Treatment device execution data determination method, apparatus, device, and storage medium

Abstract

The application provides an execution data determination method, device and equipment of a treatment device and a storage medium, applied to the technical field of medical instruments, device parameters of a treatment device used for performing helical tomotherapy are acquired, and detection data corresponding to a target user is acquired; thread effect model data corresponding to the treatment device is determined; variable pitch trajectory data corresponding to the target user is determined according to the device parameters, the detection data and the thread effect model data; a reverse dose optimization operation is performed based on the variable pitch trajectory data corresponding to the target user, and current execution data of the treatment device for the target user is determined. The application provides a systematic and high-robustness execution data determination method, effectively reduces the thread effect, and guarantees the treatment process of the target user.

Description

Technical Field

[0001] This application relates to the field of medical device technology, and more specifically, to a method, apparatus, device, and storage medium for determining execution data of a treatment device. Background Technology

[0002] Radiation therapy is a treatment for tumors that uses high doses of ionizing radiation (such as X-rays, gamma rays, or protons) to destroy cancer cells and shrink tumors. In helical radiation therapy techniques (such as helical tomotherapy and helical volumetric modulated radiotherapy), the gantry rotates continuously and the treatment bed moves forward at a constant or variable speed, creating a "helical" irradiation effect to treat tumors.

[0003] In helical radiotherapy, due to the geometric and dose characteristics of this superimposed helical beam, periodic dose fluctuations occur in the head-to-toe direction of the patient, appearing as "screws / stripes" in the isodose distribution—this is the helical effect. This effect leads to uneven dose distribution during radiotherapy, affecting target coverage and endangering the user's safety. Therefore, proposing a method to reduce the helical effect is particularly important. Summary of the Invention

[0004] In view of this, this application provides a method, apparatus, device, and storage medium for determining execution data of a treatment device.

[0005] Specifically, this application is implemented through the following technical solution: In a first aspect, embodiments of this application provide a method for determining execution data of a treatment device, including: Acquire the equipment parameters of the treatment device used to perform helical radiotherapy, as well as the detection data corresponding to the target user; Determine the thread effect model data corresponding to the treatment device; Based on the equipment parameters, the detection data, and the thread effect model data, determine the variable pitch trajectory data corresponding to the target user; Based on the variable pitch trajectory data corresponding to the target user, an inverse dose optimization operation is performed to determine the current execution data of the treatment device for the target user.

[0006] In one optional implementation, determining the thread effect model data corresponding to the treatment device includes: Under the preset execution conditions of the treatment device, multiple longitudinal slit widths, multiple pitches, and multiple off-axis radii were sampled and obtained. Multiple sampling conditions are determined based on the multiple longitudinal slit widths, the multiple pitches, and the multiple off-axis radii; For each of the sampling conditions, a spiral delivery operation is performed under the sampling conditions using a determined reference phantom model to determine longitudinal dose profile data; and the spiral dose ripple index under the sampling conditions is determined using the longitudinal dose profile data. The thread dose ripple indexes corresponding to the multiple sampling conditions are processed to generate thread effect model data corresponding to the treatment device.

[0007] In one optional implementation, determining the variable pitch trajectory data corresponding to the target user based on the device parameters, the detection data, and the thread effect model data includes: Along the treatment axis direction of the treatment device, the treatment axis range of the treatment device is divided into a preset number of local segments; Based on the detection data of the target user and the thread effect model data, a target function is constructed with the pitch of the local segment as the variable; Based on the equipment parameters of the treatment device, determine the mechanical constraints corresponding to the objective function; Under the mechanical constraints, the pitch values ​​corresponding to the multiple local segments are determined using the objective optimization algorithm and the objective function. Based on the pitch values ​​corresponding to the multiple local segments, the variable pitch trajectory data corresponding to the target user is determined.

[0008] In one optional implementation, the step of constructing an objective function with the pitch of the local segment as a variable based on the detection data of the target user and the thread effect model data includes: Based on the detection data of the target user and the thread effect model data, a risk item reflecting the intensity of the thread effect is generated; Based on the pitch difference between adjacent local segments, a smoothing term is generated to reflect the smoothness of the pitch. Based on the sequential execution time of each local segment at the corresponding pitch, an efficiency term reflecting the execution efficiency is generated; Based on the risk term, the smoothing term, and the efficiency term, an objective function is constructed with the pitch of the local segment as the variable.

[0009] In an optional implementation, determining the variable pitch trajectory data corresponding to the target user based on the pitch values ​​corresponding to the plurality of local segments further includes: Perform trajectory smoothing and boundary correction operations on the pitch values ​​corresponding to the multiple local segments respectively to generate adjusted pitch values ​​corresponding to the multiple local segments; Based on the adjusted pitch values ​​corresponding to the multiple local segments, the variable pitch trajectory data corresponding to the target user is determined.

[0010] In one optional implementation, the step of performing inverse dose optimization based on the variable pitch trajectory data corresponding to the target user to determine the current execution data of the treatment device for the target user includes: Based on the variable pitch trajectory data, the position of the treatment bed of the treatment device at multiple control points is determined; The dosage model corresponding to the target user is determined based on the position of the treatment bed of the treatment device at multiple control points; Based on the dose model and the determined clinical dosimetry constraints, the execution parameters corresponding to each control point are determined, wherein the execution parameters include dose output parameters and multi-leaf collimator control parameters; Based on the execution parameters corresponding to the multiple control points, the current execution data of the treatment device for the target user is generated.

