Method for adaptive radiotherapy dose consistency fast verification for tomo system

Abstract

The application discloses a kind of adaptive radiotherapy dose consistency fast verification methods for TOMO system, it is related to medical information technology field, especially the field of TOMO radiotherapy information processing.The method comprises the following steps: obtaining the flux distribution data in original radiotherapy plan and original region of interest;According to the beam geometry parameter of original radiotherapy plan, original region of interest is projected to flux projection space, and mapping relationship is established as reference index;Obtain the target region of interest in adaptive radiotherapy stage;Based on flux distribution data and target region of interest, update mapping relationship, and mapping relationship is used as target index;Detect the data consistency between target index and reference index, determine the dose distribution change information of target object.The present application solves the technical problems that the adaptive radiotherapy dose consistency verification in the prior art must rely on high-load dose calculation engine to carry out data reprocessing, resulting in large amount of calculation data, high system response delay, and unable to meet the timeliness requirements of online verification.

Description

Technical Field

[0001] This application relates to the field of medical information system technology, especially to the field of TOMO radiotherapy information processing, and specifically to a rapid verification method for adaptive radiotherapy dose consistency in the TOMO system. Background Technology

[0002] In helical tomotherapy systems, adaptive radiotherapy improves treatment precision by acquiring anatomical images of patients daily and dynamically adjusting the radiotherapy plan to address changes such as organ deformation, body position shift, and target displacement.

[0003] In existing adaptive radiotherapy workflows, validating the impact of anatomical changes on the treatment plan requires invoking a high-load dose calculation engine. This process involves complex data processing operations such as HU value correction and three-dimensional spatial interpolation, with a single validation data processing time typically exceeding 5-15 minutes. This data processing mode results in high system response latency, failing to meet the real-time data feedback requirements of online adaptive radiotherapy workflows and hindering the clinical deployment efficiency of adaptive radiotherapy systems.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This application provides a method for rapid verification of adaptive radiotherapy dose consistency in the TOMO system, which at least solves the technical problems in the prior art where adaptive radiotherapy dose consistency verification must rely on a high-load dose calculation engine for data reprocessing, resulting in large amounts of computational data, high system response latency, and inability to meet the timeliness requirements of online verification.

[0006] According to one aspect of the embodiments of this application, a rapid verification method for adaptive radiotherapy dose consistency in a TOMO system is provided, comprising: acquiring flux distribution data and an original region of interest (ROI) from an original radiotherapy plan, wherein the original radiotherapy plan is a radiotherapy plan developed for a target object; projecting the original ROI onto a flux projection space based on the beam geometry parameters of the original radiotherapy plan, and establishing a mapping relationship between the original ROI and the flux distribution data as a reference index; acquiring a target ROI for the adaptive radiotherapy phase; updating the mapping relationship between the original ROI and the flux distribution data based on the flux distribution data and the target ROI, and obtaining the mapping relationship between the target ROI and the flux distribution data as a target index; detecting the data consistency between the target index and the reference index, and determining the dose distribution change information of the target object during the adaptive radiotherapy phase based on the detection results.

[0007] Optionally, the consistency between the target indicator and the reference indicator is detected, and the dose distribution change information of the target object in the adaptive radiotherapy phase is determined based on the detection results, including: if the consistency between the target indicator and the reference indicator meets the preset conditions, the dose distribution change of the target object in the adaptive radiotherapy phase is determined to meet the expected requirements when formulating the original radiotherapy plan; if the consistency between the target indicator and the reference indicator does not meet the preset conditions, the dose distribution change of the target object in the adaptive radiotherapy phase does not meet the expected requirements when formulating the original radiotherapy plan.

[0008] Optionally, the flux distribution data includes sinusoidal data and / or beam modulation sequence data; the original region of interest includes the target area and organs at risk; wherein, the sinusoidal data represents: two-dimensional projection data of the opening time of the multi-leaf grating blades and the beam intensity distribution at different gantry angles during helical tomotherapy; the beam modulation sequence data represents: time-series data of the changes in the position of the multi-leaf grating blades and the number of machine jumps at different time points and different gantry angles during radiotherapy.

[0009] Optionally, the original region of interest is projected onto the flux projection space according to the beam geometry parameters of the original radiotherapy plan, including: determining the incident direction, gantry angle and collimator parameters for each beam direction according to the beam geometry parameters; and mapping the three-dimensional original region of interest onto the two-dimensional projection plane of each beam direction according to the incident direction, gantry angle and collimator parameters for each beam direction, to obtain the projection area of ​​the original region of interest in each beam direction.

[0010] Optionally, the reference indicators include at least one of the following indicators: a first indicator, used to characterize the coverage of the original region of interest in the flux projection direction; a second indicator, used to characterize the contribution of different flux units to the original region of interest; and a third indicator, used to characterize the positional relationship between the flux center and the center of the original region of interest.

[0011] Optionally, the first index data is determined through the following steps: obtaining the flux cell set under each beam direction; calculating the number of flux cells in the flux cell set that intersect with the projected region of the original region of interest; dividing the number of intersecting flux cells by the total number of flux cells in the flux cell set to obtain the coverage of the original region of interest in the flux projection direction under each beam direction; and determining the first index data based on the coverage of the original region of interest in the flux projection direction under all beam directions.

[0012] Optionally, detecting the data consistency between the target indicator and the reference indicator includes: calculating the similarity of the corresponding indicator data in the target indicator and the reference indicator; and determining the data consistency between the target indicator and the reference indicator based on the similarity.

[0013] Optionally, calculating the similarity between the target indicator and the reference indicator data corresponding to each other includes: when the reference indicator includes the first indicator data, obtaining the original coverage of the original region of interest based on each beam direction in the flux projection direction; obtaining the target coverage of the target region of interest based on each beam direction in the flux projection direction; for each beam direction, calculating the absolute value of the difference between the original coverage and the target coverage corresponding to that beam direction; and summing the absolute values ​​calculated for all beam directions to obtain the similarity between the target indicator and the reference indicator regarding the first indicator data.

[0014] Optionally, the preset condition is that the data consistency between the target indicator and the reference indicator is less than or equal to a preset threshold; if the data consistency indicator is less than or equal to the preset threshold, it is determined that the dose distribution change meets the expected requirements when the original radiotherapy plan was formulated, and the original radiotherapy plan is retained; if the data consistency indicator is greater than the preset threshold, it is determined that the dose distribution change does not meet the expected requirements when the original radiotherapy plan was formulated, and the original radiotherapy plan is adjusted.

[0015] According to another aspect of the embodiments of this application, an adaptive radiotherapy dose consistency rapid verification device for a TOMO system is also provided, comprising: a first acquisition unit, configured to acquire flux distribution data and an original region of interest in an original radiotherapy plan, wherein the original radiotherapy plan is a radiotherapy plan formulated for a target object; a first processing unit, configured to project the original region of interest onto a flux projection space according to the beam geometry parameters of the original radiotherapy plan, and establish a mapping relationship between the original region of interest and the flux distribution data as a reference index; a second acquisition unit, configured to acquire a target region of interest in the adaptive radiotherapy stage; an update unit, configured to update the mapping relationship between the original region of interest and the flux distribution data based on the flux distribution data and the target region of interest, and obtain the mapping relationship between the target region of interest and the flux distribution data as a target index; and a detection unit, configured to detect the data consistency between the target index and the reference index, and determine the dose distribution change information of the target object in the adaptive radiotherapy stage based on the detection results.

