A particle implantation planning system, method and apparatus

Abstract

Embodiments of the present specification provide a particle implantation planning system, method and device, wherein the system comprises a processor configured to perform the following operations: obtaining medical image data of a target object; determining object features of the target object based on the medical image data; the object features include a target region; determining a needle entry path of a reference puncture needle based on the object features of the target object; determining a candidate puncture needle group that satisfies a preset condition based on at least the needle entry path of the reference puncture needle; performing particle distribution optimization and needle group optimization on the puncture needles in the candidate puncture needle group to determine a target puncture needle group; the target puncture needle group includes a puncture path of a target puncture needle and a particle distribution in the target puncture needle.

Description

TECHNICAL FIELD

[0001] The present specification relates to the field of radiotherapy, and in particular to a particle implant planning system, method and apparatus. BACKGROUND

[0002] Particle implantation is a kind of brachytherapy technology, which is suitable for the treatment of various tumors. In particle implantation, radioactive particles are delivered into the target area through a puncture needle to kill tumor cells from the inside. The puncture technology is one of the key factors that determine the efficacy of particle implantation.

[0003] The present specification provides a particle implant planning system, method and apparatus, which plans particle implantation before operation to ensure accurate puncture during operation. SUMMARY

[0004] One or more embodiments of the present specification provide a particle implant planning system, which comprises a processor configured to perform the following operations: obtaining medical image data of a target object; determining object features of the target object based on the medical image data; the object features comprise a target area; determining an entry path of a reference puncture needle based on the object features of the target object; determining a candidate puncture needle group that meets a preset condition based on at least the entry path of the reference puncture needle; performing particle distribution optimization and needle group optimization on the puncture needles in the candidate puncture needle group to determine a target puncture needle group; the target puncture needle group comprises a puncture path of a target puncture needle and a particle distribution in the target puncture needle.

[0005] One or more embodiments of the present specification provide a particle implant planning method, which comprises: obtaining medical image data of a target object; determining object features of the target object based on the medical image data; the object features comprise a target area; determining an entry path of a reference puncture needle based on the object features of the target object; determining a candidate puncture needle group that meets a preset condition based on at least the entry path of the reference puncture needle; performing particle distribution optimization and needle group optimization on the puncture needles in the candidate puncture needle group to determine a target puncture needle group; the target puncture needle group comprises a puncture path of a target puncture needle and a particle distribution in the target puncture needle.

[0006] One or more embodiments of the present specification provide a particle implant planning apparatus comprising a processor configured to perform a particle implant planning method.

[0007] One or more embodiments of the present specification provide a computer readable storage medium storing computer instructions, wherein when a computer reads the computer instructions in the storage medium, the computer performs a particle implant planning method. BRIEF DESCRIPTION OF DRAWINGS

[0008] The present specification will be further explained in the way of example embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting, in these embodiments, the same numbers represent the same structures.

[0009] Figure 1 is a schematic diagram of an exemplary application scenario of a particle implantation planning system according to some embodiments of the present specification;

[0010] Figure 2 is an exemplary flowchart of a particle implantation planning method according to some embodiments of the present specification;

[0011] Figure 3 is an exemplary flowchart of determining an entry path of a reference puncture needle according to some embodiments of the present specification;

[0012] Figure 4 is an exemplary flowchart of determining a candidate puncture needle set according to some embodiments of the present specification;

[0013] Figure 5 is an exemplary schematic diagram of an iterative judgment method according to some embodiments of the present specification;

[0014] Figure 6 is an exemplary flowchart of calculating a target region coverage according to some embodiments of the present specification;

[0015] Figure 7 is an exemplary flowchart of determining a target puncture needle set according to some embodiments of the present specification;

[0016] Figure 8 is an exemplary flowchart of a particle distribution optimization method according to some embodiments of the present specification;

[0017] Figure 9 is an exemplary flowchart of updating a punctured particle distribution according to some embodiments of the present specification;

[0018] Figure 10 is an exemplary schematic diagram of a puncture needle length and angle according to some embodiments of the present specification;

[0019] Figure 11 is an exemplary schematic diagram of a reference puncture needle according to some embodiments of the present specification;

[0020] Figure 12 is an exemplary schematic diagram of a preset range according to some embodiments of the present specification;

[0021] Figure 13 is an exemplary schematic diagram of a target region coverage according to some embodiments of the present specification;

[0022] Figure 14 is an exemplary schematic diagram of updating target area coverage according to some embodiments of the present specification;

[0023] Figure 15 is an exemplary schematic diagram of a candidate set of puncture needles according to some embodiments of the present specification;

[0024] Figure 16 is an exemplary schematic diagram of preset equidistant scales in a puncture needle according to some embodiments of the present specification;

[0025] Figure 17 is an exemplary schematic diagram of a non-coplanar template according to some embodiments of the present specification. DETAILED DESCRIPTION

[0026] In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some examples or embodiments of the present specification, and for those skilled in the art, the present specification can also be applied to other similar scenarios without creative labor. Unless it is clear from the language context or otherwise indicated, the same reference numbers in the drawings represent the same structures or operations.

[0027] It should be understood that the "system", "device", "unit" and / or "module" used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0028] As shown in the specification and claims, unless the context clearly indicates otherwise, "one", "a", "an", and / or "the" do not specify a single number, but can also include a plurality. Generally, the terms "comprise" and "include" only indicate the inclusion of the steps and elements explicitly identified, and these steps and elements do not constitute an exclusive list, and the method or device can also include other steps or elements.

[0029] Flowcharts are used in the present specification to illustrate the operations performed by the system according to the embodiments of the present specification. It should be understood that the preceding or subsequent operations are not necessarily performed in sequence. On the contrary, each step can be processed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or more steps of the operation can be removed from these processes.

[0030] In particle implantation, a template is often used to limit the position and direction of a puncture needle (referred to as a needle). The template used in particle implantation is divided into two types: a coplanar template and a non-coplanar template. The puncture needles determined by the coplanar template are parallel, that is, the directions of all the puncture needles are consistent. For a specific site, due to the shielding of important organs (such as the shielding of a large number of ribs and blood vessels in the chest and abdomen), it is difficult to plan a dose coverage plan that meets the standard by using a coplanar template. The puncture needles determined by the non-coplanar template can be non-planar, and the puncture needles can be punctured from any angle, which has high flexibility. Under the premise of avoiding dangerous organs, the above-mentioned problem of dose coverage not meeting the standard can be effectively alleviated.

[0031] Although the non-planar needle has high flexibility and can solve the problem of target area dose coverage, the search space and the amount of calculation when planning the puncture path of the puncture needle will be dramatically expanded, which will reduce the efficiency and increase the difficulty of particle implantation planning. There are few studies on non-planar puncture needle planning algorithms for particle implantation in the related art, and a large amount of manual interaction (such as manually adjusting the number of needles and the number of particles repeatedly) is required, which is highly dependent on subjective experience.

[0032] Therefore, some embodiments of the present specification provide a particle implantation planning system and method, which can quickly and accurately plan a non-planar puncture needle group and particle distribution in the puncture needle.

[0033] Figure 1 FIG. 1 is an example application scenario of a particle implantation planning system according to some embodiments of the present specification.

[0034] In some embodiments, as shown in FIG. 1, the particle implantation planning system 100 includes a surgical robot device 110, a network 120, a terminal 130, a processing device 140, and a storage device 150. Figure 1

[0035] In some embodiments, the particle implantation planning system 100 is applied to the medical field that needs to implement complex operations, for example, surgical planning for completing particle implantation, execution of an interventional surgery (including a particle implantation surgery and other interventional surgeries), and the like. Particle implantation planning refers to determining a puncture needle and particle distribution in the puncture needle through imaging before surgery, so that the puncture needle can avoid important tissues in the target area, and the implanted particles can minimize the radiation to the surrounding important tissues when radiating the target area, thereby obtaining better therapeutic effect. In some embodiments, the puncture needle group and particle distribution in the needle planned by the particle implantation planning system can also be applied to the execution of manual surgery, which is not limited in the present specification.

[0036] ​The surgical robotic device 110 can perform corresponding operations according to received instructions (e.g., control signal instructions). In some embodiments, the surgical robotic device 110 includes a surgical robot, a medical device, and a mobile patient bed. When the surgical robotic device 110 receives data or instructions sent by other devices or system components, the mobile patient bed can move the patient to a position indicated by the instructions, the medical device can start working according to the instructions, and the surgical robot can perform surgery (e.g., perform a particle implantation surgery) based on the instructions. In some embodiments, the surgical robot includes a mechanical arm, an end instrument (e.g., an end gripper, a puncture needle, or other surgical instrument), and when the surgical robot receives data or instructions sent by other devices or system components, the mechanical arm on the surgical robot moves to move the end instrument to a position indicated by the instructions and performs a surgical action (e.g., inserts a puncture needle into a patient). In some embodiments, the surgical robot can be integrated with the medical device or can be separate, for example, the bottom of the surgical robot includes a bottom cart, through which the surgical robot can be moved to a suitable position to perform an interventional surgery. In some embodiments, the surgical robot can also be provided with a sensor to detect kinematic parameters (e.g., position, angle, speed, etc.) when the linkage moves, and feed the kinematic parameters back to the processing device 140 or the terminal 130. In some embodiments, the surgical robot can also be provided with a camera to obtain an image of the surgical robot and its environment, and send the image to the processing device 140 or the terminal 130.

