Transcranial magnetic stimulation robot hybrid calibration method, device, equipment, medium and product

Abstract

The invention discloses a transcranial magnetic stimulation robot hybrid calibration method and device, equipment, a medium and a product, and relates to the field of transcranial magnetic stimulation, and the method comprises the steps: obtaining a first transformation matrix from a camera coordinate system to a robot base coordinate system, so as to achieve the global calibration of a camera and a robot base; acquiring a second transformation matrix from the coil marker coordinate system to the coil coordinate system to realize local calibration of the coil; on the basis of global calibration of the camera and the robot base, global preliminary positioning from a robot end effector to the head of the patient is completed; and based on the local calibration of the coil and the global preliminary positioning, completing local positioning of the brain stimulation target from the coil to the head of the patient, thereby improving the positioning precision of the brain stimulation target.

Description

Technical Field

[0001] The present application relates to the field of transcranial magnetic stimulation, and in particular to a transcranial magnetic stimulation robot hybrid calibration method, device, equipment, medium and product. Background Art

[0002] Transcranial magnetic stimulation (TMS) is an advanced method that utilizes electromagnetic fields to achieve noninvasive neuromodulation. Its unique non-invasive nature and significant therapeutic effects make it a valuable tool for clinical psychiatric intervention, neurological function testing, and research on higher-order cognitive mechanisms. Traditional TMS systems rely primarily on operator experience for spatial positioning. This manual positioning approach presents significant technical bottlenecks: the operator relies on visual feedback for spatial calibration, resulting in low spatial matching accuracy.

[0003] To address the issues with traditional TMS systems, researchers have developed a robot-assisted TMS system, which uses calibration to determine the spatial relationship between the robot's arm and visual sensor. Existing calibration methods are based on a hand-eye calibration model, solving equations to establish the spatial mapping relationship between the arm base, end effector (TMS coil), and camera coordinate system. The calibration process relies on the kinematic parameters of the robot itself. However, errors inherent in the robot (such as manufacturing tolerances and joint backlash) can directly affect the calibration results, resulting in calibration accuracy that does not meet the requirements of TMS treatment. Summary of the Invention

[0004] The purpose of this application is to provide a hybrid calibration method, device, equipment, medium and product for a transcranial magnetic stimulation robot, which can improve the positioning accuracy of brain stimulation targets.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a hybrid calibration method for a transcranial magnetic stimulation robot, comprising:

[0007] Get the first transformation matrix from the camera coordinate system to the robot base coordinate system To achieve global calibration of the camera and robot base;

[0008] Get the second transformation matrix from the coil marker coordinate system to the coil coordinate system To achieve local calibration of the coil;

[0009] Based on the global calibration of the camera and the robot base, a preliminary global positioning of the robot end effector to the patient's head is completed;

[0010] Based on the local calibration of the coil and the global preliminary positioning, local positioning of the coil to the brain stimulation target on the patient's head is completed.

[0011] In a second aspect, the present application provides a transcranial magnetic stimulation robot hybrid calibration device, comprising:

[0012] The first acquisition module is used to obtain the first transformation matrix from the camera coordinate system to the robot base coordinate system To achieve global calibration of the camera and robot base;

[0013] The second acquisition module is used to obtain the second transformation matrix from the coil marker coordinate system to the coil coordinate system To achieve local calibration of the coil;

[0014] A first positioning module is used to complete the global preliminary positioning of the robot end effector to the patient's head based on the global calibration of the camera and the robot base;

[0015] The second positioning module is used to complete the local positioning of the coil to the brain stimulation target on the patient's head based on the local calibration of the coil and the global preliminary positioning.

[0016] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the transcranial magnetic stimulation robot hybrid calibration method described in any one of the above.

[0017] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the above-mentioned transcranial magnetic stimulation robot hybrid calibration methods.

[0018] In a fifth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of any one of the above-mentioned transcranial magnetic stimulation robot hybrid calibration methods.

[0019] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0020] The present application provides a transcranial magnetic stimulation robot hybrid calibration method, device, equipment, medium and product. The method obtains the first transformation matrix from the camera coordinate system to the robot base coordinate system. To achieve global calibration of the camera and the robot base; obtain the second transformation matrix from the coil marker coordinate system to the coil coordinate system To achieve local calibration of the coil; based on the global calibration of the camera and the robot base, complete the global preliminary positioning of the robot end effector to the patient's head; based on the local calibration of the coil and the global preliminary positioning, complete the local positioning of the coil to the brain stimulation target on the patient's head. The present application establishes a mapping relationship between the camera coordinate system and the robot base coordinate system through a first transformation matrix to achieve coordinate unification in the global space, ensuring that the robot and the camera work in the same reference frame and avoiding systematic errors caused by inconsistent coordinate systems. Based on the global calibration results, the robot end effector can quickly locate the approximate area of ​​the patient's head and control the initial error within a reasonable range. On the basis of the global preliminary positioning, the system can further adjust the coil to the accuracy of the brain stimulation target through local calibration. Global positioning provides a feasible search space for local positioning, while local positioning makes up for the shortcomings of global positioning in details, forming a complementary effect. The hybrid global positioning and local positioning method improves the positioning accuracy of the brain stimulation target. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 This is a diagram of an application environment for a hybrid calibration method for a transcranial magnetic stimulation robot in one embodiment of the present application;

