Lattice arrangement method and system for three-dimensional lattice radiotherapy

Abstract

The invention provides a three-dimensional lattice radiotherapy lattice arrangement method and system. The three-dimensional lattice radiotherapy lattice arrangement method comprises the following steps: acquiring a computed tomography (CT) image and a delineation structure sequence; according to the method, a series of operations from image acquisition to lattice arrangement scheme planning are realized through an automatic algorithm process, a large amount of time and energy are saved, lattice arrangement can be rapidly completed, the method is suitable for making a radiotherapy plan of a large-size tumor, and the design efficiency of the radiotherapy plan is greatly improved; lattice arrangement is carried out according to a unified standard based on an established algorithm and parameters, so that the consistency of lattice arrangement under the operation of different patients or different doctors is ensured, and the quality stability of a radiotherapy plan is improved; through targeted processing on the structural binary image sequences of the GTV and the OARPRV and in combination with reasonable lattice globule arrangement conditions, the lattice position can be more accurately planned, damage to endangered organs is reduced, three-dimensional dose distribution is improved, the tumor treatment effect is improved, and the risk of complications is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of radiotherapy, and in particular to a three-dimensional lattice radiotherapy lattice arrangement method and system. Background Art

[0002] External beam radiation therapy has been committed to providing a uniform dose of radiation to the tumor. However, despite some progress in radiation accuracy, organ-threatening toxicity is still the key factor limiting the efficacy of most external beam radiation therapies. For larger tumors, the treatment effect is often poor due to surgical difficulties, ineffective chemotherapy and interventional treatment, and resistance to external beam radiation.

[0003] Spatially Fractionated Radiation Therapy (SFRT) breaks the traditional theory of uniform dose within the target area and adopts highly non-uniform dose distribution. It has significant effects in treating large and refractory tumors that are resistant to conventional fractionated radiotherapy. In addition to the direct / indirect cell killing effect of peak dose radiation, its potential mechanism of action also involves multiple aspects such as vascular damage, systemic immune response and tumor microenvironment regulation, but the specific mechanism has not yet been fully clarified. At present, there are many hypotheses to explain the strong killing effect of tumor tissue in the valley dose area in spatial fractionation, such as the bystander effect, that is, the adjacent unirradiated cells will suffer radiation damage or death due to the adjacent damaged / dead cells after irradiation; the group effect, which refers to the interaction of cells in the peak and valley dose areas within the millimeter-level area of ​​the target area; the diffusion of reactive oxygen molecules (such as H2O2) in the peak and valley dose intervals; the damage effect of peak dose on blood vessels; and more tumor immune-related lymphatic infiltration and antigen release-induced killing of distant lesions (abroad effect). At the same time, mechanisms such as the activation of specific signal transduction pathways and the migration and proliferation of stem cells in the peak and trough dose intervals may be related to the protective effect of spatial segmentation on normal tissues.

[0004] Lattice radiotherapy (LRT) is an important technical means to achieve SFRT at this stage. The parameters of its lattice, including diameter, spacing and position, have a crucial impact on the three-dimensional dose distribution. The ideal lattice spatial distribution can effectively improve the treatment effect of patients. However, in current clinical practice, there are still some problems: 1. The position of the lattice is usually arbitrarily drawn by the doctor. In order to obtain a more ideal three-dimensional dose distribution, it is often necessary to draw the lattice parameters multiple times. This method is not only inefficient, but also the quality of the plan depends to a large extent on the doctor's drawing, which is highly subjective. Second, the existing radiotherapy planning system does not take into account the special requirements of SFRT plan design. Professionals can only use the conventional radiotherapy planning system to manually place spheres as lattices by using simple geometric tools such as distance measurement, checkerboard display, and three-dimensional display. This method is barely feasible when the tumor is small, but when the tumor is huge, the workload becomes unbearable, and it is impossible to ensure that the position of the sphere is optimal. There are problems such as low efficiency, large workload, strong subjectivity, and poor consistency; To this end, a three-dimensional lattice radiotherapy lattice arrangement method and system are proposed. Summary of the invention

[0005] In view of this, the embodiments of the present invention hope to provide a three-dimensional lattice radiotherapy lattice arrangement method and system to solve or alleviate the technical problems existing in the prior art and at least provide a beneficial option.

[0006] In order to solve the above technical problems, a technical solution adopted in the present application is: a three-dimensional lattice radiotherapy lattice arrangement method, comprising the following steps: Step 1: acquiring a computed tomography (CT) image sequence, wherein the CT image sequence is composed of a plurality of CT image planes arranged in spatial order and parallel to each other; Step 2: loading contour lines for each structure according to the CT image plane, each structure comprising one or more contour lines, and the structure comprises a human organ or a tumor structure; Step 3: Based on the grid filling algorithm, generate a structural binary image sequence of the same size as the CT image sequence for each structure; Step 4: Based on the erosion algorithm and the dilation algorithm, the structural binary image sequences of the tumor target volume (GTV) and the planned risk volume of the organ at risk (OAR_PRV) are processed respectively, and the processed image region is generated; Step 5: Determine the position of the lattice sphere center by layering and scanning the processed image area; Step 6: Plan a three-dimensional lattice radiotherapy lattice layout plan based on the determined lattice sphere center position and lattice sphere layout conditions.

[0007] As a further preferred embodiment of the present technical solution, in step 4, the method for generating the processed image area comprises the following steps: Step 401: Perform three-dimensional shrinkage (m+d / 2) millimeters on the structure binary image sequence of GTV based on the erosion algorithm to obtain the shrunk image sequence V L , where m is GTV to V L The retraction distance is d, and d is the diameter of the lattice ball; Step 402: Perform two-dimensional expansion (rP +d / 2) mm and perform a merge operation to obtain the merged image sequence V PRV , where r P is the distance between the lattice ball and OAR_PRV; Step 403: Find V PRV The median value is 1, and V L Middle and V PRV and V L The voxel values ​​in the intersection are set to 0.

[0008] As a further preferred embodiment of the present technical solution, in step 5, the method for determining the position of the center of the lattice sphere comprises the following steps: Step 501: Find V L The middle layer S0 of the foreground area is scanned in the order of x, y, z, and the foreground voxel points are added to the candidate list L possible middle; Step 502: Get list L possible The first voxel point in is added to the lattice sphere center list L0, and from L possible Removed; Step 503: Remove the candidate list L possible All voxel points whose distance to the last center of the lattice center list L0 is less than r mm, where r is the minimum distance between the centers of the lattice balls; Step 504: Repeat the above steps 502-503 until the list L possible is empty; Step 505: With the middle layer S0 as the center, search for layers with a layer spacing of s mm in both the upper and lower directions, that is, satisfying S i+1 With S i The distance between the two layers is s mm; Step 506: align S in the order of x, y, and z. i The layer image is scanned and compared with the previous layer lattice sphere center list L i-1 (when i>0) or L i+1 (When i < 0) Foreground voxel points whose center distance is not less than d1 mm are added to the candidate list L possible In which, d1 is the two-dimensional distance between the center of each layer of lattice sphere and the center of the adjacent layer; Step 507: Get list L possible The first voxel point in is added to the lattice sphere center list L i and from L possible Removed; Step 508: Remove the candidate list L possible List of all lattice sphere centers in L iThe distance between the last sphere center is less than r mm; Step 509: Repeat the above steps 507-508 until the list L possible is empty, and all layers are processed, and finally the lattice sphere center list L of each layer is i The center positions of all lattices are obtained.

[0009] As a further preferred embodiment of the present technical solution, the lattice ball arrangement conditions include: The diameter of the lattice sphere is d mm; GTV to V L The adduction distance is m millimeters; The minimum distance between the centers of the lattice balls is r mm; The distance between the lattice ball and OAR_PRV is not less than r P mm; The interlayer distance is s mm; The two-dimensional distance between the center of each layer of lattice balls and the center of the adjacent layer of lattice balls is not less than d1 mm; because the center position of the first ball may not be the best, after the initial ball arrangement is completed, the balls are moved in the three-dimensional direction according to a fixed step size Δl (valued at 2.5 mm), where the movement range of x, y, and z is ±d1 / 2. After the cycle is completed, the ball that can arrange the most lattice balls and the distance between the ball and the OAR is r is given priority. P The largest layout is chosen as the final solution.

[0010] As a further preferred embodiment of the present technical solution, in step 3, the method of generating a structure binary image sequence of the same size as the CT image sequence for each structure comprises the following steps: Step 301: setting the initial value of all pixels in the structure binary image sequence to 0; Step 302: for each contour line of each structure, start a grid-based filling algorithm, and when executing the algorithm, scan the CT image pixels line by line starting from the starting line of the image; Step 303: During the scanning process, for each row of pixels, the boundary of the area to be filled is determined according to a rule; the rule is formulated based on the pixel features of the image and the definition of the contour line; Step 304: for each pixel currently scanned, a judgment is made according to the previously determined boundary conditions. If the pixel is inside the contour line, its pixel value is set to 1; if it is not inside the contour line, its pixel value is kept to 0; Step 305: Continue the above scanning, judging and assigning operations until the filling process of all contour lines is completed.

