Spine surgery positioning intelligent guiding system

Abstract

The invention relates to the technical field of medical positioning, in particular to a spine surgery positioning intelligent guiding system which comprises an image obtaining and processing module, an operation plan and display module, a quantum dot self-adaptive navigation module, a positioning guiding module and a feedback and adjustment module. The image acquisition and processing module is used for acquiring spine image data of a patient and generating a spine three-dimensional model; the operation plan and display module formulates an operation plan based on the spine three-dimensional model; the quantum dot self-adaptive navigation module provides a real-time image and carries out positioning and navigation on a surgical instrument; the positioning and guiding module monitors and guides the position and angle of the surgical instrument in real time; and the feedback and adjustment module feeds back the position and the operation state of the surgical instrument in real time. According to the invention, a clearer real-time image with a higher contrast ratio is provided, so that the accuracy and safety of surgical operation are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical positioning, and particularly to an intelligent guidance system for spinal surgery positioning. Background Art

[0002] Spinal surgery is a complex and high-risk medical operation that involves precise treatment of the patient's spine. To ensure the success of the surgery and the safety of the patient, doctors need to obtain high-resolution imaging data and precise navigation guidance during the surgery. Currently, advanced imaging technologies such as CT (Computed Tomography), MRI (Magnetic Resonance Imaging), and X-rays have been widely used in the acquisition of spinal imaging data. However, how to efficiently integrate these imaging data and use them for surgical navigation remains a technical challenge.

[0003] Existing spinal surgery navigation systems have some limitations. Firstly, there are certain noises and insufficient contrast in the acquisition and processing of imaging data, which affect the clarity and accuracy of the images. Secondly, existing surgical navigation systems lag in real-time correction of the path and position of surgical instruments and cannot respond in a timely manner to changes during the surgery, resulting in insufficient surgical precision. In addition, there is a lack of an effective feedback mechanism during the surgery, making it difficult for doctors to adjust the surgical plan in a timely manner, increasing the risk and difficulty of the surgery.

[0004] The present invention aims to provide an intelligent guidance system for spinal surgery positioning to ensure the safety and success rate of the surgery and significantly improve the effect of spinal surgery and the safety guarantee of the patient. Summary of the Invention

[0005] Based on the above objectives, the present invention provides an intelligent guidance system for spinal surgery positioning.

[0006] An intelligent guidance system for spinal surgery positioning includes an image acquisition and processing module, a surgical plan and display module, a quantum dot adaptive navigation module, a positioning guidance module, and a feedback and adjustment module, wherein; The image acquisition and processing module acquires the spinal imaging data of the patient, including two-dimensional and three-dimensional images, and processes the spinal imaging data to generate a three-dimensional spinal model; The surgical plan and display module formulates a surgical plan based on the three-dimensional spinal model, including the surgical path and operation steps, and real-time displays the three-dimensional spinal model, the surgical path, and the surgical progress; The quantum dot adaptive navigation module provides real-time images and corrects the image quality through adaptive optical technology. Combining image processing and navigation algorithms, it locates and navigates the surgical instruments, specifically including: Quantum dot imaging and excitation: Label the spine and surrounding tissues to provide fluorescence signals, and excite the quantum dots through a laser exciter to generate fluorescence. At the same time, a camera captures the fluorescence signals emitted by the quantum dots to generate real-time images; Optical component detection: Detect distortions and scattering in the optical components in real time through a wavefront sensor and a deformable mirror; Control algorithm: Based on the adaptive optics algorithm, calculate and control the adjustment parameters of the deformable mirror to optimize the contrast and clarity of the real-time image; Real-time image processing: Based on the optimized real-time image, use image enhancement algorithms and multi-modal image fusion algorithms to optimize the three-dimensional model of the spine; Surgical navigation: Based on the optimized real-time image and the three-dimensional model of the spine, provide surgical path planning and navigation guidance, and correct the path and position of the surgical instruments in real time; The positioning and guiding module, based on the results of surgical instrument positioning and navigation, monitors and guides the position and angle of the surgical instrument in real time during the operation; The feedback and adjustment module real-time feedbacks the position and operating status of the surgical instrument, and adjusts the surgical plan according to the feedback information.

