A radiotherapy respiratory control threshold self-adaptive adjustment method based on image feedback

Abstract

The application provides a radiotherapy breathing control threshold adaptive adjustment method based on image feedback, which comprises the following steps: before any fraction treatment, guiding a patient to perform deep inhalation breath holding, and acquiring chest three-dimensional image data of the patient through an image acquisition device; determining whether a breathing control threshold optimization condition is met based on the chest three-dimensional image data; when the breathing control threshold optimization condition is met, optimizing the breathing control threshold, and determining a new breathing control threshold for triggering radiotherapy beam output. The application uses the real lung volume calculated by three-dimensional image as a feedback variable to adaptively correct the control threshold based on inhaled gas volume in the traditional ABC system, so as to establish a closed-loop breathing control mechanism based on internal anatomical state.

Description

Technical Field

[0001] This application relates to the field of radiotherapy technology, and in particular to a method for adaptive adjustment of respiratory control threshold in radiotherapy based on image feedback. Background Technology

[0002] With the continuous advancement of radiotherapy technology, precise radiotherapy for thoracic and abdominal tumors (such as breast cancer, lung cancer, and liver cancer) has become a routine clinical practice. Because respiratory motion causes significant displacement of the target area and organs at risk—often several millimeters, and in severe cases exceeding 20 mm—it increases target localization error and can lead to uncertainty in dose distribution. To reduce the impact of respiratory motion on radiotherapy accuracy, the Deep Inspiration Breath Hold (DIBH) technique is widely used clinically. This technique involves the patient holding their breath briefly after a deep inspiration, allowing the chest cavity to expand and temporarily stabilize the position of the tumor and surrounding organs, thereby reducing the impact of respiratory motion on radiotherapy dose distribution. DIBH can be achieved through respiratory management devices such as Active Breathing Control (ABC). This system typically monitors the patient's respiratory status in real time using a respiratory monitoring device and triggers valve control when a preset lung volume or respiratory threshold is reached, maintaining the patient in a specific breath-hold state. However, in actual clinical treatment, the patient's respiratory pattern, lung volume, and degree of chest cavity expansion may change between different treatment fractions. Because the respiratory control threshold in the existing ABC system is usually set once during the localization CT phase and remains fixed throughout the treatment, when the patient's actual respiratory status changes, the original respiratory control threshold may not accurately reflect the current optimal breath-holding state, resulting in a decrease in the consistency of anatomical structure positions between different fractions, which in turn affects the dose accuracy and treatment safety of radiotherapy. Summary of the Invention

[0003] The purpose of this application is to provide a method, device, electronic device, and storage medium for adaptive adjustment of respiratory control threshold in radiotherapy based on image feedback. By introducing lung volume and / or key organ location information during radiotherapy, dynamic adjustment of the ABC breath-hold threshold is achieved, thereby improving the consistency of the patient's internal anatomical structure between different treatment fractions under deep inspiration and breath-hold, reducing the impact of respiratory control errors on radiotherapy dose distribution, and reducing the risk of target area under-irradiation or excessive irradiation of endangered organs.

[0004] In a first aspect, the present invention provides an adaptive adjustment method for respiratory control threshold in radiotherapy based on image feedback. The method includes guiding the patient to perform deep inspiration and breath-holding before any fraction of treatment, acquiring three-dimensional chest image data (e.g., CBCT or other three-dimensional images) of the patient through an image acquisition device; determining whether the respiratory control threshold optimization conditions are met based on the chest three-dimensional image data; and optimizing the respiratory control threshold when the respiratory control threshold optimization conditions are met to determine a new respiratory control threshold for triggering radiotherapy beam output.

[0005] In an optional implementation, the step of determining whether the respiratory control threshold optimization conditions are met based on three-dimensional chest imaging data specifically includes: Based on three-dimensional chest imaging data, the real-time lung volume of the current patient is identified, and the corresponding inspiratory volume parameter value is determined; The lung volume deviation value is calculated based on real-time lung volume and reference lung volume; Determine whether the lung volume deviation value is greater than the preset deviation value; If so, then the conditions for optimizing the respiratory control threshold are met.

[0006] In an optional implementation, the step of calculating the lung volume deviation value specifically includes: Calculate the difference between real-time lung volume and reference lung volume; The ratio between the difference and the reference lung volume is calculated as the lung volume deviation value.