[0011] As a further preferred embodiment of the present technical solution, in step one, the CT image plane includes a set of sampling grids representing a specific thickness, length and width in a three-dimensional human body space, the grids are voxels, and the voxel value represents a density distribution sampling value at the location of the voxel, i.e., a CT value.

[0012] As a further preferred embodiment of the present technical solution, in step 2, the contour line is drawn according to the grayscale distribution of the human organ in the CT image, and is composed of a plurality of coordinate points arranged in sequence.

[0013] To solve the above technical problems, another technical solution adopted by the present application is: a three-dimensional lattice radiotherapy lattice arrangement system, the system comprising: an image data acquisition module, a contour loading module, a binary image generation module, an image transformation processing module, a lattice sphere center determination module and a lattice arrangement planning module; The image data acquisition module is configured to acquire a CT image sequence, wherein the CT image sequence is composed of a plurality of CT image planes arranged in spatial order and parallel to each other; The contour loading module is configured to load contours for each structure according to the CT image plane, each structure comprising one or more contours, and the structure comprises a human organ or a tumor structure; The binary image generation module is configured to generate a structural binary image sequence of the same size as the CT image sequence for each structure based on a grid filling algorithm; The image transformation processing module is configured to process the structural binary image sequences of the tumor target volume (GTV) and the planned risk volume of the organ at risk (OAR_PRV) based on the corrosion algorithm and the expansion algorithm respectively, and generate a processed image region; The lattice sphere center determination module is configured to determine the lattice sphere center position by layering and scanning the processed image area; The lattice arrangement planning module is configured to plan a three-dimensional lattice radiotherapy lattice arrangement plan based on the determined lattice sphere center position and lattice sphere arrangement conditions.

[0014] As a further preferred embodiment of the present technical solution, the image transformation processing module is further configured with a data storage unit for storing intermediate data generated during the three-dimensional shrinking processing of the structure binary image sequence of GTV and the two-dimensional expansion processing of the structure binary image sequence of OAR_PRV, including the image sequence V after shrinking. L , the image sequence V after union PRV .

[0015] As a further preferred embodiment of the present technical solution, the lattice arrangement planning module includes a collision detection unit, and the collision detection unit is configured to detect whether there is a collision or interference between adjacent lattice spheres when planning a three-dimensional lattice radiotherapy lattice arrangement plan according to the determined lattice sphere center positions and lattice sphere arrangement conditions.

[0016] The embodiment of the present invention has the following advantages due to the adoption of the above technical solution: 1. The present invention realizes a series of operations from image acquisition to lattice layout planning through an automated algorithm process. Doctors do not need to manually adjust lattice parameters many times, saving a lot of time and energy. The lattice layout can be completed quickly. It is particularly suitable for radiotherapy planning of large tumors, greatly improving the design efficiency of radiotherapy plans. 2. The present invention reduces the differences caused by human factors by performing lattice arrangement according to unified standards based on established algorithms and parameters, ensures the consistency of lattice arrangement under the operation of different patients or different doctors, and improves the quality stability of radiotherapy plans; 3. The present invention can plan the lattice position more accurately by targeted processing of the structural binary image sequences of GTV and OAR_PRV, combined with reasonable lattice ball arrangement conditions, to ensure that while effectively irradiating the tumor, the damage to the endangered organs is minimized, the three-dimensional dose distribution is improved, the tumor treatment effect is improved, and the risk of complications is reduced.

[0017] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 It is a schematic flow chart of the method for arranging a lattice for three-dimensional lattice radiotherapy according to the present invention; Figure 2 A schematic diagram of a process of generating a processed image region according to the present invention; Figure 3 A schematic diagram of a process for determining the position of a lattice sphere center according to the present invention; Figure 4A schematic flow chart of a method for generating a structure binary image sequence of the same size as a CT image sequence for each structure according to the present invention; Figure 5 It is a schematic diagram of the functional modules of the lattice arrangement system of the three-dimensional lattice radiotherapy of the present invention. DETAILED DESCRIPTION

[0020] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0021] It should be clear that the following embodiments of the present disclosure are described by specific specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other in the absence of conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present disclosure.

[0022] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0023] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present disclosure. The drawings only show components related to the present disclosure rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0024] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects described may be practiced without these specific details.

[0025] Figure 13D lattice radiotherapy lattice arrangement method according to an embodiment of the present invention. It should be noted that if there is substantially the same result, the method of the present application is not limited to the method of the present invention. Figure 1 The process sequence shown is limited. Figure 1-Figure 4 As shown: A three-dimensional lattice radiotherapy lattice arrangement method, comprising the following steps: Step 1: acquiring a computed tomography (CT) image sequence, wherein the CT image sequence is composed of a plurality of CT image planes arranged in spatial order and parallel to each other; Specifically, first, assist the patient to lie on the CT scanning bed, adjust the patient's position according to the scanning site and the requirements of the radiotherapy plan, ensure that the patient's position is comfortable and stable, and avoid image artifacts or inaccuracies due to changes in the patient's position during the scanning process. Usually, position fixation devices such as vacuum pads and thermoplastic films are used to fix the patient to ensure the consistency of the patient's position during each scan; for example, for patients undergoing head radiotherapy, thermoplastic film may be used to fix the head in a specific position; for patients undergoing chest radiotherapy, vacuum pads may be used to fix the body to ensure that the impact of respiratory movement on the scanning results is minimized.

[0026] After confirming that the patient's position and scanning parameters are set correctly, start the CT scanning program. During the scanning process, closely observe the patient's condition to ensure that the patient remains still. At the same time, observe the real-time status of the scanned image through the monitoring system. If any abnormality is found in the image, such as artifacts or blur, the scanning should be stopped in time, the cause should be found, and adjustments should be made before rescanning; After scanning, the CT device will generate a series of image data, which are usually transmitted to the image storage and processing system through the network or storage media. During the transmission process, to ensure the integrity and accuracy of the data and avoid data loss or damage, a reliable transmission protocol and data verification mechanism are generally used to verify and correct the transmitted data; Finally, the acquired CT image sequence is stored in a dedicated image storage system to provide data support for the subsequent radiotherapy plan. The storage system should have sufficient storage space and good data management functions to facilitate the effective organization, retrieval and backup of large amounts of image data. At the same time, in order to ensure data security, corresponding data protection measures should be taken, such as setting access rights, regularly backing up data, etc., to prevent data leakage or loss.

[0027] Step 2: loading contour lines for each structure according to the CT image plane, each structure comprising one or more contour lines, and the structure comprises a human organ or a tumor structure; Specifically, first, the CT image sequence obtained in step 1 is imported into professional medical image processing software. Such software usually has powerful image display, processing and analysis functions and can meet the requirements of loading contour lines; ensure that the software can correctly identify the spatial order and inter-layer relationship of the CT image so as to accurately operate on each CT image plane; Then, according to different scanning parts and imaging characteristics, adjust the image window width and window position. The window width determines the grayscale range of the image display, and the window position determines the central grayscale value of the display. Appropriate window width and window position settings can make human organs and tumor structures appear more clearly in the image, which is convenient for subsequent contour delineation. For example, for lung CT images, the window width can usually be set to 1500-2000HU and the window position to -600--800HU, so that the subtle structure of the lungs can be better observed. Next, professional doctors or medical imaging technicians observe and analyze the CT images to identify the human organs and tumor structures that need to be loaded with contour lines. Different diseases and radiotherapy plans may focus on different structures. Common human organs include the heart, lungs, liver, kidneys, etc., and the tumor structure depends on the specific condition. During the identification process, it is necessary to combine the patient's medical history, clinical symptoms and other relevant examination results to ensure accurate positioning and differentiation of each structure. For complex structures or situations with multiple substructures, a reasonable delineation strategy should be formulated. For example, for the liver, it may be necessary to delineate different areas such as the left lobe and right lobe of the liver separately. For tumors, it may be necessary to distinguish between the main part of the tumor and the marginal infiltration area. Determine whether to draw contour lines one by one on all CT image planes, or to use the method of first delineating on the key level and then expanding to other levels through interpolation or automatic segmentation algorithms. Subsequently, starting from the first layer of the CT image sequence, the target structure is outlined on each image plane in sequence. During the outline process, the boundary features of the structure, such as grayscale changes and morphological contours, should be carefully observed to ensure that the contour line depicts the actual boundary of the structure as accurately as possible. For some structures with unclear boundaries, a comprehensive judgment can be made by combining image information from multiple levels, or different observation angles and image enhancement methods can be used to assist in the outline. When a structure contains multiple contours, each contour is outlined separately, and it is ensured that the logical relationship between them is correct. For example, for a tumor with multiple nodules, the contour of each nodule needs to be outlined separately. For some structures with regular shapes or obvious features, medical images can be used to assist in the outline. The automatic segmentation algorithm in the image processing software is used to assist in the generation of contour lines. Common automatic segmentation algorithms include threshold segmentation, region growing, and segmentation based on machine learning. For example, the threshold segmentation algorithm separates the target structure from the background in the image by setting an appropriate grayscale threshold. The region growing algorithm starts from one or more seed points and gradually expands the region according to the similarity of adjacent pixels until the boundary of the structure is reached. The automatic segmentation algorithm may not be able to completely and accurately segment the target structure, and the segmentation result needs to be manually corrected to check whether the automatically generated contour line is consistent with the boundary of the actual structure. For inaccurate parts, use manual outlining tools to adjust and correct them. At the same time, care should be taken to avoid excessive correction that causes the contour line to lose its original characteristics and accuracy. After all structures are outlined, the outline data is saved in the database of the medical image processing software. The outline data is usually stored in a specific file format, such as the DICOM RT Structure Set format, which can contain detailed information such as the geometric information of the outline, the name and type of the structure to which it belongs, etc. Another professional verifies the outlined outline to check its accuracy and completeness. The verification process can be carried out by comparing it with the original CT image, checking the continuity of the outlines at different levels, and measuring the size and position of the structure. If any problems are found in the outline, it is modified and adjusted in a timely manner to ensure that the outline can accurately reflect the actual situation of the human organs and tumor structure, and provide a reliable basis for the formulation of subsequent treatment plans. In actual operation, when importing CT image sequences and RTSS into the Treatment Planning System (TPS), the import fails due to the same image sequence UID (Unique Identifier). To solve this problem, an option to rewrite the image sequence UID is added in the image export process. The specific rewriting rule is: add 1 to the last digit of the UID of the original CT and RTSS. When implementing this rewriting rule, the system first reads the UID of the original CT and RTSS, and extracts the last digit through a programming algorithm. Taking Python language as an example, assuming that the original UID is stored in the string variable uid, you can use uid[-1] to get the last digit, then convert it to an integer type and add 1, and then reassemble the result back into the UID string. During the processing, it is necessary to consider the case where the last digit of the original UID is 9. At this time, adding 1 will cause a carry, and the number of digits and format of the entire UID need to be adjusted accordingly. For example, if the original UID is "123456789", it should become "123456790" after adding 1; After the rewriting is completed, the system will automatically perform a check to see if the rewritten UID is the same as the existing UID in the database; if a duplication is found, the system will add 1 to the last digit of the UID and recheck until a unique UID is obtained. This process can effectively avoid import failures caused by UID duplication, ensuring that CT image sequences and RTSS can be smoothly imported into the treatment planning system, providing a stable data foundation for subsequent radiotherapy plan formulation and implementation.

