Surgical robot navigation plate and navigation method

Abstract

The invention discloses a surgical robot navigation plate and a navigation method, and relates to the technical field of surgical robot navigation plates, and the method comprises the following steps: obtaining preoperative preparation data of a puncture operation; determining an initial position of the navigation plate, establishing a unified coordinate system, acquiring skin deformation data of the patient, and calculating a first reference position of the navigation plate based on a deformation model; a deformation comprehensive value is obtained based on skin deformation data obtained in real time, the real-time pressure adjusting value of each micro air bag is calculated, descending order arrangement is carried out according to the real-time pressure adjusting values, the pressure of the corresponding micro air bags is adjusted in sequence, and a second initial position is obtained; performing depth calibration on the second initial position to obtain a second reference position; the navigation plate is adjusted twice based on the skin deformation data of the patient, the effects of accurately fixing the navigation plate and accurately positioning the surgical robot are achieved, navigation errors caused by skin deformation are reduced, and the positioning accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of surgical robot navigation boards, and in particular to a surgical robot navigation board and a navigation method. Background Art

[0002] Traditional surgical robot navigation uses preoperative planning, combined with intraoperative X-ray image fusion matching measurement technology and real-time optical image tracking technology. This navigation technology requires computers to have powerful computing power to track the movement of the target in a timely manner and prevent the robot's movement from lagging.

[0003] Chinese invention patent application number 202210333133.5 discloses a surgical robot navigation board with multiple correction targets distributed throughout the board, including a central correction target. Using the navigation board as a bridge, the board matches intraoperative X-ray images of stable tissue structures with preoperative MRI images, establishing a unified coordinate system for the robot. This unified coordinate system guides the robot's positioning.

[0004] However, when a single integral navigation board is used for navigation positioning in the existing technology, different degrees of deformation will occur when it is close to the skin. The error will accumulate at the central correction target, resulting in a large positioning error of the navigation board, affecting the positioning accuracy. Reducing the positioning error of the navigation board and thus improving the positioning accuracy has become the top priority of the surgical robot navigation board. Summary of the Invention

[0005] The present application solves the problem of low positioning accuracy caused by positioning errors in the prior art by providing a surgical robot navigation board and navigation method, thereby achieving the technical effect of reducing the positioning error of the navigation board and thereby improving the positioning accuracy.

[0006] The present application provides a surgical robot navigation method, which is applied to a navigation board, and the method includes: S100: Obtaining preoperative preparation data for the puncture surgery through preoperative MRI image data, wherein the preoperative preparation data includes a percutaneous puncture point, a needle insertion angle, and a puncture depth to the percutaneous puncture point; S200: Determine the initial position of the navigation board according to the preoperative preparation data and establish a unified coordinate system, obtain the patient's skin deformation data, and calculate the first reference position of the navigation board based on the deformation model; S300: Determine the coordinate positions of the central navigation panel and several auxiliary navigation panels based on the first reference position and mark them to form a three-dimensional coordinate matrix; obtain a deformation comprehensive value based on the real-time acquired skin deformation data; if the deformation comprehensive value is greater than a preset deformation threshold, calculate the real-time pressure adjustment value of each micro-airbag, arrange the real-time pressure adjustment values ​​in descending order, and adjust the pressure of the corresponding micro-airbags in sequence to obtain a second initial position; perform depth calibration on the second initial position to obtain a second reference position; S400: Fix the navigation board on the human body based on the second reference position, and under the guidance of the laser cross target on the working end of the surgical robot, align the laser cross with the central target and cross guide line of the central navigation board. The central target and cross guide line can be visualized under X-rays and overlap with the target tissue structure; calculate the values ​​of the surgical entry point, entry angle and entry depth based on the unified coordinate system, and input the values ​​into the surgical robot. After the operation is completed, an analysis report is generated.

[0007] Furthermore, obtaining the patient's skin deformation data and calculating the first reference position of the navigation board based on the deformation model include: Acquiring patient skin surface data using a three-dimensional scanning device to generate point cloud data, thereby obtaining skin deformation data; gridding the skin deformation data, inputting the processed skin deformation data into a deformation model, extracting skin deformation features, calculating an error value based on changes in the deformation features, and calculating a first reference position based on the error value and an initial position; The deformation characteristics include tension and curvature of the skin.

[0008] Furthermore, the processed skin deformation data is input into the deformation model, the deformation characteristics of the skin are extracted, and the error value is calculated according to the change of the deformation characteristics, including: based on the finite element analysis method, a collective model is established according to the processed skin deformation data, the collective model is divided into a finite number of units, each unit contains a number of nodes, and a finite element solver is used to solve the mechanical equations of each unit to obtain the displacement and stress results of the node, and then the tension and curvature changes of the skin are calculated, and the weighted average of multiple deformation characteristic changes is summed to obtain the error value.

[0009] Furthermore, the real-time pressure adjustment value of each micro airbag is calculated, including: The total pressure value is calculated based on the comprehensive deformation value, deformation threshold and mapping formula, the corresponding pressure demand value is obtained based on the weight coefficient of each micro airbag, and the corresponding real-time pressure adjustment value is obtained according to the current pressure and pressure demand value of each micro airbag.

[0010] Furthermore, the mapping formula is as follows:

[0011] in, is the total pressure value, is the comprehensive deformation value, is the deformation threshold, is the conversion coefficient, which ranges from [0, 1] and is used to measure the conversion relationship between deformation and pressure. The pressure demand value is calculated as follows:

[0012] in, is the pressure requirement of the i-th micro airbag, is the weight coefficient of the i-th micro airbag.

[0013] Furthermore, in step S300, depth calibration is performed on the second initial position to obtain a second reference position, including: S310: Obtaining the position of each correction target point at the first reference position and marking it as the first original position, and obtaining the position of each correction target point at the second initial position and marking it as the second current position; S320: Calculating the position deviation of each correction target point based on the first original position and the second current position, and then calculating the overall offset of the navigation board, and selecting a number of correction target points as candidate target points according to the flotation mechanism; S330: Perform depth calibration based on the candidate target point to obtain a second reference position.

