Radiation irradiation experiment strategy generation method and verification method thereof

Abstract

The invention provides a radiation irradiation experiment strategy generation method and a verification method thereof, and relates to the technical field of radiotherapy and computers. The method comprises the following steps: acquiring respective beam parameters of n beams adopted by a beam experiment on a predetermined experiment object, wherein the beam parameters comprise beam weights; inputting the beam weights corresponding to the n beams into a Monte Carlo dose calculation engine, and outputting actual dose deposition results of the n beams in the predetermined experimental subject; inputting the actual dose deposition result and the target dose deposition result into a dose optimization model so as to optimize respective beam weights of the n beams by using the optimization model, and outputting respective optimization weights of the n beams; and generating an irradiation strategy aiming at the experiment object by using the respective optimization weights of the n beams. By means of the experimental strategy generation method, the irradiation strategy closer to the ideal target dose deposition result can be obtained, and meanwhile the beam parameters of the radiation experiment can be optimized with high precision.

Description

Technical Field

[0001] The present invention relates to the field of radiotherapy technology and computer technology, and in particular to a radiation irradiation experiment strategy generation method and a verification method thereof. Background Art

[0002] Radiation exposure experiments involve a series of experimental procedures designed to determine the direction and intensity of the beam. Beam parameters determine the accuracy of the experiment. For example, small animal radiotherapy involves applying radiotherapy techniques to small animal models (such as mice and rats) to simulate and evaluate the effectiveness of tumor treatment. Radiotherapy experiments in animal models help uncover potential radiobiological mechanisms and provide experimental data for the clinical translation of new treatment strategies and technologies. The experimental process for small animal radiotherapy is generally designed based on the clinical radiotherapy process. After completing the imaging scan, a radiotherapy plan is developed based on the imaging results, and finally, the irradiation is performed.

[0003] However, due to the small target volume and large dose gradients in small animal radiotherapy, even a small deviation between the calculated and actual doses can significantly affect experimental results. This makes precise control of dose delivery a major challenge in small animal radiotherapy. Therefore, a more precisely controlled radiation experimental strategy is needed. Summary of the Invention

[0004] In view of the above problems, the present invention provides a radiation exposure experiment strategy generation method and a verification method thereof.

[0005] According to a first aspect of the present invention, a method for generating a radiation irradiation experiment strategy is provided, which is performed using an electronic device. The method comprises: obtaining beam parameters of each of n beams used to perform a radiation irradiation experiment on a predetermined experimental object, wherein the beam parameters include beam weights; inputting the beam weights corresponding to each of the n beams into a Monte Carlo dose calculation engine, and outputting actual dose deposition results of each of the n beams in the body of the predetermined experimental object; inputting the actual dose deposition results and the target dose deposition results into a dose optimization model, so as to optimize the beam weights of each of the n beams using the optimization model, and outputting the optimized weights of each of the n beams; and generating an irradiation strategy for the experimental object using the optimized weights of each of the n beams.

[0006] A second aspect of the present invention provides a method for verifying an experimental strategy, comprising: placing a dose measurement film into a heterogeneous phantom structure prepared according to the structure of an experimental object; performing irradiation based on an irradiation strategy obtained by the above-mentioned radiation irradiation experimental strategy generation method to obtain a dose measurement film after irradiation; and comparing the actual dose distribution obtained from the dose measurement film after irradiation with the theoretically calculated dose distribution result to obtain a verification result of the irradiation strategy.

[0007] According to an embodiment of the present invention, by utilizing the actual dose deposition results of multiple beams and the target dose deposition results obtained by a Monte Carlo dose calculation engine, and inputting them into a dose optimization model for calculation, the preset beam weights can be optimized, and an irradiation strategy that is closer to the ideal target dose deposition result can be obtained, and the optimal combination of beam angle distribution and beam weights can be obtained. Taking full account of the heterogeneity of the complex tissues inside the experimental object, the radiation dose deposition can be precisely controlled, the experiment can be improved, and the experimental goal can be completed with fewer beams, thereby improving the experimental efficiency while simplifying the experimental process and saving experimental resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 The figure is a flow chart of the experimental strategy generation method according to an embodiment of the present invention.

[0009] Figure 2 Schematic diagram of the verification method flow of the experimental strategy of an embodiment of the present invention.

[0010] Figure 3 Schematic diagram of a heterogeneous simulated mouse phantom according to an embodiment of the present invention.

[0011] Figure 4 The figure shows the comparison results of the irradiation plan before and after optimization in the mouse lung cancer case according to the embodiment of the present invention. Figure 4 (a) in the figure indicates the delineation results of tumor and organs at risk. Figure 4 (b) shows the comparison of the dose-volume histograms of tumors and organs at risk in the two plans. Figure 4 (c) and (d) in the figure represent the isodose line distributions corresponding to 20%, 40%, 60%, 80% and 100% of the prescription dose for the manual forward plan and the inverse optimization plan, respectively.

[0012] Figure 5 The figure shows the comparison of the dose profile in the horizontal direction between the verification measurement results and the calculated results according to an embodiment of the present invention, where (a) is the dose profile comparison result under a 5 mm collimator aperture, and (b) is the comparison result under a 7.5 mm collimator aperture.

[0013] Figure 6 Gamma analysis results are shown to verify measurement results and calculation results according to an embodiment of the present invention.

[0014] Figure 7 A structural block diagram of an experimental strategy generating device according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0015] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present invention. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of embodiments of the present invention. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concept of the present invention.

[0016] The terms used herein are only for describing specific embodiments and are not intended to limit the present invention. The terms "comprise," "include," etc. used herein indicate the presence of features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0017] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0018] When expressions such as "at least one of A, B, and C, etc." are used, they should generally be interpreted in accordance with the meaning commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0019] During the development of this invention, researchers discovered that early small animal radiation experiments relied on modified clinical radiotherapy equipment, which was primarily designed for humans and could not meet the high precision requirements of small animal treatments. Therefore, researchers developed a radiotherapy research platform specifically designed for small animals to improve treatment precision and better support related research.