[0016] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, which stores a computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located performs the above-described method for rapid verification of adaptive radiotherapy dose consistency for the TOMO system.

[0017] According to another aspect of the embodiments of this application, an electronic device is also provided, including one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to perform the above-described adaptive radiotherapy dose consistency rapid verification method for the TOMO system.

[0018] According to another aspect of the embodiments of this application, a computer program product is also provided, including a computer program or instructions that, when executed by a processor, implement the above-described method for rapid verification of adaptive radiotherapy dose consistency for the TOMO system.

[0019] In this embodiment, the region of interest (ROI) is projected onto the flux projection space based on beam geometry parameters. By calculating the coverage consistency index of the ROI to the fixed flux distribution in the original and adaptive phases, the impact of anatomical changes on the treatment flux distribution can be quantified without recalculating the dose. This achieves the technical effect of indirectly reflecting dose distribution consistency through changes in geometric mapping relationships, thereby solving the technical problem in existing technologies where adaptive radiotherapy dose consistency verification relies on high-load dose calculation engines for data reprocessing, resulting in large amounts of computational data, high system response latency, and inability to meet online verification timeliness requirements. For example, in this embodiment, by projecting the ROI onto the flux projection space based on beam geometry parameters and calculating the coverage consistency index of the ROI to the fixed flux distribution in the original and adaptive phases, the complex dose calculation problem is transformed into a spatial index data comparison problem. This achieves the technical effect of rapid verification of planning data without calling a high-load dose calculation engine, thereby reducing data processing complexity and improving the data response efficiency of the adaptive radiotherapy system. Attached Figure Description

[0020] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0021] Figure 1 This is a flowchart of an optional rapid verification method for adaptive radiotherapy dose consistency for a TOMO system according to an embodiment of this application;

[0022] Figure 2 This is a schematic diagram of the projection of an optional original region of interest in different beam directions according to an embodiment of this application;

[0023] Figure 3 This is a schematic diagram of an optional adaptive radiotherapy dose consistency rapid verification device for the TOMO system according to an embodiment of this application. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0026] According to an embodiment of this application, a method embodiment for a rapid verification method of adaptive radiotherapy dose consistency for a TOMO system is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0027] According to the embodiments of this application, an adaptive radiotherapy dose consistency rapid verification system for the TOMO system (hereinafter referred to as the system) can be used as the execution subject of the adaptive radiotherapy dose consistency rapid verification method for the TOMO system in the embodiments of this application. The adaptive radiotherapy dose consistency rapid verification system for the TOMO system can be a software system or an embedded system combining software and hardware. Of course, the execution subject of the method in the embodiments of this application can also be other forms of execution subject, such as devices or equipment. Those skilled in the art should know that this application does not particularly limit the specific form of the execution subject.

[0028] Figure 1 This is a flowchart of an optional rapid verification method for adaptive radiotherapy dose consistency in a TOMO system according to an embodiment of this application, such as... Figure 1As shown, the method includes the following steps:

[0029] Step S101: Obtain flux distribution data and original region of interest from the original radiotherapy plan, wherein the original radiotherapy plan is a radiotherapy plan developed for the target subject.

[0030] In some embodiments, the system can directly read flux distribution data from the planning database of the radiotherapy planning system. The flux distribution data is stored in the form of a sine wave, containing the intensity of each flux unit at all gantry angles. Simultaneously, the system extracts the three-dimensional contour data of the original region of interest corresponding to the target object from the region of interest set file of the radiotherapy plan. This contour is drawn by the clinician during the radiotherapy planning stage and serves as the geometric boundary information of the radiotherapy target area and organs at risk.

[0031] It should be noted that the flux distribution data refers to the beam intensity distribution generated by the TOMO system in the original treatment plan. It is stored in the form of a sine graph and includes the weight value of the beam unit corresponding to the opening of each multi-leaf grating at each gantry angle, reflecting the radiation intensity distribution in the radiotherapy planning stage.

[0032] It should also be noted that the original region of interest refers to the target area or organ at risk delineated in the radiotherapy plan. It is the core area for dose control, and the geometry and location of the original region of interest determine the dose distribution pattern of the original radiotherapy plan.

[0033] Step S102: Based on the beam geometry parameters of the original radiotherapy plan, project the original region of interest onto the flux projection space and establish a mapping relationship between the original region of interest and flux distribution data as a reference index.

[0034] In some embodiments, the system orthogonally projects the three-dimensional contour of the original region of interest (ROI) along each beam direction based on the gantry angle sequence, collimator rotation angle, and multi-leaf grating (MLC) blade position set recorded in the original radiotherapy plan, generating a two-dimensional binary projection mask that is perfectly aligned with the flux cell grid space. In the two-dimensional binary projection mask, each flux cell is marked as covered or uncovered, forming a Boolean spatial correspondence between the ROI and flux on the projection plane, serving as a reference indicator for subsequent dose consistency assessment.

[0035] It should be noted that at each rack angle θ, the three-dimensional voxels of the original region of interest are projected parallel to the beam incident direction to obtain the two-dimensional contour of the original region of interest in the flux plane at angle θ. Then, this contour is superimposed on the flux cell mesh at angle θ, and it is calculated whether each flux cell is covered by the original region of interest. This is mathematically quantified as a coverage ratio. Finally, the coverage ratios at all angles form a one-dimensional curve as a reference indicator. The formula for calculating the coverage ratio of the original region of interest in the flux projection direction is Formula (1):

[0036] (1)

[0037] Where s represents the original region of interest, and θ represents the flux projection direction. This represents the geometric projection of the original region of interest s onto the direction θ. This represents the set of flux cells under this projection direction; Indicates the total number of flux units. Indicates an indicator function, This indicates whether the flux cell at each angle intersects with the projection of the original region of interest. If it intersects, it is recorded as 1; otherwise, it is recorded as 0. The judgment results of all flux cells in each angle are summed to obtain the number of flux cells that hit the original region of interest at that angle. i represents the i-th flux cell.

[0038] It should be noted that the system can simplify the complex three-dimensional dose problem into a geometric coverage relationship under two-dimensional projection. It does not rely on tissue density or physical dose models, and can characterize the dose intention of the original radiotherapy plan simply through the spatial relationship between the original region of interest and the flux unit.

[0039] In some embodiments, before projection, the system performs edge smoothing filtering on the 3D contour of the original region of interest to eliminate pixel-level jaggedness caused by image segmentation or delineation errors. Edge smoothing filtering only affects the geometric representation of the contour, using bilinear interpolation or median smoothing algorithms to preserve the overall boundary shape and area of ​​the original region of interest without changing its spatial location. The projection result remains a binary mask, and a mapping is established through the intersection of the original region of interest and the flux unit.

[0040] It should be noted that edge smoothing filtering is only used to improve the geometric continuity of the projection mask and reduce spurious flux coverage fluctuations introduced by pixel discretization, thereby improving the stability of the mapping relationship. This processing does not change the clinically defined boundaries of the original region of interest, nor does it introduce any tissue equivalent density or Hounsfield unit (HU) transformation, thus avoiding the interference of HU instability commonly found in cone-beam CT (CBCT) images on the mapping results.

