A method and system for preoperative planning of a particle implantation

Abstract

The embodiment of the specification provides a particle implantation preoperative planning method and system, the method comprises the following steps: obtaining image data of a segmented target region and a tissue region of a target object; determining an initial needle distribution based on a representation model associated with particle implantation, the representation model comprising a needle candidate position representation and a particle candidate position representation related to the image data, wherein one or more particle candidate positions correspond to a needle candidate position; based on the initial needle distribution, optimizing the needle distribution by a first optimization algorithm to obtain a candidate particle implantation plan; based on the candidate particle implantation plan, optimizing the particle distribution by a second optimization algorithm to obtain a target particle implantation plan.

Description

Technical Field

[0001] This specification relates to the field of particle implantation technology, and in particular to a method and system for preoperative planning of particle implantation. Background Art

[0002] Particle implantation, known as "radioactive particle implantation therapy," is a type of brachytherapy. It's a localized treatment method that can precisely destroy tumor cells. The mechanism of tumor treatment with particle implantation involves puncturing with imaging-guided techniques (such as CT and ultrasound) to implant particles loaded with radioactive nuclides (such as I-125) into or around the tumor. The radioactive nuclides continuously release low-energy gamma rays within the tumor, which are highly lethal to tumor cells while minimizing damage to adjacent tissues, thereby achieving the desired localized tumor treatment effect.

[0003] In order to improve the efficacy and local control rate of tumors and ensure the smooth progress of the operation, before performing the seed implantation surgery, a corresponding seed implantation plan must be formulated according to the seed implantation treatment plan to ensure the smooth implementation of seed implantation.

[0004] Currently, preoperative seed implantation planning is primarily done through trial and error, with the number and placement of puncture needles and seeds manually adjusted until the seed distribution within the target area meets clinical dose requirements. This approach is time-consuming, prolongs the treatment cycle, and relies heavily on subjective experience, introducing further uncertainty into the procedure.

[0005] Therefore, a method and system for preoperative planning of seed implantation are provided, which can alleviate the shortcomings of existing preoperative planning systems and achieve more efficient and accurate seed implantation plans. Summary of the Invention

[0006] One of the embodiments of the present specification provides a method for preoperative planning of particle implantation, comprising: acquiring image data of a segmented target area and tissue area of ​​a target object; determining an initial puncture needle distribution based on a representation model associated with the particle implantation, the representation model comprising a puncture needle candidate position representation and a particle candidate position representation associated with the image data, wherein the puncture needle candidate position corresponds to one or more particle candidate positions; based on the initial puncture needle distribution, optimizing the puncture needle distribution using a first optimization algorithm to obtain a candidate particle implantation plan; based on the candidate particle implantation plan, optimizing the particle distribution using a second optimization algorithm to obtain a target particle implantation plan.

[0007] One of the embodiments of the present specification provides a particle implantation preoperative planning system, comprising: an acquisition module configured to acquire image data of a segmented target region and a tissue region of a target object; a determination module configured to determine an initial needle distribution based on a representation model associated with the particle implantation, the representation model comprising a needle candidate position representation and a particle candidate position representation related to the image data, wherein the needle candidate position corresponds to one or more particle candidate positions; a first optimization module configured to optimize the needle distribution by a first optimization algorithm based on the initial needle distribution to obtain a candidate particle implantation plan; and a second optimization module configured to optimize the particle distribution by a second optimization algorithm based on the candidate particle implantation plan to obtain a target particle implantation plan.

[0008] One of the embodiments of the present specification provides a particle implantation preoperative planning device, comprising a processor configured to execute the above-mentioned particle implantation preoperative planning method. BRIEF DESCRIPTION OF DRAWINGS

[0009] The present specification will be further illustrated in the manner of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same numbers represent the same structures, wherein:

[0010] Figure 1 is an application scenario diagram of the particle implantation preoperative planning method according to some embodiments of the present specification.

[0011] Figure 2 is an exemplary schematic diagram of the module of the particle implantation preoperative planning system according to some embodiments of the present specification.

[0012] Figure 3a is an exemplary flowchart of the particle implantation preoperative planning method according to some embodiments of the present specification.

[0013] Figure 3b is an exemplary schematic diagram of the initial needle distribution according to some embodiments of the present specification.

[0014] Figure 3c is an exemplary schematic diagram of the needle candidate position representation according to some embodiments of the present specification.

[0015] Figure 3d is an exemplary schematic diagram of the particle candidate position representation according to some embodiments of the present specification.

[0016] Figure 4a is an exemplary flowchart of the method for determining the initial needle distribution according to some embodiments of the present specification.

[0017] Figure 4bSchematic diagram of puncture needle insertion according to preoperative planning of particle implantation according to some embodiments of the present specification.

[0018] Figure 5a is an exemplary flow chart of optimizing a particle implantation plan through an optimization algorithm according to some embodiments of this specification.

[0019] Figure 5b is an exemplary schematic diagram of the first transformation process according to some embodiments of this specification.

[0020] Figure 5c FIG. 1 is an exemplary schematic diagram of a target particle implantation plan for particle implantation according to some embodiments of the present specification.

[0021] Figure 6 is an exemplary flow chart of another method for preoperative planning of target seed implantation according to some embodiments of this specification.

[0022] Figure 7 is an exemplary schematic diagram of a device for preoperative planning of seed implantation according to some embodiments of the present specification. DETAILED DESCRIPTION

[0023] To more clearly illustrate the technical solutions of the embodiments of this specification, the following briefly describes the drawings required for describing the embodiments. Obviously, the drawings described below are merely examples or embodiments of this specification. Those skilled in the art can apply this specification to other similar scenarios based on these drawings without inventive effort. Unless otherwise apparent from the context or otherwise noted, the same reference numerals in the figures represent the same structure or operation.

[0024] It should be understood that the terms "system," "device," "unit," and / or "module" used herein are a method for distinguishing different components, elements, parts, portions, or assemblies at different levels. However, if other terms can achieve the same purpose, the terms may be replaced by other expressions.

[0025] Unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not refer to the singular but include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements specifically identified, and these steps and elements do not constitute an exclusive list. A method or apparatus may also include other steps or elements.

[0026] Flowcharts are used throughout this specification to illustrate the operations performed by systems according to embodiments of this specification. It should be understood that preceding or following operations do not necessarily need to be performed in exact order. Instead, the steps may be processed in reverse order or simultaneously. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0027] Figure 1 This is a schematic diagram of an application scenario of a particle implantation preoperative planning system according to some embodiments of this specification.

[0028] like Figure 1 As shown, the application scenario 100 of the particle implantation preoperative planning system may include a medical scanning device 110 (exemplarily, Figure 1 CT scanner in this example), network 120, terminal 130, processing device 140 and storage device 150. The components in application scenario 100 can be connected in various ways. For example, Figure 1 As shown in FIG, the medical scanning device 110 can be connected to the processing device 140 via the network 120. The medical scanning device 110 can be directly connected to the processing device 140 (as shown by the double-headed arrow in the dashed line connecting the medical scanning device 110 and the processing device 140). The storage device 150 can be connected to the processing device 140 directly or via the network 120.

[0029] The medical scanning device 110 can scan a scanned object and / or generate multiple data about the scanned object. In this specification, the scanned object may also be referred to as a scanning object, a target object, a target, or a detected object. In some embodiments, the scanned object may be a patient, an animal, etc. When the scanned object needs to be scanned, after the scanned object enters the scanning area 115, the medical scanning device 110 can perform tube anode exposure based on a preset scanning protocol to emit radiation (e.g., an X-ray beam), and the radiation is irradiated on the scanned object to obtain a corresponding medical image. In some embodiments, the medical image may include information such as the outline of the target area, the organs or tissue areas surrounding the target area, etc.

[0030] The network 120 may include any suitable network that facilitates information and / or data exchange for the application scenario 100. In some embodiments, one or more components of the application scenario 100 (e.g., the medical scanner 110, the terminal 130, the processing device 140, or the storage device 150) may transmit information and / or data to one or more other components of the application scenario 100 via the network 120. For example, the processing device 140 may obtain a medical image of a scanned object from the medical scanner 110 via the network 120. In some embodiments, the network 120 may be any one or more of a wired network and a wireless network. In some embodiments, the network may have various topologies, such as point-to-point, shared, or centralized, or a combination of multiple topologies.

[0031] The terminal 130 may include a mobile device 130-1, a tablet computer 130-2, a laptop computer 130-3, etc., or any combination thereof. In some embodiments, the terminal 130 may interact with other components in the application scenario 100 via the network 120. For example, the terminal 130 may receive data such as medical images sent by the medical scanning device 110. In some embodiments, the terminal 130 may receive information and / or instructions input by a user (e.g., a user of the medical scanning device 110, such as a doctor), and send the received information and / or instructions to the medical scanning device 110 or the processing device 140 via the network 120. For example, a doctor may input operating instructions for the medical scanning device 110 via the terminal 130. In some embodiments, the terminal 130 may display a report of the seed implantation plan for the user to view and / or select.

[0032] The processing device 140 may process data and / or information obtained from the medical scanning device 110, the terminal 130, and / or the storage device 150. For example, the processing device 140 may acquire a medical image of a scanned person.

