Proton rotation intensity modulated radiation therapy plan planning method

Abstract

The invention belongs to the technical field of medical treatment, and discloses a proton rotation intensity modulated radiation therapy plan planning method, which comprises the following steps: constructing an energy transmission time model of a proton therapy system; determining an energy layer rising constraint of the energy transmission time model, and constructing an energy distribution function including a plan quality target and an energy layer constraint target; solving the energy distribution function to obtain optimal energy distribution; after the optimal energy distribution is determined, robust optimization is carried out on the beam spot weight in combination with a dosimetry target, the optimal beam spot distribution is obtained, and the optimal plan quality is determined based on the optimal beam spot distribution. According to the technical scheme, the planning quality can be improved, so that treatment is efficient, the treatment time is saved, meanwhile, the probability of occurrence of treatment errors is reduced by considering planning robustness, and clinical application of proton rotation intensity modulated radiation treatment is promoted.

Description

Technical Field

[0001] The present invention belongs to the field of medical technologies, and particularly relates to a method for planning a proton intensity modulated radiotherapy plan. Background Art

[0002] Due to the Bragg peak effect of proton beams, compared with traditional radiotherapy technologies, proton radiotherapy has significant dosimetric advantages, that is, higher target conformity and sparser doses to critical organs. Proton radiotherapy is developing rapidly. In addition, existing research has shown that proton intensity modulated radiotherapy can achieve better treatment quality and conformity compared with traditional proton radiotherapy. Therefore, proton intensity modulated radiotherapy is currently recognized as the next-generation development technology of proton radiotherapy.

[0003] Existing proton intensity modulated radiotherapy technologies face the bottleneck of low delivery efficiency, resulting in their inability to be widely applied clinically. To solve this problem, existing research mainly focuses on optimizing the energy layer distribution and beam spot in proton intensity modulated radiotherapy by reducing the number of upward energy layer switches, so as to make its treatment plan more efficient. However, compared with the traditional proton radiotherapy currently used clinically, combined with the transmission characteristics of cyclotrons, there is still much room for improvement in the delivery efficiency and plan quality of these technologies. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for planning a proton intensity modulated radiotherapy plan to solve the problems existing in the above-mentioned prior art.

[0005] To achieve the above purpose, the present invention provides a method for planning a proton intensity modulated radiotherapy plan, including:

[0006] Constructing an energy transmission time model of a proton therapy system;

[0007] Determining the energy layer rise constraint of the energy transmission time model, and constructing an energy distribution function including a plan quality target and an energy layer constraint target;

[0008] Solving the energy distribution function to obtain an optimal energy distribution;

[0009] After determining the optimal energy distribution, robustly optimizing the beam spot weights in combination with the dosimetric target to obtain an optimal beam spot distribution, and determining the best plan quality based on the optimal beam spot distribution.

[0010] Optionally, the constructing of the energy transmission time model of the proton therapy system specifically includes:

[0011] The new proton cyclotron uses magnetic field regulation technology to significantly reduce the energy layer rise time, obtain the machine log file of the energy transfer model of the cyclotron proton therapy system, and record the beam spot position of each radiation layer;

[0012] Fit the energy transfer time model based on the machine log file and the beam spot position.

[0013] Optionally, the energy transfer time model is specifically:

[0014]

[0015] In the formula, ΔE i is the energy layer rise magnitude, is the energy layer switching time.

[0016] Optionally, the determination process of the energy layer rise constraint specifically includes:

[0017] Compare the energy transfer model based on the cyclotron proton therapy system with the Belgian proton therapy system, and determine the energy layer rise constraint based on the comparison result.

[0018] Optionally, the energy distribution function is specifically:

[0019]

[0020] In the formula, x is the spot weight vector to be optimized, A is the dose influence matrix, d is the dose distribution, y is the energy vector, W is the linear matrix operator, N B is the number of angles, N E represents the number of all optional energy layers, F(d) is the dose optimization target, R(y) is the energy layer constraint target, the elements in y correspond to the sum of all beam spot weights on the corresponding energy layer, and if the element is zero, the corresponding energy layer does not exist.

[0021] Optionally, the solution of the energy distribution function specifically includes:

[0022] Decompose the energy distribution function based on the forward-backward operator splitting algorithm of the proximity operator to obtain several sub-objective optimization functions, and iteratively solve each sub-objective optimization function to obtain the optimal energy distribution.