[0023] Figure 2 A schematic flow chart of a hybrid calibration method for a transcranial magnetic stimulation robot provided in one embodiment of the present application;

[0024] Figure 3 This is the structural diagram of the transcranial magnetic stimulation robot for this application;

[0025] Figure 4 This is a schematic diagram of the global calibration of the camera and robot base in this application;

[0026] Figure 5 This is a schematic diagram of the global initial positioning motion control for this application;

[0027] Figure 6 This is a schematic diagram of the end coil calibration for this application;

[0028] Figure 7 This is a schematic diagram of local positioning motion control for this application;

[0029] Figure 8A functional module schematic diagram of a transcranial magnetic stimulation robot hybrid calibration device provided by an embodiment of the present application is shown in the figure.

[0030] Figure 9 A structural schematic diagram of a computer device provided by an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0031] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0032] The above purposes, features and advantages of the present application can be more obvious and easy to understand. The present application will be further described in detail below with reference to the drawings and specific embodiments.

[0033] The transcranial magnetic stimulation robot hybrid calibration method provided by the embodiments of the present application can be applied in an application environment as shown in the figure. Figure 1 The terminal 102 communicates with the server 104 through a network. The data storage system can store data required to be processed by the server 104. The data storage system can be separately arranged, can be integrated on the server 104, or can be placed on a cloud or other server. The terminal 102 can send the first transformation matrix and the second transformation matrix to be processed to the server 104. After receiving the first transformation matrix and the second transformation matrix to be processed, the server 104 completes the global preliminary positioning of the robot end effector to the patient's head based on the global calibration of the camera and the robot base for the first transformation matrix and the second transformation matrix to be processed; and completes the local positioning of the coil to the brain stimulation target point of the patient's head based on the local calibration of the coil and the global preliminary positioning. The server 104 can feed back the obtained local calibration of the coil and the global preliminary positioning to the terminal 102. In addition, in some embodiments, the transcranial magnetic stimulation robot hybrid calibration method can also be implemented by the server 104 or the terminal 102 alone, for example, the terminal 102 can directly process the first transformation matrix and the second transformation matrix to be processed, or the server 104 can obtain the first transformation matrix and the second transformation matrix to be processed from the data storage system and process the first transformation matrix and the second transformation matrix to be processed.

[0034] The terminal 102 can be, but is not limited to, various desktop computers, notebook computers, smart phones, tablet computers and the like. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers, and can also be a cloud server.

[0035] The existing calibration technology of transcranial magnetic stimulation (TMS) robots faces multiple bottlenecks, which are rooted in the static characteristics of the calibration framework and the excessive dependence on the kinematic model of the robot. The traditional hand-eye calibration method is based on the assumption of a static coordinate system, which requires the camera and the robot base to maintain an absolutely fixed relationship. However, in actual clinical operations, factors such as intraoperative device displacement and visual angle adjustment can easily cause disturbances in the camera pose. Once such situations occur, the pre-calibrated parameters will be completely invalid, and the operator will have to repeatedly perform the multi-pose calibration process. This process not only consumes a lot of time and significantly reduces surgical efficiency, but also significantly increases the risk of collision with the patient due to the frequent large-scale movement of the robot, which poses a potential safety hazard for clinical applications.

[0036] From a technical principle perspective, existing calibration methods highly depend on the kinematic parameters of the robot itself, such as joint angles and link lengths. Although robots usually have high repeatability positioning accuracy of 0.02-0.15 mm, their absolute positioning error can be as high as 8 mm due to factors such as manufacturing tolerances and joint backlash. These errors will directly pass to the calibration results, making it difficult for the final positioning accuracy to meet the stringent requirements of TMS treatment for sub-millimeter accuracy. Even with complex compensation algorithms, the residual errors of calibration are still difficult to control effectively, which severely restricts the precision of treatment effectiveness.

[0037] In addition, a single calibration strategy has significant limitations in function implementation and cannot meet the dual requirements of large-scale fast positioning and end-of-arm fine operation. Global calibration can cover the entire workspace of the robot, but its accuracy is limited by the absolute error of the robot, making it difficult to meet the high-precision requirements of TMS treatment for target positioning. While end-of-arm local calibration can achieve higher positioning accuracy, it is prone to losing the marker due to the limited visual tracking range, resulting in the interruption of the calibration process. This contradiction makes it difficult for existing calibration technology to achieve an ideal balance in clinical applications.

