A method for rapid system registration based on optical positioner

Abstract

The present invention relates to the field of positioning and registration technology, and in particular to a method for rapid system registration based on an optical positioner. The method comprises the following steps: by fixing an infrared camera and an LED lamp above the patient's surgical site area of ​​a CT bed, and optimizing the position coverage distribution of a calibration ball based on the patient's surgical site area of ​​the CT bed, so as to obtain an optical calibration ball adaptation coverage distribution position; based on the optical calibration ball adaptation coverage distribution position, the calibration ball is staggeredly distributed on the patient's surgical site area of ​​the CT bed; the patient's surgical site area of ​​the CT bed is subjected to CT image scanning and calibration ball positioning processing to obtain the CT image coordinates of the surgical site calibration ball; the calibration needle tip is placed on the calibration ball by holding a calibration tool handle, and the calibration ball is subjected to position tracking analysis and image registration processing to obtain the patient's surgical site CT image registration result. The present invention can greatly improve the image registration efficiency of the analysis system.

Description

Technical Field

[0001] The present invention relates to the technical field of positioning and registration, and in particular to a method for rapid system registration based on an optical positioner. Background Art

[0002] In the application of medical robot positioning guidance, the optical locator collects images in real time, and the medical image (usually CT image) is systematically registered with the intermediate sensor (infrared camera, etc.), and then the robot arm is guided and positioned. The target is positioned by using the optical locator to obtain the target's position information, and the acquired position information is associated with the real-time acquired image in the system. The image is transformed or corrected by the image processing algorithm so that the position of the target in different imaging systems can match. This process can be achieved through image registration technologies such as feature point matching and transformation matrix calculation. At the same time, by adjusting the image, the images acquired by different imaging systems in the system can be accurately superimposed or overlapped to achieve the purpose of system registration. However, the traditional system registration method is usually based on feature matching or region matching, which takes a lot of time in clinical preoperative preparation, generally from ten to thirty minutes, which generally causes a serious waste of time and energy, and there are also problems of inaccurate matching and long calculation time. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a method for rapid system alignment based on an optical locator to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for rapid system registration based on an optical positioner comprises the following steps:

[0005] Step S1: An infrared camera and an LED lamp are installed and fixed above the patient's surgical site area of ​​the CT bed, and the calibration ball is optimized for position coverage distribution based on the patient's surgical site area of ​​the CT bed to obtain an optical calibration ball adaptation coverage distribution position; the calibration ball is staggeredly distributed on the patient's surgical site area of ​​the CT bed based on the optical calibration ball adaptation coverage distribution position;

[0006] Step S2: Performing CT image scanning of the surgical site of the patient on the CT bed by using an infrared camera to obtain a CT scan image of the surgical site of the patient; uploading the CT scan image of the surgical site of the patient to a CT image analysis system, and using the CT image analysis system to perform calibration ball positioning processing on the CT scan image of the surgical site of the patient to obtain calibration ball CT image coordinates of the surgical site;

[0007] Step S3: placing the calibration needle tip on the calibration ball by holding the calibration tool handle, and performing position tracking analysis on the calibration ball based on the infrared camera and the infrared recognition ball to obtain the calibration ball camera coordinates of the surgical site;

[0008] Step S4: performing image registration processing on the CT image coordinates of the surgical site calibration sphere based on the camera coordinates of the surgical site calibration sphere to obtain the CT image registration result of the patient's surgical site.

[0009] The present invention firstly installs and fixes an infrared camera and an LED lamp above the surgical site area of ​​the patient on the CT bed. The purpose of this operation is to provide necessary equipment support for subsequent surgical site identification and position positioning calibration. By fixing the infrared camera, real-time monitoring and image acquisition of the surgical site can be achieved, and the installation of the LED lamp helps to provide sufficient light conditions, ensuring that the camera can accurately capture the image information of the surgical area, thereby providing a reliable data basis for subsequent analysis and operation. By optimizing the position coverage distribution of the calibration ball in combination with the surgical site area of ​​the patient on the CT bed, the adaptive coverage distribution position of the optical calibration ball is determined. This step can ensure that the position distribution of the calibration ball is uniform and covers the entire surgical area through optimized distribution, thereby achieving comprehensive calibration and positioning of the surgical area. By staggering the calibration ball on the surgical site area of ​​the patient on the CT bed according to the adaptive coverage distribution position of the optical calibration ball, this step can further ensure that the position distribution of the calibration ball is uniform and covers a wide area through staggered distribution, effectively improving the accuracy and reliability of calibration and calibration, and providing a reliable positioning reference for subsequent calibration and registration operations, thereby reducing the corresponding preoperative preparation. Secondly, the CT image scanning of the surgical site of the patient on the CT bed is performed by using an infrared camera. The key to this step is to use an infrared camera to perform non-contact scanning of the surgical site of the patient, which can achieve high-precision imaging of the surgical site. In this way, accurate structural information of the surgical site of the patient can be obtained, providing a reliable data basis for subsequent image processing and analysis. The CT scan image of the surgical site of the patient is uploaded to the CT image analysis system by using network transmission technology, and the system is used to perform calibration ball positioning processing on the CT scan image of the surgical site of the patient. The purpose of this step is to identify and locate the calibration ball and determine their accurate coordinate position in the CT image. The accurate positioning of the calibration ball can provide an important basis for subsequent image calibration and calibration positioning, ensure the accuracy and safety of the calibration positioning operation, and provide doctors with more reliable navigation and positioning information. Then, by holding the calibration tool handle, the calibration needle tip is placed on the calibration ball, and the position of the calibration ball is tracked and analyzed using the infrared camera and the infrared identification ball on the calibration needle tip, a more stable and accurate calibration ball camera coordinate can be obtained, thereby realizing the precise positioning of the calibration ball position at the surgical site. The key to this step is to obtain the camera coordinates through calculation, providing an accurate basis for subsequent image acquisition and processing, and ensuring the accuracy and safety of the calibration registration process. Finally, by performing image registration processing on the CT image coordinates of the surgical site calibration ball based on the camera coordinates of the surgical site calibration ball, the position of the surgical site calibration ball in the CT image can be matched with the position in the camera coordinates, thereby realizing the accurate matching of the image data and the actual surgical scene.This processing can ensure the consistency between the CT image of the patient's surgical site and the actual surgical position, providing accurate image guidance and positioning information for subsequent processing, thereby improving the accuracy of optical locator image registration and reducing the calculation time of the registration process. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0011] Figure 1 A schematic diagram of the steps of the method for rapid system registration based on an optical locator according to the present invention;

[0012] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0013] Figure 3 for Figure 2 Detailed step flow chart of step S13 in FIG. DETAILED DESCRIPTION

[0014] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0015] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0016] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0017] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for rapid system registration based on an optical positioner, the method comprising the following steps:

[0018] Step S1: An infrared camera and an LED lamp are installed and fixed above the patient's surgical site area of ​​the CT bed, and the calibration ball is optimized for position coverage distribution based on the patient's surgical site area of ​​the CT bed to obtain an optical calibration ball adaptation coverage distribution position; the calibration ball is staggeredly distributed on the patient's surgical site area of ​​the CT bed based on the optical calibration ball adaptation coverage distribution position;

[0019] Step S2: Performing CT image scanning of the surgical site of the patient on the CT bed by using an infrared camera to obtain a CT scan image of the surgical site of the patient; uploading the CT scan image of the surgical site of the patient to a CT image analysis system, and using the CT image analysis system to perform calibration ball positioning processing on the CT scan image of the surgical site of the patient to obtain calibration ball CT image coordinates of the surgical site;

[0020] Step S3: placing the calibration needle tip on the calibration ball by holding the calibration tool handle, and performing position tracking analysis on the calibration ball based on the infrared camera and the infrared recognition ball to obtain the calibration ball camera coordinates of the surgical site;

[0021] Step S4: performing image registration processing on the CT image coordinates of the surgical site calibration sphere based on the camera coordinates of the surgical site calibration sphere to obtain the CT image registration result of the patient's surgical site.