[0033] In some embodiments, the processing device 140 can be a single server or a server group. The server group can be centralized or distributed. In some embodiments, the processing device 140 can be local or remote. The processing device 140 can be directly connected to the medical scanning device 110, the terminal 130, and the storage device 150 to access stored or acquired information and / or data. In some embodiments, the processing device 140 can be implemented on a cloud platform. By way of example only, the cloud platform can include private clouds, public clouds, hybrid clouds, community clouds, distributed clouds, internal clouds, multi-layer clouds, and the like, or any combination thereof.

[0034] The storage device 150 can store data and / or instructions. In some embodiments, the storage device 150 can store data obtained from the medical scanning device 110, the terminal 130 and / or the processing device 140. For example, the storage device 150 can store medical images obtained by the user scanning device, reports on particle implantation plans, etc. In some embodiments, the storage device 150 can store data and / or instructions that the processing device 140 can execute or use to execute the exemplary methods described in this specification. For example, the storage device 150 can store instructions for the processing device 140 to execute the methods shown in the various flowcharts. In some embodiments, the storage device 150 may include a large-capacity storage device, a removable storage device, a volatile read-write memory, a read-only memory (ROM), etc., or any combination thereof. In some embodiments, the storage device 150 can be implemented on a cloud platform.

[0035] In some embodiments, the storage device 150 can be connected to the network 120 to communicate with one or more components of the application scenario 100 (e.g., the medical scanning device 110, the terminal 130, the processing device 140, etc.). One or more components of the application scenario 100 can access data or instructions stored in the storage device 150 via the network 120. In some embodiments, the storage device 150 can be directly connected to or communicate with one or more components of the application scenario 100. In some embodiments, the storage device 150 can be part of the processing device 140.

[0036] The above description is for illustrative purposes only, and actual application scenarios may vary.

[0037] It should be noted that application scenario 100 is provided for illustrative purposes only and is not intended to limit the scope of this application. A person skilled in the art can make various modifications or variations based on the description of this specification. For example, application scenario 100 may further include a display device. However, such variations and modifications do not deviate from the scope of this application.

[0038] Figure 2 1 is an exemplary schematic diagram of modules of a preoperative planning system for seed implantation according to some embodiments of this specification.

[0039] like Figure 2 As shown, the pre-operative planning system 200 for seed implantation (hereinafter referred to as the planning system 200 ) may include an acquisition module 210 , a determination module 220 , a first optimization module 230 , and a second optimization module 240 .

[0040] The acquisition module 210 may be configured to acquire image data of the segmented target region and tissue region of the target object.

[0041] The determination module 220 can be used to determine the initial puncture needle distribution based on a representation model associated with particle implantation, the representation model including a puncture needle candidate position representation and a particle candidate position representation associated with the image data, wherein the puncture needle candidate position corresponds to one or more of the particle candidate positions.

[0042] In some embodiments, the determination module 220 can also be used to preprocess the candidate puncture needle positions based on the representation model, and the preprocessing includes one or more of the following: eliminating the candidate puncture needle positions that do not meet the preset conditions for the puncture needle, eliminating the candidate particle positions that do not meet the preset conditions for the particles; and determining the initial puncture needle distribution based on the preprocessed candidate puncture needle positions.

[0043] The first optimization module 230 may be configured to optimize the puncture needle distribution based on the initial puncture needle distribution by using a first optimization algorithm to obtain a candidate seed implantation plan.

[0044] In some embodiments, the first optimization module 230 can also be used to: optimize the puncture needle distribution through a first optimization algorithm with the goal of making the candidate particle implantation plan satisfy the first constraint and reducing the function value of the first objective function to obtain a candidate particle implantation plan; wherein, in the puncture needle distribution, particles are placed at any particle candidate position on the puncture needle candidate position; and, the first constraint is related to the target area dose coverage, and the first objective function is related to the total number of puncture needles and the total number of particles.

[0045] In some embodiments, the first optimization module 230 can also be used to: perform optimization processing based on at least one round of iterative processing, wherein one round of iterative processing includes: obtaining an initial population, the initial population including multiple individuals, each individual corresponding to a particle implantation plan; based on the initial population, generating a new population through a preset deduction algorithm; based on constraints or based on constraints and objective functions, screening target individuals from the initial population and the new population to form a target population, and the initial population in the next round of iterative processing is the target population; determining the target result in the target population obtained after the optimization processing; wherein, for optimizing the puncture needle distribution through the first optimization algorithm, the constraint condition is the first constraint condition, the objective function is the first objective function, the initial population of the first round of iteration is obtained based on the initial puncture needle distribution, and the target result is a candidate particle implantation plan.

[0046] The second optimization module 240 may be configured to optimize the particle distribution based on the candidate particle implantation plan by using a second optimization algorithm to obtain a target particle implantation plan.

[0047] In some embodiments, the second optimization module 240 can also be used to: optimize the particle distribution through a second optimization algorithm with the goal of making the target particle implantation plan satisfy the second constraint and reducing the function value of the second objective function, so as to obtain a target particle implantation plan; wherein the second constraint is related to the target area dose coverage and the total number of puncture needles and / or the total number of particles; the second objective function is related to the particle dose conformity and / or the uniformity of the particle dose distribution.

[0048] In some embodiments, the second optimization module 240 can also be used to: during the optimization process, obtain the difference particles in the particle distribution of the candidate particle implantation plan corresponding to the optimized particle implantation plan and the optimized particle implantation plan; when the number of difference particles is less than the preset total number of particles, determine the dose at the difference particles.

[0049] In some embodiments, the first optimization module 240 can also be used to: perform optimization processing based on at least one round of iterative processing, wherein one round of iterative processing includes: obtaining an initial population, the initial population including multiple individuals, each individual corresponding to a particle implantation plan; based on the initial population, generating a new population through a preset deduction algorithm; based on constraints or based on constraints and objective functions, screening target individuals from the initial population and the new population to form a target population, and the initial population in the next round of iterative processing is the target population; determining the target result in the target population obtained after the optimization processing; wherein, for optimizing the particle distribution through the second optimization algorithm, the constraints are the second constraints, the objective function is the second objective function, the initial population of the first round of iteration is obtained based on the candidate particle implantation plan, and the target result is the target particle implantation plan.

[0050] In some embodiments, the seed implantation preoperative planning system 200 may further include an output module 250. The output module 250 may be configured to output a report corresponding to the candidate seed implantation plan and / or the target seed implantation plan.

[0051] It should be noted that the above description of the particle implantation preoperative planning system 200 and its modules is for convenience of description only and does not limit this specification to the scope of the embodiments cited. It is understandable that for those skilled in the art, after understanding the principle of the system, it is possible to arbitrarily combine the modules or form a subsystem to connect with other modules without deviating from this principle. The modules can be different modules in a system, or a module can realize the functions of two or more modules mentioned above. For example, the modules can share a storage device, and each component can also have its own storage device. Such variations are within the scope of protection of this specification.

[0052] Figure 3ais an exemplary flow chart of a method for preoperative planning of seed implantation according to some embodiments of the present specification.

[0053] In some embodiments, process 300 may be performed by planning system 200. Figure 3a As shown, process 300 includes the following steps:

[0054] Step 310: Acquire image data of the segmented target area and tissue area of ​​the target object.

[0055] The target object may refer to an individual who needs to undergo seed implantation, which may be a human patient, an animal, or the like.

[0056] The target region may refer to the area of ​​the body where the seed implantation procedure is to be performed. For example, the target region may include an area of ​​the body where an abnormal lesion has occurred, such as a tumor region within the chest cavity.

[0057] The tissue region may refer to a body region related to the seed implantation procedure. For example, the tissue region may include organs (such as the heart), bones, blood vessels, etc. surrounding the target area. In some embodiments, the tissue region may include a first predetermined tissue and a second predetermined tissue.

[0058] The first preset tissue refers to non-puncturable tissue, which can be determined based on the actual circumstances of the seed implantation procedure. For example, the degree of negative impact of puncturing a tissue region on the target subject (such as the possibility of serious complications or massive bleeding) can be assessed. The greater the impact, the more likely it is to be marked as the first preset tissue. For another example, for tissue regions that do not require puncture (such as bone or calcified tissue), the difficulty of puncturing them can be assessed. The greater the difficulty, the more likely it is to be marked as the first preset tissue.

[0059] The second preset tissue refers to the tissue that requires particle dose limitation. The particle dose limitation may include dose limit indicators such as volume dose and point dose of the tissue area (such as maximum volume dose, maximum point dose, etc.). It should be understood that the particle dose values ​​of different tissue areas are different. When the particle dose value is exceeded, the possibility of negative effects on the target object (such as causing serious complications, etc.) is greater. The second preset tissue can be determined based on the actual situation of this particle implantation, for example, based on the physical constitution of different target objects, the location of the target area and the type of surrounding tissue.

[0060] It should be noted that the first preset tissue and the second preset tissue are determined according to the actual scenario of the particle implantation. In different types of particle implantation (such as liver cancer, gastric cancer, and lung cancer), the first preset tissue and the second preset tissue may be different. In addition, a certain tissue area may be both the first preset tissue and the second preset tissue (for example, a non-puncturable heart that also has dose limit constraints). In some embodiments of the present specification, by marking different tissue areas in the planning of particle implantation and taking into account their non-puncturability and particle dose limitations, the particle implantation plan can be made more accurate.

[0061] The image data may include, but is not limited to, medical image data such as CT images and ultrasound images, which may be one or more pictures or one or more frames of video data. In some embodiments, the image data may be obtained by scanning a target object with a medical scanning device (such as a CT device).