[0014] Furthermore, the flotation mechanism is as follows: based on the position deviation and overall offset of each correction target point, the comprehensive impact value of each correction target point is calculated, the comprehensive impact value of each correction target point is arranged in descending order according to size, and several correction target points are selected as candidate target points according to the extraction ratio.

[0015] Furthermore, the comprehensive impact value is obtained by comprehensively calculating the deformation value and contribution value of each correction target point, where the deformation value refers to the ratio of the position deviation of each correction target point to the average value of the position deviation; the contribution value refers to the product of the pre-set weight value of each correction target point and the ratio of the position deviation of each correction target point to the overall offset; the extraction ratio is set according to the ratio of the overall offset and the pre-set offset threshold. If the ratio is not less than 1, all correction targets are selected as candidate targets; if the ratio is less than 1, several correction targets are selected as candidate targets according to the ratio.

[0016] A surgical robot navigation board, used in the above-mentioned surgical robot navigation method, the navigation board comprising a central navigation board, several auxiliary navigation boards, and several micro airbags, the central navigation board and the several auxiliary navigation boards being connected by micro airbags; each micro airbag being connected to an air pump; Several auxiliary navigation panels are arranged in a square shape, with the central navigation panel located at the center of the panels. Two adjacent auxiliary navigation panels are fixedly connected by micro airbags, and the central navigation panel is fixedly connected to the four nearest auxiliary navigation panels by micro airbags on all sides. Both the central navigation panel and the auxiliary navigation panel are provided with a central target point, a group of cross guide lines and a plurality of correction target points; the center of the central target point coincides with the center of the cross guide lines.

[0017] One or more technical solutions provided in this application have at least the following technical effects or advantages: By obtaining the patient's skin deformation data, adjusting the initial position according to the deformation characteristics to obtain the first reference position, obtaining the skin deformation data in real time, calculating the comprehensive deformation value and the real-time pressure adjustment value of each micro airbag, adjusting the pressure of the corresponding micro airbag in turn to obtain the second initial position, and performing depth calibration on the second initial position to obtain the second reference position; based on the patient's skin deformation data, the navigation board is adjusted twice to achieve the effect of precise fixation of the navigation board and precise positioning of the working end of the surgical robot, further reducing the navigation error caused by skin deformation, and improving the positioning accuracy of the surgical robot. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a schematic flow chart of a surgical robot navigation method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the overall structure of a surgical robot navigation panel according to an embodiment of the present invention; Figure numerals: 1. Central navigation panel; 2. Auxiliary navigation panel; 3. Central target; 4. Correction target; 5. Cross guide line; 6. Micro airbag. DETAILED DESCRIPTION

[0019] To facilitate understanding of the present invention, the present application will be described more comprehensively below with reference to the relevant drawings; the drawings show preferred embodiments of the present invention, but the present invention can be implemented in many different forms and is not limited to the embodiments described herein; on the contrary, the purpose of providing these embodiments is to enable a more thorough and comprehensive understanding of the disclosed content of the present invention.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains; the terms used herein in the specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention; the term "and / or" used herein includes any and all combinations of one or more of the associated listed items.

[0021] Example 1: Figure 1 As shown, a surgical robot navigation method is applied to a navigation board, the method comprising: S100: Obtaining preoperative preparation data for the puncture surgery through preoperative MRI image data, wherein the preoperative preparation data includes a percutaneous puncture point, a needle insertion angle, and a puncture depth to the percutaneous puncture point.

[0022] In some embodiments, puncture point information is obtained through intraoperative X-ray and preoperative MR or CT measurements, and needle angle information and puncture depth information are obtained preoperatively through preoperative MR or CT measurements. The specific information needs to be obtained according to actual conditions, and this embodiment does not impose any specific restrictions.

[0023] S200: Determine the initial position of the navigation board according to the preoperative preparation data and establish a unified coordinate system, obtain the patient's skin deformation data, and calculate the first reference position of the navigation board based on the deformation model.

[0024] In some embodiments, the initial position of the navigation board is determined based on preoperative preparation data, specifically based on the center position of the percutaneous puncture point and the central navigation plate 1 of the navigation board to ensure position overlap. After determining the position of the central navigation plate 1, the positions of the remaining auxiliary navigation plates 2 are adaptively distributed.

[0025] In some embodiments, a unified coordinate system is established. This coordinate system provides a uniform metric for all points within the surgical field, enabling data from diverse sources (e.g., MRI images, 3D scan data, navigation board positions, etc.) to be compared and calculated in the same space. The precise location of key elements such as the navigation board, puncture points, and skin deformation ensures surgical accuracy and safety. In this embodiment, the center target 3 of the central navigation board 1 serves as the coordinate system origin.

[0026] In some embodiments, the patient's skin deformation data is obtained, and the first reference position of the navigation board is calculated based on the deformation model, including: obtaining the patient's skin surface data based on a three-dimensional scanning device, generating point cloud data, and then obtaining skin deformation data; gridding the skin deformation data, inputting the processed skin deformation data into the deformation model, extracting the deformation characteristics of the skin, calculating the error value based on the change of the deformation characteristics, and calculating the first reference position based on the error value and the initial position; wherein the deformation characteristics include but are not limited to the tension and curvature of the skin.

[0027] Specifically, a 3D scanning device is used to acquire patient skin surface data, generating a point cloud with a density of 100 points per square millimeter. Noise is removed using a point cloud registration algorithm to obtain skin deformation data. The design is tailored to the specific patient site and is suitable for areas such as the back, chest, or joints. Taking the back as an example, the skin deformation data is gridded. Using a pre-established deformation model, finite element analysis is used to calculate skin tension and curvature, thereby determining the deformation characteristics of the back skin. The entire process begins with the target positioning of the central navigation pad and auxiliary navigation pads. These navigation pads are typically placed at key anatomical locations on the patient's back, such as spinal prominences or the scapular margins. For example, for patients with scoliosis, the central navigation pad might be placed at the point of maximum curvature, while auxiliary navigation pads are positioned on either side to capture overall curvature changes. Based on the deformation characteristics of the back skin, a least-squares curve fitting method is used to obtain error values. The initial position coordinates of each navigation pad and the error values ​​are used to determine the first reference position of the navigation pads.

