determination method according to claim 2, characterized in that, Determining the internal radiation dose distribution based on the initial dose and the first image includes: Based on the initial dose or the first image, determine the cumulative decay number of the radioactive material; The internal radiation dose distribution is determined based on the cumulative decay number.

4. The dosage determination method as described in claim 3, characterized in that, Based on the first image, the cumulative decay number of the radioactive material is determined, including: If, after the radioactive material enters the target body, there is no data on the radioactivity of the target body's excrement at different times, then based on the first image, the relationship between the radioactivity of the radioactive material in the target body and time is determined. Based on the aforementioned change relationship, the cumulative decay number is determined.

5. The dosage determination method as described in claim 3, characterized in that, Determining the cumulative decay number of the radioactive material based on the initial dose includes: After the radioactive material enters the target body, if there is radioactivity measurement data of the target body's excrement at different times, the current remaining radioactivity in the target body is calculated based on the initial dose and each of the excrement radioactivity measurement data. The cumulative decay number is calculated based on the current remaining radioactivity and the half-life of the radionuclides in the radioactive material.

6. The dosage determination method as described in claim 3, characterized in that, Determining the internal radiation dose distribution based on the cumulative decay number includes: Determine the average absorbed dose per unit decay number; The internal radiation dose distribution is calculated based on the cumulative decay number and the average absorbed dose.

7. The dosage determination method as described in claim 3, characterized in that, Determining the internal radiation dose distribution based on the cumulative decay number includes: Obtain the X-ray energy spectrum data corresponding to the radioactive material, as well as the photon reaction cross section data and electron reaction cross section data of the specified material. Based on the first image, the distribution of radioactivity concentration and the physical density of tissues within the target object are determined; Based on the aforementioned radioactivity concentration distribution, a sampling process is performed to obtain multiple first sampled particles; Based on the X-ray energy spectrum data, the photon reaction cross section data, the electron reaction cross section data, and the physical density, parallel particle transport simulations are performed on multiple first sampled particles to obtain the internal radiation dose distribution.

8. The dosage determination method as described in claim 1, characterized in that, When the radiation delivery includes external radiation to the target, the external radiation dose distribution is determined according to the following manner: Acquire a second image of the target object after the radioactive material corresponding to the radiation emitted from the outside of the object enters the interior, and acquire photon reaction cross-section data and electron reaction cross-section data of the set material. The external radiation dose distribution is determined based on the second image, the photon reaction cross-section data, and the electron reaction cross-section data.

9. The dosage determination method as described in claim 1, characterized in that, Determining the target dose distribution based on the internal radiation dose distribution and / or the external radiation dose distribution includes: If a biological effect is detected within the target object, a first biological effect weight corresponding to radiation within the object and / or a second biological effect weight corresponding to radiation outside the object are determined based on the biological effect. The target dose distribution is obtained by weighting and summing the internal radiation dose distribution and / or the external radiation dose distribution based on the first biological effect weight and / or the second biological effect weight.

10. A computing device comprising a memory, a processor, and instructions stored in the memory and executable on the processor, characterized in that, When the processor executes the instructions, it implements the dose determination method as described in any one of claims 1 to 9.

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