[0020] Despite the continuous development and improvement of small animal radiotherapy platforms, existing technologies still have some shortcomings. First, existing platforms require operators to manually complete radiotherapy plan design, which makes it difficult to effectively balance tumor coverage and organ protection in complex scenarios, resulting in poor radiotherapy experimental quality. Second, small animal radiotherapy platforms lack a comprehensive quality assurance process, which can easily lead to deviations between the planned and actual delivered doses, thereby affecting the reliability of experimental results. Therefore, new methods and technologies are needed to improve the quality and reproducibility of small animal radiotherapy experiments.

[0021] It should be noted that the method for optimizing the relevant beam parameters of radiotherapy involved in the present invention is only aimed at optimizing the experimental method of radiation, and does not involve direct clinical diagnosis and treatment of diseases.

[0022] Figure 1 Schematic diagram of the flow of a method for generating a radiation exposure experiment strategy according to an embodiment of the present invention.

[0023] Specifically, according to an embodiment of one aspect of the present invention, a method for generating a radiation exposure experiment strategy is provided, wherein the method is executed by an electronic device, such as Figure 1 As shown, the following operations S101 to S104 are included.

[0024] Operation S101: Obtaining beam parameters of n beams respectively used for performing a radiation exposure experiment on a predetermined experimental object, wherein the beam parameters include beam weights.

[0025] According to an embodiment of the present invention, the predetermined experimental subject may be an experimental subject to be irradiated by the beam, for example, an experimental animal for small animal conformal arc radiotherapy, such as a mouse. It may also be a specific biological tissue, such as a tumor area, etc.; it may also be a physical model such as a phantom. Beam parameters refer to a set of key parameters that describe the physical characteristics of the beam, and are used to control the energy distribution, propagation characteristics and effect of the beam. Beam parameters include beam weight, energy, duration, spot size and shape, etc. Among them, the beam weight represents the relative proportion of the beam in the total dose distribution. For example, in a radiotherapy plan, the weight determines the contribution ratio of different beams to the target area or normal tissue, and needs to be adjusted through an optimization model to achieve the optimal solution for the dose distribution.

[0026] In some specific embodiments of the present invention, the selection of n beams can be determined based on the experimental platform where the experimental object is located, and the n beams have different beam directions. For example, when the experimental object is a small animal, the experimental platform can be a small animal radiotherapy platform, that is, based on the structure of the small animal radiotherapy platform, the candidate n beam sets are defined.

[0027] Optionally, when the illumination condition is coplanar illumination, n candidate beams are generated at certain angular intervals within a 360° range. For example, 72 candidate beams are generated at angular intervals of 5°. The present invention does not impose any specific limitation on the number of n candidate beams, where n is a positive integer greater than zero.

[0028] Optionally, one or more initial values may be set for the beam weights of the n beams. The initial values may be set based on experience or as random values.

[0029] In a specific embodiment of the present invention, the experimental subject is an experimental animal, and the experimental platform is a small animal radiotherapy platform. Before operation S101, the following operations S1101 to S1103 are also included.

[0030] Operation S1101: Anesthetize the experimental animal and complete positioning.

[0031] Optionally, the experimental animal is placed in an anesthesia box. When the experimental animal has no obvious reaction when the anesthesia box is shaken, the experimental animal is taken out and fixed on an animal stand in a vertical position using tape, and then the animal stand is fixed on a small animal support table.

[0032] Operation S1102: Scan the experimental animal using cone beam computed tomography (CBCT) to obtain three-dimensional image data.

[0033] Operation S1103: Locate the spatial position of the tumor based on the image data, and outline the tumor and organs at risk.

[0034] Alternatively, an algorithm is used to determine the centroid of the tumor region and the maximum diameter of the tumor, and the centroid of the tumor is used as the irradiation isocenter. The collimator diameter is selected based on the maximum diameter of the tumor, wherein the irradiation isocenter coincides with the 3D center of the tumor.

[0035] Optionally, the collimator is a device made of a specific material and having a through hole of a specific geometric shape, which is used to limit the shape of the X-ray beam. The material can be lead and the geometric shape can be designed to be a circular hole.

[0036] Optionally, multiple collimator apertures are selected according to the size of the tumor. When defining candidate beam sets, a beam set is defined for each aperture, and beam sets corresponding to different apertures are finally merged to form a final candidate beam set for optimization.

[0037] Operation S102: inputting the beam weights corresponding to the n beams into a Monte Carlo dose calculation engine, and outputting actual dose deposition results of the n beams in a predetermined experimental subject.

[0038] According to an embodiment of the present invention, a Monte Carlo dose calculation engine refers to a dose calculation tool based on random sampling and statistical principles. It simulates the transport of a large number of particles (such as photons, electrons, and protons) through a medium and calculates their energy deposition distribution within the subject. Actual dose deposition results refer to the energy deposition distribution of various regions within the subject obtained through Monte Carlo simulation, presented as a three-dimensional dose map or voxelized data. Specifically, representation methods include displaying the dose range received by different tissue volumes, a dose-volume histogram (DVH) for assessing target coverage and organ-at-risk protection, and an isodose map that visualizes the dose distribution and directly reflects the spatial relationship between high-dose and low-dose areas.

[0039] Operation S103: inputting the actual dose deposition result and the target dose deposition result into a dose optimization model, so as to optimize the beam weights of the n beams using the optimization model and output the optimized weights of the n beams.

[0040] According to an embodiment of the present invention, the target dose deposition result refers to a preset ideal dose distribution which is usually specified according to an experimental goal.

[0041] For example, the target dose deposition result can be that the tumor area needs to reach the prescribed dose, and normal tissue needs to be below the tolerance dose (such as spinal cord ≤45Gy).

[0042] For example, the target dose deposition result may be a completely ideal dose deposition result, such as the tumor region reaches a prescribed dose that can eliminate the tumor, and the target dose deposition results of other normal tissues are set to 0.

[0043] A dose optimization model is an algorithm used to optimize beam weights. By comparing actual and target doses, the beam weights are iteratively adjusted to minimize the difference. The optimized weights for each of the n beams, calculated using the dose optimization model, represent the optimal contribution of each beam to the total dose. This balances dose coverage of the target (e.g., tumor) with protection of normal tissue, ensuring that the actual dose distribution approaches the target.