[0011] In one optional implementation, the method further includes: A joint verification is performed based on the variable pitch trajectory data and the current execution data to obtain a verification result, wherein the joint verification includes at least one of the following: mechanical constraint verification, equipment execution constraint verification, and control time base consistency verification. When the verification result is successful, a current control table is generated based on the variable pitch trajectory data and the current execution data, wherein the current control table is used to control the operation of the treatment device.

[0012] Secondly, embodiments of this application also provide an execution data determination device for a treatment device, comprising: The acquisition module is used to acquire the equipment parameters of the treatment equipment used to perform helical radiotherapy, as well as the detection data corresponding to the target user; The first determining module is used to determine the thread effect model data corresponding to the treatment device; The second determining module is used to determine the variable pitch trajectory data corresponding to the target user based on the equipment parameters, the detection data, and the thread effect model data. The third determining module is used to perform inverse dose optimization operations based on the variable pitch trajectory data corresponding to the target user, and to determine the current execution data of the treatment device for the target user.

[0013] This application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for determining execution data of a treatment device.

[0014] This application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the above-described method for determining execution data of the treatment device.

[0015] This application obtains the equipment parameters of the treatment device used to perform helical radiotherapy and the detection data corresponding to the target user. After determining the helical effect model data corresponding to the treatment device, it determines the variable pitch trajectory data corresponding to the target user based on the equipment parameters, detection data, and helical effect model data. This variable pitch trajectory data is the optimal pitch trajectory determined based on the helical effect model data, which can effectively reduce the helical effect. Moreover, this variable pitch trajectory data is compatible with the detection results of the target user, that is, it is compatible with the treatment process of the treatment device for the target user. Then, based on the variable pitch trajectory data corresponding to the target user, an inverse dose optimization operation can be performed to determine the current execution data of the treatment device for the target user, so that the accuracy of the current execution data is high, ensuring the treatment process of the target user. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of this specification and form part of this specification, illustrate exemplary embodiments and are used to explain this specification, but do not constitute an undue limitation thereof. In the drawings: Figure 1 This is a schematic diagram of the thread effect generated by the treatment device; Figure 2 This is a flowchart illustrating an exemplary embodiment of a method for determining execution data of a treatment device; Figure 3 This is a flowchart illustrating a method for determining execution data of a treatment device, as shown in another exemplary embodiment of this application; Figure 4 This is a schematic diagram of an execution data determination device for a treatment device according to an exemplary embodiment of this application; Figure 5 This is a schematic diagram of the structure of a computer device provided in this application. Detailed Implementation

[0017] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0018] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0019] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0020] like Figure 1 As shown, in helical radiotherapy, the gantry rotates continuously while the treatment bed advances at a constant / variable speed, creating a "helical" irradiation pattern. Due to the geometric and dose characteristics of this superimposed helical beam, periodic dose fluctuations occur in the head-to-toe direction (i.e., longitudinally) during helical radiotherapy, appearing as "threads / stripes" in the isodose distribution—this is the helical effect. This effect leads to uneven dose distribution during radiotherapy, affecting target area coverage and potentially endangering the user's safety.

[0021] In related technologies, by testing under certain conditions, it was found that when the empirical correction coefficient pitch = 0.86 × 1 / n (n is a positive integer), the thread effect reaches a minimum. However, the above research method is an empirical heuristic method with specific application conditions. When faced with complex projection techniques, its robustness is limited (i.e., the optimal point drifts when conditions change).

[0022] To alleviate the aforementioned problems, this application obtains the equipment parameters of the treatment device used to perform helical radiotherapy and the detection data corresponding to the target user. After determining the helical effect model data corresponding to the treatment device, it determines the variable pitch trajectory data corresponding to the target user based on the equipment parameters, detection data, and helical effect model data. This variable pitch trajectory data is the optimal pitch trajectory determined based on the helical effect model data, which can effectively reduce the helical effect. Moreover, this variable pitch trajectory data is compatible with the detection results of the target user, that is, it is compatible with the treatment process of the treatment device for the target user. Furthermore, an inverse dose optimization operation can be performed based on the variable pitch trajectory data corresponding to the target user to determine the current execution data of the treatment device for the target user, so that the accuracy of the current execution data is high, ensuring the treatment process of the target user.

[0023] To facilitate understanding of this embodiment, a detailed description of the method for determining execution data of a treatment device disclosed in this application embodiment will be provided first. The execution subject of the method for determining execution data of a treatment device provided in this application embodiment is generally a computer device with a certain computing capability. This computer device may include, for example, a terminal device, a server, or other processing devices. The terminal device may be a user equipment (UE), a mobile device, a user terminal, an embedded device, etc. In some possible implementations, the method for determining execution data of the treatment device can be implemented by a processor calling computer-readable instructions stored in memory.