[0062] The image data contains information about the target area and / or tissue area. For example, the image data may include information such as the location (such as the chest, neck, abdomen, etc.), shape, outline, size, etc. of the target area in the target object. It may also include information such as whether there are important organs or body tissues (such as the heart, carotid artery, lungs, etc.) around the target area and their shape, outline, etc.

[0063] The planning system 200 can process the acquired image data to delineate the target volume and tissue regions, thereby obtaining processed image data of the segmented target volume and tissue regions. In some embodiments, during a seed implantation procedure, different target regions require different implantation doses, and tissue regions associated with the target region (e.g., a second predetermined tissue region surrounding the target region) must also meet dose limit conditions.

[0064] In some embodiments, the planning system 200 may analyze grayscale changes, color distortion, etc. of a single-frame image, and may also extract features of the target area and / or tissue area (such as tissue type) in the single-frame image.

[0065] In some embodiments, the planning system 200 may process the image data through an image processing algorithm. For example, the image data may be segmented using methods including but not limited to a threshold method and a region growing method to obtain the above-mentioned image data.

[0066] In some embodiments, the planning system 200 may also utilize a trained machine learning or deep learning model to process the image data. For example, the image data may be segmented using a U-Net-based segmentation model to obtain target image data.

[0067] It can be understood that segmenting the target area and tissue area in the image can be achieved through various feasible methods.

[0068] In some embodiments of this specification, by segmenting the target area and tissue area, the contours of the target area and surrounding important tissues or organs can be accurately obtained, which helps the puncture needle of the seed implantation surgery avoid important organs and reduce the negative impact of the dose.

[0069] Step 320 : determining an initial puncture needle distribution based on a representation model associated with particle implantation, wherein the representation model includes a puncture needle candidate position representation and a particle candidate position representation associated with the image data, wherein a puncture needle candidate position corresponds to one or more particle candidate positions.

[0070] The initial puncture needle distribution may refer to the spatial distribution information of candidate puncture needles. The candidate puncture needles may be used to characterize feasible puncture paths for the puncture needles. The spatial distribution information of the candidate puncture needles may include the number of candidate puncture needles and information such as the candidate puncture needle entry point, needle tip, insertion depth, and needle direction.

[0071] The initial puncture needle distribution may also include spatial distribution information of candidate particles. The candidate particles can be used to characterize the source and dose information of the implanted particles. The spatial distribution information of the candidate particles may include information such as the location and number of the candidate particles.

[0072] In some embodiments, the initial puncture needle distribution can be represented by a representation model associated with particle implantation. The representation model can refer to a method for representing and analyzing the initial puncture needle distribution information, which can include a representation of candidate puncture needle positions and a representation of candidate particle positions, where each candidate puncture needle position corresponds to one or more candidate particle positions.

[0073] In some embodiments, the planning system 200 can determine a representation model based on image data of the segmented target volume and tissue region through mathematical modeling or computer simulation. For example, the representation model can be obtained by analyzing and processing information such as the location, outline, and size of the target volume in the image data, as well as information such as the types of vital organs surrounding the target volume and their particle dose requirements. The representation of candidate needle and particle positions in the representation model can be expressed in various ways, such as numerical values, symbols, and codes.

[0074] In some embodiments, the representation model may include a representation model based on a coplanar needle group, and the puncture needle candidate position representation and / or the particle candidate position representation are binary representations. Binary representation refers to a representation with only two possible output results. Among them, the binary representation of the puncture needle candidate position is determined based on whether the puncture needle candidate position is placed with a puncture needle, and the binary representation of the particle candidate position is determined based on whether the particle is placed with a particle. Exemplarily, the binary representation of the puncture needle candidate position and the particle candidate position may include one or a combination of binary representations such as A / B, Y / N, + / -. In some embodiments, the binary representation can be a binary number (i.e., 0 / 1). Among them, when the puncture needle candidate position is placed with a puncture needle and the particle candidate position is placed with a particle, they can be represented by 1 respectively, otherwise they can be represented by 0.

[0075] A coplanar needle group can refer to puncture needles determined based on a particle implantation template. The puncture needles are parallel, and the particle implantation template is a plane. For example, the particle implantation template can be a rectangular area that defines the initial puncture needle distribution. Adjustments to the initial puncture needle distribution can be made by adjusting the center position of the particle implantation template and the template's angle or orientation. The particle implantation template includes holes that can be used to indicate candidate puncture needle locations, and the needle insertion direction can be perpendicular to the template plane.

[0076] refer to Figure 3b , Figure 3b is an exemplary schematic diagram of the initial puncture needle distribution according to some embodiments of the present specification.

[0077] like Figure 3b As shown, the rectangular grid area is the seed implantation template 321 , the elliptical area represents the target area 322 , each black solid dot represents a candidate puncture needle position 323 , and the two dotted circular areas represent non-punctureable areas 324 .

[0078] The seed implantation template 321 may be pre-set and may include a plurality of pre-set holes (e.g., intersections of a grid). The contours, relative positions, and other related information of the target region 322 and the non-punctureable region 324 may be determined based on image data of the segmented target region and tissue region.

[0079] The seed implantation template 321 can determine multiple candidate puncture needle positions 323 based on the relevant information of the target area 322 and the non-puncture area 324 by adjusting the center position (such as translation along the x-axis and y-axis directions) and rotating the angle (such as rotation along the XY axis plane and the XZ axis plane based on the center position point). Figure 3bAs shown, there are 22 candidate puncture needle positions 323, each of which corresponds to a candidate puncture needle. The candidate puncture needle is within the target area 322 and does not pass through any non-punctureable area 324. The candidate puncture needles corresponding to the multiple candidate puncture needle positions 323 constitute a coplanar needle group, or candidate needle group.

[0080] It should be noted that the representation model can also include a representation model based on a heterogeneous needle group. A heterogeneous needle group can mean that the puncture needles are not parallel. For example, a feasible needle insertion area can be drawn on the body surface corresponding to the target area of ​​the target object, where the feasible needle insertion area avoids the non-puncture area. A needle insertion point is randomly selected within the feasible needle insertion area and paired with a random point in the target area to generate a candidate puncture needle. After repeated operation, a heterogeneous needle group can be generated, and the initial puncture needle distribution is determined based on the heterogeneous needle group.

[0081] This manual mainly uses the coplanar needle group as an example to illustrate the preoperative planning method of seed implantation.

[0082] In some embodiments, the candidate puncture needle position representation may be in the form of a binary number representation, which may be represented as 0 or 1. 0 indicates that no puncture needle is placed at the candidate puncture needle position; 1 indicates that a puncture needle is placed at the candidate puncture needle position, and the puncture needle is the recommended puncture needle, and the candidate position is the recommended puncture needle position.

[0083] Combine Figure 3c , Figure 3c is an exemplary schematic diagram showing candidate positions of a puncture needle according to some embodiments of the present specification.

[0084] like Figure 3c The figure shows the representation of the candidate puncture needle positions corresponding to the candidate needle group, which can be represented as a binary sequence (or array) with n elements, wherein the value of n is determined based on the number of candidate puncture needle positions (eg, 22).

[0085] For example, Figure 3c As shown in the figure, the value of the first candidate needle position is 0, indicating that the needle is not placed at this position; the value of the second candidate needle position is 1, indicating that the needle is placed at this position. Based on this principle, a binary sequence corresponding to n candidate needle positions can be obtained. Among them, the candidate needle position corresponding to the binary value of 1 in this sequence is the recommended needle position.

[0086] In some embodiments, whether a candidate puncture needle position is used as a recommended puncture needle position, or whether a candidate puncture needle is used as a recommended puncture needle, needs to meet the puncture needle preset conditions. For more information about the puncture needle preset conditions, see Figure 4a and its description.

[0087] In some embodiments, one or more particle candidate positions can be associated with one puncture needle candidate position. It can be understood that one puncture needle candidate position corresponds to one candidate puncture needle. In some embodiments, the puncture needle is marked with equally spaced scales, and each scale corresponds to a particle candidate position. It should be noted that when performing particle implantation, the puncture needle first passes through the target region, and then gradually retreats according to the scales and places particles. For example, a puncture needle is marked with m (such as 10) equally spaced scales, such as scale 1, scale 2, …, scale m, which indicates that the puncture needle corresponds to m particle candidate positions where particles can be placed.

[0088] In some embodiments, the particle candidate position can be represented in the form of a binary number, which can be represented as 0 or 1. Wherein 0 represents that the particle candidate position does not place particles; 1 represents that the particle candidate position places particles.

[0089] In combination with Figure 3d , Figure 3d is an exemplary schematic diagram of the particle candidate position representation according to some embodiments of the present specification.

[0090] Figure 3d The representation of the particle candidate positions corresponding to the k puncture needles is shown. For the representation of the particle candidate positions of a certain puncture needle (such as puncture needle 1, puncture needle k), it can be represented as a binary sequence (or array) with m elements. Wherein the value of m is determined based on the number of puncture needle scales (such as 10), and the value of k is the number of recommended puncture needles in the candidate needle group, which is less than or equal to the total number n of candidate puncture needles or candidate puncture needles.

[0091] For example, as shown in Figure 3d , in the puncture needle 1, the m (such as 10) scales correspond to m particle candidate positions, the binary value corresponding to the scale 1 is 0, indicating that the particle candidate position does not place particles; the binary value corresponding to the scale 2 is 1, indicating that the particle candidate position needs to place particles. Based on the binary values of the m particle candidate positions, the representation of the particle candidate positions of the puncture needle 1 can be obtained according to this principle. Similarly, the representations of the particle candidate positions of the k puncture needles can be obtained, which can be a matrix of k rows and m columns of binary values.