[0038] To address the above technical problems, the present disclosure constructs a dynamic and efficient calibration system by integrating the technical advantages of base-camera offline global calibration (BCGC) and end-coil precise calibration (ECPC) in stages. The BCGC stage uses offline calibration to establish a global coordinate mapping between the camera and the robot base in a stable environment, providing a reliable base coordinate system for subsequent operations. The ECPC stage focuses on the fine calibration of the end effector, dynamically adjusts the coil position through real-time visual feedback, and ensures high-precision positioning of the stimulation target. This hierarchical calibration mechanism not only effectively avoids the shortcomings of traditional methods but also achieves a synergistic optimization of positioning accuracy and operational efficiency, providing a new technical path for the precision and intelligent development of TMS treatment.

[0039] In an exemplary embodiment, as shown in Figure 2 As shown, a hybrid calibration method for transcranial magnetic stimulation robot is provided. The method is executed by a computer device, specifically a computer device such as a terminal or a server, or a terminal and a server. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate the process, including the following steps S201 to S204.

[0040] In step S201, the first transformation matrix from the camera coordinate system to the robot base coordinate system is obtained To achieve global calibration of the camera and robot base.

[0041] In one embodiment, the step S201 of "obtaining the first transformation matrix between the camera coordinate system and the robot base coordinate system" ” includes the following sub-steps S2011-S2015:

[0042] Step S2011: Obtain the third transformation matrix from the camera coordinate system to the base marker coordinate system

[0043] Step S2012: Obtain the fourth transformation matrix from the camera coordinate system to the end marker coordinate system

[0044] Step S2013: Obtain the fifth transformation matrix from the robot base coordinate system to the end flange coordinate system

[0045] Step S2014: According to the third transformation matrix The fourth transformation matrix And the fifth transformation matrix Determine the sixth transformation matrix from the base marker coordinate system to the robot base coordinate system

[0046] Step S2015: According to the third transformation matrix and the sixth transformation matrix Determine the first transformation matrix

[0047] Specifically, if Figure 3-4As shown, a rigid marker with an optical tracking mark is fixedly installed on the robot base (or trolley), namely the base marker 1, and the base marker coordinate system is recorded as basemark, ensuring that the basemark and the robot base coordinate system (base) maintain a rigid connection. At the same time, another set of rigid markers is installed at the end flange of the robot, namely the end marker 3, and the end marker coordinate system is recorded as efmark, which is used to establish the association between the end flange coordinate system (ef) and the camera coordinate system (cam).

[0048] Need to explain, such as Figure 4 As shown, during visual measurement and data acquisition, an external optical tracking camera is used to measure and obtain the third transformation matrix from the camera coordinate system to the base marker coordinate system in real time. At the same time, measure and obtain the fourth transformation matrix from the camera coordinate system to the end marker coordinate system Through the robot control system, read or obtain the fifth transformation matrix from the robot base coordinate system to the end flange coordinate system Among them, the third transformation matrix, the fourth transformation matrix and the fifth transformation matrix are all homogeneous transformation matrices.

[0049] The sixth transformation matrix from the base marker coordinate system to the robot base coordinate system Calculated by the following formula:

[0050]

[0051] in, Indicates the rigid body transformation from the end marker coordinate system to the end flange coordinate system. If the CAD model of the end marker is known, it can be calculated directly from the model data. This simplifies the calibration process. You can also use the classic hand-eye calibration method (such as the AX=ZB model) to drive the robot to multiple different postures and collect multiple sets of corresponding and Finally, Stored as a global transformation matrix.

[0052] During the treatment process, if the camera position deviates, the system will track the position of the base marker in real time. The mapping relationship between the base coordinate system and the camera coordinate system is dynamically updated using the following formula:

[0053]

[0054] This mechanism ensures that the accuracy of the transformation matrix is ​​maintained even if the camera moves, without the need to repeat the calibration process.

[0055] It should be noted that in the global calibration calculation, the homogeneous transformation matrix used (i.e. the transformation from the robot base coordinate system to the end flange coordinate system obtained from the robot control system) includes the robot absolute positioning error, so the calculated global transformation matrix There is a large error.

[0056] In step S202, the second transformation matrix from the coil marker coordinate system to the coil coordinate system is obtained. To achieve local calibration of the coil.

[0057] The End Effector Coil Precision Calibration (ECPC) module, described in this paper, aims to overcome the limitations of the Base-Camera Global Calibration (BCGC) method, which incorporates the robot's absolute positioning errors. This module instead achieves high-precision calibration that relies solely on the geometry and optical measurements of the end effector (e.g., the TMS coil). The core concept is that the calibration results should be independent of the robot's kinematic errors.

[0058] In one embodiment, the step S202 of "obtaining a second transformation matrix from the coil marker coordinate system to the coil coordinate system" ” includes the following sub-steps S2021-S2026:

[0059] Step S2021: Establish a coil coordinate system with the center of the robot end flange as a reference.