[0028] The processed skin deformation data is input into the deformation model, the deformation characteristics of the skin are extracted, and the error value is calculated according to the change of the deformation characteristics, including: based on the finite element analysis method, a collective model is established according to the processed skin deformation data, the collective model is divided into a finite number of units, each unit contains a number of nodes, according to the mechanical properties of the skin, the elastic modulus, Poisson's ratio and other properties of the material are set, according to the stress conditions of the skin in different states, the corresponding boundary conditions and loads are applied, and the finite element solver is used to solve the mechanical equations of each unit to obtain the displacement, stress and strain results of the node, and according to the displacement and stress results of the node, the tension and curvature changes of the skin are calculated, and the weighted average of multiple deformation characteristic changes is summed to obtain the error value.

[0029] Specifically, tension is the pulling force exerted on the skin in the direction of the applied force. In finite element analysis, the distribution of skin tension can be determined by calculating the stress at the nodes. Curvature is the degree of curvature of the skin surface. In finite element analysis, the surface shape of the skin can be determined by calculating the displacement of the nodes, which in turn can be used to calculate the curvature.

[0030] The deformation model is obtained by pre-training, and the method is not specifically limited. In this embodiment, a nonlinear viscoelastic model is used as the basic mathematical model to describe skin deformation. The basic parameters are determined by fitting experimental data, including: relaxation time, initially set to 0.5 seconds, which represents the time required for the skin to return to its original shape after being subjected to force; elastic modulus, set to 10 MPa, which represents the stiffness of the skin within the elastic range; viscosity coefficient: set to 1 MPa·s, which represents the damping characteristics of the skin within the viscous range.

[0031] Based on the basic form and parameters of the nonlinear viscoelastic model, a constitutive equation for the skin is established. This constitutive equation uses an integral form to describe the stress-strain relationship of the skin under stress. The skin surface mesh data of the patient's back is imported into the finite element analysis software. The viscoelastic properties of the material are set, that is, the basic parameters entered into the constitutive equation. Boundary conditions and loads are applied to simulate the stress conditions on the skin during surgery. For example, the displacement boundary conditions of the skin under pressure from the surgical instrument are set, as well as the load applied by the surgical instrument to the skin. The finite element solver is run to solve the mechanical equations and obtain results such as node displacement, stress, and strain.

[0032] The established viscoelastic model was validated using skin deformation data from actual surgical procedures. The model's predictive capabilities were evaluated, such as the error in the calculated skin deformation and the degree of agreement with experimental results. In this embodiment, the model-predicted skin deformation error was controlled within 5%, which was in good agreement with the experimental results. Based on the validation results, the model was adjusted and optimized as necessary. For example, parameters such as relaxation time, elastic modulus, and viscosity coefficient could be adjusted to improve the model's accuracy and robustness.

[0033] The optimized viscoelastic model is applied to a surgical robot navigation method. During surgery, the patient's skin deformation data is acquired in real time and input into the viscoelastic model. Based on the model's predictions, the position of the navigation pad is dynamically adjusted. Preferably, the skin deformation data range is monitored in real time. If the curvature change rate of the skin deformation data exceeds a preset threshold (e.g., 0.5), the elastic coefficient and Poisson's ratio parameters of the deformation model are adjusted using a gradient descent method, and the error value is recalculated. Based on the adjusted deformation model parameters, the deformation model database is updated, and a new model configuration file is generated. The updated deformation model is optimized using a random forest regression model within a machine learning algorithm. Hyperparameters are adjusted through cross-validation to obtain the optimized deformation model. Using the optimized deformation model, the Newton iteration method is combined with the nonlinear equations to solve the system, ultimately determining the precise error value of each navigation pad with an accuracy of 0.1 mm. The first reference position of the central navigation pad and the auxiliary navigation pads is set based on the error values, and an interpolation algorithm is used to generate coordinate marker data in the form of a three-dimensional coordinate matrix.

[0034] This embodiment can automatically identify and adapt to patients of different body shapes, ages and skin conditions, improving the versatility and accuracy of the model. The precise position determined by the optimization model is the basis for the final placement of the navigation board. This process takes into account multiple factors, including skin deformation characteristics, anatomical structure and surgical needs. For example, for thoracic surgery, the model may recommend placing more navigation boards in the intercostal space to capture subtle deformations caused by breathing. The final generated coordinate marking data is the output of the entire process. These data contain not only the three-dimensional coordinates of the navigation boards, but also the relative position and orientation information between them. These precise coordinate data provide a reliable reference point for the surgical navigation system, which helps to improve surgical accuracy, reduce operation time and patient radiation exposure. S300: Determine the coordinate positions of the central navigation panel 1 and several auxiliary navigation panels 2 according to the first reference position and mark them to form a three-dimensional coordinate matrix; obtain a comprehensive deformation value based on the skin deformation data acquired in real time. If the comprehensive deformation value is greater than the preset deformation threshold, calculate the real-time pressure adjustment value of each micro airbag 6, arrange them in descending order according to the size of the real-time pressure adjustment value, and adjust the pressure of the corresponding micro airbag 6 in turn to obtain a second initial position; perform depth calibration on the second initial position to obtain a second reference position.

[0035] In some embodiments, a three-dimensional coordinate matrix is ​​a data structure used to represent the coordinates of multiple points in three-dimensional space. It appears as an array or matrix, where each element (or row) represents the coordinates of a point in three-dimensional space. These coordinates are expressed in the form (x, y, z), where x, y, and z represent the horizontal, vertical, and vertical coordinates of the point in three-dimensional space, respectively. In this space, the position of each point can be uniquely identified by three numerical values.