[0024] See Figure 2 The diagram shows a flowchart of a method for determining execution data of a treatment device provided in an embodiment of this application. The method includes steps S201 to S204, wherein: S201. Obtain the equipment parameters of the treatment device used to perform helical radiotherapy, as well as the detection data corresponding to the target user; S202. Determine the thread effect model data corresponding to the treatment device; S203. Based on the equipment parameters, the detection data, and the thread effect model data, determine the variable pitch trajectory data corresponding to the target user; S204. Perform inverse dose optimization operation based on the variable pitch trajectory data corresponding to the target user to determine the current execution data of the treatment device for the target user.

[0025] The following provides a detailed explanation of S201-S204.

[0026] Regarding S201: The equipment parameters of the treatment equipment for performing helical radiotherapy may include, but are not limited to, equipment attribute parameters and execution constraint parameters. For example, equipment attribute parameters may include: source-axis distance (SAD), longitudinal slit width, gantry rotation speed range, control point period, etc.; execution constraint parameters may include: upper limit of bed speed, upper limit of acceleration, upper limit of jerk, upper limit of dose rate, minimum switching time of multi-leaf collimator (MLC), etc.

[0027] The detection data corresponding to the target user may include, but is not limited to: computed tomography (CT), planning target volume (PTV) / organ at risk (OAR) contours and prescription constraints, etc.

[0028] During implementation, the detection data of the target user can also be standardized so that the current execution data of the target user can be determined based on the processed detection data. For example, the standardization process may include, but is not limited to: coordinate system and position standardization, contour integrity check, treatment axis range standardization, voxelization and resampling (resolution uniformity), etc.

[0029] For example, image coordinate system standardization processing can make the spatial coordinate system of CT images uniform and consistent in direction, avoiding left-right reversal, up-down reversal, and positional misalignment, making subsequent calculations of treatment bed position, dose deposition position, etc. more accurate; contour integrity standardization processing can check and repair PTV and OAR contours, ensuring continuity, closure, no voids, and no misalignment; treatment axis range standardization is used to determine from head to toe (Z-axis) which layer to treat, and unify the treatment range.

[0030] Through the above standardization process, the CT images, anatomical contours, treatment range, and clinical constraints are unified and standardized, ensuring that subsequent processes such as variable pitch trajectory optimization, bed position sequence generation, dose calculation, and joint verification are performed based on the same set of standard data. This allows for a more accurate determination of the target user's current execution data, ensuring the accuracy and executability of the treatment plan executed based on the current execution data.

[0031] During implementation, the width of the jaw used in this treatment plan can also be determined, which is based on the actual situation of the target user.

[0032] Regarding S202: Considering that different treatment devices have different basic thread effects, the thread effect model data corresponding to the treatment device can be determined so that the variable pitch trajectory can be determined more accurately based on the thread effect model data.

[0033] Optionally, determining the thread effect model data corresponding to the treatment device includes: Step a1: Under the preset execution conditions of the treatment device, multiple longitudinal slit widths, multiple pitches, and multiple off-axis radii are sampled and obtained; Step a2: Determine multiple sampling conditions based on the multiple longitudinal slit widths, the multiple pitches, and the multiple off-axis radii; Step a3: For each of the sampling conditions, perform a spiral delivery operation under the sampling conditions using the determined reference phantom model to determine longitudinal dose profile data; and use the longitudinal dose profile data to determine the spiral dose ripple index under the sampling conditions. Step a4: Process the thread dose ripple indexes corresponding to the multiple sampling conditions to generate thread effect model data corresponding to the treatment device.

[0034] The preset execution conditions can be open field (i.e., MLC fully open) or weak modulation conditions, etc. Under the preset execution conditions, multiple longitudinal slit widths, multiple pitches, and multiple off-axis radii r are sampled. The axial movement distance of the treatment table when the gantry rotates one revolution is assumed to be... The width of the longitudinal beam at the center is Then pitch can be positioned as: , Indicates the pitch.

[0035] Based on multiple longitudinal slit widths, multiple pitches, and multiple off-axis radii, multiple sampling conditions are determined, namely, each sampling condition includes a jaw width, a pitch, and an off-axis radius r.

[0036] For each sampling condition, a spiral delivery operation is performed using a defined reference phantom model under the sampling conditions to determine the longitudinal dose profile data D(z); and the spiral dose ripple index R(r) under the sampling conditions is determined using the longitudinal dose profile data. The spiral dose ripple index R(r) is determined according to the following formula:

[0037] in , These are the peak and trough values ​​(i.e., extreme values ​​within a specified window) in the longitudinal dose profile data.

[0038] The above process yields thread dose ripple indices R(r) corresponding to multiple sampling conditions. Interpolation and smoothing fitting of these indices R(r) yields the thread risk surrogate function. That is, the thread effect model data corresponding to the treatment device was obtained, in which Indicates the off-axis radius. Indicates the pitch. This indicates the width of the jaw.

[0039] After obtaining the thread effect model data, the thread effect model data and its metadata (such as model, energy, jaw, phantom conditions, version number, etc.) can be stored in the database and their validity verified and version managed, so that the thread effect model data and its metadata can be read from the database for subsequent processing.

[0040] Regarding S203: During implementation, the variable pitch trajectory data corresponding to the target user can be determined based on equipment parameters, test data, and thread effect model data, and the outer layer geometry optimization process can be completed to reduce the thread effect.

