A method and device for simulating the distribution of radioactive seeds in a body cavity
By simulating the distribution of radioactive particles within the cavity, the deformation of the cavity and target area after stent implantation was simulated, and the distribution of radioactive particles was optimized. This solved the problem of dose field incongruity caused by metal stent placement, improved treatment efficacy, and reduced complications.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- BEIHANG UNIV
- Filing Date
- 2023-11-16
- Publication Date
- 2026-07-14
AI Technical Summary
Existing technologies lack integrated planning design for particles and scaffolds. The placement of metal scaffolds into expanding cavities can lead to tumor and cavity deformation, dose field incongruity, affecting treatment efficacy and causing serious complications.
By establishing a simulation method for the distribution of radioactive particles within a cavity in medical imaging space and 3D model space, the deformation of the cavity and target area after stent implantation is simulated. Finite element mechanical simulation is used to simulate the morphology of the metal stent after placement, optimize the distribution of radioactive particles, and achieve precise conformal adaptation of the dose field to the target area.
It has achieved automated simulation of the distribution of radioactive particles within cavities, which has improved treatment outcomes, reduced complications, and decreased reliance on physician experience.
Smart Images
Figure CN117744425B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of simulation technology, and in particular to a method and apparatus for simulating the distribution of radioactive particles within a cavity. Background Technology
[0002] Particle implantation therapy is an important brachytherapy technique that can be used to treat target areas throughout the body. Its principle involves implanting multiple radioactive particles into the target area using a puncture needle; the gamma rays released by these particles then kill tumor cells in the target area. In the treatment of cavity tumors, the particle stent has a "combined effect" of dual therapeutic efficacy. On one hand, the stent can alleviate obstruction caused by the tumor; on the other hand, the stent can carry radioactive particles to deliver continuous brachytherapy to the tumor target area.
[0003] However, there is currently a lack of integrated planning and design research on particles and scaffolds. When metal scaffolds are placed to expand cavities, the tumor and the cavity deform accordingly. At present, the deployment of particle sources does not specifically consider the shape of the metal scaffold itself after placement and the deformation caused by the placement of the metal scaffold, resulting in an incongruent dose field, affecting the treatment effect and causing serious complications. Summary of the Invention
[0004] In view of this, the present invention proposes a method and device for simulating the distribution of radioactive particles in cavities. The simulation results provide a reference for clinicians to alleviate the shortcomings of the existing technology.
[0005] In a first aspect, the present invention provides a method for simulating the distribution of radioactive particles within a cavity, including: in a medical imaging space Mark therapeutic target areas and cavity targets in three-dimensional model space. Establish a three-dimensional model including the treatment target area and the cavity target area; select the stent length and diameter based on the cavity target centerline and the cavity target stenosis caused by the treatment target area, and then apply this model to the three-dimensional model space. Establish a stent model; establish a stent-cavity-target area model after stent implantation into the target cavity, and simulate the expansion effect of the stent on the target cavity and treatment target area. After the stent-cavity-target area model stabilizes, extract the simulation results of the stent-cavity-target area model; calculate the medical imaging space. and three-dimensional model space The relationship matrix T is used to inversely delineate the stent, cavity, and target area in the stent-cavity-target area model simulation results onto the medical image and reassign CT values to obtain the postoperative simulated CT image; the stent unfolding surface is rasterized to obtain the position coordinates of candidate particles; based on the target area and candidate particle positions delineated by the postoperative CT, a heuristic optimization method is used to optimize the dose and particle number plan.
[0006] Optionally, the medical imaging space Refers to the DICOM 3D image coordinate system, 3D model space The coordinate system of a three-dimensional model defined by mechanical simulation software.
[0007] Optionally, it also includes: measuring the stenosis length using the target centerline of the cavity, with the stent length being 5mm at both ends of the stenosis, and selecting the stent diameter using the following formula, where all length units are in mm:
[0008]
[0009] Where D is the estimated normal diameter of the target stenotic segment of the cavity, d is the narrowest diameter of the stenotic segment, and T c denoted as , where is the length of the target midline in the narrow segment of the cavity, and t is the straight-line distance between the two ends of the target midline in the narrow segment of the cavity. To select the decision variable for the stent diameter, when ≥50%, the support diameter is selected as D+2, when <50%, the support diameter is selected as D+1.