[0092] In some embodiments, whether the particle candidate position places particles or the particle candidate position needs to meet the particle preset condition as a recommended particle position. For related content of the particle preset condition, please refer to Figure 4a and the description thereof.

[0093] Step 330, based on the initial puncture needle distribution, the puncture needle distribution is optimized by a first optimization algorithm to obtain a candidate particle implantation plan.

[0094] In some embodiments, the planning system 200 can determine one or more initial needle distributions and particle distributions based on the representation model. An initial needle distribution and particle distribution can represent an initial seed implantation plan. For example, the aforementioned matrix of binary values ​​with k rows and m columns can represent an initial seed implantation plan.

[0095] The candidate particle implantation plan may refer to a particle implantation plan that meets preset conditions.

[0096] In some embodiments, the candidate seed implantation plan obtained through the optimization in step 330 can be used as a feasible solution for seed implantation, which can meet the preoperative planning requirements for seed implantation to a certain extent. For example, Figure 6 The method shown.

[0097] In some embodiments, the planning system 200 may determine candidate seed implantation plans based on prior knowledge or medical experience. For example, based on image data of the target volume and tissue region, historical seed implantation plans for similar cases may be obtained, and one or more historical seed implantation plans with good treatment outcomes (e.g., patient feedback or postoperative medical observation evaluation results) may be selected as candidate seed implantation plans.

[0098] In some embodiments, the planning system 200 can adjust the candidate puncture needle distribution parameters (such as the position and number of puncture needles) and the parameters of the particle candidate position distribution (such as the placement position and number of particles) in the initial particle implantation plan. When the adjusted initial particle implantation plan meets the preset conditions, the candidate particle implantation plan is obtained.

[0099] In some embodiments, in the optimization stage of step 330 (which may be referred to as the first optimization stage), particles may be placed at all candidate particle positions on each puncture needle in the determined initial puncture needle distribution. Thus, in the optimization stage of step 330, the puncture needle distribution may be optimized and a candidate particle implantation plan may be obtained.

[0100] In some embodiments, the planning system 200 can optimize the puncture needle distribution through the first optimization algorithm to obtain a candidate seed implantation plan. Figure 5a and its description.

[0101] Step 340 : Based on the candidate particle implantation plan, optimize the particle distribution by a second optimization algorithm to obtain a target particle implantation plan.

[0102] The target seed implantation plan may refer to a seed implantation plan that is ultimately used to perform seed implantation surgery.

[0103] The optimization phase of step 340 may be referred to as a second optimization phase. In some embodiments, the planning system 200 may compare and analyze multiple candidate particle implantation plans and select a candidate particle implantation plan that meets preset conditions from the multiple candidate particle implantation plans as a target particle implantation plan.

[0104] In some embodiments, the planning system 200 can optimize the particle distribution based on the candidate particle implantation plan using a second optimization algorithm to obtain a target particle implantation plan. Figure 5a and its description.

[0105] In some embodiments of this specification, a planning system automatically determines the target particle implantation plan, enabling efficient acquisition of a precise particle implantation plan and avoiding the uncertainty associated with subjectively determined particle implantation plans. Furthermore, through two separate optimization phases (first optimization algorithm optimization and second optimization algorithm optimization), the simultaneous optimization of needle and particle distribution can be avoided, potentially weakening the optimization effect of needle and particle counts (e.g., if both needle and particle counts are optimized, the optimization of needle and particle counts may be weakened because the optimization targets also include optimization of dose and other aspects). This allows for a more targeted and stable optimization direction for the particle implantation plan, resulting in a more reliable and practically appropriate particle implantation plan.

[0106] Figure 4a is an exemplary flow chart of a method for determining an initial puncture needle distribution according to some embodiments of the present specification.

[0107] In some embodiments, process 400 may be performed by planning system 200. Figure 4a As shown, process 400 includes the following steps:

[0108] Step 410 : Preprocess the candidate puncture needle positions based on the representation model.

[0109] In some embodiments, the planning system 200 can obtain a binary representation of the candidate puncture needle positions based on the representation model, which includes a binary representation of the candidate puncture needle positions and a binary representation of the candidate particle positions corresponding to each candidate puncture needle position. For more information about the representation model, the candidate puncture needle positions, and the binary representation of the candidate particle positions, see Figure 3a and its description.

[0110] In some embodiments, the planning system 200 may initialize the binary representation of the candidate puncture needle positions, for example, generating binary numbers that are all 0, or all 1, or a random combination of 0 and 1.

[0111] In some embodiments, the pre-processing may include the following steps 411 and 412:

[0112] Step 411: Eliminate candidate puncture needle positions that do not meet preset puncture needle conditions. The preset puncture needle conditions include one or more of the following: the puncture needle at the candidate puncture needle position cannot penetrate the target tissue, or the insertion depth of the puncture needle at the candidate puncture needle position is less than the instrument length of the puncture needle.

[0113] The target tissue can be the first preset tissue around the target area. For example, blood vessels, bones, etc. near the target area. For more information about the first preset tissue, see Figure 3a and its description.

[0114] In some embodiments, the puncture needle at the candidate puncture needle position cannot pass through the target tissue may also include expanding the boundary of the current contour of the target tissue based on a preset safety distance (for example, 10 mm, etc.), and the puncture needle at the candidate puncture needle position cannot pass through the target tissue after the boundary expansion.

[0115] In some embodiments of this specification, by introducing a preset safety distance, the distribution of pre-treated puncture needles can be made safer.

[0116] The insertion depth of the puncture needle may include the first depth from the insertion point of the puncture needle on the body surface to the first time the needle tip of the puncture needle touches the target area. When the instrument length (physical length) of the puncture needle is less than the first depth, it indicates that the candidate position of the puncture needle does not meet the preset conditions of the puncture needle.

[0117] The insertion depth of the puncture needle may also include a second depth from the insertion point on the body surface to a preset needle tip point (such as the last voxel in the target area). When the instrument length of the puncture needle is less than the second depth, it indicates that the candidate puncture needle position does not meet the preset puncture needle conditions.

[0118] The first depth and the second depth can be determined based on actual conditions (e.g., the shape and size of the target area). The planning system 200 can determine the distance between the needle entry point on the body surface and the point where the needle tip first touches the target area, as well as the preset needle tip point, based on information such as the target area contour and the voxels on the contour, and the puncture direction of the puncture needle. The system can then compare the aforementioned distances with the instrument length of the puncture needle to determine whether the candidate puncture needle position meets the preset puncture needle conditions.

[0119] In some embodiments, the puncture needle candidate positions that do not meet the puncture needle preset conditions are eliminated as follows: when a puncture needle candidate position does not meet the puncture needle preset conditions, the planning system 200 may set the binary number of the puncture needle candidate position to 0; otherwise, it is set to 1.

[0120] Step 412: Eliminate candidate particle positions that do not meet preset particle conditions. The preset particle conditions include one or more of the following: the candidate particle position is inside the target area, and the distance between the candidate particle position and the first or last punctured voxel of the target area is less than a preset value.

[0121] The first puncture voxel of the target area refers to the voxel point where the puncture needle first contacts the target area; the last puncture voxel refers to the voxel point corresponding to the position of the needle tip after the puncture needle is inserted.

[0122] In some embodiments, the candidate particle positions that do not meet the particle preset conditions are eliminated by: when a candidate particle position does not meet the particle preset conditions, the planning system 200 can set the binary number of the candidate particle position to 0; otherwise, it is set to 1.

[0123] Figure 4b Schematic diagram of puncture needle insertion according to preoperative planning of particle implantation according to some embodiments of the present specification.

[0124] like Figure 4b As shown, for a human body region (indicated by a dotted line in the figure), a puncture needle 4121 is an exemplary candidate puncture needle in the preoperative planning of seed implantation. The voxel points (indicated by the hollow points in the figure) after the puncture needle 4121 penetrates the target area 4120 include: the body surface needle entry point 412-1, the first puncture voxel 412-2 of the target area, the last puncture voxel 412-3 of the target area, and the last puncture voxel 412-4. Among them, the last puncture voxel 412-4 can correspond to the needle tip position of the puncture needle 4121. In some embodiments, the planning system 200 can place the needle tip at a preset extension distance (e.g., 5 mm) along the direction of the puncture path after the puncture needle 4121 reaches the last puncture voxel 412-3 of the target area to determine the last puncture voxel 412-4. If the puncture needle 4121 passes through the first preset tissue and / or the second preset tissue after being extended, the needle tip retracts to the surface of the target area 4120. At this time, the last punctured voxel 412-4 overlaps with the last punctured voxel 412-3 of the target area.

[0125] In some embodiments, the planning system 200 can respectively determine the distance between the body surface needle entry point 412-1 and the first puncture voxel 412-2 of the target area, and the distance between the body surface needle entry point 412-1 and the last puncture voxel 412-4 to obtain the first depth and the second depth, and determine whether the puncture needle 4121 needs to be eliminated based on the processing of step 411. The planning system can also determine whether the particle candidate position (the scale on the needle, not shown in the figure) is located inside the target area 4120, and whether the distance between the particle candidate position outside the target area 4120 and the first puncture voxel 412-2 and the last puncture voxel 412-4 of the target area is less than a preset value (e.g., 5 mm), and then determine whether each particle candidate position needs to be eliminated.

[0126] At step 420, an initial needle distribution is determined based on the pre-processed needle candidate positions.