[0060] Specifically, the coil's CAD model is used to precisely define the coil's functional coordinate system, which is typically established with the center of the robot's end flange as a reference.

[0061] Step S2022: Determine at least three rigid identification points P for the coil structure in the coil coordinate system. coilref .

[0062] Specifically, a set of (at least 3) rigid marking points P with known spatial positions are pre-designed and determined on the coil structure. coilref The coordinates of these points are known in the defined coil coordinate system.

[0063] Step S2023: According to the rigid identification point P coilref , determine each rigid identification point P coilref The measured coordinate P in the coil marker coordinate system gain .

[0064] Specifically, if Figure 6 As shown in the figure, the optical tracking probe touches the physical marking points on the coil in sequence to obtain the measured coordinates P in the end rigid body marker coordinate system. gain :

[0065]

[0066] wherein, represents a fourteenth transformation matrix of the camera coordinate system to the probe coordinate system, P pointer represents a needle tip point in the probe coordinate system.

[0067] Step S2024, according to the coil CAD model, obtaining the theoretical coordinates P coilref of the rigid marking points in the coil coordinate system.

[0068] Step S2025, according to the least squares rigid registration algorithm, reducing the error between the measured coordinates P gain and the theoretical coordinates P coilref , and determining the first rotation matrix and the first translation vector when the error between the measured coordinates and the theoretical coordinates is minimized.

[0069] Specifically, the goal of the least squares rigid registration algorithm is to find a suitable rotation matrix R1and translation vector t1, by calculating the error sum of squares between the results of P gain after R1rotation and t1translation and the theoretical coordinates P coilref , by continuously adjusting R1and t1through mathematical optimization methods (such as singular value decomposition), the error sum of squares is minimized, when the error cannot be reduced, at this time, the corresponding R1is the first rotation matrix, t1is the first translation vector, thereby realizing the error reduction and optimal transformation parameter determination of the measured coordinates and the theoretical coordinates.

[0070] wherein, the least squares rigid registration is represented by the following formula:

[0071]

[0072] Step S2026, according to the first rotation matrix and the first translation vector, determining the second transformation matrix

[0073] Specifically, the second transformation matrix is represented by the following formula:

[0074]

[0075] It should be noted that the above second transformation matrix accurately describes the relative pose of the coil marking object coordinate system and the coil coordinate system, and finally is stored as the coil fine calibration parameter.

[0076] In step S203, based on the global calibration of the camera and the robot base, the global preliminary positioning of the robot end effector to the patient's head is completed.

[0077] The global initial positioning motion control module proposed in this paper aims to use the global spatial relationship established by the robot base-camera global calibration (BCGC) module to guide the robot to perform large-scale and rapid initial positioning motion to move the TMS coil to a safe position near the target area.

[0078] In one embodiment, step S203 includes the following sub-steps S2031-S2038:

[0079] Step S2031: Acquire brain stimulation target points and set safety points based on the brain stimulation target points

[0080] like Figure 5 As shown, in order to obtain the target pose and calibration parameters, once the stimulation target on the cerebral cortex is determined, the safety point can be determined. This safety point is usually set 5cm around the final stimulation target to leave space for subsequent precise positioning. Call or obtain the sixth transformation matrix from the base marker coordinate system to the robot base coordinate system calculated by the robot base-camera global calibration (BCGC) module Obtain the third transformation matrix from the camera coordinate system measured by the optical tracking camera to the base marker coordinate system in real time And the seventh transformation matrix from the camera coordinate system to the head marker coordinate system Get the eighth transformation matrix from the pre-registered head marker coordinate system to the head coordinate system And accurately calculate the ninth transformation matrix from the coil coordinate system to the end flange coordinate system through the CAD model

[0081] Step S2032: Obtain the coordinate point set P in the head coordinate system headmark .

[0082] Specifically, the optical tracking probe touches the predefined anatomical landmarks on the patient's head (such as the root of the nose, the tip of the nose, the bony points of the left and right ears, etc.) in sequence to obtain the coordinate point set P in the head coordinate system. headmark .

[0083] Step S2033: Based on the MRI image data of the patient's head, obtain the coordinates of the anatomical landmark point P MRI .

[0084] Specifically, the MRI image data of the patient's head is imported into the system, and the corresponding anatomical landmark coordinates P are automatically extracted. MRI .

[0085] Step S2034: Obtain the seventh transformation matrix from the camera coordinate system to the head marker coordinate system

[0086] In one embodiment, step S2034 includes the following sub-steps S20341-S20342:

[0087] Step S20341: Reduce the coordinates of the anatomical landmark point P according to the least squares rigid registration algorithm. MRI and the coordinate point set P in the head coordinate system headmark and determining a second rotation matrix and a second translation vector when the error between the coordinates of the anatomical landmark point and the coordinate point set in the head coordinate system is minimized;

[0088] Step S20342: Determine the seventh transformation matrix from the camera coordinate system to the head marker coordinate system based on the second rotation matrix and the second translation vector.

