Modeling method of dose calculation model, radiation delivery device and readable storage medium

Abstract

The invention relates to a dose calculation model modeling method, a radiation delivery device and a readable storage medium. The method comprises the following steps: acquiring a first dose calculation model and actual measurement verification data; determining whether the actually measured verification data meets verification data checking conditions or not; and if the actual measurement verification data meets the verification data checking condition, adjusting the first dose calculation model to obtain a second dose calculation model based on the actual measurement verification data. In the modeling method, two modeling processes of rapid modeling and accurate modeling are set, and actual measurement verification data used for verifying a first dose calculation model obtained by rapid modeling is checked in advance so as to ensure the accuracy of the actual measurement verification data; the problems that the accurate modeling process is interrupted due to errors of the actual measurement verification data, and the overall modeling time is long due to the fact that the actual measurement verification data needs to be obtained again are solved, so that the overall modeling time can be shortened, and the modeling efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of radiation delivery technologies, and particularly to a method for modeling a dose calculation model, a radiation delivery device, and a readable storage medium. Background Art

[0002] When performing radiation delivery (such as radiotherapy, radiation flaw detection, radiation processing, radiation testing, etc.), it is necessary to calculate the spatial distribution of radiation dose (such as the radiation dose values at various points in space), and thereby determine a radiation delivery plan.

[0003] As a non-limiting example, a medical radiation device, as a radiation device for a medical diagnosis and treatment procedure, for example: one of the radiotherapy devices can use radiation to treat tumors. Generally, a radiotherapy device can generate a corresponding radiation delivery plan based on a radiotherapy plan of a patient, and thereby control a treatment delivery system (TDS) to irradiate a tumor region of the patient with a radiation dose based on the radiation delivery plan, so as to achieve radiotherapy. When performing radiotherapy, the radiotherapy plan is usually determined by a treatment planning system (TPS). For the TPS, it is necessary to first accurately model a specific TDS, so as to obtain a dose calculation model matching the TDS. In this way, the TPS can accurately calculate the dose distribution in the patient's body based on the dose calculation model matching the TDS, and thereby determine an accurate radiotherapy plan for the TDS.

[0004] Traditionally, when the TPS models a specific TDS, it usually first collects the data required for modeling, and then models the specific TDS based on the collected data to obtain a dose calculation model matching the TDS.

[0005] However, the traditional modeling method has the problem of a long modeling time. Summary of the Invention

[0006] Based on this, it is necessary to provide a method, device, computer device, computer-readable storage medium, and computer program product for modeling a dose calculation model, which can reduce the modeling time of the dose calculation model and improve the modeling efficiency of the dose calculation model, in view of the above technical problems.

[0007] In a first aspect, the present application provides a method for modeling a dose calculation model, including:

[0008] Obtain a first dose calculation model and measured verification data;

[0009] Determine whether the measured verification data meets the verification data check condition;

[0010] If the measured verification data meets the verification data check conditions, then based on the measured verification data, adjust the first dose calculation model to obtain the second dose calculation model.

[0011] In one embodiment, obtaining the first dose calculation model includes:

[0012] Obtain measured phantom data and determine whether the measured phantom data meets the phantom data check conditions;

[0013] If the measured phantom data meets the phantom data check conditions, then perform modeling based on the measured phantom data to obtain the first dose calculation model.

[0014] In one embodiment, if the measured verification data does not meet the verification data check conditions, then obtain new measured verification data again, and iteratively execute the step of determining whether the measured verification data meets the verification data check conditions until the measured verification data meets the verification data check conditions.

[0015] In one embodiment, determining whether the measured verification data meets the verification data check conditions includes:

[0016] Based on the first dose calculation model, obtain calculated verification data;

[0017] Determine whether the measured verification data meets the verification data check conditions by determining whether the verification data difference between the calculated verification data and the measured verification data is within a predetermined threshold range.

[0018] In one embodiment, determining whether the measured verification data meets the verification data check conditions includes:

[0019] Based on the first dose calculation model, obtain calculated verification data;

[0020] Obtain historical verification reference data corresponding to the measured verification data;

[0021] According to the calculated verification data and the historical verification reference data, determine whether the measured verification data meets the verification data check conditions.

[0022] In one embodiment, based on the measured verification data, adjusting the first dose calculation model to obtain the second dose calculation model includes:

[0023] Adjust the parameters of the first dose calculation model according to the verification data difference between the measured verification data and the calculated verification data obtained based on the first dose calculation model to obtain the second dose calculation model.

[0024] In one embodiment, it further includes:

[0025] Determine whether the parameters of the second dose calculation model meet the model parameter check conditions;

[0026] If the parameters of the second dose calculation model do not meet the model parameter check conditions, re-adjust the first dose calculation model to obtain a new second dose calculation model, and iteratively execute the step of determining whether the parameters of the second dose calculation model meet the model parameter check conditions until the parameters of the second dose calculation model meet the model parameter check conditions.

[0027] In a second aspect, the present application provides a method for modeling a dose calculation model, including:

[0028] Obtain measured phantom data, and determine whether the measured phantom data meets the phantom data check conditions;

[0029] If the measured phantom data meets the phantom data check conditions, perform modeling based on the measured phantom data to obtain a dose calculation model.

[0030] In a third aspect, the present application further provides a device for modeling a dose calculation model, including:

[0031] An acquisition module, configured to acquire a first dose calculation model and measured verification data;

[0032] A first determination module, configured to determine whether the measured verification data meets the verification data check conditions;

[0033] An adjustment module, configured to, when the measured verification data meets the verification data check conditions, adjust the first dose calculation model based on the measured verification data to obtain a second dose calculation model.

[0034] In a fourth aspect, the present application further provides a device for modeling a dose calculation model, including:

[0035] An acquisition module, configured to acquire measured phantom data and determine whether the measured phantom data meets the phantom data check conditions;

[0036] A modeling module, configured to, if the measured phantom data meets the phantom data check conditions, perform modeling based on the measured phantom data to obtain a dose calculation model.

[0037] In a fifth aspect, the present application further provides a radiation delivery device, including:

[0038] A modeling module, configured to obtain a dose calculation model by the modeling method according to any one of claims 1 to 8; and

[0039] A radiation delivery module, configured to perform radiation delivery based on the dose calculation model.

[0040] In a sixth aspect, the present application further provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the methods in the above first aspect and second aspect are implemented.

[0041] In a seventh aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for modeling the dose calculation model in the above first aspect are implemented.

[0042] In an eighth aspect, the present application further provides a computer program product, including a computer program. When the computer program is executed by a processor, the steps of the methods in the above first aspect and second aspect are implemented.

