Helical fault radiotherapy Lattice structure integrated generation system and method based on active geometric blocking

Abstract

The invention relates to an integrated generation system and method for a spiral tomography Lattice structure based on active geometric blocking, and the method comprises the steps: receiving Lattice reference parameters and blocking unit parameters inputted by a user, generating array three-dimensional model data corresponding to regularly distributed Lattice vertex units through a space filling algorithm according to the Lattice reference parameters, and carrying out the processing of the array three-dimensional model data, according to the vertex array three-dimensional model data, determining an implantation position coordinate corresponding to the blocking unit through a preset topological geometric rule, according to the blocking unit parameters and the implantation position coordinate, generating three-dimensional virtual geometry model data, and merging the array three-dimensional model data and the three-dimensional virtual geometry model data to obtain a three-dimensional virtual geometry model; and generating three-dimensional treatment structure data. Therefore, a Lattice treatment structure which can be used for realizing different dose peak-to-valley ratio characteristics can be actively, accurately and predictably generated by a user through adjusting visual geometric parameters, and the clinical working efficiency is improved.

Description

Technical Field

[0001] This disclosure relates to the field of biomedical therapeutics, and more specifically, to an integrated generation system and method for helical tomotherapy Lattice structures based on active geometric blocking. Background Technology

[0002] Lattice radiotherapy (also known as grid radiotherapy) is an emerging precision oncology treatment technology. Its core is to create a three-dimensional dose pattern with alternating "high-dose peaks" and "low-dose troughs" within the tumor target area, in order to effectively kill tumors while protecting normal tissues to the greatest extent.

[0003] The current mainstream technical approach for implementing lattice therapy on helical tomotherapy platforms can be categorized as an "algorithm optimization-driven" model. For example, the approach published by Seol Y et al. in *Frontiers in Oncology* (2025) relies on the following core technology: within the treatment planning system, complex and irregular "avoidance regions" are manually defined around pre-defined lattice vertices, and the algorithm is entirely dependent on inverse optimization to attempt to reduce the dose in these regions. This approach has three fundamental drawbacks: First, the results are highly uncertain; the final dose distribution is severely limited by the convergence of the optimization algorithm and the skill required for parameter settings, requiring physicists to repeatedly try and fail, resulting in low efficiency. Second, the control lacks physical intuition; users indirectly influence the dose by adjusting abstract optimization structures and weights, unable to achieve precise and predictable control through an intuitive parameter directly related to the physical effect. Third, the workflow is fragmented and highly dependent; it typically requires the use of third-party software to generate the lattice structure before importing it into the planning system, a cumbersome process, and its effectiveness is deeply tied to the optimization engine of a specific commercial planning system, resulting in poor versatility. Summary of the Invention

[0004] In order to overcome the technical problem of inaccurate generation of Lattice structures in related technologies, the purpose of this disclosure is to provide an integrated system and method for generating Lattice structures in helical tomotherapy based on active geometric blocking.

[0005] To achieve the above objectives, the first aspect of this disclosure provides an integrated method for generating helical tomotherapy lattice structures based on active geometric blocking, the method comprising: Receives user input of Lattice reference parameters and blocking unit parameters; Based on the Lattice reference parameters, array 3D model data corresponding to regularly distributed Lattice vertex units are generated using a space filling algorithm; Based on the vertex array 3D model data, the implantation position coordinates of the blocking unit are determined by preset topological geometry rules; Based on the blocking unit parameters and the implantation location coordinates, generate three-dimensional virtual geometric model data; The array 3D model data and the 3D virtual geometry model data are merged to generate 3D treatment structure data.

[0006] Optionally, in some embodiments, the Lattice reference parameters include at least one of the following: the target diameter of the Lattice vertex unit and the target center distance between adjacent Lattice vertex units.

[0007] Optionally, in some embodiments, determining the implantation location coordinates of the blocking unit based on the vertex array 3D model data using preset topological geometry rules includes: Based on the vertex array 3D model data, identify multiple triangles in 3D space formed by three adjacent Lattice vertex units; Determine the multiple geometric centroids corresponding to the multiple triangles respectively; The plurality of geometric centroids are used as the implantation sites of the blocking units; The implantation location coordinates are determined based on the positions of the plurality of geometric centroids in the three-dimensional space.

[0008] Optionally, in some embodiments, the blocking unit parameters include the dimensional parameters of the blocking geometry, and the method further includes: Receives a parameter sequence containing multiple different size values; For each size value in the sequence, repeat the steps above, from generating three-dimensional treatment structure data based on the blocking unit parameters and the implantation position coordinates, to generate multiple three-dimensional treatment structure data files corresponding to multiple different size values; Based on the multiple three-dimensional treatment structure data files, the preset dose peak-to-trough ratio characteristics are determined.

[0009] According to a second aspect of the present disclosure, an integrated apparatus for generating helical tomotherapy Lattice structures based on active geometrical blocking is provided, the apparatus comprising: The receiving module is used to receive Lattice reference parameters and blocking unit parameters input by the user; The first generation module is used to generate array 3D model data corresponding to regularly distributed Lattice vertex units according to the Lattice reference parameters through a space filling algorithm. The determination module is used to determine the implantation position coordinates of the blocking unit based on the vertex array three-dimensional model data and by using preset topological geometry rules. The second generation module is used to generate three-dimensional virtual geometric model data based on the blocking unit parameters and the implantation position coordinates. The execution module is used to merge the array three-dimensional model data and the three-dimensional virtual geometry model data to generate three-dimensional treatment structure data.

[0010] According to a third aspect of the present disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps of the method described in any of the first aspects of the present disclosure.

[0011] According to a fourth aspect of the present disclosure, an electronic device is provided, comprising: A memory on which computer programs are stored; A processor for executing the computer program in the memory to implement the steps of the method according to any one of the first aspects of this disclosure.

[0012] According to a fifth aspect of the present disclosure, a helical tomotherapy Lattice structure integrated generation system based on active geometric blocking is provided, the system comprising an integrated reference input module, a Lattice structure generation module, an intelligent geometric correlation calculation module, a blocking construction module, and a structure integration output module; The integrated reference input module is used to receive and integrate the Lattice reference parameters and blocking unit parameters input by the user, and send the Lattice reference parameters to the Lattice structure generation module and the blocking unit parameters to the blocking construction module; The Lattice structure generation module is used to generate corresponding vertex array three-dimensional model data according to the Lattice reference parameters and the blocking unit parameters through a space filling algorithm, and send the vertex array three-dimensional model data to the intelligent geometric association calculation module and the structure integration output module; The intelligent geometric association calculation module is used to determine the implantation position coordinates of the blocking unit according to the vertex array three-dimensional model data and through preset topological geometric rules, and send the implantation position coordinates to the blocking construction module; The blocking construction module is used to generate three-dimensional virtual geometric model data according to the blocking unit parameters and the implantation position coordinates, and send the three-dimensional virtual geometric model data to the structure integration output module; The structure integration output module is used to seamlessly integrate the vertex array 3D model data and the 3D virtual geometry model data to generate 3D treatment structure data.