[0089] Specifically, the goal of the least squares rigid registration algorithm is to find a suitable rotation matrix R2 and translation vector t2 by calculating the coordinate point set P headmark The result after R2 rotation and t2 translation is consistent with the coordinates of the anatomical landmark point P MRI The sum of squared errors is continuously adjusted by mathematical optimization methods (such as singular value decomposition) to minimize the sum of squared errors. When the error can no longer be reduced, the corresponding R2 is the second rotation matrix and t2 is the second translation vector, thereby achieving the coordinate point set P headmark and the coordinates of the anatomical landmark point P MRI Error reduction and optimal transformation parameters determination.

[0090] Among them, the least squares rigid registration is expressed by the following formula:

[0091]

[0092] Eighth transformation matrix It is expressed by the following formula:

[0093]

[0094] Eighth transformation matrix The relative position of the head marker coordinate system and the head coordinate system is accurately described.

[0095] Step S2035: Obtain the eighth transformation matrix from the head marker coordinate system to the head coordinate system

[0096] Step S2036: Obtain the ninth transformation matrix from the coil coordinate system to the end flange coordinate system

[0097] Step S2037: According to the sixth transformation matrix The third transformation matrix The seventh transformation matrix The eighth transformation matrix The safety point The ninth transformation matrix Determine the first target pose that the robot end effector needs to reach

[0098] Step S2038: Set the first target posture Input the robot motion controller, the robot motion controller according to the first target pose The difference between the current position of the robot end generates the first motion trajectory and executes the motion. When the robot end effector reaches the first target position When , the global preliminary positioning is completed.

[0099] Specifically, the first target pose that the robot end effector wants to reach is Calculated by the following formula:

[0100]

[0101] Specifically, the above calculation process utilizes the global calibration results provided by the BCGC module to convert the target point (safety point) defined in the patient's head coordinate system into the target pose in the base coordinate system that can be understood by the robot's own motion control system.

[0102] Will As the motion target, it is input to the robot motion controller, and the controller is based on the current robot end position and the target position. differences, such as Figure 3 As shown, a first motion trajectory 2 from the current position to the target position is planned and executed. When the end of the robot reaches the target position The global initial positioning motion is completed. At this point, the robot end effector is near the target treatment area, ready for subsequent fine-tuning of the local high-precision positioning motion control module.

[0103] In step S204, Figure 7 As shown, based on the local calibration of the coil and the global preliminary positioning, the local positioning of the coil to the brain stimulation target on the patient's head is completed.

[0104] The disclosed local high-precision positioning motion control module enables submillimeter-level fine adjustment of the TMS coil near the target brain region. After the preceding global initial positioning motion control module quickly guides the TMS coil to the vicinity of the target safety point, this module takes over control. Using the high-precision calibration parameters provided by the End-Stage Coil Precision Calibration (ECPC) module and real-time optical tracking data, it calculates precise motion instructions, guiding the robot end-end to precisely position the TMS coil at the preset stimulation target. The core of this module is the use of a visual servoing control strategy, directly based on the precise measurements of the optical tracking camera, to drive the robot for local fine-tuning, thereby bypassing the absolute positioning error of the robot body.

[0105] In one embodiment, step S204 includes the following sub-steps S2041-S2045:

[0106] Step S2041: Obtain the tenth transformation matrix from the coil marker coordinate system to the coil coordinate system

[0107] Step S2042: Obtain the eleventh transformation matrix from the camera coordinate system to the coil marker coordinate system

[0108] Step S2043: According to the tenth transformation matrix The eleventh transformation matrix The seventh transformation matrix The eighth transformation matrix Target pose Determine the twelfth transformation matrix from the current coil pose to the target coil pose

[0109] Step S2044: Obtain the thirteenth transformation matrix from base to coil And according to the twelfth transformation matrix and the thirteenth transformation matrix Determine the second target pose that the robot end effector needs to reach

[0110] Step S2045: Set the second target posture Input robot motion controller, the robot motion control according to the second target posture The difference between the current position and posture of the robot end generates a second motion trajectory and performs motion. When the robot end effector reaches the second target posture When the coil is localized to the brain stimulation target on the patient's head, local positioning is completed.