[0044] Specifically, the radiotherapy plan output by the dose optimization model is evaluated, such as by evaluating the generated plan based on the dose-volume histogram and the isodose line distribution map. If the expected standards are not met, the optimization parameters are adjusted and the solution is re-solved until the plan requirements are met.

[0045] Operation S104: Generate an irradiation strategy for the experimental object using the optimized weights of the n beams.

[0046] According to an embodiment of the present invention, the irradiation strategy refers to a combination of beam parameters and an implementation plan generated based on the optimized weights, which is used to guide the precise irradiation of the experimental object, and may include beam angle (such as multi-field coplanar / non-coplanar layout), energy, dose rate (such as continuous or pulsed mode), etc.

[0047] Optionally, the irradiation strategy may also include timing control for dose distribution of fractionated irradiation (such as large fractionation or conventional fractionation) and beam switching sequence.

[0048] According to an embodiment of the present invention, by utilizing the actual dose deposition results of multiple beams and the target dose deposition results obtained by using a Monte Carlo dose calculation engine, and inputting them into a dose optimization model for calculation, the preset beam weights can be optimized, thereby obtaining an irradiation strategy that is closer to the ideal target dose deposition result, and obtaining an optimal combination of beam angle distribution and beam weights. Taking full account of the heterogeneity of the complex tissues inside the experimental object, the radiation dose deposition can be precisely controlled, the experiment can be improved, and the experimental goal can be completed with fewer beams, thereby improving the experimental efficiency while simplifying the experimental process and saving experimental resources.

[0049] Specifically, for example, when the experimental subject is a small animal tumor radiotherapy experiment, the use of conformal arc irradiation technology can replace reliance on manual planning to achieve precise dose delivery to the small animal tumor target area, while minimizing the dose received by normal tissue, reducing radiation-induced toxicity to healthy tissues and animal mortality, and improving the accuracy and efficiency of small animal radiotherapy experimental plans.

[0050] Furthermore, the experimental results of the irradiation strategy can be used as an intermediate reference result, not as a disease diagnosis or treatment result or method.

[0051] According to an embodiment of the present invention, in operation S102, the beam weights corresponding to the n beams are input into the Monte Carlo dose calculation engine, and outputting the actual dose deposition results of the n beams in the predetermined experimental object includes operations S2001 to S2003.

[0052] Operation S2001: inputting beam weights corresponding to the n beams into a Monte Carlo dose calculation engine to calculate the dose deposition values of the n beams in each volume pixel of the experimental object.

[0053] Operation S2002: Constructing a dose deposition matrix according to the dose deposition values within each volume pixel.

[0054] Operation S2003: obtaining actual dose deposition results according to the metrology deposition matrix and the beam weights of the n beams.

[0055] According to an embodiment of the present invention, in operation S2001, a volume pixel, or voxel, is the smallest computational unit in three-dimensional space, demarcated from computed tomography (CT) or CBCT images. It contains information such as tissue density and elemental composition and is used to record the location of particle energy deposition. The dose deposition value refers to the accumulated energy (in Gy) within each volume pixel and is calculated by summing the energy losses of all particles passing through that voxel.

[0056] According to an embodiment of the present invention, in operation S2002 , the dose deposition matrix dimension is voxel number×beam number. The dose deposition matrix can be constructed by independently running a Monte Carlo simulation for each beam, recording the dose distribution of all voxels, and forming a column vector of the matrix.

[0057] According to an embodiment of the present invention, in operation S2003, the actual dose deposition result refers to the integrated dose distribution of all beams under optimized weights, obtained by linearly superimposing the dose contributions of each beam. A three-dimensional dose distribution can be generated as the actual dose deposition result by multiplying the beam weight vector by the dose deposition matrix.

[0058] According to an embodiment of the present invention, the Monte Carlo engine has a low dose calculation error in heterogeneous tissues (such as lungs and bones), can achieve high-precision actual deposition dose calculation, convert physical simulation results into linear algebraic models, and support subsequent rapid optimization calculations.

[0059] According to an embodiment of the present invention, in operation S103, the method for constructing the dose optimization model includes determining an objective function of the optimization model based on deviations between preset target dose deposition results and actual dose deposition results for each organ, as well as preset weights for each organ, wherein the objective function includes an optimization target term and an optimization penalty term.

[0060] According to an embodiment of the present invention, the deviation between the preset target dose deposition results and the actual dose deposition results for each organ can be understood as a difference. The preset weight of each organ is a pre-set weight value that can be set based on experience. For example, if an organ is considered important and the deposition dose is as small as possible or as close to the target deposition dose as possible, a higher organ weight can be set. Alternatively, if an organ is considered generally important and the beam dose deposition received by it has little impact on the optimization target, a lower organ weight can be set. The preset weight of an organ can also be reflected by the relative importance factor of the light.

[0061] Specifically, the objective function is the optimization goal of the optimization model and its core. It is composed of a weighted combination of the optimization target term and the penalty term. It defines the optimization direction and evaluation criteria, and the optimization model integrates constraints and algorithms to achieve the goal. The optimization target term is the part of the objective function that directly reflects the main optimization requirements; the optimization penalty term is used to constrain the dose distribution or beam parameters, constrain undesirable results, and prevent solutions that do not meet physical or experimental requirements from appearing during the optimization process. For example, when the actual dose to an organ at risk exceeds a safety threshold, the penalty term increases, forcing the model to reduce the dose in that area. Specifically, if the liver dose exceeds the tolerance value (such as 30Gy), the penalty term imposes additional costs, prompting the model to reallocate beam weights.

[0062] In a specific embodiment of the present invention, the dose optimization model further includes constraints; the constraints include a constraint on the number of beams with non-zero beam weights in the optimized n candidate beams, and a constraint on the non-negative values of the beam weights.

[0063] According to embodiments of the present invention, the beam number constraint limits the number of non-zero beam weights (e.g., only five non-zero beam weights are allowed), simplifying experimental strategy planning complexity and reducing machine execution time. The beam weight non-negative value constraint requires that the beam weights must be non-negative (≥0), consistent with the irreversibility of physical beam energy deposition.