[0037] The network 120 includes any suitable network that can facilitate the exchange of information and / or data of the surgical robotic device 110. In some embodiments, at least one component of the particle implantation planning system 100 (e.g., the surgical robotic device 110, the terminal 130, the processing device 140, and the storage device 150) can exchange information and / or data with at least one other component of the particle implantation planning system 100 through the network 120. For example, the processing device 140 obtains medical images of a target object from the terminal 130 through the network 120. For another example, the processing device 140 performs particle implantation planning based on the obtained medical images of the target object, and sends results of the particle implantation planning to other components of the system, such as to the terminal 130 for display for user confirmation, etc. In some embodiments, the network 120 includes at least one network access point. For example, the network 120 includes wired and / or wireless network access points (e.g., base stations and / or Internet exchange points), through which at least one component of the particle implantation planning system 100 can connect to the network 120 to exchange data and / or information.

[0038] The terminal 130 can communicate and / or connect with the surgical robotic device 110, the processing device 140, and / or the storage device 150. In some embodiments, the terminal 130 includes a mobile device 131, a tablet 132, a laptop 133, or the like, or any combination thereof. For example, the mobile device 131 includes a mobile control handle, a personal digital assistant (PDA), a smartphone, or the like, or any combination thereof. In some embodiments, the terminal 130 includes a display device, e.g., a display. The display device is used to display information or images related to the particle implantation surgery, e.g., particle implantation planning results, medical images of a patient, a three-dimensional model, or an operation panel related to the particle implantation surgery, etc. In some embodiments, the terminal 130 is part of the processing device 140.

[0039] The processing device 140 can be used to process data and / or information obtained from the surgical robotic device 110, the terminal 130, the storage device 150, or other components of the particle implantation planning system 100. For example, the processing device 140 can obtain medical image data of a target object; determine object features of the target object based on the medical image data; the object features include a target volume; determine an entry path of a reference puncture needle based on the object features of the target object; determine a candidate puncture needle set that satisfies a preset condition based on at least the entry path of the reference puncture needle; perform particle distribution optimization and needle set optimization on the puncture needles in the candidate puncture needle set to determine a target puncture needle set. In some embodiments, the processing device 140 is a single server or a server group. The server group is centralized or distributed. In some embodiments, the processing device 140 is local or remote. For example, the processing device 140 can access information and / or data from the surgical robotic device 110, the storage device 150, and / or the terminal 130 through the network 120. For another example, the processing device 140 can be directly connected to the surgical robotic device 110, the terminal 130, and / or the storage device 150 to access information and / or data. For yet another example, the processing device 140 can be installed on the surgical robotic device 110. In some embodiments, the processing device 140 can be implemented on a cloud platform. For example, the cloud platform includes a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud, a multi-cloud, or the like, or any combination thereof.

[0040] The storage device 150 can be used to store data, instructions, and / or any other information. For example, the storage device 150 stores data obtained from the surgical robotic device 110, the terminal 130, and / or the processing device 140. In some embodiments, the storage device 150 stores data and / or instructions used by the processing device 140 to perform or use to complete the example methods described in this specification. In some embodiments, the storage device 150 includes mass storage, removable storage, volatile read / write storage, read-only memory (ROM), etc., or any combination thereof. In some embodiments, the storage device 150 is implemented on a cloud platform.

[0041] In some embodiments, the storage device 150 can be connected to the network 120 to communicate with at least one other component in the particle implant planning system 100 (e.g., the processing device 140, the terminal 130). At least one component in the particle implant planning system 100 can access data stored in the storage device 150 through the network 120. In some embodiments, the storage device 150 is part of the processing device 140. In some embodiments, the processing device 140 and the storage device 150 can be integrated in the surgical robotic device 110.

[0042] It should be noted that the foregoing description is provided for illustrative purposes only and is not intended to limit the scope of this specification. Various changes and modifications can be made to the exemplary embodiments described in this specification within the scope of the present disclosure, and features, structures, methods, and other characteristics of the exemplary embodiments described in this specification can be combined in various ways, and can be used in combination with additional and / or alternative exemplary embodiments described in this specification. For example, the storage device 150 is a data storage device 150 including a cloud computing platform, such as a public cloud, a private cloud, a community and hybrid cloud, etc. However, these changes and modifications do not depart from the scope of this specification.

[0043] Figure 2 is an example flowchart of a particle implant planning method according to some embodiments of the present specification. As shown in Figure 2 the flowchart 200 includes the following steps. In some embodiments, the flowchart 200 can be performed by a particle implant planning system (e.g., a processor included in the particle implant planning system).

[0044] Step 210, obtaining medical image data of a target object.

[0045] The target object includes the whole or part of a biological object and / or a non-biological object involved in the scanning process. For example, the target object is a living or non-living organic and / or inorganic substance, such as the head, ear-nose, mouth, neck, chest, abdomen, liver-gallbladder-pancreas-spleen, kidney, spine, etc.

[0046] The medical image data refers to images obtained by scanning a target object using an imaging device. In some embodiments, the medical image data includes CT (Computed Tomography) images, MRI (Magnetic Resonance Imaging) images, and the like.

[0047] In some embodiments, the medical image data can be obtained by scanning a target object using an imaging device (e.g., a CT device, an MRI device, or the like) before performing a particle implantation surgery, and then reconstructing images based on the scanning data. Alternatively, the medical image data can be obtained by reading data from a database, a storage device, or an imaging device, where the medical image data has been pre-scanned and stored.

[0048] At step 220, object features of the target object are determined based on the medical image data.

[0049] The object features refer to attributes, characteristics, or properties of the target object. In some embodiments, the object features include a target region of the target object and adjacent tissues of the target region.

[0050] In a particle implantation surgery, the target region refers to a site or tissue that needs to be treated by particle implantation. In some embodiments, the target region can be any location that needs to be treated, such as the brain, the spine, the lungs, or a specific area (e.g., a tumor), and the like. Before performing a particle implantation surgery, a physician can determine the location and size of the target region using various imaging techniques to ensure the accuracy and effectiveness of the treatment, e.g., by determining the target region from the medical image data.

[0051] The adjacent tissues refer to tissues adjacent to the target region, e.g., tissues having a distance less than a certain value (e.g., 1 mm, 2 mm, or 5 mm, or the like) from the target region. In some embodiments, the adjacent tissues include organs, such as the heart, the lungs, the stomach, the kidneys, the skin, the liver, and the like, which are usually composed of two or more types of tissues.

[0052] In some embodiments, the adjacent tissues vary depending on the target region. For example, for particle implantation of a brain tumor, the adjacent tissues of the target region include brain tissue, the optic nerve, the auditory nerve, the pituitary, the carotid artery, and the like. For example, for particle implantation of prostate cancer, the adjacent tissues of the target region include the rectum, the bladder, the urethra, and the like.

[0053] In some embodiments, the processing device can segment the target region and the adjacent tissues of the target region of the target object from the medical image data by image segmentation. For example, the image segmentation can segment sites including, but not limited to, a target region lesion, adjacent important organs, skin, bones, and part of blood vessels from the medical image data.

[0054] In some embodiments, the method of image segmentation includes manual segmentation, semi-automatic segmentation and automatic segmentation. The way of manual segmentation includes outlining the region contour by using interactive software (such as ITK-SNAP, etc.); the way of semi-automatic segmentation includes segmenting the target region by using traditional image processing algorithms through manual setting of parameters, and the specific algorithms include but are not limited to threshold method, region growing method and level set; the way of automatic segmentation includes segmenting the target region in the image by using a trained machine learning or deep learning model, such as a segmentation model based on U-Net, etc. The specific segmentation method is not limited in the embodiment, and the corresponding function can be implemented.

[0055] In the embodiment, the object feature of the target object can be used to assist the execution of the subsequent particle implantation planning step. For example, if some important organs are punctured, serious complications (such as pneumothorax caused by puncturing the lung) will be caused, and the puncture needle should be avoided to pass through these organs when planning the needle arrangement; for example, in the particle implantation plan, a certain dose level (such as minimum limit, etc.) needs to be reached in the target region, and a certain dose limit (such as maximum limit) needs to be met in the important organs. The contours of the target region and the adjacent tissues are segmented, which can help to calculate the dose distribution in the target region and help to avoid passing through the important tissues when planning the puncture needle.

[0056] In step 230, the entry path of the reference puncture needle is determined based on the object feature of the target object.

[0057] The puncture needle is a medical device that can be used to inject high-energy particles (such as metal particles wrapped with I125, Pd103, etc. radioactive nuclide) into the target region in the patient's body. The puncture needle has different specifications, such as size (diameter), length, etc. Different specifications of the puncture needle can reach different parts of the patient's body to adapt to different treatment scenarios.

[0058] The reference puncture needle refers to the puncture needle that can be used as a reference for planning the puncture needle. In some embodiments, the reference puncture needle is the first puncture needle planned in the puncture needle group planning. In the process of planning the puncture needle, the best entry point and puncture angle need to be selected according to the position and size of the target region, considering various factors (such as the position of the surrounding tissues, the entry angle and depth of the puncture needle, etc.).

[0059] The entry path of the reference puncture needle refers to the entry point of the first puncture needle on the skin surface determined in the process of particle implantation planning. Assuming that the reference puncture needle passes through the center of the target region, the puncture path of the reference puncture needle can be represented by the entry point of the reference puncture needle on the skin surface and the center of the target region. In order to describe simply, the puncture path of the reference puncture needle is described as the entry path of the reference puncture needle.

[0060] In some embodiments, the reference puncture needle is arranged to pass through the center of the target region. In consideration of the fact that the reference puncture needle is arranged to pass through the center of the target region (e.g., the centroid, the geometric center, or a designated point in the target region), the reference puncture needle can be arranged to pass through the center of the target region (e.g., the centroid, the geometric center, or a designated point in the target region), and the puncture path of the reference puncture needle can be determined by connecting the entry point and the center of the target region.