[0022] In the embodiment of the present invention, please refer to Figure 1 FIG. 1 is a schematic diagram of the process flow of the method for rapid system alignment based on an optical locator according to the present invention. In this example, the method for rapid system alignment based on an optical locator includes the following steps:

[0023] Step S1: An infrared camera and an LED lamp are installed and fixed above the patient's surgical site area of ​​the CT bed, and the calibration ball is optimized for position coverage distribution based on the patient's surgical site area of ​​the CT bed to obtain an optical calibration ball adaptation coverage distribution position; the calibration ball is staggeredly distributed on the patient's surgical site area of ​​the CT bed based on the optical calibration ball adaptation coverage distribution position;

[0024] The embodiment of the present invention prepares an infrared camera and an LED lamp as well as brackets and fixing devices required for the installation process, selects a suitable position above the patient's surgical site area on the CT bed to install and fix the infrared camera and the LED lamp, and uses corresponding statistical methods to perform range measurement and shape analysis on the patient's surgical site area on the CT bed to analyze and determine the size of the area range and shape characteristics, so that the boundary range and shape contour of the surgical site can be accurately determined. Secondly, by combining the area range size and area shape obtained by analysis, a corresponding distribution area prediction method is used to monitor and analyze the regional distribution area, so as to measure and predict the distribution area of ​​the corresponding patient's surgical site area. At the same time, the size of the optical calibration sphere and the scanning range size are obtained through statistical analysis and the effective distribution is used. The corresponding calibration balls are analyzed by the area statistics method to determine the actual effective area that each calibration ball can cover, and the maximum number of calibration balls that can be accommodated in the patient's surgical area is determined by comparing the effective scanning distribution area obtained by statistical analysis with the corresponding distribution area of ​​the patient's surgical area. Then, the corresponding number of optical calibration balls are distributed and arranged at the corresponding positions of the patient's surgical area of ​​the CT bed according to the preset distribution positions by combining the determined maximum number of optical calibration balls, and the distribution positions of the optical calibration balls are rearranged according to their coverage conditions to distribute the corresponding calibration balls in the patient's surgical area of ​​the CT bed in a staggered manner to further ensure that the positions of the calibration balls are evenly distributed and avoid image aliasing or occlusion caused by dense distribution of the calibration balls.

[0025] Step S2: Performing CT image scanning of the surgical site of the patient on the CT bed by using an infrared camera to obtain a CT scan image of the surgical site of the patient; uploading the CT scan image of the surgical site of the patient to a CT image analysis system, and using the CT image analysis system to perform calibration ball positioning processing on the CT scan image of the surgical site of the patient to obtain calibration ball CT image coordinates of the surgical site;

[0026] The embodiment of the present invention adjusts the position and angle of the infrared camera by determining the accurate position of the surgical site of the patient in the CT bed, so that the infrared camera is aligned with the surgical site of the patient in the CT bed, and the infrared camera is started to scan the surgical site in real time, thereby obtaining a CT scan image of the surgical site of the patient. Secondly, the CT scan image of the surgical site of the patient obtained by real-time scanning is uploaded to the CT image analysis system by using network transmission technology, and the position of the calibration ball in the pre-processed CT scan image of the surgical site of the patient is identified and located by using the corresponding image processing algorithm in the CT image analysis system, so as to map the coordinates of the calibration ball in the image space coordinate system according to the position of the calibration ball in the CT image as the coordinates of the surgical site calibration ball CT image, and finally obtain the coordinates of the surgical site calibration ball CT image.

[0027] Step S3: placing the calibration needle tip on the calibration ball by holding the calibration tool handle, and performing position tracking analysis on the calibration ball based on the infrared camera and the infrared recognition ball to obtain the calibration ball camera coordinates of the surgical site;

[0028] In the embodiment of the present invention, the calibration needle tip is accurately placed on the calibration ball by holding the corresponding calibration tool handle, and the motion trajectory of the infrared recognition ball on the calibration needle tip is tracked by using an infrared camera to achieve real-time tracking of the motion trajectory of the calibration needle tip, and the real-time motion position of the infrared recognition ball in the camera coordinate system is calculated by using image processing and computational geometry methods (such as background subtraction, thresholding, morphological processing, etc.) in combination with the size and shape information of the calibration ball. Then, the relative position of the calibration ball (the middle part of the area surrounded by the infrared recognition ball) is averaged by using the geometric mean method, so as to fully consider the geometric shape of the calibration ball and the characteristics of the camera to quantitatively calculate the accurate position of the surgical site calibration ball in the camera coordinate system, and finally obtain the camera coordinates of the surgical site calibration ball.

[0029] Step S4: performing image registration processing on the CT image coordinates of the surgical site calibration sphere based on the camera coordinates of the surgical site calibration sphere to obtain the CT image registration result of the patient's surgical site.

[0030] The embodiment of the present invention processes the CT image coordinates of the surgical site calibration ball and the camera coordinates of the surgical site calibration ball by using a time synchronization method, so as to ensure that the time information of the image taken by the camera is aligned with the time information of the CT image by recording and matching the timestamp, and performs statistical analysis of local features by using local feature descriptors (such as SIFT, SURF, etc.) to extract the local feature information of each coordinate point, and at the same time, extracts the feature corner points of the extracted image coordinate local features and camera coordinate local features by using a corner detection algorithm (such as Harris corner detection, Shi-Tomasi corner detection, etc.) to extract the corresponding feature corner points, and converts the corner points of the surgical site calibration ball image coordinates and the camera coordinates into vector form, and then uses a feature matching algorithm in computer vision (such as SIFT (Scale Invariant Feature Transform), SURF (Speeded Robust Features) or ORB (Oriented FAST and Rotated BRIEF, etc.) match the converted calibration sphere image coordinate corner point vector and the corresponding coordinate corner point in the calibration sphere camera coordinate corner point vector to establish the coordinate matching correspondence between the calibration sphere in the image and camera coordinate systems, and identify the non-rigid deformation between the calibration sphere image coordinates and the camera coordinates, and then estimate the non-rigid deformation registration matrix between the calibration sphere coordinates. Perform image registration processing on the non-rigid deformation existing in the CT image coordinates of the calibration sphere of the corresponding surgical part, so as to accurately map the position of the surgical part calibration sphere in the CT image to the actual surgical scene, and realize the precise alignment of the CT image of the patient's surgical part, and finally obtain the CT image registration result of the patient's surgical part.

[0031] The present invention firstly installs and fixes an infrared camera and an LED lamp above the surgical site area of ​​the patient on the CT bed. The purpose of this operation is to provide necessary equipment support for subsequent surgical site identification and position positioning calibration. By fixing the infrared camera, real-time monitoring and image acquisition of the surgical site can be achieved, and the installation of the LED lamp helps to provide sufficient light conditions, ensuring that the camera can accurately capture the image information of the surgical area, thereby providing a reliable data basis for subsequent analysis and operation. By optimizing the position coverage distribution of the calibration ball in combination with the surgical site area of ​​the patient on the CT bed, the adaptive coverage distribution position of the optical calibration ball is determined. This step can ensure that the position distribution of the calibration ball is uniform and covers the entire surgical area through optimized distribution, thereby achieving comprehensive calibration and positioning of the surgical area. By staggering the calibration ball on the surgical site area of ​​the patient on the CT bed according to the adaptive coverage distribution position of the optical calibration ball, this step can further ensure that the position distribution of the calibration ball is uniform and covers a wide area through staggered distribution, effectively improving the accuracy and reliability of calibration and calibration, and providing a reliable positioning reference for subsequent calibration and registration operations, thereby reducing the corresponding preoperative preparation. Secondly, the CT image scanning of the surgical site of the patient on the CT bed is performed by using an infrared camera. The key to this step is to use an infrared camera to perform non-contact scanning of the surgical site of the patient, which can achieve high-precision imaging of the surgical site. In this way, accurate structural information of the surgical site of the patient can be obtained, providing a reliable data basis for subsequent image processing and analysis. The CT scan image of the surgical site of the patient is uploaded to the CT image analysis system by using network transmission technology, and the system is used to perform calibration ball positioning processing on the CT scan image of the surgical site of the patient. The purpose of this step is to identify and locate the calibration ball and determine their accurate coordinate position in the CT image. The accurate positioning of the calibration ball can provide an important basis for subsequent image calibration and calibration positioning, ensure the accuracy and safety of the calibration positioning operation, and provide doctors with more reliable navigation and positioning information. Then, by holding the calibration tool handle, the calibration needle tip is placed on the calibration ball, and the position of the calibration ball is tracked and analyzed using the infrared camera and the infrared identification ball on the calibration needle tip, a more stable and accurate calibration ball camera coordinate can be obtained, thereby realizing the precise positioning of the calibration ball position at the surgical site. The key to this step is to obtain the camera coordinates through calculation, providing an accurate basis for subsequent image acquisition and processing, and ensuring the accuracy and safety of the calibration registration process. Finally, by performing image registration processing on the CT image coordinates of the surgical site calibration ball based on the camera coordinates of the surgical site calibration ball, the position of the surgical site calibration ball in the CT image can be matched with the position in the camera coordinates, thereby realizing the accurate matching of the image data and the actual surgical scene.This processing can ensure the consistency between the CT image of the patient's surgical site and the actual surgical position, providing accurate image guidance and positioning information for subsequent processing, thereby improving the accuracy of optical locator image registration and reducing the calculation time of the registration process.

[0032] Preferably, step S1 comprises the following steps:

[0033] Step S11: installing and fixing an infrared camera and an LED lamp above the surgical site of the patient on the CT bed;

[0034] Step S12: performing regional range and shape recognition analysis on the surgical site of the patient on the CT bed to obtain the size of the surgical site and the shape of the surgical site;

[0035] Step S13: Calculate and determine the number of calibration balls based on the size of the surgical site area and the shape of the surgical site area of ​​the patient to obtain the maximum number of optical calibration balls to be adapted;

[0036] Step S14: optimizing the position coverage distribution of the surgical site of the patient on the CT bed through the calibration sphere based on the maximum number of optical calibration sphere adaptations, so as to obtain the distribution position of the optical calibration sphere adaptation coverage;

[0037] Step S15: Distribute the calibration spheres in a staggered manner over the surgical site of the patient on the CT bed based on the adaptive coverage distribution positions of the optical calibration spheres.