[0013] Optionally, in some embodiments, the barrier construction module is also used to generate multiple sets of three-dimensional barrier geometry model data in batches based on a set of different barrier unit size parameters; The structure integration output module is also used to batch output multiple integrated three-dimensional treatment structure data files.

[0014] Optionally, in some embodiments, the structure integration output module is further configured to determine a preset dose peak-to-trough ratio characteristic based on the plurality of three-dimensional treatment structure data files.

[0015] The above technical solution receives Lattice baseline parameters and blocking unit parameters input by the user. Based on the Lattice baseline parameters, a space-filling algorithm generates an array of 3D model data corresponding to regularly distributed Lattice vertex units. Based on the vertex array 3D model data, the implantation position coordinates of the blocking units are determined according to preset topological geometric rules. Based on the blocking unit parameters and implantation position coordinates, 3D virtual geometric model data is generated. The array 3D model data and the 3D virtual geometric model data are merged to generate 3D treatment structure data. This provides a method that allows users to actively, accurately, and predictably generate Lattice treatment structures that can achieve different dose peak-valley characteristics by adjusting intuitive geometric parameters, thus improving clinical work efficiency.

[0016] Other features and advantages of this disclosure will be described in detail in the following detailed description section. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate the present disclosure and form part of the specification. They are used together with the following detailed description to explain the present disclosure, but do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart illustrating an integrated generation method for helical tomotherapy Lattice structures based on active geometric blocking, according to an exemplary embodiment.

[0018] Figure 2 This is a block diagram illustrating an integrated generation system for helical tomotherapy Lattice structures based on active geometric blocking, according to an exemplary embodiment.

[0019] Figure 3 This is a flowchart illustrating a method for generating Lattice therapeutic structures with adjustable peak-to-valley ratios, according to an exemplary embodiment.

[0020] Figure 4 This is a schematic diagram comparing the effects of different blocking ball diameter schemes according to an exemplary embodiment.

[0021] Figure 5 This is a schematic diagram of a dose distribution curve extracted from a specific cross-section according to an exemplary embodiment.

[0022] Figure 6 This is a block diagram illustrating an integrated device for generating helical tomotherapy Lattice structures based on active geometric blocking, according to an exemplary embodiment.

[0023] Figure 7 This is a block diagram illustrating an electronic device 700 according to an exemplary embodiment. Detailed Implementation

[0024] The specific embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit this disclosure.

[0025] For example, this application aims to completely solve the problems inherent in the existing "algorithm optimization-driven" model, such as uncertainty in results, lack of intuitive control, and low process efficiency. By proposing a novel "geometry design-driven" paradigm, it provides an integrated method and system that enables users to actively, accurately, and predictably generate Lattice treatment structures that can be used to achieve different dose peak-valley characteristics by adjusting intuitive geometric parameters.

[0026] Within the same integrated design environment, dose distribution control is moved forward to the geometric design stage. Through built-in deterministic geometric rules, the user's macroscopic treatment intentions (such as the desired peak-to-valley ratio) are directly transformed into a series of three-dimensional geometric structures with specific spatial layouts. These structures can be set to "complete blocking" mode after being imported into the treatment planning system, thereby actively shaping the dose distribution at the physical principle level.

[0027] Figure 1 This is a schematic flowchart illustrating an integrated generation method for helical tomotherapy lattice structures based on active geometric blocking, according to an exemplary embodiment. Figure 1 As shown, the method includes: Step S101: Receive the Lattice reference parameters and blocking unit parameters input by the user.

[0028] For example, in the same user interface, user input is received for Lattice generation reference parameters and blocking unit generation parameters; wherein, the Lattice generation reference parameters include at least the size and spacing of the vertex units, and the blocking unit generation parameters include at least the size of the blocking geometry.

[0029] Step S102: Based on the Lattice baseline parameters, generate array 3D model data corresponding to regularly distributed Lattice vertex units using a space filling algorithm.

[0030] Based on the Lattice generation reference parameters, the system automatically calculates and generates a three-dimensional geometric model of regularly arranged Lattice vertex units and their spatial coordinate set within the target treatment area using a built-in deterministic space filling algorithm.

[0031] For example, the system first receives three-dimensional medical image data of the target treatment area, typically CT or MRI scan sequences in DICOM format. This image data is arranged into a voxel grid in three orthogonal directions, with each voxel containing the tissue's electron density information or grayscale value. The system then extracts the tumor target area from the images using thresholding, region growing, or deep learning segmentation algorithms, forming a binary three-dimensional mask matrix where a value of 1 represents the inside of the target area, and a value of 0 represents the outside of the target area or normal tissue.

[0032] After segmentation, the system establishes a standardized three-dimensional coordinate system. This coordinate system follows international conventions in medical imaging: the X-axis represents the patient's left-right direction, with the left side negative and the right side positive; the Y-axis represents the patient's front-back direction, with the back negative and the abdomen positive; and the Z-axis represents the patient's head-to-toe direction, with the feet negative and the head positive. The origin of the coordinate system is usually set at the geometric center of the patient or a reference corner point of the image volume. All subsequent Lattice vertex coordinates will be defined and calculated within this coordinate system.

[0033] The system extracts geometric features from the segmented target region, calculating its volume, surface area, 3D bounding box, and principal axis directions. The bounding box is defined as the smallest cuboid that can completely enclose the target region, determined by six extreme coordinates: minimum and maximum values ​​in the X, Y, and Z directions. These geometric parameters provide an initial spatial container for the subsequent arrangement of lattices.

[0034] In some embodiments, the system sets the following baseline parameters for lattice based on clinical needs and physical constraints: Lattice diameter refers to the diameter of the spherical or near-spherical region occupied by a single lattice unit in three-dimensional space. This parameter determines the physical extent of the high-dose zone, typically ranging from 5 mm to 20 mm, and needs to be adjusted according to tumor type and the tolerance of surrounding sensitive tissues. Lattice spacing is defined as the Euclidean distance between the center points of two adjacent lattices. This parameter directly affects the peak-to-trough ratio of the dose distribution, i.e., the ratio between the high-dose peak and the low-dose trough. Too small a spacing will cause lattice overlap, losing the meaning of spatial segmentation; too large a spacing will result in insufficient target coverage. Typical values ​​range from 10 mm to 40 mm. Edge distance specifies the minimum safe distance between the lattice center and the target boundary. This parameter ensures that the lattice does not get too close to the target edge, preventing high-dose leakage into surrounding normal tissues. It is typically set to 5 mm to 10 mm, depending on the uncertainty of the target boundary delineation and the range of organ movement. Filler density characterizes the percentage relationship between the total volume of the lattice and the total volume of the target area. This parameter is directly related to treatment efficacy and the probability of complications in normal tissues, and is generally controlled between 10% and 50%. Excessive filler density increases the risk of damage to normal tissues, while insufficient filler density may lead to a decrease in tumor control rates.