[0010] Optionally, it also includes: deleting voxels in medical images used to construct three-dimensional models of the treatment target area and cavity; reverse-drawing the stent, cavity, and target area from the stent-cavity-target area model simulation results to the medical images using the relation matrix T; assigning CT values to the cavity and target areas in the reverse-drawn area according to the original CT values; assigning CT values to the stent area according to the stent CT values in the actual postoperative CT; assigning CT values to the overlapping parts of the reverse-drawn area and the medical images according to the newly drawn area; and assigning CT values to the missing parts according to other surrounding tissues.
[0011] Optionally, it further includes: dividing the unfolded surface of the support into multiple grids according to Cartesian coordinates at a preset interval, determining the grid point coordinates of each grid, and using the grid point coordinates as the candidate particle position coordinates.
[0012] Optionally, it also includes: optimizing the objective function, which is related to the dose and the number of particles, and whose form is based on the following formula:
[0013]
[0014] Where P represents the arrangement of particles on the unfolding support. This represents the dose received by the j-th voxel in the therapeutic target volume (TV). This represents the lower bound of the dose to the therapeutic target area. N(P) is the number of particles used. H(·) is the step function. and These are the weights of the dose constraint and the particle number constraint, respectively.
[0015] Optionally, it also includes: initializing the algorithm starting point, which is the closest distance to the treatment target area. All candidate particle positions are set up to initialize the particle arrangement. Set initial iteration variables Current iteration variable Lower bound of iteration variables Given the process decay rate q and the number of iterations N, calculate the initial target value. Change the current state; specifically, with a 50% probability, discard or add a particle that has been deployed or not deployed, resulting in a new particle arrangement. ; Calculate the target value of the new particle arrangement If the target value obtained from the new particle arrangement is less than that obtained in the previous step, that is... Accepting new particle arrangements Otherwise, with a certain probability Accepting a new particle arrangement, in which It is a hyperbolic tangent function; it linearly reduces the value of the iteration variable. Repeat the particle arrangement modification and iteration variable reduction operations until the number of iterations N is reached or the iteration variable reaches its lower limit. When the time is reached, the iterative optimization operation is stopped, and the optimized particle arrangement is obtained. .
[0016] Optionally, the preset spacing between adjacent particle sources is set to 10 mm.
[0017] Optionally, objective function weights and Based on experience, the values are set to 1 and 10.
[0018] Optionally, set the nearest distance. The initial iteration variable is 3mm. =1000, lower bound of iteration variable q=1, N=1000.
[0019] Secondly, the present invention provides a simulation device for the distribution of radioactive particles within a cavity, comprising: a stent implantation morphology simulation module, which is used in medical imaging space. Mark therapeutic target areas and cavity targets in three-dimensional model space. Establish a three-dimensional model including the treatment target area and the cavity target area; select the stent length and diameter based on the cavity target centerline and the cavity target stenosis caused by the treatment target area, and then apply this model to the three-dimensional model space. Establish a stent model; establish a stent-cavity-target area model after stent implantation into the target cavity, and simulate the expansion effect of the stent on the target cavity and treatment target area. After the stent-cavity-target area model stabilizes, extract the simulation results of the stent-cavity-target area model; calculate the medical imaging space. and three-dimensional model space The relation matrix T is used to reverse-draw the stent, cavity, and target area from the stent-cavity-target area model simulation results onto the medical image and reassign CT values to obtain the postoperative simulated CT image; the particle distribution module is used to rasterize the stent deployment surface and obtain the position coordinates of candidate particles; based on the target area and candidate particle positions drawn by the postoperative CT, an optimization objective function is established and a heuristic optimization method is used to optimize the planned dose and particle number.
[0020] The beneficial effects of this invention are as follows:
[0021] The technical solution provided by this invention can include the following beneficial effects: It proposes a method and device for simulating the placement of radioactive particle sources in cavities. By finite element mechanical simulation, it simulates the placement of a metal stent into a cavity, obtains the shape of the metal stent itself after placement, as well as the shape of the target area and cavity after placement, and places radioactive particle sources according to the expanded stent, target area and cavity shape to meet the precise conformal fit between the particle source dose field and the target area. It realizes the automated simulation of the placement of radioactive particle sources in cavities, provides auxiliary reference for clinicians, and alleviates the technical problems of low conformal fit of the dose field obtained by traditional stent particle source placement technology and high dependence on physician experience.
[0022] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description
[0023] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are one embodiment of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0024] Figure 1 This is a schematic flowchart of a simulation method for the distribution of radioactive particles within a cavity, according to the first embodiment of the present invention.