[0041] In some embodiments, the system divides the original region of interest into multiple sub-regions according to anatomical function, performs projection operations independently on each sub-region, and generates an independent set of sub-maps. Each sub-map retains the semantic label of its original region of interest and establishes a dimensionally consistent index mapping with the flux unit set to form a hierarchical reference index, which supports subsequent tracking of differential responses in different clinical regions.

[0042] Step S103: Obtain the target region of interest for the adaptive radiotherapy phase.

[0043] In some embodiments, the system acquires cone-beam computed tomography (CBCT) or megavolt-level computed tomography (MVCT) images of the patient before treatment on the day of adaptive radiotherapy. Clinicians or automated delineation tools manually or semi-automatically delineate the target region of interest on the images. The target region of interest includes the target area and critical organs at risk, and its boundary definition is anatomically corresponding to the original region of interest in the original radiotherapy plan. The system uses the delineation result as the target region of interest in the adaptive phase.

[0044] In some embodiments, the system uses an image registration algorithm to non-rigidly register the adaptive CBCT images of the current day with the CT simulation images used in the original plan, and automatically maps the contour of the target region of interest in the original radiotherapy plan to the CBCT space to form an initial estimated contour. Subsequently, the system prompts clinicians to visually verify and fine-tune the mapping results, and finally confirms and saves the target region of interest for the adaptive phase, thereby reducing the burden of manual delineation while contributing to anatomical consistency.

[0045] In some embodiments, the system calls a pre-trained deep learning segmentation model, inputs the CBCT images of the day into the model, and directly outputs the three-dimensional segmentation results of the target region of interest. The output results are optimized by the system's built-in morphological post-processing and serve as the target region of interest in the adaptive stage without the need for manual intervention.

[0046] It should be noted that the pre-trained deep learning segmentation model has learned the morphological distribution of target areas and organs at risk on CBCT in a large number of historical adaptive radiotherapy cases during the training phase.

[0047] Step S104: Based on the flux distribution data and the target region of interest, update the mapping relationship between the original region of interest and the flux distribution data to obtain the mapping relationship between the target region of interest and the flux distribution data as the target index.

[0048] In some embodiments, the system uses the flux distribution data from the original radiotherapy plan and, based on the target region of interest (ROI) obtained during the adaptive phase, re-executes the same geometric projection process as the original mapping. Specifically, for each beam direction, the three-dimensional contour of the ROI is projected onto the corresponding flux projection space, generating a two-dimensional binary mask with the same resolution as the original mapping. In the two-dimensional binary mask, each flux cell is marked as covered or uncovered, thereby forming a new mapping relationship between the ROI and the flux distribution data, which is stored as a target index. For example, when establishing the mapping relationship, the system establishes a spatial index relationship between the contour data of the original ROI and the flux distribution data. This spatial index is stored in a B+ tree data structure, supporting fast retrieval of the ROI coverage status corresponding to each flux cell. During the adaptive validation phase, the system only needs to recalculate the spatial index of the ROI and compare it with the original index; it does not need to recalculate the dose distribution data.

[0049] It should be noted that updating the mapping relationship means keeping the original flux distribution data unchanged, only replacing the original region of interest with the target region of interest, and re-performing the same projection and coverage calculation as step S102 to obtain a new coverage ratio curve.

[0050] It should also be noted that the target index refers to the spatial correspondence regenerated in the adaptive phase based on the target region of interest and the original flux data. Essentially, it is an updated expression of the geometric projection results, rather than a recalculation of the dose distribution.

[0051] In some embodiments, during the mapping update process, the system performs subpixel-level interpolation on the boundary of the target region of interest to eliminate edge discretization errors caused by image registration or delineation accuracy. Subpixel-level interpolation is only used to improve the spatial continuity of the projection mask; it does not change the clinical boundary definition of the target region of interest, nor does it introduce intensity weighting or region weighting. The generated target index remains a binary Boolean mapping used to reflect whether the flux unit is covered by the target region of interest.

[0052] It should be noted that subpixel interpolation refers to performing bilinear or cubic spline interpolation on the 3D boundary before projecting the contour of the target region of interest, so that the projected edge is closer to the real anatomical shape, thereby reducing the spurious flux coverage fluctuations introduced by pixel quantization.

[0053] In some embodiments, the system divides the target region of interest into multiple clinically significant sub-regions, independently performs geometric projection based on flux distribution on each sub-region, generates an independent set of sub-maps, retains the target region of interest label for each sub-map, and establishes a dimensionally consistent index relationship with the flux unit set to form a multi-channel target index.

[0054] It should be noted that the multi-channel target index refers to a hierarchical representation of the target region of interest by decomposing a single target region of interest into multiple semantic sub-maps.

[0055] Step S105: Detect the data consistency between the target index and the reference index, and determine the dose distribution change information of the target object in the adaptive radiotherapy stage based on the detection results.

[0056] In some embodiments, the system compares the coverage status of the target index and the reference index for each angle of the region of interest, and counts the changes in the occlusion of the flux unit by the target region of interest in each beam direction compared to the original region of interest. It also summarizes the difference patterns of all angles to generate change information reflecting the overall degree of offset, which serves as a basis for judging whether there is a potential anomaly in the dose distribution.

[0057] It should be noted that the dose distribution change information is not the actual dose value, but a risk signal that the dose may deviate from the original radiotherapy plan based on the change in geometric blockage. This avoids relying on time-consuming dose recalculation and can quickly identify treatment deviations that need attention.

[0058] In some embodiments, during the comparison process, the system assigns different importance levels to coverage differences at different angles based on the flux contribution intensity of each beam direction in the original radiotherapy plan. Directions with larger flux contributions have a greater impact on overall change information due to changes in coverage along those directions. By weighted aggregating the differences across all angles, the system makes the final generated change information closer to the actual dose-sensitive region in clinical practice, thereby improving the accuracy of change assessment.

[0059] It should be noted that flux contribution intensity refers to the total number of flux units or modulation weights carried in a certain beam direction in the original plan.

[0060] In some embodiments, the system performs consistency detection on the target indicator and the reference indicator according to the sub-regions of the clinical region of interest, generates multiple local change information, and combines the generated local change information into a region of interest-specific change description to identify different types of change patterns, such as decreased target coverage or increased occlusion of organs at risk.

[0061] In one optional embodiment, detecting the data consistency between the target indicator and the reference indicator, and determining the dose distribution change information of the target object in the adaptive radiotherapy phase based on the detection results, includes: if the data consistency between the target indicator and the reference indicator meets the preset conditions, determining that the dose distribution change of the target object in the adaptive radiotherapy phase meets the expected requirements when formulating the original radiotherapy plan; if the data consistency between the target indicator and the reference indicator does not meet the preset conditions, determining that the dose distribution change of the target object in the adaptive radiotherapy phase does not meet the expected requirements when formulating the original radiotherapy plan.

[0062] For example, the system detects data consistency between the target metric and the reference metric by calculating the difference in the coverage ratio of the region of interest between the two at various rack angles. For each angle θ, the system calculates the absolute difference between the coverage ratio of the reference metric and the target metric, and sums up the differences for all angles to obtain the consistency metric.

[0063] It should be noted that the consistency index reflects the degree of deviation of the target region of interest from the original region of interest in terms of flux distribution in the original radiotherapy plan. The preset condition is a threshold range for this index, for example, set to not exceed 15% of the average coverage ratio of the original plan.