[0007] Optionally, the image acquisition and processing module includes: Image acquisition: Use CT, MRI, and X-ray imaging technologies to obtain two-dimensional and three-dimensional image data of the patient's spine; Image preprocessing: Denoise, enhance the contrast, and segment the obtained two-dimensional and three-dimensional image data; Three-dimensional reconstruction: Based on volume rendering and surface rendering algorithms, reconstruct the two-dimensional image data into a three-dimensional spine model, specifically including: Volume rendering: Use the volume rendering algorithm to stack voxels of the two-dimensional image data to generate a three-dimensional model, expressed as: ; Wherein, is the voxel value, is the transfer function; Surface rendering: Use the Marching Cubes algorithm to extract the isosurface from the three-dimensional data to generate a surface mesh model, expressed as: ; Wherein, and are vertices, is the scalar field value, is the isovalue.

[0008] Optionally, the surgical planning and display module includes: Surgical path planning: Based on the three-dimensional spinal model, use the Dijkstra algorithm to calculate the optimal surgical path; Operation step generation: Generate surgical operation steps according to the planned surgical path, including cutting, separation, and fixation; Real-time display: Use computer graphics technology to present the three-dimensional spinal model, surgical path, and surgical progress on the display in real time; Interactive interface: Provide an interactive interface that allows doctors to view and adjust the surgical plan in real time.

[0009] Optionally, the quantum dot imaging and excitation include: Quantum dot labeling agent: Introduce the quantum dot labeling agent into the patient's body by injection. The quantum dot labeling agent selectively attaches to the spine and its surrounding tissues, providing a fluorescence signal; Laser exciter: Use a laser exciter to excite the quantum dots injected into the patient's body to generate a fluorescence signal, expressed as: ; Wherein, is the fluorescence intensity after excitation, is the fluorescence intensity before excitation, is the excitation distance, is the excitation wavelength; Camera capture: Configure a camera to capture the fluorescence signal emitted by the quantum dots and convert the captured fluorescence signal into a real-time image, expressed as: ; Wherein, is the fluorescence intensity captured by the camera, is the capture efficiency of the camera.

[0010] Optionally, the optical component detection includes: Wavefront sensor capture: Detect the wavefront information of light passing through the optical component through a wavefront sensor, including aberration and scattering; Data analysis: Analyze the captured wavefront information to identify the aberration and scattering information in the optical summary, expressed as: ; Wherein, is the wavefront error function, is the Zernike polynomial, is the corresponding Zernike coefficient.

[0011] Optionally, the control algorithm includes: Wavefront detection: Real-time detect the wavefront aberration in the optical component through a wavefront sensor to obtain the wavefront error distribution ; Wavefront fitting: Use Zernike polynomials to fit the wavefront error distribution and calculate the coefficients of each order of Zernike polynomials , expressed as: ; Among them, is the th order Zernike polynomial, is the corresponding coefficient, is the order of the polynomial; Control signal generation: Calculate the adjustment parameters of the deformable mirror based on the fitted wavefront error , expressed as: ; Among them, is the adjustment amount of the deformable mirror at position ; Mirror adjustment: Adjust the shape of the deformable mirror in real time according to the adjustment parameters to compensate for wavefront distortion.

[0012] Optionally, the real-time image processing includes: Image enhancement algorithm: Enhance the image contrast by stretching the gray histogram, expressed as: ; Among them, is the gray value of the input image, is the gray value of the output image, and are the minimum and maximum gray values of the input image respectively, is the number of gray levels; Multi-modal image fusion algorithm: Perform weighted fusion on images of different modalities (CT images and MRI images) to comprehensively utilize the advantages of each modality, expressed as: ; Among them, and are the gray values of the CT image and the MRI image respectively, and are the weight coefficients; Spinal three-dimensional model optimization: Optimize the spinal three-dimensional model using a three-dimensional reconstruction algorithm, expressed as: ; Among them, is the pixel value of the output image, is the opacity of the th voxel, The color value of each voxel, is the total number of voxels in the volume data.

