A particle source distribution method and system for particle implantation therapy

Abstract

The present invention belongs to the technical field of tumor radiotherapy dose calculation and optimization, and provides a particle source distribution method and system for particle implantation therapy. The method includes the following steps: obtaining medical images of the tumor and surrounding tissue; determining the size data of the tumor based on the medical images, and calculating the number of particles to be implanted based on the size data; and determining the optimal source distribution position of the particles through a hybrid optimization algorithm based on a standard dose template determined by the radiation dose of a single particle. The advantage of the present invention is that when the particles are initially distributed, an abstract dose template is used to make the particle arrangement more reasonable and orderly, further reducing the computational cost of the algorithm and improving the quality of algorithm optimization; at the same time, the radiation of the particles to the surrounding area is pre-packaged in the system as a standard dose template, which can be used at any time, eliminating the need for complex calculations and only requiring an almost cost-free addition calculation to determine the optimal source distribution position.

Description

Technical Field

[0001] The present invention relates to the technical field of tumor radiotherapy dose calculation and optimization, and in particular to a particle source distribution method and system used in particle implantation therapy. Background Art

[0002] Brachytherapy is a widely used internal radiation therapy method for cancer treatment. The basic principle of this method is to use high-energy radiation (usually gamma rays or X-rays) emitted by radioactive isotopes (such as iodine-125 and palladium-103) to disrupt tumor cell division, thereby achieving a therapeutic effect. Because the radiation range of radioactive particles is limited, the radiation dose is primarily concentrated in the tumor area, effectively reducing damage to surrounding healthy tissue compared to external beam radiation therapy.

[0003] Previous radioactive seed implantation methods relied heavily on the physician's experience for both the calculation of the radiation dose and the placement of the seed source. The advent of the radioactive seed implantation therapy system (TPS) allows physicians to calculate the radiation dose, but the placement of the seed source still relies heavily on the physician's clinical experience.

[0004] The Monte Carlo method used in particle radiation dose calculations, as outlined in the Chinese patent "CN113181563A - Method, System, and Medium for Particle Implantation in Tumor Radiotherapy Dose Planning," is widely accepted by researchers due to its high dose calculation accuracy, earning it the nickname "gold standard" for dose calculations. However, its disadvantages are also significant: First, the Monte Carlo method requires a large number of random sampling and simulations to achieve the required accuracy, resulting in a long calculation time. Second, to speed up calculations, high-performance computing resources, such as multi-core processors or GPUs, are often required, which may be difficult to implement in certain clinical settings. Furthermore, the Monte Carlo method requires accurate medium parameters to simulate interparticle motion in order to achieve high metrological accuracy. Existing commercial software does not provide detailed medium preset parameters, so in actual applications, even at a high computational cost, the accuracy achieved by doctors is compromised.

[0005] The simulated annealing method used in particle dose optimization has certain flexibility and global search capabilities in particle source optimization, but it has limitations such as long calculation time, difficult parameter tuning, and the possibility of falling into local optimality. Summary of the Invention

[0006] This invention addresses the challenges of existing TPS systems, which rely too heavily on the physician's clinical experience, suffer from high computational costs, and are time-consuming. By developing a highly accurate, portable, and user-friendly automated seed placement method for radioactive seed implantation, the system addresses the challenges. The physician simply provides the system with medical images of the patient's target area, and the system automatically determines the optimal seed placement based on the target area's volume and surrounding tissue information.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is:

[0008] A particle source distribution method for particle implantation therapy, comprising the steps of:

[0009] Obtain medical images of tumors and surrounding tissues;

[0010] determining size data of the tumor according to the medical image, and calculating the number of particles required to be implanted based on the size data;

[0011] The standard dose template determined by the radiation dose of a single particle is used to determine the optimal source position of the particles through a hybrid optimization algorithm;

[0012] The hybrid optimization algorithm is configured to initialize the particle positions based on the block source placement algorithm, then perform a global search using a particle swarm algorithm to obtain a preliminary range of source placement positions, and determine the optimal source placement position within the preliminary range of the solution using a simulated annealing algorithm;

[0013] The block source distribution algorithm is configured to determine the particle initialization position that meets the dose requirement according to the dose value corresponding to the abstract dose template formed by converting the standard dose template.