[0025] Figure 2 This is a schematic diagram of a simulation device for the distribution of radioactive particles within a cavity, according to a second embodiment of the present invention. Detailed Implementation
[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. The described embodiments are some embodiments of the present invention, but not all embodiments.
[0027] First embodiment:
[0028] Figure 1 This is a schematic diagram of a simulation method for the distribution of radioactive particles within a cavity, as described in the first embodiment of the present invention. Figure 1 As shown, the method includes steps S1, S2, S3, S4 and S5.
[0029] Step S1: In the medical imaging space Mark therapeutic target areas and cavity targets in three-dimensional model space. Establish a three-dimensional model that includes the therapeutic target area and the cavity target area.
[0030] It should be noted that the aforementioned medical imaging space Refers to the DICOM 3D image coordinate system, 3D model space The coordinate system of a three-dimensional model defined by mechanical simulation software.
[0031] Optionally, detection can be performed based on the temporal or frequency domain characteristics of the treatment target area and cavity target region, i.e., detection can be performed using color and frequency features. Alternatively, the treatment target area and cavity target region in the medical image can be marked manually. For example, CT medical images can be constructed into a three-dimensional model from a two-dimensional CT image sequence using a multi-layer reconstruction method.
[0032] Step S2: Select the stent length and diameter based on the target centerline of the cavity and the target stenosis of the cavity caused by the treatment target area, and then apply the stent to the three-dimensional model space. Establish a stent model. Establish a stent-cavity-target area model after stent implantation into the target cavity, and simulate the expansion effect of the stent on the target cavity and treatment target area. After the stent-cavity-target area model stabilizes, extract the simulation results of the stent-cavity-target area model.
[0033] For example, the stent length and diameter are selected based on the target stenosis of the cavity caused by the target centerline and the treatment target area. The method is characterized by measuring the stenosis length using the target centerline, with the stent length being 5mm at both ends of the stenosis, and selecting the stent diameter using the following formula, where all length units are in mm:
[0034]
[0035] Where D is the estimated normal diameter of the target stenotic segment of the cavity, d is the narrowest diameter of the stenotic segment, and T cdenoted as , where is the length of the target midline in the narrow segment of the cavity, and t is the straight-line distance between the two ends of the target midline in the narrow segment of the cavity. To select the support diameter as the decision variable; when ≥50%, the support diameter is selected as D+2, when <50%, the support diameter is selected as D+1.
[0036] Optionally, the support is a self-expanding support made of nickel-titanium alloy, and the support model is a commercially available SMART Control mesh support.
[0037] For example, the simulation results are in OBJ files.
[0038] Alternatively, simulation can be performed using Abaqus finite element software.
[0039] Step S3: Calculate the medical image space and three-dimensional model space The relationship matrix T is used to reverse-draw the stent, cavity and target area in the stent-cavity-target area model simulation results to the medical image and reassign CT values to obtain the postoperative simulated CT image.
[0040] For example, the origins of the medical image space and the 3D model space are aligned to construct a relationship matrix T between the medical image and the 3D model surface. The 3D model outline can be extracted and mapped onto a 2D slice of the medical image through the relationship matrix T.
[0041] Step S4: Grid the unfolded surface of the support frame to obtain the position coordinates of candidate particles. Specifically, based on the base radius and height of the support frame, a rectangular surface is obtained. The positions of candidate particles are then gridded according to the particle placement interval.
[0042] In an optional embodiment, the spacing between adjacent particle sources is set to 10 mm.
[0043] In an optional embodiment, the unfolded surface of the support is divided into multiple grids according to Cartesian coordinates at preset intervals, the grid point coordinates of each grid are determined, and the grid point coordinates are used as candidate particle position coordinates.
[0044] Step S5: Optimize the scaffold particle placement surgical plan. Specifically, an objective function is established, and heuristic optimization operations are performed on candidate particle positions to optimize the dose distribution and number of radiation particles.
[0045] Optionally, the objective function is related to the dose and the number of particles, and its form is based on the following formula:
[0046]
[0047] Where P represents the arrangement of particles on the unfolding support. This represents the dose received by the j-th voxel in the therapeutic target volume (TV). This represents the lower bound of the dose to the therapeutic target area. N(P) is the number of particles used. H(·) is the step function. and These are the weights of the dose constraint and the particle number constraint, respectively.