[0023] Optionally, the sub-objective optimization function is specifically:

[0024] F(x)=(1 / 2)||Ax - b|| 2

[0025]

[0026] Wherein, F(x) is the planned quality objective function, b is the prescription dose requirement, R(x) is the energy layer constraint objective function, λ is the penalty parameter of the regularization term, DVSWx represents the energy layer switching vector, and d0 is the threshold for ignoring the penalty of the adjacent energy layer rising transition.

[0027] Optionally, the beam spot weight is robustly optimized in combination with the dosimetric objective, specifically including:

[0028] After determining the optimal energy distribution, regenerate the dose influence matrix for secondary optimization and isolate the key energy layer. The dose influence matrix includes the influence of the energy distribution of the beam spot at different positions on the dose distribution;

[0029] Integrate the uncertainty factors into the optimization process based on the probability formula of robust optimization to obtain the beam spot optimization model; the uncertainty factors include range uncertainty and setting uncertainty;

[0030] Solve the beam spot optimization model based on the alternating direction multiplier method to obtain the optimal beam spot weight distribution.

[0031] The technical effects of the present invention are as follows:

[0032] The present invention determines the energy layer selection rule according to the machine-specific transmission characteristics, eliminates the unnecessary restriction on the number of energy layer rises. This new energy layer transmission characteristic may be introduced into all proton cyclotron systems; while ensuring the planned quality, obtain the optimal energy distribution and reduce the energy layer switching time; and robustly optimize the beam spot weight in combination with the dosimetric objective to ensure the robustness of the plan, thereby generating a highly efficient proton intensity modulated radiotherapy plan, improving the treatment efficiency, being conducive to reducing the probability of errors occurring during the patient's treatment and thus improving the treatment safety, and is expected to clear the obstacles for the proton intensity modulated technology to be put into clinical use, which is conducive to promoting the development of proton therapy technology. Description of the Drawings

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0034] The drawings constituting a part of this application are used to provide a further understanding of this application. The schematic embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation to this application. In the drawings:

[0035] Figure 1 It is a schematic flowchart of the proton intensity modulated radiotherapy plan planning method in the embodiment of the present invention;

[0036] Figure 2 This is a schematic diagram of the energy layer rise time model and comparison in the embodiments of the present invention;

[0037] Figure 3 This is a schematic diagram of the optimal energy layer distribution in the embodiments of the present invention;

[0038] Figure 4 This is a dose slice diagram and the corresponding dose volume histogram band diagram in the embodiments of the present invention. Detailed implementation manners

[0039] Now, various exemplary implementation manners of the present invention will be described in detail. This detailed description should not be considered as a limitation of the present invention, but rather as a more detailed description of certain aspects, characteristics, and implementation schemes of the present invention.

[0040] It should be understood that the terms described in the present invention are only for describing specific implementation manners and are not used to limit the present invention. Additionally, for the numerical ranges in the present invention, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Each intermediate value within any stated value or stated range, as well as each smaller range between any other stated value or intermediate value within the stated range, is also included in the present invention. The upper and lower limits of these smaller ranges can be independently included or excluded from the range.

[0041] Without departing from the scope or spirit of the present invention, various improvements and changes can be made to the specific implementation manners of the description of the present invention, which are obvious to those skilled in the art. Other implementation manners obtained from the description of the present invention are obvious to those skilled in the art. The description and embodiments of this application are only exemplary.

[0042] Regarding the terms "comprising", "including", "having", "containing", etc. used herein, they are all open-ended terms, meaning including but not limited to.

[0043] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The following will refer to the accompanying drawings and combine with the embodiments to detail this application.

[0044] Such as Figure 1 - Figure 4As shown in the figure, in this embodiment, a method for planning a proton rotational intensity modulated radiotherapy plan is provided, including: constructing an energy transmission time model of a proton therapy system; determining an energy layer rise constraint of the energy transmission time model, and constructing an energy distribution function including a plan quality target and an energy layer constraint target; solving the energy distribution function to obtain an optimal energy distribution; after determining the optimal energy distribution, robustly optimizing the beam spot weight in combination with a dosimetric target to obtain an optimal beam spot distribution, and determining the best plan quality based on the optimal beam spot distribution.