[0064] For example, after constrained optimization, the number of beams with non-zero weights is 5, and the beam weights of n beams must be greater than or equal to 0. That is, there are 5 beams used for the experiment at the end, and the beam weights of the other (n-5) beams are 0. When the beam weight is 0, it can be understood that the beam is not used.

[0065] In one embodiment of the present invention, a dose optimization model is established through the following steps: Optimization parameters are selected, including target doses for different organs, relative organ importance factors, the expected number of beams, and penalty weights. These optimization parameters are then input into an optimization model based on beam angle and irradiation time optimization, and an iterative algorithm is used to find the global optimal solution.

[0066] The Monte Carlo dose calculation engine is used to calculate the dose deposition results of n beams in the experimental animal. The dose calculation engine will output n files independently, and reconstruct the dose distribution of all candidate beams and integrate them into a dose deposition matrix containing the dose contributions of all beams as shown in the following formula (1): .

[0067] (1).

[0068] In formula (1), the dose deposition matrix Each column in corresponds to the nth candidate beam with unit weight in The dose contribution in each voxel is shown in Figure 2, while each row represents the dose contribution in the target or normal tissue. The dose deposited in each voxel. The actual dose deposition result is the matrix is the dose deposition matrix and beam weight The product of α represents the total dose deposition in each voxel, where α 1,1 It represents the dose deposited by the first voxel in the preset area such as the first organ, tissue or target area, and so on.

[0069] The square of the difference between the target dose and the planned dose is calculated, and the beam weight optimization target term is formed after weighting different organs; the portion of the dose exceeding the target dose in each organ is calculated as a penalty term; the weight non-negativity constraint and the angle number constraint are set to ensure that the number of angles obtained after angle optimization does not exceed the actual expectation; finally, the beam weight optimization target term and the penalty term are weighted separately to form a complete objective function; the objective function and the constraint conditions together constitute an optimization model based on beam angle and irradiation time optimization, where the objective function is shown in the following formula (2).

[0070] (2).

[0071] In formula (2), f(x) is the objective function, is the planned dose deposition result, is the target dose deposition result, is the weight of the penalty term, is the organ weight, is the number of non-zero beam weights, is the beam weight, “‖…‖” represents the norm, and st represents the constraint condition.

[0072] Optionally, the first term in the objective function aims to minimize the planned dose Target dose deposition results The deviation between them is to ensure that the target area can receive sufficient prescribed dose as expected, while the dose distribution of normal tissues is controlled at the lowest possible level.

[0073] Optionally, the target dose to the target volume is 100% of the prescribed dose; for organs at risk, the target dose is usually set to zero or a corresponding dose limit.

[0074] Optionally, the second term of the objective function is a penalty term, which is mainly used to constrain the portion of the maximum planned dose in the organ that exceeds the target dose, so as to limit the dose received by the organ at risk to not exceed a set safety threshold. is the weight of the penalty term, which is used to adjust the penalty intensity for the portion of the dose to the organ at risk that exceeds the target dose.

[0075] Alternatively, the relative importance factor in the objective function can be understood as the organ weight The optimization process allows users to adjust the optimization intensity according to the treatment needs of different tissues, so that the optimization process can grade the optimization of each critical organ. For organs with a larger relative importance factor, the optimization process will give higher priority to ensure that these organs are more strictly protected.

[0076] Optionally, in the constraints, The constraint is used to limit the number of non-zero beam weights in the final treatment plan The non-negativity constraint on beam weights is used to ensure that the beam weights are physically feasible. When the beam weight is zero, it means that the beam is not selected. By determining whether the beam weight is zero, both beam angle optimization and weight optimization can be achieved simultaneously.

[0077] For example, s can be an integer from 1 to 10, or can be set according to actual experimental requirements.

[0078] According to an embodiment of the present invention, by defining an objective function including an optimization target term and an optimization penalty term, the target area dose deviation can be minimized, ensuring that the tumor area reaches the prescribed dose to improve the local control rate while constraining the dose limit of the endangered organs and reducing the risk of radiation damage; a dynamic balance of multiple objectives is achieved according to the preset weights of the organs, and customized adjustments can be made to improve the safety of the experimental subjects while improving the accuracy and efficiency of the experimental plan.

[0079] Furthermore, limiting the number of non-zero beams in the optimization model can shorten the accelerator gantry angle switching time, improve experimental efficiency, and reduce experimental complexity. It can achieve better experimental results using a relatively small number of beams. At the same time, it also has the physical rationality guarantee of non-negative constraints, conforms to physical reality, and ensures the consistency of dose calculation and equipment execution.

[0080] According to an embodiment of the present invention, a method for solving a dose optimization model includes the following steps S1 to S5.

[0081] Step S1: The beams are arranged in order of their influence on the dose distribution.

[0082] Step S2: Filter out the target beam for directional optimization according to the sequence arrangement results and the constraint conditions.

[0083] Step S3: Calculating the descent gradient of the objective function based on multiple combinations of target beams, wherein the descent gradient represents the direction of influence of the beam weights of each beam on the dose deposition optimization.

[0084] Step S4: Project the gradient vector into the subspace corresponding to the set of target beams to obtain a corrected gradient vector.

[0085] Step S5: Update the beam weights of the n beams using the pre-calculated step size and the corrected gradient vector.

[0086] Repeat steps S2 to S5 until a preset termination condition is met, and then obtain and output the optimization weights of the n beams.