[0014] Furthermore, the step of calculating the number of particles to be implanted includes:

[0015] determining a minimum cuboid surrounding the tumor based on the size data of the tumor, wherein the boundary of the cuboid is tangent to the outer surface of the tumor;

[0016] The number of particles to be implanted is determined based on the size data of the cuboid.

[0017] Furthermore, the number of implanted particles required is expressed as:

[0018] ;

[0019] Wherein, L, W, and H represent the length, width, and height of the cuboid, respectively, in mm; A0 is the activity of the particle source, in mci.

[0020] Furthermore, the radiation dose of a single particle was obtained by a theoretical calculation method based on AAPM TG-43 report.

[0021] Furthermore, when there are multiple particle sources in the space, the cumulative dose value of the multiple particle sources at the same observation point is the accumulation of the radiation dose of each particle source to the observation point.

[0022] Furthermore, the step of initializing the particle positions based on the block source distribution algorithm includes:

[0023] The standard dose template is represented as an abstract dose template in the form of a first cuboid embedded in a second cuboid. The second cuboid is represented as a high-dose area, and the area within the first cuboid but not within the second cuboid is represented as a low-dose area. The location where the centers of the two cuboids overlap is represented as the location of the particle source.

[0024] The particle initialization position that meets the dose requirement is determined according to the dose value corresponding to the abstract dose template.

[0025] Furthermore, the boundary between the high-dose area and the low-dose area is determined based on the normal distribution; according to the normal distribution trajectory, when the percentage of the accumulated value to the total dose of all points on the axis reaches a preset percentage, this point is used as the dividing point between the high-dose area and the low-dose area.

[0026] Furthermore, the dose values of the high-dose area and the low-dose area are determined according to the average dose values in their respective areas.

[0027] Furthermore, the method further includes the steps of: performing three-dimensional reconstruction on the acquired medical image to form a three-dimensional model; after determining the particle distribution source, superimposing the isodose surface formed by all particle radiations on the three-dimensional model.

[0028] The present invention also provides a particle source distribution system for particle implantation therapy, comprising at least one processor and at least one memory, wherein the memory stores a computer program. When the program is executed by the processor, the processor is enabled to execute the above-mentioned particle source distribution method for particle implantation therapy.

[0029] Compared with the prior art, the present invention has at least the following beneficial effects:

[0030] (1) When designing the particle source distribution algorithm, a pre-packaged standard dose template was used to speed up the calculation and reduce the calculation cost;

[0031] (2) When initially distributing the particles, an abstract dose template method was used to make the particle arrangement more reasonable and orderly, further reducing the computational cost of the algorithm and improving the optimization quality of the algorithm;

[0032] (3) The particle scattering algorithm uses PSO-SA to overcome the shortcomings of PSO and SA, two more common algorithms in the field of particle scattering, and designs relevant parameters to enable the algorithm to have both convergence speed and convergence quality, greatly avoiding the occurrence of pseudo-optimal situations. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0034] Figure 1 is a flow chart of the steps of the particle source distribution method provided by an embodiment of the present invention;

[0035] Figure 2 is a schematic structural diagram of a particle source provided by an embodiment of the present invention;

[0036] Figure 3 Schematic diagram of a particle dose calculation model recommended by AAPM provided in an embodiment of the present invention;

[0037] Figure 4 is a flowchart of the steps of the hybrid optimization algorithm provided by an embodiment of the present invention;

[0038] Figure 5 1 is a schematic top view of an abstracted dosage template provided by an embodiment of the present invention;

[0039] Figure 6 It is a schematic diagram of forming an abstract dose template provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0040] It should be noted that terms such as "first," "second," and "one" in this disclosure are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of this disclosure, "plurality" means at least two, such as two or three, unless otherwise specifically defined.

[0041] In addition, the technical solutions between the various embodiments of the present invention can be combined with each other, but it must be based on the fact that ordinary technicians in this field can implement it. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0042] The following are specific embodiments of the present invention, and the technical solutions of the present invention are further described in conjunction with the accompanying drawings, but the present invention is not limited to these embodiments.

[0043] In this embodiment, if Figure 1 As shown, a particle source distribution method for particle implantation therapy is provided, comprising the steps of:

[0044] S1. Acquire medical images of the tumor and surrounding tissues, and perform three-dimensional reconstruction on the acquired medical images to form a three-dimensional model;

[0045] S2. Determine the size of the tumor based on the medical image and calculate the number of particles required for implantation based on the size data;

[0046] S3. Determine the optimal source position of particles using a hybrid optimization algorithm based on a standard dose template determined by the radiation dose of a single particle;

[0047] S4. After determining the particle source distribution, the isodose surface formed by all particle radiation is superimposed on the three-dimensional model.