[0043] The above method for modeling the dose calculation model, the device for modeling the dose calculation model, the radiation delivery device, the computer device, the storage medium, and the computer program product. Among them, the modeling method includes obtaining a first dose calculation model and measured verification data, and determining whether the measured verification data meets the verification data check conditions; then, when the measured verification data meets the verification data check conditions, based on the measured verification data, adjusting the first dose calculation model to obtain a second dose calculation model. That is to say, the modeling method proposed in the embodiments of the present application sets up two modeling processes: rapid modeling and accurate modeling, and advances the inspection of the measured verification data used to verify the first dose calculation model obtained by rapid modeling to ensure the accuracy of the measured verification data, and avoid the situation that the result of accurate modeling is in error due to errors in the measured verification data, thereby improving the accuracy of accurate modeling; in addition, when the rapid modeling process and the measured verification data acquisition process are executed in parallel, it is also possible to couple the modeling and data acquisition processes, thereby shortening the overall modeling time; and, while performing rapid modeling, checking the obtained measured verification data can also avoid the problem that data errors are found during accurate modeling, resulting in the interruption of the modeling process and the need to re-obtain the measured verification data, which prolongs the modeling time. It can be seen that by adopting this method, not only can the modeling time be shortened, the modeling efficiency be improved, but also the modeling accuracy can be improved, thereby improving the accuracy of the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0045] Figure 1Application environment diagram of the modeling method for the dose calculation model in an embodiment;

[0046] Figure 2 Flow schematic diagram of the modeling method for the dose calculation model in an embodiment;

[0047] Figure 3 Flow schematic diagram of the modeling method for the dose calculation model in another embodiment;

[0048] Figure 4 Flow schematic diagram of the modeling method for the dose calculation model in another embodiment;

[0049] Figure 5 Flow schematic diagram of the modeling method for the dose calculation model in another embodiment;

[0050] Figure 6 Flow schematic diagram of the modeling method for the dose calculation model in another embodiment;

[0051] Figure 7 Specific flow schematic diagram of the modeling method for the dose calculation model in an embodiment;

[0052] Figure 8 Flow schematic diagram of the modeling method for the dose calculation model in another embodiment;

[0053] Figure 9 Structural block diagram of the modeling device for the dose calculation model in an embodiment;

[0054] Figure 10 Structural block diagram of the modeling device for the dose calculation model in another embodiment;

[0055] Figure 11 Structural block diagram of the radiation delivery device in an embodiment;

[0056] Figure 12 Internal structure diagram of a computer device in an embodiment. Detailed implementation manners

[0057] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0058] When performing radiation delivery (such as radiotherapy, radiation flaw detection, radiation processing, radiation testing, etc.), it is necessary to calculate the spatial distribution of the radiation dose (such as the radiation dose values at various points in space), and thereby determine the radiation delivery plan.

[0059] As a non - restrictive example, a medical radiation device, as a radiation device for medical diagnosis and treatment procedures, for example: one of the radiation therapy devices can use radiation to treat tumors. Generally, a radiation therapy device can generate a corresponding radiation delivery plan based on a patient's radiotherapy plan, and thus control a treatment delivery system (TDS) to irradiate a tumor area of the patient with a radiation dose based on the radiation delivery plan to achieve radiotherapy. During the radiotherapy process, a very crucial issue is whether the dose distribution in the patient's body can be calculated quickly and accurately; and an important prerequisite for accurately calculating the dose distribution in the patient's body is the ability to accurately model the dose distribution that the radiation therapy device can generate, that is, to accurately model the TDS in the radiation therapy device.

[0060] Since the treatment plan of a radiation therapy device is determined by a treatment planning system (TPS), therefore, when modeling the TDS in a radiation therapy device, it is usually necessary to collect water tank curve data for modeling and measured verification data corresponding to the verification plan by means of the radiation therapy device, and adjust the parameters of the dose calculation algorithm in the TPS based on the collected data, so as to model and obtain a dose calculation model that matches the TDS of the radiation therapy device.

[0061] Currently, the common modeling method of a radiation therapy device is to first collect water tank curve data and measured verification data for modeling, and then adjust the modeling parameters after the data collection is completed. The two processes are independent of each other and do not affect each other. However, during the data collection process, due to various factors, there may be errors in the water tank curve data or the measured verification data; in this way, during the modeling process, when using the water tank curve data or the measured verification data, if it is found that some data is problematic, it cannot be used for modeling or verification. At this time, only the data collection needs to be carried out again, which not only prolongs the modeling time but also increases the modeling cost additionally.

[0062] Based on this, the embodiment of the present application proposes a modeling method for a dose calculation model, which couples the data collection process and the modeling process, and checks the collected data in real time, which can reduce the probability of errors in the collection of water tank curve data or measured verification data during the entire modeling process, improve the modeling efficiency, shorten the modeling time, and reduce the modeling cost. In addition, after the modeling is completed, the modeling parameters will be compared with the reference modeling data, so as to further improve the rationality of the modeling parameters and the accuracy of the dose calculation model.

[0063] The modeling method for the dose calculation model provided by the embodiment of the present application can be applied to such as Figure 1In the application environment shown. Among them, a Treatment Planning System (TPS) can be installed in the medical radiation device 102. In the TPS of the medical radiation device 102, a mathematical model can be established for the Treatment Delivery System (TDS) in the medical radiation device 102 to obtain a dose calculation model that matches the TDS in the medical radiation device. Based on this dose calculation model, the radiation dose can be accurately estimated for different treatment plans, thereby realizing precise radiotherapy. Exemplarily, the medical radiation device 102 can include, but is not limited to, radiotherapy devices, and other types of radiation devices, such as radiation calculation devices, etc.

[0064] It should be noted that in addition to being installed in the medical radiation device 102, the TPS can also be installed in other computing devices other than the medical radiation device 102. Among them, other computing devices include, but are not limited to, computing terminals and servers, etc.

[0065] In addition, the applicant wants to state that although the following embodiments are described by taking the medical radiation device as an example, the technical content involved in this article can also be used in any other type of radiation delivery device (for example, radiation processing devices, radiation flaw detection devices), and is not limited to the medical radiation device.

[0066] In an exemplary embodiment, as Figure 2 shown, a method for modeling a dose calculation model is provided. Taking the application of this method to the Figure 1 medical radiation device as an example, the following steps 202 to 206 are included. Among them:

[0067] Step 202, obtain the first dose calculation model and measured verification data.

[0068] Exemplarily, the first dose calculation model can be a dose calculation model corresponding to the TDS in the medical radiation device obtained by adjusting the parameters of the dose calculation algorithm in the TPS based on the dose output information of the medical radiation device under specific measurement conditions. Among them, the dose output information of the medical radiation device under specific measurement conditions can include, but is not limited to, Percentage Depth Dose (PDD), Profile, output factors, etc. The first dose calculation model can be pre-stored in the memory or can be calculated in real time by a computing device. The computing device includes, but is not limited to, medical radiation devices, computing terminals or servers communicating with the medical radiation device, etc.

[0069] Exemplarily, the dose output information may include the phantom curve data obtained after irradiating a water tank or other phantom of a preset size with a medical radiation device. The phantom curve data may include the PDD curve, Profile curve, output factor, etc. of the phantom; that is, the first dose calculation model may be obtained by modeling the collected phantom curve data.

