Tunnel position determination system and method for anterior / posterior cruciate ligament reconstruction

Abstract

The invention relates to the technical field of medical image navigation, in particular to a tunnel position determination system and method for anterior / posterior cruciate ligament reconstruction, and the system comprises a medical image processing module, a tunnel planning module, a navigation registration module and a display unit. And calculating a virtual tunnel position based on the parameterized anatomical rule base, and introducing a biomechanical simulation unit to optimize tunnel planning. In the operation, high-precision space registration is achieved through an optical navigation system, multi-view real-time navigation guidance is provided, and precise drilling can be completed in combination with a robot execution platform. According to the method, the problems that a traditional operation depends on experience, and static planning and dynamic functions are not matched are solved, spanning from anatomical reconstruction to functional reconstruction is achieved, and the operation precision and the postoperative effect are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of medical image navigation technology, specifically to a tunnel location determination system and method for anterior / posterior cruciate ligament reconstruction. Background Technology

[0002] Anterior cruciate ligament (ACL) and posterior cruciate ligament (PCL) reconstruction is a common and technically challenging procedure in knee surgery. Its success hinges on the precise placement of the bone tunnel. Even a deviation of a few millimeters in tunnel positioning can lead to abnormal tension in the transplanted ligaments, limited joint mobility, impingement, or even reconstruction failure. In traditional surgery, surgeons primarily rely on intraoperative direct visualization of anatomical landmarks combined with two-dimensional X-ray fluoroscopy for positioning. This lack of quantitative analysis and dynamic functional assessment of the complex three-dimensional structures limits the accuracy to the surgeon's experience and visual judgment, making it difficult to guarantee consistent postoperative results.

[0003] With the development of computer-aided technology, medical image-based navigation systems have been introduced to improve surgical precision. However, existing navigation systems are mostly limited to the reproduction of static anatomical positions, providing only real-time positional feedback of instruments relative to the skeletal model, without fully considering the biomechanical behavior of ligaments during knee flexion and extension. For example, while the tunnel location may conform to anatomical standards under static conditions, it may undergo excessive length changes or impinge on the intercondylar fossa roof during joint movement, leading to postoperative joint stiffness, pain, or ligament re-tears. Furthermore, existing systems generally lack intelligent means to efficiently combine planned pathways with intraoperative manipulation. Passive navigation still relies on manual operation by the surgeon, resulting in insufficient stability and repeatability; while some active robotic systems can improve execution precision, they often lack deep integration of preoperative planning and intraoperative biomechanical simulation functions, leading to low inter-system collaboration efficiency and difficulty in achieving personalized and functionally optimized surgical plans.

[0004] Therefore, there is an urgent need to develop an intelligent surgical system that integrates three-dimensional anatomical reconstruction, biomechanical simulation, and precise execution, so as to achieve function-oriented tunnel positioning based on individual anatomical structure, thereby improving the long-term efficacy and reliability of cruciate ligament reconstruction surgery. Summary of the Invention

[0005] The purpose of this invention is to provide a tunnel location determination system and method for anterior / posterior cruciate ligament reconstruction. It achieves precise tunnel planning through three-dimensional reconstruction, parametric rules and biomechanical simulation, and improves surgical accuracy and functional prognosis by combining optical navigation and robotic execution.

[0006] To achieve the above-mentioned technical objectives and effects, the present invention is implemented through the following technical solution:

[0007] A tunnel location determination system for anterior / posterior cruciate ligament reconstruction, comprising:

[0008] Medical image data processing module: Receives DICOM data from CT or MRI, extracts the bony contours of the distal femur and proximal tibia through image segmentation algorithms, and performs three-dimensional reconstruction based on Marching Cubes or surface rendering algorithms to generate a three-dimensional model of the target bone containing the geometry of the intercondylar fossa.

[0009] Tunnel planning module: Calculates virtual tunnels on the 3D model based on a preset anatomical rule base;

[0010] The anatomical rule base includes:

[0011] Femoral tunnel positioning rules: The center point P of the virtual femoral tunnel is... femur The region located on the medial wall of the intercondylar fossa of the lateral femur, between the top of the femur and the center of the footprint area, is defined by a parametric proportional relationship:

[0012] P femur =f(Blumensaat's line, posterior cortical contour line, intercondylar fossa width W);

[0013] In the sagittal plane, P femur The distance D from Blumensaat's line (the projection of the subchondral bone plate of the intercondylar fossa on a lateral radiograph) sag With the total length L of Blumensaat's line blum Satisfy: D sag =α·L blum , where α is an empirical coefficient, α∈[0.6, 0.8];

