Interventional instrument state data acquisition method and device and medium

Abstract

The invention belongs to the field of vascular interventional surgical robots, and discloses a state data acquisition method and device of an interventional instrument and a medium, and the method comprises the steps: constructing a virtual interventional scene corresponding to a real interventional scene; a control instruction is generated through the controller; controlling the movement of the interventional instrument in the real interventional scene according to the control instruction, obtaining a corresponding DSA image, and obtaining first state data of the interventional instrument in the real interventional scene based on the DSA image; according to the control instruction, controlling the movement of a virtual interventional instrument in the virtual interventional scene, and obtaining second state data of the virtual interventional instrument; and fusing the first state data and the second state data to obtain fused state data. Finally obtained fusion state data is more accurate and reliable, a doctor can master the state of the interventional instrument in the operation more accurately, the safety and accuracy of the operation operation are improved, and a more powerful basis is provided for operation risk prejudgment.

Description

Technical Field

[0001] The present invention relates to the field of vascular interventional surgical robots, and in particular to a method, device and medium for acquiring status data of an interventional instrument. Background Art

[0002] During the operation of the interventional surgical robot, the status data such as the position information and the head end force information of the interventional instrument are very important information. The doctor or the main end controller can predict the surgical risk based on the above status data. Therefore, the accuracy of the above status data is very important. The current method of obtaining status data is generally to obtain it through analysis of the force feedback system of the slave robot and DSA (Digital Subtraction Angiography) images, which is prone to analysis errors. For example, when analyzing the position information and head end force information of the interventional instrument based on DSA images, because there is only image information, the blood vessels are sometimes mistaken for interventional instruments, thereby obtaining erroneous status data, which will affect the accuracy of the doctor or controller's prediction of surgical risks.

[0003] Therefore, how to accurately obtain the status data of interventional devices is a technical problem that needs to be solved urgently. Summary of the invention

[0004] The present invention provides a method, device and medium for acquiring status data of an interventional device, aiming to solve the technical problem that the prior art cannot accurately acquire the status data of the interventional device.

[0005] In order to achieve the above-mentioned object of the invention, the first aspect of the present invention provides a method for obtaining status data of an interventional device, comprising the steps of:

[0006] Constructing virtual intervention scenarios that correspond to real intervention scenarios;

[0007] Obtain control instructions generated by the master controller;

[0008] Controlling the movement of the interventional instrument in the real interventional scene according to the control instruction, acquiring a corresponding digital subtraction angiography image, and obtaining first state data of the interventional instrument in the real interventional scene based on the digital subtraction angiography image;

[0009] controlling the movement of the virtual interventional instrument in the virtual interventional scene according to the control instruction, and acquiring second state data of the virtual interventional instrument;

[0010] The first state data and the second state data are fused to obtain fused state data.

[0011] Furthermore, after the step of fusing the first state data and the second state data to obtain fused state data, the method further comprises:

[0012] The first state data is modified based on the fused state information, and the second state data is modified based on the fused state information.

[0013] Furthermore, the step of constructing a virtual intervention scene corresponding to the real intervention scene includes:

[0014] Acquire interventional device data, slave robot data, and preoperative computed tomography angiography data of patients in real interventional scenarios;

[0015] constructing the virtual interventional instrument in the virtual interventional scene according to the interventional instrument data;

[0016] constructing virtual slave robot data in the virtual intervention scene according to the slave robot data;

[0017] A blood vessel model in the virtual intervention scene is constructed according to the computed tomography angiography data.

[0018] Furthermore, the step of constructing a blood vessel model in the virtual intervention scene according to the computed tomography angiography data comprises:

[0019] reconstructing a three-dimensional blood vessel model based on the computed tomography angiography data;

[0020] increasing the thickness of the blood vessel wall in the three-dimensional blood vessel model to obtain a volume model;

[0021] The body model is imported into a preset simulation software to obtain the blood vessel model, wherein the simulation software assigns elastic physical collision parameters to the blood vessel model.

[0022] Furthermore, the step of fusing the first state data and the second state data to obtain fused state data includes:

[0023] The first state data and the second state data are fused by a multimodal data fusion method to obtain the fused state data.

[0024] Furthermore, after the step of constructing a virtual intervention scene corresponding to the real intervention scene, the following steps are included:

[0025] Computer graphics technology and visualization algorithms are used to generate visualized virtual intervention scenes.