[0087] According to an embodiment of the present invention, arranging beams according to their impact on the dose distribution prioritizes them based on their contribution to the dose distribution in the subject (e.g., target coverage and OAR protection). For example, the actual dose deposition results output by the Monte Carlo dose calculation engine can be used to quantify the weight of each beam's dose contribution to the target and normal tissues. For example, beams with high target coverage are prioritized, while beams with excessive OAR dose contribution may be eliminated. Target beam selection involves selecting candidate beams that significantly impact the dose optimization objective function, based on constraints such as the number of non-zero beams. The descent gradient refers to the partial derivative of the objective function with respect to the beam weights, reflecting the sensitivity of weight adjustment to dose deviations (e.g., target underdose and organ overdose). Gradient projection and subspace correction involves projecting the gradient vector onto the subspace corresponding to the target beam, retaining only the optimized direction of the selected beam and preventing invalid beam weight updates from interfering with convergence. This approach can accelerate the calculation by limiting the optimization dimension, for example, by updating only the weights of the first five beams while keeping the weights of the remaining beams fixed, thereby improving efficiency. The preset termination condition can be set to a specific preset convergence threshold (such as the objective function change rate <1% or the upper limit of the number of iterations) to ensure that the algorithm outputs a stable solution within a reasonable time.

[0088] In some specific embodiments of the present invention, the step length may be determined by an exact search method and / or a backtracking heuristic method. It should be noted that the step length may also be determined by other conventional calculation methods in the art, and this application does not limit this.

[0089] Specifically, the exact search method determines the step size by solving a one-dimensional optimization problem, so that the objective function reaches a minimum in the search direction. The backtracking heuristic method starts with a larger initial step size and gradually reduces it proportionally (e.g., β = 0.5) until the sufficient descent condition is met.

[0090] According to an embodiment of the present invention, the optimization variable dimension is reduced from n dimensions to k dimensions (k n), can reduce the number of calls to the Monte Carlo dose calculation engine. The subspace acceleration method of gradient projection avoids full-space search, reducing the number of iterations and improving computational efficiency. By adjusting only the weights of key beams, it reduces dose distribution oscillations caused by fluctuations in secondary beams and enhances dose distribution stability. Furthermore, the precise search method for determining the step size can achieve the optimal step size when the objective function is smooth, ensuring rapid convergence. The backtracking method for determining the step size avoids overcomputation and maintains practicality in non-smooth or high-dimensional problems. Both methods can improve the efficiency of iterative solutions to dose optimization models.

[0091] In a specific embodiment of the present invention, the method for solving the dose optimization model in steps S1 to S5 may specifically include the following operations S3001 to S3007.

[0092] In operation S3001, set the optimization variable to the beam weight , introduce auxiliary optimization variables After that, the optimization variables and iteration counters are initialized so that , , .

[0093] In operation S3002, the auxiliary variable is calculated The set of indices of the non-zero elements in .

[0094] In operation S3003, the objective function is calculated The gradient result is projected into the subspace . Subspace By optimizing the variables All elements in are sorted by absolute value, and the first The space composed of the elements with the largest absolute value is designed to ensure that The constraint conditions are calculated as shown in the following formula (3), where n represents the nth step in the iterative calculation, i represents the index number or position of a specific component in the vector, represents the gradient after projection, Represents the objective function In auxiliary variables The complete gradient at In the subspace The projection operation on the if condition means that only the original gradient is retained Those corresponding to the important components (index ) values. For the less important components (index ), and its gradient value is forced to 0.

[0095] (3).

[0096] In operation S3004, the step size of the gradient descent is determined .

[0097] In one implementation, an exact line search method is used to find the exact solution for the optimal step size, which is determined by minimizing the one-dimensional function value of the objective function in the current gradient direction.

[0098] . (4)

[0099] Among them, in formula (4), yes The gradient calculation result of , A is the dose kernel matrix.

[0100] In another implementation method, as shown in the following equations (5) and (6), the step size is determined by backtracking , the step size is multiplied by a shrinkage factor Gradually reduce the distance until the sufficient descent condition (Armijo condition) is met.

[0101] (5).

[0102] (6).

[0103] Among them, in formula (5) and formula (6), It is a constant, usually a small value (such as 0.1 or 0.01), used to control the extent of the objective function decrease; is an integer representing the shrinkage factor The number of contractions, “‖……‖” represents the norm.

[0104] In operation S3005, the optimization variables are optimized based on the gradient descent concept. Update and project the updated optimization variables into the subspace As shown in the following formula (7), is the optimization variable for the n+1th iteration, Represents the objective function In auxiliary variables The full gradient at is the step length, is an auxiliary variable, n is the current number of iterations, Represents a vector Projected onto the aforementioned subspace superior.

[0105] (7).

[0106] In operation S3006, the momentum method is used to introduce historical gradient information, and the new step size is determined by the above method. Then, for the auxiliary variables Update as shown in the following formula (8).

[0107] (8).

[0108] In operation S3007, it is determined whether the termination condition is met. If the termination condition is met, the iterative process is terminated and the final beam weight optimization result is output. ; Otherwise, return to operation S3002 and continue to iteratively update the optimization variables and auxiliary variables until the termination condition is met.

[0109] Optionally, the termination condition is set to .

[0110] According to an embodiment of the present invention, in operation S104, generating an irradiation strategy for the experimental object using the optimized weights of each of the n beams includes: determining the irradiation times of the n beams based on the optimized weights of each of the n beams and a calibration factor obtained in advance; and determining the irradiation strategy for the experimental object based on the irradiation times of the n beams.

[0111] According to embodiments of the present invention, calibration factors are pre-measured based on the experimental platform and equipment used, or derived from dose measurement data. They are used to convert beams of different weights into corresponding irradiation times. For example, a beam with a weight of 0.8 might correspond to a 0.9-second irradiation time using a calibration factor of 1.1. An irradiation strategy is a radiation execution plan generated by integrating parameters such as irradiation time, angle, and energy for all beams.

[0112] According to an embodiment of the present invention, the optimized beam weights of the n beams are converted into a final irradiation strategy based on the experimental platform used and the parameters of the device itself. The irradiation time is directly generated by optimizing the weights, avoiding traditional manual trial-and-error adjustments, eliminating differences in beam characteristics between devices, and forming a closed loop in terms of accuracy, efficiency, and safety. This is particularly suitable for complex scenarios such as equipment replacement and multimodal radiation experiments.

[0113] Figure 2 Schematic diagram of the verification method flow of the experimental strategy of an embodiment of the present invention.