[0064] In this embodiment, when the consistency index is less than or equal to the threshold, the system determines that the data consistency between the target index and the reference index meets the preset conditions, indicating that although the target region of interest has deformed or shifted compared to the original region of interest, the spatial correspondence between the flux and the original region of interest has not been significantly changed, and the irradiation intention of the original radiotherapy plan can still be achieved. Therefore, it is determined that the dose distribution change of the target object in the adaptive radiotherapy stage meets the expected requirements when formulating the original radiotherapy plan, and no intervention is required. The original radiotherapy plan can be executed directly.

[0065] Conversely, when the consistency index exceeds the threshold, the system determines that the data consistency between the target index and the reference index does not meet the preset conditions, indicating that the geometric changes in the target region of interest have caused a significant deviation between the flux distribution and the radiotherapy plan. At this time, the system determines that the dose distribution changes of the target object in the adaptive radiotherapy phase do not meet the expected requirements when the original radiotherapy plan was formulated, and a replanning process needs to be triggered.

[0066] For example, in some embodiments, the system compares the target indicator with the reference indicator according to clinical sub-regions, and independently determines whether the change in the region of interest coverage of each sub-region is within the allowable range. If the data consistency of all sub-regions meets the preset conditions, the overall dose distribution change is determined to meet the expected requirements; if any sub-region shows a significant deviation, such as a decrease in target coverage or an increase in spinal cord coverage, the overall dose distribution change is determined to not meet the expected requirements.

[0067] It should be noted that clinical sub-regions refer to clinically critical areas within the target population that have independent clinical protective significance, such as the target area and critical organs at risk. The system differentiates between different risk patterns, such as insufficient target area irradiation and excessive irradiation of critical organs, making the assessment results more clinically targeted.

[0068] For example, the system can also dynamically weight the coverage differences at different angles based on the flux contribution intensity of each beam direction in the original radiotherapy plan. That is, the changes in the original region of interest coverage of the beam direction with high flux contribution have a greater impact on the determination of data consistency. After comprehensively evaluating the weighted differences of all angles, if the overall consistency level remains within the preset range, the dose distribution change is determined to meet the expected requirements; conversely, if the high contribution direction deviates significantly, it is determined to not meet the expected requirements.

[0069] It should be noted that flux contribution intensity refers to the total number of flux units or modulation weights carried by a certain beam direction in the original plan. The flux contribution intensity is only used to adjust the relative importance of the coverage change of that beam direction in the overall consistency assessment, and does not involve any physical dose modeling or energy deposition calculation. Dynamic weighting is a priority adjustment mechanism based on prior information from the original radiotherapy plan, which can improve the sensitivity to clinically critical beams and reduce the interference of small fluctuations in low-intensity angles on the overall judgment.

[0070] In one optional embodiment, the flux distribution data includes sinusoidal data and / or beam modulation sequence data; the original region of interest includes the target area and organs at risk; wherein, the sinusoidal data represents: two-dimensional projection data of the opening time of the multi-leaf grating blades and the beam intensity distribution recorded at different gantry angles during helical tomotherapy; the beam modulation sequence data represents: time-series data of the changes in the position of the multi-leaf grating blades and the number of machine jumps recorded at different time points and different gantry angles during radiotherapy.

[0071] For example, the system stores and uses only sinusoidal data as the source of flux distribution data in the original radiotherapy plan. During the adaptation phase, the system uses the stored sinusoidal data, maps it to the target region of interest acquired that day, and calculates a consistency index. The system does not access or reconstruct beam modulation sequence data; it relies solely on the gantry angle and flux intensity distribution information encoded in the sinusoidal data to complete the verification.

[0072] It should be noted that the sine curve data is a two-dimensional matrix generated by the system after the plan optimization is completed. The rows of the two-dimensional matrix correspond to the rack rotation angle, and the columns correspond to each blade channel of the multi-leaf grating. The value of each element in the matrix represents the beam intensity weight corresponding to the blade opening at that angle, which is used to inversely map the intensity distribution pattern of the beam in space.

[0073] It should also be noted that beam modulation sequence data records the leaf movement trajectory and machine hop count that change over time during treatment, and is logically equivalent to sine wave data, but is more refined in the temporal dimension. In this embodiment, the system preferentially uses sine wave data as the primary data source for flux distribution.

[0074] For example, the system can also combine sinusoidal data and beam modulation sequence data to construct a more complete flux distribution characterization. The sinusoidal data is used to determine the flux distribution baseline at each gantry angle, while the beam modulation sequence data is used to reconstruct the temporal trajectory of the multi-leaf grating blades at that angle, thereby calculating the effective coverage area of ​​each flux unit during radiotherapy. The system spatially matches the contour of the original region of interest with the dynamic flux interval to generate a reference index. During the adaptive phase, the same flux distribution data and the target region of interest are used to generate a target index. By calculating the consistency of the region of interest coverage between the reference index and the target index at all angles, the system determines whether the dose distribution changes meet the expected requirements.

[0075] For example, the system can also type-label flux distribution data when the original radiotherapy plan is generated: if the sine wave data is complete and stable, it is used as the flux distribution data source first; if the sine wave data is missing or insufficient in accuracy, it automatically switches to beam modulation sequence data for reconstruction. Maintaining a flux data source consistent with the original radiotherapy plan during the adaptive phase helps ensure that the reference and target indicators are constructed on the same basis, thereby determining whether the dose distribution changes meet the expected requirements through consistency comparison.

[0076] In one optional embodiment, the original region of interest is projected onto the flux projection space according to the beam geometry parameters of the original radiotherapy plan, including: determining the incident direction, gantry angle, and collimator parameters for each beam direction according to the beam geometry parameters; and mapping the three-dimensional original region of interest onto a two-dimensional projection plane for each beam direction according to the incident direction, gantry angle, and collimator parameters for each beam direction, to obtain the projected region of the original region of interest in each beam direction.

[0077] For example, the system reads the beam geometry parameters recorded in the original radiotherapy plan, including the gantry angle, collimator rotation angle and beam center coordinates for each beam direction. Based on the above beam geometry parameters, the system projects the original three-dimensional region of interest into a two-dimensional plane perpendicular to the beam in a parallel projection manner along the incident direction of each beam direction, generating a projection region of the original region of interest under that beam direction. Subsequently, all projection regions together constitute the complete geometric representation of the original region of interest in the flux projection space, which is used for consistency comparison with the target indicators of the adaptive phase.

[0078] It should be noted that the beam geometry parameters include the incident direction, gantry angle, and collimator parameters for each beam direction. The incident direction refers to the straight path of the X-ray beam from the radiotherapy head, through the patient's body surface, and to the detector. The gantry angle is the azimuth angle of the radiotherapy head as it rotates around the patient; helical tomotherapy systems typically rotate continuously from 0° to 360°, with each 1° or 2° interval serving as a sampling point. The collimator parameters refer to the opening and closing positions of the multi-leaf grating at the gantry angle, used to determine the boundary of the irradiation field in the two-dimensional plane, corresponding to the sine wave data.

[0079] Figure 2 This is a schematic diagram of the projection of an optional original region of interest in different beam directions according to an embodiment of this application, such as... Figure 2 As shown, when the beam direction is θ, the projection of the original three-dimensional region of interest s onto the beam direction is... When there is spatial overlap with the set of flux cells, flux cells that intersect with the projected region of the original region of interest are considered to cover the original region of interest, i.e., hit the original region of interest, while those that do not intersect are considered to be uncovered.

