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CN-121982165-A - Automatic generation method of hexagonal closest packing-based space division radiotherapy ball target

CN121982165ACN 121982165 ACN121982165 ACN 121982165ACN-121982165-A

Abstract

The invention relates to the technical field of medical image processing, in particular to an automatic generation method of a space division radiotherapy ball target based on hexagonal closest packing. The method comprises the steps of automatically loading and analyzing medical images and structural data, reconstructing a three-dimensional target area model and calculating characteristics of the three-dimensional target area model, generating candidate spherical center lattices in the target area based on the hexagonal closest packing model, adopting a two-stage intelligent screening algorithm to ensure that all points are positioned inside the target area contour, directly generating high-precision sphere section contours in an image pixel coordinate system, and finally synthesizing a standard DICOM-RT structural file strictly related to an original image. The invention can solve the problems of low efficiency, poor spatial distribution, insufficient sphere precision, complex and error-prone data association and the like in the prior art, thereby realizing high efficiency, distribution optimization and precision standardization of sphere target generation.

Inventors

  • YANG XIONG
  • Dai Zeyi
  • ZHANG JUN
  • RUAN CHANGLI

Assignees

  • 武汉大学人民医院（湖北省人民医院）

Dates

Publication Date
20260505
Application Date
20260407

Claims (10)

  1. 1. The automatic generation method of the space division radiotherapy ball target based on hexagonal closest packing is characterized by comprising the following steps: s1, loading and analyzing a CT image sequence and radiotherapy data standard structure file which meet preset standards, extracting image metadata in the file, and establishing a spatial transformation relation from an original three-dimensional coordinate system of an object to a two-dimensional pixel coordinate system of each layer of CT image; S2, extracting a target area structure from the radiotherapy data standard structure file, collecting two-dimensional contour points of the target area structure on all CT slices, endowing the two-dimensional contour points with corresponding Z-axis coordinates, reconstructing a three-dimensional point cloud model of the target area, and calculating a geometric centroid and a three-dimensional axial bounding box of the target area; S3, generating a candidate spherical center lattice based on a hexagonal closest packing method, and screening out spherical centers which are positioned in the outline of a target area and uniformly distributed in the candidate spherical center lattice through a two-stage intelligent screening algorithm and interval consistency filtering; s4, in a CT image two-dimensional pixel coordinate system, aiming at each sphere center reserved after screening, calculating an intersecting section of a lattice point sphere corresponding to the sphere center and each CT slice so as to generate a sphere two-dimensional slice contour with pixel-level precision; s5, synthesizing and outputting an updated radiotherapy data standard structure file, wherein the updated radiotherapy data standard structure file contains all sphere three-dimensional contour data associated with the original CT image sequence.
  2. 2. The method for automatically generating a hexagonal closest packed space division radiotherapy ball target according to claim 1, wherein in step S1, the image metadata comprises image positions Image line vector Image column vector Pixel pitch ; The spatial transformation relation is as follows: Wherein the method comprises the steps of For the original three-dimensional coordinate system of the object, Is a two-dimensional pixel coordinate system after space transformation, and The calculation formulas of u and v are: ; 。
  3. 3. The method for automatically generating a hexagonal closest packed space division radiotherapy ball target according to claim 1, wherein in step S2, the geometric centroid The three-dimensional axial bounding box is the arithmetic mean value of coordinates of all points in the three-dimensional point cloud model Wherein 、 、 The minimum value and the maximum value of the X-axis coordinate, the minimum value and the maximum value of the Y-axis coordinate and the minimum value and the maximum value of the Z-axis coordinate of the point cloud are respectively obtained.
  4. 4. The automatic generation method of space-division radiotherapy ball targets based on hexagonal closest packing according to claim 1, wherein in step S3, the generation method of candidate ball center lattices based on hexagonal closest packing specifically comprises: S31, the lattice constant a input by the user is used for determining the horizontal spacing between the sphere centers, and the system automatically calculates the vertical interlayer spacing according to a preset formula ; S32, using geometric mass center of target area Defining lattice basis vectors for origin 、 、 Offset vector of B layer relative to A layer ; S33, traversing the integer index combination in the range of the three-dimensional bounding box of the target area Generating candidate A-layer close-packed layer points And B layer close-packed layer points 。
  5. 5. The method for automatically generating a hexagonal closest packing-based space-division radiotherapy sphere target according to claim 4, wherein in step S3, the two-stage intelligent screening algorithm comprises: performing Delaunay triangulation on the three-dimensional point cloud of the target area to generate a corresponding convex hull, and removing candidate spherical center points positioned outside the convex hull; And judging the inclusion of the second-stage accurate contour, namely extracting a CT (computed tomography) slice with the nearest Z coordinate and a target area two-dimensional contour polygon corresponding to the CT slice from the candidate spherical center points which pass through the pre-screening, projecting the candidate spherical center points to the slice plane, judging whether the projection points are inside the two-dimensional contour polygon or not by a ray method, and only reserving the candidate spherical center points judged to be inside.
  6. 6. The method for automatically generating space-division radiotherapy ball targets based on hexagonal closest packing according to claim 5, wherein in step S3, the interval consistency filtering is that Euclidean distance between every two candidate ball center points reserved by the two-stage intelligent screening algorithm is calculated, and candidate ball center points with the distance from the adjacent ball center being smaller than 0.5a are removed, wherein a is a lattice constant.
  7. 7. The automatic generation method of the hexagonal closest packing-based space division radiotherapy sphere target according to claim 1, wherein in step S4, the generation of the two-dimensional slice profile of the sphere with pixel-level precision specifically comprises: S41, calculating the vertical distance from the sphere center to each CT slice plane Wherein Is the Z-axis coordinate of the sphere center, Z-axis coordinates of the slice plane; S42, if d Smaller than the physical radius of the sphere Judging that the sphere intersects with the corresponding slice plane, and calculating the physical radius of the corresponding cross-section circle ; S43, converting the projection of the sphere center on the slice plane into the pixel coordinate system of the current slice through the space transformation relation established in the step S1 to obtain the pixel center coordinate And obtain the physical radius of the cross-section circle Pixel radius mapped in the pixel coordinate system ; S44, under the pixel coordinate system, according to a preset parameter equation Generating a set of discrete points, wherein Sampling is uniformly performed in the range of 0 to 2 pi to form a sphere two-dimensional slice profile.
  8. 8. The automatic generation method of a hexagonal closest packing-based space division radiotherapy sphere target according to claim 7, wherein in step S5, the synthesizing the updated radiotherapy data standard structure file includes: s51, copying the unique identifier of the original CT image, and writing the unique identifier into the newly generated head part of the radiotherapy data standard structure file and the corresponding data field; S52, converting all pixel coordinate point sets of the two-dimensional slice contour of the sphere in the step S4 back to the original three-dimensional coordinate system through inverse coordinate transformation, and carrying out flattening storage according to a DICOM standard format; S53, creating a complete DICOM data set containing file meta-information, object information, a Study reference sequence, an ROI definition sequence and an ROI profile sequence, and outputting the complete DICOM data set as a dcm file.
  9. 9. An electronic device comprising a memory, a processor and a program stored on the memory and executable on the processor, wherein the processor implements the hexagonal closest packed based automatic generation method of spatially segmented radiotherapy sphere targets according to any one of claims 1 to 8 when executing the program.
  10. 10. A non-transitory readable storage medium having stored thereon a program, which when executed by an electronic device, implements the hexagonal closest packing-based space-division radiotherapy sphere target automatic generation method according to any one of claims 1 to 8.