[0114] According to another embodiment of the present invention, a method for verifying an experimental strategy is provided, such as Figure 2 As shown, the following operations S201 to S204 are included:

[0115] Operation S201: placing a dose measurement film into a heterogeneous phantom structure prepared according to the structure of an experimental object.

[0116] Operation S202: performing irradiation according to the irradiation strategy obtained based on the above method, and obtaining a dose measurement film after irradiation.

[0117] Operation S203: determining an actual dose distribution corresponding to the dose measurement film after irradiation.

[0118] Operation S204: comparing the actual dose distribution with the theoretically calculated dose distribution result to obtain a verification result of the irradiation strategy.

[0119] According to an embodiment of the present invention, the dose measurement film is a special film used to record the radiation dose distribution. It can quantify the absorbed dose in different areas by undergoing a chemical color change reaction after radiation exposure. A heterogeneous phantom structure refers to a phantom that simulates the physical structure and density differences of the experimental subject, and may contain bone and soft tissue equivalent materials for real-world scene reproduction. The actual dose distribution obtained by measuring the film refers to the real dose data obtained by film scanning, which reflects the actual deposition result of the beam in the phantom. The theoretically calculated dose distribution result refers to the expected dose result output by the Monte Carlo dose calculation engine or calculation model based on the optimized weights in the irradiation strategy. The verification result of the irradiation strategy can be obtained by comparing the actual dose distribution with the theoretically calculated dose distribution result, and can be presented in the form of compliance rate, pass rate, etc.

[0120] According to an embodiment of the present invention, the accuracy and robustness of the irradiation strategy in real scenarios are ensured through heterogeneous phantom simulation and high-precision film dose verification.

[0121] According to an embodiment of the present invention, a method for constructing a heterogeneous phantom structure includes: obtaining phantom construction data of an experimental subject, wherein the phantom construction data includes first modeling data created for a first structure of the experimental subject, and second modeling data created for a second structure of the experimental subject; inputting the first modeling data into a 3D printer, and printing the first structure based on the first modeling data using a first printing material; inputting the second modeling data into the 3D printer, and printing the second structure based on the second modeling data using a second printing material; and assembling the printed first and second structures to obtain the heterogeneous phantom structure.

[0122] According to embodiments of the present invention, phantom construction data can be obtained based on the subject's anatomical imaging data, such as structural data obtained using tomography or magnetic resonance imaging. The first and second structures simulate the subject's actual structure, having different densities. For example, if the subject is an animal, the first structure can be low-density soft tissue or organs, while the second structure can be high-density bone structure.

[0123] The densities of the first and second printed materials correspond to the first and second structures. For example, if the density of the first structure is greater than that of the second structure, then the density of the first printed material is also higher than that of the second printed material. This allows for a closer approximation to the actual structure of the experimental object and the physical density distribution within that structure.

[0124] According to an embodiment of the present invention, the radiation attenuation characteristics of the experimental object are reproduced by combining heterogeneous materials (such as bones and soft tissues), thereby improving the accuracy of experimental strategy verification.

[0125] According to an embodiment of the present invention, the experimental subject is an experimental animal; the first structure is the soft tissue of the experimental animal, and the second structure is the skeleton of the experimental animal; the first printing material includes a transparent resin material, and the second printing material includes a nylon material.

[0126] According to an embodiment of the present invention, a transparent resin-nylon heterogeneous material combination is used to achieve high-fidelity anatomical simulation, coordinated optimization of mechanical-chemical properties, and efficient and safe preparation of experimental animal models, thereby enabling more realistic and accurate verification of the authenticity and accuracy of irradiation experimental strategies. 3D printing technology is used to produce heterogeneous simulated mouse phantoms to simulate the actual anatomical structure and tissue heterogeneity of mice; the heterogeneous simulated mouse phantoms are used to perform the end-to-end dose verification process of the small animal radiotherapy platform, comparing the average dose difference, maximum dose difference, and two-dimensional dose distribution difference between the measured dose distribution and the calculated dose distribution, to ensure the accuracy of the irradiation platform in the implementation of irradiation radiotherapy, improve the reliability and repeatability of small animal radiotherapy experiments, and promote the application and transformation of new treatment plans.

[0127] In addition, the method provided in the embodiment of the present invention is applicable to any experimental animal and any animal tumor model, and can be applied to both arc radiation therapy experimental technology and other radiation experimental technologies.

[0128] Specifically, in a specific embodiment of the present invention, the experimental subject is a mouse, and the experimental platform is an image-guided small animal drawn arc radiotherapy system. The verification process of the above experimental strategy can be as follows: Figure 3 As shown in the figure, a simulated mouse phantom was established.

[0129] Figure 3 Schematic diagram of a heterogeneous simulated mouse phantom according to an embodiment of the present invention.

[0130] like Figure 3As shown, a heterogeneous simulated mouse phantom was fabricated using 3D printing technology. First, the phantom data file was designed based on a 3D mouse anatomical atlas. The phantom structure primarily consists of soft tissue, lungs, and skeleton. Different printing materials were selected based on the physical properties of each tissue: a water-equivalent transparent resin was used for the soft tissue, a hollow cavity design was used for the lungs, and nylon was used for the skeleton. Furthermore, the phantom was designed into two halves, upper and lower, to provide greater operational flexibility and facilitate the placement of dose measurement tools.

[0131] The film slot inside the phantom is parallel to the phantom's coronal plane and perpendicular to the beam's incident direction. The slot is slightly thicker than the film to prevent it from shifting or deforming during the experiment. Preferably, a slot is designed inside the phantom to accommodate the film (film model EBT3).

[0132] More preferably, the soft tissue portion of the phantom has a mass density of 1.06 g / cm 3 The skeleton is made of transparent resin material with a mass density of 1.30 g / cm 3 The phantom is made of nylon. After the upper and lower parts are joined, water is filled into the phantom through a hole drilled in the top to better simulate the radiation absorption characteristics of soft tissue.

[0133] Optionally, the kidney portion can be made of a transparent resin material consistent with soft tissue, or filled with other appropriate materials as needed to simulate the characteristics of different tissues.