[0035] The number of lattices is determined by the ratio of the target volume to the effective occupied volume of a single lattice. The effective occupied volume includes the volume of the lattice itself and the volume of the surrounding low-dose region required to maintain the spatial segmentation effect. The system determines the maximum number of lattices that can be placed by iterative calculations while satisfying edge distance constraints.

[0036] For example, an initial regular mesh is first established within the 3D bounding box of the target region using space-filling computation. The mesh step size is equal to the preset lattice spacing. The system generates a series of potential lattice center point coordinates in the X, Y, and Z directions, starting from the minimum coordinates of the bounding box and increasing with a fixed step size. These points form a 3D lattice, whose topology can be a simple cubic arrangement, a body-centered cubic arrangement, or a hexagonal close-packed arrangement, depending on the required degree of isotropy and computational complexity. For each candidate point in the initial mesh, the system performs an inclusion check. This check uses an implicit surface model or a voxel lookup table to determine whether the candidate point is located inside the target mask. The implicit surface model transforms the discrete target boundary into a continuous mathematical description through non-uniform rational B-spline functions or level set functions, thereby achieving sub-voxel accuracy in position determination. Only candidate points completely located inside the target region can proceed to the next stage. Candidate points that pass the inclusion screening need to undergo further edge distance checks. The system calculates the nearest distance from each candidate point to the target boundary, which can be quickly obtained using the Euclidean distance transformation algorithm. The distance transformation spreads outward from the target boundary, marking the precise distance to the boundary for each internal voxel. The distance marker value of a candidate point must be greater than or equal to a preset edge distance parameter; otherwise, it will be discarded. This step ensures that all lattices maintain a safe distance from the target boundary. Candidate points filtered through the first two steps enter the collision detection stage. The system checks whether there is physical overlap between any two lattices. Since lattices are modeled as spherical regions with a certain diameter, collision detection is transformed into determining whether the distance between the centers of two spheres is greater than the sum of their radii. To accelerate computation, the system uses a spatial partitioning data structure, such as an octree or KD-tree, to recursively subdivide the three-dimensional space, performing distance calculations only on points within adjacent spatial units, reducing the time complexity from quadratic to logarithmic linear.

[0037] If a collision is detected, the system retains one point and removes the other according to deterministic rules. The retention rules can be based on the spatial priority of the points, such as prioritizing points closer to the center of the target area or points that appeared earlier in the grid generation order. This deterministic conflict resolution strategy ensures that, under the same input parameters, the algorithm always generates a completely consistent Lattice arrangement, meeting the stringent requirements of repeatability and traceability in medical systems.

[0038] Each selected Lattice vertex is assigned a specific three-dimensional geometry. In the basic implementation, a Lattice unit is defined as an ideal sphere, centered at a coordinate point determined by the algorithm, with a radius half the lattice diameter. In advanced implementations, Lattice units can employ more complex geometries, such as helical surfaces or diamond surfaces within triple periodic minimal surfaces. These complex surfaces are defined through implicit functions; for example, a helical surface satisfies an equation where a specific linear combination of sine and cosine functions equals a constant, enabling the formation of interconnected pore structures that facilitate tissue fluid flow and cell migration.

[0039] The system assigns a unique identifier to each Lattice vertex and records its precise 3D coordinates. The coordinate values ​​are stored as floating-point numbers with sub-millimeter precision. In addition to spatial location, each vertex is associated with a set of physical attributes, including design diameter, prescription dose weight, and priority level. These attributes will be used later for dose calculation and optimization.

[0040] Information about all Lattice vertices is organized into a structured dataset, including the total number of vertices, coordinate system definition, vertex list, and adjacency relationships between vertices. Adjacency relationships are determined by calculating the Euclidean distance matrix between vertices, and vertex pairs with a distance less than a certain threshold are marked as nearest neighbors for subsequent dose stacking calculations and connectivity analysis.

[0041] The system constructs a complete 3D geometric model based on a set of Lattice vertices. This model is stored in the form of boundary representation or constructed solid geometry, containing a surface mesh for each Lattice element. The surface mesh is extracted from implicit functions using the marchingcubes algorithm or a similar method, generating closed surfaces composed of triangular patches. The model file uses standard medical image interaction formats, such as STL or OBJ, or specialized radiotherapy planning formats, such as RTSTRUCT, for import into treatment planning systems for further dose calculation and validation.

[0042] Step S103: Based on the three-dimensional model data of the vertex array, determine the implantation position coordinates of the blocking unit through preset topological geometric rules.

[0043] Geometric positioning and generation of blocking units: (1) Based on the Lattice vertex space coordinates generated in step S102, according to the predefined topological geometry rules, all basic geometric units composed of multiple adjacent vertices are automatically identified, and the geometric center point of each basic geometric unit is calculated to form a set of target implantation sites for blocking units; (2) According to the blocking unit generation parameters, a three-dimensional virtual geometry is generated for each site in the set of target implantation sites; the geometry is designed to be set to "complete blocking" mode in the subsequent imported treatment planning system to physically block rays from penetrating its area.

[0044] The data includes the 3D model of the vertex array, which contains a set of Lattice vertex cells generated by the hexagonal densest stacking algorithm. Each cell has a defined spatial location, geometric parameters, and topological relationships. Specifically, this includes: 3D coordinates of the sphere center, target diameter, sphere radius, layer type identifier, globally unique identifier, and twelve-neighborhood connectivity.

[0045] Blocking units are additional units inserted between or at specific geometric locations of lattice apex units to modulate dose distribution, enhance mechanical support, or achieve specific clinical functions. These units typically have different physical properties from the lattice apex units, such as different diameters, material densities, or radioactivity.

[0046] The preset topological geometry rules define the spatial positioning strategy of the blocking unit relative to the Lattice vertex unit. Based on pure geometric topological relationships rather than random or empirical placement, it ensures determinism and repeatability.

[0047] In a hexagonal close-packed structure, four contacting Lattice spheres form a tetrahedral geometry, with a naturally occurring tetrahedral void at its center. This void possesses definite geometric characteristics: The distance from the void center to any four sphere centers is equal, equal to the interstitial radius of a hexagonal close-packed tetrahedron, and approximately 0.225 times the lattice constant. The four sphere centers are located at the four vertices of a regular tetrahedron, and the void center is the geometric center of the tetrahedron.