[0070] Exemplarily, the first dose calculation model may also be obtained by modeling based on the Golden Beam Data (GBD); wherein, the Golden Beam Data may be obtained by analyzing a large number of accelerator beam curves. Compared with the method of modeling using phantom curve data, modeling using the Golden Beam Data can, to a certain extent, reduce the workload of collecting phantom curve data, that is, reduce the workload of collecting data such as PDD and Profile curves; at the same time, it can also reduce the probability of errors occurring during the collection of phantom curve data and improve the accuracy of the first dose calculation model.

[0071] Exemplarily, for the first dose calculation model obtained by modeling, the first dose calculation model should be further verified to ensure the accuracy of the first dose calculation model, that is, to verify whether the adjustment of the dose calculation algorithm parameters is correct. Therefore, when verifying the first dose calculation model, it is also necessary to obtain measured verification data to verify the first dose calculation model based on the measured verification data.

[0072] Exemplarily, the measured verification data may be the dose data collected in real time according to the verification plan, or the data to be verified selected from the dose data collected historically. For example: The medical radiation device may irradiate the object to be measured with ray radiation according to the verification plan and actually measure the dose data received by the object to be measured, which may include point dose data and / or surface dose data, etc. Optionally, the object to be measured may be a three-dimensional water tank or other types of test phantoms, etc.; the type of the object to be measured is not specifically limited in the embodiments of the present application.

[0073] In addition, it should be noted that the process of obtaining the first dose calculation model and the process of obtaining the measured verification data may be executed in parallel simultaneously, or may be executed sequentially according to a preset order, etc.

[0074] Step 204, determine whether the measured verification data meets the verification data check conditions.

[0075] That is to say, when verifying the first dose calculation model based on the measured verification data, it is not directly based on the collected measured verification data to verify the first dose calculation model; instead, the measured verification data is first checked to avoid errors in the measured verification data, thereby avoiding the problem of inaccurate verification when using incorrect measured verification data to verify the first dose calculation model.

[0076] Exemplarily, the medical radiation device can check the correctness of the measured verification data based on preset verification data check conditions. As an example, for incorrect measured verification data, the incorrect measured verification data can be excluded. As another example, new measured verification data can also be re-collected based on the verification plan until a preset number of measured verification data that meet the verification data check conditions are obtained.

[0077] Exemplarily, in the case where it is determined that some or all of the measured verification data do not meet the verification data check conditions, the user can also be prompted to perform a maintenance check on the medical radiation device, including but not limited to checking the treatment head for emitting rays in the medical radiation device, checking the acquisition device, or checking other devices related to rays in the medical radiation device, etc. Then, as an example, after performing a maintenance check on the medical radiation device, new measured verification data can be re-obtained based on the verification plan, and the step of determining whether the measured verification data meets the verification data check conditions can be iteratively executed until the measured verification data meets the verification data check conditions. As another example, after performing a maintenance check on the medical radiation device, if it is determined that there are major problems with the medical radiation device, the modeling operation is ended and waiting for the manufacturer to arrive at the scene for maintenance.

[0078] Exemplarily, the verification data check conditions can include whether the error between the measured verification data and the reference verification data is less than or equal to a preset error threshold; wherein, the reference verification data can include reference dose data corresponding to the verification plan determined based on a preset correspondence relationship. As an example, the reference dose data can be determined based on experience, determined based on international standards or domestic standards, determined based on theoretical formulas, etc., and no special limitation is made here. By comparing the differences between the measured verification data and the reference verification data, abnormal measured verification data is screened out. This abnormal measured verification data is probably data with measurement errors; therefore, excluding the abnormal measured verification data and retaining the measured verification data that meet the verification data check conditions to verify the first dose calculation model can ensure the accuracy of the verification.

[0079] As another example, the verification data check condition may include whether the measured verification data is abnormal, for example, whether it falls within a preset range of abnormal values. If the measured verification data falls within the preset range of abnormal values, it can be determined that the measured verification data is abnormal data with errors. Another example is whether it falls within a preset range of normal values. If the measured verification data does not fall within the preset range of normal values, it can also be determined that the measured verification data is abnormal data with errors.

[0080] The above only lists non-limiting examples of the verification data check condition. The content of the verification data check condition can be determined according to the actual situation and requirements, and no special limitation is made here.

[0081] Step 206, if the measured verification data meets the verification data check condition, then based on the measured verification data, adjust the first dose calculation model to obtain the second dose calculation model.

[0082] Exemplarily, steps 204 and 206 can be executed simultaneously, that is, while determining whether the measured verification data meets the verification data check condition, the measured verification data that has passed the check is used to adjust the first dose calculation model, so as to achieve adjusting the model while checking the data.

[0083] Exemplarily, when it is determined that the measured verification data meets the verification data check condition, or when a preset number of measured verification data that meet the verification data check condition are collected, the medical radiation device can adjust the parameters in the first dose calculation model based on the measured verification data that meet the verification data check condition, that is, to achieve further accurate modeling and obtain a second dose calculation model with higher accuracy.

[0084] Exemplarily, when accurately modeling the first dose calculation model based on the measured verification data, the parameters in the first dose calculation model can be adjusted in a manual and / or automatic manner; that is, the user can manually adjust the parameters in the first dose calculation model and recalculate the theoretical dose data based on the adjusted first dose calculation model, and repeat this process until the difference between the calculated theoretical dose data and the measured verification data is less than a preset difference threshold; at this time, the corresponding adjusted first dose calculation model can be used as the second dose calculation model after accurate modeling.

[0085] Exemplarily, the medical radiation device can also automatically adjust the parameters in the first dose calculation model; for example: according to a preset adjustment strategy, adjust the parameters in the first dose calculation model until the difference between the theoretical dose data calculated based on the adjusted first dose calculation model and the measured verification data is less than a preset difference threshold; at this time, the corresponding adjusted first dose calculation model can be used as the second dose calculation model after accurate modeling. Exemplarily, the preset adjustment strategy can include increasing / decreasing the parameter values of each parameter according to a preset value.

[0086] Exemplarily, when performing automatic adjustment, it is also possible to determine the parameter range of each parameter corresponding to the measured verification data based on the measured verification data; and within the parameter range of each parameter, adjust the corresponding parameters in the first dose calculation model; until the difference between the theoretical dose data calculated based on the adjusted first dose calculation model and the measured verification data is less than a preset difference threshold; at this time, the corresponding adjusted first dose calculation model can be used as the second dose calculation model after accurate modeling.

[0087] Exemplarily, when performing automatic adjustment, it is also possible to adjust the specific parameters in the first dose calculation model based on the measured verification data and the correlation between the verification data obtained based on experience and the specific parameters in the dose calculation model; until the difference between the theoretical dose data calculated based on the adjusted first dose calculation model and the measured verification data is less than a preset difference threshold; at this time, the corresponding adjusted first dose calculation model can be used as the second dose calculation model after accurate modeling.

[0088] It should be noted that when adjusting parameters, different parameter adjustment methods can be used alone or in combination.

[0089] Exemplarily, in the case of accurately modeling the first dose calculation model to obtain the second dose calculation model, the medical radiation device can use this second dose calculation model as the dose calculation model of the TDS in the medical radiation device, or further update and adjust the parameters of this second dose calculation model based on other adjustment strategies, so as to obtain the dose calculation model of the TDS in the medical radiation device.