[0038] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S1 in FIG. 1 , in this embodiment, step S1 includes the following steps:

[0039] Step S11: installing and fixing an infrared camera and an LED lamp above the surgical site of the patient on the CT bed;

[0040] The embodiment of the present invention prepares an infrared camera and an LED lamp as well as brackets and fixing devices required for the installation process, and selects a suitable position above the patient's surgical site area on the CT bed to install and fix the infrared camera and the LED lamp, ensuring that the installation is firm and can accurately illuminate the patient's surgical site.

[0041] Step S12: performing regional range and shape recognition analysis on the surgical site of the patient on the CT bed to obtain the size of the surgical site and the shape of the surgical site;

[0042] The embodiment of the present invention uses a regional range statistical method to measure the range of the patient's surgical area on the CT bed, so as to fully consider the dimensions including length, width and height to measure the range of the patient's surgical area, thereby obtaining the range of the patient's surgical area. Then, by performing shape analysis on the patient's surgical area on the CT bed to analyze and determine its shape characteristics, the boundary range and shape contour of the surgical area are accurately determined, and finally the shape of the patient's surgical area is obtained.

[0043] Step S13: Calculate and determine the number of calibration balls based on the size of the surgical site area and the shape of the surgical site area of ​​the patient to obtain the maximum number of optical calibration balls to be adapted;

[0044] The embodiment of the present invention uses a corresponding distribution area prediction method to monitor and analyze the regional distribution area by combining the size of the patient's surgical site area and the shape of the patient's surgical site area obtained by analysis, so as to measure and predict the distribution area of ​​the corresponding patient's surgical site area. At the same time, the size of the optical calibration ball and the scanning range size are obtained through statistical analysis, and the corresponding calibration ball is analyzed using an effective distribution area statistical method to analyze and determine the actual effective area that each calibration ball can cover. Then, the effective scanning distribution area of ​​the cursor calibration ball obtained through statistical analysis is compared with the corresponding patient's surgical site area distribution area to determine the maximum number of calibration balls that can be accommodated in the patient's surgical site area, ensure sufficient coverage and accurate imaging, and finally obtain the maximum number of optical calibration ball adaptation limits.

[0045] Step S14: optimizing the position coverage distribution of the surgical site of the patient on the CT bed through the calibration sphere based on the maximum number of optical calibration sphere adaptations, so as to obtain the distribution position of the optical calibration sphere adaptation coverage;

[0046] The embodiment of the present invention distributes a corresponding number of optical calibration balls at corresponding positions of the patient's surgical site area on the CT bed according to a preset distribution position in combination with the determined adaptation limit number of optical calibration balls, and uses computer-aided design software or image simulation tools to identify and analyze the imaging distribution area of ​​the calibration balls at each initial allocation position to identify the imaging distribution of each calibration ball, and evaluate and determine the imaging area coverage of each calibration ball at the initial allocation distribution position, thereby finding out the insufficient or excessive coverage areas, and at the same time, according to the identified insufficient or excessive coverage areas, the layout of the corresponding initial allocation position is adjusted and optimized, so as to rearrange the distribution position of the optical calibration balls so that they can better cover the surgical site area, and finally obtain the optical calibration ball adaptation coverage distribution position.

[0047] Step S15: Distribute the calibration spheres in a staggered manner over the surgical site of the patient on the CT bed based on the adaptive coverage distribution positions of the optical calibration spheres.

[0048] The embodiment of the present invention distributes the corresponding calibration balls in a staggered manner on the patient's surgical area on the CT bed by combining the optimized optical calibration ball adaptation coverage distribution position, so as to further ensure that the calibration balls are evenly distributed and avoid image aliasing or occlusion caused by dense distribution of the calibration balls.

[0049] The present invention firstly installs and fixes an infrared camera and an LED lamp above the surgical site area of ​​the patient on the CT bed. The purpose of this operation is to provide necessary equipment support for subsequent surgical site identification and position positioning calibration. By fixing the infrared camera, real-time monitoring and image acquisition of the surgical site can be achieved, and the installation of the LED lamp helps to provide sufficient light conditions, ensuring that the camera can accurately capture the image information of the surgical area, thereby providing a reliable data basis for subsequent analysis and operation. Secondly, by performing regional range and shape recognition analysis on the surgical site area of ​​the patient on the CT bed, this helps to determine the specific position and shape characteristics of the surgical site. This step can accurately determine the boundary range and shape contour of the surgical site through recognition analysis, providing an accurate reference basis for subsequent calibration and positioning, and ensuring the accuracy and safety of the operation. Then, the calibration ball is quantitatively calculated based on the size of the surgical site area of ​​the patient and the shape of the surgical site area of ​​the patient to determine the adaptation limit number of the optical calibration ball. This step can reasonably determine the number of calibration balls through quantitative calculation, ensuring that a sufficient number of calibration balls are arranged in the surgical area to achieve the accuracy and reliability of subsequent calibration and calibration. Next, the position coverage distribution of the patient's surgical area on the CT bed is optimized based on the consideration of the maximum number of optical calibration balls and the coverage of the calibration balls to determine the distribution position of the optical calibration balls. This step can ensure that the calibration balls are evenly distributed and cover the entire surgical area through optimized distribution, thereby achieving comprehensive calibration and positioning of the surgical area. Finally, the calibration balls are staggered in the surgical area of ​​the patient on the CT bed according to the distribution position of the optical calibration balls. This step can further ensure that the calibration balls are evenly distributed and cover a wide area through staggered distribution, effectively improving the accuracy and reliability of calibration and calibration, and providing a reliable positioning reference for subsequent calibration and registration operations.

[0050] Preferably, step S13 comprises the following steps:

[0051] Step S131: monitoring the regional distribution area according to the size of the surgical site and the shape of the surgical site to obtain the regional distribution area of ​​the surgical site;

[0052] Step S132: measuring the size of the calibration sphere to obtain the size of the optical calibration sphere;

[0053] Step S133: monitoring and analyzing the scanning range of the calibration sphere to obtain the scanning range size of the optical calibration sphere;

[0054] Step S134: performing statistical analysis on the effective scanning distribution area of ​​the calibration ball according to the size of the optical calibration ball and the scanning range of the optical calibration ball to obtain the effective scanning distribution area of ​​the cursor calibration ball;

[0055] Step S135: Quantify and determine the effective scanning distribution area of ​​the cursor calibration ball based on the distribution area of ​​the surgical site of the patient, so as to obtain the maximum number of optical calibration ball adaptations.

[0056] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 2 FIG. 1 is a schematic diagram of a detailed step flow chart of step S13 in FIG. 1 . In this embodiment, step S13 includes the following steps:

[0057] Step S131: monitoring the regional distribution area according to the size of the surgical site and the shape of the surgical site to obtain the regional distribution area of ​​the surgical site;

[0058] The embodiment of the present invention combines the size of the patient's surgical site area and the shape of the patient's surgical site area obtained by analysis to adopt a corresponding distribution area prediction method to monitor and analyze the regional distribution area, so as to measure and predict the distribution area of ​​the corresponding patient's surgical site area, and finally obtain the distribution area of ​​the patient's surgical site area.

[0059] Step S132: measuring the size of the calibration sphere to obtain the size of the optical calibration sphere;

[0060] The embodiment of the present invention uses appropriate measuring tools (such as a ruler, a laser rangefinder, etc.) to measure the size of the selected calibration sphere to accurately measure the size of the calibration sphere, including the diameter or radius, and ensures that each calibration sphere meets the specified size standards, and finally obtains the size of the optical calibration sphere.

[0061] Step S133: monitoring and analyzing the scanning range of the calibration sphere to obtain the scanning range size of the optical calibration sphere;

[0062] The embodiment of the present invention monitors and analyzes the scanning range of the calibration ball to monitor and record the scanning range of the calibration ball during the scanning process, including the horizontal and vertical ranges of the scanning, and finally obtains the scanning range size of the optical calibration ball.

[0063] Step S134: performing statistical analysis on the effective scanning distribution area of ​​the calibration ball according to the size of the optical calibration ball and the scanning range of the optical calibration ball to obtain the effective scanning distribution area of ​​the cursor calibration ball;

[0064] The embodiment of the present invention analyzes the corresponding calibration balls by combining the size of the optical calibration ball and the scanning range of the optical calibration ball obtained by analysis using an effective distribution area statistical method to analyze and determine the actual effective area that each calibration ball can cover, and finally obtain the effective scanning distribution area of ​​the cursor calibration ball.

[0065] Step S135: Quantify and determine the effective scanning distribution area of ​​the cursor calibration ball based on the distribution area of ​​the surgical site of the patient, so as to obtain the maximum number of optical calibration ball adaptations.