[0048] The location of the blocking unit implantation was determined to be the center coordinates of the tetrahedral void. The calculation method was to take the arithmetic mean of the coordinates of the four sphere centers: The position coordinates are equal to the sum of the x-coordinates of the four sphere centers divided by four, the sum of the y-coordinates divided by four, and the sum of the vertical coordinates divided by four.

[0049] Tetrahedral voids are divided into two categories: upward-pointing voids and downward-pointing voids, which are alternately distributed in hexagonal close packing, forming two unequal families of blocking unit positions.

[0050] When six Lattice spheres form an octahedral geometry, an octahedral void exists at its center. The geometric characteristics of this void are as follows: The distance from the void center to the centers of the six spheres is equal, equal to the interstitial radius of a hexagonal close-packed octahedron, and approximately 0.414 times the lattice constant. The six sphere centers are located at the six vertices of a regular octahedron, and the void center is the geometric center of the octahedron.

[0051] The location of the blocking unit is determined by the center coordinates of the octahedral void, and the calculation method is to take the arithmetic mean of the coordinates of the six sphere centers.

[0052] In hexagonal close packing, octahedral voids are arranged linearly along the hexagonal axis of symmetry, forming a channel structure.

[0053] The midpoint of the edge connecting two nearest-neighbor lattice vertices can be used as the implantation site for blocking units. The geometric features of this location are: The position coordinates are equal to the arithmetic mean of the coordinates of the two sphere centers, that is, the horizontal, vertical and vertical coordinates are respectively taken as the average of the corresponding coordinates of the two sphere centers.

[0054] The blocking units generated by this rule are located at the center of the edge of the Lattice framework, which is suitable for enhancing structural connectivity or modulating dose distribution along the edge direction.

[0055] The face-centered position rule states that the geometric center of the triangular face formed by the three lattice vertices can be used as the implantation location for the blocking unit. The coordinates of this location are equal to the arithmetic mean of the coordinates of the three spherical centers.

[0056] Within the hexagonal layer of the hexagonal densest packing, each sphere participates in forming six triangular faces, creating a honeycomb-like array of face-centered positions.

[0057] Extending the neighbor rule beyond the nearest neighbor range, the location of blocking cells is determined based on the set of Lattice vertices with specific topological distances. For example: Second nearest neighbor rule: Select Lattice vertex pairs with a distance of √2 times the lattice constant, with the midpoint as the blocking unit position.

[0058] The third nearest neighbor rule: select a pair of Lattice vertices with a distance of √3 times the lattice constant, with the midpoint as the blocking unit position.

[0059] Interlayer-specific rule: Select a pair of Lattice vertices that are two layers apart and whose horizontal projections coincide, and use the midpoint as the blocking unit position.

[0060] The system first parses the input 3D model data of the Lattice vertex array and constructs a complete topological graph structure. Each Lattice vertex is used as a node in the graph, and the twelve-neighbor connections are used as edges, forming a 3D topological network.

[0061] The system identifies the basic geometric units in the graph: it traverses all nodes and identifies tetrahedral units consisting of four nodes, octahedral units consisting of six nodes, edge units consisting of two nodes connected by an edge, and face units consisting of three nodes connected by a triangle.

[0062] Based on clinical needs and physical constraints, select an appropriate combination of topological geometry rules. For example: Pure tetrahedral filling mode: Only apply rule one, and implant blocking units at all tetrahedral gap positions.

[0063] Pure octahedral filling mode: Only apply rule 2, and implant blocking units at all octahedral gap positions.

[0064] Hybrid fill mode: Multiple rules are applied simultaneously, but conflict detection is required to prevent the positions generated by different rules from being too close together.

[0065] The system generates a set of candidate locations for each rule and performs uniqueness filtering to eliminate locations that are counted repeatedly.

[0066] The locations of candidate blocking elements must pass the following geometric constraint checks: Inclusion test: The center of the blocking unit must be located inside the target area and maintain a safe distance from the target area boundary.

[0067] Non-overlap test: The blocking cell maintains a minimum distance from all Lattice vertex cells. This distance is typically set to the sum of the blocking cell radius and the Lattice cell radius, or a larger spacing based on clinical requirements.

[0068] Non-conflict check: The blocking units generated by different rules maintain the minimum distance between each other to avoid overlapping or being too dense.

[0069] Candidate locations that pass all constraint tests are determined as the final blocking unit implantation locations. The following data are determined for each location: Position coordinates: Precise coordinates in three-dimensional space, inheriting the precision standard of Lattice vertex data.

[0070] Belonging rule: Identifies the type of topological geometry rule that generated this location.

[0071] Associated Lattice: Records the set of Lattice vertex identifiers on which this location was generated.

[0072] Geometric parameters: The target diameter of the blocking element, which may be the same as or different from that of the Lattice element.

[0073] Topological role: Identifies the functional role of this location within the overall structure, such as structural support, dose modulation, boundary marker, etc.

[0074] The data structure of the blocking cell array is parallel to that of the Lattice vertex array, and includes: Header information: Description of blocking unit generation rules, associated Lattice array identifier, generation timestamp.

[0075] Location data block: A list of blocking units arranged by rule type or spatial order. Each unit contains a global identifier, 3D coordinates, geometric parameters, generation rules, and an associated Lattice list.

[0076] Topology data blocks: the connection relationship between blocking cells and Lattice vertices, and the proximity relationship between blocking cells.

[0077] Geometric data block: Blocking element surface mesh data, generated using the same method as Lattice elements.

[0078] The system integrates the Lattice vertex array and the blocking element array into a composite 3D model: Spatial index structure: Establish a unified spatial hash or octree index to support fast proximity queries.

[0079] Hierarchical grouping: Group units by layer, region, or function for easy selective display and editing.

[0080] Visual attributes: Assign different visual attributes to Lattice vertices and blocking units, such as color, transparency, and wireframe mode, to distinguish the structure type.

[0081] Step S104: Generate three-dimensional virtual geometric model data based on the blocking unit parameters and implantation location coordinates.

[0082] The Lattice vertex model generated in step S102 is geometrically merged with the blocking unit model generated in step S103-(2) to form a complete integrated three-dimensional treatment structure that can be directly used for treatment plan calculation, and then output as a standardized treatment structure data file.

[0083] Step S105: Merge the array 3D model data and the 3D virtual geometry model data to generate 3D treatment structure data.

[0084] Multi-parameter scheme batch generation (expansion capability): By changing the size value in the generation parameters of the blocking unit, the system can automatically and batch repeat steps S103-(2) to S104, thereby quickly generating a series of corresponding integrated treatment structure schemes with different geometric blocking characteristics, providing users with a set of optional schemes that can be used to achieve different preset dose peak-valley bit characteristics.