[0041] Optionally, determining the variable pitch trajectory data corresponding to the target user based on the device parameters, the detection data, and the thread effect model data includes: Step b1: Divide the treatment axis range of the treatment device into a preset number of local segments along the treatment axis direction of the treatment device; Step b2: Based on the detection data of the target user and the thread effect model data, construct an objective function with the pitch of the local segment as the variable; Step b3: Determine the mechanical constraints corresponding to the objective function based on the equipment parameters of the treatment device; Step b4: Under the mechanical constraints, using the objective optimization algorithm and the objective function, determine the pitch values ​​corresponding to the multiple local segments respectively; Step b5: Based on the pitch values ​​corresponding to the multiple local segments, determine the variable pitch trajectory data corresponding to the target user.

[0042] The treatment axis direction can be from head to toe. Along the treatment axis of the treatment device, the treatment axis range is divided into a preset number of local segments, where the preset number can be set according to actual conditions. In this application, each local segment can correspond to a pitch variable.

[0043] Based on the target user's detection data and the thread effect model data, the thread effect strength index can be determined. Then, an objective function with the pitch of the local segment as the variable can be constructed based on the thread effect strength index. For example, the thread effect strength index can be determined as an objective function with the pitch of the local segment as the variable; or the pitch difference between adjacent local segments can be determined, and an objective function with the pitch of the local segment as the variable can be constructed based on the thread effect strength index and the pitch difference between adjacent local segments.

[0044] In one optional implementation, the step of constructing an objective function with the pitch of the local segment as a variable based on the target user's detection data and the thread effect model data includes: generating a risk term reflecting the intensity of the thread effect based on the target user's detection data and the thread effect model data; generating a smoothing term reflecting the smoothness of the pitch based on the pitch difference between adjacent local segments; generating an efficiency term reflecting the execution efficiency based on the sequential execution time of each local segment at the corresponding pitch; and constructing an objective function with the pitch of the local segment as a variable based on the risk term, the smoothing term, and the efficiency term.

[0045] During implementation, the objective function is constructed as follows:

[0046] in, This is a risk item. For smoothing terms, For efficiency, S is the number of local segments (i.e., the preset number). Let be the pitch of the s-th local segment. Let be the pitch of the (s-1)th local segment. Characterizes the execution time of the sequence; For the weight of the thread penalty term, For the weights of the smoothing term, As the weight of the efficiency term, , ,and The value can be set according to actual needs.

[0047] After determining the objective function, the pitch can be converted into bed velocity, and mechanical constraints can be applied. These mechanical constraints can be determined based on the equipment parameters of the treatment device, ensuring that the resulting variable pitch trajectory data is achievable and reasonable. These mechanical constraints include, but are not limited to, velocity constraints, acceleration constraints, jerk constraints, and upper and lower bound constraints on the pitch.

[0048] Under mechanical constraints, the optimal solution of the objective function is obtained by using an objective optimization algorithm, which yields the pitch values ​​corresponding to multiple local segments. The objective optimization algorithm can be, for example, dynamic programming (DP) discrete optimization algorithm, sequential quadratic programming (SQP) / gradient optimization algorithm, etc.

[0049] During implementation, a thread effect sensitivity factor can be set to dynamically adjust the pitch candidate value. For example, the thread effect sensitivity factor can be determined based on the determined weight function W(z) and the effective off-axis distance r. The thread effect sensitivity factor is calculated as follows: S(z) = W(z) / times f(r), where f(r) is the correlation function between the off-axis distance and the thread effect. The larger r is, the larger f(r) is.

[0050] When optimizing pitch using DP / SQP, when S(z) ≥ the threshold (i.e., sensitive areas, such as the outer layer of PTV or areas far from the axis), the range of candidate pitch values ​​should be narrowed (e.g., 0.215~0.43), and the weight of low candidate pitch values ​​should be increased to force the use of a small pitch in this area. This will smooth out the spiral ripples by increasing the overlap of the radiation field. When S(z) < the threshold (i.e., non-sensitive areas, such as normal tissue or the central area), the candidate pitch values ​​can be appropriately expanded (e.g., 0.43~0.86) to balance treatment efficiency.

[0051] Furthermore, the variable pitch trajectory data corresponding to the target user can be determined based on the pitch values ​​corresponding to multiple local segments. For example, the pitch values ​​corresponding to multiple local segments can be used to determine the variable pitch trajectory data corresponding to the target user; or, the pitch values ​​corresponding to multiple local segments can be fitted, and the fitted expression can be used to determine the variable pitch trajectory data corresponding to the target user.

[0052] This application determines the pitch corresponding to each local segment through the above process, realizing the optimized determination process of variable pitch, so that the determined variable pitch trajectory data can effectively reduce the thread effect of the treatment device and ensure the accuracy of the treatment process for the target user.

[0053] In one optional implementation, determining the variable pitch trajectory data corresponding to the target user based on the pitch values ​​corresponding to the plurality of local segments further includes: performing trajectory smoothing and boundary correction operations on the pitch values ​​corresponding to the plurality of local segments to generate adjusted pitch values ​​corresponding to the plurality of local segments; and determining the variable pitch trajectory data corresponding to the target user based on the adjusted pitch values ​​corresponding to the plurality of local segments.