[0066] The embodiment of the present invention compares the effective scanning distribution area of ​​the cursor calibration ball obtained through statistical analysis with the corresponding distribution area of ​​the patient's surgical site area to determine the maximum number of calibration balls that can be accommodated in the patient's surgical site area, ensure sufficient coverage and accurate imaging, and finally obtain the maximum number of optical calibration balls that can be adapted.

[0067] The present invention monitors and analyzes the regional distribution area by combining the size of the patient's surgical site area and the shape of the patient's surgical site area obtained by analysis, so as to accurately measure the actual distribution area of ​​the surgical site, and take into account the shape complexity and size differences of the surgical sites of different patients, to ensure that the subsequent calibration and layout work can be based on accurate regional data, thereby improving the accuracy and efficiency of the overall operation. Secondly, by measuring the size of the calibration ball, the accurate size of the optical calibration ball is obtained. This step ensures that each calibration ball meets the prescribed size standard by accurately measuring the size of the calibration ball. This is the basis for ensuring the accuracy and reliability of the subsequent calibration process, and the size of the calibration ball directly affects its layout density and coverage effect in the surgical area, so accurate measurement of the size is crucial. Then, by monitoring and analyzing the scanning range of the calibration ball, the scanning range of the optical calibration ball is obtained. This step involves evaluating the area range that each calibration ball can effectively cover, taking into account the working characteristics of the optical system and the layout density of the calibration ball, to ensure the scanning and positioning capabilities of each calibration ball in the entire surgical area. Next, by performing a statistical analysis of the effective scanning distribution area of ​​the calibration ball according to the size of the optical calibration ball and the scanning range of the optical calibration ball, the size and scanning range of the calibration ball can be comprehensively considered, and the actual effective area that each calibration ball can cover can be determined. This analysis can provide a scientific basis for determining the optimal layout density and position of the calibration ball, thereby ensuring comprehensive coverage and positioning accuracy during the calibration process. Finally, by quantitatively calculating the effective scanning distribution area of ​​the cursor calibration ball based on the distribution area of ​​the patient's surgical site, the number of calibration balls can be accurately calculated, and sufficient calibration balls can be ensured to cover the entire surgical area to meet the calibration and positioning requirements. This step not only takes into account the size and shape of the surgical site, but also the layout density and coverage effect of the calibration ball, and finally determines the number of suitable calibration balls, thereby providing a solid data foundation for the accuracy and safety of the subsequent positioning, calibration and registration process.

[0068] Preferably, step S14 comprises the following steps:

[0069] Step S141: performing initial allocation processing on the surgical area of ​​the patient on the CT bed by using the calibration sphere based on the maximum number of optical calibration sphere adaptations, and obtaining an initial allocation position of the surgical area of ​​the optical calibration sphere;

[0070] The embodiment of the present invention distributes a corresponding number of optical calibration balls according to a predetermined distribution position in the corresponding positions of the surgical area of ​​the patient on the CT bed by combining the determined adaptation limit number of optical calibration balls, so as to achieve effective calibration and positioning of the surgical area, and finally obtain the initial distribution position of the surgical area of ​​the optical calibration balls.

[0071] Step S142: performing imaging distribution area analysis on the calibration balls at the initial allocation positions of the surgical site regions of each optical calibration ball, so as to obtain the CT imaging distribution areas of the initial allocation positions of each optical calibration ball;

[0072] The embodiment of the present invention uses computer-aided design software or image simulation tools to identify and analyze the imaging distribution area of ​​the calibration balls at the initial allocation position of the surgical site area of ​​each optical calibration ball, so as to identify the imaging distribution of each calibration ball and ensure that it can cover all necessary areas of the surgical site, and finally obtain the CT imaging distribution area of ​​the initial allocation position of each optical calibration ball.

[0073] Step S143: performing regional coverage evaluation and analysis on the CT imaging distribution area of ​​each initial allocation position of the optical calibration ball to obtain imaging area coverage status data of each initial allocation position of the optical calibration ball;

[0074] The embodiment of the present invention uses an area coverage evaluation method to measure and analyze the coverage range of the CT imaging distribution area of ​​each optical calibration ball's initial allocation position, so as to evaluate and determine the imaging area coverage of each calibration ball at the initial allocation distribution position, and finally obtain the imaging area coverage status data of the initial allocation position of each optical calibration ball.

[0075] Step S144: optimizing the position layout of the initial allocation positions of the optical calibration balls in the surgical area according to the imaging area coverage status data of the initial allocation positions of the optical calibration balls, so as to obtain the optical calibration balls' adapted coverage distribution positions.

[0076] The embodiment of the present invention performs data analysis and statistics based on the analyzed imaging area coverage status data of the initial allocation positions of each optical calibration ball to find out the areas with insufficient coverage or excessive coverage, and adjusts and optimizes the layout of the initial allocation positions of the surgical site areas of the optical calibration balls corresponding to the identified areas with insufficient coverage or excessive coverage, so as to rearrange the allocation positions of the optical calibration balls so that they can better cover the surgical site area, and finally obtain the adaptive coverage distribution positions of the optical calibration balls.

[0077] The present invention first uses the calibration balls to calibrate the surgical area of ​​the patient on the CT bed by combining the maximum number of optical calibration balls obtained by analysis. The key to this step is to ensure the proper arrangement of the optical calibration balls to achieve effective calibration and positioning of the surgical area. Through accurate initial allocation processing, the position of each optical calibration ball in the surgical area can be effectively determined, thereby laying the foundation for subsequent imaging and positioning work. Secondly, by analyzing the imaging distribution area of ​​the calibration balls at the initial allocation position of the surgical area of ​​each optical calibration ball, this step aims to evaluate the imaging distribution of each calibration ball to ensure that it can cover all necessary areas of the surgical area. In this way, by analyzing the imaging distribution area, the imaging effect of each calibration ball can be determined, thereby providing guarantee for the subsequent imaging quality. Then, by performing regional coverage evaluation and analysis on the CT imaging distribution area of ​​the initial allocation position of each optical calibration ball, this step aims to evaluate the imaging area coverage of each calibration ball to ensure the complete imaging of the entire surgical area. Through detailed coverage evaluation and analysis, the problem of insufficient imaging area coverage can be discovered and solved in time, thereby ensuring the accuracy of imaging quality and surgical calibration and registration. Finally, the initial allocation position of the optical calibration sphere in the surgical area is optimized based on the imaging area coverage data of the initial allocation position of each optical calibration sphere. This step aims to maximize the coverage and uniformity of the imaging area by optimizing the layout, thereby ensuring comprehensive imaging of the entire surgical area. Through carefully designed position layout optimization, the imaging quality can be effectively improved, providing more reliable support for the accuracy and safety of the subsequent calibration and registration process.

[0078] Preferably, step S2 comprises the following steps:

[0079] Step S21: performing a CT image scan of the surgical site of the patient on the CT bed by using an infrared camera to obtain a CT scan image of the surgical site of the patient;

[0080] The embodiment of the present invention determines the exact position of the surgical site of the patient in the CT bed to adjust the position and angle of the infrared camera so that it is aimed at the surgical site of the patient in the CT bed, and starts the infrared camera to scan it in real time, thereby achieving high-precision imaging of the surgical site, and finally obtaining a CT scan image of the surgical site of the patient.

[0081] Step S22: uploading the CT scan image of the surgical site of the patient to the CT image analysis system through network transmission technology, and using the CT image analysis system to perform image denoising on the CT scan image of the surgical site of the patient to obtain a CT denoised image of the surgical site of the patient;

[0082] The embodiment of the present invention uses network transmission technology to upload the CT scan image of the patient's surgical site obtained by real-time scanning to a CT image analysis system, and uses the denoising algorithm in the CT image analysis system to denoise the CT scan image of the patient's surgical site to eliminate noise interference in the image, and finally obtains a denoised CT image of the patient's surgical site.

[0083] Step S23: performing contrast enhancement processing on the denoised CT image of the surgical site of the patient to obtain a CT image with clear contrast of the surgical site of the patient;

[0084] The embodiment of the present invention uses the corresponding contrast enhancement algorithm (such as histogram equalization, grayscale transformation, image smoothing, etc.) in the CT image analysis system to process the denoised CT image of the patient's surgical site to enhance the contrast in the image, making the image clearer and easier to analyze, and finally obtaining a CT image of the patient's surgical site with clear contrast.

[0085] Step S24: performing calibration ball positioning processing on the contrast-clear CT image of the surgical site of the patient to obtain the calibration ball CT image coordinates of the surgical site.

[0086] The embodiment of the present invention uses a corresponding image processing algorithm to identify and locate the position of a calibration ball in a contrast-clear CT image of a patient's surgical site after contrast enhancement processing, so as to map the coordinates of the calibration ball in the image space coordinate system according to the position of the calibration ball in the CT image as the coordinates of the surgical site calibration ball CT image, and finally obtain the coordinates of the surgical site calibration ball CT image.