[0127] In some embodiments, the planning system 200 can perform a traversal process on the binary sequence of the pre-processed needle candidate positions, and obtain the needle candidate positions with a binary value of 1 in the binary sequence as one or more recommended needle positions in the initial needle distribution.

[0128] Meanwhile, for each recommended needle position, the planning system 200 can perform a traversal process on the binary sequence of the particle candidate positions corresponding to the recommended needle position, and obtain the particle candidate positions with a binary value of 1 in the binary sequence as one or more particle placement positions on each recommended needle position in the initial needle distribution, which are specifically one or more scales of the recommended needle.

[0129] Based on the processing of step 420, the planning system 200 can obtain the initial needle distribution.

[0130] According to some embodiments of the present specification, the pre-processing can be used to screen the needle candidate positions and candidate particle positions, so as to quickly eliminate the needle candidate positions and candidate particle positions that do not meet the conditions, and provide a good foundation for subsequent optimization.

[0131] Figure 5a is an exemplary flowchart of particle implantation plan optimization by an optimization algorithm according to some embodiments of the present specification.

[0132] In some embodiments, the flow 500 can be performed by the planning system 200.

[0133] In some embodiments, the method flow of optimizing the needle distribution by the first optimization algorithm and the method flow of optimizing the particle distribution by the second optimization algorithm described in some embodiments of the present specification can be similarly implemented according to the flow 500.

[0134] In some embodiments, the planning system 200 can perform optimization processing based on at least one iteration process to optimize the needle distribution by the first optimization algorithm or to optimize the particle distribution by the second optimization algorithm. As shown in the flow 500, the flow 500 includes the following steps: Figure 5a

[0135] At step 510, an initial population is obtained, and the initial population includes a plurality of individuals, each individual corresponding to a particle implantation plan.

[0136] The initial population can refer to a set of one or more particle implantation plans before optimization processing by the first optimization algorithm or the second optimization algorithm. ​

[0137] For optimizing the needle distribution by the first optimization algorithm, the individuals in the initial population of the first iteration can be based on the initial needle distribution.

[0138] For optimizing the particle distribution by the second optimization algorithm, the initial population of the first iteration can be based on the candidate particle implant plan. The candidate particle implant plan at this time can be the multiple candidate particle implant plans obtained after being optimized by the first optimization algorithm.

[0139] Each individual in the initial population can be a binary number representation of the candidate particle implant plan, which can be a binary number matrix of k rows and m columns. For related content about the binary number representation of the candidate particle implant plan, see Figure 3a and the description thereof.

[0140] In some embodiments, for optimizing the needle distribution by the first optimization algorithm, the planning system 200 can determine one or more individuals based on the preprocessed initial needle distribution and compose the initial population. Illustratively, in the preprocessed needle distribution, all particle candidate positions in any needle candidate position are set to place particles (the value of the binary bit corresponding to the particle candidate position satisfying the preset particle condition is set to 1), thereby forming an individual. For related content about preprocessing, the initial needle distribution, and the particle preset condition, see Figure 4a and the description thereof.

[0141] Illustratively, for the binary number matrix corresponding to each individual, the planning system 200 can randomly set the value of one or more binary bits in a needle (such as needle 1, needle 2, etc.) in a certain individual to 0 or 1 according to a preset random probability (such as 0.5), to generate one or more new individuals. Based on the generated new individuals, the initial population is composed. In some embodiments, the number of individuals in the initial population can be a preset number of individuals (such as 10, 30).

[0142] It should be noted that when the value of one or more binary bits is set from 0 to 1, the particle position corresponding to the binary bit needs to satisfy the particle preset condition.

[0143] In some embodiments, the preset random probability is related to the number of recommended needles and the number of recommended particles. The preset random probability p can be determined based on the formula (1) as follows:

[0144]

[0145] wherein p in formula (1) is the random probability, N rn and N rs represent the number of recommended needles and the number of recommended particles, respectively, and N pnand N ps Respectively represent the number of candidate puncture needles and the number of candidate particles. The number of recommended particles can be determined based on prior knowledge or medical experience. For example, the number of recommended particles can be based on the medical experience formula: N rs =4.674×V 0.562 / A is determined, where V(cm 3 ) represents the target volume, A(mCi) represents the particle activity; the number of candidate particles can be determined based on the number of candidate particle positions in the initial puncture needle distribution. The planning system 200 can adjust the value of the binary bit in the initial puncture needle distribution based on the preset random probability to generate an initial population of a preset number of individuals (e.g., 30). For more information about candidate particle positions, see Figure 3a and its description.

[0146] In some embodiments of this specification, a preset random probability is determined by introducing the relationship between the number of recommended puncture needles and the number of recommended particles, which is conducive to obtaining a good initial population, improving the efficiency of subsequent optimization processing, and reducing the workload and time consumption of calculations.

[0147] Step 520: Generate a new population based on the initial population using a preset deduction algorithm.

[0148] The preset inference algorithm may refer to an algorithm used to perform inference on the initial population to generate new individuals.

[0149] In some embodiments, the preset deduction algorithm may include the following steps S1 to S3:

[0150] Step S1: Determine the first individual based on the initial population.

[0151] The first individual may refer to at least two individuals selected from the initial population. In some embodiments, for multiple individuals in the initial population, the planning system 200 may determine the first individual based on a preset individual ratio. The preset individual ratio may be set according to actual needs.

[0152] In some embodiments, the preset individual ratio is related to the total number of individuals in the initial population. Planning system 200 can determine the ratio of the first individual based on the total number of individuals in the initial population. For example, when the total number of individuals in the initial population is less than 40, the preset individual ratio can be 100%; when the total number of individuals in the initial population is greater than 100, the preset individual ratio can be 50%, and so on.

[0153] By presetting the individual ratio to determine the number of the first individuals, the load of each round of iterative processing can be balanced.

[0154] In some embodiments, the planning system 200 may determine the first individual through a preset selection function based on the evaluation values ​​of individuals in the initial population.

[0155] The evaluation value can represent the quality of an individual.

[0156] In some embodiments, for optimizing the puncture needle distribution by the first optimization algorithm, the evaluation value can be determined based on the function value of the first objective function; for optimizing the particle distribution by the second optimization algorithm, the evaluation value can be determined based on the function value of the second objective function.

[0157] The selection function can be any of a variety of preset selection operators, such as a roulette wheel selection operator, an expected value selection operator, or a uniform sorting operator. The greater the evaluation value of an individual, the greater the probability that the individual will be selected as the first individual by the selection function.

[0158] The first objective function is related to the total number of puncture needles and the total number of particles. For example, the function value of the first objective function can be the sum of the total number of puncture needles and the total number of particles corresponding to an individual. The smaller this sum, the higher the evaluation value corresponding to the individual, indicating a higher quality of the individual. The first objective function is used here for illustrative purposes only; the total number of puncture needles and the total number of particles can have different weighting coefficients.

[0159] The second objective function is related to particle dose conformity and / or particle dose distribution uniformity.

[0160] The particle dose conformity can characterize the degree of influence of the particle dose of the seed implantation on the tissues surrounding the target area. In some embodiments, the particle dose conformity can be determined based on the following formula (2):

[0161]

[0162] In the above formula (2), CN represents the particle dose conformity, V T,ref It represents the volume of the area within the target area that is higher than the reference dose (PD), V T represents the target volume, V ref Represents the volume receiving a dose higher than the reference dose (PD). A larger CN value indicates less dose leakage and less impact on tissues surrounding the target. A higher CN value indicates a better individual.

[0163] In some embodiments of this specification, by introducing particle dose conformity to evaluate the individual's quality, it is possible to avoid high doses to tissues around the target area while ensuring the efficacy of seed implantation, thereby protecting organs around the target area.

[0164] The particle dose distribution uniformity can characterize the uniformity of the dose distribution within the target area. In some embodiments, the particle dose distribution uniformity can be determined based on the following formula (3):

[0165]

[0166] In the above formula (3), DNR represents the uniformity of particle dose distribution, V ref represents the volume receiving a dose higher than the reference dose (PD), V 1.5ref It represents the volume receiving a dose greater than 1.5 times the reference dose (1.5×PD). A smaller DNR value indicates a more uniform dose distribution and fewer local high-dose areas within the target volume. A higher DNR value indicates a better individual outcome.

[0167] In some embodiments of this specification, by introducing the uniformity of particle dose distribution to evaluate the quality of an individual, it is possible to avoid the occurrence of uneven local high doses or local low doses in the target area and improve the treatment effect.

[0168] In some embodiments, the function value of the second objective function can be determined based on the particle dose conformity (CN value) and the particle dose distribution uniformity (DNR value). As an example only, the function value of the second objective function can be determined by the sum of the inverse of the CN value and the DNR value. As the CN value increases and the DNR value decreases, the function value of the second objective function decreases, and the individual evaluation value increases, i.e., the individual is more optimal.

[0169] Step S2: Perform a first transformation on the first entity to generate a second entity.

[0170] The first transformation process may refer to a process of pairing and processing the first individual to generate a new individual. The second individual may refer to a set of multiple new individuals generated by the first transformation process.

[0171] In some embodiments, the planning system 200 may randomly pair any two individuals in the first individual and adjust the puncture needle distribution and / or particle distribution of the two individuals.

[0172] In some embodiments, the first transformation process may include exchanging particle distributions on the same puncture needle of two individuals.

[0173] Combine Figure 5b , Figure 5b is an exemplary schematic diagram of the first transformation process according to some embodiments of this specification.