[0080] For example, during the projection process, the system can also dynamically trim the projection boundary based on the collimator parameters. When the collimator opening is smaller than the projection range of the original region of interest, the system performs a spatial intersection operation between the projection area of ​​the original region of interest and the collimator opening area, retaining only the projection portion within the effective illumination range of the collimator as the final projection area. This aligns the projection area with the spatial coverage of the flux distribution data, reducing invalid mapping caused by collimator occlusion.

[0081] It should be noted that the collimator parameters include the opening and closing boundary coordinates of the collimator blades, which determine the range of flux cells allowed to pass through in each beam direction. The spatial intersection operation is a geometric Boolean operation, retaining only the overlap between the projection of the original region of interest and the effective illumination area of ​​the collimator.

[0082] For example, the system can also project the target area and the organ at risk separately from the original region of interest. That is, the target area and the organ at risk are subsets of the original region of interest, and during the projection process, they are mapped to a two-dimensional projection plane with the same beam direction, forming two independent projection regions. The system records the coverage status of the target area projection region and the organ at risk projection region at various angles.

[0083] It should be noted that during the projection stage, the system does not merge the target area and the organs at risk, but maintains their spatial independence, which helps to calculate the changes in target area coverage and the changes in organs at risk coverage separately during consistency comparison.

[0084] In one optional embodiment, the reference index includes at least one of the following index data: a first index data, used to characterize the coverage of the original region of interest in the flux projection direction; a second index data, used to characterize the contribution of different flux units to the original region of interest; and a third index data, used to characterize the positional relationship between the flux center and the center of the original region of interest.

[0085] For example, the system constructs a reference index using only the first index data. For each beam direction, the system calculates the proportion of flux cells covered by the projected region of the original region of interest in the beam direction to the total number of flux cells, which is used as the first index data for that direction. Finally, the reference index is constructed from the first index data for all directions.

[0086] It should be noted that the first index data refers to the geometric coverage ratio between the projected area of ​​the original region of interest and the set of flux cells under a certain beam direction. The calculation of the first index data is based solely on binary coverage judgment: if there is spatial overlap between the flux cells and the projected area, it is recorded as 1; otherwise, it is recorded as 0. The final average value is taken as the first index data. In addition, the first index data does not involve flux intensity, treatment time, or machine hops; it only reflects the extent of occlusion of the flux space by the original region of interest.

[0087] For example, the system can combine the first indicator data and the second indicator data to construct a reference indicator: based on the calculated coverage, the system further evaluates the contribution of each flux unit to the original region of interest, i.e., whether the flux unit is completely covered, partially covered, or uncovered by the original region of interest, and assigns different weight levels, such as complete coverage = 1.0, partial coverage = 0.5, and uncovered = 0. The system performs a weighted average of the contributions of all flux units to generate the second indicator data, and combines the first indicator data and the second indicator data into a multidimensional reference indicator for subsequent consistency analysis.

[0088] It should be noted that the second indicator data is a refined supplement to the first indicator data. Its core lies in quantifying the difference in coverage of the original region of interest by the flux unit, rather than flux intensity or physical dose. The contribution is graded and assigned solely based on the spatial overlap ratio between the flux unit and the projected region, without introducing density, mass, or energy parameters. This enhances the sensitivity to deformation of the local original region of interest (such as organ atrophy or tumor shrinkage). Even if the overall coverage does not decrease significantly, the second indicator data can still capture the change in coverage of some key areas from complete to partial, thereby improving the clinical precision of the judgment.

[0089] For example, the system can further introduce a third index data based on the first and second index data: the system calculates the geometric center point of the original region of interest projection area and calculates the centroid position of all covered flux cells as the flux center. Then, the system calculates the Euclidean distance between the flux center and the region center on the two-dimensional projection plane as the third index data, and combines the first, second, and third index data into a high-dimensional reference index for subsequent consistency evaluation.

[0090] It should be noted that the third index data represents the relative geometric alignment between the original region of interest and the flux space. Its essence is the offset between the center of the projected region and the center of the effective flux distribution. The flux center is not the dose center, but the geometric centroid of the flux unit covered by the original region of interest. The region center is the geometric centroid of the projected contour of the original region of interest, calculated only based on the contour coordinates, without relying on any image intensity or density information.

[0091] It should also be noted that the third indicator data is highly sensitive to the overall translation or rotation of the original region of interest. For example, if the projection center of the target area shifts by 5 mm due to breathing, the third indicator data will still change significantly even if the coverage remains unchanged. This effectively identifies the dose distribution risk caused by systematic displacement or rotation, making up for the global shift problem that cannot be detected by coverage and contribution alone, and thus has the ability to perceive two-dimensional projection changes in the three-dimensional original region of interest.

[0092] In one optional embodiment, the first index data is determined through the following steps: obtaining a set of flux cells for each beam direction; calculating the number of flux cells in the set that intersect with the projected region of the original region of interest; dividing the number of intersecting flux cells by the total number of flux cells in the set to obtain the coverage of the original region of interest in the flux projection direction for each beam direction; and determining the first index data based on the coverage of the original region of interest in the flux projection direction for all beam directions.

[0093] For example, the system performs the following operations for each beam direction: First, it extracts the set of flux units corresponding to that beam direction from the original radiotherapy plan. This set consists of all independently controllable flux output units divided by a multi-leaf grating under that beam direction. Second, it uses the two-dimensional projection region of the original region of interest under that beam direction as the polygon contour input. Then, the system uses a geometric intersection judgment algorithm to determine whether each flux unit spatially overlaps with the projection region; if overlap exists, it is counted as an intersection unit. Finally, the number of intersection units is divided by the total number of flux units in that beam direction to obtain the coverage rate of that beam direction. The coverage rates of all beam directions constitute the first indicator data, used for consistency comparison with the first indicator data in the target indicator of the adaptive phase.

[0094] It should be noted that the flux cell set refers to the set of the smallest radiation output units that can be independently modulated on the flux projection plane under a certain beam direction, determined by the position of the multi-leaf grating blades. Their number and position are fixed in the original radiotherapy plan. Geometric intersection algorithms, such as polygon-rectangle intersection, help to make the calculation results accurate and reproducible, thereby achieving precise quantification of coverage and avoiding misjudgments caused by simplified models. They are particularly robust when the boundary of the original region of interest is close to the edge of the flux cell.

[0095] For example, the system can introduce the effective region of dynamic flux cells when calculating the intersection. Since some flux cells are located at the edge of the collimator or the boundary of the beam field, their actual illumination range is limited. Therefore, the system first determines the effective illumination region of each flux cell in a certain beam direction based on the collimator parameters. That is, the sub-region that the collimator allows to pass through. The intersection cell count is only included when the effective illumination region of the flux cell intersects with the projected region of the original region of interest. Otherwise, even if the geometric center of the flux cell overlaps with the projected region, it is not counted.

[0096] It should be noted that the effective irradiation area is the actual radiable portion of the flux unit under the collimator constraint. Its range is smaller than or equal to the complete geometric unit of the flux unit, thus ensuring that the intersection is calculated only within the actual irradiable space, rather than the theoretical unit space. The system directly encodes the physical limitations of the collimator into the coverage calculation, ensuring that the primary indicator data accurately reflects the coverage status of the clinically achievable original region of interest. This eliminates false coverage caused by collimator obstruction. For example, if a flux unit geometrically covers the target area but is not actually irradiated due to collimator obstruction, then that unit should not be counted in the coverage calculation.