[0045] As Figure 1 shown in the figure, this embodiment provides a method for planning a proton rotational intensity modulated radiotherapy plan, including: Step S1, exploring the beam current transmission characteristics of a proton therapy system based on a specific cyclotron; Step S2, designing an energy ceiling optimization algorithm according to the polynomial increasing characteristic of the energy layer and the dose optimization target to find the optimal energy distribution; Step S3, robustly optimizing the beam spot weight by using the alternating multiplier algorithm in combination with the dosimetric target. By adopting the technical solution of this embodiment, the optimal energy layer distribution and the corresponding optimal beam spot weight can be obtained according to the beam current time characteristics of the accelerator, the unnecessary limitation on the total number of energy layer rises can be eliminated, the delivery efficiency can be significantly improved, and at the same time the plan quality can be improved, so as to make the treatment efficient, save the treatment time, and at the same time consider the plan robustness to reduce the probability of treatment errors, and promote the clinical application of proton rotational intensity modulated radiotherapy.

[0046] The specific implementation process of this embodiment includes:

[0047] Step S1: Explore the energy transmission model of the cyclotron proton therapy system of the University Medical Center Groningen in the Netherlands, and compare the energy layer rise switching time with the ONE proton therapy system in Belgium to determine the energy layer rise constraint.

[0048] S1.1: Establishment of the energy transmission model. Based on the cyclotron proton therapy system of the University Medical Center Groningen in the Netherlands, this system uses magnetic field regulation technology to significantly reduce the energy layer rise time. Each test beam is created in a commercial treatment planning system with different beam spot positions. These test beams are provided in the clinical mode, and the machine log file records the beam spot positions of each radiation layer at a sampling frequency of 4 kHz. Then, a retrospective analysis of the log file is carried out, and the transmission time model of this proton system is obtained by fitting, focusing on exploring the energy rise transmission time characteristics of this specific cyclotron. The fitted energy rise transmission model is:

[0049]

[0050] (unit: s)

[0051] In the formula, ΔE iis the magnitude of the energy layer rise, used to calculate the energy layer switching time Exploring the energy layer transmission model based on machine characteristics can more precisely constrain the energy layer selection and eliminate unnecessary restrictions.

[0052] S1.2: Determination of the energy layer rise constraint. By comparing the energy transmission model of the cyclotron proton therapy system at the University Medical Center Groningen in the Netherlands with the ONE proton therapy system in Belgium, the optimal energy layer rise constraint can be determined, as Figure 2 shown. This cyclotron system greatly reduces the energy layer rise time, shows a polynomial increasing characteristic, and realizes that the energy can rise within a certain gap while still shortening the beam transmission time. This may provide greater freedom for the proton intensity modulated radiotherapy plan and thus improve the plan quality. When the energy rise gap is less than 10 MeV, the energy layer transmission time is less than 2 s. Therefore, in this embodiment, the energy rise constraint is limited to 10 MeV.

[0053] Therefore, through the exploration of the energy transmission model of the cyclotron proton therapy system at the University Medical Center Groningen in the Netherlands, this embodiment can develop an energy ceiling optimization algorithm based on the polynomial increasing characteristic.

[0054] Step S2: Use the forward-backward operator splitting algorithm of the proximal operator to determine the optimal energy distribution based on the optimal energy selection rule and the dose optimization objective to ensure the plan quality and reduce the energy layer switching time.

[0055] S2.1: Construction of the objective function. When optimizing the energy layer distribution, both the plan quality objective and the energy layer constraint objective are considered. The objective function model is as follows:

[0056]

[0057] In the formula, x is the spot weight vector to be optimized, A is the dose influence matrix, d is the dose distribution, y is the energy vector, W is the linear matrix operator, N B represents the number of angles, that is, the total number of beams; N E represents the number of all optional energy layers, F(d) is the dose optimization objective, R(y) is the energy layer constraint objective. The optimization aims to minimize the value of the objective function F(d)+R(y). The elements in y correspond to the sum of all spot weights on the corresponding energy layer. If the element is zero, it means that the corresponding energy layer does not exist. Specifically, it is expressed as:

[0058]

[0059] In the formula, y is an energy layer vector with a size of N B *N E and y idenotes the total weight of beam spot on the \(i\)-th energy layer; when \(i = b*e\), it indicates that the \(i\)-th energy layer is the \(e\)-th energy corresponding to the \(b\)-th angle. \(N\) i denotes the number of beam spots on the \(i\)-th energy layer.

[0060] S2.2: Solving the optimal energy distribution. Based on the forward-backward operator splitting algorithm with proximal operator, the algorithm design for solving the above model is carried out. First, the above model can be decomposed into two sub-objective functions, that is, the following two optimization problems:

[0061]

[0062] In the formula, \(b\) represents the prescription dose requirement, and the objective function \(F\) aims to make the actual dose distribution closer to the specified dose distribution. \(\lambda\) is the penalty parameter of the regularization term, \(DVSWx\) represents the energy layer switching vector, and its elements represent the energy change between adjacent energy layers. \(d_0\) represents the threshold for ignoring the penalty of the upward transition between adjacent energy layers, and in this study, it is taken as 10 MeV.