[0036] Specifically, first determine a reference position or origin, which is the starting point for all coordinate calculations. In this embodiment, the position of the central navigation panel 1 is used as the origin. For each navigation panel (including the central navigation panel 1 and the auxiliary navigation panel 2), it is necessary to calculate its coordinates in three-dimensional space based on the first reference position. The calculated coordinate data is organized into a matrix form. Each row represents the coordinates of a navigation panel, and the columns correspond to the x, y, and z coordinates respectively. In actual situations, it may be necessary to use an interpolation algorithm to generate denser coordinate data points. The interpolation algorithm can estimate the coordinates of unknown points based on known coordinate points, thereby generating a more complete and detailed coordinate matrix. The generated three-dimensional coordinate matrix also needs to be verified and corrected to ensure the accuracy and reliability of the data. The specific needs should be set according to the actual situation, and this embodiment does not impose specific restrictions.

[0037] In some embodiments, the initial adjustment point can also be set according to the coordinate position of the auxiliary navigation panel 2, and the auxiliary navigation panel 2 located at the edge corner in the three-dimensional coordinate matrix is ​​selected as the initial adjustment point. The auxiliary navigation panel 2 located at the "edge corner" refers to those auxiliary navigation panels 2 that are at the four extremes or corners of the overall layout in terms of spatial position. Specifically, the coordinates of all auxiliary navigation panels 2 in the three-dimensional coordinate matrix are traversed. Find the maximum and minimum points in the x, y, and z coordinate values. These points are the auxiliary navigation panels 2 located at the edge corners, and arbitrarily select an auxiliary navigation panel 2 as the initial adjustment point.

[0038] In some embodiments, real-time skin deformation data is acquired using a 3D scanning device or optical tracking system to capture changes in the patient's skin surface in real time. The device is aimed at the patient's surgical area, and the appropriate scanning frequency and resolution are set to begin real-time acquisition of 3D skin surface data. The collected data is processed in real time using the device's built-in software or custom algorithms to generate point cloud or mesh data representing the skin's current morphology.

[0039] Based on the real-time skin deformation data, deformation features are extracted and quantified. For example, the extracted tension features are calculated to obtain a numerical representation of the tension. For example, the average tension can be calculated by summing all the tension data and dividing it by the number of data points. The extracted curvature features are calculated to obtain a numerical representation of the curvature. For example, the rate of change of curvature can be calculated by comparing the curvature values ​​at different time points or spatial locations.

[0040] According to the influence of each deformation feature on the overall deformation degree, a weight is determined for each feature. The quantified deformation features are combined using the weighted average or summation method to obtain a comprehensive deformation value representing the overall deformation degree.

[0041] In some embodiments, based on historical surgical data or clinical experiments, deformation data under different surgical types, patient body shapes and skin conditions are collected, and statistical analysis is performed on the collected data to determine the normal range and abnormal range of the deformation characteristics. According to the statistical analysis results, one or more deformation thresholds are set to determine whether the real-time deformation data exceeds the normal range. The specific needs need to be pre-set according to actual conditions, and this embodiment does not impose any specific restrictions.

[0042] In some embodiments, calculating the real-time pressure adjustment value of each micro-airbag 6 includes: calculating a total pressure value based on the deformation comprehensive value, the deformation threshold, and a mapping formula; obtaining a corresponding pressure demand value based on a weight coefficient of each micro-airbag 6; and obtaining a corresponding real-time pressure adjustment value based on the current pressure and the pressure demand value of each micro-airbag 6; the mapping formula is as follows:

[0043] in, is the total pressure value, is the comprehensive deformation value, is the deformation threshold, is a conversion coefficient with a value range of [0, 1]. It measures the conversion relationship between deformation and pressure. Its purpose is to obtain corresponding pressure data based on the current deformation data. The conversion coefficient k can be used to convert deformation data into pressure values. The specific setting needs to be based on actual conditions and experimental data. It can also be determined through experimental fitting. For example, in the laboratory, different deformation composite values ​​are set, and the corresponding pressure demand values ​​are measured. The deformation composite value is used as the independent variable and the corresponding total pressure value as the dependent variable. A scatter plot is then drawn. The mapping relationship between the deformation composite value and the total pressure value is then fitted using linear regression. During the fitting process, the conversion coefficient k is determined as a parameter in the regression equation. New skin deformation data is used for verification to check the accuracy and reliability of the fitting equation. Based on the verification results, the fitting equation and the conversion coefficient k are adjusted and optimized as necessary.

[0044] In some embodiments, the pressure requirement value is calculated as follows:

[0045] in, is the pressure requirement value of the i-th micro airbag 6, is the weight coefficient of the i-th micro airbag 6. Based on the current pressure of each micro airbag 6 and the calculated pressure demand value, the real-time pressure adjustment value of each airbag can be calculated. The real-time pressure adjustment value is equal to the pressure demand value minus the current pressure value.

[0046] For example, there are three micro airbags 6A, B, and C, and their weight coefficients are 0.4, 0.3, and 0.3 respectively. The deformation comprehensive value is 1.5, the deformation threshold is 1.0, and the conversion coefficient is 2.0. The calculated total pressure value is 1.0; the pressure demand value of each micro airbag 6 is ; ; The current pressure value of each micro airbag 6 is initially set to 0.1, and the real-time pressure adjustment values ​​of each micro airbag 6 are 0.3, 0.2, and 0.2 respectively.

[0047] In some embodiments, each micro-airbag 6 is adjusted based on its real-time pressure adjustment value, with the pressure adjustment range being 10 to 50 kPa. The airbag pressures of the central navigation panel and the auxiliary navigation panel are dynamically adjusted to adapt to skin deformation, thereby obtaining a second initial position. A depth calibration is performed on the second initial position. The depth calibration involves performing a secondary correction on the adjusted navigation panel based on the correction target 4 on the navigation panel. The coordinate position of the navigation panel after depth calibration is used to obtain a second reference position.

[0048] In the unified coordinate system, an auxiliary navigation panel 2 at an edge corner is selected as the starting point for adjustment. This is because the auxiliary navigation panel 2 at the edge corner is relatively independent and its deformation has little effect on the position of the overall navigation panel. By monitoring the relative position changes between adjacent navigation panels and the real-time update of skin deformation data, the air pressure of the micro airbag 6 is adjusted one by one with the edge corner as the starting point, so that each navigation panel can better adapt to the dynamic deformation of the skin. For example, when the skin undergoes a large deformation, the airbag pressure can be increased to better fit the skin; when the skin deformation is small, the airbag pressure can be reduced to avoid excessive pressure. By dynamically adjusting the position of the navigation panel and the airbag pressure, the positioning accuracy of the surgical robot and the success rate of the operation are improved.