[0111] Specifically, the target pose and calibration parameters are obtained: the system obtains the homogeneous transformation matrix from the end rigid body marker coordinate system measured by the optical tracking camera to the camera coordinate system in real time And the homogeneous transformation matrix from the head marker coordinate system to the camera coordinate system At the same time, the stored precise transformation matrix from the coil coordinate system to the end rigid body marker coordinate system calculated by the end coil precision calibration (ECPC) module is called Get the transformation from the pre-registered head marker to the head coordinate system and precise stimulation target position determined by the clinician

[0112] The twelfth transformation matrix from the current coil pose to the target coil pose Calculated by the following formula:

[0113]

[0114] The second target position that the end effector needs to reach is calculated through the coordinate system transformation chain in the robot's base coordinate system. The second target pose is calculated using the following formula:

[0115]

[0116] This calculation process converts the stimulation target defined in the patient's head coordinate system into the target pose in the base coordinate system that can be understood by the robot's own motion control system. As the motion target, it is input to the robot motion controller, such as Figure 3 As shown, the controller plans a second motion trajectory 4 from the current position to the target position and executes the motion. When the end of the robot reaches the target position The local high-precision positioning motion is completed. It should be noted that this module combines the high-precision calibration results of ECPC with the real-time visual closed loop, completely avoiding the introduction of the kinematic parameters of the robot body.

[0117] The present invention integrates the robot base-camera offline global calibration (BCGC) and the end coil fine calibration (ECPC) in stages, and realizes the coordinated optimization of efficiency and accuracy through a two-layer architecture of global coarse positioning and local fine adjustment. Based on the optical marker fixed on the robot base, the camera posture disturbance is tracked in real time during the operation, and the global mapping relationship between the base and the camera is dynamically updated to avoid repeated calibration due to camera movement. In the ECPC module, the absolute positioning error introduced by the robot kinematic parameters is completely avoided through the geometric characteristics of the coil and the visual closed-loop control, and sub-millimeter end positioning accuracy is achieved. The BCGC parameters are used to complete a large range of rapid initial positioning, and after approaching the target area, they are switched to the ECPC parameters for fine adjustment to balance the efficiency and accuracy requirements. The functional coordinate system is defined using the CAD model of the coil, and the accurate solution of the end calibration parameters is achieved by combining optical measurement and point cloud registration.

[0118] Traditional methods rely on the robot kinematic model, and its absolute positioning error (1-8mm) is directly transferred to the calibration result. However, the present invention uses the ECPC module to decouple the robot body error based on optical measurement and CAD model, and relies solely on visual closed-loop control to achieve submillimeter positioning accuracy. It breaks through the accuracy bottleneck of traditional calibration and meets the clinical needs of TMS treatment for submillimeter accuracy. The existing technology requires the camera to be fixed to the base, and camera offset during surgery will cause calibration failure. The present invention uses the BCGC module to track the base marker in real time and dynamically update the calibration parameters without the need for repeated calibration. It significantly improves the system's anti-interference ability in complex clinical environments and reduces the risk of operation interruption. Traditional methods require frequent multi-posture calibration, which is time-consuming and increases the risk of patient collision. The hierarchical control strategy of the present invention (BCGC large-scale positioning + ECPC fine adjustment) reduces redundant motion of the robotic arm. It shortens treatment preparation time, improves operational efficiency, and reduces the safety hazards of large-scale motion of the robotic arm. Combined with optical marker tracking and CAD alignment, the system's dependence on the absolute accuracy of the robot is reduced and it is compatible with different models of robotic arms. This technology solution expands its clinical application scope, adapting to a variety of robotic platforms and treatment scenarios. The ECPC module streamlines the calibration process through predefined CAD models, reducing manual intervention; while the BCGC module's automated parameter updates reduce operational complexity. This reduces reliance on operator experience and improves the system's usability and scalability.

[0119] Based on the same inventive concept, embodiments of the present application also provide a device for implementing the aforementioned transcranial magnetic stimulation robot hybrid calibration. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the transcranial magnetic stimulation robot hybrid calibration device provided below can be found in the limitations of the transcranial magnetic stimulation robot hybrid calibration method described above, and will not be repeated here.

[0120] In one exemplary embodiment, as shown in Figure 8 A transcranial magnetic stimulation robot hybrid calibration device is provided, comprising:

[0121] A first acquisition module 810 is configured to acquire a first transformation matrix from a camera coordinate system to a robot base coordinate system to achieve global calibration of the camera and the robot base;

[0122] A second acquisition module 820 is configured to acquire a second transformation matrix from a coil marker coordinate system to a coil coordinate system to achieve local calibration of the coil;

[0123] A first positioning module 830 is configured to complete global preliminary positioning of a robot end effector to a patient's head based on the global calibration of the camera and the robot base;

[0124] A second positioning module 840 is configured to complete local positioning of a coil to a brain stimulation target point of the patient's head based on the local calibration of the coil and the global preliminary positioning.