[0054] After determining the optimal solution of the objective function, trajectory smoothing and boundary correction operations can be performed on the pitch values ​​corresponding to multiple local segments to generate adjusted pitch values ​​corresponding to multiple local segments. The adjusted pitch values ​​corresponding to multiple local segments can be used as the variable pitch trajectory data corresponding to the target user. Alternatively, the adjusted pitch values ​​corresponding to multiple local segments can be fitted, and the fitted expression can be used as the variable pitch trajectory data corresponding to the target user.

[0055] By performing trajectory smoothing and boundary correction operations on the pitch values ​​of multiple local segments, the adjusted pitch values ​​meet the actual execution requirements, ensuring the executability of the determined pitch values.

[0056] Regarding S204: Based on the variable pitch trajectory data corresponding to the identified target user, an inverse dose optimization operation is performed to determine the current execution data of the treatment device for the target user. The current execution data includes the treatment device control parameters, dose parameters, position geometry parameters, etc.

[0057] In one optional implementation, the step of performing inverse dose optimization based on the variable pitch trajectory data corresponding to the target user to determine the current execution data of the treatment device for the target user includes: Step c1: Based on the variable pitch trajectory data, determine the position of the treatment bed of the treatment device at multiple control points; Step c2: Determine the dosage model corresponding to the target user based on the position of the treatment bed of the treatment device at multiple control points; Step c3: Based on the dose model and the determined clinical dosimetry constraints, determine the execution parameters corresponding to each control point, wherein the execution parameters include dose output parameters and multi-leaf collimator control parameters; Step c4: Based on the execution parameters corresponding to the multiple control points, generate the current execution data of the treatment device for the target user.

[0058] In implementation, the variable pitch trajectory can be unfolded into a geometric-temporal mapping consistent with the time base of the gantry feet and control points to generate a sequence of treatment bed positions. For example, multiple control points can be identified, and the position of the treatment bed at each control point can be determined based on the variable pitch trajectory data, thus determining the position of the treatment bed of the treatment equipment at multiple control points. Alternatively, the distance the treatment bed moves between adjacent control points can be determined, i.e., the position increment of the treatment bed can be determined.

[0059] Through the aforementioned outer geometry optimization process, delivery geometry data such as gantry feet, treatment bed positions, and MLC shapes corresponding to each control point were determined. Based on this determined delivery geometry data, a dosage model corresponding to the target user can then be determined. This enables the construction of a forward dosage calculation model that is completely consistent with the delivery geometry of the treatment equipment, building upon a fixed real-world geometric mapping, thus ensuring both dosage calculation accuracy and executability consistency.

[0060] For example, by fixing the beam direction, bed position, and MLC shape at each control point, the relative positional relationship between voxels and the beam is established. A suitable dose calculation algorithm (such as Collapsed Cone, Monte Carlo, etc.) is selected to construct a forward dose calculation model. The model can quickly and accurately calculate the dose deposition of each voxel in the target user's body based on the beam intensity and MLC shape at any control point, ensuring that the geometry used for dose calculation (bed position, angle, field) is completely consistent with the actual execution geometry of the equipment, and avoiding dose deviation caused by geometric approximation.

[0061] Furthermore, under the premise of satisfying clinical dosimetry constraints and device execution constraints, the execution parameters of each control point are solved through numerical optimization methods. Execution parameters include dose output parameters and MLC control parameters. For example, an optimization function is constructed based on the built dose model, and clinical dosimetry constraints and device execution constraints are imported. The clinical dosimetry constraints include, but are not limited to: prescription dose of PTV target area, dose uniformity, maximum / minimum dose constraints, dose limits of each OAR, dose gradient, etc.; the device execution constraints include, but are not limited to: upper and lower limits of dose rate, minimum opening of MLC blades, minimum movement time, maximum movement speed, blade non-collision constraints, etc. Then, using the control point as the optimization unit, the dose output weight / dose rate / MU of each control point and the MLC blade position of each control point are automatically adjusted through iterative optimization algorithms. In each iteration, the forward dose model is called to calculate the dose distribution and compared with the clinical constraints to gradually approach the optimal solution. After optimization convergence, the MLC blade position sequence and dose rate or output weight sequence (i.e., output dose output parameters and MLC control parameters) of the control point with complete time-synchronization are output. The parameters obtained can be directly used to generate a treatment control table, achieving optimal dosage, geometric accuracy, and safe operation of the equipment.

[0062] The execution parameters corresponding to the multiple control points are determined as the current execution data of the treatment device for the target user; or, a control table can be generated based on the execution parameters of the multiple control points to control the execution operation of the treatment device.

[0063] Optionally, the method further includes: performing joint verification based on the variable pitch trajectory data and the current execution data to obtain a verification result, wherein the joint verification includes at least one of the following: mechanical constraint verification, device execution constraint verification, and control time base consistency verification; when the verification result is a successful verification, generating a current control table based on the variable pitch trajectory data and the current execution data, wherein the current control table is used to control the operation of the treatment device.

[0064] After completing the inverse dose optimization and obtaining the MLC blade position and dose output parameters for each control point, it is necessary to uniformly verify the outer layer geometric trajectory and the inner layer dose optimization results to ensure that all motion parameters, equipment parameters, and timing relationships meet the physical limitations of the accelerator. Finally, the plan can be executed safely, stably, and accurately by the treatment equipment without problems such as motion exceeding limits, timing misalignment, or dose abnormality.