[0061] In some embodiments, the processing device can determine the entry path of the reference puncture needle by screening the entry point of the target object surface based on the object features of the target object and the first preset constraint condition. The details of determining the entry path of the reference puncture needle can be found in the description of Figure 3 .

[0062] In some embodiments, the entry path of the reference puncture needle can also be directly specified by the user, and this embodiment is not limited in this regard.

[0063] At step 240, a candidate puncture needle group satisfying the preset condition is determined based on at least the entry path of the reference puncture needle.

[0064] The candidate puncture needle group refers to a set of puncture needles that are screened out and can be used as the planning result. In some embodiments, the candidate puncture needle group includes the reference puncture needle and one or more candidate puncture needles determined based on at least the reference puncture needle. The candidate puncture needle refers to a puncture needle determined after the reference puncture needle is determined. In some embodiments, the candidate puncture needle group can include only one or more candidate puncture needles determined based on at least the reference puncture needle. For example, when the candidate puncture needle group is further optimized (see the description of Figure 7 later), the reference puncture needle can be removed from the candidate puncture needle group.

[0065] In some embodiments, each candidate puncture needle (referred to as a puncture needle or a needle for short) in the candidate puncture needle group includes an entry point and a target point. The puncture path of the candidate puncture needle can be determined by connecting the entry point and the target point.

[0066] In some embodiments, the processing device can gradually obtain the candidate puncture needle by screening according to the preset condition based on at least the obtained entry path of the reference puncture needle. The preset condition includes a preset needle group obtaining strategy and a second preset constraint condition.

[0067] The preset needle set acquisition strategy refers to a preset assumption for acquiring the puncture needle set. For example, to enable the puncture needle to gradually cover the target region, it is assumed that the next needle is within a certain range of the current acquired needle set. The current acquired needle set is a set of puncture needles for which the puncture path has been determined. For example, when the 6th needle is acquired, the current acquired needle set is the 1st-5th needles (a total of five puncture needles) that have been acquired. The certain range is a region determined at the tip of each needle in the acquired needle set, such as a circular region. The union of the adjacent ranges of the plurality of acquired needles is taken as the range in which the tip of the next needle to be acquired is located.

[0068] The preset second constraint condition refers to a screening condition for acquiring the candidate puncture needle.

[0069] For detailed descriptions of the preset second constraint condition and the determination of the candidate puncture needle set, refer to the description of Figure 4 .

[0070] In step 250, the particle distribution optimization and the needle set optimization are performed on the puncture needles in the candidate puncture needle set to determine the target puncture needle set.

[0071] The target puncture needle set refers to a set of puncture needles that the particle implantation planning wants to acquire. In some embodiments, the target puncture needle set includes the puncture path of the target puncture needle and the particle distribution in the target puncture needle. The particle distribution refers to the spatial position of the particles in the puncture needle.

[0072] The particle distribution optimization refers to adjusting the spatial position of the particles in the puncture needle to enable the particles in the puncture needle to better cover the target region.

[0073] The needle set optimization refers to further screening the puncture needles in the candidate puncture needle set. For example, removing redundant puncture needles from the candidate puncture needle set.

[0074] In some embodiments, the processing device can optimize the particle distribution in the puncture needle based on the candidate puncture needle set by using a preset particle distribution optimization algorithm, and screen the puncture needles in the candidate puncture needle set based on the result of the particle distribution optimization to determine the target puncture needle. For example, there may be a situation in which the particles in some candidate puncture needles are empty in the particle distribution optimization result. The part of the puncture needles can be removed from the candidate puncture needle set, and the remaining set of puncture needles is the target puncture needle set.

[0075] For detailed descriptions of the determination of the target puncture needle set, refer to the description of Figures 7 to 9 , which will not be described here.

[0076] In some embodiments, after the particle implantation planning is completed, the particle implantation planning system can output a complete planning report for the user to refer to. The specific content of the report includes: basic information of the patient, such as name, age, occupation, gender, etc.; image data of the patient, including medical images near the target region, target region and tissue contours, etc.; instrument information, including needle length, scale, particle type, particle activity, etc., and if a template is used, template information can also be provided, such as whether a coplanar template or a non-coplanar template is used, etc.; needle entry point and target point of the puncture needle, etc.; distribution of particles inside the needle channel; visual display of the needle channel and the spatial distribution of particles; isodose lines of the dose distribution generated by the particles; DVH (Dose and Volume Histogram) curve display of the dose distribution generated by the particles, which can reflect the surrounding situation of the isodose lines to the target region and organs; quantitative evaluation results of the dose distribution, including indicators such as conformity and uniformity.

[0077] In some embodiments of the present disclosure, the target region can be extracted from the medical image data of the target object, and a candidate puncture needle group (including the entry path of the reference puncture needle and the puncture path of the subsequent needle) can be adaptively determined according to the shape and position of the target region. The target puncture needle group can be planned in the potential needle group by using an optimization algorithm, thereby providing a reference for subsequent accurate puncture.

[0078] In some embodiments of the present disclosure, by planning the first puncture needle alone and planning the subsequent needle based on at least the first puncture needle planning, it can be applicable to more complex clinical scenarios, such as particle implantation surgery for tumors of different positions, sizes and shapes. Since this scheme screens the puncture needle based on the preset first constraint condition and the preset condition, it can automatically avoid obstacles according to the user-defined non-punctureable area, and plan a potential needle group according to the shape and position of the target region. This scheme further improves the effect of the puncture needle group planning by using an optimization algorithm to plan the distribution of the needle and the particle in the potential needle group. The scheme is also easy to implement, for example, the number of puncture needles and the number of particles do not need to be defined by humans, and the scheme is not subject to the model of the puncture needle.

[0079] The above descriptions of the various processes are merely for example and illustration, and are not intended to limit the specific steps of particle implantation planning disclosed in the present specification. For example, in some embodiments, particle implantation planning can be performed according to the following steps: determining object features of the target object based on the medical image data; determining a candidate needle set that meets a preset condition based on the object features of the target object; performing particle distribution optimization and needle set optimization on the needles in the candidate needle set to determine a target needle set. In this embodiment, a candidate needle set that meets the preset condition is planned based on the object features of the target object, and needle set optimization and particle distribution optimization are performed based on the candidate needle set, further improving the effect of needle set planning and making the scheme easy to implement. For another example, in some embodiments, particle implantation planning can also be performed according to the following steps: obtaining medical image data of a target object; determining object features of the target object based on the medical image data; determining a needle entry path of a reference needle based on the object features of the target object; and determining a candidate needle set that meets a preset condition based on at least the needle entry path of the reference needle. In this embodiment, the planning of the first needle is stripped out, and the planning of the subsequent needles is performed based on at least the first needle planning, which can be applied to more complex clinical scenarios and meet the needle set planning requirements in complex scenarios. Detailed descriptions of each of the above steps can be found in the description of Figure 2 , which will not be repeated here.

[0080] Figure 3 is an exemplary flowchart of determining a needle entry path of a reference needle according to some embodiments of the present specification. As shown in Figure 3 , the flow 300 includes the following operations. In some embodiments, the flow 300 can be performed by a particle implantation planning system or a processing device.

[0081] Step 310, discretizing a target body surface region of a target object into a plurality of body surface units.

[0082] A body surface unit refers to a small block or unit into which the skin surface of a target object is divided. Each small block or unit is referred to as a body surface unit. By discretizing the target body surface region into a plurality of body surface units, the search space when determining the needle entry point can be reduced, and the algorithm processing speed can be improved.

[0083] Since the number of body surface voxels of the target object is huge, full traversal requires a lot of time and computing resources, which can greatly reduce the efficiency of the algorithm. At the same time, adjacent voxels as needle entry points can be relatively similar in planning results, which can bring some unnecessary repeated calculations. Discretizing the body surface can alleviate this problem.

[0084] The target body surface region refers to a potential needle entry position region of the target object body surface. For example, the target body surface region is the skin surface adjacent to the target region (e.g., when the target region is the heart, the target body surface region is the chest of the target object).

[0085] In some embodiments, the processing device can discretize the target body surface region into triangular facets by various discrete algorithms, such as the marching cube algorithm, etc., each of which can serve as a body surface unit. The discretization interval can be set empirically, such as 5 mm, 6 mm, etc. In some embodiments, the discretized body surface units can also be other shapes, such as quadrilaterals, hexagons, or irregular polygons, etc., which are not limited in the present embodiment.

[0086] In some embodiments, the plurality of body surface units can be used to construct a plurality of candidate needle entry points. The candidate needle entry point refers to a body surface unit that can be a needle entry point of the puncture needle on the body surface of the target object.

[0087] Step 320, based on the preset first constraint condition, screening the plurality of candidate needle entry points.

[0088] In some embodiments, the preset first constraint condition includes a puncture needle length constraint, a puncture angle constraint, and a tissue collision detection constraint.

[0089] The puncture needle length constraint is used to constrain the puncture path length (needle entry depth) of the puncture needle. For example, to ensure that the needle tip can reach the corresponding planned target point in the subsequent puncture process, the puncture depth of the needle is compared with the needle length, and the needle entry points with a puncture depth greater than the needle length are removed.

[0090] The puncture angle constraint is used to constrain the entry angle of the puncture needle, so that the puncture needle enters the skin surface as vertically as possible. For example, the angle between the puncture needle and the normal vector of the skin surface when the puncture needle enters the body is constrained. Constrained puncture angle can reduce the deformation of the tissue during puncture, improve puncture accuracy, and ensure that the puncture needle accurately enters the target region.