[0049] S400: Fix the navigation board on the human body based on the second reference position, and under the guidance of the laser cross target on the working end of the surgical robot, align the laser cross with the central target 3 and the cross guide line 5 of the central navigation board 1. The central target 3 and the cross guide line 5 can be developed under X-rays and overlap with the target tissue structure; calculate the values ​​of the surgical entry point, entry angle and entry depth based on the unified coordinate system, and input the values ​​into the surgical robot. After the operation is completed, an analysis report is generated.

[0050] In some embodiments, the target tissue structure is a target puncture site on the human body. The specific operation is as follows: an X-ray source positioned above the navigation board is turned on, so that the navigation board and the target tissue structure are imaged on a DR imaging flatbed; the navigation board image distortion is corrected to obtain the positional relationship between the navigation board and the robot; a ranging device with a ranging function is installed on the robot, which obtains the distance to any multiple positions on the navigation board, thereby obtaining the angular positional relationship between the robot and the navigation board on a unified plane. MR images are used to calibrate the edges and surface of the target tissue structure, and the distance between the target tissue structure and the human body surface is obtained through the MR images, thereby obtaining the angular positional relationship between the navigation board and the target tissue structure on a unified plane. Numerical values ​​for the surgical entry point, entry angle, and entry depth based on a unified coordinate system are calculated and input into the robot. During a puncture procedure, when the navigation board is used to guide and correct the surgical robot, the surgical entry point is the percutaneous puncture point, the entry angle is the needle insertion angle, and the entry depth is the puncture depth. Determining the final surgical entry point of the robot based on the determined position of the navigation board using an X-ray machine and MR images is a technique well known to those skilled in the art and will not be elaborated on in detail in this embodiment.

[0051] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages: The present application obtains the patient's skin deformation data, adjusts the initial position according to the deformation characteristics to obtain a first reference position, obtains the skin deformation data in real time, calculates the comprehensive deformation value and the real-time pressure adjustment value of each micro airbag 6, adjusts the pressure of the corresponding micro airbag 6 in turn to obtain a second initial position, and performs depth calibration on the second initial position to obtain a second reference position; based on the patient's skin deformation data, the navigation board is adjusted twice to achieve the effect of precise fixation of the navigation board and precise positioning of the working end of the surgical robot, further reducing the navigation error caused by skin deformation and improving the positioning accuracy of the surgical robot.

[0052] Embodiment 2: In embodiment 1, depth calibration is performed to obtain the second reference position. This embodiment makes further improvements based on the above content.

[0053] In step S300, depth calibration is performed on the second initial position to obtain a second reference position, including: S310: Obtaining the position of each correction target point 4 at the first reference position and marking it as the first original position, and obtaining the position of each correction target point 4 at the second initial position and marking it as the second current position; S320: Calculating the position deviation of each correction target point 4 according to the first original position and the second current position, and then calculating the overall offset of the navigation board, and selecting a number of correction target points 4 as candidate target points according to the flotation mechanism; S330: Perform depth calibration based on the candidate target point to obtain a second reference position.

[0054] In some embodiments, the navigation board is adjusted to reflect the displacement of the navigation board relative to its initial position at the start of surgery due to factors such as skin deformation, tissue movement, or surgical machine manipulation. After determining the first reference position, the navigation board is photographed using an X-ray machine, and the positions of all correction targets 4 are recorded and marked as the first initial position. After the pressure of the micro-airbag 6 is adjusted, the navigation board's position shifts, and the navigation board is re-photographed, and the positions of all correction targets 4 are again recorded and marked as the second current position.

[0055] For each correction target point 4, the first original position and the second current position are compared to calculate the position deviation. The position deviation is calculated using the distance formula in three-dimensional space, as follows:

[0056] in, is the position deviation, are the coordinates of the first original position, are the coordinates of the second current position.

[0057] In some embodiments, the flotation mechanism is as follows: based on the position deviation and overall offset of each corrective target point 4, a comprehensive impact value of each corrective target point 4 is calculated. The comprehensive impact values ​​of each corrective target point 4 are sorted in descending order by size, and a number of corrective targets 4 are selected as candidate targets based on the extraction ratio. The first original position and second current position of each corrective target point 4 are obtained, and the position deviation of each corrective target point 4 is calculated using a coordinate difference formula. For example, if the original position of target point A is (10, 20, 30) and the current position is (12, 22, 40), its position deviation is 2.83. Based on the position deviations of all corrective targets 4, the average value of the position deviations is calculated using the average value formula. If the position deviation of target point B is 1.41 and the position deviation of target point C is 3.16, the average value is 2.46. The deformation value is determined by the ratio of the position deviation of each corrective target point 4 to the average value of the position deviations. For example, the deformation value of target point A is 2.83 / 2.46≈1.15. Based on the deformation value of each corrective target 4 and the preset contribution weight, for example, a weight coefficient of 0.7, the contribution value of target A is calculated to be 1.15 × 0.7 = 0.805. A weighted comprehensive calculation is performed on the deformation value and contribution value of each corrective target 4. For example, if the combined weight is 60% for the deformation value and 40% for the contribution value, the combined impact value of target A is 1.15 × 0.6 + 0.805 × 0.4 ≈ 1.02. The combined impact values ​​of all corrective targets 4 are sorted in descending order from largest to smallest.

[0058] In some embodiments, the comprehensive impact value is calculated by comprehensively calculating the deformation value and contribution value of each corrective target point 4. The deformation value refers to the ratio of the position deviation of each corrective target point 4 to the average position deviation; the contribution value refers to the product of the preset weight value of each corrective target point 4 and the ratio of the position deviation of each corrective target point 4 to the overall offset. The deformation value and contribution value mentioned in this embodiment are both used to assess the actual importance of the corrective target point 4. Specifically, the position deviation data of each corrective target point 4 is collected by a sensor. For example, the deviation of target point A is 0.5 mm, the deviation of target point B is 0.8 mm, and the deviation of target point C is 1.2 mm. The average position deviation of all targets is calculated to obtain an average deviation of 0.83 mm.