[0028] Step 3: Based on the grid filling algorithm, generate a structural binary image sequence of the same size as the CT image sequence for each structure; Specifically, first, a structural binary image sequence of the same size as the CT image sequence is created, and the initial values ​​of all pixels are set to 0. This step provides a unified basis for subsequent filling operations. The 0 value represents the background area, and the area subsequently filled with 1 will represent the corresponding human organ or tumor structure; Then, for each contour line of each structure, a grid-based filling algorithm is started, starting from the starting line of the image, and scanning the CT image pixels line by line. During the scanning process, for each row of pixels, the boundary of the area to be filled is determined according to the rules based on the pixel features of the image and the definition of the contour line. These rules may involve multiple factors such as the gray value of the pixel and the distance from the contour line, so as to accurately determine which pixels belong to the area that needs to be filled; Next, for each pixel currently scanned, a judgment is made according to the previously determined boundary conditions. If the pixel is inside the contour line, its pixel value is set to 1; if it is not inside the contour line, its pixel value is kept at 0. In this way, each pixel is judged and assigned a value one by one, and a binary image representing the structure of human organs or tumors is gradually constructed. The above scanning, judgment and assignment operations are continued until all contour lines are filled. At this point, in the obtained structure binary image sequence, pixels with a value of 1 represent the corresponding human organ or tumor structure area, and pixels with a value of 0 represent the background area, realizing the conversion of each structure into a binary image representation of the same size as the CT image sequence, which facilitates subsequent image analysis and processing.

[0029] Step 4: Based on the erosion algorithm and the dilation algorithm, the structural binary image sequences of the tumor target volume (GTV) and the planned risk volume of the organ at risk (OAR_PRV) are processed respectively, and the processed image region is generated; Specifically, first, the structural binary image sequence of GTV is subjected to three-dimensional shrinkage (m+d / 2) millimeters based on the erosion algorithm to obtain the shrunk image sequence V L Here, the erosion algorithm gradually erodes the foreground pixels (pixels with a value of 1) in the image to shrink the GTV area; where m is the distance from GTV to V L The inward retraction distance is d, and d is the diameter of the lattice ball. In actual operation, the erosion algorithm is used to process each layer of the binary image of the GTV according to the pre-set parameters, so that the GTV area shrinks inward by (m+d / 2) mm in three-dimensional space. Then, all the binary image sequences of OAR_PRV were expanded in two dimensions based on the dilation algorithm (r P +d / 2) mm and perform a union operation to obtain the image sequence V after union. PRV ; The dilation algorithm is opposite to the erosion algorithm. It will expand the foreground pixels in the image outward. For each layer of binary image of OAR_PRV, according to the set parameters (r P +d / 2) mm for two-dimensional expansion, and then all the expanded images are combined to obtain a comprehensive V PRV image; where r P is the distance between the lattice ball and OAR_PRV; Next, find V PRV The median value is 1, and V L The voxel value at the corresponding position is set to 0. This step is to ensure that when the lattice is arranged, the lattice balls are not placed in areas that may cause high doses of radiation to endangered organs. L Corresponding to V PRV The voxel positions with a median value of 1 are set to 0. When the lattice sphere center position is determined and the lattice spheres are arranged later, these areas will be excluded, thereby achieving protection of endangered organs.

[0030] Step 5: Determine the position of the lattice sphere center by layering and scanning the processed image area; Specifically, first find V in the processed image area L The middle layer S0 of the foreground area in the (tumor target area image sequence after shrinking) is usually the area composed of pixels with a value of 1 in the image, representing the effective part of the tumor target area after processing. It can be calculated by V L The range of all foreground areas in the axial direction, take the layer corresponding to the middle position as S0; scan the S0 layer image in the order of x, y, z, and add the foreground voxel points (voxels with a value of 1) to the candidate list L possible In this step, the purpose is to find all possible points in the middle layer that can be the center of the lattice sphere; Then, take the list L possible The first voxel point in the lattice is added to the lattice sphere center list L0, and the L possible Remove this point from the candidate list, L0 will be used to store the lattice sphere center position of the middle layer S0; remove the candidate list L possible All voxel points whose distance from the last sphere center in the lattice sphere center list L0 is less than r mm, where r is the minimum distance between the lattice sphere centers. This is to ensure that there is enough spacing between the lattice spheres to avoid overlapping or being too close to each other, which will affect the radiotherapy effect; repeat the above steps of selecting sphere centers and screening candidate points until the list L0 is full. possible is empty. At this time, L0 stores the positions of all lattice sphere centers in the middle layer S0 that meet the spacing requirements; Next, with the middle layer S0 as the center, look for layers with a layer spacing of s mm in both the up and down directions, that is, satisfying S i+1 With S i The distance between the two layers is s mm; the interlayer spacing s here is a pre-set parameter used to control the distribution of the lattice in the z-axis direction; S is set in the order of x, y, and z. i The layer image is scanned and compared with the previous layer lattice sphere center list L i-1 (When i>0) or (When i<0) the foreground voxel points whose sphere center distance is not less than d1 mm are added to the candidate list L possible In which, d1 is the two-dimensional distance between the center of each layer of lattice balls and the center of the adjacent layer. This step is to ensure that there is enough spacing between the lattice balls of adjacent layers. Take the list L possible The first voxel point in is added to the lattice sphere center list L i and from the list L possible Remove the point from the candidate list L possible List of all lattice sphere centers in L i The distance between the last sphere center of the voxel point is less than r mm; repeat the above steps of scanning, selecting sphere centers and screening candidate points until all layers are processed, and finally select the lattice sphere center list L of each layer. iThe center positions of all lattices are obtained.

[0031] Step 6: According to the determined lattice sphere center position and lattice sphere arrangement conditions, a three-dimensional lattice radiotherapy lattice arrangement plan is planned; Specifically, firstly, based on the determined position of the lattice ball center, a virtual model is constructed in three-dimensional space, which covers all the determined lattice ball centers, and their positions in three-dimensional space are determined according to the coordinates of the ball centers. At the same time, according to the requirements of the lattice ball arrangement conditions, such as the layer spacing of s mm, the two-dimensional distance between the lattice ball center of each layer and the lattice ball center of the adjacent layer being not less than d1 mm, the relative position relationship of each ball center in space is clarified, and the framework structure of the lattice is preliminarily constructed; Then, according to the diameter d mm in the arrangement conditions of the lattice spheres, the radius of the lattice spheres is calculated to be d / 2 mm. In the above-constructed three-dimensional model, a sphere is drawn with the center of each lattice sphere as the center and a radius of d / 2 mm to represent the actual lattice spheres, ensuring that the size of each lattice sphere meets the set standard, providing an accurate geometric shape basis for subsequent precise planning of radiotherapy doses; Next, the constructed lattice arrangement is comprehensively evaluated against the lattice ball arrangement conditions to check whether the distance between the lattice balls meets the requirement that the minimum distance between the lattice ball centers is r mm, and whether the distance between the lattice balls and the OAR_PRV is not less than r mm. P Millimeters. If there are situations where the conditions are not met, such as some lattice balls are too close or not far enough from the OAR_PRV, the position of the lattice ball center is fine-tuned. During the adjustment, the area with less impact on the radiotherapy effect is given priority. The lattice layout is optimized by changing the ball center coordinates to ensure that all layout conditions are met. After evaluation and adjustment, the three-dimensional lattice radiotherapy lattice layout that meets all the lattice sphere layout conditions is determined, and detailed information such as the final lattice sphere center coordinates, lattice sphere radius, and spatial relationship between each lattice sphere is compiled into a complete plan document. The document should contain clear text descriptions and intuitive three-dimensional schematic diagrams to facilitate the understanding and implementation of radiotherapy team members (such as doctors, physicists, etc.), and provide accurate guidance for the subsequent formulation and implementation of radiotherapy plans.