[0091] Referring to Figure 10 , Figure 10 is an exemplary schematic diagram of the puncture needle length and angle according to some embodiments of the present specification. Wherein 1010 represents the target region in the target object, 1020 represents the skin of the body surface of the target object, the puncture depth is L ST (the distance between S and T), S is the body surface needle entry point, T is the target point, θ represents the angle between the puncture needle and the normal vector of the body surface , O is a reference point inside the target region through which the puncture needle passes, for example, if the reference puncture needle is at this position, O can be the center point (such as the centroid or the center of gravity, etc.) of the target region.

[0092] The collision detection constraint refers to specifying the spatial position of the puncture needle to avoid collision with sensitive tissues in the adjacent tissues. The sensitive tissues refer to tissues that need to be paid special attention to avoid extrusion or puncture with the puncture needle. Through collision detection, it can be ensured that the puncture needle will not extrude or puncture the sensitive tissues such as nerves, blood vessels, etc. during puncture. In particle implantation, multiple puncture needles are implanted at the same time, and the puncture needles themselves have a certain volume and occupy a certain space. Through the collision detection constraint, the physical collision between the puncture needles can be avoided.

[0093] In some embodiments, when performing collision detection, the organ boundary can be inflated to set a certain safety distance for the organ.

[0094] One or more puncture needle points meeting the conditions can be selected from a plurality of candidate puncture needle points through the preset first constraint condition. For example, the processing device selects the plurality of candidate puncture needle points by performing puncture needle length detection, puncture angle detection and collision detection, and removes the candidate puncture needle points that do not meet the preset first constraint condition, so as to obtain the selected candidate puncture needle points. For example, if the puncture path passes through important organs, the candidate puncture needle points corresponding to the puncture needle should be removed.

[0095] Step 330, determining the puncture path of the reference puncture needle based on the preset first optimization target and the selected candidate puncture needle points.

[0096] The preset first optimization target refers to the optimization target set according to the surgical requirements for the selected candidate puncture needle points. In some embodiments, the preset first optimization target includes at least one of minimizing the puncture depth, maximizing the distance from the tissue, and minimizing the puncture angle. For example, the preset first optimization target can be to minimize the puncture depth, or a combination of minimizing the puncture depth and maximizing the distance from the tissue.

[0097] In some embodiments, the processing device can further select the selected candidate puncture needle points based on the preset first optimization target, and take the puncture needle meeting the preset first optimization target as the reference puncture needle. For example, the puncture needle with the shortest puncture depth is taken as the puncture path of the reference puncture needle.

[0098] When the preset first optimization target is a combination of multiple optimization targets, a weight can be assigned to each optimization target, the score of each candidate puncture needle point under each optimization target is calculated respectively, and then the score of each puncture needle under each optimization target is weighted, and the puncture point of the reference puncture needle is determined according to the final weighted score. For example, the candidate puncture needle point with the highest weighted score is taken as the puncture point of the reference puncture needle.

[0099] In some embodiments, when determining the first puncture needle, it is assumed that the first puncture needle passes through a preset point of the target region. The preset point is a certain point in the target region determined according to a preset rule. For example, the preset point can be a certain point in the central region of the target region (such as within 1-5 mm around the central region of the target region), can be a central point of the target region (such as a centroid or a barycenter, etc.), or can be an arbitrary point in the target region. Preferably, the puncture path of the reference puncture needle passes through the central point of the target region, and the puncture path of the reference puncture needle (which can be represented by the puncture path of the reference puncture needle) can be determined according to the entry point and the central point of the target region. The needle tip of the puncture needle steps in the direction from the entry point to the central point of the target region, and the farthest point of the target region that can be reached is the position of the needle tip of the reference puncture needle.

[0100] For example, the planned reference puncture needle is as shown in Figure 11 , Figure 11 is an exemplary schematic diagram of a reference puncture needle according to some embodiments of the present specification, wherein 1110 represents the reference puncture needle, and 1120 represents the target region. The needle tip of the reference puncture needle passes through a preset point of the target region.

[0101] Figure 4 is an exemplary flowchart for determining a candidate puncture needle set according to some embodiments of the present specification. As shown in Figure 4 , the flowchart 400 includes the following operations. In some embodiments, the flowchart 400 can be executed by a particle implantation planning system or a processing device.

[0102] Step 410, discretizing a target body surface region of the target object into a plurality of body surface units.

[0103] In some embodiments, the plurality of body surface units obtained by discretization are used to construct a plurality of candidate entry points. The discretization manner can be the same as that in step 310 of Figure 3 , which will not be described here again.

[0104] In some embodiments, the candidate entry points here can be the same as or different from the candidate entry points obtained by discretization when planning the first needle.

[0105] Step 420, screening the plurality of candidate entry points based on the uncovered region of the target region to obtain a plurality of screened candidate entry points.

[0106] For the coverage of the target region, it is assumed that the puncture needle is regarded as a cylinder, and the region within the cylinder represents the region that can be covered by the puncture needle (corresponding to the region that can be irradiated by the particles in the needle). The uncovered region of the target region refers to the region in the target region that is not covered by the determined puncture needle. For example, when the puncture needle is regarded as a cylinder, the uncovered region of the target region refers to the region in the target region outside the cylinder of the puncture needle (there can be an intersection between the needle and the cylinder region of the needle).

[0107] In some embodiments, the processing device can pre-set the coverage range of each puncture needle based on the radiation range of the particle, for example, set a certain distance to draw a circle with the puncture needle as the center, and the whole puncture needle will form a cylinder, and the corresponding area in the target area is considered as the coverage area of the puncture needle.

[0108] In some embodiments, the uncovered area of the target area is determined based on the determined puncture needle group. The position of the determined puncture needle group in the target area is determined, and when the coverage area of each puncture needle is determined, the area in the target area other than the covered area is the uncovered area.

[0109] In some embodiments, the processing device can screen the plurality of candidate needle entry points based on the uncovered area of the target area and the preset first constraint condition. The specific way of screening can refer to the description of step 320 of Figure 3 , which are the same in the specific way of screening, and the difference is that step 320 screens the plurality of candidate needle entry points in the whole target area based on the first preset condition, and here the plurality of candidate needle entry points in the uncovered area of the target area are screened, and the purposes of the two are the same, which is to reduce the search space and improve the processing efficiency of the algorithm.

[0110] Step 430, discretizing the surface of the target area to determine a plurality of candidate target points.

[0111] The target point refers to the position of the surface of the target area aimed at by the puncture needle in the puncture process. In some embodiments, the target point can also be understood as the position that the needle tip of the puncture needle needs to reach. The candidate target point refers to the surface block obtained after discretizing the surface of the target area. A surface block can be a candidate target point.

[0112] In some embodiments, the discretization of the surface of the target area can be the same as the discretization of the surface area of the target object in step 310 of Figure 3 , and therefore will not be described here.

[0113] Step 440, based on at least the needle entry path of the reference puncture needle, the preset condition and the plurality of candidate target points, determining a candidate puncture needle group that meets the preset condition through a plurality of iterations.

[0114] The plurality of iterations refers to a series of iterative processes to achieve the final purpose. In some embodiments, the plurality of iterations can be used to determine the next puncture needle based on the determined puncture path of the puncture needle. For example, the acquisition of the 3rd puncture needle (the 2nd subsequent needle) should be based on the 1st puncture needle (the reference puncture needle) and the 2nd puncture needle (the candidate puncture needle), that is, the 3rd puncture needle is acquired based on the information of the 1st puncture needle and the 2nd puncture needle.

[0115] In some embodiments, the operations of each of the plurality of iterations include steps 441-443.

[0116] Step 441, based on a preset condition, screening the plurality of candidate target points.

[0117] The preset condition includes a preset second constraint condition and a preset needle set acquisition strategy.

[0118] The preset second constraint condition includes a puncture needle length constraint, a puncture angle constraint, a tissue collision detection constraint, a collision detection constraint of an acquired puncture needle, and an included angle constraint with a reference puncture needle.

[0119] The puncture needle length constraint is used to constrain the puncture path length (needle depth) of the puncture needle. For example, to ensure that the needle tip can reach the corresponding planned target point in the subsequent puncture process, the puncture depth of the needle is compared with the needle length, and the needle entry point with a puncture depth greater than the needle length is removed.

[0120] The puncture angle constraint is used to constrain the entry angle of the puncture needle, so that the puncture needle enters the skin surface as vertically as possible. For example, the angle between the puncture needle and the normal vector of the skin surface when the puncture needle enters the body is constrained. Constrained puncture angle can reduce the deformation of the tissue during puncture, improve puncture accuracy, and ensure that the puncture needle accurately enters the target area.

[0121] The tissue collision detection constraint refers to specifying the spatial position of the puncture needle to avoid collision with adjacent tissues. Collision detection can ensure that the puncture needle does not squeeze or pierce adjacent tissues or sensitive tissues such as nerves, blood vessels, etc. during puncture. In particle implantation, multiple puncture needles are implanted simultaneously during puncture, and the puncture needles themselves have a certain volume and occupy a certain space. The tissue collision detection constraint can avoid physical collision between the puncture needles.

[0122] The collision detection constraint of the acquired puncture needle refers to limiting the distance between the candidate puncture needle and the acquired puncture needle to avoid collision between the puncture needles in the planned plan.

[0123] The included angle constraint with the reference puncture needle refers to limiting the included angle between the candidate puncture needle and the reference puncture needle to avoid the distribution of the puncture needles being too dispersed in the planned plan. For example, when planning subsequent needles, the included angle between the subsequent needle and the reference puncture needle (such as the reference puncture needle) is limited to be less than a certain threshold (such as an included angle less than 30°).