[0097] For example, the system can also divide the original region of interest into multiple sub-regions and independently calculate the first indicator data for each sub-region. The system generates separate projection regions for the target area and organs at risk, and calculates the intersection of each projection region with the flux unit set, resulting in two independent coverage sequences. The final first indicator data is a composite data structure including the target area coverage sequence and the organs at risk coverage sequence. During consistency comparison, the system evaluates the changes in the first indicator data of the target area and organs at risk separately to identify different risk patterns such as decreased target area coverage or increased organs at risk coverage.

[0098] In one optional embodiment, detecting data consistency between a target indicator and a reference indicator includes: calculating the similarity of one-to-one correspondences of indicator data between the target indicator and the reference indicator; and determining the data consistency between the target indicator and the reference indicator based on the similarity.

[0099] For example, the system calculates the sum of the absolute differences between each data point in the target and reference metrics as the similarity score. For the first metric, the system calculates the absolute difference between the coverage rates of the target and reference stages in each beam direction and sums these differences over all beam directions to obtain the first similarity score. Similarly, for the second and third metrics, the system calculates the sum of the absolute differences between the corresponding target and reference values ​​to obtain the second and third similarities, respectively. Finally, the system merges all similarities into a comprehensive similarity score. If the comprehensive similarity score is lower than a preset threshold, the system determines that the data consistency between the target and reference metrics meets the requirements.

[0100] It should be noted that similarity is a negative measure of difference; that is, the smaller the difference, the higher the similarity. In essence, it is the reverse expression of dissimilarity.

[0101] For example, the system can use a normalized similarity function to calculate the matching degree between the target indicator and the reference indicator. For each indicator data, the system first performs normalization processing to uniformly map its value range to the [0,1] interval. Then, it uses the cosine similarity formula or 1 minus the normalized Euclidean distance to calculate the similarity between the target indicator and the reference indicator. For example, for the first indicator data sequence, its cosine value in multidimensional space is calculated; the closer the value is to 1, the higher the directional consistency. Finally, the system performs a weighted average of the similarities of the three indicators to generate a comprehensive similarity score, which is then compared with a threshold.

[0102] It should be noted that normalization refers to using the maximum / minimum value of each indicator data in the original radiotherapy plan as a benchmark and linearly scaling it to [0,1] to eliminate the differences in the dimensions of different indicators; cosine similarity measures the directional consistency of two sets of indicator data in multidimensional space.

[0103] For example, the system can also employ a tiered consistency determination mechanism. The system first determines whether the similarity of each indicator data is below an independent threshold. If all three indicators meet the threshold, high consistency is determined. If only some are met, a secondary determination is made, where different indicators are weighted according to clinical priority (e.g., target coverage is weighted greater than changes in organ coverage), and a weighted similarity is calculated. If the weighted similarity is still below the composite threshold, moderate consistency is determined, and an early warning can be issued, but the radiotherapy plan is not reset temporarily. If any indicator exceeds its independent threshold, low consistency is determined, triggering plan re-optimization.

[0104] It should be noted that the hierarchical consistency determination mechanism means that the system does not use a single global threshold, but rather establishes a multi-level decision-making logic. The independent threshold is the empirical or calibration value of each indicator data, and the weighted similarity is a comprehensive assessment driven by clinical priorities.

[0105] It should also be noted that the risk grading consistency determination mechanism allows the system to differentiate risk levels without immediately replanning. For example, if the coverage of organs at risk increases slightly but the target area coverage does not decrease, intervention may not be necessary for the time being, while a decrease in target area coverage will trigger intervention immediately, thereby upgrading the system from a yes / no judgment to a risk grading decision.

[0106] In one optional embodiment, calculating the similarity between the target indicator and the reference indicator data corresponding one-to-one includes: when the reference indicator includes the first indicator data, obtaining the original coverage of the original region of interest based on each beam direction in the flux projection direction; obtaining the target coverage of the target region of interest based on each beam direction in the flux projection direction; for each beam direction, calculating the absolute value of the difference between the original coverage and the target coverage corresponding to that beam direction; and summing the absolute values ​​calculated for all beam directions to obtain the similarity between the target indicator and the reference indicator regarding the first indicator data.

[0107] For example, the system pre-stores the original coverage of the region of interest (ROI) for each beam direction in the original radiotherapy plan as the first indicator data for reference. During the adaptive phase, based on the ROI acquired that day, the system recalculates the target coverage for each beam direction using the same beam geometry parameters. Subsequently, for each beam direction, the system calculates the absolute value of the difference between the original coverage and the target coverage, and sums the absolute values ​​for all beam directions to obtain a first similarity. When the first similarity is less than a preset threshold, it is determined that the first indicator data remains consistent, thus supporting the decision not to re-plan.

[0108] It should be noted that the original coverage is the output of the original region of interest (ROI) mapped to flux during the original radiotherapy planning stage. It is calculated as follows: the number of flux cells intersecting with the projected ROI in a given beam direction divided by the total number of flux cells in that direction. The target coverage is an equivalent metric obtained during the adaptive stage after remapping the projected ROI under the same beam geometry.

[0109] For example, the system can assign different weights to the absolute differences of different beam directions. Based on clinical importance or beam geometry, the system assigns higher weights to the absolute differences in high-angle density regions (such as the main irradiation direction of the target area) and lower weights to low-angle regions (such as dorsal and lateral low-dose areas). Similarity is the sum of weighted absolute values. Furthermore, the weights are automatically calculated and fixed by the system during the generation of the original radiotherapy plan based on the three-dimensional spatial distribution of the target area and the gantry trajectory.

[0110] It should be noted that the weights are numerical parameters tied to the beam direction, and their values ​​are determined by the projection coverage density of the target area in space. For example, if the target area is mainly located on the anterior ventral side, the weight of the forward gantry angle (0°-180°) is higher than that of the backward angle (180°-360°). The weight values ​​are floating-point numbers between 0 and 1, and their sum can be normalized to 1. Through the above mechanism, the system ensures that the first similarity no longer uniformly reflects coverage changes in all directions, but focuses on the clinically critical irradiation area, thereby improving the sensitivity to deformation of the core target area, reducing false triggering of re-optimization due to small drifts in non-critical directions, thus reducing unnecessary recalculation of the plan and improving treatment efficiency.

[0111] In one optional embodiment, the preset condition is that the data consistency between the target indicator and the reference indicator is less than or equal to a preset threshold; if the data consistency indicator is less than or equal to the preset threshold, it is determined that the dose distribution change meets the expected requirements when the original radiotherapy plan was formulated, and the original radiotherapy plan is retained; if the data consistency indicator is greater than the preset threshold, it is determined that the dose distribution change does not meet the expected requirements when the original radiotherapy plan was formulated, and the original radiotherapy plan is adjusted.

[0112] For example, the system uses a fixed preset threshold for consistency determination. This preset threshold is calibrated and set by a clinical physicist based on historical case data during system deployment. For instance, it might be set to a total similarity of less than or equal to 0.15 for the first indicator data. During the adaptive phase, after calculating the data consistency index between the target indicator and the reference indicator, if the data consistency index is less than or equal to 0.15, the dose distribution change is determined to be within the tolerance range of the original radiotherapy plan design, and the original plan is automatically retained and treatment continues. If the data consistency index is greater than 0.15, a plan re-optimization process is triggered.