[0065] In practical implementation, joint verification can also be performed based on variable pitch trajectory data and current execution data to verify the feasibility of the current execution data. For example, joint verification includes at least one of the following: 1. Mechanical constraint verification, such as verifying whether the actual bed speed of the treatment bed is less than the upper limit of bed speed, whether the acceleration is less than the upper limit of acceleration, and whether the jerk is less than the upper limit of jerk, etc.; 2. Equipment execution constraint verification, such as verifying whether the blade speed is less than or equal to the maximum blade speed, whether the time required for blade movement is less than or equal to the control point time interval, whether the dose rate is less than or equal to the maximum dose rate of the equipment, whether the number of jumps (Monitor Unit, MU) of a single control point is not less than the minimum beam output unit, and whether the dose rate changes drastically between control points, etc.; 3. Control time base consistency verification, that is, ensuring that the gantry angle, bed position, MLC shape, and dose rate are strictly synchronized at the same control point; for example, verifying that each control point corresponds to a unique: gantry angle, bed position, MLC, etc. Shape, dose rate, and all other parameters must correspond one-to-one in time sequence, without leading or lagging behind. Furthermore, changes in bed position must be geometrically consistent with pitch and gantry rotation speed, with no control point misalignment, missing points, or time sequence reversal. This ensures that computational geometry is equivalent to delivery geometry and avoids dose distribution distortion caused by misalignment of the helical trajectory.

[0066] When the verification result is successful, a current control table for the treatment equipment can be generated based on the variable pitch trajectory data and the current execution data, so as to control the operation of the treatment equipment using the current control table. The current control table includes, but is not limited to, the following: control point number, gantry angle, bed position or bed increment, control point duration, dose output parameters (MU or dose rate), MLC control parameters, and jaw settings.

[0067] While the treatment device is performing treatment under the current control table, routine trajectory tracking, out-of-tolerance termination, and execution log recording can also be performed for quality assurance (QA) traceability.

[0068] The method proposed in this application is illustrated below by way of example; see [link to relevant documentation]. Figure 3 As shown, the method includes: S301: System initialization and parameter loading.

[0069] Load the device parameters of the treatment equipment, such as device attribute data, execution constraint data, and agent model version information, and complete the global parameter initialization of the current plan.

[0070] Equipment attribute data includes: SAD, jaw width, rack speed range, control point cycle, etc.

[0071] The execution constraint data includes: bed velocity limit, acceleration limit, jerk limit, dose rate limit, minimum MLC switching time, etc.

[0072] The proxy model version information includes: machine type, power, Jaw configuration, database version number, etc.

[0073] S302: Offline Phase: Agent Model Calibration and Database Construction.

[0074] Determine the thread effect model data corresponding to the treatment device during the offline phase.

[0075] S303: User data import and preprocessing.

[0076] Import the target user's CT, PTV / OAR contour, and prescription constraint data, standardize the data to obtain processed data, and determine the jaw width setting to be used in this project.

[0077] S304: Outer layer geometry optimization.

[0078] Under the premise of satisfying mechanical constraints, the variable pitch trajectory p(z) or discrete pitch trajectory sequence {ps} is solved based on the thread effect model data, that is, the variable pitch trajectory data is determined to reduce the thread risk term and take into account the trajectory smoothness and delivery efficiency.

[0079] S305: Trajectory unfolding and control timing mapping.

[0080] The variable pitch trajectory obtained in step S304 is expanded into a geometric temporal mapping consistent with the gantry angle and control point time base to generate a treatment bed position sequence (or bed increment).

[0081] S306: Inner layer dose optimization.

[0082] Under the true geometric mapping determined in step S305, conventional inverse dose optimization is performed to solve for the MLC control parameters and dose output parameters, thereby determining the current execution data to ensure that the clinical dosimetric target is met.

[0083] S307: Conformity and executability check.

[0084] The outer layer geometry results and the inner layer dose optimization results were jointly verified to confirm that they met mechanical constraints, control time base consistency, and equipment execution constraints.

[0085] S308: Generate an executable control list.

[0086] After the joint verification is passed, the current control table is generated. This involves merging the geometric timing mapping, dose output parameters, and MLC control parameters to generate a time synchronization control table sorted by control points, which is then executed by the equipment.

[0087] If the verification fails, you can return to the outer geometry optimization process and regenerate the variable pitch trajectory.

[0088] S309: Delivery Execution and Logging.

[0089] The treatment device executes the treatment according to the control table output in step S308, and performs routine trajectory tracking, out-of-tolerance termination, and execution log recording for QA traceability.

[0090] Corresponding to the embodiments of the aforementioned method for determining execution data of a treatment device, this application also provides embodiments of a device for determining execution data of a treatment device. Figure 4 A schematic diagram of the execution data determination device for the treatment device provided in this application is provided, specifically including: The acquisition module 401 is used to acquire the equipment parameters of the treatment equipment used to perform helical radiotherapy and the detection data corresponding to the target user; The first determining module 402 is used to determine the thread effect model data corresponding to the treatment device; The second determining module 403 is used to determine the variable pitch trajectory data corresponding to the target user based on the equipment parameters, the detection data, and the thread effect model data. The third determining module 404 is used to perform a reverse dose optimization operation based on the variable pitch trajectory data corresponding to the target user, and to determine the current execution data of the treatment device for the target user.