[0063] The overall optimization uses the forward-backward operator splitting algorithm with proximal operator, which can handle complex and non-smooth optimization problems by iteratively improving the solution, and can effectively handle the interaction between the plan quality objective and the energy layer penalty term. The overall optimization model is as follows:

[0064]

[0065] subject to \(x\in0\cup[g_0,+\infty)\)

[0066] In the formula, \(n\) is the total number of beam spots. The constraint term of \(x\) here is to ensure that the beam spot weight is non-negative, and adding the minimum jump number constraint for each beam spot weight can improve the plan quality while ensuring high delivery efficiency of the proton intensity modulated radiotherapy plan. Figure 3 Show the optimal energy layer layout of a case.

[0067] Step S3: Use the alternating multiplier algorithm combined with the dosimetric objective to robustly optimize the beam spot weights, further improve the plan quality while obtaining the optimal beam spot distribution, thereby improving the robustness of the plan.

[0068] S3.1: Construction of the beam spot optimization model. After determining the optimal energy distribution, regenerate the dose influence matrix for secondary optimization to improve the plan quality and robustness. The key point of the secondary optimization is to adjust the beam spot weights within the determined non-critical energy layers, with priority given to the plan quality target. By isolating the critical energy layers, the optimization process becomes more efficient and targeted, ensuring that the final dose distribution more accurately reaches the specified treatment target. At the same time, robust optimization is adopted considering the uncertainties affecting the treatment effect. Specifically, range uncertainty (variation in proton penetration depth caused by tissue heterogeneity) and setup uncertainty (variation in the position of the patient or tumor during treatment) are considered. The probability formula of robust optimization is used to integrate these uncertainties into the optimization process, ensuring that the final plan remains effective under various possible circumstances. The considered beam spot optimization model is as follows:

[0069]

[0070] In the formula, d k , A k , k = 1, …, K respectively represent the dose distributions and dose influence matrices under different scenarios; c k represents the probability weight of each scenario occurring.

[0071] S3.2: Solving the optimal beam spot distribution. Design the solution algorithm for the above model based on the alternating direction method of multipliers, which is the following optimization problem:

[0072]

[0073] In the formula, A k , b k , k = 1, …, K respectively represent the dose influence matrices and dose targets under different scenarios.

[0074] The optimal beam spot weight distribution is obtained by solving using the alternating direction method of multipliers, thereby further obtaining the best plan quality. At the same time, robust optimization is carried out to obtain a robust and efficient treatment plan that can provide accurate and consistent treatment doses under uncertain circumstances. As Figure 4 shows the optimized dose slice diagram and its corresponding dose volume histogram band diagram for a case.

[0075] This embodiment simultaneously optimizes the energy distribution and beam spot distribution of proton rotational intensity-modulated radiotherapy, eliminates the unnecessary restrictions on the number of energy layer ascents, reduces the energy layer switching time, improves the treatment efficiency, increases the degree of freedom of the plan, enhances the plan quality and robustness, and provides a method for planning proton rotational intensity-modulated radiotherapy with stable, efficient, and high-quality treatment. In the method of this embodiment, the energy layer selection rule is determined according to the specific transmission characteristics of the machine; the optimal energy distribution is obtained to reduce the energy layer switching time; and on the basis of further optimizing the plan quality, the beam spot distribution is robustly optimized, thereby generating a stable and efficient proton rotational intensity-modulated radiotherapy plan, improving the treatment efficiency, facilitating the reduction of the probability of errors occurring during the patient's treatment, thus improving the treatment safety, and is expected to clear the way for the clinical use of proton rotational intensity-modulated technology, which is conducive to the development of proton therapy technology.

[0076] This embodiment also provides a device for planning proton rotational intensity-modulated radiotherapy, including:

[0077] The first processing module is used to determine the energy layer selection rule;

[0078] The second processing module is used to find the optimal energy distribution according to the beam current time characteristics;

[0079] The third processing module is used to robustly optimize the beam spot weights in combination with the dosimetric target;

[0080] As an implementation manner of this embodiment, the first processing module is used to explore the energy transmission model of the cyclotron proton therapy system of the University Medical Center Groningen in the Netherlands, and compare the energy layer ascent switching time with the ONE proton therapy system in Belgium to determine the energy layer ascent constraint.