[0026] Furthermore, the method for acquiring status data of an interventional device further includes:

[0027] Obtain data from sensors on the slave robot;

[0028] According to each of the sensor data, determining whether a component corresponding to each of the sensor data fails;

[0029] If so, mapping the failed component to the visualized virtual intervention scene;

[0030] If not, the components of the slave robot are displayed normally in the virtual intervention scene.

[0031] Further, after the step of fusing the first state data and the second state data to obtain fused state data, the method further comprises:

[0032] Using the fusion status data, predicting whether there is a risk in surgery;

[0033] If so, a reminder message is generated;

[0034] If not, new fusion status data is obtained to continue predicting whether the surgery is risky.

[0035] A second aspect of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method for acquiring status data of an interventional device as described in any one of the above are implemented.

[0036] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the method for acquiring status data of an interventional device as described in any one of the above are implemented.

[0037] Beneficial effects:

[0038] The present invention provides a method, device and medium for obtaining status data of an interventional instrument, wherein the method can simulate the surgical operation process in a virtual environment by constructing a virtual intervention scene, providing comparison and reference for real surgery. The control instructions of the main-end controller are used to realize the synchronous movement of the instruments in the real and virtual intervention scenes, and obtain the status data in the two scenes respectively. By integrating the two, the rich physical simulation information in the virtual intervention scene and the actual image information in the real intervention scene can be fully utilized, and the advantages are complementary. The fused status data finally obtained is more accurate and reliable, which helps doctors to more accurately grasp the status of the interventional instrument during the operation, improve the safety and accuracy of the surgical operation, and provide a more powerful basis for predicting surgical risks, thereby improving the quality and success rate of the entire interventional surgery. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1A schematic diagram of a flow chart of a method for acquiring status data of an interventional device according to an embodiment of the invention;

[0040] Figure 2 A schematic diagram of a flow chart of a method for acquiring status data of an interventional device according to an embodiment of the invention;

[0041] Figure 3 The figure is a schematic diagram of the structure of a computer device according to an embodiment of the invention.

[0042] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0044] It will be understood by those skilled in the art that, unless expressly stated, the singular forms "a", "an", "above", and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of features, integers, steps, operations, elements, modules, modules and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, modules, modules, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be an intermediate element. In addition, the "connection" or "coupling" used herein may include wireless connection or wireless coupling. The term "and / or" used herein includes all or any module and all combinations of one or more associated listed items.

[0045] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as generally understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined as herein.

[0046] Reference Figure 1 The embodiment of the present invention provides a method for obtaining status data of an interventional device, comprising the steps of:

[0047] S1. Construct a virtual intervention scenario corresponding to the real intervention scenario;

[0048] S2, obtaining the control instruction generated by the master controller;

[0049] S3, controlling the movement of the interventional instrument in the real interventional scene according to the control instruction, acquiring a corresponding digital subtraction angiography image, and obtaining first state data of the interventional instrument in the real interventional scene based on the digital subtraction angiography image;

[0050] S4, controlling the movement of the virtual interventional instrument in the virtual interventional scene according to the control instruction, and acquiring second state data of the virtual interventional instrument;

[0051] S5. Fuse the first state data and the second state data to obtain fused state data.

[0052] For step S1, the above-mentioned real intervention scene is the real intervention scene corresponding to when the interventional surgical robot performs the interventional task. The interventional surgical robot generally includes a master controller and a slave robot. The relationship between the master controller and the slave robot is: the master controller controls the actions of the slave robot, and the slave robot will feed back the relevant signals generated by its actions to the master controller. In this embodiment, the surgery-related data obtained before the operation, including interventional instrument data, slave robot data and CAT (Computed Tomography Angiography) data, are used to construct a virtual intervention scene similar to the real surgical scene in a virtual environment. The interventional instrument data is used to construct a virtual interventional instrument, the slave robot data is used to construct a virtual slave robot, and the CAT data is used to construct a vascular model, thereby providing a basis for subsequent simulation operations and data comparison.

[0053] For step S2, the doctor operates the master controller with force feedback function, and the master controller generates corresponding control instructions. These instructions are the command signals for the entire surgical operation, which determine the movement direction, speed, rotation and other operating parameters of the instruments in the real and virtual intervention scenes. In this embodiment, the control signal of the master controller is sent to the slave robot and the virtual intervention scene in the form of CAN bus.