[0014] In the coronal position, P femur Distance D from the posterior cortical contour line cor The intercondylar fossa width W satisfies: D cor =β·W, where β is an empirical coefficient, β∈[0.25, 0.35];

[0015] Tibial tunnel localization rule: The center point P of the virtual tibial tunnel is... tibia Located between the posterolateral and anteromedial bundles of the ACL footprint on the tibial plateau, its position satisfies:

[0016] P tibia Located in the posterior 42%–52% region of the anteroposterior diameter (AP) of the tibial plateau and the medial 45%–55% region of the medial diameter (ML);

[0017] Navigation registration module: Receives spatial pose information from the calibration device of the optical navigation system. This spatial pose information is derived from a group of optical markers M fixed to a reference frame attached to the skeleton and an instrument holder attached to the surgical instruments. bone and Mtool Spatial coordinates (x, y, z, θ) x θ y θ z Define; the spatial transformation matrix T from the 3D model coordinate system to the surgical site skeleton coordinate system is calculated using the iterative nearest neighbor algorithm or singular value decomposition algorithm. reg This allows for the planning of virtual tunnel paths. final Registered to the actual surgical space;

[0018] The display unit, communicatively connected to the processing unit, is used for real-time rendering and display of: the 3D model, the virtual tunnel planning path, and the registered and superimposed display of the actual pose P of the surgical instruments relative to the planning path. actual With ideal pose P ideal The deviation ΔP = |P actual -P ideal |

[0019] Furthermore, the tunnel planning module also includes a biomechanical simulation unit: defining the knee flexion angle θ as a variable, simulating the movement process of the knee joint from full extension to 120° flexion; at each flexion angle θ i Below, calculate the change in length L(θ) of the transplanted ligament. i The minimum Euclidean distance D between the lateral condyle and the top of the intercondylar fossa. imp (θ) i );

[0020] Tunnel location optimization is based on the following judgment logic:

[0021] The primary optimization objective is to minimize the change in ligament length ΔL = max(L(θ)). i )) - min(L(θ) i If ΔL is greater than the threshold L threshold If the value is typically 2mm, it is determined that the equal length principle is not met, and the tunnel entrance point needs to be adjusted.

[0022] Second optimization objective: Ensure D within the full buckling range imp (θ) i )>0, and a safety margin δ (typically 1.5mm) is allowed, i.e., min(D) imp (θ) i If any θ > δ; i D imp (θ) i If the value is less than or equal to δ, then a collision risk is identified, and the tunnel path needs to be adjusted.

[0023] The processing unit is configured to iteratively adjust the position and orientation of the virtual tunnel based on the above judgment logic, using either gradient descent or a genetic algorithm, until a tunnel is found that simultaneously satisfies ΔL < L. threshold and min(D) imp (θ) i The optimal solution is δ.

[0024] Furthermore, the system also includes:

[0025] A surgical robot execution platform includes a robotic arm and an end effector, the end effector being used to hold a drill bit or a guide needle;

[0026] The processing unit further includes a robot control module, which receives the optimal virtual tunnel planning path calculated by the tunnel planning module, the path starting from the tunnel entrance point P. in Exit point P out and the central axis vector V tunnel definition;

[0027] According to the spatial transformation matrix T reg , will P in P out V tunnel Transform to the robot's base coordinate system to obtain the target pose P. robot and direction vector V robot ;

[0028] By solving the inverse kinematics equations of the robotic arm, the joint angles θ are calculated. joint =[θ1, θ2, ..., θ n ] T This makes the tool center point of the end effector align with P. robot Coincident, and the tool axis is V robot parallel;

[0029] Drive the robotic arm to the calculated joint angle and control the end effector along V. robot The drilling operation is completed in the correct direction.

[0030] Furthermore, the calibration device is an optical navigation system, which includes multiple infrared optical cameras for tracking passive reflective spheres or active infrared emitting markers on the reference frame and instrument frame; the calculation of the spatial pose information is based on the principle of binocular vision and rigid body transformation theory.

[0031] Furthermore, the image segmentation algorithm is a semantic segmentation model based on deep learning. This model is trained using a medical image dataset including labeled femurs and tibias, and can automatically and accurately segment the target skeletal structure. The model is one of U-Net, V-Net, or Deeplab V3+ architecture.

[0032] Furthermore, when planning the virtual tunnel, the processing unit considers the diameter d of the selected graft. graft Automatically calculates the required drill bit diameter d for the tunnel. drill Based on this, a solid with a radius r = d is generated on the 3D model. drill A 2 / 2 cylindrical tunnel model is used for more accurate collision detection and visualization of bone removal.