[0124] The preset needle group acquisition strategy includes that the candidate puncture needle is located in a preset range of the acquired puncture needle group. In the scheme for acquiring the puncture needle group in the embodiments of the present specification, the puncture needle is gradually expanded outward from the first needle, and therefore, when the next needle is planned, a certain range around the current acquired needle group needs to be considered. For example, when the target point is screened, the target point that is more than a certain distance away from the acquired needle group can be removed. This operation can reduce the search space and improve the algorithm efficiency. For example, refer to Figure 12 , Figure 12 is an exemplary schematic diagram of the preset range according to some embodiments of the present specification. In the diagram, 1210 represents the first acquired puncture needle, 1220 represents the second acquired puncture needle, 1211 represents the area that can be covered by the first puncture needle, 1221 represents the area that can be covered by the second puncture needle, and 1230 represents the target point range of the acquired puncture needle group. The candidate target point is selected from the surrounding area (for example, the distance from the area is less than a certain value) of the part of the area. In some embodiments, the preset range can be manually set or automatically calculated according to machine learning, neural network or big data algorithm, which is not limited in the embodiments.

[0125] The screening of the plurality of candidate target points according to the preset condition can filter out a part of the target points, reduce the search space in subsequent planning and improve the processing efficiency of the algorithm.

[0126] In step 442, based on the plurality of screened candidate entry points and the plurality of screened candidate target points, the target area coverage rate of the puncture needle is calculated to determine the plurality of first candidate puncture needles and the corresponding target area coverage rates.

[0127] Under the premise that the particle distribution in the needle is unknown (the current is the planning stage of the puncture needle group, and the particle position has not been planned), the coverage rate of the puncture needle is calculated, and it is assumed that each position in the puncture needle is provided with a particle source. Under this assumption, the puncture needle can be regarded as a cylinder, and the area inside the cylinder represents the area that can be covered by the puncture needle. As shown in Figure 13 , Figure 13 is an exemplary schematic diagram of the target area coverage according to some embodiments of the present specification. In the diagram, 1310 represents the entry point of the needle on the body surface, 1320 represents the puncture needle, 1330 represents the cylindrical coverage range of the puncture needle, 1340 represents the target area in the form of point cloud, and 1350 represents the needle tip position of the puncture needle 1320. The gray range at the position of 1320 represents the skin surface of the target object, and the gray square at the position of 1350 represents the boundary of the target area (the actual target area boundary is mostly a curved surface, and the planar quadrilateral is used for convenience). The target area coverage rate of the puncture needle can be represented as the ratio of the target area volume inside the cylinder of the puncture needle to the total target area volume. In some embodiments, the volumes are respectively represented by the number of point clouds in the cylindrical range and the number of point clouds in the entire target area.

[0128] In some embodiments, the processing device can calculate a puncture needle with the largest target area coverage based on the plurality of filtered candidate entry points and the plurality of filtered candidate target points. The calculation of the target area coverage can be based on the puncture path of the puncture needle, wherein one candidate entry point and one candidate target point can form one puncture path. Thus, for one candidate entry point (denoted as S i ), assuming the puncture needle passes through S i , after the needle passes through S i , when the needle tip reaches different filtered candidate target points, a plurality of puncture paths will be formed, each of which has a corresponding target area coverage.

[0129] In some embodiments, the processing device can determine the target area coverage by a preset target area coverage calculation method, for example, by calculating the ratio of the number of point clouds of the puncture needle in the target area to the total number of point clouds in the target area.

[0130] After the target area coverage of the puncture needle is calculated for each filtered candidate entry point, a plurality of first candidate puncture needles and their corresponding target area coverages can be determined. The first candidate puncture needle refers to the puncture needle corresponding to the puncture path with the largest target area coverage in the puncture path formed by the filtered candidate entry point and its corresponding target point. For example, the puncture needle corresponding to the puncture path with the largest target area coverage in the puncture path formed by each candidate entry point and the plurality of candidate target points is determined as the first candidate puncture needle.

[0131] In some embodiments, the dose information and the coverage of the target area dose can also be introduced when calculating the target area coverage. For example, for each puncture needle, the particle radiation dose gradually decreases along the direction away from the puncture needle. For each target point in the target area, the corresponding dose is the radiation dose of the plurality of puncture needles at the target point. After superimposing the radiation doses of the plurality of puncture needles, the dose of the target point can be obtained. The dose is compared with a preset threshold value. If the threshold value is exceeded, it is considered to be covered. After calculating all the target points in the radiation range of the puncture needle, the coverage of the puncture needle can be determined.

[0132] For more information about the calculation of the target area coverage, please refer to the description of Figure 6 , which will not be repeated here.

[0133] Step 443, adding the first candidate puncture needle with the largest target area coverage to the current candidate puncture needle group.

[0134] In the above step 442, the target area coverage of each first candidate puncture needle has been calculated. After sorting or comparing the target area coverages of the plurality of first candidate puncture needles, the first candidate puncture needle with the largest target area coverage can be determined.

[0135] The current candidate needle set refers to a set of first candidate needles with the maximum target coverage calculated in each iteration. Since the determination of subsequent needles is carried out through multiple iterations, the first candidate needle with the maximum target coverage in the un-covered area of the current target is calculated in each iteration. Therefore, after each iteration, the first candidate needle with the maximum target coverage corresponding to the current iteration can be added to the current candidate needle set.

[0136] The needle in the current candidate needle set will be involved in subsequent planning, such as particle distribution optimization and needle set optimization, so as to obtain the target needle set.

[0137] In some embodiments, the target of needle planning can be set, for example, to achieve complete coverage of the target area with as few needles as possible. Under this optimization target, needles can be added one by one in a greedy manner (as described in the embodiments of the present specification, the next needle is gradually obtained on the basis of the determined needle), so that each added needle can cover as much of the remaining target area as possible.

[0138] Exemplarily, the way of adding needles one by one in a greedy manner is shown in the following embodiments.

[0139] Before performing iteration to obtain the candidate needle set, the parameters involved in the iteration can be initialized, for example, the candidate needle set N = {} ({} represents an empty set), the target coverage CR = 0, and the set of un-covered target points P NC , the points corresponding to the entire target area P T , and P Nc = P T .

[0140] After the needle is obtained, the parameters are gradually updated, for example, after the first needle n1 is obtained, N = {n1} is updated, the target coverage CR is updated, for example, the target coverage of the first needle is 5%, then CR = 5% is updated, and the points covered by n1 are removed from the point set P NC ; after the second needle n2 is obtained, N = {n1, n2} is updated, the target coverage of the second needle is 5%, then CR = 10% is updated, and the points covered by n2 are further removed from the point set P NC .

[0141] The iteration process is shown in Figure 14 , and Figure 14 is an exemplary schematic diagram for updating the target coverage according to some embodiments of the present specification.

[0142] First, the first needle N = {n1} is obtained; the first needle is also the reference needle, and its acquisition process is described in Figure 3 ;

[0143] updating the target coverage CR;

[0144] determining whether the target is completely covered;

[0145] if the target is not completely covered, performing the following operations:

[0146] preprocessing; the preprocessing includes operations described in steps 410-430;

[0147] obtaining the next needle n i ; the obtaining the next needle includes operations of steps 441 and 443;

[0148] adding the obtained next needle to the needle set N = N + {n i};

[0149] if the target is completely covered, outputting the candidate needle set.

[0150] In some embodiments, the specific operation of determining whether the target is completely covered is as shown in Figure 5 .

[0151] Figure 5 is an exemplary schematic diagram of the iterative determination method according to some embodiments of the present specification. As shown in Figure 5 , the flow 500 includes the following operations. In some embodiments, the flow 500 can be performed by a particle implantation planning system or processing device.

[0152] Step 510, removing the point corresponding to the puncture path with the largest target coverage from the target point set.

[0153] The target point set refers to a set of point clouds obtained by discretizing the target region. In some embodiments, the target point set includes target points obtained by discretizing the surface of the target region and points discretized inside the target region.

[0154] As described in the above embodiments, the puncture path can be represented by the entry point of the body surface and the target point of the target region surface. After determining the puncture path with the largest target coverage, the point corresponding to the puncture path inside the target region and the target point of the target region surface can be determined, and the point corresponding to the puncture path can be removed from the target point set. For example, the point covered by n2 is removed from the target point set P NC , where n2 corresponds to the puncture needle corresponding to the puncture path with the largest target coverage determined in the first iteration.

[0155] Step 520, determining whether the target coverage reaches a preset value.

[0156] The preset value is a threshold value of the target coverage in the pre-set optimization target. For example, the preset value is 99%, 98%, or 97%, etc.

[0157] In some embodiments, the processing device determines whether the preset value has been reached by comparing the updated target coverage with a preset value. For example, in the example above, the target coverage of the second injection is 5%, and the updated target coverage is CR = 10%, which does not reach the preset value of 99%.

[0158] After removing the points corresponding to the puncture paths with the highest target coverage from the target point set, the remaining points in the target area are the target uncovered area. Whether the target coverage reaches a preset value can be determined by determining whether the number of points removed from the target point set reaches a certain ratio of the total number of points in the target point set, for example, whether it reaches a preset value, or by determining whether the remaining target uncovered area is less than a threshold, for example, whether it is less than 1%, 2%, or 3%.

[0159] In some embodiments, after determination, if the preset value is not reached, step 540 is executed; if the preset value is reached, step 530 is executed.

[0160] Step 530: Output the current candidate puncture needle group.

[0161] For example, the current candidate puncture needle group N is output, which may include multiple candidate puncture needles.

[0162] In some embodiments, the output candidate puncture needle group can be as follows: Figure 15 As shown, Figure 15 This is an exemplary schematic diagram of a candidate puncture needle group according to some embodiments of this specification. 1510 illustrates multiple candidate puncture needles, and 1520 is an enlarged view of the area shown in 1510. The numbers in 1520, such as #1, #2, and #3, represent the sequence of the identified puncture needles. For example, #1 represents the first identified puncture needle.