[0174] For example only, Figure 5bAs shown, for the two first individuals, individual 521 and individual 522 respectively include 3 puncture needles (one row represents one puncture needle), and the particle distribution on each puncture needle (1 represents placement of particles, 0 represents no placement of particles).

[0175] The planning system 200 can exchange the particle distribution in the puncture needle 1 of individual 521 and the particle distribution in the puncture 1 of individual 522. Figure 5b Individual 521-1 and individual 522-1 are shown.

[0176] It is understandable that by exchanging the particle distribution on the same puncture needle of two individuals, the generated second individual also meets the particle preset conditions. For relevant content on the particle preset conditions, see Figure 4a and its description.

[0177] In some embodiments, with the goal of increasing the evaluation value of an individual, the planning system 200 can traverse the puncture needles in the two paired individuals respectively, and obtain the number of binary bits with a value of 1 in each puncture needle (hereinafter referred to as the number of particles), and then use the particle distribution of the puncture needle corresponding to the maximum number of particles for the first transformation process. Figure 5b For the three puncture needles in individual 521, the particle counts are 8, 2, and 3, respectively; for the three puncture needles in individual 522, the particle counts are 5, 3, and 4, respectively. Among them, puncture needle 1 in individual 521 has the largest number of particles, and the particle distribution of puncture needle 1 can be used to perform the first transformation process described above.

[0178] It can be understood that the first transformation process favors retaining puncture needles with fewer particles within an individual. Simultaneously, the particle distribution of the puncture needle with the largest number of particles is swapped with the particle distribution of the puncture needle corresponding to the paired individual, which helps reduce the number of particles on that puncture needle, thereby optimizing the particle distribution of that individual toward a lower value of the first objective function. Furthermore, optimizing the particle distribution through the first transformation process at the puncture needle level facilitates rapid expansion of the individual search space, improving the speed and efficiency of obtaining the optimal individual.

[0179] The first transformation process described herein is merely exemplary. For example, the first transformation process may involve exchanging the particle distribution on one or more puncture needles. In some embodiments, the first transformation process may also involve exchanging the particle placement states (0 or 1) of one or more particles (e.g., particles at the same particle candidate position) on corresponding puncture needles (e.g., puncture needles at the same puncture needle candidate position) in two paired individuals to generate new individuals. Exemplarily, the particle placement states corresponding to multiple particle candidate positions on one or more puncture needles may be obtained randomly or based on a preset probability to form a combination for performing the first transformation process.

[0180] In some embodiments of this specification, the first transformation process can speed up the acquisition of new individuals, which is beneficial to the expansion of the population and the speed of obtaining excellent individuals.

[0181] Step S3: Perform a second transformation on the individuals in the initial population and / or the second individual to generate a third individual.

[0182] The second transformation process may refer to a process of adjusting the particle distribution of a single individual to generate a new individual. The third individual may refer to a set of one or more new individuals generated by the second transformation process.

[0183] In some embodiments, the planning system 200 may select one or more individuals from the initial population and / or the second individual and adjust the particle distribution on the puncture needle of each individual. Figure 5b The particle distribution in the puncture needles (e.g., puncture needle 1, puncture needle 2, etc.) of individual 521-1 (shown) can be adjusted. For example, the value of the first binary bit in puncture needle 1 of individual 521-1 can be set from 1 to 0; the value of the second binary bit can be set from 0 to 1; or a combination of multiple binary bits (e.g., 2, 3, etc.) can be adjusted to generate one or more third individuals.

[0184] It should be noted that, during the second transformation process, when the value of a binary bit is set from 0 to 1, the particle position corresponding to the binary bit needs to meet the particle preset condition.

[0185] In some embodiments of this specification, the second transformation process is used to achieve local optimization of the particle distribution of an individual at the particle level, while increasing the diversity of the particle distribution of an individual, which is conducive to obtaining individuals with higher evaluation values.

[0186] Step 530 : Based on the constraint conditions or based on the constraint conditions and the objective function, target individuals are selected from the initial population and the new population to form a target population. The initial population in the next round of iterative processing is the target population.

[0187] Wherein, for optimizing the puncture needle distribution by the first optimization algorithm, the constraint condition is the first constraint condition, and the objective function is the first objective function;

[0188] For optimizing the particle distribution by the second optimization algorithm, the constraint condition is the second constraint condition, and the objective function is the second objective function.

[0189] The new population may refer to a population composed of new individuals obtained through the first transformation process and / or the second transformation process, and may include a second individual generated through the first transformation process and / or a third individual generated through the second transformation process.

[0190] In some embodiments, with respect to optimizing the puncture needle distribution through the first optimization algorithm, the planning system 200 may screen individuals in the initial population and the new population based on the first constraint condition so that the candidate particle implantation plan satisfies the first constraint condition and the function value of the first objective function gradually decreases with the optimization (the individual evaluation value gradually increases).

[0191] In order to optimize the particle distribution through the second optimization algorithm, the planning system 200 can screen the individuals in the initial population and the new population based on the second constraint condition so that the target particle implantation plan satisfies the second constraint condition and the function value of the second objective function gradually decreases with the optimization (the individual evaluation value gradually increases).

[0192] For the convenience of the following description, the optimization of the puncture needle distribution by the first optimization algorithm can be referred to as the first stage optimization; the optimization of the particle distribution by the second optimization algorithm can be referred to as the second stage optimization.

[0193] The first constraint or the second constraint may refer to a discriminant condition for screening individuals in the initial population and the new population, and may be set according to actual needs.

[0194] In some embodiments, the first constraint is related to target dose coverage. The target dose coverage can be used to characterize the dose at the target volume that must reach a certain level to ensure therapeutic efficacy. The target dose coverage must meet a preset target dose coverage. The preset target dose coverage can be determined based on clinical needs or requirements. For example, it can be related to D90. D90 represents the dose level of the isodose line within the target volume that encompasses 90% of the target volume.

[0195] In some embodiments, the first constraint condition may be determined based on the following expression (4):

[0196] c=δ[D90 <PD]×(D90-PD) (4)

[0197] Wherein, δ[x] is an indicator function, which returns 1 when x is true, and returns 0 otherwise. In the above expression (4), PD represents the reference dose (or prescription dose), which can be determined based on scientific research institutions or medical experience. c represents the discriminant value of the second constraint. Expression (4) means that when the value of D90 is lower than the reference dose PD (i.e., D90 <PD),则约束判别值c取值为(c90-PD)部分,即D90与PD二者的差值,此时,判别值c为负值,表示个体不满足第一约束条件;否则为0,表示个体满足第一约束条件。也就是说,第一约束条件可以是靶区内包含靶区90%体积的等剂量线的剂量水平(即D90)需要高于参考剂量PD。

[0198] In some embodiments, the first constraint is also related to the dose limit of the second preset tissue. For example, the second constraint may also include that the volume dose and point dose of the second preset tissue around the target area are less than the preset maximum volume dose and the preset maximum point dose, respectively. The preset maximum volume dose and the preset maximum point dose can be determined according to the type of the second preset tissue, which can be obtained based on medical prior knowledge. For example, the second preset tissue around the target area is the spinal cord, and its corresponding maximum volume dose is 10Gy, and its maximum point dose is 14.0Gy. For another example, the second preset tissue around the target area is the main bronchus, and its corresponding maximum volume dose is 10.5Gy, and its maximum point dose is 16.0Gy. For more information about the second preset tissue and its dose limits, see Figure 3a and its description.

[0199] In some embodiments, the planning system 200 can obtain the D90 value of each individual in the initial population and the new population respectively, and obtain the target individuals that meet the first constraint condition based on the discriminant value of the above expression (4), form a target population, and use the target population as the initial population for the next round of iterative processing.

[0200] In some embodiments, the second constraint relates to target dose coverage and the total number of puncture needles and / or the total number of particles. The total number of puncture needles is related to the incremental threshold between the total number of puncture needles per individual before and after the second-stage optimization; the total number of particles is related to the incremental threshold between the total number of particles per individual before and after the second-stage optimization. In other words, after the second-stage optimization, the change in the total number of puncture needles and the change in the total number of particles per individual must satisfy the second constraint.

[0201] The increment thresholds for the total number of puncture needles and the total number of particles can be determined based on actual circumstances, for example, the size of the actual target area, the type of tissue surrounding the target area (such as heart, lungs), and the like.

[0202] In some embodiments, as an example, the increment threshold of the total number of puncture needles may be set to 3, and the increment threshold of the total number of particles may be set to 5. The second constraint condition may be determined based on the following expression (5):

[0203] c=δ[D90 <PD]×(D90-PD)+δ[N n >N n,0 +3]×(N n,0 +3-N n )+δ[N s >N s,0 +5]×(N s,0 +5-N s ) (5)

[0204] In the above expression (5), δ[x] is an indicator function, which returns 1 if x is true, and 0 otherwise. n,0 and N a,0 They represent the number of needles and particles corresponding to the individual (target particle implantation plan) obtained after the second stage of optimization. For example, the number of needles for an individual is 10 and the number of particles is 20.