[0134] Optionally, the soft tissue portion of the transparent phantom is made using stereolithography (SLA) technology, and the bone portion of the phantom is made using fused deposition modeling (FDM) technology.

[0135] Cut the film into 2 cm × 2 cm dimensions according to the phantom size. A 0.5 mm diameter metal ball marker was placed at the center of the film to calibrate the isocenter position.

[0136] After marking the experiment number and scanning direction on each film, the film was inserted into the slot inside the phantom and fixed.

[0137] Using the positioning and fixation method used in mouse radiotherapy, the heterogeneous simulated mouse phantom was fixed in a vertical position on the animal holder, and then the animal holder was fixed on the animal support table.

[0138] The phantom was imaged based on cone beam computed tomography (CBCT) imaging technology, and the irradiation isocenter position was determined by metal ball marking.

[0139] Irradiation is performed using the irradiation strategy generated by the experimental strategy generation method of the present invention.

[0140] Considering the dose response range of the film, the prescription dose at the isocenter was set to 6 Gy.

[0141] After irradiation, the film was removed from the phantom and stored in the dark for at least 24 hours before being scanned. The scanning resolution was set at 200 dpi, with a bit depth of 16 bits per color channel. Each film was scanned three times.

[0142] Film scan results are converted into absorbed doses, and three scans of the same film are averaged to obtain a measured dose distribution. The measured dose distribution data are then compared with the calculated results. This comparison includes the difference in mean and maximum dose between the measured and calculated values, with both differences required to be less than 5%. Gamma analysis is also used to verify the matching of the two-dimensional dose distributions, with a dose difference of 3%, a distance consistency of 0.3 mm, a reference point dose threshold of 10% of the prescribed dose, and a gamma pass rate of at least 90%.

[0143] Figure 4 The figure shows the comparison results of the irradiation plan before and after optimization in the mouse lung cancer case according to the embodiment of the present invention. Figure 4 (a) in the figure indicates the delineation results of tumor and organs at risk. Figure 4 (b) shows the comparison of the dose-volume histograms of tumors and organs at risk in the two plans. Figure 4 (c) and (d) in the figure represent the isodose line distributions corresponding to 20%, 40%, 60%, 80% and 100% of the prescription dose for the manual forward plan and the inverse optimization plan, respectively.

[0144] According to an embodiment of the present invention, in the radiation plan before optimization, the beam angles used were evenly spaced over a 360-degree range, with each beam having the same weight. Furthermore, the number of beams used in the radiotherapy plan before and after optimization was consistent. The target volume was a lung tumor, and organs at risk included the right and left lungs, heart, and spinal cord. The forward plan represents an empirically developed radiation strategy, while the inverse plan represents an radiation strategy derived using the calculation method of the present invention.

[0145] according to Figure 4 It can be seen that after executing the above-mentioned radiotherapy optimization process, the optimized radiotherapy plan results are obtained. The dose-volume curves of the heart, spinal cord and the lung on the non-tumor side in the plan obtained by reverse optimization are lower than those of the forward plan, and the isodose line distribution is more concentrated, the radiation range to normal tissues is smaller, and the radiation dose to the ribs is significantly reduced.

[0146] Figure 5 The figure shows the comparison of the dose profile in the horizontal direction between the verification measurement results and the calculated results according to an embodiment of the present invention, where (a) is the dose profile comparison result under a 5 mm collimator aperture, and (b) is the comparison result under a 7.5 mm collimator aperture.

[0147] according to Figure 5 It can be seen that the normalized dose curves of the measured dose and the calculated dose are basically consistent in the horizontal direction.

[0148] Figure 6 Gamma analysis results are shown to verify measurement results and calculation results according to an embodiment of the present invention.

[0149] according to Figure 6 It can be seen that the measured dose and the calculated dose have a high degree of match in the two-dimensional dose distribution, and the gamma pass rate reaches more than 90%, which meets the requirements, proving that the radiotherapy method proposed in the present invention has certain accuracy and reliability in actual dose delivery.

[0150] Based on the above experimental strategy generation method, the present invention also provides an experimental strategy generation device. Figure 7 The device is described in detail.

[0151] Figure 7 A structural block diagram of an experimental strategy generating device according to an embodiment of the present invention is shown.

[0152] like Figure 7 As shown, the experimental strategy generating device 700 of this embodiment includes an acquisition module 710 , a calculation module 720 , an optimization module 730 and a generation module 740 .

[0153] The acquisition module 710 is used to obtain beam parameters of each of the n beams used in the radiation exposure experiment for the predetermined experimental object, wherein the beam parameters include beam weights. In one embodiment, the acquisition module 710 can be used to perform the operation S101 described above, which will not be repeated here.

[0154] The calculation module 720 is configured to input the beam weights corresponding to each of the n beams into a Monte Carlo dose calculation engine, and output the actual dose deposition results of each of the n beams within the predetermined experimental subject. In one embodiment, the calculation module 720 can be configured to perform the operation S102 described above, which will not be further described here.

[0155] The optimization module 730 is configured to input the actual dose deposition results and the target dose deposition results into a dose optimization model, thereby optimizing the beam weights of each of the n beams using the optimization model and outputting the optimized weights of each of the n beams. In one embodiment, the optimization module 730 may be configured to perform operation S103 described above, which will not be further described here.

[0156] The generation module 740 is used to generate an irradiation strategy for the experimental object using the optimized weights of the n beams. In one embodiment, the generation module 740 can be used to perform the operation S104 described above, which will not be repeated here.

[0157] According to an embodiment of the present invention, by utilizing the actual dose deposition results of multiple beams and the target dose deposition results obtained by utilizing a Monte Carlo dose calculation engine, and inputting them into a dose optimization model for calculation, the preset beam weights can be optimized, an irradiation strategy that is closer to the ideal target dose deposition result can be obtained, and an optimal combination of beam angle distribution and beam weights can be obtained. Taking full account of the heterogeneity of complex tissues within the experimental subject, the radiation dose deposition can be precisely controlled to improve the experimental effect. Specifically, when the experimental subject is small animal tumor radiotherapy, the use of image-guided conformal arc irradiation technology can replace reliance on manual planning to achieve precise dose delivery to the small animal tumor target area, while minimizing the dose received by normal tissue as much as possible, reducing radiation-induced toxicity to healthy tissues and animal mortality, and improving the accuracy and efficiency of small animal radiotherapy experimental plans.