[0007] In an optional implementation, lung volume is identified in the following manner: Acquire three-dimensional chest images of a patient during deep inspiration and breath-holding, captured by an image acquisition device; Three-dimensional segmentation of the bilateral lung regions is performed based on three-dimensional chest images to determine the three-dimensional voxel set of the bilateral lungs; Based on the slice thickness and pixel spatial resolution of the three-dimensional chest image, the three-dimensional voxel set is accumulated to determine the lung volume.

[0008] In an optional implementation, the new respiratory control threshold is determined in the following manner: Determine whether the preset number of iterations has been reached; If not, calculate the ratio between the real-time lung volume and the reference lung volume, calculate the product of the ratio and the current respiratory control threshold, determine the new respiratory control threshold, and perform the step of determining whether the respiratory control threshold optimization conditions are met.

[0009] In an optional implementation, it further includes: Based on three-dimensional chest imaging data, the patient's diaphragm displacement deviation value was determined; The respiratory control threshold is corrected based on the diaphragm displacement deviation value to determine a new respiratory control threshold.

[0010] In an optional implementation, the patient's real-time respiratory amplitude parameter value is obtained; Determine whether the patient's real-time respiratory amplitude parameter value is within the range corresponding to the respiratory control threshold; if so, generate a control signal to the accelerator to trigger the radiotherapy beam output.

[0011] Secondly, the present invention provides an adaptive adjustment device for respiratory control threshold in radiotherapy based on image feedback, the device comprising: The acquisition module is used to guide the patient to take a deep breath and hold their breath before any treatment session, and to acquire three-dimensional chest image data of the patient through the image acquisition device. The analysis module is used to determine whether the conditions for optimizing the respiratory control threshold are met based on three-dimensional chest imaging data. The optimization module is used to optimize the breathing control threshold if the conditions are met, and to determine a new breathing control threshold for triggering the radiotherapy beam output.

[0012] Thirdly, the present invention provides an electronic device, comprising: a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the memory via the bus, and the processor executes the machine-readable instructions to perform the steps of any of the aforementioned embodiments of the radiotherapy respiratory control threshold adaptive adjustment method based on image feedback.

[0013] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of any of the image feedback-based adaptive adjustment methods for respiratory control thresholds in radiotherapy as described in the foregoing embodiments. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 A flowchart illustrating a control method for an image feedback-based adaptive adjustment system for respiratory control thresholds in radiotherapy, provided in an embodiment of this application; Figure 2 A schematic diagram of a radiotherapy respiratory control threshold adaptive adjustment device based on image feedback provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0016] In radiotherapy for thoracic and abdominal tumors (such as breast cancer, lung cancer, and liver cancer), the patient's respiratory movements cause periodic displacement of the target area and organs at risk, introducing geometric errors and dose uncertainties. To reduce the impact of respiratory movements on the accuracy of radiotherapy, the Deep Inspiration Breath Hold (DIBH) technique can be used. In this system, the ABC system monitors the inhaled gas flow rate using a flow sensor and integrates the flow signal to obtain the inhaled gas volume. When a preset threshold is reached, the airway is automatically shut off, thus achieving breath-holding.

[0017] Although the ABC system can improve the repeatability of the breathing process to some extent, it still has the following shortcomings in actual clinical application: ABC monitors the volume of inhaled gas, not the total lung volume or the geometry of internal organs. Even if the volume of inhaled gas is the same, differences in residual gas in the lungs, lung compliance, and breathing patterns can still lead to changes in lung volume and organ position during different breath-holding sessions or different breath-holding processes.

[0018] The ABC threshold is typically set once during the localization CT scan, lacking a dynamic adjustment mechanism during treatment. Existing systems fail to utilize pre-treatment or in-treatment imaging information to perform real-time or phased corrections to the ABC threshold.

[0019] The existing ABC system is an open-loop control system. That is, the system does not verify whether the current breath-holding state is consistent with the planned state based on image feedback, thus it cannot avoid the situation of "the volume meets the standard but the target area misses the target".

[0020] Based on this, this application provides an adaptive breathing control system that combines image-guided information to dynamically adjust the ABC control parameters, thereby improving the geometric reproducibility of internal anatomical structures during breath-holding.