[0113] It should be noted that the data consistency index refers to the overall degree of deviation between the calculated target index and the reference index. For example, the data consistency index can be the total deviation value of the first index data, which is the sum of the absolute values ​​of the differences between the original coverage and the target coverage at all rack angles.

[0114] For example, the data consistency index is calculated using formula (2):

[0115] (2)

[0116] in, Indicates the reference index at angle θ. This represents the target index at the θ angle.

[0117] It should also be noted that the preset threshold refers to a fixed numerical boundary set by clinical experts based on experience or historical data during the system initialization or plan generation phase. The value of the preset threshold is determined based on the statistical results of a large number of validated cases and is fixed in the system configuration file. This preset threshold is not dynamically adjusted with patients, time, or devices.

[0118] For example, when the data consistency index is less than or equal to a preset threshold, the system determines that the change in the target region of interest has not exceeded the design tolerance of the original radiotherapy plan. This means that although the patient has some degree of organ movement or deformation, the original flux distribution can still cover the target area, and the radiation dose to the endangered organs is still within a safe range, so there is no need to re-optimize the plan. In this case, the system will automatically retain the original radiotherapy plan and directly execute the treatment, thereby saving the time and resources required for replanning.

[0119] Conversely, when the data consistency index exceeds a preset threshold, it indicates that the match between the target region of interest and the original plan has exceeded an acceptable range. Continuing to use the original plan may result in underdosing of the target area or overdosing of organs. At this point, the system will trigger an alert and recommend redefining the target area and optimizing the plan.

[0120] For example, in some embodiments, the system uses a dynamically preset threshold. Based on the target volume, organ sensitivity, treatment site, and beam modulation complexity of the original radiotherapy plan, the system automatically calculates and binds a personalized threshold when the radiotherapy plan is generated. For example, for lung tumors, which are easily affected by respiratory movements, the system automatically widens the threshold to 0.25, and for head and neck tumors, which are densely affected by organs, the system automatically tightens it to 0.10. In the adaptive phase, each personalized threshold is used to determine consistency.

[0121] It's important to note that the dynamically preset threshold refers to a personalized threshold model automatically constructed by the system during the original radiotherapy plan generation stage, based on multidimensional features of plan parameters such as target volume, organ distance, MLC modulation entropy, and beam angle distribution. This model is trained using historical clinical data and outputs a continuous value, not a fixed one. For example, when the target volume is >300cm³, the threshold is increased by 0.03; when the distance between the endangered organ and the target is <5mm, the threshold is decreased by 0.05. By using dynamically preset thresholds, the system avoids a one-size-fits-all approach and adaptively adjusts the judgment criteria according to the clinical risk level. This reduces the missed diagnosis rate for high-risk patients and the false trigger rate for low-risk patients, achieving risk-driven intelligent thresholding and improving universality and safety in complex clinical scenarios.

[0122] For example, the system can also employ a tiered preset threshold mechanism, which sets independent thresholds for multiple indicator data in the target and reference indicators. For instance, when the consistency of the first indicator data is less than or equal to 0.15 and the second indicator data is less than or equal to 0.10, the overall consistency is deemed to meet the requirements, and the plan is retained; if only the first indicator exceeds the threshold but the second indicator does not, only the target area coverage risk is marked, and the plan is not immediately re-planned, but rather a physicist is prompted to evaluate; if any indicator exceeds its independent threshold, a forced re-optimization is triggered.

[0123] Figure 3 This is a schematic diagram of an optional adaptive radiotherapy dose consistency rapid verification device for a TOMO system according to an embodiment of this application. According to another aspect of an embodiment of this application, as... Figure 3 As shown, an adaptive radiotherapy dose consistency rapid verification device for the TOMO system is also provided, including: a first acquisition unit 301, a first processing unit 302, a second acquisition unit 303, an update unit 304, and a detection unit 305.

[0124] The system comprises the following components: a first acquisition unit 301, used to acquire flux distribution data and the original region of interest (ROI) from the original radiotherapy plan, wherein the original radiotherapy plan is a radiotherapy plan developed for the target object; a first processing unit 302, used to project the original ROI onto the flux projection space based on the beam geometry parameters of the original radiotherapy plan, and establish a mapping relationship between the original ROI and the flux distribution data as a reference index; a second acquisition unit 303, used to acquire the target ROI during the adaptive radiotherapy phase; an update unit 304, used to update the mapping relationship between the original ROI and the flux distribution data based on the flux distribution data and the target ROI, obtaining the mapping relationship between the target ROI and the flux distribution data as a target index; and a detection unit 305, used to detect the data consistency between the target index and the reference index, and determine the dose distribution change information of the target object during the adaptive radiotherapy phase based on the detection results.

[0125] Optionally, the detection unit 305 includes: a first determining subunit, configured to determine that the dose distribution change of the target object in the adaptive radiotherapy phase meets the expected requirements when formulating the original radiotherapy plan, provided that the data consistency between the target indicator and the reference indicator meets the preset conditions; and a second determining subunit, configured to determine that the dose distribution change of the target object in the adaptive radiotherapy phase does not meet the expected requirements when formulating the original radiotherapy plan, provided that the data consistency between the target indicator and the reference indicator does not meet the preset conditions.

[0126] Optionally, the adaptive radiotherapy dose consistency rapid verification device for the TOMO system further includes: flux distribution data including sine wave data and / or beam modulation sequence data; the original region of interest including the target area and organs at risk; wherein, the sine wave data represents: two-dimensional projection data of the opening time of the multi-leaf grating blades and the beam intensity distribution recorded at different gantry angles during helical tomotherapy; the beam modulation sequence data represents: time-series data of the changes in the position of the multi-leaf grating blades and the number of machine jumps recorded at different time points and different gantry angles during radiotherapy.

[0127] Optionally, the first processing unit 302 includes: a determination subunit, used to determine the incident direction, frame angle and collimator parameters of each beam direction according to the beam geometry parameters; and a mapping subunit, used to map the original three-dimensional region of interest to a two-dimensional projection plane of each beam direction according to the incident direction, frame angle and collimator parameters of each beam direction, to obtain the projection area of ​​the original region of interest in each beam direction.

[0128] Optionally, the mapping subunit includes at least one of the following index data modules: a first index data module for characterizing the coverage of the original region of interest in the flux projection direction; a second index data module for characterizing the contribution of different flux units to the original region of interest; and a third index data module for characterizing the positional relationship between the flux center and the center of the original region of interest.

[0129] Optionally, the first index data module includes: an acquisition submodule for acquiring a set of flux cells under each beam direction; a first calculation submodule for calculating the number of flux cells in the set of flux cells that intersect with the projected region of the original region of interest; a second calculation submodule for dividing the number of intersecting flux cells by the total number of flux cells in the set of flux cells to obtain the coverage of the original region of interest in the flux projection direction under each beam direction; and a determination submodule for determining the first index data based on the coverage of the original region of interest in the flux projection direction under all beam directions.

[0130] Optionally, the detection unit 305 further includes: a calculation subunit for calculating the similarity between the target indicator and the reference indicator data that correspond one-to-one; and a data consistency determination subunit for determining the data consistency between the target indicator and the reference indicator based on the similarity.