[0085] Furthermore, the deterministic space-filling algorithm in step S102 is preferably a hexagonal densest packing algorithm. The predefined topological geometry rule in step S103-(1) is preferably "calculating the geometric centroid of the triangle formed by every three spatially adjacent Lattice vertices" and using the centroid as the implantation site of the blocking unit.

[0086] Optionally, in some embodiments, the space filling algorithm is a hexagonal densest stacking algorithm, and the Lattice reference parameters include at least one of the following: the target diameter of the Lattice vertex cell and the target center distance between adjacent Lattice vertex cells.

[0087] For example, the system receives two core baseline parameters. The first is the target diameter, which is the diameter of the spherical region occupied by each Lattice vertex unit in three-dimensional space. This parameter directly determines the physical extent of the high-dose region and affects the steepness of the dose gradient; a larger diameter results in a slower dose drop. The second is the target center distance, which is the Euclidean distance between the centers of adjacent Lattice vertex units. This parameter controls the strength of the spatial segmentation and must be greater than or equal to the target diameter to ensure that low-dose valleys are preserved between adjacent units.

[0088] The system calculates the spacing-to-diameter ratio, which is the ratio of the distance from the target center to the target diameter. This ratio is a key design parameter. When the ratio equals one, the lattice cells are in contact with each other, preventing the formation of low-dose troughs; when the ratio is greater than one, alternating high and low dose distributions occur. Clinically, this ratio is typically controlled between 1.5 and 4.0 to balance the spatial segmentation effect with target coverage.

[0089] The system directly maps the target center distance to the lattice constant of the hexagonal close-packed structure, i.e., the distance between the centers of adjacent spheres within the same layer. Based on this lattice constant, the system derives a complete set of geometric parameters.

[0090] The stacking height is the vertical distance between two adjacent layers, equal to the lattice constant multiplied by the square root of two-thirds, approximately 0.8165 times the lattice constant. The axial ratio is the ratio of the height to the side length of the base of the hexagonal structure, with an ideal value of the square root of eight-thirds, approximately 1.633. The interlayer horizontal offset comprises two components: a lateral offset of half the lattice constant and a longitudinal offset of two square root of three-thirds of the lattice constant. This offset ensures that the center of the upper sphere falls precisely into the center of the depression formed by the three tangent spheres in the lower layer.

[0091] Each Lattice vertex cell is modeled as a standard sphere, with a radius equal to half the target diameter, which is also equal to the lattice constant divided by twice the spacing-to-diameter ratio. The surface equations of the sphere are defined with the sphere's center coordinates as the origin and the sphere's radius as the radius.

[0092] The system establishes a coordinate system aligned with the patient's anatomical structure, aligning the hexagonal axis of the hexagonal densest packing with the head-to-toe direction of the patient, so that the layered structure is horizontally distributed in the cross-section.

[0093] For even-numbered layers, i.e., layer A, the system generates sphere center coordinates by row and column. The horizontal axis is constructed by multiplying the column index by the lattice constant, plus a half-cell offset determined by the parity of the row index. The vertical axis is constructed by multiplying the row index by the lattice constant at square root of 3. The vertical column is constructed by multiplying the layer index by the stacking height.

[0094] For odd-numbered layers, i.e., layer B, the system adds an interlayer horizontal offset to the coordinates of layer A, so that the center of the sphere is offset by a lateral distance of half the lattice constant and a vertical distance of two square roots of three lattice constants relative to the lower layer, thus achieving optimal stacking.

[0095] The system uses a unified two-level coordinate system. The horizontal axis includes the offset determined by the parity of the sum of the column index, row index, and layer index. The vertical axis includes the base offset of the row index and the additional offset determined by the layer parity. The vertical axis is determined solely by the layer index and the stacking height.

[0096] The system calculates the value ranges of the layer index, row index, and column index based on the target area's boundary range in three directions. The layer index range is determined by dividing the target area's head-to-foot boundary by the stacking height. The row index range for each layer is dynamically calculated based on the layer's vertical offset and the target area's front and rear boundaries. The column index range within each row is dynamically calculated based on the row's horizontal offset and the target area's left and right boundaries.

[0097] The system performs three constraint checks on the generated candidate points in sequence.

[0098] The first step is the target inclusion test. The system uses the implicit surface function or binary mask matrix of the target area to determine whether candidate points are strictly located inside the target area. When using an implicit function, a function value less than or equal to zero indicates that the point is inside the target area; when using a voxel mask, a voxel value of one indicates that the point is inside the target area.

[0099] The second aspect is the edge distance constraint. The system calculates the minimum Euclidean distance from the candidate point to the target area boundary, which can be quickly obtained through a pre-calculated distance transformation field. Only candidate points with a distance greater than or equal to the preset edge distance parameter are retained to ensure that the Lattice sphere does not exceed the effective treatment area and to avoid high-dose leakage into the surrounding normal tissue.

[0100] The third step is collision detection. The system checks whether the distance between any two Lattice points is greater than or equal to the target diameter. Due to the regularity of hexagonal close-packing, the nearest neighbor distance within the same layer and the nearest neighbor distance between layers are both equal to the target center distance, naturally satisfying the collision-free condition. However, after trimming the target area boundary, irregular arrangements may appear locally, requiring verification.

[0101] Candidate points that pass all constraint checks are identified as official Lattice vertex cells. Each cell defines the following core data: a globally unique identifier for traceability and indexing; precise 3D coordinates of the sphere's center, expressed as floating-point numbers in millimeters with a precision of one-hundredth of a millimeter; the target diameter and target center distance, i.e., the input baseline parameters; the derived sphere radius; a layer type identifier to distinguish between layer A and layer B; and the layer row and column indexes generated for debugging and verification.

[0102] The system generates a spherical surface geometric model for each unit. Using a latitude and longitude parameterization method, the longitude direction is divided into sixteen to thirty-two equal parts, and the latitude direction into eight to sixteen equal parts. Through the conversion from spherical coordinates to rectangular coordinates, the three-dimensional coordinates of the sphere's surface vertices are generated, and the connection relationships of triangular facets are constructed to form a closed manifold surface.

[0103] The system determines the topological neighbor relationships of each cell. In a hexagonal close-packed structure, each cell has twelve equidistant nearest neighbors, including six neighbors within the same layer and three neighbors from each of the two adjacent layers. The system records the global identifiers of these neighbors for subsequent dose superposition calculations and connectivity analysis.