[0021] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0022] Example 1 In one embodiment of this application, an adaptive active breathing control system is provided, including a breathing control unit, an image acquisition unit, an image segmentation and processing unit, a lung volume and organ geometric parameter calculation unit, a reference state storage unit, an error assessment and threshold adaptive calculation unit, and a control and feedback unit.

[0023] The respiratory control unit is used to monitor the volume of inhaled gas and control breath-holding. The respiratory control unit can be connected to existing ABC equipment or equivalent volumetric respiratory control devices.

[0024] The ABC device may include a breathing circuit, a flow sensor, a valve control module, and a human-machine interface, used to monitor the volume of gas inhaled by the patient in real time, and automatically shut off the airway when a set threshold is reached to achieve deep inhalation and breath-holding.

[0025] The image acquisition unit is used to acquire patient image data during the radiotherapy positioning phase and / or before and during treatment. The image data includes CT, CBCT or other three-dimensional imaging data.

[0026] The image segmentation and processing unit is used to automatically or semi-automatically process the image data and extract geometric information of lung tissue, diaphragm, target area and / or organs at risk.

[0027] Lung volume and organ geometric parameter calculation unit: used to calculate lung volume, organ centroid position, organ boundary position or other geometric parameters based on the segmentation results.

[0028] Reference state storage unit: used to store reference breath-hold state parameters obtained during the localization imaging phase, including reference lung volume, reference organ position, and corresponding respiratory control threshold.

[0029] Error assessment and threshold adaptive calculation unit: used to compare the lung volume and / or organ geometry parameters at the current treatment stage with the reference state, calculate the deviation, and generate an updated respiratory control threshold based on preset rules or models.

[0030] The control and feedback unit is used to transmit updated respiratory control thresholds to the respiratory control unit or to output adjustment suggestions to the operator, thereby forming a closed-loop control driven by image feedback.

[0031] Figure 1 A flowchart illustrating a control method for a radiotherapy system provided in an embodiment of this application. Figure 1 As shown in the embodiment of this application, a control method for an adaptive adjustment system for respiratory control threshold in radiotherapy based on image feedback includes: Before step S1, reference breath-holding data needs to be established for the patient. During the radiotherapy localization phase, the patient can be guided to wear an ABC breathing device and perform multiple deep breath-holds under the guidance of medical staff. The localization CT image obtained from the breath-holding state where the patient can stably maintain the breath and the inspiratory volume is closest to the CT localization is used as the reference image.

[0032] Based on the reference image, the lungs are segmented in three dimensions using an image processing module, and the reference lung volume is calculated by adding the volumetric values ​​of the segmented lung voxels in three-dimensional space. And record the corresponding ABC inspiratory volume thresholds. .

[0033] S1. Before any treatment session, guide the patient to take a deep breath and hold it, and acquire three-dimensional chest imaging data of the patient through an imaging acquisition device.

[0034] Before each treatment fraction, the patient is guided to perform a deep inhalation and breath-hold within the current ABC control threshold. When the ABC device detects that the inhaled gas volume has reached the threshold and the patient has entered the breath-hold state, CBCT imaging is initiated to acquire CBCT images of the patient in the breath-hold state before treatment.

[0035] S2. Based on the three-dimensional chest imaging data, determine whether the conditions for optimizing the respiratory control threshold are met.

[0036] The steps for determining whether the conditions for optimizing respiratory control thresholds are met based on three-dimensional chest imaging data include: Based on 3D chest imaging data, the real-time lung volume of the current patient is identified, and the corresponding inspiratory volume parameter value is determined. Specifically, the image processing and analysis module can be used to segment lung tissue from pre-treatment CBCT images, and the current lung volume can be calculated by adding the volumetric values ​​of the segmented lung tissue voxels in 3D space. .

[0037] The lung volume deviation value is calculated based on the real-time lung volume and the reference lung volume.

[0038] The steps for calculating the lung volume deviation value specifically include: Calculate the difference between the real-time lung volume and the reference lung volume. Calculate the ratio of this difference to the reference lung volume as the lung volume deviation value. Determine whether the lung volume deviation value is greater than the preset deviation value. If so, determine that the breathing control threshold optimization condition is met.