[0087] The present invention first uses an infrared camera to perform CT image scanning on the surgical site of the patient on the CT bed. The key to this step is to use an infrared camera to perform non-contact scanning on the surgical site of the patient, which can achieve high-precision imaging of the surgical site. In this way, accurate structural information of the surgical site of the patient can be obtained, providing a reliable data basis for subsequent image processing and analysis. Secondly, by using network transmission technology, the CT scan image of the surgical site of the patient is uploaded to the CT image analysis system, and the image is processed using the image denoising processing algorithm in the system. The purpose of this step is to eliminate noise interference in the image, improve image quality and clarity, and provide a more reliable data basis for subsequent image processing and analysis. Then, by contrast enhancement processing of the CT denoised image of the surgical site of the patient, this step can highlight the detailed information in the image by enhancing the contrast, so that the calibration ball position information at the surgical site image can be more clearly observed, thereby improving the accuracy and reliability of the subsequent processing process. Finally, the calibration ball positioning is performed on the clear contrast CT images of the patient's surgical site. The purpose of this step is to identify and locate the calibration balls and determine their exact coordinate positions in the CT images. The accurate positioning of the calibration balls can provide an important basis for subsequent image calibration and calibration positioning, ensure the accuracy and safety of the calibration positioning operation, and provide doctors with more reliable navigation and positioning information.

[0088] Preferably, step S24 includes the following steps:

[0089] Step S241: performing calibration ball position recognition analysis on the CT image of the surgical site with clear contrast, and obtaining the relative position of the calibration ball in the CT image of the surgical site;

[0090] The embodiment of the present invention uses an image processing algorithm to identify and analyze the position of the calibration ball in the clear contrast CT image of the patient's surgical site, so as to accurately locate the relative position of the calibration ball in the image, and finally obtain the relative position of the calibration ball in the CT image of the patient's surgical site.

[0091] Step S242: performing image space conversion analysis on the CT image with clear contrast at the surgical site of the patient to obtain a CT image space model of the surgical site of the patient;

[0092] The embodiment of the present invention uses an appropriate image space conversion algorithm (such as affine transformation, perspective transformation and other algorithms) to perform spatial conversion on the contrast-clear CT image of the patient's surgical site obtained after contrast enhancement, so as to convert the two-dimensional image information mapping into a specific image space model, and finally obtain the CT image space model of the patient's surgical site.

[0093] Step S243: performing spatial structural feature analysis on the spatial model of the CT image of the surgical site of the patient to obtain spatial structural feature data of the CT image of the surgical site of the patient;

[0094] The embodiment of the present invention analyzes the spatial model of the CT image of the patient's surgical site by using a spatial feature analysis method to extract and analyze the geometric structure and features of the CT image in space, including the size, edge, texture, shape, positional relationship, etc. of each part, and finally obtains the spatial structure feature data of the CT image of the patient's surgical site.

[0095] Step S244: performing three-dimensional reconstruction processing on the CT contrast clear image of the patient's surgical site based on the spatial structural feature data of the CT image of the patient's surgical site, and obtaining a three-dimensional reconstruction spatial coordinate system of the CT image of the surgical site;

[0096] The embodiment of the present invention optimizes and reconstructs the corresponding clear contrast CT image of the patient's surgical part by combining the spatial structural feature data of the CT image of the patient's surgical part obtained by analysis and using appropriate three-dimensional reconstruction technology (such as voxelization, triangulation, etc.), so as to reconstruct the spatial coordinate system of the CT image according to the characteristics of the image spatial feature changes, and ensure that its spatial position and direction in the medical device or surgical navigation system are correct, and finally obtain the three-dimensional reconstructed spatial coordinate system of the CT image of the surgical part.

[0097] Step S245: performing calibration ball positioning processing on the three-dimensional reconstruction space coordinate system of the surgical site CT image according to the relative position of the calibration ball of the surgical site CT image to obtain the calibration ball CT image coordinates of the surgical site.

[0098] The embodiment of the present invention performs positioning processing on the relative position of the calibration ball in the CT image of the patient's surgical site obtained by combining the relative position of the calibration ball in the three-dimensional reconstruction space coordinate system of the CT image of the surgical site, so as to associate the position of the calibration ball in the two-dimensional image coordinate system space with the three-dimensional reconstruction space coordinate system, so as to locate and determine the coordinate position of the calibration ball in the three-dimensional reconstruction space coordinate system, and finally obtain the CT image coordinates of the calibration ball of the surgical site.

[0099] The present invention firstly analyzes the position of the calibration ball through the clear contrast CT image of the patient's surgical site. The purpose of this step is to accurately locate the relative position of the calibration ball in the image through the image processing algorithm. The key to this step is to accurately identify the calibration ball and determine their position relative to the image, so as to provide accurate reference coordinates for subsequent image processing and analysis, thereby ensuring the accurate positioning and positioning accuracy of the surgical site. Secondly, the image space conversion analysis is performed on the clear contrast CT image of the patient's surgical site, the purpose of which is to convert the image into a spatial model. This step can convert the two-dimensional image information into a three-dimensional spatial model through spatial conversion analysis, thereby more accurately describing the spatial structural characteristics of the surgical site, and providing a reliable basis for subsequent three-dimensional reconstruction and positioning. Then, the spatial structural feature analysis is performed on the spatial model of the CT image of the patient's surgical site to obtain the spatial structural feature data of the image. The purpose of this step is to analyze the geometric structure and characteristics of the image in space, including the size, shape, positional relationship, etc. of each part, so as to provide detailed reference information for subsequent three-dimensional reconstruction and positioning. Next, by performing three-dimensional reconstruction of the CT contrast-clear image of the patient's surgical site based on the spatial structural feature data of the CT image of the patient's surgical site, the two-dimensional image can be converted into a three-dimensional spatial coordinate system to achieve three-dimensional reconstruction of the surgical site, thereby providing more accurate spatial coordinate information for subsequent surgical navigation and positioning. Finally, by performing calibration ball positioning processing on the three-dimensional reconstruction spatial coordinate system of the CT image of the surgical site according to the relative position of the calibration ball of the CT image of the patient's surgical site, the purpose of this step is to use the known position of the calibration ball to accurately position the three-dimensional reconstructed image, thereby determining the CT image coordinates of the calibration ball of the surgical site, and providing accurate coordinate reference for subsequent surgical navigation and positioning.

[0100] Preferably, step S244 includes the following steps:

[0101] Based on the spatial structural feature data of the CT image of the patient's surgical site, a three-dimensional spatial reconstruction analysis is performed on the CT image of the patient's surgical site with clear contrast, to obtain a three-dimensional reconstructed spatial model of the CT image of the patient's surgical site;

[0102] The embodiment of the present invention uses three-dimensional reconstruction technology to reconstruct the corresponding clear contrast CT image of the patient's surgical part by combining the spatial structural feature data of the CT image of the patient's surgical part obtained by analysis (including information on regional boundaries, tissue structure, density changes, etc.), so as to convert the original two-dimensional image into a more realistic and three-dimensional three-dimensional spatial model, and finally obtain a three-dimensional reconstructed spatial model of the CT image of the patient's surgical part.

[0103] Preferably, a surface fitting process is performed on the three-dimensional reconstructed spatial model of the CT image of the patient's surgical site to obtain a three-dimensional fitting spatial model of the CT image of the surgical site;

[0104] The embodiment of the present invention uses a surface fitting method to fit the surface within the three-dimensional reconstructed spatial model of the patient's surgical site CT image after reconstruction to obtain a smoother and continuous surface for representation, and reduces the noise and discontinuity of the model to more accurately describe the morphological characteristics of the surgical site, while making the reconstructed model smoother and continuous, and finally obtaining a three-dimensional fitting spatial model of the surgical site CT image.

[0105] Preferably, texture recognition analysis is performed on the CT image with clear contrast at the surgical site of the patient to obtain texture information data of the CT image of the surgical site of the patient;

[0106] The embodiment of the present invention uses an image processing algorithm (such as gray level co-occurrence matrix, local binary pattern, etc.) to perform image texture recognition and analysis on the contrast-clear CT image of the patient's surgical site obtained after contrast enhancement, so as to extract texture information in the CT image, such as skin texture, tissue texture, etc., and finally obtain the texture information data of the CT image of the patient's surgical site.

[0107] Preferably, the three-dimensional fitting spatial model of the surgical site CT image is texture-mapped and enhanced based on the texture information data of the surgical site CT image of the patient, so as to obtain a three-dimensional detail-enhanced model of the surgical site CT image;

[0108] The embodiment of the present invention enhances the texture information of the corresponding area within the three-dimensional fitting space model of the surgical site CT image by combining the texture information data of the patient's surgical site CT image obtained by analysis, so as to map the identified texture change information to the reconstructed three-dimensional fitting space model, thereby enhancing the texture detail expression of the three-dimensional model, and finally obtaining a three-dimensional detail enhanced model of the surgical site CT image.

[0109] Preferably, the three-dimensional detail enhancement model of the surgical site CT image is subjected to a spatial coordinate system mapping transformation to obtain a three-dimensional reconstructed spatial coordinate system of the surgical site CT image.