[0054] For step S3, in a real intervention scenario, the slave robot receives the control command and executes the action to move the interventional instrument, including controlling the axial movement and radial movement of the interventional instrument. At the same time, the DSA device collects images in real time, and extracts the two-dimensional position of the interventional instrument, the bending state of the head end, and other information from the DSA two-dimensional image based on the image recognition algorithm, and can estimate the force on the head end of the interventional instrument. This information constitutes the first state data. For example, in vascular intervention surgery, the real-time position of the interventional instrument in the blood vessel and the force on its head end caused by factors such as the blood vessel wall are both first state data.

[0055] For step S4, in the virtual intervention scene, the virtual environment drives the movement of the virtual interventional instrument according to the main end control instruction. In this embodiment, the virtual intervention scene is constructed using the SOFA (Simulation Open Framework Architecture, an open source software framework for real-time physical simulation) simulation software. Through the elastic physics engine and collision algorithm in the SOFA simulation software, the software function library's built-in function interface is used to obtain the three-dimensional position information and head end force state of the virtual interventional instrument. This information is the second state data. For example, the motion trajectory, position coordinates, and simulated calculated head end force magnitude and direction of the virtual interventional instrument in the virtual vascular model.

[0056] For step S5, a multimodal data fusion method, such as a Kalman filter algorithm, is used to fuse the first state data and the second state data, thereby obtaining fused state data, which generally includes more accurate information on the head end force and head end position of the interventional device. Taking the head end force as an example, a head end force A is obtained through the API program interface and the elastic simulation environment in the virtual intervention environment, and another head end force B is obtained according to the DSA image. The Kalman filter method assigns different weights according to the credibility of different data to fit a more accurate head end force value.

[0057] In this implementation, a data acquisition system based on virtual-reality interaction is constructed. By constructing a virtual intervention scene, the surgical operation process can be simulated in a virtual environment, providing comparison and reference for real surgery. Based on the control instructions of the main controller, the synchronous movement of the instruments in the real and virtual intervention scenes is realized, and the state data in the two scenes are obtained respectively. By integrating the two, the rich physical simulation information in the virtual intervention scene and the actual image information in the real intervention scene can be fully utilized to complement each other. For example, the three-dimensional position information and force calculation in the virtual intervention scene can supplement the deficiencies of the two-dimensional image in the real intervention scene in spatial information and force estimation, while the actual image in the real intervention scene can correct the model deviation that may exist in the virtual intervention scene. The final fusion state data is more accurate and reliable, which helps doctors to more accurately grasp the state of the interventional instrument during surgery, improve the safety and accuracy of surgical operations, and provide a more powerful basis for predicting surgical risks, thereby improving the quality and success rate of the entire interventional surgery.

[0058] Reference Figure 2 In one embodiment, after the step S5 of fusing the first state data and the second state data to obtain the fused state data, the following steps are included:

[0059] S6, modifying the first state data based on the fusion state information; and

[0060] S7. Modify the second state data based on the fusion state information.

[0061] For step S6, after obtaining the fusion state data, the first state data obtained in the real interventional scene is corrected with reference to the accurate information in the fusion state data. In the process of interventional device segmentation, the traditional algorithm based solely on the AI ​​model may make recognition errors due to only image information, such as mistaking blood vessels for interventional devices. The fusion state data contains physical attribute information in the virtual environment (such as the collision between the interventional device and the vascular model, etc.). By comparing the two-dimensional image obtained in the virtual environment according to the registration situation (labeled with the interventional device projection and fused with physical attributes) with the actual DSA image, the erroneous judgments about the identification and position of the interventional device, force, etc. in the first state data can be corrected, thereby making the first state data more accurate.

[0062] For step S7, the second state data in the virtual interventional scene is also optimized based on the fused state data. Although the virtual environment can simulate and calculate the state data of the interventional instrument, there may be problems such as model error or calculation deviation. The fused state data integrates the actual situation in the real interventional scene, such as the motion feedback of the real interventional instrument. This information can be used to adjust the parameters in the virtual interventional instrument model, and correct the second state data such as the three-dimensional position and head end force of the virtual interventional instrument to make it more in line with the actual surgical situation.