[0090] In the modeling method of the above dose calculation model, the medical radiation device obtains the first dose calculation model and the measured verification data, and determines whether the measured verification data meets the verification data check condition; then, when the measured verification data meets the verification data check condition, based on the measured verification data, the first dose calculation model is adjusted to obtain the second dose calculation model. That is to say, the modeling method proposed in the embodiments of the present application sets two modeling processes: rapid modeling and accurate modeling, and advances the inspection of the measured verification data used to verify the first dose calculation model obtained by rapid modeling to ensure the accuracy of the measured verification data, and avoid the situation where the result of accurate modeling is in error due to incorrect measured verification data, thereby improving the accuracy of accurate modeling; in addition, when the rapid modeling process and the measured verification data acquisition process are executed in parallel, the coupling of the modeling and data acquisition processes can also be realized, thereby shortening the overall modeling time; and, while performing rapid modeling, checking the obtained measured verification data can also avoid the problem that the modeling process is interrupted when data errors are found during accurate modeling and the modeling time is lengthened due to the need to re-obtain the measured verification data. It can be seen that by adopting this method, not only can the modeling time be shortened, the modeling efficiency be improved, but also the modeling accuracy can be improved, thereby improving the accuracy of the model.

[0091] In an exemplary embodiment, as Figure 3 shown, the obtaining of the first dose calculation model in step 202 above may include steps 302 to 306. Among them:

[0092] Step 302, obtain the measured phantom data, and determine whether the measured phantom data meets the phantom data check condition.

[0093] Among them, the phantom may include, but is not limited to, dose detection phantoms such as a three-dimensional water tank and water-equivalent tissue. Exemplarily, the phantom can be placed at a preset position of the medical radiation device, and the medical radiation device is controlled to apply rays to the phantom, and the dose data passing through the phantom is collected as the measured phantom data. Exemplarily, the medical radiation device can also select part or all of the dose data of the phantom collected historically as the measured phantom data.

[0094] Exemplarily, the measured phantom data may include the above-mentioned phantom curve data, that is, it may include the PDD curve, Profile curve, and output factor of the phantom.

[0095] Then, after obtaining the measured phantom data, based on the phantom data check condition, the measured phantom data can be checked to determine whether the measured phantom data meets the phantom data check condition; Exemplarily, the phantom data check condition may be the same as or different from the above verification data check condition.

[0096] Exemplarily, during the acquisition process of the phantom curve data, key indicators in the phantom curve data can be checked manually or automatically in real time. For example, the value of a key indicator is compared with the reference value corresponding to the key indicator. When it is determined that the difference between the value of the key indicator and the reference value corresponding to the key indicator is less than or equal to a preset difference threshold, it can be determined that the phantom curve data meets the phantom data check condition. Among them, the reference value corresponding to the key indicator can include the standard value corresponding to the key indicator, the historical value, or the value of the key indicator in the phantom curve data of other medical radiation devices, etc. It should be noted that other medical radiation devices can be radiation devices of the same model or different models as this medical radiation device. Additionally, when the model of other medical radiation devices is different from that of this medical radiation device, the phantom curve data of other medical radiation devices can also be adjusted, and the adjusted phantom curve data of other medical radiation devices can be used as the reference data for the phantom curve data of this medical radiation device, etc.

[0097] By checking the data in real time during the data acquisition process, a real-time check function can be provided for the acquired phantom curve data, errors or problems that may exist in the acquired phantom curve data can be discovered in a timely manner, and the probability of errors occurring during the data acquisition process can be reduced. Additionally, when comparing the differences between the phantom curve data of this medical radiation device and the phantom curve data of other medical radiation devices, the parameter consistency of medical radiation devices in different scenarios and positions can also be improved.

[0098] Furthermore, it should be noted that when there are multiple key indicators, it can be determined that the phantom curve data meets the phantom data check condition when it is determined that the difference between each key indicator and the corresponding indicator reference value is less than or equal to the preset difference threshold. Among them, the preset difference thresholds corresponding to different indicators can be the same or different.

[0099] Exemplarily, the key indicators of the phantom curve data can include but are not limited to at least one of the maximum dose depth, flatness, and symmetry. Through these key indicators, the inspection of the PDD curve and the Profile curve can be realized, thereby realizing the inspection of the phantom curve data.

[0100] Step 304, if the measured phantom data meets the phantom data check condition, then a first dose calculation model is obtained based on the measured phantom data.

[0101] Exemplarily, when it is determined that the measured phantom data meets the phantom data inspection conditions, it can be shown that there is no incorrect data in the measured phantom data. Then, at this time, modeling can be performed based on the measured phantom data, that is, the parameters in the dose calculation algorithm of the TPS are adjusted based on the measured phantom data, so as to obtain the first dose calculation model corresponding to the TDS. As an example, the dose calculation model can include a calculation model based on the Monte Carlo algorithm, a calculation model based on the pencil beam algorithm, etc., and the specific form of the dose calculation model is not limited herein.

[0102] Step 306, if the measured phantom data does not meet the phantom data inspection conditions, new measured phantom data is re-obtained.

[0103] Exemplarily, when it is determined that the measured phantom data does not meet the phantom data inspection conditions, at this time, it may be that some or all of the measured phantom data does not meet the phantom data inspection conditions, then it can be shown that there is incorrect data in the measured phantom data; at this time, new measured phantom data can be re-obtained, and it is determined whether the new measured phantom data meets the phantom data inspection conditions; this cycle continues until the measured phantom data that meets the phantom data inspection conditions is obtained; then, modeling can be performed based on the measured phantom data that meets the phantom data inspection conditions, so as to obtain the first dose calculation model.

[0104] Exemplarily, when re-obtaining new measured phantom data, the parameters or equipment related to ray emission, acquisition, etc. in the medical radiation equipment can also be adjusted first, or the phantom can be replaced, etc.; then, based on the adjusted medical radiation equipment and / or the replaced phantom, new measured phantom data is re-obtained. Exemplarily, the parameters related to ray emission, acquisition, etc. can include but are not limited to the field size and shape, accelerator monitor unit (MU), radiation time length, etc.

[0105] In this embodiment, when constructing the first dose calculation model, measured phantom data is obtained, and it is determined whether the measured phantom data meets the phantom data inspection conditions; when the measured phantom data meets the phantom data inspection conditions, modeling is performed based on the measured phantom data to obtain the first dose calculation model; when the measured phantom data does not meet the phantom data inspection conditions, new measured phantom data is obtained again until the measured phantom data that meets the phantom data inspection conditions is obtained. That is, by using the method in this embodiment, during the process of collecting measured phantom data, the collected measured phantom data can be inspected in real time to ensure that there are no incorrect data in the measured phantom data required for modeling, thereby improving the accuracy of the first dose calculation model obtained by modeling based on the measured phantom data. In addition, during the process of collecting the measured phantom data, inspecting the measured phantom data in real time, that is, coupling the data collection process and the data inspection process, can also shorten the modeling time of the first dose calculation model and improve the modeling efficiency of the first dose calculation model; furthermore, it can improve the efficiency of the entire modeling process.