[0048] In one specific embodiment, a heuristic optimization method is used to optimize the dosage and particle number plan, including: initializing the algorithm starting point and determining the nearest distance to the treatment target area. All candidate particle positions are set up to initialize the particle arrangement. Set initial iteration variables Current iteration variable Lower bound of iteration variables Given the process decay rate q and the number of iterations N, calculate the initial target value. Change the current state; specifically, with a 50% probability, discard or add a particle that has been deployed or not deployed, resulting in a new particle arrangement. ; Calculate the target value of the new particle arrangement If the target value obtained from the new particle arrangement is less than that obtained in the previous step, that is... Accepting new particle arrangements Otherwise, with a certain probability Accepting a new particle arrangement, in which It is a hyperbolic tangent function; it linearly reduces the value of the iteration variable. Repeat the particle arrangement modification and iteration variable reduction operations until the number of iterations N is reached or the iteration variable reaches its lower limit. When the time is reached, the iterative optimization operation is stopped, and the optimized particle arrangement is obtained. .
[0049] Second embodiment:
[0050] Figure 2 This is a schematic diagram of a simulation device for the distribution of radioactive particles within a cavity, according to an embodiment of the present invention. Figure 2 As shown, the intracavitary radioactive particle distribution simulation device 200 includes: a stent implantation simulation module 201 and a particle distribution module 202.
[0051] Stent implantation simulation module 201, which is used in medical imaging space Mark therapeutic target areas and cavity targets in three-dimensional model space. Establish a three-dimensional model including the treatment target area and the cavity target area; select the stent length and diameter based on the cavity target centerline and the cavity target stenosis caused by the treatment target area, and then apply this model to the three-dimensional model space. Establish a stent model; establish a stent-cavity-target area model after stent implantation into the target cavity, and simulate the expansion effect of the stent on the target cavity and treatment target area. After the stent-cavity-target area model stabilizes, extract the simulation results of the stent-cavity-target area model; calculate the medical imaging space. and three-dimensional model space The relationship matrix T is used to reverse-draw the stent, cavity and target area in the stent-cavity-target area model simulation results to the medical image and reassign CT values to obtain the postoperative simulated CT image.
[0052] The particle distribution module 202 is used to grid the scaffold deployment surface to obtain the position coordinates of candidate particles; based on the target area and candidate particle positions delineated by postoperative CT, a heuristic optimization method is used to optimize the planned dose and particle number.
[0053] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.
Claims
1. A method for simulating the distribution of radioactive particles within a cavity, characterized in that, include: In the medical imaging space Mark therapeutic target areas and cavity targets in three-dimensional model space. Establish a three-dimensional model that includes the therapeutic target area and the cavity target area; The stent length and diameter are selected based on the target stenosis of the cavity caused by the target centerline and the treatment target area, and then in the three-dimensional model space. Establish a scaffold model; A stent-cavity-target model was established after stent implantation into the target cavity, and the expansion effect of the stent on the target cavity and the treatment target area was simulated. After the stent-cavity-target model stabilized, the simulation results of the stent-cavity-target model were extracted. Computational medical image space and three-dimensional model space The relationship matrix T is used to inversely delineate the stent, cavity and target area in the stent-cavity-target area model simulation results to the medical image and reassign CT values to obtain the postoperative simulated CT image. Based on the postoperative stent, cavity and target area model, the rasterized cylindrical stent unfolded surface is used to obtain the position coordinates of candidate particles; Based on the target area and candidate particle locations delineated by postoperative CT, an optimization objective function was established, and a heuristic optimization method was used to optimize the planned dose and particle number.
2. The method according to claim 1, characterized in that, The medical imaging space Refers to the DICOM 3D image coordinate system, 3D model space The coordinate system of a three-dimensional model defined by mechanical simulation software.
3. The method according to claim 1, characterized in that, The selection of stent length and diameter based on the target stenosis of the cavity caused by the target centerline and the treatment target area includes: The length of the stenosis is measured using the target centerline of the cavity. The stent length is 5mm at both ends of the stenosis. The stent diameter is selected using the following formula, with all length units in mm: Where D is the estimated normal diameter of the target stenotic segment of the cavity, d is the narrowest diameter of the stenotic segment, and T c denoted as , where is the length of the target midline in the narrow segment of the cavity, and t is the straight-line distance between the two ends of the target midline in the narrow segment of the cavity. To select the decision variable for the stent diameter, when ≥50%, the support diameter is selected as D+2, when <50%, the support diameter is selected as D+1.