[0063] In this embodiment, by correcting the first state data and the second state data based on the fusion state information, the accuracy of the two state data can be continuously improved. For the first state data, the corrected result can provide doctors with a more reliable decision-making basis in real surgical operations and reduce surgical risks caused by erroneous data. For the second state data, the optimized virtual intervention scene data can more accurately simulate the surgical process and improve the guiding significance of the virtual environment for real surgery. For example, in subsequent surgical simulations and rehearsals, the corrected virtual intervention scene data can more realistically reflect the situations that may occur during surgery, help doctors better plan surgical plans, and improve the success rate and safety of surgery. At the same time, it also makes the virtual-reality state synchronization more accurate, and the intelligent perception ability of the entire interventional surgical robot system is further improved.

[0064] In one embodiment, the step S1 of constructing a virtual intervention scene corresponding to the real intervention scene includes:

[0065] S11, obtain the interventional device data, slave robot data and patient's preoperative CAT data in the real interventional scenario;

[0066] S12, constructing the virtual interventional instrument in the virtual interventional scene according to the interventional instrument data;

[0067] S13, constructing virtual slave robot data in the virtual intervention scene according to the slave robot data;

[0068] S14. Constructing a blood vessel model in the virtual intervention scene according to the computed tomography angiography data.

[0069] The above-mentioned interventional device data covers the model, specifications, physical properties and other information of the device, which is crucial for accurately building virtual interventional devices in a virtual environment. The slave robot data includes the specific parameters of the robot's delivery cart, delivery rollers, delivery rails and other structures, which are used to build a virtual slave robot to achieve synchronous simulation of the motion of the virtual and real robots. The patient's preoperative CAT data provides detailed structural information of the patient's blood vessels and is the basic data for building a vascular model. For example, for a specific vascular interventional surgery, the interventional device data can clarify the length, diameter, etc. of the device, the slave robot data determines how the virtual robot simulates the motion of the real robot, and the CAT data clearly shows the direction and branches of the patient's blood vessels.

[0070] Using the acquired interventional device data, a virtual interventional device model similar to the real interventional device in appearance, physical properties, etc. is created in a virtual environment. The model can simulate motion according to control instructions in the virtual intervention scene, and its interaction with the virtual vascular model can also accurately reflect the situation in the real operation. For example, according to the elastic modulus and other material parameters of the real interventional device, the corresponding properties are set in the virtual model so that its behavior such as force deformation in the virtual environment is consistent with that of the real device.

[0071] Based on the slave robot data, a virtual slave robot model and its related motion control logic are constructed. The movement mode and state of this virtual slave robot in the virtual intervention scene are consistent with those of the real slave robot, receiving the same control instructions and feeding back similar state information. For example, the movement of the delivery cart of the real slave robot is accurately simulated in the virtual slave robot model, so as to fully display the operating status of the surgical robot system in the virtual environment.

[0072] According to the CAT data, the vascular model in the virtual interventional scene is constructed. First, the three-dimensional vascular model is reconstructed according to the CAT data. The model can present the three-dimensional structure of the blood vessel. Then the thickness of the vascular wall in the three-dimensional vascular model is increased to make it a volume model. This step helps to more accurately simulate the physical properties of the blood vessels, such as elasticity. Finally, the volume model is imported into the preset simulation software (such as SOFA simulation software), and the elastic physical collision parameters are given to the vascular model to construct a complete vascular model. The vascular model constructed in this way can perform real collision simulation with virtual interventional instruments, providing a closer to real environment for surgical simulation.

[0073] In this embodiment, by accurately acquiring and utilizing various types of data in the real intervention scene to construct corresponding elements in the virtual intervention scene, a high degree of correspondence between the virtual intervention scene and the real intervention scene is achieved. The construction of virtual interventional instruments forms a digital twin system of the real intervention scene. The construction of virtual slave robot data helps to optimize the robot's control algorithm and motion planning. The accurate vascular model can not only simulate the deformation and force conditions of blood vessels during surgery, but also provide accurate environmental references for the operation of interventional instruments, thereby improving the accuracy and safety of the surgery. The construction of the entire virtual intervention scene provides doctors with an intuitive and realistic surgical platform, which helps to improve doctors' understanding and control of the surgical process, thereby improving the success rate of the operation.