[0032] In one embodiment, specifically, in step 4, the method for generating a processed image region comprises the following steps: Step 401: Perform three-dimensional shrinkage (m+d / 2) millimeters on the structure binary image sequence of GTV based on the erosion algorithm to obtain the shrunk image sequence V L , where m is GTV to V L The retraction distance is d, and d is the diameter of the lattice ball; Among them, the determination of m needs to comprehensively consider the biological characteristics of the tumor, the requirements of radiotherapy dose distribution and clinical experience; for actively growing and highly invasive tumors, the m value will be appropriately increased to ensure thorough radiotherapy; for relatively slow-growing and clearly bordered tumors, the m value can be appropriately reduced; clinicians determine the appropriate m value based on long-term accumulated treatment experience and relevant research data, combined with the specific patient's tumor situation; d is determined based on the performance of the radiotherapy equipment, the size and location of the tumor, and the accuracy requirements of the radiotherapy plan; the radiotherapy equipment has high accuracy and can achieve more accurate dose control, so the d value can be relatively reduced; the tumor is small and in a special location, which requires high radiotherapy accuracy, and the d value also needs to be adjusted accordingly; in actual operation, doctors and physicists discuss together to determine the d value based on the specific radiotherapy plan; Specifically, first, among the many corrosion algorithms, choose an algorithm suitable for medical images. Common corrosion algorithms include morphology-based corrosion algorithms, such as using structural elements (such as squares, circles, etc.) to perform convolution operations on images. In medical image processing, considering the irregularity of tumor shapes, it is more appropriate to choose algorithms with flexible structural elements. Disk-shaped structural elements can better adapt to the circular or approximately circular areas of tumors, while cross-shaped structural elements may have more advantages in processing directional tumor boundaries. You can also choose corrosion algorithms based on distance transformation, which implement corrosion operations by calculating the distance from each pixel to the background pixel, and can more accurately control the depth and range of corrosion. When selecting an algorithm, consider the efficiency and accuracy of the algorithm to ensure that the processing is completed within a reasonable time and meets the accuracy requirements; Then, the structural binary image sequence of GTV is imported into the medical image processing software. The software needs to have powerful image processing functions and can support three-dimensional image processing and the operation of the selected erosion algorithm. The algorithm implementation module of the software is used to perform three-dimensional shrinkage (m+d / 2) millimeter processing on the structural binary image sequence of GTV according to the selected erosion algorithm. During the processing, each layer and each pixel of the image is operated. According to the algorithm rules, it is determined whether each pixel should be eroded according to the structural elements and the shrinkage distance. For three-dimensional images, not only the pixel relationship within the plane should be considered, but also the pixel connection between layers should be considered to ensure that the shrinkage operation is performed accurately in the three-dimensional space. After processing all images, the shrunk image sequence V is obtained. L ; Finally, the processed image sequence V LPerform a visual inspection and compare the structural binary image sequence of the original GTV to observe whether the tumor area is evenly retracted, whether the boundary after retraction is smooth and continuous, and whether there are abnormal cavities or protrusions. Use image analysis tools to measure parameters such as the tumor volume and surface area after retraction, and compare them with the theoretical calculated values. Calculate the theoretical value of the tumor volume after retraction according to the formula, and compare the measured value with the theoretical value. If the error is within a reasonable range (such as within 5%), it indicates that the processing result is relatively accurate. If the error is large, it is necessary to check whether there are problems with the parameter setting, algorithm selection, and execution process, and adjust and reprocess in time.

[0033] Step 402: Perform two-dimensional expansion (r P +d / 2) mm and perform a merge operation to obtain the merged image sequence V PRV , where r P is the distance between the lattice ball and OAR_PRV; Among them, the parameter r P The determination of the radiotherapy plan should take into account many factors. The doctor should set the radiotherapy plan based on the patient's specific condition, the sensitivity of the organs at risk, and the expected effect of the radiotherapy plan. For organs with high sensitivity, such as the eyes and spinal cord, P The value of is usually set larger to give these organs a wider safety margin; for relatively less sensitive organs, r P The value can be appropriately reduced, but it is still necessary to ensure that the organs can be effectively protected from excessive radiation doses; d is the diameter of the lattice sphere, and its size will also affect the degree of expansion. When determining d, it is necessary to refer to factors such as the performance of the radiotherapy equipment, the size and location of the tumor, etc., to ensure the accuracy and effect of radiotherapy; Specifically, first, select a dilation algorithm. Common dilation algorithms include morphological dilation algorithms, which dilate images by performing convolution operations on images using structural elements (such as squares, circles, crosses, etc.). When processing the structural binary image sequence of OAR_PRV, it is necessary to select appropriate structural elements according to the characteristics of the image and processing requirements; if the shape of OAR_PRV is relatively regular, the circular structural element may make the expansion effect more uniform; if OAR_PRV has obvious directionality, the cross structural element may be more suitable and can perform more effective expansion in a specific direction; in addition, a dilation algorithm based on distance transformation can also be selected, which determines the degree of dilation by calculating the distance from each pixel to the background pixel, and can more accurately control the range of expansion; in practical applications, the efficiency and accuracy of the algorithm should be comprehensively considered to ensure that the image processing is completed within a reasonable time and the accuracy requirements are met; Then, all the binary image sequences of OAR_PRV were imported into the software or system with image processing function, and the binary image of each OAR_PRV was expanded in two dimensions (r P +d / 2) mm processing. During the processing, each pixel in the image is judged and processed according to the algorithm rules. Taking the morphological expansion algorithm as an example, the structural element slides on the image. If there is a foreground pixel (pixel with a value of 1) in the area covered by the structural element, the current center pixel is set as the foreground pixel, thereby realizing the expansion of the image. In the two-dimensional expansion process, operations are only performed in the horizontal and vertical directions, and no changes in the depth direction are involved. After completing the two-dimensional expansion processing of the structural binary images of all OAR_PRVs, these expanded images are merged. The purpose of the merging operation is to merge the expanded areas of each OAR_PRV into a comprehensive area to obtain the merged image sequence V PRV ; During the AND operation, for each pixel position, if the value of the pixel is 1 in any of the expanded OAR_PRV images, then PRV The value of this pixel in the image is also set to 1; only when the values ​​of all expanded images at this pixel position are 0, V PRV The value of this pixel in the image is 0; in this way, all risk areas of endangered organs are integrated together, providing a basis for avoiding these areas when subsequently determining the position of the lattice sphere center.

[0034] Step 403: Find V PRV The median value is 1, and V L Middle and V PRV and V L The voxel values ​​in the intersection of are set to 0; Specifically, first, use medical image processing software or programming tools to PRV Each image in the image sequence is traversed pixel by pixel, starting from the first pixel in the upper left corner of the image, and checking the value of each pixel in turn from left to right and from top to bottom. PRV It is a binary image sequence, and the pixel values ​​are only 0 and 1, so during the traversal process, focus on the pixels with a value of 1; When V PRV When a voxel with a value of 1 is found in V (a pixel in a two-dimensional image and a voxel in a three-dimensional image, collectively referred to as a voxel here), the voxel is determined according to the spatial coordinate system of the image. L The corresponding position in the image, because V L and V PRV are obtained based on the same CT image sequence, and their spatial coordinates have a corresponding relationship; for example, if VPRV The coordinates of a voxel in V are (x, y, z), then L The corresponding voxel coordinates in are also (x, y, z); Find V L After finding the voxel at the corresponding position in the code, set its value from the original value (which may be 0 or 1) to 0. This step is implemented by programming. In the code, use the assignment statement to set V L The pixel value of the corresponding voxel in the image is changed to 0; for example, when using the NumPy library to process images in Python, the value of the corresponding element in the array can be directly modified through indexing operations; repeat the above traversal, determination, and modification operations until all voxels in V are traversed. PRV For all images in the image sequence, ensure that V PRV All voxels with value 1 in V L The voxel values ​​at the corresponding positions are set to 0.