[0048] During the above process, the radiation dose of a single radioactive particle to the surrounding area needs to be calculated in advance and the calculated results are packaged into a radiation dose unit, which serves as a standard dose template. All subsequent calculations only need to be performed around this standard dose template.

[0049] When calculating the radiation dose of a single particle, I 125 -6702 particle source as an example, Figure 2 As shown, the standard model of its particle source is: I 125 The shell of the particle is a cylinder with a total length of 4.5 mm, a diameter of 0.8 mm, and a thickness of 0.05 mm. 125 The radioactive source is attached to the inside of the cylinder and is located on a silver rod with a length of 3 mm and a diameter of 0.5 mm.

[0050] Here we select the dose calculation method used by AAPM (American Association of Physicists in Medicine) in TG-43 report, that is, the theoretical calculation method based on AAPM TG-43 report, to obtain the radiation dose of a single particle, such as Figure 3 The calculation formula is as follows:

[0051] ;

[0052] in, is the air kerma intensity, is the dose rate constant. Other variables in the formula are functions related to the spatial position of the particle source and the observation point. is the geometric factor, is the radial dose function, is an anisotropy function.

[0053] The calculation formula is:

[0054] ;

[0055] Where r is the distance from the particle source to the observation point, L is the length of the radiation source, θ is the angle between the observation point and the positive direction of the x-axis (the direction of the titanium tube), and β is the angle between the observation point and the two end points of the particle source.

[0056] is the radial dose function, and the multi-order linear fit used by AAPM is as follows, where: and is the fitting coefficient, r is the radial distance;

[0057] ;

[0058] against The AAPM report does not provide a specific calculation formula or method, but rather provides the measurement results at different distances and angles in the form of a table. In actual calculations, it is impossible for all coordinates of the particle source and the observation point to be included in the table. In order to accurately calculate the required distance from the particle source at any coordinate to the observation point, Function value, the data in the table needs to be fitted to ensure the universal applicability and accuracy of the model.

[0059] Data fitting is achieved through polynomial fitting method, and the implementation process is as follows:

[0060] (1) Construct a quadratic polynomial: The independent variables in the function are r and θ, which are the x and y of the polynomial, and the function value is is the z in the polynomial:

[0061] .

[0062] (2) Construct the matrix X: where n is the number of (x, y) value pairs;

[0063] .

[0064] (3) Solve the polynomial coefficients by the least squares method:

[0065] ;

[0066] in, , z is The function value of .

[0067] Since the treatment cycle of particle implantation lasts for about five half-lives, the cumulative radiation dose of a single particle source must be calculated over time. The cumulative dose can be derived based on the attenuation dose formula of radioactive elements in nuclear physics.

[0068] Assume that the dose rate at a point in space is , air kerma intensity (k is a constant coefficient, is the activity of an ideal point source, which will produce exactly the same irradiance as an actual sealed source at the same reference point in air), then:

[0069] ;

[0070] According to the law of radioactive decay:

[0071] ;

[0072] in is the physical decay constant of the radioactive nuclide corresponding to the particle:

[0073] ;

[0074] Therefore, it is deduced that a single particle at point Absorbed dose at for:

[0075] ;

[0076] where t is the time interval from seed implantation to the time when the absorbed dose is calculated.

[0077] If there are multiple particle sources in space, the cumulative dose value of multiple particle sources at the same observation point is the accumulation of the radiation dose of each particle source to the observation point.

[0078] From the above formula, we can see that the radiation dose of the particle is only related to two variables: the relative position of the particle source and the observation point, and time. In other words, at the same time, as long as the relative position of the particle position and the observation point does not change, the radiation dose received by the observation point will not change.

[0079] Therefore, this embodiment proposes to pre-calculate the radiation dose of a single particle based on the particle model and particle source activity (unit: mci) as a standard dose template, and encapsulate and save it. The user can use the preset standard dose template by selecting the corresponding particle source model and activity.

[0080] After obtaining the coordinate information of the tumor region from the medical image, the number of seeds required for implantation needs to be calculated. The steps include:

[0081] S21. Determine a minimum cuboid surrounding the tumor based on the size data of the tumor, where the boundary of the cuboid is tangent to the outer surface of the tumor;

[0082] S22. Determine the number of particles to be implanted based on the size data of the cuboid.