[0074] In one implementation, the step S14 of constructing the blood vessel model in the virtual intervention scene according to the computed tomography angiography data comprises:

[0075] S141, reconstructing a three-dimensional blood vessel model according to the computed tomography angiography data;

[0076] S142, increasing the thickness of the blood vessel wall in the three-dimensional blood vessel model to obtain a volume model;

[0077] S143, importing the body model into a preset simulation software to obtain the blood vessel model, wherein the simulation software assigns elastic physical collision parameters to the blood vessel model.

[0078] CAT data contains the tomographic information of the patient's blood vessels. Through specific image processing algorithms and three-dimensional reconstruction technology, these two-dimensional tomographic information is converted into a three-dimensional vascular model. This process requires accurate extraction and reconstruction of information such as the vascular contour and branch structure in the CAT data to generate a three-dimensional model that can accurately reflect the true morphology of the patient's blood vessels. For example, for complex cerebrovascular structures, the reconstruction algorithm needs to identify the starting point, direction and connection relationship of each vascular branch, so as to construct a complete three-dimensional cerebrovascular model. On the basis of the reconstructed three-dimensional vascular model, the thickness of the vascular wall is increased to transform it from a simple surface model to a volume model with a certain volume. The importance of this step lies in that the real vascular wall has a certain thickness and physical properties. The volume model with increased thickness can more accurately simulate the mechanical behavior of the vascular wall when it is affected by interventional instruments, such as elastic deformation, collision response, etc. For example, in vascular interventional surgery, contact and interaction between interventional instruments and vascular walls are common situations, and the volume model can more realistically reflect this interaction process. Import the constructed volume model into the selected simulation software (such as SOFA simulation software), which has powerful physical simulation functions. During the import process, the software will assign elastic physical collision parameters to the vascular model according to the preset parameter configuration, including material parameters such as elastic modulus and Poisson's ratio of the vascular wall, as well as collision detection and response algorithms. In this way, the vascular model can perform a real elastic collision simulation with the virtual interventional instrument in the virtual environment. When the virtual interventional instrument contacts or collides with the vascular model, the software can accurately calculate the deformation and force of the vascular wall according to the set parameters, providing a highly realistic vascular environment for surgical simulation.

[0079] This embodiment proposes a specific construction process that focuses on the vascular model, which plays a key role in improving the authenticity and practicality of the virtual interventional scene. Through accurate three-dimensional vascular model reconstruction, detailed information on the patient's vascular structure can be provided to doctors, which is helpful for surgical planning and risk assessment. Increasing the thickness of the vascular wall to obtain a volume model makes the virtual blood vessel closer to the real blood vessel in terms of physical simulation, thereby improving the accuracy of the surgical simulation. By importing the volume model into the simulation software and assigning elastic physical collision parameters, a real interactive simulation of blood vessels and interventional instruments in a virtual environment is achieved. Doctors can more intuitively observe the operation process of interventional instruments in blood vessels in the virtual intervention scene, evaluate the impact of different operations on blood vessels, discover potential risks in advance, and optimize surgical plans, thereby improving the safety and success rate of interventional surgery.

[0080] In one embodiment, the step S5 of fusing the first state data and the second state data to obtain fused state data includes:

[0081] S51. Fusing the first state data and the second state data by a multimodal data fusion method to obtain the fused state data.

[0082] For step S51, the multimodal data fusion method is a technical means to comprehensively process data from different data sources or different modes to obtain more accurate and comprehensive information. In an embodiment, the first state data comes from DSA image analysis and interventional instrument tail force detection in a real intervention scene, and the second state data is obtained through simulation calculation in a virtual intervention scene. These data from different sources have their own characteristics and advantages, but also have certain limitations. For example, DSA images provide two-dimensional image information in real surgical scenes, but may not be accurate enough in terms of spatial positioning and force calculation; and although the data in the virtual intervention scene can be accurately calculated in three dimensions, there may be a problem that the model does not fully match the actual situation. A multimodal data fusion method such as the Kalman filtering algorithm is used to fuse the two state data. The Kalman filtering algorithm is based on the linear system state equation and estimates the optimal state of the system by predicting the system state and correcting the measured value. In this application, it takes the first state data (such as the head force B estimated based on DSA images) and the second state data (such as the head force A obtained through the virtual environment API) as input, assigns different weights according to the credibility of the two data (such as the calculation accuracy of the data in the virtual environment, the resolution of the DSA image, etc.), and then calculates a fused head force value. Similar fusion methods are also used for other state data such as the position information of the interventional device to obtain fused state data.