[0205] The first part of the above expression (5) δ[D90 <PD]×(D90-PD)表示当该个体的D90低于参考剂量PD时,则第一部分的约束判别值为二者差值,即为负值,否则为0;

[0206] The second part δ[N n >N n,0 +3]×(N n,0 +3-N n ), N n Indicates the number of needles of the individual before optimization, N n,0 It represents the number of needles after the individual is optimized. The second part represents the increment N of the number of needles after the individual is optimized. n -N n,0 When it exceeds 3, the constraint judgment value of the second part is N n,0 +3-N n , the score is negative, otherwise it is 0;

[0207] Part III δ[N s >N s,0 +5]×(N s,0 +5-N s ), N s Indicates the number of individual particles before optimization, N s,0 Indicates the number of particles after the individual is optimized. The third part indicates the increment N of the number of particles after the individual is optimized. s -N s,0 When it exceeds 5, the constraint judgment value of the third part is Ns,0 +5-N s , that is, a negative number; otherwise, it is 0.

[0208] In the above expression (5), c represents the discriminant value of the entire expression (5), which is determined based on the sum of the discriminant values ​​of the first part, the second part, and the third part of expression (5). If the value of c is negative, it means that the individual does not satisfy the second constraint condition. Otherwise, the value of c is 0, which means that the individual satisfies the second constraint condition.

[0209] It should be noted that the puncture needle increment threshold of 3 and the particle increment threshold of 5 are only used as examples. They can be set according to actual conditions (for example, the puncture needle increment threshold is 4, the particle increment threshold is 6, etc.), and the above formula expression (5) is modified accordingly.

[0210] In some embodiments of the present specification, by introducing a puncture needle increment threshold and a particle increment threshold, the second stage optimization can appropriately limit the number of puncture needles and the number of particles while satisfying the first constraint condition, thereby avoiding the situation where the number of puncture needles and / or particles is excessive in the individuals generated by the deduction in the second optimization stage.

[0211] In some embodiments, the second constraint condition is further related to a dose limit of a second predetermined tissue. Similar to the first constraint condition, the second constraint condition may include that the volume dose and point dose of a second predetermined area around the target area are respectively less than a predetermined maximum volume dose and a predetermined maximum point dose.

[0212] In some embodiments, during the optimization process of the second optimization stage, the planning system 200 may further obtain difference particles in the particle distribution between the optimized particle implantation plan and the candidate particle implantation plan corresponding to the optimized particle implantation plan. When the number of difference particles is less than a preset total number of particles, the dose at the difference particle is determined. The optimized particle implantation plan refers to the second or third individual generated by the second optimization stage. It is understood that the particle distributions of a candidate particle implantation plan (i.e., the individual before the second optimization stage) and the optimized particle implantation plan (i.e., the individual after the second optimization stage) obtained after the second optimization stage may differ. The preset total number of particles can be determined based on the maximum number of current particles in the two individuals. The number of difference particles can be determined based on the number of different particle placement states (0 or 1) at the same particle candidate position in the two individuals. For example, if the particle placement states of the first particle candidate position on a puncture needle in individuals A and B are different (e.g., 0 in the former and 1 in the latter), while all other positions are consistent, the number of difference particles is 1.

[0213] It should be noted that after the first stage of optimization, during the optimization process of the second optimization stage, the particle distribution of each individual obtained by optimization through the above-mentioned preset deduction algorithm (such as the second individual or the third individual generated in the second optimization stage) is smaller than the particle distribution of the individual before optimization. By comparing the difference in particle distribution of the individual before and after optimization, when the difference value is less than the preset total number of particles, the dose at the difference particle can be directly calculated, thereby accelerating the dose calculation (such as the calculation of spatial dose distribution), and further accelerating the optimization process.

[0214] As an example, for a candidate seed implantation plan (e.g., individual A) and individual B, obtained after the second-stage optimization, the particle distributions of these two individuals are 1100 and 1110, respectively. The number of different particles between the two is 1 (i.e., individual B has one additional particle placed at the third position, while all other positions remain the same), which is less than the maximum number of particles between the two individuals, 3 (i.e., the number of particles in individual B). In this case, the planning system 200 can directly calculate the dose at the different particle (the third position of individual B). The difference in particle distributions of multiple puncture needles and the dose at the different particles are determined in a similar manner.

[0215] It should be noted that before calculating the CN value and DNR value of an individual, it is necessary to calculate the spatial dose distribution (such as the dose at multiple particles). In some embodiments of this specification, by calculating the dose at the difference particles, it is possible to avoid recalculating the dose of all particles in the individual particle distribution, reduce the calculation time, and improve the optimization efficiency while accelerating the dose calculation.

[0216] In some embodiments, the planning system 200 may further perform a ranking process on the target population that satisfies the first and / or second constraints. This may be a pre-set algorithm based on actual conditions. For example, the population may be ranked by evaluation value (e.g., in descending order) and the top-ranked individuals may be selected.

[0217] In some embodiments, the sorting process may include a non-dominated sorting algorithm or other available algorithms with similar functions. As an example, the target population is stratified by the non-dominated sorting algorithm to obtain non-dominated individuals in the hierarchy. The higher the hierarchy of an individual, the higher the non-dominated state of the individual, and the greater the probability of being screened out and entering the next round of iteration. At the same time, the crowding degree of the individuals in each dominance hierarchy can also be calculated. When the crowding distance between an individual and its adjacent individuals is greater, the sparser the area around the individual is, and the greater the crowding degree of the individual. The greater the crowding degree, the lower the similarity between the individual and other individuals, and the greater the probability of being screened out and entering the next round of iteration.

[0218] In some embodiments of this specification, individuals selected through a non-dominated sorting algorithm are used as a target population, and the target population is used as the initial population for the next round of iterative processing. This can make the individuals in the population diverse, which is conducive to obtaining a better particle implantation plan during the optimization process.

[0219] Step 540 : determining a target result in the target population obtained after at least one round of iterative processing.

[0220] In some embodiments, the planning system 200 may iteratively repeat steps 520 and 530 until a predetermined termination condition is met, thereby terminating the iterative process and obtaining the target population. The predetermined termination condition may include, but is not limited to, reaching a predetermined number of iterations or reaching a predetermined number of individuals in the target population.

[0221] The planning system 200 may determine a target outcome based on a target population.

[0222] For the first optimization phase, the target result can serve as a candidate particle implantation plan, which can be used as the initial population for the second optimization phase. For the second optimization phase, the target result can serve as the target particle implantation plan. It should be noted that the target result can be multiple candidate particle implantation plans and / or a target particle implantation plan, which can also be selected or adjusted by the user. For example, the user can make appropriate adjustments based on experience.

[0223] In some embodiments, the target seed implantation plan generated by the planning system 200 can be used to guide users (eg, medical personnel) to perform seed implantation on the target object. Figure 5c , Figure 5c FIG. 1 is an exemplary schematic diagram of a target particle implantation plan for particle implantation according to some embodiments of the present specification.

[0224] like Figure 5cAs shown, the cylinder represents a target region 322 (the tissues such as organs around the target region are not shown) in the target object, and for a certain target particle implantation plan, the user can determine a plurality of recommended puncture needles 541 (represented by vertical lines in the figure perpendicular to the particle implantation template 321, such as 5 recommended puncture needles) actually inserted into the target region 322 according to a plurality of recommended puncture needle positions (the binary bits corresponding to the puncture needle candidate positions have a value of 1) in the particle implantation template 321, and determine the placement of the particles based on the particle distribution information on each puncture needle in the target particle implantation plan. For example, for the recommended puncture needle 541, of the 5 candidate particle positions corresponding to the 5 scales (shown by black solid points and hollow points) on the needle, each black solid point 542-1 corresponds to a position on the needle with a binary bit value of 1, indicating that a particle needs to be placed at the position, and the hollow point 543-1 corresponds to a position on the needle with a binary bit value of 0, indicating that no particle is placed. The same applies to other recommended puncture needles, thereby achieving treatment of the target object.

[0225] In some implementations, the planning system 200 can also generate one or more individual corresponding particle implantation plan reports based on the target result. The particle implantation plan report can include, but is not limited to, basic information of the target object (such as a patient); image data (such as contours of the target region and tissue region); instrument information (such as puncture needle length, scale, particle type, particle activity, particle implantation template information, etc.); position and direction of the puncture needle; particle distribution on the puncture needle (such as particle number, position); visual display of the puncture needle path and particle spatial distribution; isodose line display of the dose distribution generated by the particles; DVH curve display of the dose distribution generated by the particles; quantitative evaluation results of the dose distribution, including particle dose conformity, particle dose distribution uniformity, etc.

[0226] In some embodiments of the present specification, the first optimization algorithm and / or the second optimization algorithm can automatically obtain an optimized target particle implantation plan, which can reduce the labor and time costs brought by manual deduction. Meanwhile, the combination of the first optimization algorithm and the second optimization algorithm can make the optimization direction of the individual more targeted, stable and reliable based on the individual obtained by the optimization of the first optimization algorithm, and thus realize a more accurate target particle implantation plan that meets the actual needs.

[0227] It should be noted that the first optimization algorithm and / or the second optimization algorithm can also be other feasible optimization algorithms or search algorithms.

[0228] In some embodiments, the optimization strategy of the first optimization algorithm and / or the second optimization algorithm may be related to the time complexity and / or space complexity of the optimization process. For example, when the target area is small (such as a small cross-sectional area, a small volume, etc.), the number of puncture needles required for particle implantation is small, and / or the number of particles is small, then the number of particle implantation plans is small. At this time, the time complexity and / or space complexity of the optimization process is low. The first optimization algorithm and / or the second optimization algorithm may be a set composed of enumerating different combinations of 0 or 1 (a combination of binary values ​​corresponding to all candidate particle positions is an individual) to determine the candidate particle implantation plan and / or the target particle implantation plan; otherwise, the first optimization algorithm and / or the second optimization algorithm may be such as Figure 5a The strategy shown can also be implemented by other combinatorial optimization algorithms (such as simulated annealing algorithm, etc.).