[0125] As an optional implementation, the first acquisition module 810 is specifically configured to:

[0126] acquire a third transformation matrix from the camera coordinate system to a base marker coordinate system

[0127] acquire a fourth transformation matrix from the camera coordinate system to an end marker coordinate system

[0128] acquire a fifth transformation matrix from the robot base coordinate system to an end flange coordinate system

[0129] determine a sixth transformation matrix from the base marker coordinate system to the robot base coordinate system according to the third transformation matrix the fourth transformation matrix and the fifth transformation matrix

[0130] determine the first transformation matrix according to the third transformation matrix and the sixth transformation matrix

[0131] As an optional implementation, the second acquisition module 820 comprises:

[0132] establishing a coil coordinate system with the robot end flange center as a reference;

[0133] ​​Determine at least three rigid identification points P for the coil structure on the coil coordinate system coilref ;

[0134] According to the rigid identification point P coilref , determine each of the rigid identification points P coilref The measured coordinate P in the coil marker coordinate system gain ;

[0135] According to the coil CAD model, the theoretical coordinates of the rigid identification point in the coil coordinate system are obtained;

[0136] Combining the measured coordinates with the theoretical coordinates to generate a set of coordinate pairs;

[0137] reducing the error between the measured coordinates and the theoretical coordinates according to a least squares rigid registration algorithm, and determining a first rotation matrix and a first translation vector when the error between the measured coordinates and the theoretical coordinates is minimized;

[0138] Determine the second transformation matrix based on the first rotation matrix and the first translation vector

[0139] As an optional implementation manner, the first positioning module 830 is specifically configured to:

[0140] Obtaining brain stimulation targets and setting safety points based on the brain stimulation targets

[0141] Get the coordinate point set P in the head coordinate system headmark ;

[0142] Based on the MRI image data of the patient's head, the coordinates of the anatomical landmark point P are obtained. MRI ;

[0143] Get the seventh transformation matrix from the camera coordinate system to the head marker coordinate system

[0144] Get the eighth transformation matrix from the head marker coordinate system to the head coordinate system

[0145] Get the ninth transformation matrix from the coil coordinate system to the end flange coordinate system

[0146] According to the sixth transformation matrix The third transformation matrix The seventh transformation matrix The eighth transformation matrix The safety point The ninth transformation matrix Determine the first target pose that the robot end effector needs to reach

[0147] The first target pose Input the robot motion controller, the robot motion controller according to the first target posture The difference between the current position of the robot end effector and the first motion trajectory is generated and the motion is performed. When the robot end effector reaches the first target position When , the global preliminary positioning is completed.

[0148] As an optional implementation, in the step of obtaining the seventh transformation matrix from the camera coordinate system to the head marker coordinate system In terms of the first positioning module 830, it is specifically configured to:

[0149] reducing the error between the coordinates of the anatomical landmark points and the coordinate point set in the head coordinate system according to a least squares rigid registration algorithm, and determining a second rotation matrix and a second translation vector when the error between the coordinates of the anatomical landmark points and the coordinate point set in the head coordinate system is minimized;

[0150] According to the second rotation matrix and the second translation vector, the seventh transformation matrix from the camera coordinate system to the head marker coordinate system is determined.

[0151] As an optional implementation manner, the second positioning module 840 is specifically configured to:

[0152] Get the tenth transformation matrix from the coil marker coordinate system to the coil coordinate system

[0153] Get the eleventh transformation matrix from the camera coordinate system to the coil marker coordinate system

[0154] According to the tenth transformation matrix The eleventh transformation matrix The seventh transformation matrix The eighth transformation matrix Target pose Determine the twelfth transformation matrix from the current coil pose to the target coil pose

[0155] Get the thirteenth transformation matrix from base to coil And according to the twelfth transformation matrix and the thirteenth transformation matrix Determine the second target pose that the robot end effector needs to reach

[0156] The second target pose Input robot motion controller, the robot motion control according to the second target posture The difference between the current position and the end effector of the robot generates a second motion trajectory and performs motion. When the end effector of the robot reaches the second target position When , local positioning of the coil to the brain stimulation target on the patient's head is completed.

[0157] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 9 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of 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 system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store video tag processing data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a video tag processing method is implemented.

[0158] Those skilled in the art will understand that Figure 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0159] In an exemplary embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0160] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0161] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0162] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0163] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented 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, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0164] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0165] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0166] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A hybrid calibration method for a transcranial magnetic stimulation robot, characterized in that: The transcranial magnetic stimulation robot hybrid calibration method comprises: Get the first transformation matrix from the camera coordinate system to the robot base coordinate system To achieve global calibration of the camera and robot base; Get the second transformation matrix from the coil marker coordinate system to the coil coordinate system To achieve local calibration of the coil; Based on the global calibration of the camera and the robot base, a preliminary global positioning of the robot end effector to the patient's head is completed; Based on the local calibration of the coil and the global preliminary positioning, local positioning of the coil to the brain stimulation target on the patient's head is completed.