[0013] Optionally, the surgical navigation includes: Surgical path planning: According to the optimized three-dimensional spinal model, use the Dijkstra algorithm to calculate the optimal surgical path; Navigation guidance: Based on the planned surgical path, use real-time image data and the three-dimensional spinal model for navigation guidance of surgical instruments. Align the real-time image with the three-dimensional spinal model through an image registration algorithm, expressed as: ; where, is the registered image, is the transformation matrix, are the registration parameters; Real-time correction: Use the Extended Kalman Filter (EKF) algorithm to correct the path and position of the surgical instrument in real time, expressed as: ; where, is the updated state estimate, is the predicted state, is the Kalman gain, is the observation value, is the observation matrix; Error correction: Based on the deviation between the position of the surgical instrument in real-time feedback and the predetermined path, dynamically adjust the operating path of the instrument, expressed as: ; where, is the error, is the predetermined path, is the actual path.

[0014] Optionally, the positioning guidance module includes: Surgical instrument positioning: Use a positioning sensor (optical tracking system or electromagnetic tracking system) to obtain the position and angle data of the surgical instrument in real time, expressed as: ; where, is the pose matrix, is the rotation matrix, is the translation vector; Real-time monitoring: Obtain the current pose and the pre-positioned pose of the surgical instrument in real time, and calculate the deviation between the current pose and the pre-positioned pose, expressed as: ; where, is the pose deviation matrix; Path correction: Based on the pose deviation matrix , the position and angle of the surgical instrument are adjusted using the proportional-integral-differential (PID) control algorithm, expressed as: ; where is the control output, is the error signal, are the proportional, integral, and differential coefficients respectively; Real-time guidance: According to the adjusted control output, the surgical instrument is guided to operate along the predetermined path in real time.

[0015] Optionally, the feedback and adjustment module includes: Data analysis: Collect the real-time position and operation status data of the instrument during the operation , including position coordinates, angles, and operation status, and analyze the real-time data to calculate the deviation value , expressed as: ; where is the expected pose matrix, is the current pose matrix, is the pose deviation; Feedback processing: Based on the deviation value and the operation status data , generate feedback information , expressed as: ; where is the control output signal, is the feedback processing function; Surgical plan adjustment: According to the feedback information , the surgical plan is dynamically adjusted using the particle swarm optimization algorithm.

[0016] Advantages of the present invention: In the present invention, high-resolution two-dimensional and three-dimensional image data of the spine are obtained by using multi-modal imaging technologies such as CT, MRI, and X-ray, and combined with advanced image preprocessing and three-dimensional reconstruction technologies to generate an accurate three-dimensional model of the spine, which provides a solid foundation for the formulation of the surgical plan, ensures the accuracy of the surgical path planning, avoids key nerve and blood vessel structures, and significantly improves the safety and success rate of the surgery.

[0017] In the present invention, by marking the spine and surrounding tissues, high-brightness and stable fluorescence signals are provided. Combining with adaptive optical technology to correct the image quality, precise positioning and navigation of surgical instruments are ensured. At the same time, the real-time image processing module uses image enhancement algorithms and multimodal image fusion algorithms to optimize the three-dimensional spine model, providing clearer and higher-contrast real-time images, thereby improving the accuracy and safety of surgical operations.

[0018] In the present invention, by real-time monitoring the position and operating state of surgical instruments, based on adaptive optical algorithms, extended Kalman filter algorithms, and particle swarm optimization algorithms, the surgical plan is dynamically adjusted to ensure that the surgical instruments operate along a predetermined path. This intelligent positioning guidance and feedback adjustment mechanism improves the adaptability and robustness of the system to complex surgical environments, significantly enhancing the surgical effect and patient safety guarantee. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only those of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0020] Figure 1 It is a schematic diagram of the system functional modules of the embodiment of the present invention; Figure 2 It is a schematic diagram of the quantum dot adaptive navigation module of the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] The present invention will be described in detail below in combination with the drawings and specific embodiments. At the same time, it should be noted here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments. For some well-known technologies, those skilled in the art can also adopt other alternative methods for implementation; moreover, the drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0022] It should be pointed out that in the specification, it is mentioned that "an embodiment", "embodiments", "exemplary embodiments", "some embodiments", etc. indicate that the described embodiments may include specific features, structures, or characteristics, but not necessarily every embodiment includes such specific features, structures, or characteristics. In addition, when combining embodiments to describe specific features, structures, or characteristics, implementing such features, structures, or characteristics in combination with other embodiments (whether explicitly described or not) should be within the knowledge of those skilled in the relevant art.