[0110] The embodiment of the present invention uses a spatial coordinate system conversion method to perform spatial coordinate system mapping conversion on the three-dimensional detail enhancement model of the surgical site CT image after detail enhancement processing, so as to map the model to a specific spatial coordinate system and ensure that its spatial position and orientation in the medical device or surgical navigation system are correct, and finally obtain the three-dimensional reconstructed spatial coordinate system of the surgical site CT image.

[0111] The present invention firstly performs a three-dimensional spatial reconstruction analysis on the CT contrast clear image of the patient's surgical site by combining the spatial structural feature data of the CT image of the patient's surgical site obtained by analysis. This step can convert the original two-dimensional image into a more realistic and three-dimensional three-dimensional spatial model by analyzing and reconstructing the anatomical structure, organ position and other features of the CT image. Such a reconstruction process can not only provide more intuitive and comprehensive surgical site information, but also provide a reliable basis for subsequent processing. Secondly, by performing surface fitting processing on the obtained three-dimensional reconstructed spatial model of the CT image of the patient's surgical site, the morphological characteristics of the surgical site can be more accurately described, making the model smoother and more continuous. This processing helps to reduce the noise and discontinuity of the model, improve the accuracy and credibility of the model, and provide a better basis for subsequent analysis and application. Then, by performing texture recognition analysis on the CT contrast clear image of the patient's surgical site, the texture information data in the image is extracted. This step can better understand the subtle changes in the tissue structure by analyzing the texture features of the image, and provide more information for subsequent processing and analysis. These texture information can be used to distinguish different tissue types, thereby helping to better provide basic data guarantee for the subsequent calibration and registration process. Next, by performing texture mapping enhancement processing on the three-dimensional fitting spatial model of the surgical site CT image based on the texture information data of the patient's surgical site CT image, the texture information can be mapped to the three-dimensional model, which can enhance the detail performance of the model and make the model more realistic and visual. Such processing helps to better observe the detailed structure of the surgical site and improve the accuracy and credibility of the subsequent processing process. Finally, by performing spatial coordinate system mapping conversion on the three-dimensional model of the surgical site CT image with enhanced details, the model can be mapped to a specific spatial coordinate system, which can facilitate the positioning and measurement of the spatial position. Such processing provides a reliable foundation for the subsequent calibration and registration process, so that the calibration and registration work of the calibration ball can be performed more accurately.

[0112] Preferably, step S3 comprises the following steps:

[0113] Step S31: placing the calibration needle tip on the calibration ball by holding the calibration tool handle, and performing trajectory tracking processing on the infrared recognition ball on the calibration needle tip based on the infrared camera to obtain the real-time motion trajectory of the infrared recognition ball;

[0114] The embodiment of the present invention holds the corresponding calibration tool handle to accurately place the calibration needle tip on the calibration ball, uses an infrared camera to take a real-time photo of the infrared recognition ball on the calibration needle tip, and uses computer vision technology to track the motion trajectory of the captured image, so as to achieve real-time tracking of the motion trajectory of the calibration needle tip, identify the position change of the infrared recognition ball, and finally obtain the real-time motion trajectory of the infrared recognition ball.

[0115] Step S32: Performing position analysis on the real-time motion trajectory of the infrared recognition ball to obtain the real-time motion positioning position of the infrared recognition ball;

[0116] The embodiment of the present invention calculates the real-time motion position of the infrared recognition ball in the camera coordinate system by tracking the real-time motion trajectory of the infrared recognition ball and combining the size and shape information of the calibration ball using image processing and computational geometry methods (such as background subtraction, thresholding, morphological processing, etc.), and finally obtains the real-time motion positioning position of the infrared recognition ball.

[0117] Step S33: performing geometric mean calculation on the calibration ball based on the real-time motion positioning position of the infrared recognition ball to obtain the camera coordinates of the calibration ball of the surgical site.

[0118] The embodiment of the present invention combines the real-time motion positioning position of the infrared recognition ball obtained by positioning and uses the geometric mean method to average the relative position of the calibration ball (the middle part of the area surrounded by the infrared recognition ball), so as to fully consider the geometric shape of the calibration ball and the characteristics of the camera to quantitatively calculate the exact position of the surgical site calibration ball in the camera coordinate system, and finally obtain the camera coordinates of the surgical site calibration ball.

[0119] The present invention first places the calibration needle tip on the calibration ball by holding the calibration tool handle, and uses the infrared camera to track the infrared recognition ball on the calibration needle tip. The key to this step is to achieve real-time tracking of the calibration needle tip motion trajectory. Through the position change of the infrared recognition ball, the movement path of the calibration tool can be accurately tracked. Such a processing method can achieve accurate positioning and tracking of the surgical site, and provide accurate data support for subsequent positioning and operation. Then, by performing position positioning analysis on the real-time motion trajectory of the infrared recognition ball, the real-time position of the calibration tool can be determined, and then the surgical site can be accurately located. The key to this step is to use the data captured by the infrared camera for accurate positioning, and provide accurate positioning information for subsequent registration operations. Finally, by performing geometric mean calculation on the calibration ball based on the real-time motion positioning position of the infrared recognition ball, a more stable and accurate calibration ball camera coordinate can be obtained, thereby achieving accurate positioning of the calibration ball position at the surgical site. The key to this step is to obtain the camera coordinates by calculation, provide an accurate basis for subsequent image acquisition and processing, and ensure the accuracy and safety of the calibration registration process.

[0120] Preferably, step S4 comprises the following steps:

[0121] Step S41: performing time-series synchronization processing on the surgical site calibration ball CT image coordinates and the surgical site calibration ball camera coordinates to obtain the surgical site calibration ball image time-series synchronization coordinates and the surgical site calibration ball camera time-series synchronization coordinates;

[0122] The embodiment of the present invention processes the surgical site calibration sphere CT image coordinates and the surgical site calibration sphere camera coordinates by using a timing synchronization method, so as to ensure that the image taken by the camera is aligned with the time information of the CT image by recording and matching the timestamps, and ensure that they have a consistent reference at the same time point, and finally obtain the surgical site calibration sphere image timing synchronization coordinates and the surgical site calibration sphere camera timing synchronization coordinates.

[0123] Step S42: performing coordinate local feature analysis on the surgical site calibration ball image time-series synchronization coordinates and the surgical site calibration ball camera time-series synchronization coordinates to obtain the surgical site calibration ball image coordinate local feature data and the surgical site calibration ball camera coordinate local feature data;

[0124] The embodiment of the present invention uses local feature descriptors (such as SIFT, SURF, etc.) to perform statistical analysis of local features of the surgical site calibration sphere image time-series synchronized coordinates and the surgical site calibration sphere camera time-series synchronized coordinates to extract the local feature information of each coordinate point, and use this information to describe the texture, color and other features of the pixels around the coordinate point, and finally obtain the local feature data of the surgical site calibration sphere image coordinates and the local feature data of the surgical site calibration sphere camera coordinates.

[0125] Step S43: performing coordinate feature corner point extraction processing on the local feature data of the surgical site calibration sphere image coordinates and the local feature data of the surgical site calibration sphere camera coordinates to obtain a surgical site calibration sphere image coordinate feature corner point set and a surgical site calibration sphere camera coordinate feature corner point set;

[0126] The embodiment of the present invention uses a corner point detection algorithm (such as Harris corner point detection, Shi-Tomasi corner point detection, etc.) to perform feature corner point extraction processing on the extracted local feature data of the surgical site calibration sphere image coordinates and the local feature data of the surgical site calibration sphere camera coordinates to extract the corresponding feature corner points, and enable them to describe the position and features of the surgical site calibration sphere in the image and camera coordinates, and finally obtain the feature corner point set of the surgical site calibration sphere image coordinates and the feature corner point set of the surgical site calibration sphere camera coordinates.

[0127] Step S44: performing vector conversion processing on the surgical site calibration sphere image coordinate feature corner point set and the surgical site calibration sphere camera coordinate feature corner point set to obtain the surgical site calibration sphere image coordinate corner point vector and the surgical site calibration sphere camera coordinate corner point vector;

[0128] The embodiments of the present invention perform vector transformation on the extracted feature corner point set of the surgical site calibration sphere image coordinates and the feature corner point set of the surgical site calibration sphere camera coordinates by using corresponding vector transformation methods, so as to convert the relationship between the surgical site calibration sphere image coordinates and the camera coordinates into a vector form, and convert the local feature data into a more abstract and operable vector form, and finally obtain the corner point vector of the surgical site calibration sphere image coordinates and the corner point vector of the surgical site calibration sphere camera coordinates.

[0129] Step S45: performing image registration processing on the surgical site calibration sphere CT image coordinates based on the surgical site calibration sphere image coordinate corner point vectors and the surgical site calibration sphere camera coordinate corner point vectors to obtain the patient's surgical site CT image registration result.