[0083] In this embodiment, by integrating the data from real and virtual interventional scenarios, the advantages of the two types of data are fully utilized and their respective shortcomings are compensated. The fused state data can provide doctors with more accurate surgical decision-making basis. For example, during the operation of interventional instruments, more accurate prediction of the force on the head end can help doctors better control the operation force and avoid unnecessary damage to blood vessels; more accurate head end position information can help improve the positioning accuracy of interventional instruments in blood vessels and reduce operational errors. This not only improves the safety of surgery, but also improves surgical efficiency, shortens operation time, and makes interventional surgical robots more reliable and intelligent in complex surgical operations, further promoting the development of interventional surgical technology.

[0084] In one embodiment, after the step S1 of constructing a virtual intervention scene corresponding to the real intervention scene, the following steps are included:

[0085] S10. Generate a visualized virtual intervention scene using computer graphics technology and visualization algorithms.

[0086] For step S10, after the virtual intervention scene corresponding to the real intervention scene is constructed, various elements in the virtual intervention scene, such as virtual intervention instruments, virtual blood vessel models, virtual slave robots, etc., are displayed in an intuitive graphical interface using computer graphics technology and visualization algorithms. This includes converting the three-dimensional model into a two-dimensional image and displaying it on the screen, while enhancing the realism and three-dimensionality of the scene through effects such as color, light and shadow. For example, the virtual blood vessel model can be represented by colors such as red or blue, and the virtual intervention instrument can be displayed in gray or silver, and dynamically rendered according to its position and state in the scene, such as when the intervention instrument moves in the blood vessel, its position in the visualization interface is updated in real time. Furthermore, various data can also be displayed on the screen, such as the specific value of the force on the head end.

[0087] Visualizing virtual interventional scenes is of great significance in many aspects. For doctors, visualized virtual interventional scenes provide an intuitive surgical rehearsal and real-time monitoring platform. Doctors can plan surgical paths, assess surgical risks, and develop more optimized surgical plans in advance by observing instrument operations and vascular reactions in virtual interventional scenes before surgery. During the operation, the visualization interface can display the interaction between virtual instruments and vascular models in real time, helping doctors better understand the surgical process and adjust the operation strategy in time. At the same time, the visualized virtual interventional scenes also facilitate communication and collaboration between doctors and other medical personnel to jointly discuss the improvement and optimization of surgical plans. From the perspective of the overall surgical system, visualization enhances the friendliness of human-computer interaction, makes it easier for doctors to accept and operate the interventional surgical robot system, improves the practicality and ease of use of the system, and helps promote the widespread application of interventional surgical robots in clinical practice.

[0088] In one embodiment, the method for acquiring status data of the interventional device further includes:

[0089] S7, obtaining data from each sensor on the slave robot;

[0090] S8. judging, based on the sensor data, whether a component corresponding to each sensor data fails;

[0091] S91, if yes, mapping the failed component to the visualized virtual intervention scene;

[0092] S92: If not, then displaying the components of the slave robot normally in the virtual intervention scene.

[0093] The slave robot is equipped with a variety of sensors, such as position sensors and force sensors, which collect the operating status data of each component of the slave robot in real time. The data collected by each sensor is analyzed by the preset fault judgment algorithm and threshold to determine whether the component corresponding to the sensor has failed. For example, for force sensor data, if the detected force value exceeds the normal operating range, or the force change trend is abnormal (such as sudden increase or decrease), it may indicate that the components related to the force sensor, such as the transmission device, are at risk of failure. If the position sensor data has a large deviation or unstable data jump, it may indicate that the corresponding moving parts have problems such as inaccurate positioning or motion jamming. Once a component is determined to be faulty, the faulty component is displayed in a striking manner in the virtual intervention scene through a specific mapping algorithm and graphic identification technology. For example, in the visual interface, the faulty motor is marked with a red flashing icon, or a warning box is displayed around the faulty component, with the fault type and related prompt information attached. In this way, when doctors observe the virtual intervention scene, they can intuitively see the location and type of the fault and take corresponding measures in time, such as suspending the operation, performing emergency repairs, or adjusting the surgical strategy. Of course, if the data of each sensor is normal, it means that no component is faulty. Therefore, in the virtual intervention scene, the components of the slave robot can be displayed normally without special marking processing.