[0023] Generally, terms can be understood, at least in part, from their use in context. For example, depending at least in part on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey a set of exclusive factors, but rather can alternatively, at least in part depending on the context, allow for the existence of other factors that are not necessarily explicitly described.

[0024] As Figure 1 - Figure 2 shown, a spinal surgery positioning intelligent guidance system includes an image acquisition and processing module, a surgical planning and display module, a quantum dot adaptive navigation module, a positioning guidance module, and a feedback and adjustment module, wherein; The image acquisition and processing module acquires the spinal image data of the patient, including two-dimensional and three-dimensional images, and processes the spinal image data to generate a three-dimensional spinal model; The surgical planning and display module formulates a surgical plan based on the three-dimensional spinal model, including the surgical path and operation steps, and real-time displays the three-dimensional spinal model, the surgical path, and the surgical progress; The quantum dot adaptive navigation module provides real-time images, corrects the image quality through adaptive optical technology, and combines image processing and navigation algorithms to position and navigate surgical instruments, specifically including: Quantum dot imaging and excitation: Mark the spine and surrounding tissues, provide high-brightness and stable fluorescence signals, and excite the quantum dots through a laser exciter to generate high-brightness fluorescence. At the same time, the camera captures the fluorescence signals emitted by the quantum dots to generate real-time images; Optical component detection: Real-time detect the distortion and scattering in the optical components through a wavefront sensor and a deformable mirror; Control algorithm: Based on the adaptive optical algorithm, calculate and control the adjustment parameters of the deformable mirror to optimize the contrast and clarity of the real-time images, and ensure the clarity and accuracy of the images; Real-time image processing: Based on the optimized real-time images, use image enhancement algorithms and multi-modal image fusion algorithms to optimize the three-dimensional spinal model; Surgical navigation: Based on the optimized real-time images and the three-dimensional spinal model, provide surgical path planning and navigation guidance, and real-time correct the path and position of the surgical instruments to ensure the safety and accuracy of the surgical operation; The positioning guidance module, based on the results of the positioning and navigation of the surgical instruments, monitors and guides the position and angle of the surgical instruments in real time during the operation to ensure that the surgical instruments operate along the predetermined path; The feedback and adjustment module real-time feedbacks the position and operation status of the surgical instruments, and adjusts the surgical plan according to the feedback information; Through the above, high-resolution real-time images and precise surgical navigation are provided, ensuring the precise positioning and path correction of surgical instruments during the operation. Through adaptive optical technology and multi-modal image processing, the image quality and surgical precision are improved, significantly enhancing the safety, success rate, and efficiency of spinal surgery.

[0025] The image acquisition and processing module includes: Image acquisition: CT (Computed Tomography), MRI (Magnetic Resonance Imaging), and X-ray imaging technologies are used to acquire two-dimensional and three-dimensional image data of the patient's spine. Image preprocessing: Denoising, contrast enhancement, and image segmentation are performed on the acquired two-dimensional and three-dimensional image data, where; Denoising processing uses Gaussian filtering to denoise the original image data, expressed as: ; where, is the image value after denoising processing, is the standard deviation of the Gaussian filter, controlling the smoothness of the filter, is the radius of the filter, determining the size of the filter, is the pixel value of the original image at position , is the Gaussian weight function; Contrast enhancement uses histogram equalization method to improve the contrast of the image, expressed as: ; where, is the image value after contrast enhancement, is the pixel value of the original image at position , is the minimum pixel value of the original image, is the maximum pixel value of the original image; Image segmentation uses the Otsu algorithm for image segmentation to extract the contour of the spine, expressed as: ; where, is the threshold, and are the probabilities of the classes, and are the means of the classes; Three-dimensional reconstruction: Based on volume rendering and surface rendering algorithms, the two-dimensional image data is reconstructed into a three-dimensional spine model, specifically including: Volume rendering: Using the volume rendering algorithm, the two-dimensional image data is stacked by voxels to generate a three-dimensional model, expressed as: ; Among them, is the voxel value, is the transfer function; Surface rendering: Using the Marching Cubes algorithm, extract the isosurface from the three-dimensional data to generate a surface mesh model, expressed as: ; Among them, and are vertices, is the scalar field value, is the isovalue; Through the above content, the accuracy and reliability of the image data are improved, providing a solid foundation for subsequent surgical planning and navigation, thereby improving the safety and success rate of the surgery.