[0163] Step 540: Perform the next iteration based on the puncture needles in the current candidate puncture needle group.

[0164] For example, based on the puncture needles in the current candidate puncture needle group, the uncovered area of ​​the target area is determined, and the preset second constraint condition detection is performed based on the puncture needles in the current candidate puncture needle group. The iterative process can be referred to Figure 4 Description.

[0165] Figure 6 FIG6 is an exemplary flow chart for calculating target coverage according to some embodiments of the present specification. In some embodiments, process 600 may be executed by a seed implantation planning system or processing device.

[0166] In some embodiments, for each screened candidate needle entry point, the processing device may calculate its target coverage by performing the following steps 610 - 630 .

[0167] Step 610: Determine multiple potential target sites from the multiple screened candidate target sites.

[0168] Potential target points refer to candidate target points that may be combined with the screened candidate needle entry points to form a puncture path.

[0169] In some embodiments, multiple potential target sites can be identified from multiple screened candidate targets by screening using preset conditions.

[0170] Step 620 , calculate the target area coverage based on the multiple potential target points, and determine the potential target point with the largest target area coverage.

[0171] In some embodiments, S i The candidate needle entry point is calculated, and the potential target point T with the largest target area coverage can be calculated from multiple potential target points. For example, the calculation method is shown in formula (1) and formula (2).

[0172]

[0173]

[0174] Wherein, T in formula (1) represents the potential target point with the largest target coverage, S i represents the i-th candidate needle entry point, T j represents the jth potential target, S i T j Indicates the puncture path determined by two points, Indicates adding puncture path S i T j The target coverage rate after . S in formula (2) i represents the i-th candidate needle entry point, T j represents the jth potential target, S i T j Indicates the puncture path determined by two points, Indicates that the current candidate puncture needle group (the puncture needles that have been added + the current puncture needle S i T j ) is the target point set covered by .

[0175] Step 630 : Based on the potential target point with the largest target coverage, determine the first candidate puncture needle of the screened candidate needle entry point and the corresponding target coverage.

[0176] For the candidate needle entry point S i , the potential target point with the largest target coverage has been calculated to be T, and the corresponding first candidate puncture needle can be expressed as S iT, whose target zone coverage can be expressed as (CR represents target zone coverage, subscript S i T represents a puncture needle).

[0177] For ease of understanding, the following examples illustrate an embodiment of determining a candidate puncture needle set in a greedy manner, with the optimization goal of achieving complete coverage of the target zone with as few puncture needles as possible. The complete steps are shown in the following embodiment.

[0178] S1: initialization, set the candidate puncture needle set to {}, the target zone coverage CR = 0, and the set of un-covered target zone points P NC = PT (in the initialization, PT is the set of points in the entire target zone, including target points on the surface of the target zone and points inside the target zone);

[0179] S2: add the first needle (reference puncture needle) to the candidate puncture needle set and update the needle set to {n1}; update the coverage of the first needle; remove the points covered by n1 from P NC ;

[0180] S3: assuming that the newly added needle (e.g., the second needle) passes through the center of P NC , the search space of the subsequent needle entry point is narrowed down by a preset second constraint condition. The center is the center of the remaining part after the covered target zone is removed (the center needs to be in P NC ), which can be the center of a geometric shape or other forms of center (such as the centroid).

[0181] S4: traverse the screened candidate entry points, for each candidate entry point S i : make the puncture needle pass through S i , determine the potential target points to narrow down the search space of the target points; then calculate the target point T with the maximum target zone coverage, which can be calculated as formula (1) above.

[0182] S5: calculate the needle with the maximum target zone coverage. Exemplarily, the calculation method is shown in formula (3).

[0183]

[0184] where ST represents the puncture needle with the maximum target zone coverage, S i represents the i-th candidate entry point, represents the target zone coverage of the puncture path corresponding to the candidate entry point S i and the target point T.

[0185] S6: add the needle N = N + {ST} to the needle set;

[0186] S7: update the set of un-covered target zone points P NCremove the points covered by the ST from P NC

[0187] S8: Determine whether the target region is completely covered: if the coverage rate CR ST > 99%, it is considered to be completely covered, and the candidate needle set N can be output, otherwise, return to S3 for the next iteration to obtain the next subsequent needle.

[0188] After obtaining the candidate needle set, the particle distribution optimization and needle set optimization can be performed on the needles in the candidate needle set to obtain the target needle set.

[0189] Figure 7 is an example flowchart for determining a target needle set according to some embodiments of the present specification. As Figure 7 shown, the flow 700 includes the following steps. In some embodiments, the flow 700 can be performed by a particle implant planning system or processing device.

[0190] Step 710, determine the initial particle distribution of each candidate needle in the candidate needle set based on the preset equal-interval scale on the needle.

[0191] The preset equal-interval scale refers to the marks with equal intervals that are pre-set on the needle. For example, if the length of the needle is 5 cm, the preset equal-interval scale can be an equal-interval scale of 10 mm.

[0192] The preset equal-interval scale is used to indicate the potential particle positions of the particles in the needle. For example, in order to facilitate the subsequent optimization of the distribution of the particles in the needle, a binary variable can be set for each scale to store whether the position corresponding to the scale is finally placed with a particle or not. For example, a value of 1 indicates that a particle is placed, and a value of 0 indicates that no particle is placed. Exemplarily, a schematic diagram thereof is shown in Figure 16 . Figure 16 is an example schematic diagram of the preset equal-interval scale in the needle according to some embodiments of the present specification. Wherein, the horizontal 10->0 respectively represents the equal-interval scale set, the gray square represents the particle placed at the scale position in the needle, and the vertical 1-13 represents the number of the needle. The binary variable is shown below the arrow in the figure, 1-K respectively represents the first binary variable that needs to be optimized, and (M, N) represents the Mth potential particle of the Nth needle.

[0193] Step 720, perform particle distribution optimization based on the initial particle distribution on the candidate needle.

[0194] The initial particle distribution refers to randomly selecting a certain number of positions to place particles among all potential particle positions of the needle.

[0195] ​In some embodiments, the processing device can perform particle distribution optimization on the initial particle distribution on the candidate set of needles by executing a plurality of iterations.

[0196] In some embodiments, the processing device can perform particle distribution optimization on the initial particle distribution on the candidate set of needles by executing a plurality of iterations.

[0197] In some embodiments, the processing device can perform particle distribution optimization on the initial particle distribution on the candidate set of needles by executing a plurality of iterations.

[0198] For detailed description of the process of particle distribution optimization, please refer to the description of Figure 8 , which will not be repeated here.

[0199] At step 730, the processing device can perform needle set optimization based on the result of particle distribution optimization, and determine the target set of needles according to the result of needle set optimization.

[0200] The result of particle distribution optimization refers to the adjusted particle distribution in the needle.

[0201] The needle set optimization refers to the optimization of the candidate set of needles. For example, according to the result of particle distribution optimization, the candidate set of needles with no particles (i.e., the number of particles is 0) can be removed. Specifically, the processing device can restore the binary variable after particle optimization to the particle distribution in the needle, and determine whether there is a needle with the number of particles being 0 according to the restored needle.

[0202] The set of candidate needles after needle set optimization is the target set of needles.

[0203] Figure 8 is an exemplary flowchart of the particle distribution optimization method according to some embodiments of the present specification. As shown in Figure 8 , the flowchart 800 includes the following operations. In some embodiments, the flowchart 800 can be executed by a particle implant planning system or a processing device.

[0204] At step 810, the processing device can perform particle distribution optimization on the initial particle distribution on the candidate set of needles by executing a plurality of iterations.

[0205] Each iteration can include the operations shown in steps 811-815.

[0206] At step 811, the processing device can obtain a first objective function value of the objective function of particle distribution optimization.

[0207] The objective function is a function set to reflect whether the optimization of the particle distribution achieves the optimization goal. For example, the optimization goal includes that V100 needs to exceed 90%, and V150 is the smaller the better. V100 and V150 are indicators used to evaluate the dose distribution in the particle implant planning, which respectively represent the ratio of the volume exceeding 100% of the prescribed dose and the volume exceeding 150% of the prescribed dose in the target region to the total volume of the target region. In some embodiments, when using its optimization algorithm, for example, using a genetic algorithm or a binary integer linear programming, the optimization goal reflected by the objective function can be the number of particles used (the fewer the better), the number of puncture needles (the fewer the better), or the conformality of the dose field (the higher the better).

[0208] For example, the objective function is as shown in the following formula (4).

[0209] minf t = w1 x ReLU (90% - V100) + w2 x V150 (4)

[0210] wherein, w1 and w2 represent the weights of the two function items (represented in the form of V100 and V150), f t is the objective function value, minf t indicates that the optimization goal is to minimize the objective function value. The weight values can be set according to actual needs, for example, the weights of the two function items can be 0.5 and 0.5 respectively, or 0.7 and 0.3 respectively.

[0211] The first objective function value can be understood as the initial function value of the objective function in the current round of iteration. That is, the objective function value before the optimization of the particle distribution in the puncture needle in the current round of iteration. The first objective function value corresponding to the first round of iteration is calculated based on the initial particle distribution of the candidate puncture needle, and the first objective function value corresponding to the subsequent rounds of iteration is the first objective function value of the last round of iteration. It can be understood that the initial objective function value in the subsequent round of iteration is the objective function value after the optimization of the particle distribution in the puncture needle in the last round of iteration.