[0039] In a specific embodiment, the current lung volume can be... Compared with reference lung volume Compare and calculate the relative volume deviation. : ; When | When the current breath-holding state is less than the preset allowable threshold (e.g., 5%-10%, preferably 3%-5%), it is determined that the current breath-holding state is consistent with the reference state, and the radiotherapy step is initiated.

[0040] S3. When the breathing control threshold optimization condition is met, the breathing control threshold is optimized to determine a new breathing control threshold for triggering the radiotherapy beam output.

[0041] In step S3, the new respiratory control threshold can be determined in the following way: Determine if the preset number of iterations has been reached. The number of iterations can be 3 to 5.

[0042] If not, calculate the ratio between the real-time lung volume and the reference lung volume, and then calculate the ratio relative to the current respiratory control threshold. The product of is determined as the new respiratory control threshold, and the step of determining whether the respiratory control threshold optimization condition is met is performed.

[0043] The new respiratory control threshold can be determined in the following ways. : .

[0044] In other feasible implementations, nonlinear functions, empirical models, or prediction models trained based on historical fractional data can also be used to calculate and update the breathing control threshold.

[0045] In other feasible implementations, an adaptive ABC control method combining SGRT and CBCT can also be used.

[0046] In another embodiment, the system may also include an optical surface guidance device (SGRT) for real-time monitoring of geometric changes on the patient's body surface.

[0047] In this implementation, SGRT is used to guide the patient into a deep inhalation and breath-hold state and to perform real-time monitoring, while CBCT is used to periodically verify the internal lung volume or organ location.

[0048] When SGRT is within the target range but CBCT shows excessive internal geometric deviations, the ABC threshold adaptive adjustment procedure is initiated. By combining external surface monitoring with internal imaging feedback, the safety and reliability of deep inspiration breath-hold radiotherapy can be further improved.

[0049] The updated ABC control threshold is sent to the ABC device to guide the patient to perform a deep inhalation and breath-hold again, and the steps are repeated. When the lung volume deviation meets the preset conditions or reaches the maximum allowable number of iterations, radiotherapy is performed under a breath-hold state that meets the geometric consistency requirements. Here, the lung volume deviation can be 3% to 5%.

[0050] Specifically, the system can acquire the patient's real-time respiratory amplitude parameters. It then determines whether these parameters fall within the range corresponding to the respiratory control threshold. If so, a control signal is generated for the accelerator to trigger the radiotherapy beam output for radiotherapy.

[0051] The present application provides a control method for a radiotherapy system that adaptively adjusts the ABC control threshold based on real lung volume information. This effectively reduces the differences in lung volume and related organ positions during deep inspiration and breath-holding between different fractions, thereby improving the geometric reproducibility of the target area and organs at risk.

[0052] Example 2 In one embodiment of this application, lung volume can be identified in the following way: Acquire three-dimensional chest images of the patient during deep inspiration and breath-holding, captured by an image acquisition device. Perform three-dimensional segmentation of the bilateral lung regions based on the chest images to determine the three-dimensional voxel set of both lungs. Accumulate the three-dimensional voxel set based on the slice thickness and pixel spatial resolution of the chest images to determine the lung volume.

[0053] After acquiring the localization CT image of the patient in a deep inhalation and breath-holding state, the image processing module can perform three-dimensional segmentation of the bilateral lung regions. The segmentation method can include, but is not limited to, initial lung parenchyma segmentation based on CT grayscale thresholds; the segmentation results can be corrected by combining region growth, morphological operations or model constraints; and three-dimensional voxel sets of the left and right lungs can be obtained respectively.

[0054] After completing the 3D segmentation of the lung region, the volumetric values ​​of the segmented lung voxels are accumulated based on the slice thickness and pixel spatial resolution of the CT images to calculate the total volume of both lungs, which serves as the reference lung volume. .

[0055] Example 3 In one embodiment of this application, based on iterative adjustment of the respiratory control threshold, the patient's diaphragm displacement deviation value can also be determined based on three-dimensional chest imaging data. The respiratory control threshold is then corrected based on the diaphragm displacement deviation value to determine a new respiratory control threshold.

[0056] Here, based on CBCT images acquired during breath-holding before treatment, the image processing module can identify and locate the diaphragm structure while completing lung tissue segmentation.