[0083] The number of implanted particles required is expressed as:

[0084] ;

[0085] Among them, L, W, and H represent the length, width, and height of the cuboid respectively, in mm; A0 is the particle source activity, in mci.

[0086] Then, in step S4, the optimal source position of each particle needs to be determined by a hybrid optimization algorithm (PSO-SA) based on the standard dose template and the number of particles required to be implanted.

[0087] In the hybrid optimization algorithm, PSO (Particle Swarm Optimization) is responsible for global search, quickly exploring the entire search space to find a better solution, and then SA (Simulated Annealing Algorithm) is used for local optimization to further optimize the solution found by PSO, thereby avoiding falling into the local optimum. The process is as follows Figure 4 As shown, the steps include:

[0088] B1. Initialization parameters;

[0089] B2. Initialize the particle population position and randomly initialize the velocity of each particle;

[0090] B3. Calculate the fitness of each particle i, that is, the value of the objective function;

[0091] B4. Update individual optimal position: If the current particle's fitness value is better than its historical optimal value, update its historical optimal position;

[0092] B5. Update the global optimal position: If the fitness value of the current particle is better than the global optimal value, the global optimal position is updated.

[0093] B6. Update speed and position: Each iteration records the position of each (i-th) particle, updates the global optimal position, and the fitness corresponding to the current position and the global optimal fitness.

[0094] If the stopping condition is met (the maximum number of iterations is reached or the particle position no longer changes), stop; otherwise, return to step B3.

[0095] However, the above hybrid optimization algorithm does not solve the problem that the convergence quality of the PSO algorithm itself depends on the initial particle distribution.

[0096] If the initial distribution of the particle source can maintain the particle spacing that conforms to the scene and the particle arrangement is relatively orderly, it can not only greatly reduce the computational burden of the source algorithm, but also improve the convergence quality of the optimization algorithm.

[0097] Therefore, in this embodiment, a method for initializing particle positions based on a block source distribution algorithm is proposed, which includes the following steps:

[0098] A1. Representing the standard dose template as an abstract dose template in the form of a first cuboid embedded in a second cuboid;

[0099] A2. Determine a particle initialization position that meets the dose requirement according to the dose value corresponding to the abstract dose template.

[0100] like Figure 5 As shown in the figure, the area inside the second cuboid is represented as the high-dose area, the area inside the first cuboid but not belonging to the second cuboid is represented as the low-dose area, and the center of the cuboid is represented as the location of the particle source. The dose values in the two areas are uniform, that is, the dose in the low-dose area is D min The dose in the high-dose area is D max .

[0101] The high-dose zone is defined as a specific area centered on the particle source, where the cumulative dose accounts for 95% of the total dose for the entire region. The low-dose zone is the entire region minus the high-dose zone. When selecting the initial particle positions, an abstract dose template is placed within the tumor, much like stacking empty houses with rectangular boxes, with the center of each box representing the particle source.

[0102] To identify this high-dose region, this embodiment uses a method that uses the 95% confidence interval of a normal distribution as the high-dose region. Particle radiation dose decays exponentially with distance, exhibiting a symmetrical structure with high in the middle and low on both sides along a certain axis.

[0103] The specific implementation method is: imagine two points moving from the origin to the positive and negative half axes simultaneously along the x-axis, record the coordinate values corresponding to the two points for each movement, and add up these values. When the accumulated value reaches 95% of the total dose of all points on this axis, the specific point on the positive and negative half axes of the x-axis is found, which is called the "endpoint". This endpoint indicates where the high-dose area starts and ends on the x-axis. Using the same method, do the same operation along the y-axis and z-axis to find the endpoints on the y-axis and z-axis. For example Figure 6 As shown, Figure 6The upper left side shows the curve gradually attenuating as the distance from the particle source to the observation point increases; the right side shows a schematic diagram of the high-dose region formed by the endpoints of the three axes. Then, the average value of the high-dose region and the low-dose region can be taken to determine D max and D min value.

[0104] Therefore, when the particles are initially distributed, the abstract dose template method is used to make the particle arrangement more reasonable and orderly, further reducing the computational cost of the algorithm and improving the algorithm optimization quality.