[0212] For the first objective function value of the first round of iteration, the processing device can calculate the dose volume of the needle particles in the target region based on the initial distribution of the particles in the candidate puncture needle group, and substitute the dose volume into the above formula (4) to calculate the first objective function value. For the first objective function value of the subsequent rounds of iteration, the processing device can directly obtain the second objective function value of the last round of iteration and take it as the first objective function value of the current round of iteration. The description of the second objective function value can be referred to the description in step 814 below.

[0213] Step 812, adjusting the current particle distribution of the candidate puncture needle to determine the random particle distribution of the candidate puncture needle.

[0214] In some embodiments, the adjusting is performed by randomly selecting a number (e.g., 1-3) of potential particle positions from all potential particle positions of the current particle distribution of the candidate needle, and changing the state of the selected particle positions, such as from 0→1 or 1→0 (representing changing from a non-placed particle to a placed particle and from a placed particle to a non-placed particle, respectively). The particle distribution after the state change is the random particle distribution of the candidate needle.

[0215] At step 813, a second objective function value of the objective function of the particle distribution optimization is obtained based on the random particle distribution of the candidate needle.

[0216] The second objective function value refers to the objective function value calculated based on the adjusted particle distribution after the adjustment of the particle distribution of the needle in the current round of iteration.

[0217] In some embodiments, the processing device can calculate the dose volume of the needle based on the random particle distribution, and substitute the dose volume into the above formula (4) to obtain the second objective function value.

[0218] At step 814, it is determined whether to update the current particle distribution of the candidate needle based on the first objective function value and the second objective function value.

[0219] In some embodiments, the processing device determines whether to update the current particle distribution of the candidate needle by comparing the size of the first objective function value and the second objective function value.

[0220] The specific manner of the determination can be referred to the description of Figure 9 , which will not be repeated here. If yes, step 815 is performed.

[0221] At step 815, the random particle distribution of the candidate needle is used to replace the current particle distribution of the candidate needle.

[0222] In some embodiments, the current particle distribution of the candidate needle is different according to the round of the current iteration. In the first round of iteration, the current particle distribution of the candidate needle is the initialized example distribution, and in the subsequent rounds of iteration, the current particle distribution of the candidate needle is the particle distribution updated using the random particle distribution in the last round of iteration.

[0223] Figure 9 is an exemplary flowchart of updating the particle distribution of the needle according to some embodiments of the present specification. As Figure 9 shown, the flow 900 includes the following operations. In some embodiments, the flow 900 can be performed by a particle implant planning system or a processing device.

[0224] At step 910, it is determined whether the second objective function value is greater than or less than the first objective function value.

[0225] In some embodiments, the processing device can directly compare the size of the second objective function value and the first objective function value, and determine the size relationship between the second objective function value and the first objective function value.

[0226] Step 920, if the second objective function value is less than the first objective function value, updating the current particle distribution of the candidate puncture needle.

[0227] It can be understood that if the second objective function value is less than the first objective function value, it means that the current adjusted random particle distribution is closer to the optimization target, and the current particle distribution of the candidate puncture needle is selected to be updated.

[0228] Step 930, if the second objective function value is greater than or equal to the first objective function value, determining whether to update the current particle distribution of the candidate puncture needle based on a preset probability.

[0229] In some embodiments, the preset probability can be determined based on the following manner.

[0230]

[0231] Wherein, p is a preset probability value, T is a current temperature in the simulated annealing algorithm used, f is the second objective function value, and f is the first objective function value. In this embodiment, by setting the preset probability to accept a suboptimal solution (the case where the second objective function value is less than the first objective function value), it is helpful to avoid the optimization algorithm from falling into local optimum. t

[0232] For ease of understanding, the complete embodiment of particle distribution optimization using the simulated annealing algorithm is shown in the following embodiments. Specifically, it can include the following steps S1-S7.

[0233] S1: Initialize the simulated annealing parameters: set the initial temperature T0, the termination temperature T f , the cooling rate R∈(0, 1);

[0234] S2: Set the optimization target; for example, the optimization target is that V100 needs to exceed 90%, and V150 is better the smaller; and set the corresponding objective function (for example, the objective function shown in formula (4)).

[0235] S3: Initialize the state distribution of the particle: according to the empirical formula N=4.674×V 0.562 (A represents the particle activity), randomly select N positions in all potential particle positions, and calculate the current objective function value f0(the first objective function value), and set the current temperature T=T0;

[0236] S4: update the time t=t+1, initialize f t ​= f t-1 ;

[0237] S5: Randomly update the state distribution: randomly select 1-3 from all potential particle positions to change the state (0→1 or 1→0), and calculate the current objective function value f (the second objective function value);

[0238] S6: Determine whether to update the current particle distribution:

[0239] If f < f t , update the state distribution of the particle, and let f t = f;

[0240] If f ≥ f t , update according to the preset probability, and the probability is

[0241] Return to S4 for a certain number of iterations. Multiple iterations (which can be referred to as the first iteration to avoid confusion) at this point can randomly update the particle distribution multiple times, so as to approach the optimization target as much as possible, and can also make the optimization process more stable when using the preset optimization algorithm to optimize.

[0242] S7: If T > T f , then the temperature T = T x R, and return to S4; otherwise, the particle distribution optimization is completed. In the first iteration, T > T f , T = T0, and when cooling, T = T0 x R. In subsequent iterations, T is the cooling result of the last iteration, for example, in the second iteration, T = T0 x R, and in the third iteration, T = T x (T0 x R).

[0243] Optionally, in some embodiments, after the particle distribution optimization is completed using the simulated annealing algorithm, the processing device can perform S8 based on the optimization result of the particle distribution.

[0244] S8: Based on the optimization result of the particle distribution, perform needle set optimization, and determine the target puncture needle set according to the needle set optimization result. For example, the processing device can determine whether the number of particles in the puncture needle is 0 according to the optimization result of the particle distribution, if so, the puncture needle with the particle number of 0 is removed from the needle set, and the remaining set of puncture needles is the target puncture needle set.

[0245] In some embodiments, after the puncture needle set is planned, the particle implantation planning system can output the spatial position and direction of the puncture needle required. According to the spatial position and direction of the puncture needle, a non-coplanar template is modeled and personalized 3D printing is performed for the auxiliary positioning of the puncture needle in the particle implantation. Exemplarily, the printed non-coplanar template is as shown in Figure 17 Figure 17 ​is an exemplary schematic diagram of a non-coplanar template according to some embodiments of the present specification. In 1710, the holes are the planned hole columns of the puncture needles; in 1720, the holes are auxiliary holes. Specifically, due to various reasons (such as limited intraoperative puncture accuracy, patient respiratory motion, etc.), if the planned puncture needle hole column is implanted with a puncture needle during the operation, but the target area is not well covered, the puncture needle can be supplemented through these auxiliary holes.

[0246] It should be noted that the above description of each process is only for example and illustration, and does not limit the scope of the present specification. Those skilled in the art can make various modifications and changes to each process under the guidance of the present specification. However, these modifications and changes are still within the scope of the present specification. For example, a storage step is added between each process step.

[0247] In addition, each of the above processes is described for a single target area. For multiple target areas, the above process can be executed multiple times. For a single target area that is blocked by an organ and cannot be normally planned, the target area can be split into multiple parts, and the above process can be executed multiple times.

[0248] For example, in some embodiments, the processing device can split the target area and execute the above process multiple times according to the following process. The processing device can obtain medical image data of a target object; determine object characteristics of the target object based on the medical image data; split a target area of the target object to obtain multiple sub-regions of the target area; for each sub-region: determine a reference puncture needle entry path based on the object characteristics of the target object; determine a candidate puncture needle group that meets a predetermined condition based on at least the reference puncture needle entry path; perform particle distribution optimization and needle group optimization on the puncture needles in the candidate puncture needle group to determine a target puncture needle group.

[0249] Since the target area is split into multiple sub-regions and the needle group is planned for each sub-region, when the multiple sub-regions are merged, the target puncture needle groups corresponding to the multiple sub-regions can be merged. After merging, there may be a situation where the coverage ranges of the puncture needles of two adjacent sub-regions overlap. For this situation, the processing device can perform particle distribution optimization on the merged target area again based on the particle distribution optimization method described in the embodiments of the present specification.

[0250] It should be noted that when the target puncture needle set is optimized, the optimization object mainly considers optimizing the particle distribution (the particle distribution and the needle set can also be optimized at the same time) because even if the coverage areas of the puncture needles overlap, the dose can be avoided by optimizing the particle distribution. In some embodiments, even if there is a collision between the puncture needles of the target puncture needle set corresponding to different sub-regions, only the particle distribution optimization can be performed because after the sub-regions are divided, the batch puncture by grouping can avoid the problem of needle collision. In the process of implanting particles, the puncture needle is first inserted to the deepest position, then the particles are implanted, and then the needle is withdrawn a certain distance before the particles are implanted. Therefore, when the particles in a needle are implanted, the needle is basically pulled out of the patient's body. The needle collision occurs when all the needles are initially inserted to the deepest position required. If the puncture needles are grouped, all the needles in the first group can be inserted to implant the particles, and then all the needles in the second group can be inserted. Only the puncture needles in the group need to be ensured not to collide.

[0251] It should be understood that the system and its modules shown in the embodiments of the present specification can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented by special logic; the software part can be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or a specially designed hardware. Those skilled in the art can understand that the above-mentioned method and system can be implemented using computer executable instructions and / or included in processor control code, such as provided on a carrier medium, such as a magnetic disk, CD or DVD-ROM, a programmable memory, such as a read-only memory (firmware), or a data carrier, such as an optical or electronic signal carrier. The system and its modules of the present specification can not only have hardware circuit implementation, such as very large scale integrated circuits or gate arrays, semiconductors, such as logic chips, transistors, or programmable hardware devices, such as field programmable gate arrays, programmable logic devices, etc., but also can be implemented by software, for example, executed by various types of processors, and also can be implemented by a combination of the above-mentioned hardware circuit and software (for example, firmware).