[0130] The embodiment of the present invention uses a feature matching algorithm in computer vision (such as SIFT (Scale Invariant Feature Transform), SURF (Speeded Robust Features) or ORB (Oriented FAST and Rotated BRIEF), etc.) to match the converted surgical site calibration sphere image coordinate corner point vector and the corresponding coordinate corner point in the surgical site calibration sphere camera coordinate corner point vector, so as to establish a coordinate matching correspondence between the calibration sphere in the image and camera coordinate systems, and identify the non-rigid deformation between the calibration sphere image coordinates and the camera coordinates, and then estimate the non-rigid deformation registration matrix between the calibration sphere coordinates to perform image registration processing on the non-rigid deformation existing in the corresponding surgical site calibration sphere CT image coordinates, so as to accurately map the position of the surgical site calibration sphere in the CT image to the actual surgical scene, and realize accurate alignment of the patient's surgical site CT image, and finally obtain the patient's surgical site CT image registration result.

[0131] The present invention first performs time-series synchronization processing on the CT image coordinates of the surgical site calibration ball and the camera coordinates of the surgical site calibration ball, aiming to synchronize the time-series relationship between the two to ensure that they have a consistent reference at the same time point. This processing step is the basis of the entire process, which ensures the accuracy and consistency of subsequent analysis and processing steps. By performing time-series synchronization on the CT image and camera coordinates, the actual surgical scene can be effectively matched with the medical image data, providing an accurate basis for subsequent data processing. Secondly, by performing coordinate local feature analysis on the time-series synchronization coordinates of the surgical site calibration ball image and the time-series synchronization coordinates of the surgical site calibration ball camera, it is intended to extract local feature information from the data. These local feature data are crucial for subsequent analysis and processing. They can help determine the feature points of the surgical site calibration ball in the image and camera coordinates, providing an accurate data basis for subsequent corner point extraction and vector conversion. Then, by extracting the corresponding feature corner points from the local feature data of the surgical site calibration ball image coordinates and the local feature data of the surgical site calibration ball camera coordinates, the position and features of the surgical site calibration ball in the image and camera coordinates can be described more accurately, thereby providing key data support for subsequent vector conversion and registration processing. Next, by performing vector conversion processing on the feature corner point set of the surgical site calibration ball image coordinates and the feature corner point set of the surgical site calibration ball camera coordinates, the relationship between the surgical site calibration ball image coordinates and the camera coordinates can be converted into a vector form. The key to this step is to convert the local feature data into a more abstract and operable form, thereby providing an accurate data basis for subsequent image registration processing. Finally, by performing image registration processing on the CT image coordinates of the surgical site calibration ball based on the corner point vector of the surgical site calibration ball image coordinates and the corner point vector of the surgical site calibration ball camera coordinates, the position of the surgical site calibration ball in the CT image can be matched with the position in the camera coordinates, thereby achieving accurate matching of the image data and the actual surgical scene.

[0132] Preferably, step S45 includes the following steps:

[0133] Step S451: performing coordinate corner point matching processing on the surgical site calibration sphere image coordinate corner point vector and the surgical site calibration sphere camera coordinate corner point vector to obtain the calibration sphere image coordinate matching corner point vector and the calibration sphere camera coordinate matching corner point vector;

[0134] The embodiment of the present invention uses a feature matching algorithm in computer vision (such as SIFT (Scale Invariant Feature Transform), SURF (Speeded Robust Features) or ORB (Oriented FAST and Rotated BRIEF), etc.) to match the corresponding coordinate corner points in the converted surgical site calibration sphere image coordinate corner point vector and the surgical site calibration sphere camera coordinate corner point vector, so as to establish a coordinate matching correspondence between the calibration sphere in the image and camera coordinate systems, and arrange them accordingly, and finally obtain the calibration sphere image coordinate matching corner point vector and the calibration sphere camera coordinate matching corner point vector.

[0135] Step S452: performing non-rigid deformation field recognition analysis on the calibration sphere image coordinate matching corner point vector and the calibration sphere camera coordinate matching corner point vector to obtain a non-rigid deformation vector field between the calibration sphere image coordinate and the camera coordinate;

[0136] The embodiments of the present invention use corresponding image processing and computer vision technologies (including optical flow method, dense optical flow method, non-rigid deformation model, etc.) to identify and analyze the non-rigid deformation between the calibration sphere image coordinates and the calibration sphere camera coordinates, so as to identify the non-rigid deformation between the calibration sphere image coordinates and the camera coordinates, thereby helping to determine the alignment method between the images, and finally obtaining the non-rigid deformation vector field between the calibration sphere image coordinates and the camera coordinates.

[0137] Step S453: performing non-rigid registration matrix estimation on the calibration sphere image coordinate matching corner point vector and the calibration sphere camera coordinate matching corner point vector according to the non-rigid deformation vector field between the calibration sphere image coordinate and the camera coordinate, so as to obtain the calibration sphere coordinate non-rigid deformation registration matrix;

[0138] The embodiment of the present invention uses a deformation field-based registration algorithm (such as Thin-Plate Spline transformation, local affine transformation, etc.) based on the non-rigid deformation vector field between the calibration sphere image coordinates and the camera coordinates obtained by recognition analysis to estimate and calculate the registration matrix of the matching corner point vectors of the calibration sphere image coordinates and the matching corner point vectors of the calibration sphere camera coordinates, so as to estimate the non-rigid deformation registration matrix between the calibration sphere coordinates, and effectively eliminate the distortion between the calibration sphere coordinates in the image and the calibration sphere coordinates during the actual operation, and finally obtain the non-rigid deformation registration matrix of the calibration sphere coordinates.

[0139] Step S454: performing image registration processing on the calibration sphere CT image coordinates of the surgical site according to the calibration sphere coordinate non-rigid deformation registration matrix to obtain the CT image registration result of the surgical site of the patient.

[0140] The embodiment of the present invention uses the non-rigid deformation registration matrix of the calibration sphere coordinates obtained by estimation to perform image registration processing on the non-rigid deformation existing in the CT image coordinates of the corresponding surgical part calibration sphere, so as to accurately map the position of the surgical part calibration sphere in the CT image to the actual surgical scene, and realize precise alignment of the CT image of the patient's surgical part, and finally obtain the CT image registration result of the patient's surgical part.

[0141] The present invention firstly performs coordinate corner point matching processing on the image coordinate corner point vectors of the calibration ball of the surgical site and the camera coordinate corner point vectors of the calibration ball of the surgical site, so as to achieve accurate matching and alignment of the coordinates between the images. The key to this step is that it can ensure the accuracy and reliability of the subsequent processing. Through the matching processing, the corresponding relationship between the calibration ball in the image and camera coordinate systems can be established, thereby providing basic data guarantee for the subsequent non-rigid deformation analysis and registration. Secondly, by performing non-rigid deformation field recognition and analysis on the calibration ball image coordinate matching corner point vectors and the calibration ball camera coordinate matching corner point vectors, it is helpful to understand the deformation between the calibration ball image coordinates and the camera coordinates, and then help determine the registration method between the images. The key to this step is that by identifying non-rigid deformation, the accuracy and robustness of image registration are improved. The analysis of non-rigid deformation field can capture subtle changes in the image, thereby better adapting to the complex morphology of the surgical site. Then, the non-rigid registration matrix is ​​estimated for the calibration sphere image coordinate matching corner vector and the calibration sphere camera coordinate matching corner vector through the non-rigid deformation vector field between the calibration sphere image coordinate and the camera coordinate, so that accurate registration between images can be achieved. The key to this step is that it can improve the accuracy and stability of medical image registration. By accurately estimating the non-rigid deformation registration matrix, the distortion between images can be effectively eliminated to ensure the quality of image registration. Finally, by performing image registration processing on the CT image coordinates of the surgical site calibration sphere according to the non-rigid deformation registration matrix of the calibration sphere coordinates, accurate alignment of the CT image of the surgical site of the patient can be achieved. The key to this step is that it can provide accurate image data. Through image registration processing, the position of the surgical site calibration sphere in the CT image can be accurately mapped to the actual surgical scene, providing doctors with accurate navigation and positioning information, thereby improving the accuracy and safety of surgery.