[0131] Optionally, the calculation subunit includes: a first acquisition module, used to acquire the original coverage of the original region of interest based on each beam direction in the flux projection direction when the reference index includes the first index data; a second acquisition module, used to acquire the target coverage of the target region of interest based on each beam direction in the flux projection direction; an absolute value calculation module, used to calculate the absolute value of the difference between the original coverage and the target coverage corresponding to each beam direction; and a similarity calculation module, used to add the absolute values ​​calculated for all beam directions to obtain the similarity between the target index and the reference index regarding the first index data.

[0132] Optionally, the adaptive radiotherapy dose consistency rapid verification device for the TOMO system further includes: a preset condition that the data consistency between the target indicator and the reference indicator is less than or equal to a preset threshold; when the data consistency indicator is less than or equal to the preset threshold, determining that the dose distribution change meets the expected requirements when formulating the original radiotherapy plan, and retaining the original radiotherapy plan; when the data consistency indicator is greater than the preset threshold, determining that the dose distribution change does not meet the expected requirements when formulating the original radiotherapy plan, and adjusting the original radiotherapy plan.

[0133] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, which stores a computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located performs the above-described method for rapid verification of adaptive radiotherapy dose consistency for the TOMO system.

[0134] According to another aspect of the embodiments of this application, an electronic device is also provided, including one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to perform the above-described adaptive radiotherapy dose consistency rapid verification method for the TOMO system.

[0135] According to another aspect of the embodiments of this application, a computer program product is also provided, including a computer program or instructions that, when executed by a processor, implement the above-described method for rapid verification of adaptive radiotherapy dose consistency for the TOMO system.

[0136] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0137] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0138] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0139] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0140] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0141] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0142] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A rapid verification method for adaptive radiotherapy dose consistency in the TOMO system, characterized in that, include: Obtain flux distribution data and original region of interest from the original radiotherapy plan, wherein the original radiotherapy plan is a radiotherapy plan developed for the target subject; Based on the beam geometry parameters of the original radiotherapy plan, the original region of interest is projected onto the flux projection space, and a mapping relationship between the original region of interest and the flux distribution data is established as a reference index. Obtain the target region of interest during the adaptive radiotherapy phase; Based on the flux distribution data and the target region of interest, the mapping relationship between the original region of interest and the flux distribution data is updated to obtain the mapping relationship between the target region of interest and the flux distribution data as the target index; The data consistency between the target index and the reference index is detected, and the dose distribution change information of the target object in the adaptive radiotherapy phase is determined based on the detection results.

2. The method according to claim 1, characterized in that, Detecting the data consistency between the target indicator and the reference indicator, and determining the dose distribution change information of the target object in the adaptive radiotherapy phase based on the detection results, including: If the data consistency between the target indicator and the reference indicator meets the preset conditions, it is determined that the dose distribution change of the target object in the adaptive radiotherapy phase meets the expected requirements when formulating the original radiotherapy plan. If the data consistency between the target indicator and the reference indicator does not meet the preset conditions, it is determined that the dose distribution change of the target object in the adaptive radiotherapy phase does not meet the expected requirements when formulating the original radiotherapy plan.

3. The method according to claim 1, characterized in that, The flux distribution data includes sinusoidal data and / or beam modulation sequence data; the original region of interest includes the target area and organs at risk; wherein, the sinusoidal data represents: two-dimensional projection data of the opening time of the multi-leaf grating blades and the beam intensity distribution at different gantry angles during helical tomotherapy; the beam modulation sequence data represents: time-series data of the changes in the position of the multi-leaf grating blades and the number of machine jumps at different time points and different gantry angles during radiotherapy.

4. The method according to claim 1, characterized in that, Based on the beam geometry parameters of the original radiotherapy plan, the original region of interest is projected onto the flux projection space, including: The incident direction, frame angle, and collimator parameters for each beam direction are determined based on the beam geometry parameters. According to the incident direction, frame angle and collimator parameters of each beam direction, the original three-dimensional region of interest is mapped to the two-dimensional projection plane of each beam direction to obtain the projection area of ​​the original region of interest in each beam direction.

5. The method according to claim 4, characterized in that, The reference indicators include at least one of the following indicators: The first indicator data is used to characterize the coverage of the original region of interest in the flux projection direction; The second indicator data is used to characterize the contribution of different flux units to the original region of interest; The third indicator data is used to characterize the positional relationship between the flux center and the center of the original region of interest.

6. The method according to claim 5, characterized in that, The first indicator data is determined through the following steps: Obtain the set of flux units for each beam direction; Calculate the number of flux units in the set that intersect with the projected region of the original region of interest; Divide the number of flux cells that have intersections by the total number of flux cells in the set of flux cells to obtain the coverage of the original region of interest in the flux projection direction under each beam direction; The first index data is determined based on the coverage of the original region of interest in the flux projection direction under all beam directions.

7. The method according to claim 5, characterized in that, Detecting the data consistency between the target indicator and the reference indicator includes: Calculate the similarity between the target indicator and the corresponding indicator data in the reference indicators; The data consistency between the target indicator and the reference indicator is determined based on the similarity.

8. The method according to claim 7, characterized in that, Calculating the similarity between the target indicator and the corresponding indicator data in the reference indicators includes: When the reference index includes the first index data, the original region of interest is obtained based on the original coverage of each beam direction in the flux projection direction; The target region of interest is obtained based on the target coverage in the flux projection direction for each beam direction; For each beam direction, calculate the absolute value of the difference between the original coverage and the target coverage corresponding to that beam direction; The absolute values ​​calculated for each of the beam directions are summed to obtain the similarity between the target index and the reference index regarding the data of the first index.

9. The method according to claim 2, characterized in that, The preset condition is that the data consistency between the target indicator and the reference indicator is less than or equal to a preset threshold; if the data consistency indicator is less than or equal to the preset threshold, it is determined that the dose distribution change meets the expected requirements when formulating the original radiotherapy plan, and the original radiotherapy plan is retained; if the data consistency indicator is greater than the preset threshold, it is determined that the dose distribution change does not meet the expected requirements when formulating the original radiotherapy plan, and the original radiotherapy plan is adjusted.

10. A rapid verification device for adaptive radiotherapy dose consistency in a TOMO system, characterized in that, include: The first acquisition unit is used to acquire flux distribution data and original region of interest in the original radiotherapy plan, wherein the original radiotherapy plan is a radiotherapy plan formulated for the target object; The first processing unit is used to project the original region of interest onto the flux projection space according to the beam geometry parameters of the original radiotherapy plan, and establish a mapping relationship between the original region of interest and the flux distribution data as a reference index. The second acquisition unit is used to acquire the target region of interest in the adaptive radiotherapy phase. The update unit is used to update the mapping relationship between the original region of interest and the flux distribution data based on the flux distribution data and the target region of interest, and obtain the mapping relationship between the target region of interest and the flux distribution data as a target indicator; The detection unit is used to detect the data consistency between the target index and the reference index, and to determine the dose distribution change information of the target object in the adaptive radiotherapy phase based on the detection results.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein when the computer program is executed, the device containing the computer-readable storage medium performs the adaptive radiotherapy dose consistency rapid verification method for the TOMO system as described in any one of claims 1 to 9.

12. An electronic device, characterized in that, It includes one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to perform the adaptive radiotherapy dose consistency rapid verification method for the TOMO system according to any one of claims 1 to 9.

13. A computer program product, characterized in that, Includes a computer program or instructions that, when executed by a processor, implement the adaptive radiotherapy dose consistency rapid verification method for the TOMO system as described in any one of claims 1 to 9.

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