2. The hybrid calibration method for transcranial magnetic stimulation robot according to claim 1, characterized in that: Get the first transformation matrix between the camera coordinate system and the robot base coordinate system include: Get the third transformation matrix from the camera coordinate system to the base marker coordinate system Get the fourth transformation matrix from the camera coordinate system to the end marker coordinate system Get the fifth transformation matrix from the robot base coordinate system to the end flange coordinate system According to the third transformation matrix The fourth transformation matrix And the fifth transformation matrix Determine the sixth transformation matrix from the base marker coordinate system to the robot base coordinate system According to the third transformation matrix and the sixth transformation matrix Determine the first transformation matrix 3. The hybrid calibration method for transcranial magnetic stimulation robot according to claim 1, characterized in that: Get the second transformation matrix from the coil marker coordinate system to the coil coordinate system include: Establish the coil coordinate system with the center of the robot end flange as the reference; Determining at least three rigid identification points for the coil structure in the coil coordinate system; According to the rigid identification point, the measured coordinate P of each rigid identification point in the coil marker coordinate system is determined. gain ; According to the coil CAD model, the theoretical coordinates P of the rigid identification point in the coil coordinate system are obtained. coilref ; According to the least squares rigid registration algorithm, the measured coordinates P are reduced gain With the theoretical coordinate P coilref and determining a first rotation matrix and a first translation vector when the error between the measured coordinates and the theoretical coordinates is minimized; Determine the second transformation matrix based on the first rotation matrix and the first translation vector 4. The hybrid calibration method for transcranial magnetic stimulation robot according to claim 2, characterized in that: The global calibration of the camera and the robot base is used to complete the global preliminary positioning of the robot end effector to the patient's head, including: Obtaining brain stimulation targets and setting safety points based on the brain stimulation targets Get the coordinate point set P in the head coordinate system headmark ; Based on the MRI image data of the patient's head, the coordinates of the anatomical landmark point P are obtained. MRI ; Get the seventh transformation matrix from the camera coordinate system to the head marker coordinate system Get the eighth transformation matrix from the head marker coordinate system to the head coordinate system Get the ninth transformation matrix from the coil coordinate system to the end flange coordinate system According to the sixth transformation matrix The third transformation matrix The seventh transformation matrix The eighth transformation matrix The safety point The ninth transformation matrix Determine the first target pose that the robot end effector needs to reach The first target pose Input the robot motion controller, the robot motion controller according to the first target posture The difference between the current position of the robot end effector and the first motion trajectory is generated and the motion is performed. When the robot end effector reaches the first target position When , the global preliminary positioning is completed.

5. The hybrid calibration method for transcranial magnetic stimulation robot according to claim 4, characterized in that: The seventh transformation matrix from the camera coordinate system to the head marker coordinate system is obtained include: reducing the error between the coordinates of the anatomical landmark points and the coordinate point set in the head coordinate system according to a least squares rigid registration algorithm, and determining a second rotation matrix and a second translation vector when the error between the coordinates of the anatomical landmark points and the coordinate point set in the head coordinate system is minimized; According to the second rotation matrix and the second translation vector, the seventh transformation matrix from the camera coordinate system to the head marker coordinate system is determined.

6. The hybrid calibration method for transcranial magnetic stimulation robot according to claim 4, characterized in that: The local calibration of the coil and the global preliminary positioning are used to complete the local positioning of the coil to the brain stimulation target on the patient's head, including: Get the tenth transformation matrix from the coil marker coordinate system to the coil coordinate system Get the eleventh transformation matrix from the camera coordinate system to the coil marker coordinate system According to the tenth transformation matrix The eleventh transformation matrix The seventh transformation matrix The eighth transformation matrix Target pose Determine the twelfth transformation matrix from the current coil pose to the target coil pose Get the thirteenth transformation matrix from base to coil And according to the twelfth transformation matrix and the thirteenth transformation matrix Determine the second target pose that the robot end effector needs to reach The second target pose Input robot motion controller, the robot motion control according to the second target posture The difference between the current position and the end effector of the robot generates a second motion trajectory and performs motion. When the end effector of the robot reaches the second target position When , local positioning of the coil to the brain stimulation target on the patient's head is completed.

7. A transcranial magnetic stimulation robot hybrid calibration device, characterized in that: The transcranial magnetic stimulation robot hybrid calibration device comprises: The first acquisition module is used to obtain the first transformation matrix from the camera coordinate system to the robot base coordinate system To achieve global calibration of the camera and robot base; The second acquisition module is used to obtain the second transformation matrix from the coil marker coordinate system to the coil coordinate system To achieve local calibration of the coil; A first positioning module is used to complete the global preliminary positioning of the robot end effector to the patient's head based on the global calibration of the camera and the robot base; The second positioning module is used to complete the local positioning of the coil to the brain stimulation target on the patient's head based on the local calibration of the coil and the global preliminary positioning.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the transcranial magnetic stimulation robot hybrid calibration method according to any one of claims 1 to 6.

9. 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 transcranial magnetic stimulation robot hybrid calibration method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the transcranial magnetic stimulation robot hybrid calibration method according to any one of claims 1 to 6 are implemented.

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