[0057] Specifically, the diaphragm region located between the lower edge of the lung and the upper edge of the abdominal cavity can be identified in CBCT images, and the diaphragm contour can be extracted using methods such as edge detection, region growing, or model matching. Based on this, characteristic location points of the diaphragm in the cephalothorax direction are determined. These characteristic location points can be the apex of the diaphragm, the highest point on the diaphragm's arc surface, or a preset anatomical reference point.

[0058] Subsequently, the diaphragm feature position obtained in the current fractional treatment is compared with the diaphragm position in the reference breath-holding state, and the diaphragm position deviation is calculated, wherein the deviation is preferably the displacement in the head-to-toe direction.

[0059] Furthermore, the diaphragm position deviation can be used as a primary evaluation indicator to reflect the displacement of the liver during respiration in the patient's current breath-holding state. In other embodiments, the diaphragm position deviation can be combined with the lung volume deviation to form a comprehensive evaluation indicator.

[0060] In feasible embodiments, the control threshold of the ABC breathing control device is adaptively adjusted according to the direction and magnitude of the diaphragm position deviation. Specifically, this may include appropriately decreasing the ABC control threshold when the diaphragm position shifts towards the head relative to the reference position, and appropriately increasing the ABC control threshold when the diaphragm position shifts towards the feet relative to the reference position, so as to guide the patient to form a diaphragm position closer to the reference state during the next deep inhalation and breath-holding.

[0061] By using an adaptive control method based on diaphragmatic position feedback, the residual displacement of liver tumors in the head-to-foot direction can be effectively reduced, improving the stability and repeatability of target localization in stereotactic radiotherapy (SBRT).

[0062] Example 4 Figure 2 This is a schematic diagram of a radiotherapy respiratory control threshold adaptive adjustment device based on image feedback, provided as an embodiment of this application. Figure 2 As shown, based on the same inventive concept, this application also provides a radiotherapy respiratory control threshold adaptive adjustment device 20 based on image feedback, the device comprising: The acquisition module 210 is used to guide the patient to take a deep breath and hold their breath before any treatment session, and to acquire the patient's three-dimensional chest image data through the image acquisition device. Analysis module 220 is used to determine whether the conditions for optimizing respiratory control thresholds are met based on three-dimensional chest imaging data; The optimization module 230 is used to optimize the breathing control threshold if the conditions are met, and to determine a new breathing control threshold for triggering the radiotherapy beam output.

[0063] In a preferred embodiment, the analysis module 220 is specifically used to identify the real-time lung volume of the current patient based on chest three-dimensional image data and determine the corresponding inspiratory volume parameter value; calculate the lung volume deviation value based on the real-time lung volume and the reference lung volume; determine whether the lung volume deviation value is greater than a preset deviation value; if so, determine that the respiratory control threshold optimization condition is met.

[0064] In a preferred embodiment, the step of calculating the lung volume deviation value specifically includes calculating the difference between the real-time lung volume and the reference lung volume; and calculating the ratio between the difference and the reference lung volume as the lung volume deviation value.

[0065] In a preferred embodiment, lung volume is identified by the following method: Acquire three-dimensional chest images of the patient during deep inspiration and breath-holding, captured by an image acquisition device; perform three-dimensional segmentation of the bilateral lung regions based on the chest three-dimensional images to determine the three-dimensional voxel set of the bilateral lungs; accumulate the three-dimensional voxel set based on the slice thickness and pixel spatial resolution of the chest three-dimensional images to determine the lung volume.

[0066] In a preferred embodiment, the new respiratory control threshold is determined by the following method: Determine whether the preset number of iterations has been reached; if not, calculate the ratio between the real-time lung volume and the reference lung volume, calculate the product of the ratio and the current respiratory control threshold, determine it as the new respiratory control threshold, and execute the step of determining whether the respiratory control threshold optimization conditions are met.

[0067] In a preferred embodiment, the method further includes determining the patient's diaphragm displacement deviation value based on three-dimensional chest imaging data; and correcting the respiratory control threshold based on the diaphragm displacement deviation value to determine a new respiratory control threshold.

[0068] In a preferred embodiment, the patient's real-time respiratory amplitude parameter value is acquired; it is determined whether the patient's real-time respiratory amplitude parameter value is within the range corresponding to the respiratory control threshold; if so, a control signal is generated for the accelerator to trigger the radiotherapy beam output.

[0069] Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 3 As shown, the electronic device 300 includes a processor 310, a memory 320, and a bus 330.