[0059] The deformation value is then calculated based on the ratio of each target's positional deviation to the average deviation. For example, the deformation value of target A is 0.5 / 0.83=0.6, the deformation value of target B is 0.8 / 0.83=0.96, and the deformation value of target C is 1.2 / 0.83=1.45. The pre-set weight value for each target is obtained, for example, the weight of target A is 0.3, the weight of target B is 0.5, and the weight of target C is 0.2, and the overall offset is calculated. For example, the overall offset is 1.0 mm.

[0060] The contribution value is then calculated based on the ratio of the position deviation of each target to the overall offset, combined with the preset weight value. For example, the contribution value of target A is 0.3*(0.5 / 1.0)=0.15. Similarly, the contribution value of target B is 0.4, and the contribution value of target C is 0.24.

[0061] A comprehensive calculation is performed using deformation values ​​and contribution values ​​to obtain the comprehensive impact value of each target. For example, the comprehensive impact value of target A is 0.6 + 0.15 = 0.75, the comprehensive impact value of target B is 0.96 + 0.4 = 1.36, and the comprehensive impact value of target C is 1.45 + 0.24 = 1.69. The comprehensive impact values ​​of each target are sorted in descending order, for example, target C (1.69), target B (1.36), target A (0.75). Based on the extraction ratio, for example, selecting the top 50% of targets, candidate targets are selected from the sorted targets, such as targets C and B. Finally, the position deviation and comprehensive impact values ​​of the selected candidate target data, such as targets C and B, are used to optimize the correction plan or generate an adjustment report.

[0062] In some embodiments, the extraction ratio is set based on the ratio of the overall offset to a preset offset threshold. If the ratio is not less than 1, all corrective target points 4 are selected as candidate targets. If the ratio is less than 1, a number of corrective target points 4 are selected as candidate targets based on the ratio. The overall offset is obtained, and a preset offset threshold is used as a reference value. For example, if the overall offset is 1.5 mm and the offset threshold is 2 mm, the ratio of the overall offset to the offset threshold is calculated to be 0.75, and a preliminary extraction ratio is obtained, i.e., the top 75% of corrective target points 4 are sequentially selected as candidate targets.

[0063] In some embodiments, depth calibration is performed based on the candidate target points to obtain a second reference position. This specifically includes: determining calibration parameters for each candidate target point based on the position deviation values, for example, by fitting the deviation values ​​using the least squares method to obtain a calibration matrix A. Using the calibration parameters, the second initial position is adjusted to obtain a preliminary calibration position, for example, by multiplying the coordinates of the second initial position by matrix A through a matrix operation to obtain adjusted coordinates (x1, y1, z1). Based on the preliminary calibration position, updated position information for each candidate target point is obtained, for example, by capturing the target position using a high-precision camera and denoting it as (x2, y2, z2). Using the updated position information, a secondary calibration deviation value is calculated for each corrected target point 4, for example, by calculating the secondary deviation value through standard deviation analysis. Based on the secondary calibration deviation values, secondary calibration parameters are determined for each candidate target point, for example, by optimizing the calibration matrix using a gradient descent method to obtain a matrix B. Using the secondary calibration parameters, depth calibration is performed on the preliminary calibration position to obtain a second reference position, for example, by multiplying the coordinates of the preliminary calibration position by matrix B through a matrix operation to obtain final coordinates (x3, y3, z3).

[0064] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages: The present application obtains the different positions of each correction target 4, namely the first original position and the second current position, and calculates the overall offset of the navigation board by calculating the position deviation of each correction target 4; calculates the comprehensive impact value of each correction target 4, taking into account the deformation value and contribution value; arranges the comprehensive impact values ​​in descending order of size, and selects candidate targets according to the extraction ratio; achieves the effect of accurately selecting effective targets, ensuring the accuracy of surgical positioning while improving the efficiency of surgical positioning.

[0065] Embodiment 3: This embodiment makes further improvements based on the above embodiment.

[0066] The method further includes: S510: obtaining an analysis report, presetting a positioning accuracy threshold, selecting the number and layout of candidate target points corresponding to a positioning accuracy greater than the positioning accuracy threshold, and performing statistical grouping, wherein each group includes a surgical identification number, the number and layout of candidate targets; S520: Randomly select a group to perform fitness evaluation and calculate the positioning error and positioning rate; S530: Based on the fitness evaluation result, select a layout with a fitness greater than a first threshold as a parent, perform a crossover operation on the selected parent layout to generate a child layout; and perform a mutation operation on the child layout; S540: Repeat steps S520 and S530 until a preset number of iterations is reached or the fitness is greater than a second threshold; output the layout with the highest fitness as the optimal correction target point 4 layout; The analysis report includes the number and layout of candidate targets selected for each surgery and the positioning accuracy of the surgical entry points; and the first threshold is less than the second threshold.

[0067] In some embodiments, data on the number, layout, and positioning accuracy of surgical entry points for each surgery are extracted from the surgical analysis report. The layout refers to the positional distribution of the corrected target 4. For example, data with a positioning accuracy of 92%, a number of candidate targets of 5, and a layout of (10, 20, 30) is extracted. Based on the extracted positioning accuracy data, a positioning accuracy threshold is set to 90%, and candidate target numbers and layouts with positioning accuracy greater than 90% are screened. The screened candidate target numbers and layouts are statistically grouped according to the surgery identification number. For example, data with surgery identification number 001 is grouped as {001, 5, (10, 20, 30)}. Based on the statistical grouping results, the fitness of each group of candidate target layouts is calculated using a fitness function, for example, a fitness of 95 is calculated. A determination is made as to whether the fitness is greater than a first threshold of 85. If the fitness is greater than 95, the candidate target layout for that group is recorded. If the fitness is less than 85, the candidate target layout is adjusted and the fitness is recalculated. The fitness calculation and layout adjustment steps are repeated until a preset number of iterations of 100 is reached or the fitness is greater than a second threshold of 98. All recorded candidate target layouts are compared, and the layout with the highest fitness is selected as the optimal corrected target 4 layout, for example, the layout with a fitness of 98 is selected.