[0252] It should be noted that the above description of the particle implantation planning system 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 various modules, or form a subsystem to connect with other modules without deviating from this principle. In some embodiments, different modules can be different modules in a system, or a module can realize the functions of two or more modules mentioned above. For example, each module can share a storage module, or each module can have its own storage module. Such variations are all within the scope of protection of this specification.

[0253] 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.

[0254] This specification also uses specific terms to describe the embodiments of this specification. For example, "one embodiment," "an embodiment," and / or "some embodiments" refer to a feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "one embodiment," "an embodiment," or "an alternative embodiment" two or more times in different locations in this specification do not necessarily refer to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of this specification may be appropriately combined.

[0255] In addition, unless expressly stated in the claims, the order of the processing elements and sequences, the use of alphanumeric characters, or the use of other names described in this specification are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some of the invention embodiments currently considered useful through various examples, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the spirit and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0256] For simplicity and to facilitate understanding of one or more embodiments, a description of an embodiment sometimes refers to a plurality of features in a single embodiment, drawing, or description of an embodiment. However, this does not imply that a greater number of features is necessary to practice the embodiments disclosed in this specification. In fact, features fewer than those disclosed in a single embodiment described above are within the scope of the embodiments.

[0257] In some embodiments, numbers describing quantities of components, attributes, etc. are used. It should be understood that such numbers used in the description of the embodiments are, in some examples, modified by the adjectives "about," "approximately," or "substantially." Unless otherwise stated, "about," "approximately," or "substantially" indicate that the described number can vary by ±20%. Accordingly, numerical parameters such as those included in the application and claims are approximations. Although the numerical parameters are approximations, the numerical values set forth in the specific examples are reported as precisely as practicable. The numerical values in some embodiments, therefore, can vary depending upon the desired properties sought to be obtained by the individual embodiment.

[0258] Every patent, patent application, publication, document, article, book, specification, and other material cited in this specification is hereby incorporated by reference in its entirety for all purposes to the same extent as if each individual publication, document, article, book, specification, and other material were specifically and individually indicated to be incorporated by reference in its entirety for all purposes. Except in the Examples, or where otherwise explicitly indicated, all numerical quantities in this description are meant to be the approximation. None of the citation of any reference, which includes books, patents, articles, documents, or other materials, is intended to be, nor should it be construed as, an admission that any of the reference is prior art to this application. No admission is made that any reference has any pertinent relevance to this application or that it combines pertinent information concerning the present embodiments. To the extent that any reference can have a pertinent relevance, the citations are only

[0259] Finally, it should be understood that the embodiments described herein are only given by way of example and are not to be used to limit the scope of the embodiments. Other alternatives can be used in place of or in addition to those illustrated herein. In addition, the scope of the embodiments includes any alternative embodiments as can be set forth in any attached claims. Therefore, the application as described and claimed should not be limited to the embodiments described above.

Claims

1. A particle implantation planning system, comprising a processor configured to perform the following operations: Acquire medical imaging data of a target object; Determining object features of the target object based on the medical image data; the object features include a target area; determining an insertion path of a reference puncture needle based on the object features of the target object; Determine a candidate puncture needle group that meets preset conditions based at least on the insertion path of the reference puncture needle; the preset conditions include a preset second constraint and a preset needle group acquisition strategy; the preset second constraint includes a puncture needle length constraint, a puncture angle constraint, a tissue collision detection constraint, a collision detection constraint of the acquired puncture needle, and an angle constraint with the reference puncture needle; the preset needle group acquisition strategy includes the candidate puncture needle being within a preset range of the acquired puncture needle group; Particle distribution optimization and needle group optimization are performed on the puncture needles in the candidate puncture needle group to determine a target puncture needle group; the target puncture needle group includes the puncture paths of the target puncture needles and the particle distribution in the target puncture needles.

2. The system according to claim 1, wherein determining the insertion path of the reference puncture needle based on the object features of the target object comprises: discretizing a target body surface area of ​​the target object into a plurality of body surface units; The multiple body surface units are used to construct multiple candidate needle entry points; Screening the plurality of candidate needle entry points based on a preset first constraint condition; Based on the preset first optimization goal and the screened candidate needle entry points, the needle entry path of the reference puncture needle is determined; the puncture path of the reference puncture needle passes through the preset point of the target area. 3 . The system according to claim 2 , wherein the preset first constraint condition comprises a puncture needle length constraint, a puncture angle constraint, and a tissue collision detection constraint.

4. The system according to claim 2, wherein the preset first optimization goal comprises one or more of minimizing needle insertion depth, maximizing the distance between the puncture needle and the tissue, and minimizing the puncture angle.

5. The system according to claim 1, wherein determining a candidate puncture needle group that meets a preset condition based at least on the needle insertion path of the reference puncture needle comprises: discretizing a target body surface area of ​​the target object into a plurality of body surface units; The multiple body surface units are used to construct multiple candidate needle entry points; Based on the uncovered area of ​​the target area, the plurality of candidate needle entry points are screened to obtain a plurality of screened candidate needle entry points; wherein the uncovered area of ​​the target area is determined based on the determined puncture needle group; Discretizing the surface of the target area to determine a plurality of candidate target points; Determining the candidate puncture needle group that meets the preset conditions through multiple rounds of iterations based at least on the needle insertion path of the reference puncture needle, the preset conditions, and the multiple candidate target points; Each of the multiple iterations includes: Based on the preset conditions, screening the multiple candidate targets; Calculating the target coverage of the puncture needle based on the screened multiple candidate needle entry points and the screened multiple candidate target points to determine multiple first candidate puncture needles and corresponding target coverages; The first candidate puncture needle with the largest target coverage is added to the current candidate puncture needle group.

6. The system according to claim 5, further comprising: removing the point corresponding to the puncture path with the largest target area coverage from the target area point set; as well as Determining whether the coverage of the target area reaches a preset value; If yes, output the current candidate puncture needle group; If not, the next round of iteration is performed based on the puncture needles in the current candidate puncture needle group.

7. The system according to claim 5, wherein the target coverage rate of the puncture needle is calculated based on the multiple candidate needle entry points and the multiple candidate target points after screening, and the multiple first candidate puncture needles and the corresponding target coverage rates are determined, comprising: For each candidate needle entry point after screening, Screening and determining multiple potential target targets from the multiple candidate targets after screening; Calculating target coverage based on the multiple potential target points, and determining the potential target point with the largest target coverage; Based on the potential target point with the largest target area coverage, the first candidate puncture needle of the screened candidate needle entry point and the corresponding target area coverage are determined.

8. The system according to claim 1, wherein the step of performing particle distribution optimization and needle group optimization on the puncture needles in the candidate puncture needle group to determine the target puncture needle group comprises: determining an initial particle distribution of each candidate puncture needle in the candidate puncture needle group based on preset equally spaced scales on the puncture needle; performing particle distribution optimization based on the initial particle distribution on the candidate puncture needle; The needle group optimization is performed based on the result of the particle distribution optimization, and a target puncture needle group is determined according to the needle group optimization result.

9. The system according to claim 8, wherein the optimizing the particle distribution based on the initial particle distribution on the candidate puncture needle comprises: Optimizing the particle distribution of the initial particle distribution on the candidate puncture needle by performing multiple rounds of iterations; Each iteration includes: Obtaining a first objective function value of an objective function for particle distribution optimization; wherein the first objective function value corresponding to the first iteration is calculated based on the initial particle distribution of the candidate puncture needle, and the first objective function value corresponding to the subsequent iteration is the second objective function value of the previous iteration; Adjusting the current particle distribution of the candidate puncture needle to determine a random particle distribution of the candidate puncture needle; Based on the random particle distribution of the candidate puncture needle, obtaining a second objective function value of the objective function of the particle distribution optimization; Determining whether to update the current particle distribution of the candidate puncture needle based on the first objective function value and the second objective function value; If so, the current particle distribution of the candidate puncture needle is replaced with the random particle distribution of the candidate puncture needle.

10. The system according to claim 9, wherein the determining whether to update the current particle distribution of the candidate puncture needle based on the first objective function value and the second objective function value comprises: Determine the magnitude relationship between the second objective function value and the first objective function value; If the second objective function value is less than the first objective function value, updating the current particle distribution of the candidate puncture needle; If the second objective function value is greater than or equal to the first objective function value, determining whether to update the current particle distribution of the candidate puncture needle based on a preset probability.

11. A particle implantation planning method, the method comprising: Acquire medical imaging data of a target object; Based on the medical image data, the object characteristics of the target object are determined; the object characteristics include a target area determining an insertion path of a reference puncture needle based on the object features of the target object; Determine a candidate puncture needle group that meets preset conditions based at least on the insertion path of the reference puncture needle; the preset conditions include a preset second constraint and a preset needle group acquisition strategy; the preset second constraint includes a puncture needle length constraint, a puncture angle constraint, a tissue collision detection constraint, a collision detection constraint of the acquired puncture needle, and an angle constraint with the reference puncture needle; the preset needle group acquisition strategy includes the candidate puncture needle being within a preset range of the acquired puncture needle group; Particle distribution optimization and needle group optimization are performed on the puncture needles in the candidate puncture needle group to determine a target puncture needle group; the target puncture needle group includes the puncture paths of the target puncture needles and the particle distribution in the target puncture needles.

12. A particle implantation planning device, comprising a processor, wherein the processor is configured to execute the particle implantation planning method according to claim 11.

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