[0229] The planning system 200 can determine the optimization strategy of the first optimization algorithm and / or the second optimization algorithm based on the actual situation (such as the configuration of parameters), and optimize the puncture needle distribution through the first optimization algorithm with the goal of making the candidate particle implantation plan satisfy the first constraint and reducing the function value of the first objective function to obtain the candidate particle implantation plan; at the same time, with the goal of making the target particle implantation plan satisfy the second constraint and reducing the function value of the second objective function, optimize the particle distribution through the second optimization algorithm to obtain the target particle implantation plan.

[0230] Figure 6 is an exemplary flow chart of another method for preoperative planning of target seed implantation according to some embodiments of this specification.

[0231] In some embodiments, process 600 may be performed by planning system 200. Figure 6 As shown, process 600 includes the following steps:

[0232] Step 610: Acquire image data of the segmented target area and tissue area of ​​the target object.

[0233] For details about step 610, see Figure 3a Step 310 and its description.

[0234] Step 620: Determine an initial puncture needle distribution based on a representation model associated with particle implantation, wherein the representation model includes a puncture needle candidate position representation and a particle candidate position representation associated with the image data, wherein a puncture needle candidate position corresponds to one or more particle candidate positions.

[0235] For details about step 620, please refer to Figure 3a Step 320 and its description in .

[0236] At step 630, the first optimization algorithm is used to optimize the distribution of the puncture needles based on the initial distribution of the puncture needles, to obtain a target particle implantation plan.

[0237] For details about the first optimization algorithm used to optimize the distribution of the puncture needles, refer to Figure 5a and the description thereof.

[0238] In some embodiments, for one or more candidate particle implantation plans obtained by the first optimization algorithm used to optimize the distribution of the puncture needles, the planning system 200 can determine whether the candidate particle implantation plan meets a preset clinical condition. The preset clinical condition can include a second constraint condition and / or a second objective function.

[0239] For details about the second constraint condition and the second objective function, refer to Figure 5a and the description thereof.

[0240] In response to the existence of a candidate particle implantation plan that meets the preset clinical condition, the candidate particle implantation plan can be used as the target particle implantation plan.

[0241] According to some embodiments of the present specification, the first optimization algorithm is used to optimize the distribution of the puncture needles, and the candidate particle implantation plan that meets the clinical condition is used as the target particle implantation plan without a second optimization stage, which can reduce the calculation cost and time cost.

[0242] Figure 7 is an exemplary schematic diagram of a particle implantation preoperative planning device according to some embodiments of the present specification.

[0243] As shown in Figure 7 , the particle implantation preoperative planning device 700 includes a processor 710 and a memory 720 coupled to the processor 710.

[0244] The memory 720 stores program instructions for implementing the method of any of the above embodiments, and the processor 710 is configured to execute the program instructions stored in the memory 720 to implement the steps of the above method embodiments. The processor 710 can also be referred to as a CPU (Central Processing Unit). The processor 710 can be an integrated circuit chip having signal processing capability. The processor 710 can also be a general-purpose processor, a DSP, an ASIC, an FPGA, or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0245] It should be noted that the above description of the relevant processes is for illustration and purpose only and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to the processes under the guidance of this specification. However, such modifications and changes are still within the scope of this specification.

[0246] While the basic concepts have been described above, it will be apparent to those skilled in the art that the detailed disclosure is merely illustrative and does not limit this specification. Although not explicitly stated herein, various modifications, improvements, and revisions to this specification may be made by those skilled in the art. Such modifications, improvements, and revisions are suggested in this specification and remain within the spirit and scope of the exemplary embodiments of this specification.

[0247] Finally, it should be understood that the embodiments described in this specification are intended only to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly described and illustrated in this specification.

Claims

1. A method for preoperative planning of seed implantation, characterized in that: include: Acquiring image data of the segmented target area and tissue area of ​​the target object; determining an initial puncture needle distribution based on a representation model associated with the particle implantation, the representation model comprising a representation of candidate puncture needle positions and a representation of candidate particle positions associated with the image data, wherein the candidate puncture needle positions correspond to one or more candidate particle positions; Based on the initial puncture needle distribution, the puncture needle distribution is optimized by a first optimization algorithm to obtain a candidate particle implantation plan, wherein the first optimization algorithm aims to make the candidate particle implantation plan satisfy a first constraint and reduce a function value of a first objective function; Based on the candidate particle implantation plan, the particle distribution is optimized by a second optimization algorithm to obtain a target particle implantation plan, wherein the second optimization algorithm aims to make the target particle implantation plan satisfy a second constraint and reduce the function value of a second objective function.

2. The method according to claim 1, characterized in that The representation model includes a representation model based on a coplanar needle group, and the puncture needle candidate position representation and / or the particle candidate position representation are binary representations.

3. The method according to claim 1, characterized in that The determining of an initial puncture needle distribution based on a representation model associated with the seed implantation further comprises: Based on the representation model, the candidate positions of the puncture needle are preprocessed, and the preprocessing includes one or more of the following: eliminating the candidate positions of the puncture needle that do not meet the preset conditions of the puncture needle, eliminating the candidate positions of the particle that do not meet the preset conditions of the particle; wherein, The puncture needle preset conditions include one or more of the following: the puncture needle at the candidate puncture needle position cannot penetrate the target tissue, the insertion depth of the puncture needle at the candidate puncture needle position is less than the instrument length of the puncture needle; The particle preset conditions include one or more of the following: the particle candidate position is inside the target area, the distance between the particle candidate position and the first puncture voxel or the last puncture voxel of the target area is less than a preset value; and The initial puncture needle distribution is determined based on the preprocessed candidate puncture needle positions.

4. The method according to claim 1, wherein In the puncture needle distribution, particles are placed at all particle candidate positions on any of the puncture needle candidate positions; and the first constraint condition is related to the target dose coverage, and the first objective function is related to the total number of puncture needles and the total number of particles.

5. The method according to claim 1, wherein The second constraint condition is related to the target dose coverage, the total number of puncture needles and / or the total number of particles; the second objective function is related to the particle dose conformity and / or the particle dose distribution uniformity.

6. The method according to claim 5, characterized in that Further including: During the optimization process, obtaining difference particles in the particle distribution between the optimized particle implantation plan and the candidate particle implantation plan corresponding to the optimized particle implantation plan; When the number of the difference particles is less than the preset total number of particles, the dose at the difference particles is determined.

7. The method according to claim 4 or 5, characterized in that Optimizing the puncture needle distribution by the first optimization algorithm or optimizing the particle distribution by the second optimization algorithm includes: The optimization process is performed based on at least one round of iterative processing, wherein the round of iterative processing includes: obtaining an initial population, wherein the initial population includes a plurality of individuals, each individual corresponding to a seed implantation plan; Based on the initial population, a new population is generated by a preset deduction algorithm; Based on the constraint conditions or based on the constraint conditions and the objective function, target individuals are selected from the initial population and the new population to form a target population, and the initial population in the next round of iterative processing is the target population; Determine the target result in the target population obtained after the optimization process; wherein, For optimizing the puncture needle distribution using the first optimization algorithm, the constraint condition is the first constraint condition, the objective function is the first objective function, the initial population of the first iteration is obtained based on the initial puncture needle distribution, and the target result is the candidate particle implantation plan; For optimizing the particle distribution through the second optimization algorithm, the constraint condition is the second constraint condition, the objective function is the second objective function, the initial population of the first round of iteration is obtained based on the candidate particle implantation plan, and the target result is the target particle implantation plan.

8. A method for preoperative planning of seed implantation, characterized in that: include: Acquiring image data of the segmented target area and tissue area of ​​the target object; determining an initial puncture needle distribution based on a representation model associated with the particle implantation, the representation model comprising a representation of candidate puncture needle positions and a representation of candidate particle positions associated with the image data, wherein the candidate puncture needle positions correspond to one or more candidate particle positions; Based on the initial puncture needle distribution, the puncture needle distribution is optimized by a first optimization algorithm to obtain a target particle implantation plan, wherein the first optimization algorithm aims to make the candidate particle implantation plan satisfy a first constraint condition and reduce the function value of a first objective function, and the target particle implantation plan is determined based on the candidate particle implantation plan.

9. A preoperative planning system for seed implantation, characterized in that: include: an acquisition module, for acquiring image data of the segmented target area and tissue area of ​​the target object; a determination module configured to determine an initial puncture needle distribution based on a representation model associated with the particle implantation, the representation model comprising a puncture needle candidate position representation and a particle candidate position representation associated with the image data, wherein the puncture needle candidate position corresponds to one or more of the particle candidate positions; a first optimization module configured to optimize the puncture needle distribution using a first optimization algorithm based on the initial puncture needle distribution to obtain a candidate particle implantation plan, wherein the goal is to ensure that the candidate particle implantation plan satisfies a first constraint and reduces a function value of a first objective function; The second optimization module is used to optimize the particle distribution based on the candidate particle implantation plan through a second optimization algorithm to obtain a target particle implantation plan, wherein the second optimization algorithm aims to make the target particle implantation plan satisfy a second constraint condition and reduce the function value of a second objective function.

10. A device for preoperative planning of particle implantation, characterized in that: The method comprises a processor configured to execute the particle implantation preoperative planning method according to any one of claims 1 to 8.

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