[0033] Furthermore, the display unit renders the intersection of the cylindrical tunnel model and the bone model with different colors or transparency, intuitively showing the bone structure after bone tunnel removal, and calculating the volume of the removed bone to provide doctors with quantitative reference.

[0034] Furthermore, the system also includes a user input interface that allows surgeons to manually fine-tune the virtual tunnel position automatically calculated by the processing unit on the three-dimensional model; the processing unit responds to the fine-tuning operation in real time and immediately updates the biomechanical simulation results and navigation information.

[0035] Furthermore, the navigation registration module employs an iterative nearest-point algorithm whose objective function is to minimize the mean square error between the set of points X on the surface of the 3D model and the set of points Y on the bone surface measured by the calibration device.

[0036]

[0037] Where R is the rotation matrix and T is the translation vector, the optimal R and T are obtained by SVD decomposition to construct the spatial transformation matrix T. reg .

[0038] Furthermore, the processing unit is configured to perform virtual path verification before performing the drilling operation: simulating surgical instruments along the planned path. final Throughout the drilling process, the minimum distance between the instrument and surrounding key anatomical structures (such as the posterior cruciate ligament, lateral collateral ligament, and pre-defined safe area of ​​the peroneal neurovascular bundle) is monitored. If the distance is less than the safe threshold, a visual or auditory alarm is issued, prompting a replanning.

[0039] On the other hand, this invention proposes a method for determining the tunnel location for anterior / posterior cruciate ligament reconstruction, which, using the aforementioned system, includes the following steps:

[0040] S1: Data Acquisition and 3D Reconstruction: Acquire medical imaging data of the patient's knee joint and reconstruct a 3D model containing the fine structure of the intercondylar fossa using image processing algorithms;

[0041] S2: Virtual Tunnel Planning: Based on parametric anatomical rules, the femoral tunnel center point P is initially determined on a 3D model.femur and the center point P of the tibial tunnel tibia ;

[0042] S3: Biomechanical Simulation and Optimization: Simulating knee flexion and extension movements, calculating ligament length change ΔL and minimum impact distance min (D). imp Based on the judgment logic ΔL < L threshold And min(D) imp If P > δ, an optimization algorithm is used to iteratively adjust P. femur and / or P tibia The spatial coordinates are determined until biomechanical standards are met, and the final tunnel planning path is output. final ;

[0043] S4: Spatial Registration: Obtaining the spatial transformation relationship between the skeleton at the surgical site and the 3D model using a calibration device. reg , Path final Register to real space;

[0044] S5: Execution and Navigation: Select one of the following methods to execute based on the system architecture:

[0045] S5.1: Passive navigation mode: Displays surgical instruments and the planned path in real time on the display unit. final The deviation ΔP guides the doctor to manually operate the instrument along the planned path and drill;

[0046] S5.2: Proactive Execution Mode: Path final The commands are converted into robot pose instructions, which control the surgical robot platform to automatically locate and perform drilling.

[0047] Furthermore, in step S2, the parameterized anatomical rules are further defined as follows: for anterior cruciate ligament reconstruction:

[0048] The femoral tunnel center point P femur The location on a lateral X-ray or sagittal 3D slice must be such that it is located behind the extension of Blumensaat's line and at a vertical distance D from that line. sag With Blumensaat's line length L blum The proportional relationship is: D sag = (0.65 ± 0.05)·L blum ;

[0049] For posterior cruciate ligament reconstruction:

[0050] The center point P of the tibial tunnel tibia The positioning must meet the requirement of being located below the tibial plateau articular surface and at a safe distance D from the posterior cortex of the tibia. safeThe distance should be no less than 7mm to avoid damage to the posterior neurovascular bundle; this safe distance is calculated by P. tibia This is achieved by minimizing the distance to the three-dimensional surface of the posterior cortex of the tibia.

[0051] Furthermore, the spatial registration described in step S4 specifically includes:

[0052] S4.1: Point registration: Using a pointer tool with a tip, and under the tracking of the navigation system, select at least four non-coplanar anatomical feature points on the surgically exposed bone surface;

[0053] S4.2: Surface registration: Based on point registration, the pointer tool is used to continuously slide on the bone surface to collect a large amount of surface point cloud data;

[0054] S4.3: Algorithm Fusion: The feature points and the surface point cloud data are input together into the iterative nearest point algorithm and matched with the 3D model to obtain a higher-precision spatial transformation matrix T. reg .