[0106] In addition, although in this embodiment, it is illustrated by taking the example that if the measured phantom data does not meet the phantom data inspection conditions at step 306, new measured phantom data is obtained again, it is not limited to this. At step 306, other operations can also be performed. As a non-limiting example; as another non-limiting example, at step 306, when the measured phantom data does not meet the phantom data inspection conditions, the workflow can also be terminated or paused. For example, it is used to check whether the phantom and / or the medical radiation device is normal; as yet another non-limiting example, at step 306, when the measured phantom data does not meet the phantom data inspection conditions, the deviation from the phantom data inspection conditions can also be determined. If the deviation is less than the threshold, the measured phantom data is still used for modeling, but a prompt will be given through a display or an alarm to warn that the measured phantom data has a certain error.

[0107] In an exemplary embodiment, as Figure 4 shown, the above step 204 may include steps 402 to 404. Among them:

[0108] Step 402, based on the first dose calculation model, obtain calculation verification data.

[0109] Exemplarily, the medical radiation device can input a verification plan into the first dose calculation model to obtain calculation verification data corresponding to the verification plan. This calculation verification data can be used to characterize the theoretical dose data that the object to be measured can receive when controlling the medical radiation device to apply radiation to the object to be measured according to this verification plan. As an example, the calculation verification data can also include the dose distribution in a specific space.

[0110] Step 404: Determine whether the measured verification data meets the verification data check condition by determining whether the difference between the calculated verification data and the measured verification data is within a predetermined threshold range.

[0111] Among them, the measured verification data can be used to characterize the actual dose data that the object under test can receive when the medical radiation device is controlled according to the verification plan to apply radiation to the object under test.

[0112] Exemplarily, the medical radiation device can compare the calculated verification data and the measured verification data to obtain the difference between the calculated verification data and the measured verification data; for example: the absolute value of the difference between the calculated verification data and the measured verification data can be used as the difference between the calculated verification data and the measured verification data.

[0113] Next, the medical radiation device can determine whether the difference in verification data is within a predetermined threshold range. If the difference in verification data is within the predetermined threshold range, it can be determined that the measured verification data meets the verification data check condition. Conversely, if the difference in verification data is not within the predetermined threshold range, it can be determined that the measured verification data does not meet the verification data check condition.

[0114] In this embodiment, when the medical radiation device checks the measured verification data, it can use the first dose calculation model to verify the rationality of the measured verification data; specifically, based on the first dose calculation model, the calculated verification data corresponding to the verification plan can be obtained; then, by determining whether the difference between the calculated verification data and the measured verification data corresponding to the verification plan is within a predetermined threshold range, it is determined whether the measured verification data meets the verification data check condition. Since the first dose calculation model is built based on the measured phantom data or the gold standard data, although the accuracy of its model is not the best, it still meets certain accuracy requirements. Therefore, based on the first dose calculation model, the theoretical calculated verification data corresponding to the verification plan is estimated, and the rationality of the measured verification data is judged by means of this theoretical calculated verification data, which can still exclude some abnormal error data caused by equipment abnormalities or equipment failures, thereby ensuring the rationality and accuracy of the measured verification data; when accurately modeling the first dose calculation model based on reasonable measured verification data, a dose calculation model that is more suitable for the medical radiation device can be obtained, so as to make up for the differences between different medical radiation devices, obtain a more accurate dose calculation model, and improve the modeling accuracy of the dose calculation model.

[0115] In an exemplary embodiment, as Figure 5 shown, the above step 204 may further include steps 502 to 506. Among them:

[0116] Step 502: Obtain calculated verification data based on the first dose calculation model.

[0117] Exemplarily, the medical radiation device may input the verification plan into the first dose calculation model to obtain the calculated verification data corresponding to the verification plan. This calculated verification data can be used to characterize the theoretical dose data that the object to be measured can receive when the medical radiation device is controlled according to the verification plan to irradiate the object to be measured. As an example, the calculated verification data may also include the dose distribution in a specific space.

[0118] Step 504: Obtain the historical verification reference data corresponding to the measured verification data.

[0119] Among them, the historical verification reference data may be the reference dose data collected after irradiation with a similar verification plan at a historical moment. Exemplarily, the historical verification reference data may also be the actual dose data corresponding to the verification plan collected when other medical radiation devices of the same model as this medical radiation device are irradiated based on the verification plan.

[0120] Exemplarily, the medical radiation device may obtain the actual dose data corresponding to the verification plan collected at a historical moment from other medical radiation devices as the historical verification reference data corresponding to the measured verification data; since the measured verification data in this example is also the actual dose data collected during radiotherapy irradiation based on the verification plan, and the medical radiation device to be modeled in this example and the medical radiation devices in other scenarios / regions are of the same type of device; therefore, the actual dose data corresponding to the verification plan collected by the medical radiation devices in other scenarios / regions can be used as the verification reference data corresponding to the measured verification data of the medical radiation device in this example, and its reference is reasonable.

[0121] In addition, in the case where the medical radiation devices in other scenarios / regions have been accurately modeled, using the actual dose data corresponding to the verification plan collected by the medical radiation devices in other scenarios / regions as the verification reference data corresponding to the measured verification data of the medical radiation device in this example can also ensure the accuracy of the verification reference data and has a certain reference value.

[0122] Of course, when the medical radiation device in this example obtains the historical verification reference data, it may also obtain the actual dose data of other medical radiation devices of the same type as the medical radiation device in this example under the verification plan from a database or other storage devices as the historical verification reference data corresponding to the measured verification data. The embodiments of the present application do not specifically limit the manner of obtaining the historical verification reference data.

[0123] Step 506: Determine whether the measured verification data meets the verification data check condition according to the calculated verification data and the historical verification reference data.

[0124] Exemplarily, it is possible to determine whether the measured verification data meets the verification data check condition according to the first verification data difference between the calculated verification data and the measured verification data, and the second verification data difference between the historical verification reference data and the measured verification data. For example: in the case where the first verification data difference is within the first predetermined threshold range and the second verification data difference is within the second predetermined threshold range, it can be determined that the measured verification data meets the verification data check condition; wherein, the first predetermined threshold range and the second predetermined threshold range may be the same or different.

[0125] In this embodiment, when the medical radiation device checks the measured verification data, it can also use the first dose calculation model and the historical verification reference data to jointly verify the rationality of the measured verification data; specifically, it can first obtain the calculated verification data based on the first dose calculation model, and obtain the historical verification reference data corresponding to the measured verification data; then, according to the calculated verification data and the historical verification reference data, determine whether the measured verification data meets the verification data check condition. Since the first dose calculation model is trained based on the measured phantom data or the gold standard data of the medical radiation device, the first dose calculation model itself has a certain degree of accuracy and has a certain reference value; at the same time, the historical verification reference data, like the measured verification data, is based on different medical radiation devices of the same type and is the measured dose data obtained based on the same verification plan, and also has a certain reference value; therefore, using the first dose calculation model and the historical verification reference data to check the rationality of the measured verification data can greatly improve the detection rate of incorrect data and achieve a more accurate check of the measured verification data; it can improve the rationality and accuracy of the measured verification data. Furthermore, when accurately modeling the first dose calculation model based on the accurate measured verification data, the modeling accuracy of the dose calculation model can be greatly improved.