[0105] After the seed source is placed, a dose distribution map is plotted on the 3D model to show how different dose levels cover the tumor and surrounding normal tissue. Isodose lines on the isodose map are continuous lines that represent areas receiving the same dose, with each line representing a specific dose level. Different dose levels are often represented by different colors to facilitate differentiation. High-dose areas may be represented by warm colors (e.g., red, orange), while low-dose areas may be represented by cool colors (e.g., blue, green).

[0106] In this embodiment, while the computational cost of the particle placement algorithm itself increases, the dose calculation process, which was previously necessary but did not improve the algorithm's quality, is reduced. The radiation emitted by the particles to the surrounding area is pre-packaged in the system as a standard dose template, which can be used at any time. This eliminates the need for complex calculations and only requires a nearly cost-free addition calculation.

[0107] This embodiment also provides a particle source distribution system for particle implantation therapy, comprising at least one processor and at least one memory, wherein the memory stores a computer program. When the program is executed by the processor, the processor is enabled to execute the above-mentioned particle source distribution method for particle implantation therapy.

[0108] The specific embodiments described herein are merely illustrative of the spirit of the present invention. Persons skilled in the art may make various modifications, additions, or substitutions to the described specific embodiments without departing from the spirit of the present invention or exceeding the scope of the appended claims.

Claims

1. A particle source distribution method for particle implantation therapy, characterized in that: Including steps: Obtain medical images of tumors and surrounding tissues; determining size data of the tumor according to the medical image, and calculating the number of particles required to be implanted based on the size data; The standard dose template determined by the radiation dose of a single particle is used to determine the optimal source position of the particles through a hybrid optimization algorithm; The hybrid optimization algorithm is configured to initialize the particle positions based on the block source placement algorithm, then perform a global search using a particle swarm algorithm to obtain a preliminary range of source placement positions, and determine the optimal source placement position within the preliminary range using a simulated annealing algorithm; The block source distribution algorithm is configured to determine the particle initialization position that meets the dose requirement according to the dose value corresponding to the abstract dose template formed by converting the standard dose template; The steps of initializing the particle positions based on the block source distribution algorithm include: The standard dose template is represented as an abstract dose template in the form of a first cuboid embedded in a second cuboid. The second cuboid is represented as a high-dose area, and the area within the first cuboid but not belonging to the second cuboid is represented as a low-dose area. The position where the centers of the two cuboids overlap is represented as the location of the particle source. Determining a particle initialization position that meets the dose requirement according to the dose value corresponding to the abstract dose template; The boundary between the high-dose area and the low-dose area is determined based on the normal distribution; according to the normal distribution trajectory, when the percentage of the accumulated value to the total dose of all points on the axis reaches a preset percentage, this point is used as the dividing point between the high-dose and low-dose areas; the dose values of the high-dose area and the low-dose area are determined according to the average dose value in their respective areas.

2. The particle source distribution method for particle implantation therapy according to claim 1, characterized in that: The steps for calculating the number of implanted seeds are as follows: determining a minimum cuboid surrounding the tumor based on the size data of the tumor, wherein the boundary of the cuboid is tangent to the outer surface of the tumor; The number of particles to be implanted is determined based on the size data of the cuboid.

3. The particle source distribution method for particle implantation therapy according to claim 2, characterized in that: The number of implanted particles required is expressed as: ； Wherein, L, W, and H represent the length, width, and height of the cuboid, respectively, in mm; A0 is the activity of the particle source, in mci.

4. The particle source distribution method for particle implantation therapy according to claim 1, characterized in that: The single particle radiation dose is obtained by a theoretical calculation method based on AAPM TG-43 report.

5. The particle source distribution method for particle implantation therapy according to claim 4, characterized in that: When there are multiple particle sources in space, the cumulative dose value of multiple particle sources at the same observation point is the accumulation of the radiation dose of each particle source to the observation point.

6. The particle source distribution method for particle implantation therapy according to claim 1, characterized in that: Also includes the steps: The acquired medical image is three-dimensionally reconstructed to form a three-dimensional model. After the particle distribution source is determined, the isodose surface formed by all particle radiations is superimposed on the three-dimensional model.

7. A particle source system for particle implantation therapy, comprising at least one processor and at least one memory, characterized in that: The memory stores a computer program, and when the program is executed by the processor, the processor is enabled to execute the particle source distribution method for particle implantation therapy according to any one of claims 1 to 6.

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