[0026] The surgical planning and display module includes: Surgical path planning: Based on the three-dimensional spinal model, use the Dijkstra algorithm to calculate the optimal surgical path, avoiding key nerves and vascular structures to ensure the safety and effectiveness of the surgery, expressed as: ; Among them, is the shortest distance from the node to the starting point, is the edge from to the weight of; Generation of operation steps: Generate surgical operation steps according to the planned surgical path, including cutting, separating, and fixing to ensure the standardization and systematization of the surgical process; Real-time display: Using computer graphics technology, present the three-dimensional spinal model, surgical path, and surgical progress on the display in real time to provide visual surgical guidance; Interactive interface: Provide an interactive interface that allows doctors to view and adjust the surgical plan in real time to ensure the flexibility and precision of the surgery;

[0027] Quantum dot imaging and excitation include: Quantum dot labeling agent: Introduce the quantum dot labeling agent into the patient's body by injection. The quantum dot labeling agent selectively attaches to the spine and its surrounding tissues to provide a high-brightness and stable fluorescence signal; Laser exciter: A laser exciter is used to excite the quantum dots injected into the patient's body, generating a highly bright fluorescence signal, expressed as: ; wherein, is the fluorescence intensity after excitation, is the fluorescence intensity before excitation, is the excitation distance, is the excitation wavelength; Camera capture: A camera is configured to capture the fluorescence signal emitted by the quantum dots and convert the captured fluorescence signal into a high-resolution real-time image, expressed as: ; wherein, is the fluorescence intensity captured by the camera, is the capture efficiency of the camera; Through the above content, the clarity and stability of the image are ensured, thus providing precise surgical positioning and navigation support for doctors, improving the safety and success rate of the surgery, and reducing the risks of patients and the probability of postoperative complications.

[0028] Optical component detection includes: Wavefront sensor capture: The wavefront information of light passing through the optical component is detected by a wavefront sensor, including aberration and scattering; Data analysis: The captured wavefront information is analyzed to identify the aberration and scattering information in the optical summary, expressed as: ; wherein, is the wavefront error function, is the Zernike polynomial, is the corresponding Zernike coefficient; Through the above steps, it is possible to detect and identify the aberration and scattering in the optical component in real time based on the data captured by the wavefront sensor, providing detailed error data support.

[0029] The control algorithm includes: Wavefront detection: The wavefront aberration in the optical component is detected in real time by a wavefront sensor to obtain the wavefront error distribution ; Wavefront fitting: The wavefront error distribution is fitted using the Zernike polynomial to calculate the coefficients of each order of the Zernike polynomial , expressed as: ; wherein, is the th order Zernike polynomial, is the corresponding coefficient, is the order of the polynomial; Control signal generation: Based on the fitted wavefront error, calculate the adjustment parameters of the deformable mirror , expressed as: ; where, is the adjustment amount of the deformable mirror at position ; Mirror adjustment: According to the adjustment parameters adjust the shape of the deformable mirror in real time to compensate for wavefront distortion; Through the above steps, the distortion and scattering in the optical system can be effectively eliminated, ensuring that the generated image has high resolution and high contrast, not only improving the clarity and accuracy of the image, but also providing more reliable surgical navigation support for doctors.

[0030] Real-time image processing includes: Image enhancement algorithm: By stretching the gray histogram, enhance the image contrast, expressed as: ; where, is the gray value of the input image, is the gray value of the output image, and are the minimum and maximum gray values of the input image respectively, is the number of gray levels; Multi-modal image fusion algorithm: Perform weighted fusion on images of different modalities (CT images and MRI images) to comprehensively utilize the advantages of each modality, expressed as: ; where, and are the gray values of the CT image and the MRI image respectively, and are the weight coefficients, satisfying ; Spinal three-dimensional model optimization: Use the three-dimensional reconstruction algorithm to optimize the spinal three-dimensional model, expressed as: ; where, is the pixel value of the output image, is the transparency of the th voxel, is the color value of the th voxel, is the total number of voxels of the volume data; Through the above, the clarity and contrast of the three-dimensional spinal model are effectively improved, making the surgical area clearer, enhancing the accuracy and safety of the surgery. By integrating different imaging data, more comprehensive and accurate surgical navigation information is provided, significantly improving the reliability and effectiveness of the surgical plan.