[0142] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0143] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for rapid system registration based on an optical locator, characterized in that: Applied to an optical locator, wherein the optical locator includes an infrared camera, an LED light, and a calibration tool, wherein the calibration tool includes a calibration tool handle, a calibration needle tip, an infrared recognition ball, and a calibration ball, and the method for rapid system alignment based on the optical locator includes the following steps: Step S1: An infrared camera and an LED lamp are installed and fixed above the patient's surgical site area of ​​the CT bed, and the calibration ball is optimized for position coverage distribution based on the patient's surgical site area of ​​the CT bed to obtain an optical calibration ball adaptation coverage distribution position; the calibration ball is staggeredly distributed on the patient's surgical site area of ​​the CT bed based on the optical calibration ball adaptation coverage distribution position; Step S2: perform CT image scanning of the surgical site of the patient on the CT bed by using an infrared camera to obtain a CT scan image of the surgical site of the patient; upload the CT scan image of the surgical site of the patient to a CT image analysis system, and use the CT image analysis system to perform calibration ball positioning processing on the CT scan image of the surgical site of the patient to obtain the calibration ball CT image coordinates of the surgical site; wherein step S2 includes the following steps: Step S21: performing a CT image scan of the surgical site of the patient on the CT bed by using an infrared camera to obtain a CT scan image of the surgical site of the patient; Step S22: uploading the CT scan image of the surgical site of the patient to the CT image analysis system through network transmission technology, and using the CT image analysis system to perform image denoising on the CT scan image of the surgical site of the patient to obtain a CT denoised image of the surgical site of the patient; Step S23: performing contrast enhancement processing on the denoised CT image of the surgical site of the patient to obtain a CT image with clear contrast of the surgical site of the patient; Step S24: performing calibration ball positioning processing on the CT image with clear contrast of the surgical site of the patient to obtain the calibration ball CT image coordinates of the surgical site; wherein step S24 includes the following steps: Step S241: performing calibration ball position recognition analysis on the CT image of the surgical site with clear contrast, and obtaining the relative position of the calibration ball in the CT image of the surgical site; Step S242: performing image space conversion analysis on the CT image with clear contrast at the surgical site of the patient to obtain a CT image space model of the surgical site of the patient; Step S243: performing spatial structural feature analysis on the spatial model of the CT image of the surgical site of the patient to obtain spatial structural feature data of the CT image of the surgical site of the patient; Step S244: performing three-dimensional reconstruction processing on the CT contrast clear image of the patient's surgical site based on the spatial structural feature data of the CT image of the patient's surgical site to obtain a three-dimensional reconstruction spatial coordinate system of the CT image of the surgical site; wherein step S244 includes the following steps: Based on the spatial structural feature data of the CT image of the patient's surgical site, a three-dimensional spatial reconstruction analysis is performed on the CT image of the patient's surgical site with clear contrast, to obtain a three-dimensional reconstructed spatial model of the CT image of the patient's surgical site; Performing surface fitting processing on the three-dimensional reconstructed spatial model of the CT image of the patient's surgical site to obtain a three-dimensional fitting spatial model of the CT image of the surgical site; Perform texture recognition analysis on the CT image with clear contrast at the surgical site of the patient to obtain the texture information data of the CT image at the surgical site of the patient; Based on the texture information data of the CT image of the surgical site, the 3D fitting spatial model of the CT image of the surgical site is texture-mapped and enhanced to obtain a 3D detail-enhanced model of the CT image of the surgical site; Performing spatial coordinate system mapping transformation on the three-dimensional detail enhancement model of the surgical site CT image to obtain the three-dimensional reconstruction spatial coordinate system of the surgical site CT image; Step S245: performing calibration ball positioning processing on the three-dimensional reconstruction space coordinate system of the surgical site CT image according to the relative position of the calibration ball of the surgical site CT image of the patient, to obtain the calibration ball CT image coordinates of the surgical site; Step S3: placing the calibration needle tip on the calibration ball by holding the calibration tool handle, and performing position tracking analysis on the calibration ball based on the infrared camera and the infrared recognition ball to obtain the calibration ball camera coordinates of the surgical site; Step S4: performing image registration processing on the CT image coordinates of the surgical site calibration sphere based on the camera coordinates of the surgical site calibration sphere to obtain the CT image registration result of the surgical site of the patient; wherein step S4 includes the following steps: Step S41: performing time-series synchronization processing on the surgical site calibration ball CT image coordinates and the surgical site calibration ball camera coordinates to obtain the surgical site calibration ball image time-series synchronization coordinates and the surgical site calibration ball camera time-series synchronization coordinates; Step S42: performing coordinate local feature analysis on the surgical site calibration ball image time-series synchronization coordinates and the surgical site calibration ball camera time-series synchronization coordinates to obtain the surgical site calibration ball image coordinate local feature data and the surgical site calibration ball camera coordinate local feature data; Step S43: performing coordinate feature corner point extraction processing on the local feature data of the surgical site calibration sphere image coordinates and the local feature data of the surgical site calibration sphere camera coordinates to obtain a surgical site calibration sphere image coordinate feature corner point set and a surgical site calibration sphere camera coordinate feature corner point set; Step S44: performing vector conversion processing on the surgical site calibration sphere image coordinate feature corner point set and the surgical site calibration sphere camera coordinate feature corner point set to obtain the surgical site calibration sphere image coordinate corner point vector and the surgical site calibration sphere camera coordinate corner point vector; Step S45: performing image registration processing on the surgical site calibration sphere CT image coordinates based on the surgical site calibration sphere image coordinate corner point vectors and the surgical site calibration sphere camera coordinate corner point vectors to obtain the patient's surgical site CT image registration result; wherein step S45 includes the following steps: Step S451: performing coordinate corner point matching processing on the surgical site calibration sphere image coordinate corner point vector and the surgical site calibration sphere camera coordinate corner point vector to obtain the calibration sphere image coordinate matching corner point vector and the calibration sphere camera coordinate matching corner point vector; Step S452: performing non-rigid deformation field recognition analysis on the calibration sphere image coordinate matching corner point vector and the calibration sphere camera coordinate matching corner point vector to obtain a non-rigid deformation vector field between the calibration sphere image coordinate and the camera coordinate; Step S453: performing non-rigid registration matrix estimation on the calibration sphere image coordinate matching corner point vector and the calibration sphere camera coordinate matching corner point vector according to the non-rigid deformation vector field between the calibration sphere image coordinate and the camera coordinate, so as to obtain the calibration sphere coordinate non-rigid deformation registration matrix; Step S454: performing image registration processing on the calibration sphere CT image coordinates of the surgical site according to the calibration sphere coordinate non-rigid deformation registration matrix to obtain the CT image registration result of the surgical site of the patient.

2. The method for rapid system registration based on an optical locator according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: installing and fixing an infrared camera and an LED lamp above the surgical site of the patient on the CT bed; Step S12: performing regional range and shape recognition analysis on the surgical site of the patient on the CT bed to obtain the size of the surgical site and the shape of the surgical site; Step S13: Calculate and determine the number of calibration balls based on the size of the surgical site area and the shape of the surgical site area of ​​the patient to obtain the maximum number of optical calibration balls to be adapted; Step S14: optimizing the position coverage distribution of the surgical site of the patient on the CT bed through the calibration sphere based on the maximum number of optical calibration sphere adaptations, so as to obtain the distribution position of the optical calibration sphere adaptation coverage; Step S15: Distribute the calibration spheres in a staggered manner over the surgical site of the patient on the CT bed based on the adaptive coverage distribution positions of the optical calibration spheres.

3. The method for rapid system registration based on an optical locator according to claim 2, characterized in that: Step S13 includes the following steps: Step S131: monitoring the regional distribution area according to the size of the surgical site and the shape of the surgical site to obtain the regional distribution area of ​​the surgical site; Step S132: measuring the size of the calibration sphere to obtain the size of the optical calibration sphere; Step S133: monitoring and analyzing the scanning range of the calibration sphere to obtain the scanning range size of the optical calibration sphere; Step S134: performing statistical analysis on the effective scanning distribution area of ​​the calibration ball according to the size of the optical calibration ball and the scanning range of the optical calibration ball to obtain the effective scanning distribution area of ​​the cursor calibration ball; Step S135: Quantify and determine the effective scanning distribution area of ​​the optical calibration sphere based on the distribution area of ​​the surgical site of the patient, so as to obtain the maximum number of optical calibration sphere adaptations.

4. The method for rapid system registration based on an optical locator according to claim 2, characterized in that: Step S14 includes the following steps: Step S141: performing initial allocation processing on the surgical area of ​​the patient on the CT bed by using the calibration sphere based on the maximum number of optical calibration sphere adaptations, and obtaining an initial allocation position of the surgical area of ​​the optical calibration sphere; Step S142: performing imaging distribution area analysis on the calibration balls at the initial allocation positions of the surgical site regions of each optical calibration ball, so as to obtain the CT imaging distribution areas of the initial allocation positions of each optical calibration ball; Step S143: performing regional coverage evaluation and analysis on the CT imaging distribution area of ​​each initial allocation position of the optical calibration ball to obtain imaging area coverage status data of each initial allocation position of the optical calibration ball; Step S144: optimizing the position layout of the initial allocation positions of the optical calibration balls in the surgical area according to the imaging area coverage status data of the initial allocation positions of the optical calibration balls, so as to obtain the optical calibration balls' adapted coverage distribution positions.

5. The method for rapid system registration based on optical locator according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: placing the calibration needle tip on the calibration ball by holding the calibration tool handle, and performing trajectory tracking processing on the infrared recognition ball on the calibration needle tip based on the infrared camera to obtain the real-time motion trajectory of the infrared recognition ball; Step S32: Performing position analysis on the real-time motion trajectory of the infrared recognition ball to obtain the real-time motion positioning position of the infrared recognition ball; Step S33: performing geometric mean calculation on the calibration ball based on the real-time motion positioning position of the infrared recognition ball to obtain the camera coordinates of the calibration ball of the surgical site.

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