[0068] In some embodiments, based on the selected layout, the positioning error of each candidate target point in the layout is calculated. For example, the distance error between each target point and the target position is calculated using the Euclidean distance formula, with an error range of 0.5 mm to 2.0 mm. Based on the positioning error calculation results, the overall positioning error mean of the layout is calculated. For example, the sum of the error values ​​for 10 target points and dividing by 10 yields a mean of 1.2 mm. The positioning rate of the layout is calculated based on the number and distribution of candidate target points in the layout. For example, based on the average distance between target points and the movement speed of the surgical instrument, a rate of 3 targets per second is calculated. The mean positioning error and the positioning rate are input into a fitness calculation model to convert the fitness of the layout. For example, a weighted formula is used to calculate a fitness of 0.85. If the fitness is greater than a first threshold of 0.8 and less than a second threshold of 0.9, the layout is marked as a candidate optimal layout. If the fitness is greater than the second threshold of 0.9, the layout is directly determined to be the optimal correction target 4 layout. The above process is repeated until a preset number of iterations of 100 is reached or an optimal layout with a fitness greater than 0.9 is found.

[0069] In some embodiments, based on the fitness evaluation results, a layout with a fitness greater than a first threshold is selected from the statistical grouping as a parent layout set. For example, a layout with a fitness greater than 0.8 is selected as the parent, and the roulette wheel selection algorithm is used to screen the five parents with the highest fitness from the candidate layouts. A crossover operation is performed on the selected parent layout to generate a child layout. A single-point crossover algorithm is used to perform a crossover at the third site of the gene sequence to generate 10 child layouts. The gene sequence of the child layout is obtained, and the mutation site is determined by a random number generator. If the mutation site is located at the 1st to 2nd site of the gene sequence, a local mutation strategy is adopted, and a Gaussian mutation algorithm is used to perform a perturbation of ±0.1 at the site. If the mutation site is located at the 4th to 10th site of the gene sequence, a global mutation strategy is adopted, and a uniform mutation algorithm is used to randomly replace the site with 0 or 1. According to the mutation strategy, the gene sequence of the child layout is randomly mutated to generate a mutated gene sequence. The mutated gene sequence is obtained, and the child layout is reconstructed by a decoding algorithm to obtain the mutated child layout. The mutated offspring layouts are evaluated for fitness, and a fitness function is used to calculate the score of each layout. Based on the fitness evaluation results, it is determined whether the mutated offspring layout meets the second threshold of 0.9. If so, the layout is retained; otherwise, it is eliminated.

[0070] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages: This application optimizes the layout of the correction target 4 through a genetic algorithm, significantly improving the positioning accuracy of the surgical navigation system; enhancing system efficiency, and the optimized layout reduces the time required for positioning and improves the positioning rate; by setting thresholds and the number of iterations, the effect of automated optimization of the layout of the correction target 4 is achieved.

[0071] Embodiment 4: This embodiment also provides a surgical robot navigation board, such as Figure 2 As shown, the navigation board includes a central navigation board 1, several auxiliary navigation boards 2 and several micro airbags 6. The central navigation board 1 and the several auxiliary navigation boards 2 are connected by micro airbags 6. Several auxiliary navigation panels 2 are distributed in a square shape, with the central navigation panel 1 located at the center of the auxiliary navigation panels 2. Two adjacent auxiliary navigation panels 2 are fixedly connected by micro airbags 6, and the central navigation panel 1 is fixedly connected to the four nearest auxiliary navigation panels 2 by micro airbags 6 on all sides. Both the central navigation panel 1 and the auxiliary navigation panel 2 are provided with a central target point 3, a set of cross-guide lines 5, and a plurality of correction targets 4. The center of the central target point 3 coincides with the center of the cross-guide lines 5. Each micro-airbag 6 is connected to an air pump for inflating or deflating the micro-airbag 6. Preferably, the micro-airbag 6 is in the form of a sheet and an isosceles trapezoid.

[0072] In some embodiments, when the position of the navigation board is adjusted using the micro airbag 6, an air pump is used to inflate or evacuate the micro airbag 6 according to the calculated pressure adjustment value, so that the air pressure value of each micro airbag 6 reaches the required numerical range. When the air pressure of the micro airbag 6 changes, it will cause the auxiliary navigation panels 2 on the left and right sides to move, thereby achieving the effect of adjusting the navigation board.

[0073] The navigation board in this embodiment can be used for dorsal or lateral puncture positioning navigation. The board is made of a metal or non-metallic material that clearly displays under X-rays and other imaging equipment. It must be sufficiently strong and non-deformable, or flexible enough to adhere to the human body surface, and is fixed to the human body surface. According to the optimal layout obtained in the above embodiment, the corresponding number and distribution of correction targets are selected and arranged on the navigation board body. These targets are used to measure distance and direction parameters on the XY plane in the X-ray film. These distance and direction parameters are used to correct distortion of the navigation board image and the target tissue structure image under X-rays and other imaging equipment. The specific image distortion correction method is not detailed here.

[0074] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Various modifications and variations are readily apparent to those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A surgical robot navigation method, applied to a navigation board, characterized in that: The method comprises: S100: obtaining preoperative preparation data for the puncture surgery through preoperative MRI image data, wherein the preoperative preparation data includes a percutaneous puncture point, a needle insertion angle, and a puncture depth to the percutaneous puncture point; S200: determining the initial position of the navigation board according to the preoperative preparation data and establishing a unified coordinate system, acquiring the patient's skin deformation data, and calculating the first reference position of the navigation board based on the deformation model; S300: determining the coordinate positions of the central navigation panel (1) and a plurality of auxiliary navigation panels (2) based on the first reference position and marking them to form a three-dimensional coordinate matrix; obtaining a deformation comprehensive value based on the skin deformation data acquired in real time, and if the deformation comprehensive value is greater than a preset deformation threshold, calculating the real-time pressure adjustment value of each micro airbag (6), arranging them in descending order according to the size of the real-time pressure adjustment value and adjusting the pressure of the corresponding micro airbag (6) in turn to obtain a second initial position; performing depth calibration on the second initial position to obtain a second reference position; S400: The navigation board is fixed on the human body based on the second reference position, and under the indication of the laser cross target provided on the working end of the surgical robot, the laser cross is aligned with the central target (3) and the cross guide line (5) of the central navigation board (1), and the central target (3) and the cross guide line (5) can be visualized under X-rays and overlap with the target tissue structure; the numerical values ​​of the surgical entry point, entry angle and entry depth based on the unified coordinate system are calculated, and the numerical values ​​are input into the surgical robot, and an analysis report is generated after the operation is completed.