[0126] In an exemplary embodiment, the above step 206 may include: adjusting the parameters of the first dose calculation model according to the verification data difference between the measured verification data and the calculated verification data obtained based on the first dose calculation model to obtain a second dose calculation model.

[0127] Exemplarily, during the process of accurately modeling the first dose calculation model based on measured verification data, the verification plan can be input into the first dose calculation model to obtain calculation verification data corresponding to the verification plan; then, the verification data difference between the calculation verification data and the measured verification data is calculated, and based on this difference, the parameters in the first dose calculation model are adjusted to achieve accurate modeling of the first dose calculation model, thereby obtaining a second dose calculation model that is more matched to the medical radiation device.

[0128] Exemplarily, the medical radiation device can adjust the parameters of the first dose calculation model according to the verification data difference between the measured verification data and the calculation verification data to determine an intermediate dose calculation model; then, the verification plan is input into the intermediate dose calculation model to obtain new calculation verification data, and based on the verification data difference between the new calculation verification data and the measured verification data, the parameters of the intermediate dose calculation model are adjusted to obtain a new intermediate dose calculation model; through multiple iterations until the verification data difference between the measured verification data and the calculation verification data meets the iteration stop condition, the intermediate dose calculation model obtained in the last iteration can be used as the second dose calculation model for accurate modeling.

[0129] In this embodiment, the parameters of the first dose calculation model are adjusted based on the verification data difference between the measured verification data and the calculation verification data to obtain the second dose calculation model; by using this method, the modeling rate of accurately modeling the first dose calculation model can be improved, and the modeling time of the dose calculation model can be further shortened.

[0130] In an exemplary embodiment, as Figure 6 shown, the above method may further include steps 602 to 604. Among them:

[0131] Step 602, determining whether the parameters of the second dose calculation model meet the model parameter check condition.

[0132] Exemplarily, the model parameter check condition may include that the difference between the parameters of the second dose calculation model and the reference parameters of the reference dose calculation model is less than or equal to a preset difference threshold. As an example, the types of parameters may include the leaf offset of the multi-leaf collimator (MLC), the leaf movement speed of the multi-leaf collimator, the accelerator monitor units, etc., which are not limited herein.

[0133] Exemplarily, a reference dose calculation model can be obtained, and reference parameters in the reference dose calculation model can be extracted; then, the reference parameters in the reference dose calculation model are compared with the parameters of the second dose calculation model to obtain the difference therebetween; so as to determine whether the parameters of the second dose calculation model meet the model parameter check condition based on this difference. If the difference is less than or equal to a preset difference threshold, it can be considered that the parameters in the second dose calculation model are relatively close to the reference parameters in the reference dose calculation model, and it can be determined that the parameters of the second dose calculation model meet the model parameter check condition.

[0134] Exemplarily, the reference dose calculation model can include a dose calculation model obtained by modeling other medical radiation devices of the same type as the medical radiation device in this example but in different scenarios / locations.

[0135] Step 604, if the parameters of the second dose calculation model do not meet the model parameter check condition, the first dose calculation model is re-adjusted to obtain a new second dose calculation model, and the step of determining whether the parameters of the second dose calculation model meet the model parameter check condition is iteratively executed until the parameters of the second dose calculation model meet the model parameter check condition.

[0136] Exemplarily, in the case where the parameters of the second dose calculation model do not meet the model parameter check condition, it can be explained that the parameters in the second dose calculation model are quite different from the reference parameters in the reference dose calculation model; since the reference dose calculation model is an accurate dose calculation model obtained after modeling, therefore, in the case where the parameters in the second dose calculation model are quite different from the reference parameters in the reference dose calculation model, it can be explained that there are unreasonable situations in the parameters of the currently modeled second dose calculation model; at this time, the first dose calculation model can be accurately modeled again, and the step of checking the parameters of the second dose calculation model obtained by accurate modeling is repeatedly executed until a second dose calculation model that meets the model parameter check condition is obtained.

[0137] In this embodiment, after adjusting the parameters in the first dose calculation model to achieve accurate modeling, for the obtained second dose calculation model, further, the rationality of the parameters in the second dose calculation model is judged to ensure the accuracy of the second dose calculation model obtained by accurate modeling; specifically, it can be determined whether the parameters of the second dose calculation model meet the model parameter inspection conditions; if the parameters of the second dose calculation model do not meet the model parameter inspection conditions, the first dose calculation model is readjusted to obtain a new second dose calculation model, and the step of determining whether the parameters of the second dose calculation model meet the model parameter inspection conditions is iteratively executed until the parameters of the second dose calculation model meet the model parameter inspection conditions. By using the method in this embodiment, not only can high-quality modeling of the dose calculation model be achieved through two modeling stages of rapid modeling and accurate modeling, but also the rationality of the parameters in the second dose calculation model obtained by accurate modeling can be judged, thereby improving the rationality and accuracy of the second dose calculation model obtained by accurate modeling.

[0138] In an exemplary embodiment, as Figure 7 shown, a specific embodiment of a modeling method for a dose calculation model is provided, including the following steps:

[0139] Step 1, obtain measured phantom data and check the measured phantom data.

[0140] Exemplarily, during the process of collecting the measured phantom data, the measured phantom data can be checked in real time; if the check fails, that is, the measured phantom data does not meet the phantom data inspection conditions, the reason for the failed check can be determined, and the measurement conditions can be adjusted to re-collect the measured phantom data; where the measurement conditions can include but are not limited to parameters or equipment related to rays in medical radiation equipment, and test phantoms, etc.

[0141] Step 2, perform rapid modeling based on the measured phantom data that meets the phantom data detection conditions to obtain a first dose calculation model.

[0142] Step 3, obtain measured verification data and check the measured verification data.

[0143] Exemplarily, the medical radiation equipment can perform ray irradiation according to the verification plan, so as to collect the measured verification data corresponding to the verification plan; at the same time, the verification plan can be input into the first dose calculation model for dose estimation to obtain the calculated verification data corresponding to the verification plan; then, based on the difference between the calculated verification data and the measured verification data, the measured verification data is checked.

[0144] Exemplarily, historical verification reference data corresponding to the verification plan can also be obtained, and the measured verification data can be checked based on the difference between the historical verification reference data and the measured verification data.

[0145] Exemplarily, when it is determined that there is no error data in the measured verification data based on the calculated verification data, and it is also determined that there is no error data in the measured verification data based on the historical verification reference data, it can be determined that the measured verification data meets the verification data check condition. The measured verification data that meets the verification data check condition can be saved for accurate modeling based on the saved measured verification data that meets the verification data check condition at a later stage.

[0146] On the contrary, it can be determined that the measured phantom data does not meet the verification data check condition; at this time, the measurement problem can be located and the measured verification data can be re-measured.

[0147] Step 4: Based on the measured verification data that meets the verification data check condition, adjust the parameters of the first dose calculation model to obtain a second dose calculation model, thereby achieving accurate modeling.