[0091] In one optional implementation, the first determining module 402, when determining the thread effect model data corresponding to the treatment device, is used to: Under the preset execution conditions of the treatment device, multiple longitudinal slit widths, multiple pitches, and multiple off-axis radii were sampled and obtained. Multiple sampling conditions are determined based on the multiple longitudinal slit widths, the multiple pitches, and the multiple off-axis radii; For each of the sampling conditions, a spiral delivery operation is performed under the sampling conditions using a determined reference phantom model to determine longitudinal dose profile data; and the spiral dose ripple index under the sampling conditions is determined using the longitudinal dose profile data. The thread dose ripple indexes corresponding to the multiple sampling conditions are processed to generate thread effect model data corresponding to the treatment device.

[0092] In an optional implementation, the second determining module 403, when determining the variable pitch trajectory data corresponding to the target user based on the device parameters, the detection data, and the thread effect model data, is used to: Along the treatment axis direction of the treatment device, the treatment axis range of the treatment device is divided into a preset number of local segments; Based on the detection data of the target user and the thread effect model data, a target function is constructed with the pitch of the local segment as the variable; Based on the equipment parameters of the treatment device, determine the mechanical constraints corresponding to the objective function; Under the mechanical constraints, the pitch values ​​corresponding to the multiple local segments are determined using the objective optimization algorithm and the objective function. Based on the pitch values ​​corresponding to the multiple local segments, the variable pitch trajectory data corresponding to the target user is determined.

[0093] In an optional implementation, when the second determining module 403 constructs an objective function with the pitch of the local segment as a variable based on the detection data of the target user and the thread effect model data, it is used to: Based on the detection data of the target user and the thread effect model data, a risk item reflecting the intensity of the thread effect is generated; Based on the pitch difference between adjacent local segments, a smoothing term is generated to reflect the smoothness of the pitch. Based on the sequential execution time of each local segment at the corresponding pitch, an efficiency term reflecting the execution efficiency is generated; Based on the risk term, the smoothing term, and the efficiency term, an objective function is constructed with the pitch of the local segment as the variable.

[0094] In an optional implementation, when the second determining module 403 determines the variable pitch trajectory data corresponding to the target user based on the pitch values ​​corresponding to the plurality of local segments, it is further configured to: Perform trajectory smoothing and boundary correction operations on the pitch values ​​corresponding to the multiple local segments respectively to generate adjusted pitch values ​​corresponding to the multiple local segments; Based on the adjusted pitch values ​​corresponding to the multiple local segments, the variable pitch trajectory data corresponding to the target user is determined.

[0095] In an optional implementation, when the third determining module 404 performs an inverse dose optimization operation based on the variable pitch trajectory data corresponding to the target user and determines the current execution data of the treatment device for the target user, it is used to: Based on the variable pitch trajectory data, the position of the treatment bed of the treatment device at multiple control points is determined; The dosage model corresponding to the target user is determined based on the position of the treatment bed of the treatment device at multiple control points; Based on the dose model and the determined clinical dosimetry constraints, the execution parameters corresponding to each control point are determined, wherein the execution parameters include dose output parameters and multi-leaf collimator control parameters; Based on the execution parameters corresponding to the multiple control points, the current execution data of the treatment device for the target user is generated.

[0096] In an optional embodiment, the apparatus further includes: a verification module 405, configured to: A joint verification is performed based on the variable pitch trajectory data and the current execution data to obtain a verification result, wherein the joint verification includes at least one of the following: mechanical constraint verification, equipment execution constraint verification, and control time base consistency verification. When the verification result is successful, a current control table is generated based on the variable pitch trajectory data and the current execution data, wherein the current control table is used to control the operation of the treatment device.

[0097] The specific implementation process of the functions and roles of each unit in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0098] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0099] This application also provides a computer-readable storage medium storing a computer program that can be used to execute the method for determining execution data of the treatment device described in the above embodiments.

[0100] This application also provides a computer device, see [link to relevant documentation] Figure 5The diagram shown illustrates the structure of the computer device provided in this application. At the hardware level, the computer device includes a processor, an internal bus, a network interface, memory, and non-volatile memory, and may also include other hardware required for various operations. The processor reads the corresponding computer program from the non-volatile memory into memory and then runs it to implement the execution data determination method for the treatment device described in the above embodiments. Of course, besides software implementation, this specification does not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution entity of the following processing flow is not limited to individual logic units, but can also be hardware or logic devices.

[0101] Those skilled in the art will understand that embodiments of this application can be provided as methods, apparatus, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs) containing computer-usable program code. The form of a computer program product implemented on ROM, optical memory, etc.

[0102] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0103] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0104] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process.Figure 1 One or more processes and / or boxes Figure 1 The steps of the functions specified in one or more boxes. In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0105] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0106] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, and optical disc read-only memory (CD). ROM, digital multifunction optical disc (DVD) or other optical storage, magnetic cassette tape, magnetic magnetic disk storage or other magnetic storage devices or any other non-transfer media, may be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transient media, such as modulated data signals and carrier waves.