[0070] The memory 320 stores machine-readable instructions executable by the processor 310. When the electronic device 300 is running, the processor 310 and the memory 320 communicate via the bus 330. When the machine-readable instructions are executed by the processor 310, the steps of an image feedback-based radiotherapy respiratory control threshold adaptive adjustment method as described in the above method embodiment can be executed. For specific implementation details, please refer to the method embodiment, which will not be repeated here.

[0071] This application also provides a computer-readable storage medium storing a computer program. When the computer program is run by a processor, it can execute the steps of a radiotherapy respiratory control threshold adaptive adjustment method based on image feedback as described in the above method embodiments. For specific implementation details, please refer to the method embodiments, which will not be repeated here.

[0072] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0073] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0074] Furthermore, 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 network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0075] Furthermore, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0076] It should be noted that if the function is implemented as a software functional module 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 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 USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0077] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0078] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for adaptive adjustment of respiratory control threshold in radiotherapy based on image feedback, characterized in that, The method includes: Before any treatment session, the patient is guided to take a deep breath and hold it, and three-dimensional chest imaging data is acquired through an imaging acquisition device. Based on the chest 3D image data, determine whether the breathing control threshold optimization conditions are met; When the breathing control threshold optimization conditions are met, the breathing control threshold is optimized to determine a new breathing control threshold for triggering the radiotherapy beam output.

2. The method according to claim 1, characterized in that, The step of determining whether the respiratory control threshold optimization conditions are met based on the chest three-dimensional image data specifically includes: Based on the three-dimensional chest imaging data, the real-time lung volume of the current patient is identified, and the corresponding inspiratory volume parameter value is determined according to the real-time lung volume. The lung volume deviation value is calculated based on real-time lung volume and reference lung volume; Determine whether the lung volume deviation value is greater than a preset deviation value; If so, then the conditions for optimizing the respiratory control threshold are met.

3. The method according to claim 2, characterized in that, The step of calculating the lung volume deviation value specifically includes: Calculate the difference between real-time lung volume and reference lung volume; The ratio between the difference and the reference lung volume is calculated as the lung volume deviation value.

4. The method according to claim 2, characterized in that, Lung volume can be identified using the following methods: Acquire three-dimensional chest images of a patient during deep inspiration and breath-holding, captured by an image acquisition device; Three-dimensional segmentation of the bilateral lung regions is performed based on three-dimensional chest images to determine the three-dimensional voxel set of the bilateral lungs; Based on the slice thickness and pixel spatial resolution of the three-dimensional chest image, the three-dimensional voxel set is accumulated to determine the lung volume.

5. The method according to claim 1, characterized in that, The new respiratory control threshold was determined using the following method: Determine whether the preset number of iterations has been reached; If not, the ratio between the real-time lung volume and the reference lung volume is calculated, the product of the ratio and the current respiratory control threshold is calculated, the new respiratory control threshold is determined, and the step of determining whether the respiratory control threshold optimization condition is met is performed.

6. The method according to claim 1, characterized in that, Also includes: Based on the three-dimensional chest imaging data, the patient's diaphragm displacement deviation value was determined; The respiratory control threshold is corrected based on the diaphragm displacement deviation value to determine a new respiratory control threshold.

7. The method according to claim 1, characterized in that, Obtain the patient's real-time respiratory amplitude parameters; Determine whether the patient's real-time respiratory amplitude parameter value is within the range corresponding to the respiratory control threshold; if so, generate a control signal to the accelerator to trigger the radiotherapy beam output.

8. A radiotherapy respiratory control threshold adaptive adjustment device based on image feedback, characterized in that, The device includes: The acquisition module is used to guide the patient to take a deep breath and hold their breath before any treatment session, and to acquire three-dimensional chest image data of the patient through the image acquisition device. The analysis module is used to determine whether the breathing control threshold optimization conditions are met based on the three-dimensional chest image data. The optimization module is used to optimize the breathing control threshold and determine a new breathing control threshold when the breathing control threshold optimization conditions are met.

9. An electronic device, characterized in that, include: The device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus, and the processor executes the machine-readable instructions to perform the steps of the image feedback-based adaptive adjustment method for respiratory control thresholds in radiotherapy as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the radiotherapy respiratory control threshold adaptive adjustment method based on image feedback as described in any one of claims 1 to 7.

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