[0148] Exemplarily, the relevant parameters in the first dose calculation model can be adjusted based on the difference between the measured verification data and the calculated verification data, including but not limited to adjusting the multi-leaf collimator blade offset in the first dose calculation model, so that the difference between the calculated verification data obtained based on the first dose calculation model with adjusted parameters and the measured verification data is less than a preset difference threshold, thereby completing the accurate modeling of the first dose calculation model and obtaining a second dose calculation model.

[0149] Step 5: Check the parameters in the second dose calculation model.

[0150] Exemplarily, the parameters in the second dose calculation model can be compared with the reference parameters in the reference dose calculation model to determine the rationality of the parameters in the second dose calculation model. If the parameters of the second dose calculation model meet the model parameter check condition, that is, the parameters in the second dose calculation model are approximately the same or exactly the same as the reference parameters in the reference dose calculation model, the second dose calculation model can be used as the dose calculation model for TDS. If the parameters of the second dose calculation model do not meet the model parameter check condition, return to execute Step 4 and re-adjust the parameters of the first dose calculation model until the parameters of the second dose calculation model meet the model parameter check condition.

[0151] By adopting this modeling method, the probability of errors in collecting the data required for modeling (such as measured phantom data) and the planned verification data (such as measured verification data) during the entire modeling process can be reduced, the modeling efficiency can be improved, the modeling time can be shortened, and at the same time, the rationality and accuracy of the modeling parameters can be enhanced. In addition, the present modeling method includes two modeling processes, namely, rapid modeling and precise modeling. Through precise modeling, on the basis of rapid modeling, the modeling parameters can be further adjusted manually or automatically to quickly obtain the modeling parameters that meet the verification standards, thereby improving the accuracy of the dose calculation model.

[0152] In an exemplary embodiment, as Figure 8 shown, another modeling method for a dose calculation model is provided, including steps 802 to 806, where:

[0153] Step 802, obtain measured phantom data and determine whether the measured phantom data meets the phantom data inspection conditions.

[0154] Step 804, if the measured phantom data meets the phantom data inspection conditions, then perform modeling based on the measured phantom data to obtain a dose calculation model.

[0155] That is to say, in the modeling method proposed in the embodiment of the present application, by obtaining the measured phantom data collected after irradiating the phantom and checking the measured phantom data, the medical radiation device can perform modeling based on the measured phantom data without error data, so as to obtain an accurate dose calculation model. Exemplarily, during the process of collecting the measured phantom data, the collected measured phantom data can be checked in real time to detect the existing error data in advance, so as to avoid the situation where the modeling result is inaccurate due to the existence of error data when directly performing modeling based on the collected measured phantom data, or to avoid interrupting the modeling process when it is found that the measured phantom data has errors during the modeling process and re-collecting the measured phantom data, resulting in a long modeling time.

[0156] Exemplarily, in the case where the measured phantom data does not meet the phantom data inspection conditions, the measured phantom data can be re-collected, or the cause of the error data can be analyzed first, and the test conditions / test environment can be adjusted, for example: adjusting the parameters or equipment related to the ray in the medical radiation device, or replacing the test phantom, etc.; then, re-collect the measured phantom data based on the adjusted test conditions / test environment.

[0157] Exemplarily, when modeling based on measured phantom data, the dose calculation model for rapid modeling based on measured phantom data can be used as the dose calculation model of the TDS in this medical radiation device; alternatively, after rapid modeling based on measured phantom data, a first dose calculation model can be obtained; then, the first dose calculation model obtained by rapid modeling is accurately modeled through measured verification data to obtain the dose calculation model of the TDS; even further, the rationality of the model parameters of the second dose calculation model obtained by accurate modeling can be checked to perform further parameter adjustment on the second dose calculation model, and finally the dose calculation model of the TDS can be obtained, etc.

[0158] The specific implementation method can refer to any of the above embodiments, and the implementation process and technical effects will not be repeated here.

[0159] By using the modeling method in this embodiment, before modeling, the data required for modeling, that is, the measured phantom data, can be checked to ensure that there is no incorrect data in the measured phantom data, guarantee the accuracy of the measured phantom data, and further improve the accuracy of the dose calculation model when modeling based on the measured phantom data; compared with the traditional method of discovering incorrect data during the modeling process, which will cause the interruption of the modeling process and require re - collection of new measured phantom data, it can avoid the situation of modeling process interruption caused by incorrect data, thus shortening the time required for modeling and improving the modeling efficiency.

[0160] It should be understood that although the steps in the flowcharts involved in the above - mentioned embodiments are shown in sequence according to the arrows, these steps do not necessarily need to be executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above - mentioned embodiments may include multiple steps or multiple stages. These steps or stages do not necessarily need to be executed at the same time, but can be executed at different times. The execution order of these steps or stages does not necessarily need to be sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0161] Based on the same inventive concept, the embodiment of the present application also provides a modeling device for a dose calculation model for implementing the modeling method of the dose calculation model involved above. The solution provided by this device for solving problems is similar to the solution recorded in the above method. Therefore, the specific limitations in one or more embodiments of the modeling device for the dose calculation model provided below can refer to the limitations on the modeling method of the dose calculation model in the above text, and will not be repeated here.

[0162] In an exemplary embodiment, as Figure 9 shown, a modeling device for a dose calculation model is provided, including: an acquisition module 902, a first determination module 904, and an adjustment module 906, where:

[0163] The acquisition module 902 is configured to acquire a first dose calculation model and measured verification data;

[0164] The first determination module 904 is configured to determine whether the measured verification data meets the verification data check condition;

[0165] The adjustment module 906 is configured to, when the measured verification data meets the verification data check condition, adjust the first dose calculation model based on the measured verification data to obtain a second dose calculation model.

[0166] In one embodiment, the acquisition module 902 includes:

[0167] A first acquisition sub-module, configured to acquire measured phantom data and determine whether the measured phantom data meets the phantom data check condition;

[0168] A modeling sub-module, configured to, when the measured phantom data meets the phantom data check condition, perform modeling based on the measured phantom data to obtain a first dose calculation model.

[0169] In one embodiment, the first acquisition sub-module is further configured to, when the measured phantom data does not meet the phantom data check condition, re-acquire new measured phantom data.

[0170] In one embodiment, the acquisition module 902 is further configured to, when the measured verification data does not meet the verification data check condition, re-acquire new measured verification data; the first determination module 904 is further configured to iteratively execute the step of determining whether the measured verification data meets the verification data check condition until the measured verification data meets the verification data check condition.

[0171] In one embodiment, the first determination module 904 includes:

[0172] A processing sub-module, configured to obtain calculated verification data based on the first dose calculation model;

[0173] A first determination sub-module, configured to determine whether the measured verification data meets the verification data check condition by determining whether the verification data difference between the calculated verification data and the measured verification data is within a predetermined threshold range.

[0174] In one embodiment, the first determination module 904 includes:

[0175] A processing sub-module, configured to obtain calculation verification data based on a first dose calculation model;

[0176] A second acquisition sub-module, configured to acquire historical verification reference data corresponding to the measured verification data;

[0177] A second determination sub-module, configured to determine whether the measured verification data meets the verification data check condition according to the calculation verification data and the historical verification reference data.