[0107] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0108] This specification can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This specification can also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0109] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0110] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for determining execution data of a treatment device, characterized in that, The method includes: Acquire the equipment parameters of the treatment device used to perform helical radiotherapy, as well as the detection data corresponding to the target user; Determine the thread effect model data corresponding to the treatment device; Based on the equipment parameters, the detection data, and the thread effect model data, determine the variable pitch trajectory data corresponding to the target user; Based on the variable pitch trajectory data corresponding to the target user, an inverse dose optimization operation is performed to determine the current execution data of the treatment device for the target user; The step of determining the variable pitch trajectory data corresponding to the target user based on the equipment parameters, the detection data, and the thread effect model data includes: Along the treatment axis direction of the treatment device, the treatment axis range of the treatment device is divided into a preset number of local segments; Based on the detection data of the target user and the thread effect model data, a target function is constructed with the pitch of the local segment as the variable; Based on the equipment parameters of the treatment device, determine the mechanical constraints corresponding to the objective function; Under the mechanical constraints, the pitch values ​​corresponding to the multiple local segments are determined using the objective optimization algorithm and the objective function. Based on the pitch values ​​corresponding to the multiple local segments, the variable pitch trajectory data corresponding to the target user is determined.

2. The method according to claim 1, characterized in that, The determination of the thread effect model data corresponding to the treatment device includes: Under the preset execution conditions of the treatment device, multiple longitudinal slit widths, multiple pitches, and multiple off-axis radii were sampled and obtained. Multiple sampling conditions are determined based on the multiple longitudinal slit widths, the multiple pitches, and the multiple off-axis radii; For each of the sampling conditions, a spiral delivery operation is performed under the sampling conditions using a determined reference phantom model to determine longitudinal dose profile data; and the spiral dose ripple index under the sampling conditions is determined using the longitudinal dose profile data. The thread dose ripple indexes corresponding to the multiple sampling conditions are processed to generate thread effect model data corresponding to the treatment device.

3. The method according to claim 1, characterized in that, The step of constructing an objective function with the pitch of the local segment as a variable based on the detection data of the target user and the thread effect model data includes: Based on the detection data of the target user and the thread effect model data, a risk item reflecting the intensity of the thread effect is generated; Based on the pitch difference between adjacent local segments, a smoothing term is generated to reflect the smoothness of the pitch. Based on the sequential execution time of each local segment at the corresponding pitch, an efficiency term reflecting the execution efficiency is generated; Based on the risk term, the smoothing term, and the efficiency term, an objective function is constructed with the pitch of the local segment as the variable.

4. The method according to claim 1, characterized in that, The step of determining the variable pitch trajectory data corresponding to the target user based on the pitch values ​​corresponding to the multiple local segments further includes: Perform trajectory smoothing and boundary correction operations on the pitch values ​​corresponding to the multiple local segments respectively to generate adjusted pitch values ​​corresponding to the multiple local segments; Based on the adjusted pitch values ​​corresponding to the multiple local segments, the variable pitch trajectory data corresponding to the target user is determined.

5. The method according to any one of claims 1-4, characterized in that, The step of performing inverse dose optimization based on the variable pitch trajectory data corresponding to the target user, and determining the current execution data of the treatment device for the target user, includes: Based on the variable pitch trajectory data, the position of the treatment bed of the treatment device at multiple control points is determined; The dosage model corresponding to the target user is determined based on the position of the treatment bed of the treatment device at multiple control points; Based on the dose model and the determined clinical dosimetry constraints, the execution parameters corresponding to each control point are determined, wherein the execution parameters include dose output parameters and multi-leaf collimator control parameters; Based on the execution parameters corresponding to the multiple control points, the current execution data of the treatment device for the target user is generated.

6. The method according to any one of claims 1-4, characterized in that, The method further includes: A joint verification is performed based on the variable pitch trajectory data and the current execution data to obtain a verification result, wherein the joint verification includes at least one of the following: mechanical constraint verification, equipment execution constraint verification, and control time base consistency verification. When the verification result is successful, a current control table is generated based on the variable pitch trajectory data and the current execution data, wherein the current control table is used to control the operation of the treatment device.

7. A device for determining execution data of a treatment device, characterized in that, The device includes: The acquisition module is used to acquire the equipment parameters of the treatment equipment used to perform helical radiotherapy, as well as the detection data corresponding to the target user; The first determining module is used to determine the thread effect model data corresponding to the treatment device; The second determining module is used to determine the variable pitch trajectory data corresponding to the target user based on the equipment parameters, the detection data, and the thread effect model data. The third determining module is used to perform inverse dose optimization operations based on the variable pitch trajectory data corresponding to the target user, and to determine the current execution data of the treatment device for the target user; The second determining module, when determining the variable pitch trajectory data corresponding to the target user based on the device parameters, the detection data, and the thread effect model data, is used for: Along the treatment axis direction of the treatment device, the treatment axis range of the treatment device is divided into a preset number of local segments; Based on the detection data of the target user and the thread effect model data, a target function is constructed with the pitch of the local segment as the variable; Based on the equipment parameters of the treatment device, determine the mechanical constraints corresponding to the objective function; Under the mechanical constraints, the pitch values ​​corresponding to the multiple local segments are determined using the objective optimization algorithm and the objective function. Based on the pitch values ​​corresponding to the multiple local segments, the variable pitch trajectory data corresponding to the target user is determined.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by a processor, it implements the steps of the method according to any one of claims 1-6.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor performs the steps of the method according to any one of claims 1-6.

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