[0035] In one embodiment, specifically, in step five, the method for determining the position of the center of the lattice sphere includes the following steps: Step 501: Find V L The middle layer S0 of the foreground area is scanned in the order of x, y, z, and the foreground voxel points are added to the candidate list L possible middle; Specifically, first, in the medical image processing software or programming environment, read the indented image sequence V L , due to V L It is a binary image. The voxel value of the foreground area (representing the part of the tumor target area after shrinkage) is usually 1, and the voxel value of the background area is 0. L For each voxel in , count the area where the voxel value is 1 to determine the range of the foreground area; Then, after determining the foreground area, calculate the number of layers of the foreground area in the z-axis direction; assuming that the foreground area extends from the z1th layer to the z2th layer in the z-axis direction, the z coordinate of the middle layer S0 can be obtained by the formula: or To calculate, find V in this way L The middle layer S0 in the middle foreground area; Next, the middle layer S0 is scanned in the order of x, y, and z. On the two-dimensional plane (xy plane), starting from the upper left corner of the image (i.e., the position with the smallest x and y coordinates), each pixel is scanned line by line (for the two-dimensional layer in the three-dimensional image, the pixel is equivalent to the projection of the voxel in the layer). During the scanning process, it is determined whether the value of each voxel is 1. If so, the voxel point (i.e., its coordinate information in the image) is added to the candidate list L possible In; this candidate list Lpossible It will be used to subsequently screen out the appropriate lattice sphere center position.

[0036] Step 502: Get list L possible The first voxel point in is added to the lattice sphere center list L0, and from L possible Removed; Specifically, first, in the programming environment, the list L storing the candidate voxel points is accessed through the corresponding data structure operation function possible In general, a list is an ordered data structure that has built-in methods to access its elements in most programming languages; for example, in Python, if L possible is a list that can be indexed using L possible [0] to get the first element in the list; Then, after obtaining L possible After the first voxel point in the lattice is added, it is added to the list L0 specifically used to store the lattice sphere center; in Python, the list append() method can be used, such as L0.append (L possible [0]), add the selected voxel point to the end of L0, which means that this voxel point is used as a potential lattice sphere center for subsequent analysis and processing; In order to avoid repeatedly selecting the same voxel point as the center of the lattice sphere, it is necessary to select L possible Remove the voxel point that has been added to L0; in Python, you can use the pop() method of the list and specify the index as 0, that is, L possible .pop(0), so that from L possible The first element is deleted; through this operation, L possible The list is updated to retain only the remaining candidate voxel points so that the appropriate lattice sphere center position can be screened later.

[0037] Step 503: Remove the candidate list L possible All voxel points whose distance to the last center of the lattice center list L0 is less than r mm, where r is the minimum distance between the centers of the lattice balls; Specifically, first, in the program, access the lattice sphere center list L0. Since the list is an ordered data structure, the last element of the list is accessed to obtain the last determined lattice sphere center coordinates. In many programming languages, a specific index method can be used to obtain it. For example, in Python, L0[-1] can be used to obtain the last element of the list L0, which contains the coordinate information representing the sphere center position (assuming it is a three-dimensional coordinate (x, y, z)). Then, for the candidate list L possibleFor each voxel point in L0, calculate its distance to the last sphere center in L0. The distance calculation usually uses the Euclidean distance formula. For the voxel point coordinates (x1, y1, z1) and the sphere center coordinates (x2, y2, z2), the distance d is calculated as follows: ; Next, the calculated distance is compared with the minimum distance r between the centers of the lattice balls. If the distance is less than r, it means that the voxel point is too close to the determined center of the ball and does not meet the lattice layout requirements. It needs to be removed from L possible If the distance is greater than or equal to r, the voxel point is retained; Traverse L possible After completing the distance calculation and screening, all voxel points in L possible The list will change, retaining only the voxel points that are at least r mm away from the last sphere center in L0, so that the updated L possible The list provides a more reasonable set of candidate points for the subsequent determination of the lattice sphere centers, ensuring that the final determined lattice sphere centers maintain an appropriate distance and meet the requirements for lattice distribution in radiotherapy planning.

[0038] Step 504: Repeat the above steps 502-503 until the list L possible is empty; Specifically, before executing this step, it is necessary to ensure that step 501 has been completed to obtain the candidate list L possible , and has already L possible The first voxel point in is added to the lattice sphere center list L0 (that is, step 502 is completed), and L possible The first screening is performed (i.e., step 503 is completed); As long as the candidate list L possible If it is not empty, then continue to repeat steps 502 and 503; When L possible When it becomes an empty list, it means that all eligible lattice sphere centers have been screened out and added to L0, and the loop ends. At this time, L0 stores all lattice sphere center positions on the middle layer S0 that meet the minimum sphere center distance requirement.

[0039] Step 505: With the middle layer S0 as the center, search for layers with a layer spacing of s mm in both the upper and lower directions, that is, satisfying S i+1 With S i The distance between the two layers is s mm; Specifically, first, the middle layer S0 is clearly taken as the center, and the search is performed in the positive direction (upward) and negative direction (downward) of the z-axis; at the same time, the interlayer spacing parameter s is confirmed. The value of s is usually determined based on factors such as the accuracy requirements of the radiotherapy plan, the size and shape of the tumor, and the uniformity requirements of the lattice distribution. In actual operation, the value of s is generally between a few millimeters and more than ten millimeters. For example, for some tumors with large volumes and relatively low requirements for radiotherapy accuracy, s may be set to 5-10 millimeters; for tumors with small volumes and surrounding important organs, s may be set to 2-5 millimeters to ensure the effect and safety of radiotherapy. Then, starting from the middle layer S0, along the positive direction of the z-axis, the layers that meet the conditions are searched in sequence with the layer spacing s as the interval. During the search process, the target layer is determined by judging whether the distance between adjacent layers is equal to s; in medical image data, each layer of the image usually has corresponding coordinate information (such as z coordinate), and the distance between adjacent layers can be calculated based on these coordinate information; assuming that the z coordinate of the middle layer S0 is z0, then the z coordinate of the first layer s1 searched upward should be z0+s; by traversing the image sequence, the layer with the z coordinate closest to z0+s is found as s1; if a layer with a distance of exactly s is not found during the search process, the interpolation algorithm can be used to process the image to simulate a layer that meets the layer spacing requirements; or the layer with the closest distance to s and satisfying a certain error range (such as the error within ±5 mm) is selected as the approximate target layer; Next, using a method similar to the upward search, starting from the middle layer S0, along the negative direction of the z-axis, search for layers that meet the conditions with a layer spacing of s. The first layer s -1 The z coordinate of should be z0-s; similarly, by traversing the image sequence, find the layer with the coordinate closest to z0-s as s -1 ; If the layer with the exact distance s cannot be found, it can be processed in the same way as the error when searching upwards; Finally, after finding the layers that meet the layer spacing requirements (including the layers found by searching upward and downward), record the relevant information of these layers, such as the layer numbers (such as s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11, s12, s13, s14, s15, s16, s17, s18, s19, s -1 etc.), its position in the image sequence, the image data of the layer, etc. This information will be used to determine the position of the lattice sphere center of the layer in the subsequent steps.

[0040] Step 506: align S in the order of x, y, and z. i The layer image is scanned and compared with the previous layer lattice sphere center list L i-1 (when i>0) or L i+1 (When i < 0) Foreground voxel points whose center distance is not less than d1 mm are added to the candidate list L possible In which, d1 is the two-dimensional distance between the center of each layer of lattice sphere and the center of the adjacent layer; Specifically, first, in the order of x, y, z, S i The layer image is scanned in two dimensions. i On the image plane of the layer, starting from the upper left corner of the image (i.e. the position with the smallest x and y coordinates), scan each pixel from left to right row by row (in a three-dimensional image, the pixel here is actually the projection of the voxel in that layer). After completing a row of scanning, move to the next row and continue scanning until the entire S layer is traversed. i Layer image; Then, the reference lattice sphere center list is determined according to the value of i; when i>0, the reference is made to the previous layer s i-1 The lattice sphere center list L i-1 ; When i<0, refer to the next layer s i+1 The lattice sphere center list L i+1 ; These lists store the positions of the lattice sphere centers that have been determined in the previous or next layer (assuming that each sphere center position is represented by a three-dimensional coordinate (x, y, z)); For S i For each foreground voxel point scanned by the layer image (a foreground voxel point generally refers to a voxel point with a value of 1, indicating that the area may be suitable for placing the lattice sphere center), calculate its difference with the reference list (L i-1 or L i+1 ) is the two-dimensional distance (distance on the xy plane) of each sphere center in the voxel point. The Euclidean distance formula is usually used to calculate the two-dimensional distance. For the voxel point coordinates (x1, y1) and the sphere center coordinates (x2, y2), the calculation formula for the two-dimensional distance d is: ; Compare the calculated distance d1 with (the two-dimensional distance between the center of each layer of the lattice ball and the center of the adjacent layer). If the distance is not less than d1 mm, it means that the distance between the voxel point and the center of the adjacent layer meets the requirements, and it is added to the candidate list L possible If the distance is less than d1 mm, the voxel point does not meet the requirements and will not be added; After scanning S i After all voxel points of the layer image are calculated and screened, the candidate list L possible Get updated; at this time L possible Contains S i The foreground voxel points in the layer image that meet the requirements of the distance to the center of the adjacent layer will be used as candidate points of the lattice center of this layer for further screening to determine the final lattice center position.