[0081] As an implementation manner of this embodiment, the second processing module is used to determine the optimal energy distribution based on the optimal energy selection rule and the dose optimization target by using the forward-backward operator splitting algorithm of the proximal operator.

[0082] As an implementation manner of this embodiment, the third processing module is used to robustly optimize the beam spot weights in combination with the dosimetric target by using the alternating multiplier algorithm to obtain the optimal beam spot weight distribution.

[0083] This embodiment determines the energy layer selection rule according to the machine-specific transmission characteristics, eliminating the unnecessary restrictions on the number of ascending energy layers; obtaining the optimal energy distribution while ensuring the plan quality to reduce the energy layer switching time; and robustly optimizing the beam spot weight in combination with the dosimetric objective to ensure the robustness of the plan, thereby generating a proton intensity-modulated radiotherapy plan with high treatment efficiency, improving the treatment efficiency, facilitating reducing the probability of errors during the patient's treatment and thus enhancing the treatment safety, hopefully clearing the obstacles for the clinical application of the proton intensity-modulated technology and contributing to the development of the proton therapy technology

[0084] The above are only the preferred specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present application should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims

Claims

1. A proton rotational intensity modulated radiotherapy (IMRT) treatment planning method, characterized in that, Including: Constructing an energy transfer time model for a proton therapy system; Determining the energy layer rise constraint of the energy transfer time model and constructing an energy distribution function including a planned quality target and an energy layer constraint target; Solving the energy distribution function to obtain an optimal energy distribution; After determining the optimal energy distribution, robustly optimizing the beam spot weights in combination with the dosimetric target to obtain an optimal beam spot distribution, and determining the best planned quality based on the optimal beam spot distribution.

2. The proton intensity-modulated radiotherapy treatment plan planning method according to claim 1, characterized in that The construction of the energy transfer time model for the proton therapy system specifically includes: Obtaining the machine log file of the energy transfer model of the cyclotron proton therapy system and recording the beam spot positions of each radiation layer; Fitting an energy transfer time model based on the machine log file and the beam spot positions.

3. A method for planning a proton rotational intensity modulated radiotherapy plan according to claim 1, characterized in that, The energy transfer time model is specifically: where ΔE i is the magnitude of the energy layer rise, and is the energy layer switching time.

4. A method for planning a proton rotational intensity modulated radiotherapy plan according to claim 1, characterized in that, The determination process of the energy layer rise constraint specifically includes: Comparing the energy transfer model based on the cyclotron proton therapy system with the Belgian proton therapy system, and determining the energy layer rise constraint based on the comparison result.

5. A proton intensity modulated radiotherapy treatment plan planning method according to claim 1, characterized in that, The energy distribution function is specifically: Where x is the spot weight vector to be optimized, A is the dose influence matrix, d is the dose distribution, y is the energy vector, W is the linear matrix operator, and N B represents the number of angles, and N E represents the number of all optional energy layers. F(d) is the dose optimization objective, and R(y) is the energy layer constraint objective. The elements in y correspond to the sum of all spot weights on the corresponding energy layer. If the element is zero, it means that the corresponding energy layer does not exist.

6. A method for planning a proton intensity modulated radiotherapy plan according to claim 1, characterized in that, The solution of the energy distribution function specifically includes: Decomposing the energy distribution function based on the forward-backward operator splitting algorithm of the proximal operator to obtain several sub-goal optimization functions, and iteratively solving each sub-goal optimization function to obtain an optimal energy distribution.

7. A proton intensity modulated radiotherapy treatment planning method according to claim 6, characterized in that, The sub-goal optimization function is specifically: F(x) = (1 / 2)∥Ax - b∥ 2 In the formula, F(x) is the planned quality objective function, b is the prescription dose requirement, R(x) is the energy layer constraint objective function, λ is the penalty parameter of the regularization term, DVSWx represents the energy layer switching vector, and d0 is the threshold for ignoring the penalty of adjacent energy layer rise transitions.

8. A method for planning a proton rotational intensity modulated radiotherapy plan according to claim 6, characterized in that, The robust optimization of the beam spot weights in combination with the dosimetric target specifically includes: After determining the best energy distribution, regenerating the dose impact matrix for secondary optimization and isolating the key energy layers, where the dose impact matrix includes the impact of the energy distribution of the beam spot at different positions on the dose distribution; Integrating uncertainty factors into the optimization process based on the probability formula of robust optimization to obtain a beam spot optimization model; the uncertainty factors include range uncertainty and setup uncertainty; Solving the beam spot optimization model based on the alternating direction method of multipliers to obtain an optimal beam spot weight distribution.

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