[0094] This embodiment can timely discover potential hidden faults by acquiring sensor data from the end robot in real time and performing fault judgment, thereby preventing the fault from further deteriorating during the operation and causing serious harm to the patient. Mapping the faulty components to a visual virtual intervention scene provides doctors with an intuitive and convenient way to display fault information, allowing doctors to quickly locate faults without complex data analysis, greatly improving the efficiency of fault handling. During the operation, this fast and accurate fault feedback mechanism helps doctors to adjust surgical operations in a timely manner, reduce surgical risks caused by robot failures, and ensure the smooth progress of the operation. At the same time, it also facilitates the technical staff of the surgical team to monitor and maintain the robot in real time, improves the stability and maintainability of the entire surgical system, and promotes the safety and effectiveness of interventional surgical robots in clinical applications.

[0095] In one embodiment, after step S5 of fusing the first state data and the second state data to obtain fused state data, the method includes:

[0096] S51, using the fusion status data to predict whether there is a risk in surgery;

[0097] S52: If yes, generate a reminder message;

[0098] S53: If not, obtain new fusion status data and continue to predict whether the surgery has risks.

[0099] The fusion status data contains comprehensive information about the interventional device in real and virtual intervention scenarios, such as the position of the head end, force conditions, and motion trajectory. By analyzing these data, potential risks during the operation can be evaluated. For example, based on the fused head end force data, if the force value is found to be close to or exceeds the limit that the blood vessel wall can withstand, or the force changes are abnormally drastic, it may indicate the risk of blood vessel rupture. Judging from the position and motion trajectory of the interventional device, if it deviates from the preset safe operating area, or is too close to the blood vessel wall or other tissues and organs, it may also lead to risks such as collision and perforation. In addition, through trend analysis of fusion status data over a period of time, such as sudden changes in the movement speed of the interventional device and continuous increase in force, surgical risks can also be predicted in advance.

[0100] When it is predicted that there is a risk in the operation, the system will automatically generate a reminder message. The reminder message can be in various forms, such as a striking warning box popping up on the above-mentioned visual screen to display the type and severity of the risk; at the same time, a sound alarm is issued to attract the doctor's attention; and text prompts can be used to explain in detail the cause of the risk and possible countermeasures. For example, if the risk of blood vessel rupture is predicted, the warning box may display "Risk of blood vessel rupture: The head end is under too much force, please adjust the operating force", the sound alarm sounds, and specific operational suggestions are given in the prompt bar next to it, such as "Slowly withdraw the interventional device to reduce the pressure on the head end". When it is predicted that there is no risk in the operation, the current state will be maintained, and new fusion status data will continue to be obtained to continue to predict whether there is a risk in the operation. There is no need to issue other alarm reminders.

[0101] Using fusion state data for risk prediction gives full play to the advantages of multimodal data fusion and improves the accuracy and reliability of risk prediction. Timely generation of reminder information enables doctors to understand the possible risks during surgery at the first time, so that they can respond quickly, adjust surgical operations, and avoid the occurrence of risk events. This helps to reduce the incidence of surgical complications, improve the success rate of surgery, and protect the lives of patients. At the same time, this risk prediction and reminder mechanism also reduces the mental stress of doctors during surgery, allowing doctors to focus more on surgical operations and improve the quality of surgery. In the entire interventional surgical robot system, this function enhances the intelligence and safety of the system and promotes interventional surgery to develop in a more precise and safe direction.

[0102] Reference Figure 3 The embodiment of the present invention further provides a computer device, the internal structure of which can be as follows Figure 3As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. Among them, the processor designed for the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating device, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data related to the present application, etc. The network interface of the computer device is used to communicate with an external terminal through a network connection. Furthermore, the above-mentioned computer device can also be provided with an input device and a display screen, etc. When the above-mentioned computer program is executed by the processor, a method for acquiring status data of an interventional instrument is implemented, comprising the following steps:

[0103] Constructing virtual intervention scenarios that correspond to real intervention scenarios;

[0104] Control instructions generated by the controller;

[0105] Controlling the movement of the interventional instrument in the real interventional scene according to the control instruction, acquiring corresponding DSA images, and obtaining first state data of the interventional instrument in the real interventional scene based on the DSA images;

[0106] controlling the movement of the virtual interventional instrument in the virtual interventional scene according to the control instruction, and acquiring second state data of the virtual interventional instrument;

[0107] The first state data and the second state data are fused to obtain fused state data.