[0031] The surgical navigation includes: Surgical path planning: According to the optimized three-dimensional spinal model, the Dijkstra algorithm is used to calculate the optimal surgical path; Navigation guidance: Based on the planned surgical path, real-time imaging data and the three-dimensional spinal model are used for navigation guidance of surgical instruments. The real-time image is aligned with the three-dimensional spinal model through an image registration algorithm to ensure the accuracy of navigation, expressed as: ; Among them, is the registered image, is the transformation matrix, is the registration parameter; Real-time correction: The extended Kalman filter algorithm (EKF) is used to real-time correct the path and position of the surgical instrument, expressed as: ; Among them, is the updated state estimate, is the predicted state, is the Kalman gain, is the observation value, is the observation matrix; Error correction: Based on the deviation between the real-time feedback of the surgical instrument position and the predetermined path, the operation path of the instrument is dynamically adjusted, expressed as: ; Among them, is the error, is the predetermined path, is the actual path. According to the error adjust the control signal of the surgical instrument to ensure that the instrument operates along the predetermined path; Through the above steps, accurate surgical path planning and navigation guidance can be provided based on the optimized real-time image and the three-dimensional spinal model, and the path and position of the surgical instrument can be real-time corrected to ensure the accuracy and safety of the surgery.

[0032] The positioning guidance module includes: Surgical instrument positioning: Use a positioning sensor (optical tracking system or electromagnetic tracking system) to real-time obtain the position and angle data of the surgical instrument, expressed as: ; Among them, is the pose matrix, is the rotation matrix, and is the translation vector; Real-time monitoring: Obtain the current pose and the pre-set pose of the surgical instrument in real time, and calculate the deviation between the current pose and the pre-set pose, expressed as: ; where, is the pose deviation matrix; Path correction: Based on the pose deviation matrix and using the proportional-integral-derivative (PID) control algorithm to adjust the position and angle of the surgical instrument, expressed as: ; where, is the control output, is the error signal, are the proportional, integral, and derivative coefficients respectively; Real-time guidance: According to the adjusted control output, guide the surgical instrument to operate along the pre-set path in real time; Through the above steps, it is possible to monitor and guide the position and angle of the surgical instrument in real time during the operation based on the results of surgical instrument positioning and navigation, ensure that the surgical instrument operates along the pre-set path, and improve the accuracy and safety of the operation.

[0033] The feedback and adjustment module includes: Data analysis: Collect the real-time position and operation status data of the instrument during the operation, including position coordinates, angles, and operation status, and analyze the real-time data to calculate the deviation value ; where, is the expected pose matrix, is the current pose matrix, is the pose deviation; Feedback processing: Based on the deviation value and the operation status data to generate feedback information , expressed as: ; where, is the control output signal, is the feedback processing function; Surgical plan adjustment: According to the feedback information and using the particle swarm optimization algorithm to dynamically adjust the surgical plan, expressed as: ; ; Among them, is the velocity of the th particle in the th generation, is the velocity of the th particle in the th generation, is the inertia weight, which controls the inertia of the velocity, and are the learning factors, with a value of 2, and is a random number between is the personal best position of the th particle in the th generation, is the global best position of the th generation, is the position of the th particle in the th generation, is the position of the th particle in the th generation, is the position of the th particle in the th generation, is the velocity of the th particle in the th generation; Through the above content, not only the accuracy and safety of surgical operations are improved, but also the adaptability and robustness of the system to complex surgical environments are enhanced, thus significantly improving the surgical effect and the safety guarantee of patients.

[0034] Feedback processing function is the state feedback control function, expressed as: ; Among them, is the control input, is the state feedback gain matrix, is the state vector.

[0035] The present invention covers any alternatives, modifications, equivalent methods and solutions made to the essence and scope of the present invention. For the public to have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, and those skilled in the art can fully understand the present invention without the description of these details. In addition, well-known methods, processes, procedures, components and circuits, etc. are not described in detail to avoid unnecessary confusion to the essence of the present invention.