[0104] The system statistically analyzes the overall geometric properties of the array. The total number of lattices is the number of vertex cells that passed the screening. The number of layers is the difference between the maximum and minimum layer indices plus one. The average layer density is the total number divided by the number of layers. The theoretical fill rate is the space utilization constant multiplied by the cube of the inverse of the spacing-to-diameter ratio, reflecting the proportion of space occupied by the sphere. The expected peak-to-valley ratio is a function of the target diameter, target center distance, and dose kernel parameters, predicting the intensity of spatial segmentation.

[0105] The system also calculates the array's bounding box, which is the smallest cuboid containing all Lattice sphere centers, and the array's centroid coordinates, which are the arithmetic mean of all sphere center coordinates.

[0106] The system organizes the determined array data into a structured output format.

[0107] The header information includes patient identification, target area identification, generation timestamp, input baseline parameter values, geometric parameters of hexagonal close packing, coordinate system definition, and array origin position.

[0108] The vertex data block lists all Lattice cells in a layer-ordered manner. Each cell contains a global identifier, 3D coordinates of the sphere center, target diameter, layer type, generation index, and a list of neighbor identifiers.

[0109] The geometric data block contains an array of vertex coordinates and an array of face indices for the sphere's surface mesh, using a shared vertex format to reduce data redundancy.

[0110] The topology data block records the nearest neighbor connection edge list, the six-membered ring structure list, and the tetrahedral gap feature list.

[0111] The dose calculation data block contains the influence area of ​​each Lattice, a distance lookup table to the dose calculation grid points, and a Gaussian weight table to accelerate subsequent dose stacking calculations.

[0112] The system also generates visual verification data, including a 3D rendering showing the spatial distribution of Lattice spheres within the target area, a dose distribution cross-sectional diagram showing the peak-valley structure, and a statistical report summarizing key indicators.

[0113] All data is converted to standard medical image formats or proprietary formats and imported into the treatment planning system to perform final dose optimization and treatment parameter calculations.

[0114] Optionally, in some embodiments, the above step of determining the implantation location coordinates of the blocking unit according to the vertex array three-dimensional model data and by using preset topological geometry rules includes: Based on the vertex array 3D model data, identify multiple triangles in 3D space formed by three adjacent Lattice vertex units; Determine the multiple geometric centroids corresponding to the multiple triangles respectively; The plurality of geometric centroids are used as the implantation sites of the blocking units; The implantation location coordinates are determined based on the positions of the plurality of geometric centroids in the three-dimensional space.

[0115] Optionally, in some embodiments, the blocking unit parameters include the dimensional parameters of the blocking geometry, and the method further includes: Receives a parameter sequence containing multiple different size values; For each size value in the sequence, repeat the steps above, from generating three-dimensional treatment structure data based on the blocking unit parameters and the implantation position coordinates, to generate multiple three-dimensional treatment structure data files corresponding to multiple different size values; Based on the multiple three-dimensional treatment structure data files, the preset dose peak-to-trough ratio characteristics are determined.

[0116] The above technical solution receives Lattice baseline parameters and blocking unit parameters input by the user. Based on the Lattice baseline parameters, a space-filling algorithm generates an array of 3D model data corresponding to regularly distributed Lattice vertex units. Based on the vertex array 3D model data, the implantation position coordinates of the blocking units are determined according to preset topological geometric rules. Based on the blocking unit parameters and implantation position coordinates, 3D virtual geometric model data is generated. The array 3D model data and the 3D virtual geometric model data are merged to generate 3D treatment structure data. This provides a method that allows users to actively, accurately, and predictably generate Lattice treatment structures that can achieve different dose peak-valley characteristics by adjusting intuitive geometric parameters, thus improving clinical work efficiency.

[0117] Figure 2 This is a block diagram illustrating an integrated generation system for helical tomotherapy Lattice structures based on active geometric blocking, according to an exemplary embodiment. Figure 2 As shown, the system includes an integrated reference input module, a Lattice structure generation module, an intelligent geometric correlation calculation module, a barrier construction module, and a structure integration output module. The integrated reference input module is used to receive and integrate the Lattice reference parameters and blocking element parameters input by the user, and send the Lattice reference parameters to the Lattice structure generation module and the blocking element parameters to the blocking construction module. The Lattice structure generation module is used to generate corresponding vertex array 3D model data based on Lattice baseline parameters through a space filling algorithm, and then send the vertex array 3D model data to the intelligent geometric association calculation module and the structure integration output module. The intelligent geometric association calculation module is used to determine the implantation position coordinates of the blocking unit based on the vertex array 3D model data and preset topological geometric rules, and then send the implantation position coordinates to the blocking construction module. The blocking construction module is used to generate three-dimensional virtual geometry model data based on the blocking unit parameters and implantation position coordinates, and then send the three-dimensional virtual geometry model data to the structural integration output module. The structure integration output module is used to seamlessly integrate vertex array 3D model data and 3D virtual geometry model data to generate 3D treatment structure data.

[0118] Integrated parameter input module: Provides a unified interactive interface for receiving and integrating user-input Lattice and blocking unit parameters.

[0119] Lattice structure generation module: connected to the integrated parameter input module, and has at least one built-in space filling algorithm for automatically generating corresponding vertex array 3D model data based on the input Lattice parameters.

[0120] Intelligent geometric association calculation module: connected to the Lattice structure generation module, used to receive the generated vertex coordinate data and automatically derive the coordinates of all implanted positions of the blocking unit based on preset topological geometric rules (such as the triangle centroid calculation rule).

[0121] The blocking construction module is connected to both the integrated parameter input module and the intelligent geometric association calculation module. It is used to generate the three-dimensional virtual geometric model data in batches based on the input blocking parameters and the received implantation position coordinates.

[0122] Structure Integration Output Module: Connected to the Lattice structure generation module and the parameterized barrier construction module, it is used to seamlessly integrate the two types of geometric model data and encapsulate and output them as a standardized 3D structure data file that is universally applicable in the industry.

[0123] Compared with existing technologies, the "geometry design-driven" paradigm and its integrated system provided by this invention bring the following advancements: 1. Paradigm-level innovation and principle determinism: Essentially transforming dose regulation from relying on "algorithm black box" optimization to active control based on "deterministic geometric design". By generating structures with specific layouts and utilizing the mature "complete blocking" physical function of the treatment planning system, the final dose distribution (especially the peak-to-trough ratio) becomes a predictable and reproducible inevitable result in the geometric design stage.

[0124] 2. Intuitive Control and Efficient Design: Users only need to adjust a few intuitive geometric parameters, such as "blocking unit size," to directly and linearly influence key characteristics of the final dose distribution. The system supports batch parameter input and automated protocol integration, reducing the traditional time-consuming planning and design process (which can take hours or even days) to minutes, greatly improving clinical work efficiency.