Description

Automatic generation method of hexagonal closest packing-based space division radiotherapy ball target Technical Field The invention relates to the technical field of medical image processing, in particular to a method for automatically and accurately generating a high-dose spherical target area in a three-dimensional medical image by using a computer technology and a mathematical geometric model. Background Spatially segmented radiotherapy (SPATIALLY FRACTIONATED RADIOTHERAPY, SFRT) is a leading-edge RT technique whose core principle consists in deliberately designing the heterogeneity of dose distribution within a large volume tumor target (Gross Tumor Volume, GTV), i.e. creating a series of discrete high dose "peaks" (Peaks) inside the GTV, alternating with surrounding low dose "valleys" (Valleys), forming a specific "peak-valley" dose pattern. This dose distribution proved to be better able to kill tumor cells while mitigating damage to surrounding normal tissue by dose segmentation, thus achieving a higher therapeutic gain ratio. One of the key steps in its implementation is the creation of several to tens of discrete spherical or spheroid structures as "dose peaks" inside the GTV in the treatment planning system (TREATMENT PLANNING SYSTEM, TPS). Currently, in clinical practice, this operation is performed manually on computed tomography images (Computed Tomography, CT) mainly by a physicist, but has the following significant drawbacks: 1) The efficiency is low, the manual sketching process is extremely time-consuming, each case usually needs several hours, and the clinical application of the technology is severely restricted; 2) The space distribution is not optimal, the manual placement of the spheres is highly dependent on the experience of an operator, the theoretical optimal uniform distribution in the three-dimensional space is difficult to realize, the dosage 'peaks' are possibly too few or overlapped, and the final dosage engraving effect and the optimal 'peak-to-valley ratio' are affected. 3) The geometric accuracy and consistency of the high-dose ball target are difficult to ensure, the geometric shape (sphericity and dimensional consistency) and the spatial position accuracy of the three-dimensional sphere outline manually drawn on the two-dimensional CT slice are difficult to ensure, and the repeatability is poor. 4) The data association is complex and error-prone, the manually created target region structure set must be accurately registered with the original CT image data on a three-dimensional space coordinate system, and the matching precision can be affected by manual operation. Disclosure of Invention The invention aims to solve the technical problems of low efficiency, poor spatial distribution, insufficient sphere precision, complex and error-prone data association and the like in the prior art by providing an automatic generation method of a space division radiotherapy sphere target based on hexagonal closest packing, so that the high efficiency, the distribution optimization and the precision standardization of sphere target generation are realized. In order to solve the technical problems, the invention adopts the following technical scheme: A hexagonal closest packing-based space division radiotherapy ball target automatic generation method mainly comprises the following steps: s1, loading and analyzing a CT image sequence and an RT Structure file of a DICOM standard, extracting image metadata in the file, and establishing a spatial transformation relation from an original three-dimensional coordinate system of an object to a two-dimensional pixel coordinate system of each layer of CT image; S2, extracting a target area Structure from an RT Structure file, collecting two-dimensional contour points of the target area Structure on all CT slices, endowing the two-dimensional contour points with corresponding Z-axis coordinates, reconstructing a three-dimensional point cloud model of the target area, and calculating a geometric centroid and a three-dimensional axial bounding box of the target area; S3, generating a candidate spherical center lattice based on a hexagonal closest packing method (HCP), and screening out spherical centers which are positioned in the outline of a target area and uniformly distributed in the candidate spherical center lattice through a two-stage intelligent screening algorithm and interval consistency filtering; s4, in a CT image two-dimensional pixel coordinate system, aiming at each sphere center reserved after screening, calculating an intersecting section of a lattice point sphere corresponding to the sphere center and each CT slice so as to generate a sphere two-dimensional slice contour with pixel-level precision; S5, synthesizing and outputting a standard DICOM-RT Structure file, wherein the DICOM-RT Structure file contains all sphere three-dimensional contour data associated with an original CT image sequence. Further, in step S1, the image metada