[0036] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. An intelligent guiding system for spinal surgery positioning, characterized in that, It includes an image acquisition and processing module, a surgical planning and display module, a quantum dot adaptive navigation module, a positioning and guiding module, and a feedback and adjustment module, where; The image acquisition and processing module acquires the spinal image data of the patient, including two-dimensional and three-dimensional images, and processes the spinal image data to generate a three-dimensional spinal model; The surgical planning and display module formulates a surgical plan based on the three-dimensional spinal model, including the surgical path and operation steps, and real-time displays the three-dimensional spinal model, the surgical path, and the surgical progress; The quantum dot adaptive navigation module provides real-time images, corrects the image quality through adaptive optical technology, and combines image processing and navigation algorithms to position and navigate surgical instruments. Specifically, it includes: Quantum dot imaging and excitation: Mark the spine and surrounding tissues, provide fluorescence signals, and excite the quantum dots through a laser exciter to generate fluorescence. At the same time, the camera captures the fluorescence signals emitted by the quantum dots to generate real-time images; Optical component detection: Real-time detect the distortion and scattering in the optical components through a wavefront sensor and a deformable mirror; Control algorithm: Based on the adaptive optical algorithm, calculate and control the adjustment parameters of the deformable mirror to optimize the contrast and clarity of the real-time images; Real-time image processing: Based on the optimized real-time images, use image enhancement algorithms and multimodal image fusion algorithms to optimize the three-dimensional spinal model; Surgical navigation: Based on the optimized real-time images and the three-dimensional spinal model, provide surgical path planning and navigation guidance, and real-time correct the path and position of the surgical instruments; The positioning and guiding module, based on the results of the positioning and navigation of the surgical instruments, real-time monitors and guides the position and angle of the surgical instruments during the operation; The feedback and adjustment module real-time feedbacks the position and operation status of the surgical instruments, and adjusts the surgical plan according to the feedback information.

2. The intelligent guiding system for spinal surgery positioning according to claim 1, wherein The image acquisition and processing module includes: Image acquisition: Use CT, MRI, and X-ray imaging technologies to acquire the two-dimensional and three-dimensional spinal image data of the patient; Image preprocessing: Denoise, enhance the contrast, and segment the acquired two-dimensional and three-dimensional image data; Three-dimensional reconstruction: Based on volume rendering and surface rendering algorithms, reconstruct the two-dimensional image data into a three-dimensional spinal model. Specifically, it includes: Volume rendering: Use the volume rendering algorithm to stack voxels of the two-dimensional image data to generate a three-dimensional model, expressed as: ； Among them, is the voxel value, is the transfer function; Surface rendering: Use the Marching Cubes algorithm to extract the isosurface from the three-dimensional data to generate a surface mesh model, expressed as: ； Among them, and are vertices, is the scalar field value, is the isovalue.

3. An intelligent guiding system for spinal surgery positioning according to claim 2, characterized in that, The surgical planning and display module includes: Surgical path planning: Based on the three-dimensional spinal model, use the Dijkstra algorithm to calculate the optimal surgical path; Operation step generation: Generate surgical operation steps according to the planned surgical path, including cutting, separating, and fixing; Real-time display: Use computer graphics technology to present the three-dimensional spinal model, the surgical path, and the surgical progress on the display in real-time; Interactive interface: Provide an interactive interface that allows doctors to view and adjust the surgical plan in real-time.

4. An intelligent guiding system for spinal surgery positioning according to claim 1, characterized in that, The quantum dot imaging and excitation includes: Quantum dot labeling agent: The quantum dot labeling agent is introduced into the patient's body by injection. The quantum dot labeling agent selectively adheres to the spine and its surrounding tissues, providing a fluorescence signal; Laser exciter: A laser exciter is used to excite the quantum dots injected into the patient's body, generating a fluorescence signal, expressed as: ； Among them, is the fluorescence intensity after excitation, is the fluorescence intensity before excitation, is the excitation distance, is the excitation wavelength; Camera capture: A camera is configured to capture the fluorescence signal emitted by the quantum dots and convert the captured fluorescence signal into a real-time image, expressed as: ； Among them, is the fluorescence intensity captured by the camera, is the capture efficiency of the camera.

5. An intelligent guiding system for spinal surgery positioning according to claim 4, characterized in that, The optical component detection includes: Wavefront sensor capture: The wavefront information of the light passing through the optical component, including distortion and scattering, is detected by a wavefront sensor; Data analysis: The captured wavefront information is analyzed to identify the distortion and scattering information in the optical summary, expressed as: ； wherein, is the wavefront error function, is the Zernike polynomial, is the corresponding Zernike coefficient.