[0158] According to embodiments of the present invention, any multiple modules among the acquisition module 710, calculation module 720, optimization module 730, and generation module 740 may be combined into a single module, or any one of these modules may be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of other modules and implemented in a single module. According to embodiments of the present invention, at least one of the acquisition module 710, calculation module 720, optimization module 730, and generation module 740 may be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or may be implemented in hardware or firmware through any other reasonable means of circuit integration or packaging, or may be implemented in any one of software, hardware, and firmware, or any suitable combination of these. Alternatively, at least one of the acquisition module 710, calculation module 720, optimization module 730, and generation module 740 may be at least partially implemented as a computer program module that, when executed, performs the corresponding functionality.

[0159] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0160] It will be understood by those skilled in the art that the features described in the various embodiments of the present invention may be combined and / or coupled in various ways, even if such combinations or couplings are not explicitly described in the present invention. In particular, the features described in the various embodiments of the present invention may be combined and / or coupled in various ways without departing from the spirit and teachings of the present invention. All such combinations and / or couplings fall within the scope of the present invention.

[0161] The above describes embodiments of the present invention. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present invention. Although each embodiment has been described separately above, this does not mean that the measures in each embodiment cannot be advantageously used in combination. Without departing from the scope of the present invention, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present invention.

Claims

1. A method for generating a radiation exposure experiment strategy, characterized in that: The radiation exposure experiment strategy generation method is performed by using an electronic device, and the radiation exposure experiment strategy generation method includes: Obtaining beam parameters of each of n beams used in a radiation exposure experiment on a predetermined experimental object, wherein the beam parameters include beam weights; Inputting the beam weights corresponding to each of the n beams into a Monte Carlo dose calculation engine, and outputting actual dose deposition results of each of the n beams in the predetermined experimental subject; Inputting the actual dose deposition result and the target dose deposition result into a dose optimization model, so as to optimize the beam weights of the n beams respectively using the optimization model, and outputting the optimized weights of the n beams respectively; An irradiation strategy for the experimental object is generated using the optimized weights of the n beams.

2. The method for generating a radiation exposure experiment strategy according to claim 1, wherein: The method for constructing the dose optimization model includes: The objective function of the optimization model is determined according to the deviation between the preset target dose deposition results and the actual dose deposition results in each organ, as well as the preset weight of each organ, wherein the objective function includes an optimization target item and an optimization penalty item.

3. The method for generating a radiation exposure experiment strategy according to claim 2, wherein: The dose optimization model also includes constraints; The constraints include a constraint on the number of beams with non-zero beam weights in the optimized n candidate beams and a constraint on the non-negative values of the beam weights.

4. The method for generating a radiation exposure experiment strategy according to claim 3, wherein: Optimizing the beam weights of the n beams using the optimization model and outputting the optimized weights of the n beams includes steps S1 to S5: Step S1, arranging n beams according to their respective influence on dose distribution; Step S2, selecting a target beam for directional optimization according to the arrangement result and the constraint conditions; Step S3, calculating a descent gradient of the objective function based on multiple combinations of the target beams, wherein the descent gradient represents the direction in which the beam weights of each beam influence the dose deposition optimization; Step S4, projecting the gradient vector into the subspace corresponding to the set of target beams to obtain a corrected gradient vector; Step S5, updating the beam weights of the n beams using the pre-calculated step size and the corrected gradient vector; Repeat steps S2 to S5 until a preset termination condition is met, and then obtain and output the optimization weights of the n beams.

5. The method for generating a radiation exposure experiment strategy according to claim 4, wherein: The step size determination method includes an exact search method and / or a backtracking heuristic method.

6. The method for generating a radiation exposure experiment strategy according to claim 1, wherein: Inputting the beam weights corresponding to the n beams into a Monte Carlo dose calculation engine and outputting actual dose deposition results of the n beams in the predetermined experimental subject includes: Inputting the beam weights corresponding to each of the n beams into a Monte Carlo dose calculation engine to calculate the dose deposition value of the n beams in each volume pixel of the experimental object; constructing a dose deposition matrix according to the dose deposition values within each volume pixel; An actual dose deposition result is obtained according to the dose deposition matrix and the beam weights of the n beams.

7. The method for generating a radiation exposure experiment strategy according to claim 1, wherein: Generating an irradiation strategy for the experimental object by using the respective optimized weights of the n beams includes: determining the irradiation time of the n beams according to the respective optimized weights of the n beams and the calibration factors obtained by pre-measurement; An irradiation strategy for the experimental object is determined according to the irradiation times of the n beams.

8. A method for verifying a radiation exposure experimental strategy, characterized in that: include: placing a dose measurement film into a heterogeneous phantom structure prepared according to the structure of the experimental object; Obtaining an irradiation strategy based on the radiation irradiation experiment strategy generation method according to any one of claims 1 to 7, and performing irradiation to obtain a dose measurement film after irradiation; Determine the actual dose distribution corresponding to the dose measurement film after irradiation; The actual dose distribution is compared with the theoretically calculated dose distribution results to obtain the verification result of the irradiation strategy.

9. The verification method according to claim 8, characterized in that: The method for constructing the heterogeneous phantom structure comprises: Acquiring phantom construction data of the experimental subject, wherein the phantom construction data includes first modeling data created for a first structure of the experimental subject and second modeling data created for a second structure of the experimental subject; Inputting the first modeling data into a 3D printer, and printing the first structure based on the first modeling data using a first printing material; Inputting the second modeling data into a 3D printer, and printing the second structure based on the second modeling data using a second printing material; The printed first structure and the second structure are assembled to obtain the heterogeneous phantom structure.

10. The verification method according to claim 9, characterized in that: The experimental subjects are experimental animals; The first structure is the soft tissue of the experimental animal, and the second structure is the bone of the experimental animal; The first printing material includes a transparent resin material, and the second printing material includes a nylon material.

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