[0041] Step 507: Get list L possible The first voxel point in is added to the lattice sphere center list L i and from L possible Removed; Specifically, visit the candidate list L possible In most programming languages, a list is an ordered data structure, and the first element can be obtained by a specific index; for example, in Python, if L possible is a list, using L possible [0] The first voxel point can be obtained. The voxel point is usually represented by a three-dimensional coordinate (x, y, z) to represent its position in the image. Add the first voxel point obtained to the current layer S i The corresponding lattice sphere center list L i In Python, you can use the list append() method, like L_i.append(L_{possible}[0]), to add the voxel point to L i At the end, it means that this voxel point is determined as a candidate position of the lattice sphere center of the current layer; To avoid repeated selection of this voxel point in the future, it is necessary to remove it from L possible In Python, use the pop() method of the list and specify the index, i.e. L_{possible}.pop(0), so that the list is removed from L possible The first element is deleted, and the updated L possible Only the remaining candidate voxel points are included, which can be used to continue to screen the appropriate lattice sphere center position.

[0042] Step 508: Remove the candidate list L possible List of all lattice sphere centers in L i The distance between the last sphere center is less than r mm; Specifically, first, we need to start from the current layer S i The corresponding lattice sphere center list L l Get the coordinates of the last sphere center in the list; in most programming languages, since the list is an ordered data structure, the last element can be obtained through a specific index method; for example, in Python, use L l [-1] to get L l The last element of the list usually represents the position of the center of the sphere in the image in the form of three-dimensional coordinates (x, y, z); For the candidate list L possible Traverse each voxel point in L possible For each voxel point in L l The distance between the last sphere center in ; In three-dimensional space, the Euclidean distance formula is usually used to calculate the distance between two points. Suppose the voxel point coordinates are and the last sphere center coordinates are (x1, y1, z1), then the distance d between them is calculated as: ; Compare the calculated distance d with the preset minimum distance r between the centers of the lattice balls; if d < r, it means that the voxel point is close to L l The distance of the last sphere center in the L is too close and does not meet the requirements. The voxel point needs to be moved from L possible If d ≥ r, keep the voxel point; in Python, you can create a new empty list and traverse L possible When the voxel points that meet the conditions are added to the new list, and finally the new list is used to replace L possible , complete the update of the candidate list.

[0043] Step 509: Repeat the above steps 507-508 until the list L possible is empty, and all layers are processed, and finally the lattice sphere center list L of each layer is i Get the sphere center position of all lattices; Specifically, for a certain layer S i When processing, ensure that the candidate list L possible Not empty, as long as L possible If there are still voxel points in , continue to execute steps 507 and 508; When L possible When it becomes an empty list, it means that all eligible lattice sphere centers of this layer have been screened out and added to L l In the middle, the cycle of a single layer ends; After completing the processing of the middle layer S0, a series of layers S have been determined in the upward and downward directions. i (i is a positive or negative integer), and the layers are processed in order, from the lowest layer to the highest layer or vice versa; For each layer S i , repeat the above single-layer cycle operation, that is, continuously from the L possible Filter out the lattice sphere center and add it to L i In, until L possible is empty; When all layers are processed, the lattice sphere center list L of each layer is i Putting them together, we get a total list containing the positions of the sphere centers of all layers of lattices. This total list records the sphere center position information of all lattices in the entire three-dimensional space.

[0044] In one embodiment, specifically, the lattice ball arrangement conditions include: The diameter of the lattice sphere is d millimeters. The size of the lattice sphere is clearly defined, which will affect the dose distribution during radiotherapy and the interaction with surrounding tissues. A smaller diameter may achieve more precise dose delivery, but more lattice spheres may be required to cover the tumor target area. A larger diameter, on the contrary, may make the dose distribution relatively wide. GTV to V L The retraction distance is m millimeters; GTV is retracted to obtain V L The m value determines the degree of retraction. The retraction operation is to leave a certain safety margin around the tumor target area. It also prepares for the subsequent determination of the center position of the lattice ball to avoid the lattice ball being too close to the edge of the tumor, affecting the radiotherapy effect or causing unnecessary radiation to the surrounding normal tissues. The minimum distance between the centers of the lattice spheres is r millimeters. The minimum spacing between the lattice spheres is specified to ensure that there is enough distance between the lattice spheres, which can avoid excessive dose superposition between the spheres and ensure that the radiotherapy dose is more evenly distributed in the tumor target area, while also facilitating physical layout and operation. The distance between the lattice ball and OAR_PRV is not less than r P mm; OAR_PRV is the region related to the organ at risk that needs to be protected, r P The value ensures that the lattice spheres maintain a safe distance from these sensitive areas to reduce the potential damage of radiotherapy to endangered organs and ensure the normal physiological function of patients; The interlayer spacing is s millimeters; it determines the distance between different layers of the lattice spheres in three-dimensional space. Appropriate interlayer spacing helps to reasonably distribute the lattice spheres in the vertical direction, so that the radiotherapy dose can be more evenly covered in the three-dimensional space of the tumor target area, while also taking into account the anatomical structure of human tissue and the operating requirements of radiotherapy equipment; The two-dimensional distance between the center of each layer of lattice spheres and the center of the lattice spheres in the adjacent layer is no less than d1 mm; the positional relationship between the lattice spheres in adjacent layers is further constrained. In addition to the layer spacing, a certain distance is also guaranteed on the two-dimensional plane, which helps to optimize the distribution of the lattice spheres in three-dimensional space and avoid adjacent layers of spheres being too close, resulting in uneven dose distribution or adverse effects on surrounding tissues.

[0045] In one embodiment, specifically, in step 3, the method of generating a structure binary image sequence of the same size as the CT image sequence for each structure comprises the following steps: Step 301: setting the initial value of all pixels in the structure binary image sequence to 0; Specifically, in the image preprocessing stage of radiotherapy, the structural binary image sequence is used to represent the distribution of structures such as GTV and OAR_PRV. The initial values ​​of all pixels in the structural binary image sequence are set to 0, which lays the foundation for the subsequent accurate marking and processing of these structures; In medical image processing, binary images have only two pixel values, usually 0 represents the background area and 1 represents the foreground area (i.e., the structure area of ​​interest); initializing all pixels to 0 is equivalent to setting the entire image area as the background first, and then modifying the pixel value of the corresponding area to 1 based on the actual structural information, so as to highlight the tumor target area and endangered organs and other structures; For example, for a structural binary image sequence containing multiple tomographic images, each image represents a cross-section of the human body. During initialization, regardless of whether there is a tumor target area or an organ at risk in the cross-section, all pixels are marked as 0, which means that no structure of interest currently exists.

[0046] Step 302: for each contour line of each structure, start a grid-based filling algorithm, and when executing the algorithm, scan the CT image pixels line by line starting from the starting line of the image; Step 303: During the scanning process, for each row of pixels, the boundary of the area to be filled is determined according to a rule; the rule is formulated based on the pixel features of the image and the definition of the contour line; Specifically, first identify the structures to be processed, such as GTV and OAR_PRV, and for each structure, obtain its corresponding contour information. These contours are usually obtained in the previous image segmentation step. They outline the boundaries of the structure. Each contour contains a series of coordinate points that define the shape of the structure in the image. For each contour line of each structure, a grid-based filling algorithm is started, and line-by-line scanning is performed starting from the starting row of the image (usually the first row of the image, i.e., the row with y=0, where it is assumed that the axis in the image coordinate system represents the row direction). During the scanning process, each pixel in the CT image is checked in order from left to right; During the scanning process, for each pixel, it is determined whether it is located in the area surrounded by the current contour line. A variety of methods can be used to determine whether it is located in the area surrounded by the current contour line. For example, the ray method is to emit a ray from the pixel to one side of the image (such as the right side) and count the number of intersections between the ray and the contour line; if the number of intersections is an odd number, it means that the pixel is located in the area surrounded by the contour line, and the corresponding pixel value in the structural binary image sequence is set to 1 (indicating the foreground area); if the number of intersections is an even number, the pixel is located outside the contour line, and the corresponding pixel value remains 0 (indicating the background area); After completing the scanning and judgment of a row of pixels, move to the next row and repeat the above steps until all rows of the entire CT image are scanned. For each contour line of each structure, the execution process of the above filling algorithm is repeated until the contour lines of all structures are processed, thereby accurately marking the area of ​​each structure in the structural binary image sequence.

[0047] Step 304: for each pixel currently scanned, a judgment is made according to the previously determined boundary conditions. If the pixel is inside the contour line, its pixel value is set to 1; if it is not inside the contour line, its pixel value is kept to 0; Specifically, when executing this step, firstly, the previously determined contour line information must be clarified, and these contour lines constitute the boundary conditions for judging whether a pixel is within the structural region; at the same time, the coordinate position of the currently scanned pixel in the image is obtained (usually represented by (x, y), where x is the column coordinate and y is the row coordinate); for example, when a two-dimensional CT image is scanned line by line, the coordinate information of each pixel is obtained in turn as the scanning proceeds; Use a suitable algorithm to determine whether the current pixel is inside the contour line. Common methods such as the ray method, which emits a ray from the pixel to one side of the image (such as the right side) and counts the number of intersections between the ray and the contour line; if the number of intersections is an odd number, it means that the pixel is within the area surrounded by the contour line; if the number of intersections is an even number (including 0), the pixel is outside the contour line; other methods such as the angle method can also be used to determine whether it is inside the contour line by calculating the angle of the pixel relative to the vertex of the contour line; The pixel value is set according to the judgment result; if it is determined that the pixel is inside the contour line, its pixel value in the structural binary image is set to 1, indicating that the pixel belongs to the structural area (foreground); if it is determined that the pixel is not inside the contour line, its pixel value is kept at 0, indicating that the pixel belongs to the background area; in the process of scanning the entire image line by line, the above judgment and pixel value setting operations are repeated for each pixel until the processing of the entire image is completed.