4. The method according to claim 1, characterized in that, The computational medical imaging space and three-dimensional model space The relation matrix T is used to reverse delineate the stent, cavity, and target area from the stent-cavity-target area model simulation results onto the medical image and reassign CT values to obtain postoperative simulated CT images. This includes: deleting voxels used to construct the three-dimensional model of the treatment target area and cavity in the medical image; reverse delineating the stent, cavity, and target area from the stent-cavity-target area model simulation results onto the medical image through the relation matrix T; assigning CT values to the cavity and target area regions in the reverse delineated region according to the original CT values; assigning CT values to the stent region according to the stent CT values in the actual postoperative CT; assigning CT values to the overlapping parts of the reverse delineated region and the medical image according to the newly delineated region; and assigning CT values to the missing parts according to the surrounding adjacent tissues.
5. The method according to claim 1, characterized in that, The step of obtaining candidate particle position coordinates by rasterizing the unfolded surface of the cylindrical stent based on the postoperative stent, cavity and target area model includes: dividing the unfolded surface of the stent into multiple grids according to Cartesian coordinates at a preset interval, determining the grid point coordinates of each grid, and using the grid point coordinates as the candidate particle position coordinates.
6. The method according to claim 1, characterized in that, The establishment of the optimization objective function includes: Establish an optimization objective function related to dose and particle number, wherein the optimization objective function is: Where P represents the arrangement of particles on the unfolding support. This represents the dose received by the j-th voxel in the therapeutic target volume (TV). Here, N(P) represents the lower bound of the dose to the therapeutic target area, N(P) is the number of particles used, and H(·) is the step function. and These are the weights of the dose constraint and the particle number constraint, respectively.
7. The method according to claim 1, characterized in that, The optimization of the dosage and particle number plan using heuristic optimization methods includes: The algorithm starts by determining the closest distance to the treatment target area. All candidate particle positions are set up to initialize the particle arrangement. Set initial iteration variables Current iteration variable Lower bound of iteration variables Given the process decay rate q and the number of iterations N, calculate the initial target value. Change the current state; specifically, with a 50% probability, discard or add a particle that has been deployed or not deployed, resulting in a new particle arrangement. ; Calculate the target value of the new particle arrangement ; If the target value obtained from the new particle arrangement is less than that obtained in the previous step, that is... Accepting new particle arrangements Otherwise, with a certain probability Accepting a new particle arrangement, in which It is the hyperbolic tangent function; Linearly reduce the value of the iteration variable. Repeat the particle arrangement modification and iteration variable reduction operations until the number of iterations N is reached or the iteration variable reaches its lower limit. When the time is reached, the iterative optimization operation is stopped, and the optimized particle arrangement is obtained. .
8. The method according to claim 5, characterized in that, The preset spacing between adjacent particle sources is set to 10mm.
9. The method according to claim 6, characterized in that, Objective function weights and Based on experience, the values are set to 1 and 10.
10. The method according to claim 7, characterized in that, Set the closest distance The initial iteration variable is 3mm. =1000, lower bound of iteration variable The process decay rate q=1, and the number of iterations N=1000.
11. A device for simulating the distribution of radioactive particles within a cavity, characterized in that, include: Stent implantation simulation module, used in medical imaging space Mark therapeutic target areas and cavity targets in three-dimensional model space. Establish a three-dimensional model including the treatment target area and the cavity target area; select the stent length and diameter based on the cavity target centerline and the cavity target stenosis caused by the treatment target area, and then apply this model to the three-dimensional model space. Establish a stent model; establish a stent-cavity-target area model after stent implantation into the target cavity, and simulate the expansion effect of the stent on the target cavity and treatment target area. After the stent-cavity-target area model stabilizes, extract the simulation results of the stent-cavity-target area model; calculate the medical imaging space. and three-dimensional model space The relationship matrix T is used to inversely delineate the stent, cavity and target area in the stent-cavity-target area model simulation results to the medical image and reassign CT values to obtain the postoperative simulated CT image. The particle distribution module is used to rasterize the unfolded surface of the cylindrical scaffold and obtain the position coordinates of candidate particles. Based on the target area and candidate particle positions delineated by postoperative simulated CT, an optimization objective function is established and a heuristic optimization method is used to optimize the planned particle dose and particle number.