[0178] In one embodiment, the adjustment module 906 is specifically configured to adjust the parameters of the first dose calculation model according to the verification data difference between the measured verification data and the calculation verification data obtained based on the first dose calculation model, so as to obtain a second dose calculation model.

[0179] In one embodiment, the above device further includes:

[0180] A second determination module, configured to determine whether the parameters of the second dose calculation model meet the model parameter check condition;

[0181] The above adjustment module 906 is further configured to, when the parameters of the second dose calculation model do not meet the model parameter check condition, readjust the first dose calculation model to obtain a new second dose calculation model; the second determination module is further configured to iteratively execute the step of determining whether the parameters of the second dose calculation model meet the model parameter check condition until the parameters of the second dose calculation model meet the model parameter check condition.

[0182] In an exemplary embodiment, as Figure 10 shown, a modeling device for a dose calculation model is provided, including: an acquisition module 1002 and a modeling module 1004, wherein:

[0183] The acquisition module 1002 is configured to acquire measured phantom data and determine whether the measured phantom data meets the phantom data check condition;

[0184] The modeling module 1004 is configured to, if the measured phantom data meets the phantom data check condition, perform modeling based on the measured phantom data to obtain a dose calculation model.

[0185] In one embodiment, the acquisition module 1002 is further configured to, when the measured phantom data does not meet the phantom data check condition, acquire new measured phantom data again.

[0186] Each module in the modeling device of the above dose calculation model can be implemented in whole or in part by software, hardware, and their combination. Each of the above modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each of the above modules.

[0187] In an exemplary embodiment, as Figure 11 shown, a radiation delivery device is provided, including: a modeling module 1102 and a radiation delivery module 1104; wherein,

[0188] The modeling module 1102 is configured to obtain a dose calculation model by using the modeling method in any of the above embodiments; the radiation delivery module 1104 is configured to perform radiation delivery based on the dose calculation model.

[0189] It should be noted that when the modeling module 1102 performs modeling by using the modeling method described in any of the above Figures 2 to 7 embodiments, the obtained second dose calculation model can be used as the dose calculation model here. For the modeling process of the dose calculation model, reference can be made to the relevant content description in the above Figures 2 to 8 shown embodiments, and the modeling process and the corresponding effective effects will not be repeated here.

[0190] Exemplarily, in the case of obtaining the dose calculation model, the radiation delivery module 1104 can perform radiation delivery based on the dose calculation model; for example: the radiation delivery module 1104 can determine the dose data corresponding to the treatment plan according to the patient's treatment plan by using the dose calculation model, and apply the ray corresponding to the dose data to the patient based on the determined dose data to achieve radiation delivery.

[0191] In the radiation delivery device of this embodiment, by using the modeling method of the above dose calculation model to model the treatment delivery system (TDS), coupling the data acquisition process and the modeling process, and by performing real-time inspection on the data during the process of collecting modeling data, the probability of collecting data errors can be reduced, the modeling time can be shortened, and the modeling efficiency can be improved; in addition, through the two modeling processes of rapid modeling and accurate modeling, the accuracy and rationality of modeling can also be improved; furthermore, by checking the parameters in the dose calculation model of accurate modeling, the rationality of modeling can be further improved.

[0192] In an exemplary embodiment, a computer device is provided. The computer device can be a medical radiation device, such as a radiation therapy device, or any type of radiation delivery device, etc. Its internal structure diagram can be as Figure 11As shown in the figure. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it realizes a modeling method of a dose calculation model. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covered on the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0193] Those skilled in the art can understand that Figure 11 the structure shown in the figure is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0194] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, it realizes the steps of the modeling method of the dose calculation model in any of the above embodiments.

[0195] In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by the processor, it realizes the steps of the modeling method of the dose calculation model in any of the above embodiments.

[0196] In an embodiment, a computer program product is provided, including a computer program. When the computer program is executed by the processor, it realizes the steps of the modeling method of the dose calculation model in any of the above embodiments.

[0197] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0198] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., and are not limited thereto. The processors involved in the embodiments provided in this application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., and are not limited thereto.

[0199] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

[0200] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A method for modeling a dose calculation model, characterized in that, comprising: Obtaining a first dose calculation model and measured verification data; Determining whether the measured verification data meets the verification data check conditions; If the measured verification data meets the verification data check conditions, then based on the measured verification data, adjusting the first dose calculation model to obtain a second dose calculation model.

2. The modeling method according to claim 1, characterized in that, The obtaining of the first dose calculation model includes: Obtaining measured phantom data and determining whether the measured phantom data meets the phantom data check conditions; If the measured phantom data meets the phantom data check conditions, then based on the measured phantom data, performing modeling to obtain the first dose calculation model.

3. The modeling method according to claim 1, characterized in that, If the measured verification data does not meet the verification data check conditions, then re-obtaining new measured verification data and iteratively executing the step of determining whether the measured verification data meets the verification data check conditions until the measured verification data meets the verification data check conditions.

4. The modeling method according to claim 1, characterized in that, The determining whether the measured verification data meets the verification data check conditions includes: Based on the first dose calculation model, obtaining calculated verification data; Determining whether the measured verification data meets the verification data check conditions by determining whether the verification data difference between the calculated verification data and the measured verification data is within a predetermined threshold range.

5. The modeling method according to claim 1, characterized in that, The determining whether the measured verification data meets the verification data check conditions includes: Based on the first dose calculation model, obtaining calculated verification data; Obtaining historical verification reference data corresponding to the measured verification data; Determining whether the measured verification data meets the verification data check conditions according to the calculated verification data and the historical verification reference data.

6. The modeling method according to claim 1, characterized in that, The adjusting the first dose calculation model based on the measured verification data to obtain a second dose calculation model includes: Adjusting the parameters of the first dose calculation model according to the verification data difference between the measured verification data and the calculated verification data obtained based on the first dose calculation model to obtain the second dose calculation model.

7. The modeling method according to claim 1, characterized in that, further comprising: Determining whether the parameters of the second dose calculation model meet the model parameter check conditions; If the parameters of the second dose calculation model do not meet the model parameter check conditions, then re-adjusting the first dose calculation model to obtain a new second dose calculation model and iteratively executing the step of determining whether the parameters of the second dose calculation model meet the model parameter check conditions until the parameters of the second dose calculation model meet the model parameter check conditions.

8. A method for modeling a dose calculation model, characterized in that, comprising: Obtain measured phantom data and determine whether the measured phantom data meets the phantom data check conditions; If the measured phantom data meets the phantom data check conditions, perform modeling based on the measured phantom data to obtain a dose calculation model.

9. A radiation delivery device, characterized in that, it includes: a modeling module configured to obtain a dose calculation model by using the modeling method according to any one of claims 1 to 8; and a radiation delivery module configured to perform radiation delivery based on the dose calculation model.

10. A computer-readable storage medium, on which a computer program is stored, characterized in that, when the computer program is executed by a processor, the steps of the modeling method according to any one of claims 1 to 8 are implemented.

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