[0108] An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, a method for acquiring status data of an interventional device is implemented, comprising the following steps:

[0109] Constructing virtual intervention scenarios that correspond to real intervention scenarios;

[0110] Control instructions generated by the controller;

[0111] Controlling the movement of the interventional instrument in the real interventional scene according to the control instruction, acquiring corresponding DSA images, and obtaining first state data of the interventional instrument in the real interventional scene based on the DSA images;

[0112] controlling the movement of the virtual interventional instrument in the virtual interventional scene according to the control instruction, and acquiring second state data of the virtual interventional instrument;

[0113] The first state data and the second state data are fused to obtain fused state data.

[0114] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided in this application and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0115] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0116] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for acquiring status data of an interventional device, characterized in that: The method comprises the steps of: Constructing virtual intervention scenarios that correspond to real intervention scenarios; Obtain control instructions generated by the master controller; Controlling the movement of the interventional instrument in the real interventional scene according to the control instruction, acquiring a corresponding digital subtraction angiography image, and obtaining first state data of the interventional instrument in the real interventional scene based on the digital subtraction angiography image; controlling the movement of the virtual interventional instrument in the virtual interventional scene according to the control instruction, and acquiring second state data of the virtual interventional instrument; The first state data and the second state data are fused to obtain fused state data.

2. The method for acquiring status data of an interventional device according to claim 1, characterized in that: After the step of fusing the first state data and the second state data to obtain fused state data, the method further comprises: The first state data is modified based on the fused state information, and the second state data is modified based on the fused state information.

3. The method for acquiring status data of an interventional device according to claim 1, characterized in that: The step of constructing a virtual intervention scene corresponding to the real intervention scene includes: Acquire interventional device data, slave robot data, and preoperative computed tomography angiography data of patients in real interventional scenarios; constructing the virtual interventional instrument in the virtual interventional scene according to the interventional instrument data; constructing virtual slave robot data in the virtual intervention scene according to the slave robot data; A blood vessel model in the virtual intervention scene is constructed according to the computed tomography angiography data.

4. The method for acquiring status data of an interventional device according to claim 3, characterized in that: The step of constructing a blood vessel model in the virtual intervention scene according to the computed tomography angiography data comprises: reconstructing a three-dimensional blood vessel model based on the computed tomography angiography data; increasing the thickness of the blood vessel wall in the three-dimensional blood vessel model to obtain a volume model; The body model is imported into a preset simulation software to obtain the blood vessel model, wherein the simulation software assigns elastic physical collision parameters to the blood vessel model.

5. The method for acquiring status data of an interventional device according to claim 1, characterized in that: The step of fusing the first state data and the second state data to obtain fused state data comprises: The first state data and the second state data are fused by a multimodal data fusion method to obtain the fused state data.

6. The method for acquiring status data of an interventional device according to any one of claims 1 to 5, characterized in that: After the step of constructing a virtual intervention scene corresponding to the real intervention scene, the method further comprises: Computer graphics technology and visualization algorithms are used to generate visualized virtual intervention scenes.

7. The method for acquiring status data of an interventional device according to claim 6, characterized in that: The method further comprises: Obtain data from each sensor on the slave robot; According to each of the sensor data, determining whether a component corresponding to each of the sensor data fails; If so, mapping the failed component into the visualized virtual intervention scene; If not, the components of the slave robot are displayed normally in the virtual intervention scene.

8. The method for acquiring status data of an interventional device according to any one of claims 1 to 5, characterized in that: After the step of fusing the first state data and the second state data to obtain fused state data, the method further comprises: Using the fusion status data, predicting whether there is a risk in surgery; If so, a reminder message is generated; If not, new fusion status data is obtained to continue predicting whether the surgery is risky.

9. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the method for acquiring status data of an interventional device according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for acquiring status data of an interventional device according to any one of claims 1 to 8 are implemented.

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