2. A surgical robot navigation method according to claim 1, characterized in that: Acquiring the patient's skin deformation data and calculating the first reference position of the navigation board based on the deformation model, including: Acquire the patient's skin surface data based on a three-dimensional scanning device, generate point cloud data, and then obtain skin deformation data; mesh the skin deformation data, input the processed skin deformation data into a deformation model, extract the skin deformation characteristics, calculate the error value according to the change of the deformation characteristics, and calculate the first reference position based on the error value and the initial position; Wherein, the deformation characteristics include tension and curvature of the skin.

3. A surgical robot navigation method as claimed in claim 2, characterized in that: The processed skin deformation data is input into the deformation model, the deformation characteristics of the skin are extracted, and the error value is calculated according to the change of the deformation characteristics, including: based on the finite element analysis method, a collective model is established according to the processed skin deformation data, the collective model is divided into a finite number of units, each unit contains a number of nodes, and a finite element solver is used to solve the mechanical equation of each unit to obtain the displacement and stress results of the node, and then the tension and curvature changes of the skin are calculated, and the weighted average of multiple deformation characteristic changes is summed to obtain the error value.

4. A surgical robot navigation method according to claim 1, characterized in that: Calculate the real-time pressure adjustment value of each micro airbag (6), including: A total pressure value is calculated based on the deformation comprehensive value, the deformation threshold and the mapping formula, a corresponding pressure demand value is obtained based on the weight coefficient of each micro airbag (6), and a corresponding real-time pressure adjustment value is obtained based on the current pressure and pressure demand value of each micro airbag (6).

5. A surgical robot navigation method as claimed in claim 4, characterized in that: The mapping formula is as follows: ; in, is the total pressure value, is the comprehensive deformation value, is the deformation threshold, is the conversion coefficient, with a value range of [0, 1], which is used to measure the conversion relationship between deformation and pressure; the pressure demand value calculation formula is as follows: ; in, is the pressure requirement of the i-th micro airbag (6), is the weight coefficient of the i-th micro airbag (6).

6. A surgical robot navigation method according to claim 1, characterized in that: In step S300, depth calibration is performed on the second initial position to obtain a second reference position, including: S310: acquiring the position of each correction target point (4) at the first reference position and marking it as the first original position, acquiring the position of each correction target point (4) at the second initial position and marking it as the second current position; S320: calculating the position deviation of each correction target point (4) according to the first original position and the second current position, and then calculating the overall offset of the navigation board, and selecting a plurality of correction target points (4) as candidate target points according to the flotation mechanism; S330: Perform depth calibration based on the candidate target point to obtain a second reference position.

7. A surgical robot navigation method as claimed in claim 6, characterized in that: The flotation mechanism is as follows: based on the position deviation and overall offset of each correction target point (4), the comprehensive impact value of each correction target point (4) is calculated, the comprehensive impact value of each correction target point (4) is arranged in descending order according to size, and a number of correction target points (4) are selected as candidate target points according to the extraction ratio.

8. A surgical robot navigation method as claimed in claim 7, characterized in that: The comprehensive impact value is obtained by comprehensively calculating the deformation value and contribution value of each correction target point (4), wherein the deformation value refers to the ratio of the position deviation of each correction target point (4) to the average value of the position deviation; the contribution value refers to the product of the preset weight value of each correction target point (4) and the ratio of the position deviation of each correction target point (4) to the overall offset; the extraction ratio is set according to the ratio of the overall offset and the preset offset threshold value, and if the ratio is not less than 1, all the correction target points (4) are selected as candidate target points; if the ratio is less than 1, a number of correction target points (4) are selected as candidate target points according to the ratio.

9. A surgical robot navigation method as claimed in claim 1, characterized in that: The method further comprises: S510: Obtain an analysis report, pre-set a positioning accuracy threshold, select the number and layout of candidate targets corresponding to a positioning accuracy greater than the positioning accuracy threshold, and perform statistical grouping, where each group includes a surgery identification number, the number and layout of candidate targets; S520: Randomly select a group to perform fitness evaluation, and calculate the positioning error and positioning rate; S530: According to the fitness evaluation result, a layout with a fitness greater than a first threshold is selected as a parent, a crossover operation is performed on the selected parent layout to generate a child layout; and a mutation operation is performed on the child layout; S540: Repeat steps S520 and S530 until a preset number of iterations is reached or the fitness is greater than a second threshold; output the layout with the highest fitness as the optimal correction target layout; The analysis report includes the number and layout of candidate targets selected for each surgery and the positioning accuracy of surgical entry points; and the first threshold is less than the second threshold.

10. A surgical robot navigation board, used in a surgical robot navigation method according to any one of claims 1 to 9, characterized in that: The navigation board comprises a central navigation panel (1), a plurality of auxiliary navigation panels (2) and a plurality of micro airbags (6); the central navigation panel (1) and the plurality of auxiliary navigation panels (2) are connected by the micro airbags (6); each micro airbag (6) is connected to an air pump; The plurality of auxiliary navigation panels (2) are arranged in a square shape, and the central navigation panel (1) is located at the center of the plurality of auxiliary navigation panels (2); two adjacent auxiliary navigation panels (2) are fixedly connected via micro airbags (6), and the central navigation panel (1) is fixedly connected to the four nearest auxiliary navigation panels (2) via micro airbags (6) on all sides; The central navigation panel (1) and the auxiliary navigation panel (2) are both provided with a central target point (3), a group of cross guide lines (5) and a plurality of correction target points (4); the center of the central target point (3) and the center of the cross guide lines (5) coincide with each other.

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