[0125] 3. Seamless compatibility with existing clinical workflows: The standard structure file generated by this invention can be directly imported into mainstream treatment planning systems. Utilizing its built-in, mature physical blocking modes such as "Complete Block," it eliminates the need to modify existing dose calculation engines or workflows, greatly reducing the barriers and risks associated with technology implementation.

[0126] 4. Potential for Technological Decoupling and Standardization: As an independent upstream design tool, the system of this invention is decoupled from various downstream commercial treatment planning systems, and its output standardized structure documents have universal applicability. This significantly reduces the dependence of Lattice treatment technology on specific planning systems or the experience of senior physicists, laying a solid foundation for the standardized and widespread clinical application of this technology.

[0127] Example 1: Step 1: Start the system and set parameters; The user launches the integrated active dose control design system of this invention. On the system's main interface: In the "Lattice Parameters" area, set: vertex sphere diameter = 15mm, vertex spacing = 45mm, select "hexagonal densest packing" as the distribution mode, and specify the target area outline.

[0128] In the “Blocking Parameters” area, we plan to explore the effects of three different dose trough depths, so we input the target blocking ball diameter sequence: 6mm, 8mm, 10mm.

[0129] Step 2: Automation and output of the integrated structure; After the user clicks the "Generate" button, the system automatically executes the following core processes, for example. Figure 3 This is a flowchart illustrating a method for generating Lattice therapeutic structures with adjustable peak-to-valley ratios, according to an exemplary embodiment. Figure 3 As shown, the method includes the following steps: 1. The Lattice structure generation engine starts immediately. Based on the input diameter of 15mm and spacing of 45mm, it uses the hexagonal densest stacking algorithm to automatically calculate the placement of points in the target area space, accurately generating a 3D model of 27 Lattice vertex spheres and their spatial coordinate matrix.

[0130] 2. The intelligent geometric correlation calculation engine then reads the coordinate matrix, automatically traverses and identifies all triangle combinations (multiple in total) formed by three nearest neighbor spheres, and uses vector operations to efficiently calculate the three-dimensional geometric centroid coordinates of each triangle. These centroid coordinates are then determined as the center position of the blocking sphere.

[0131] 3. The parameterized blocking construction module performs batch processing based on the diameter sequence [6,8,10] mm input by the user: it generates a 3D model of a sphere with the corresponding diameter at all calculated centroid positions, based on each diameter value.

[0132] 4. The scheme assembly and standardized output interface work synchronously, pairing and encapsulating the generated Lattice vertex sphere model with three sets of blocking sphere models of different diameters, forming three independent and complete treatment structure schemes within seconds: "Scheme A (15mm vertex + 6mm blocking)", "Scheme B (15mm vertex + 8mm blocking)", and "Scheme C (15mm vertex + 10mm blocking)". Subsequently, the interface automatically exports the three schemes as three independent files conforming to the DICOM RT Structure Set standard.

[0133] Step 3: Application and effect verification of the solution (used to illustrate the effect of the invention); To verify the effectiveness of this invention, the three generated DICOM files were imported into a commercial helical tomotherapy planning system (such as Accuray Radixact). Key operation: In the planning system, the structure representing the blocking sphere was selected, and its properties were set to "Complete Block" mode. Subsequently, the same prescription dose (e.g., 20 Gy at the Lattice apex), field width, pitch, and other basic physical parameters were set for each plan, and dose calculations were performed.

[0134] • Results Analysis: Figure 4 This is a comparative schematic diagram illustrating the effects of different blocking ball diameter schemes according to an exemplary embodiment. After calculation, it can be observed that... Figure 4 The dose distribution differences shown are as follows: all three schemes created clear low-dose "cavities" at the corresponding locations of the blocking sphere, and the cavity size was positively correlated with the diameter of the blocking sphere. Furthermore, Figure 5 This is a schematic diagram of a dose distribution curve extracted from a specific cross-section, according to an exemplary embodiment. For example... Figure 5 As shown, it can be quantitatively obtained that the trough doses (relative values) of schemes A, B, and C decrease systematically and regularly. This irrefutably proves that users can directly obtain high-quality plans with different and predictable peak-to-trough bit characteristics by simply adjusting an intuitive geometric parameter (blocking ball diameter) in the system of this invention and setting it to "Complete Block" in the planning system, without going through any trial-and-error process of optimization and parameter tuning.

[0135] This embodiment clearly demonstrates how the present invention transforms complex clinical dosimetry objectives into a simple geometric parameter design problem. The collaborative relationship between the modules of the system that achieves the above functions is as follows: Figure 3 As shown.

[0136] The scope of protection of this invention is not limited to the embodiments described above. For example, the algorithm for generating Lattice vertices can be replaced with other deterministic arrangement rules such as cubic grid points, depending on clinical needs. The positioning rules for blocking units can also be extended to be based on tetrahedral centroids or specific spatial offset vectors. The shape of the blocking units can also be designed as cubes, ellipsoids, etc. Any variation or combination of the core concept of "integrated parameter input → automatic geometric rule calculation → generation of geometric structures for active blocking" proposed in this invention falls within the scope of protection of this invention.

[0137] The above technical solution receives Lattice baseline parameters and blocking unit parameters input by the user. Based on the Lattice baseline parameters, a space-filling algorithm generates an array of 3D model data corresponding to regularly distributed Lattice vertex units. Based on the vertex array 3D model data, the implantation position coordinates of the blocking units are determined according to preset topological geometric rules. Based on the blocking unit parameters and implantation position coordinates, 3D virtual geometric model data is generated. The array 3D model data and the 3D virtual geometric model data are merged to generate 3D treatment structure data. This provides a method that allows users to actively, accurately, and predictably generate Lattice treatment structures that can achieve different dose peak-valley characteristics by adjusting intuitive geometric parameters, thus improving clinical work efficiency.

[0138] Figure 6 This is a block diagram illustrating an integrated generation device for helical tomotherapy Lattice structures based on active geometric blocking, according to an exemplary embodiment. Figure 6 As shown, the device includes: The receiving module is used to receive Lattice reference parameters and blocking unit parameters input by the user; The first generation module is used to generate array 3D model data corresponding to regularly distributed Lattice vertex units according to the Lattice reference parameters through a space filling algorithm. The determination module is used to determine the implantation position coordinates of the blocking unit based on the vertex array three-dimensional model data and by using preset topological geometry rules. The second generation module is used to generate three-dimensional virtual geometric model data based on the blocking unit parameters and the implantation position coordinates. The execution module is used to merge the array three-dimensional model data and the three-dimensional virtual geometry model data to generate three-dimensional treatment structure data.

[0139] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0140] Figure 7 This is a block diagram illustrating an electronic device 700 according to an exemplary embodiment. Figure 7 As shown, the electronic device 700 may include a processor 701 and a memory 702. The electronic device 700 may also include one or more of a multimedia component 703, an input / output (I / O) interface 704, and a communication component 705.