[0048] Step 305: Continue the above scanning, judging and assigning operations until the filling process of all contour lines is completed; Specifically, before starting this step, ensure that all contour information that needs to be processed has been obtained. These contours may come from different structures (such as tumor target areas, organs at risk, etc.); at the same time, clarify the starting position of the scan, generally starting from the starting row of the image (such as the first row), and scan the CT image pixels line by line from left to right; Start scanning the image pixels line by line, starting from the starting line of the image, and perform a judgment operation for each pixel in each line; that is, according to the previously determined boundary conditions (defined by the contour line), use a suitable algorithm (such as ray method, corner method, etc.) to determine whether the pixel is inside the contour line; Perform assignment operation according to the judgment result; if the pixel is inside the contour line, set its pixel value in the structural binary image to 1; if it is not inside the contour line, keep its pixel value to 0; After completing the scanning, judgment and assignment of a row, move to the next row and repeat the above operations until all rows involved in the current contour line are scanned; For all contour lines, repeat the above scanning, judging and assigning process in turn. After processing one contour line, proceed to the next one until all contour lines are processed; When all contour lines are filled, the areas of each structure have been accurately marked in the structural binary image. The pixel value of the foreground area (the area where the structure is located) is , and the pixel value of the background area is , completing the generation of the structural binary image.

[0049] In one embodiment, specifically, in step 1, the CT image plane represents a sampling grid set of a specific thickness, length and width in a three-dimensional human body space, the grid is a voxel, and the voxel value represents a density distribution sampling value at the location of the voxel, that is, a CT value; Among them, a CT image is a two-dimensional image obtained by tomographic scanning of a three-dimensional human body space; the CT image plane is actually a cross-section in the three-dimensional human body space, which represents the sampling of the human body within a specific thickness, length and width range; this specific thickness is determined by the parameters of the CT scanning equipment, for example, the common CT scanning layer thickness may have different specifications such as 1mm, 2mm, 5mm, etc.; the length and width correspond to the range of the CT image on the plane, covering the size of the corresponding part of the human body; Taking chest CT scan as an example, the scanned CT image plane is like a slice of the chest at a certain height, showing the structural information of the human tissue at the slice. Through a series of such CT image planes (slices at different heights), a three-dimensional human chest model can be reconstructed in the computer. CT images are composed of discrete sampling points, which form a sampling grid set. Each sampling point in a CT image is called a voxel (Voxel, the abbreviation of Volume Pixel), which can be understood as a pixel in three-dimensional space. Voxel is the basic component unit of CT images. It has a certain spatial size and corresponds to a tiny volume in three-dimensional human body space. The size of the voxel depends on factors such as the resolution of the CT scanner. High-resolution CT scanners can produce smaller voxels, thus providing more detailed image information. For example, in some high-end CT devices, the size of the voxel can reach very small values, allowing doctors to observe the fine structure of human tissue more clearly. The voxel value indicates the density distribution sampling value at the location of the voxel, that is, the CT value. The CT value is a quantitative value used to measure the degree of absorption of X-rays by human tissue. Different human tissues have different CT value ranges. For example, the CT value of air is close to -1000HU (Hounsfield Unit, Hounsfield Unit is the unit of CT value), the CT value of water is about 0HU, and the CT value of bones is usually around 1000HU; By measuring the CT value of voxels, doctors can distinguish different human tissues and determine whether there are lesions. In radiotherapy planning, the CT value is also very important because it is related to the tissue's absorption and scattering characteristics of radiation, affecting the calculation and distribution of radiotherapy doses. For example, there may be differences in the CT values ​​of tumor tissue and normal tissue. The radiotherapy planning system can adjust the radiation dose based on this CT value information to achieve better treatment effects while minimizing damage to normal tissues.

[0050] In one embodiment, specifically, in step 2, the contour line is drawn according to the grayscale distribution of the human organ in the CT image, and is composed of a plurality of coordinate points arranged in sequence; Among them, CT images present the structure of human organs through the difference in the absorption degree of X-rays by different tissues, which is manifested as different grayscale values. For example, bone tissue absorbs more X-rays and presents a higher grayscale value (usually brighter) on the CT image; while soft tissue absorbs less X-rays and has a lower grayscale value (usually darker). Doctors or professionals can distinguish the boundaries of different human organs based on these grayscale distributions and then draw contours. For example, for the tumor target area, there may be a certain difference in grayscale between it and the surrounding normal tissues. By carefully observing the grayscale changes, the general outline of the tumor can be outlined. Among them, the outlined contour line is stored and represented in the computer in the form of multiple coordinate points arranged in sequence. These coordinate points determine the shape and position of the contour line. Adjacent coordinate points are connected in sequence to form continuous lines, thereby outlining the boundaries of human organs. Orderly arranged coordinate points are important because they determine the direction and closure of the contour line, which is crucial for subsequent image processing operations based on contour lines (such as filling algorithms to determine structural areas); if the order of coordinate points is chaotic, it may cause errors in the shape of the contour line, which in turn affects the accurate representation of human organs and the formulation of subsequent radiotherapy plans.

[0051] In summary, the three-dimensional lattice radiotherapy lattice arrangement method provided by the embodiment of the present invention obtains a CT image sequence and performs multi-step processing on it, first loads a contour line for each structure, then uses a grid-based filling algorithm to generate a structural binary image sequence, and then processes the image sequence of the tumor target volume (GTV) and the planned risk volume of the organ at risk (OAR_PRV) based on the corrosion and expansion algorithm, and then determines the position of the lattice sphere center by layering and scanning, and finally plans a three-dimensional lattice radiotherapy lattice arrangement scheme in combination with the lattice sphere arrangement conditions; the method realizes automation from image acquisition to lattice arrangement scheme planning, saves time and energy, and improves the efficiency of radiotherapy plan design; lattice arrangement is performed according to a unified standard, which ensures the consistency of lattice arrangement under the operation of different patients or doctors, and improves the quality stability of radiotherapy plan; through targeted processing of GTV and OAR_PRV and reasonable lattice sphere arrangement conditions, the lattice position is accurately planned, the damage to the organ at risk is reduced, the three-dimensional dose distribution is improved, the tumor treatment effect is improved, and the risk of complications is reduced.

[0052] Figure 5 is a functional module schematic diagram of a three-dimensional lattice radiotherapy lattice arrangement system according to an embodiment of the present application, such as Figure 5 As shown, a three-dimensional lattice radiotherapy lattice arrangement system, the system includes: an image data acquisition module, a contour loading module, a binary image generation module, an image transformation processing module, a lattice sphere center determination module and a lattice arrangement planning module; The image data acquisition module is configured to acquire a CT image sequence, wherein the CT image sequence is composed of a plurality of CT image planes arranged in spatial order and parallel to each other; The contour loading module is configured to load contours for each structure according to the CT image plane, each structure comprising one or more contours, and the structure comprises a human organ or a tumor structure; The binary image generation module is configured to generate a structural binary image sequence of the same size as the CT image sequence for each structure based on a grid filling algorithm; The image transformation processing module is configured to process the structural binary image sequences of the tumor target volume (GTV) and the planned risk volume of the organ at risk (OAR_PRV) based on the corrosion algorithm and the expansion algorithm respectively, and generate a processed image region; The lattice sphere center determination module is configured to determine the lattice sphere center position by layering and scanning the processed image area; The lattice arrangement planning module is configured to plan a three-dimensional lattice radiotherapy lattice arrangement plan based on the determined lattice sphere center position and lattice sphere arrangement conditions.

[0053] In one embodiment, specifically, the image transformation processing module is further configured with a data storage unit for storing intermediate data generated during the three-dimensional shrinking processing of the structure binary image sequence of GTV and the two-dimensional expansion processing of the structure binary image sequence of OAR_PRV, including the image sequence V after shrinking. L , the image sequence V after union PRV .

[0054] In one embodiment, specifically, the lattice arrangement planning module includes a collision detection unit, and the collision detection unit is configured to detect whether there is a collision or interference between adjacent lattice balls when planning a three-dimensional lattice radiotherapy lattice arrangement plan according to the determined lattice ball center position and lattice ball arrangement conditions.

[0055] In summary, the three-dimensional lattice radiotherapy lattice arrangement system provided by the embodiment of the present invention acquires a CT image sequence through an image data acquisition module, a contour loading module loads contours for each structure based on the CT image, a binary image generation module generates a structural binary image sequence using a grid filling algorithm, an image transformation processing module processes related image sequences using corrosion and expansion algorithms and stores intermediate data through a data storage unit, a lattice sphere center determination module performs layered scanning on the processed image area to determine the lattice sphere center position, and a lattice arrangement planning module plans a lattice arrangement scheme based on the lattice sphere center position and arrangement conditions, wherein the collision detection unit can also detect collision interference between lattice spheres; the system realizes an automated process from image data processing to lattice arrangement scheme planning, improves the efficiency of radiotherapy plan design, ensures the consistency of lattice arrangement, and improves the quality stability of radiotherapy plans; can accurately plan lattice positions, reduce damage to endangered organs, improve three-dimensional dose distribution, improve tumor treatment effects, and reduce the risk of complications, providing strong support for three-dimensional lattice radiotherapy.