6. The intelligent guiding system for spinal surgery positioning according to claim 5, wherein, The control algorithm includes: Wavefront detection: The wavefront distortion in the optical component is detected in real time by a wavefront sensor to obtain the wavefront error distribution ; Wavefront fitting: Use Zernike polynomials to fit the wavefront error distribution and calculate the coefficients of each order of Zernike polynomials , which is expressed as: ； Among them, is the -th order Zernike polynomial, is the corresponding coefficient, is the order of the polynomial; Control signal generation: Based on the fitted wavefront error, calculate the adjustment parameters of the deformable mirror , expressed as: ； Among them, is the adjustment amount of the deformable mirror at the position ; Mirror adjustment: According to the adjustment parameters The shape of the deformable mirror is adjusted in real time to compensate for wavefront distortion.

7. An intelligent guiding system for spinal surgery positioning according to claim 6, characterized in that, The real-time image processing includes: Image enhancement algorithm: The image contrast is enhanced by stretching the gray histogram, expressed as: ； Among them, is the gray value of the input image, is the gray value of the output image, and are the minimum and maximum gray values of the input image respectively, is the number of gray levels; Multi-modal image fusion algorithm: Different modal images are weighted and fused to comprehensively utilize the advantages of each modality, expressed as: ； Among them, and are the gray values of CT images and MRI images respectively, and are the weight coefficients; Spinal three-dimensional model optimization: The spinal three-dimensional model is optimized using a three-dimensional reconstruction algorithm, expressed as: ； Among them, is the pixel value of the output image, is the transparency of the th voxel, is the th voxel's color value, is the total number of voxels in the volume data.

8. The intelligent guiding system for spinal surgery positioning according to claim 7, characterized in that, The surgical navigation includes: Surgical path planning: According to the optimized spinal three-dimensional model, the optimal surgical path is calculated using the Dijkstra algorithm; Navigation guidance: Based on the planned surgical path, the real-time image data and the spinal three-dimensional model are used for the navigation guidance of surgical instruments. The real-time image is aligned with the spinal three-dimensional model through an image registration algorithm, expressed as: ； Among them, is the registered image, is the transformation matrix, is the registration parameter; Real-time correction: The extended Kalman filter algorithm is used to real-time correct the path and position of the surgical instrument, expressed as: ； Among them, is the updated state estimate, is the predicted state, is the Kalman gain, is the observed value, is the observation matrix; Error correction: Based on the deviation between the real-time feedback of the surgical instrument position and the predetermined path, the operation path of the instrument is dynamically adjusted, expressed as: ； wherein, is the error, is the predetermined path, is the actual path.

9. The intelligent guiding system for spinal surgery positioning according to claim 8, characterized in that The positioning and guidance module includes: Surgical instrument positioning: The position and angle data of the surgical instrument are obtained in real-time using a positioning sensor, expressed as: ； Among them, is the pose matrix, is the rotation matrix, is the translation vector; Real-time monitoring: Obtain the current pose of the surgical instrument in real time and the pre-defined pose , and calculate the deviation between the current pose and the pre-defined pose, expressed as: ； Among them, is the pose deviation matrix; Path correction: based on the pose deviation matrix , the position and angle of the surgical instrument are adjusted using a proportional-integral-derivative control algorithm, expressed as: ； wherein, is for controlling the output, is the error signal, are the proportional, integral, and differential coefficients respectively; Real-time guidance: According to the adjusted control output, the surgical instrument is guided in real-time to operate along the predetermined path.

10. The intelligent guiding system for spinal surgery positioning according to claim 1, characterized in that, The feedback and adjustment module includes: Data analysis: Collect the real-time position and operation status data of the instruments during the operation , including position coordinates, angles, and operation status, and analyze the real-time data to calculate the deviation value , expressed as: ； Among them, is the expected pose matrix, is the current pose matrix, is the pose deviation; Feedback processing: based on the deviation value and the operation status data , generate feedback information , expressed as: ； Among them, is the control output signal, is the feedback processing function; Surgical plan adjustment: Based on the feedback information , the surgical plan is dynamically adjusted using the particle swarm optimization algorithm.

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