[0141] The processor 701 controls the overall operation of the electronic device 700 to complete all or part of the steps in the aforementioned integrated generation method of helical tomotherapy Lattice structure based on active geometric barrier. The memory 702 stores various types of data to support the operation of the electronic device 700. This data may include, for example, instructions for any application or method operating on the electronic device 700, and application-related data such as contact data, sent and received messages, pictures, audio, video, etc. The memory 702 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. Multimedia component 703 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in memory 702 or transmitted via communication component 705. The audio component also includes at least one speaker for outputting audio signals. I / O interface 704 provides an interface between processor 701 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual or physical buttons. Communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wireless communication may include Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 7G, or one or more combinations thereof; therefore, the corresponding communication component 705 may include a Wi-Fi module, a Bluetooth module, or an NFC module.

[0142] In an exemplary embodiment, the electronic device 700 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described integrated generation method for helical tomotherapy Lattice structures based on active geometric barrier.

[0143] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the above-described method for integrating a helical tomotherapy Lattice structure based on active geometric barrier. For example, the computer-readable storage medium may be the memory 702 including the program instructions, which may be executed by the processor 701 of the electronic device 700 to complete the above-described method for integrating a helical tomotherapy Lattice structure based on active geometric barrier.

[0144] In another exemplary embodiment, a computer program product is also provided, which includes a computer program executable by a processor, which, when executed by the processor, implements the steps of the above-described method for generating an integrated helical tomotherapy Lattice structure based on active geometric blocking.

[0145] In another exemplary embodiment, a computer program product is also provided, which includes a computer program executable by a processor, which, when executed by the processor, implements the steps of the above-described method for generating an integrated helical tomotherapy Lattice structure based on active geometric blocking.

[0146] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction.

[0147] Furthermore, various different embodiments of this disclosure can be combined in any way, as long as they do not violate the spirit of this disclosure, they should also be regarded as the content disclosed in this disclosure.

Claims

1. A method for integrated generation of helical tomotherapy lattice structures based on active geometric blocking, characterized in that, The method includes: Receives user input of Lattice reference parameters and blocking unit parameters; Based on the Lattice reference parameters, array 3D model data corresponding to regularly distributed Lattice vertex units are generated using a space filling algorithm; Based on the vertex array 3D model data, the implantation position coordinates of the blocking unit are determined by preset topological geometry rules; Based on the blocking unit parameters and the implantation location coordinates, generate three-dimensional virtual geometric model data; The array 3D model data and the 3D virtual geometry model data are merged to generate 3D treatment structure data.

2. The integrated generation method for helical tomotherapy lattice structures based on active geometric blocking according to claim 1, characterized in that, The space filling algorithm is a hexagonal densest stacking algorithm, and the Lattice reference parameters include at least one of the following: the target diameter of the Lattice vertex cell and the target center distance between adjacent Lattice vertex cells.

3. The integrated generation method for helical tomotherapy lattice structures based on active geometric blocking according to claim 1, characterized in that, The step of determining the implantation position coordinates of the blocking unit based on the vertex array 3D model data and using preset topological geometry rules includes: Based on the vertex array 3D model data, identify multiple triangles in 3D space formed by three adjacent Lattice vertex units; Determine the multiple geometric centroids corresponding to the multiple triangles respectively; The plurality of geometric centroids are used as the implantation sites of the blocking units; The implantation location coordinates are determined based on the positions of the plurality of geometric centroids in the three-dimensional space.

4. The method according to any one of claims 1-3, characterized in that, The blocking unit parameters include the dimensional parameters of the blocking geometry, and the method further includes: Receives a parameter sequence containing multiple different size values; For each size value in the sequence, repeat the steps above, from generating three-dimensional treatment structure data based on the blocking unit parameters and the implantation position coordinates, to generate multiple three-dimensional treatment structure data files corresponding to multiple different size values; Based on the multiple three-dimensional treatment structure data files, the preset dose peak-to-trough ratio characteristics are determined.

5. An integrated device for generating helical tomotherapy Lattice structures based on active geometric blocking, characterized in that, The device includes: The receiving module is used to receive Lattice reference parameters and blocking unit parameters input by the user; The first generation module is used to generate array 3D model data corresponding to regularly distributed Lattice vertex units according to the Lattice reference parameters through a space filling algorithm. The determination module is used to determine the implantation position coordinates of the blocking unit based on the vertex array three-dimensional model data and by using preset topological geometry rules. The second generation module is used to generate three-dimensional virtual geometric model data based on the blocking unit parameters and the implantation position coordinates. The execution module is used to merge the array three-dimensional model data and the three-dimensional virtual geometry model data to generate three-dimensional treatment structure data.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method described in any one of claims 1-4.

7. An electronic device, characterized in that, include: A memory on which computer programs are stored; A processor for executing the computer program in the memory to implement the steps of the method according to any one of claims 1-4.

8. An integrated generation system for helical tomotherapy Lattice structures based on active geometric blocking, characterized in that, The system includes an integrated reference input module, a Lattice structure generation module, an intelligent geometric correlation calculation module, a barrier construction module, and a structure integration output module. The integrated reference input module is used to receive and integrate the Lattice reference parameters and blocking unit parameters input by the user, and send the Lattice reference parameters to the Lattice structure generation module and the blocking unit parameters to the blocking construction module; The Lattice structure generation module is used to generate corresponding vertex array three-dimensional model data according to the Lattice reference parameters and the blocking unit parameters through a space filling algorithm, and send the vertex array three-dimensional model data to the intelligent geometric association calculation module and the structure integration output module; The intelligent geometric association calculation module is used to determine the implantation position coordinates of the blocking unit according to the vertex array three-dimensional model data and through preset topological geometric rules, and send the implantation position coordinates to the blocking construction module; The blocking construction module is used to generate three-dimensional virtual geometric model data according to the blocking unit parameters and the implantation position coordinates, and send the three-dimensional virtual geometric model data to the structure integration output module; The structure integration output module is used to seamlessly integrate the vertex array 3D model data and the 3D virtual geometry model data to generate 3D treatment structure data.

9. The integrated generation system for helical tomotherapy Lattice structures based on active geometric blocking as described in claim 8, characterized in that... ； The barrier construction module is also used to generate multiple sets of three-dimensional barrier geometry model data in batches based on a set of different barrier unit size parameters; The structure integration output module is also used to batch output multiple integrated three-dimensional treatment structure data files.

10. The integrated generation system for helical tomotherapy Lattice structures based on active geometric blocking according to claim 9, characterized in that... ； The structure integration output module is also used to determine the preset dose peak-to-valley ratio characteristics based on the multiple three-dimensional treatment structure data files.

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