[0056] For other details about the technical solutions for implementing each module in the three-dimensional lattice radiotherapy lattice arrangement system in the above embodiment, please refer to the description of the three-dimensional lattice radiotherapy lattice arrangement method in the above embodiment, which will not be repeated here.

[0057] It should be noted that each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the embodiments can be referred to each other. For system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0058] The basic principles of the present disclosure are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present disclosure are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present disclosure. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, and are not limitations. The above details do not limit the present disclosure to the necessity of adopting the above specific details to be implemented.

[0059] In the present disclosure, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. The block diagrams of the devices, devices, equipment, and systems involved in the present disclosure are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The words "such as" used here refer to the phrase "such as but not limited to", and can be used interchangeably with them.

[0060] Additionally, as used herein, "or" used in a list of items beginning with "at least one" indicates a separate list, so that, for example, a list of "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not mean that the example described is preferred or better than other examples.

[0061] It should also be noted that in the system and method of the present disclosure, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present disclosure.

[0062] Various changes, substitutions, and modifications of the techniques described herein may be made without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of the present disclosure is not limited to the specific aspects of the processes, machines, manufactures, compositions of events, means, methods, and actions described above. Currently existing or later to be developed processes, machines, manufactures, compositions of events, means, methods, or actions that perform substantially the same functions or achieve substantially the same results as the corresponding aspects described herein may be utilized. Thus, the appended claims include such processes, machines, manufactures, compositions of events, means, methods, or actions within their scope.

[0063] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0064] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A three-dimensional lattice radiotherapy lattice arrangement method, characterized in that: The following steps are involved: Acquiring a computed tomography (CT) image sequence, wherein the CT image sequence consists of a plurality of CT image planes arranged in spatial order and parallel to each other; Loading contour lines for each structure according to the CT image plane, each structure comprising one or more contour lines, the structure comprising a human organ or a tumor structure; Based on the grid filling algorithm, each structure is generated into a structural binary image sequence of the same size as the CT image sequence; Based on the erosion algorithm and the dilation algorithm, the structural binary image sequences of the tumor target volume (GTV) and the planned risk volume of the organ at risk (OAR_PRV) are processed respectively, and the processed image regions are generated; The position of the center of the lattice sphere is determined by layering and scanning the processed image area; According to the determined lattice sphere center position and lattice sphere arrangement conditions, a three-dimensional lattice radiotherapy lattice arrangement plan is planned.

2. The three-dimensional lattice radiotherapy lattice arrangement method according to claim 1, characterized in that: The method for generating a processed image region comprises the following steps: Based on the erosion algorithm, the structural binary image sequence of GTV is subjected to three-dimensional shrinkage (m+d / 2) millimeters to obtain the shrunk image sequence V L , where m is GTV to V L The retraction distance is d, and d is the diameter of the lattice ball; Based on the dilation algorithm, all OAR_PRV structural binary image sequences were expanded in two dimensions (r P +d / 2) mm and perform a merge operation to obtain the merged image sequence V PRV , where r P is the distance between the lattice ball and OAR_PRV; Find V PRV The median value is 1, and V L Middle and V PRV and V L The voxel values ​​in the intersection are set to 0.

3. The three-dimensional lattice radiotherapy lattice arrangement method according to claim 2, characterized in that: The method for determining the position of the lattice sphere center comprises the following steps: Step 501: Find V L The middle layer S0 of the foreground area is scanned in the order of x, y, z, and the foreground voxel points are added to the candidate list L possible middle; Step 502: Get list L possible The first voxel point in is added to the lattice sphere center list L0, and from L possible Removed; Step 503: Remove the candidate list L possible All voxel points whose distance to the last center of the lattice center list L0 is less than r mm, where r is the minimum distance between the centers of the lattice balls; Step 504: Repeat the above steps 502-503 until the list L possible is empty; Step 505: With the middle layer S0 as the center, search for layers with a layer spacing of s mm in both the upper and lower directions, that is, satisfying S i+1 With S i The distance between the two layers is s mm; Step 506: align S in the order of x, y, and z. i The layer image is scanned and compared with the previous layer lattice sphere center list L i-1 or L i+1 The foreground voxel points whose distance from the center of the sphere is not less than d1 mm are added to the candidate list L possible In which, d1 is the two-dimensional distance between the center of each layer of lattice sphere and the center of the adjacent layer; Step 507: Get list L possible The first voxel point in is added to the lattice sphere center list L i and from L possible Removed; Step 508: Remove the candidate list L possible List of all lattice sphere centers in L i The distance between the last sphere center is less than r mm; Step 509: Repeat the above steps 507-508 until the list L possible is empty, and all layers are processed, and finally the lattice sphere center list L of each layer is i The center positions of all lattices are obtained.

4. The three-dimensional lattice radiotherapy lattice arrangement method according to claim 3, characterized in that: The lattice ball arrangement conditions include: The diameter of the lattice sphere is d mm; GTV to V L The adduction distance is m millimeters; The minimum distance between the centers of the lattice balls is r mm; The distance between the lattice ball and OAR_PRV is not less than r P mm; The interlayer distance is s mm; The two-dimensional distance between the center of the lattice ball in each layer and the center of the lattice ball in the adjacent layer is not less than d1 mm.

5. The three-dimensional lattice radiotherapy lattice arrangement method according to claim 1, characterized in that: The method for generating a structure binary image sequence of the same size as the CT image sequence for each structure comprises the following steps: Step 301: setting the initial value of all pixels in the structure binary image sequence to 0; Step 302: for each contour line of each structure, start a grid-based filling algorithm, and when executing the algorithm, scan the CT image pixels line by line starting from the starting line of the image; Step 303: During the scanning process, for each row of pixels, the boundary of the area to be filled is determined according to a rule; the rule is formulated based on the pixel features of the image and the definition of the contour line; Step 304: for each pixel currently scanned, a judgment is made according to the previously determined boundary conditions. If the pixel is inside the contour line, its pixel value is set to 1; if it is not inside the contour line, its pixel value is kept to 0; Step 305: Continue the above scanning, judging and assigning operations until the filling process of all contour lines is completed.

6. The three-dimensional lattice radiotherapy lattice arrangement method according to claim 1, characterized in that: The CT image plane includes a set of sampling grids representing a specific thickness, length and width in a three-dimensional human body space. The grids are voxels, and the voxel value represents a density distribution sampling value at the location of the voxel.

7. The three-dimensional lattice radiotherapy lattice arrangement method according to claim 1, characterized in that: The contour line is loaded for each structure according to the CT image plane, each structure includes one or more contour lines, and the structure includes a human organ or a tumor structure, wherein the contour line is drawn according to the grayscale distribution of the human organ in the CT image and consists of a plurality of coordinate points arranged in sequence.

8. A three-dimensional lattice radiotherapy lattice arrangement system, applied to the three-dimensional lattice radiotherapy lattice arrangement method according to any one of claims 1 to 7, characterized in that: The system comprises: an image data acquisition module, a contour loading module, a binary image generation module, an image transformation processing module, a lattice sphere center determination module and a lattice layout planning module; The image data acquisition module is configured to acquire a CT image sequence, wherein the CT image sequence is composed of a plurality of CT image planes arranged in spatial order and parallel to each other; The contour loading module is configured to load contours for each structure according to the CT image plane, wherein the structure includes a human organ or a tumor structure; The binary image generation module is configured to generate a structural binary image sequence of the same size as the CT image sequence for each structure based on a grid filling algorithm; The image transformation processing module is configured to process the structural binary image sequences of the tumor target volume (GTV) and the planned risk volume of the organ at risk (OAR_PRV) based on the corrosion algorithm and the expansion algorithm respectively, and generate a processed image region; The lattice sphere center determination module is configured to determine the lattice sphere center position by layering and scanning the processed image area; The lattice arrangement planning module is configured to plan a three-dimensional lattice radiotherapy lattice arrangement plan based on the determined lattice sphere center position and lattice sphere arrangement conditions.

9. The three-dimensional lattice radiotherapy lattice arrangement system according to claim 8, characterized in that: The image transformation processing module is also equipped with a data storage unit for storing intermediate data generated during the three-dimensional shrinking of the structure binary image sequence of GTV and the two-dimensional expansion of the structure binary image sequence of OAR_PRV, including the image sequence V after shrinking. L , the image sequence V after union PRV .

10. The three-dimensional lattice radiotherapy lattice arrangement system according to claim 8, characterized in that: The lattice arrangement planning module includes a collision detection unit, which is configured to detect whether there is a collision or interference between adjacent lattice balls when planning a three-dimensional lattice radiotherapy lattice arrangement plan based on the determined lattice ball center position and lattice ball arrangement conditions.

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