[0055] Furthermore, in the passive navigation mode of step S5.1, the display unit provides a multi-view collaborative navigation interface, including:

[0056] 3D stereoscopic rendering view: Real-time display of the spatial relationship between the skeletal model, planned tunnels, and surgical instruments;

[0057] Axial view: Viewed from the drill bit axis, showing the concentric circle deviation between the instrument and the planned path;

[0058] Probe view: A cross-sectional view along the drill bit axis, showing the drill bit depth and angular deviation;

[0059] Numerical display interface: Real-time display of the linear distance and angular components of the deviation ΔP.

[0060] Furthermore, in the active execution mode of step S5.2, a safety confirmation step is included before the robot performs drilling: the doctor manually holds the end of the robot's robotic arm and pulls it to the starting point of the planned path with a small force. The system compares the deviation between the robot's self-localization result and the planned path in real time. If the deviation is within the allowable range, the system activates and locks the current position, preparing to perform automatic drilling; if the deviation exceeds the limit, the process is interrupted and registration or planning is performed again.

[0061] Furthermore, the method also includes a postoperative verification step:

[0062] S6: Intraoperative image verification: Immediately after drilling the tunnel, intraoperative C-arm X-ray fluoroscopy was performed to obtain two-dimensional images in the frontal and lateral views;

[0063] S7: Image Fusion and Comparison: The acquired two-dimensional X-ray images are compared with those based on the three-dimensional model and the planned path to visually display the consistency between the actual bone passage and the planned path, and a surgical report is generated.

[0064] The beneficial effects of this invention are:

[0065] This invention employs deep learning-based medical image segmentation technology to automatically identify and reconstruct a three-dimensional skeletal model of the patient's knee joint region, particularly accurately reproducing complex anatomical structures such as the intercondylar fossa. Utilizing advanced semantic segmentation algorithms like U-Net, it provides a reliable anatomical basis for subsequent surgical planning. Furthermore, this invention transforms clinical anatomical knowledge into a parametric rule base, precisely calculating tunnel coordinates through mathematical relationships. Femoral tunnel localization uses proportional coefficients α and β to define the relative positional relationship between the tunnel center point and Blumensaat's line and the posterior cortical contour. This quantitative method effectively overcomes the subjective bias inherent in traditional intraoperative reliance on surgeon's visual judgment, ensuring the accuracy and repeatability of anatomical localization.

[0066] This invention introduces dynamic biomechanical simulation capabilities. By simulating the biomechanical behavior of the knee joint within its full range of flexion and extension, the system can calculate the length changes of the transplanted ligament and the distance between it and surrounding structures. Employing optimization algorithms such as gradient descent, the system can automatically adjust the tunnel position and orientation, ensuring that the surgical plan meets both anatomical requirements and biomechanical characteristics. This avoids potential postoperative problems such as abnormal ligament tension and joint impingement, achieving a significant leap from simple anatomical reconstruction to functional reconstruction.

[0067] This invention effectively solves the problem of connecting preoperative planning with intraoperative operation through multimodal navigation registration and real-time visualization technology. It utilizes an optical tracking system to collect bone surface feature data and combines this with an iterative nearest-point algorithm to calculate a precise spatial transformation matrix, achieving high-precision registration between virtual planning and the real surgical environment. The display system provides a multi-view collaborative navigation interface, displaying the spatial deviation between surgical instruments and the planned path in real time, providing the surgeon with intuitive operational guidance.

[0068] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0069] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0070] Figure 1This is a schematic diagram of the overall process of the present invention. Detailed Implementation

[0071] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0072] Example 1

[0073] This embodiment describes a tunnel location determination system for anterior / posterior cruciate ligament reconstruction, comprising:

[0074] Medical image data processing module: Receives DICOM data from CT or MRI, extracts the bony contours of the distal femur and proximal tibia through image segmentation algorithms, and performs three-dimensional reconstruction based on Marching Cubes or surface rendering algorithms to generate a three-dimensional model of the target bone containing the geometry of the intercondylar fossa.

[0075] Tunnel planning module: Calculates virtual tunnels on the 3D model based on a preset anatomical rule base;

[0076] The anatomical rule base includes:

[0077] Femoral tunnel positioning rules: The center point P of the virtual femoral tunnel is... femur The region located on the medial wall of the intercondylar fossa of the lateral femur, between the top of the femur and the center of the footprint area, is defined by a parametric proportional relationship:

[0078] P femur =f(Blumensaat's line, posterior cortical contour line, intercondylar fossa width W);

[0079] In the sagittal plane, P femur The distance D from Blumensaat's line (the projection of the subchondral bone plate of the intercondylar fossa on a lateral radiograph) sag With the total length L of Blumensaat's line blum Satisfy: D sag =α·L blum , where α is an empirical coefficient, α∈[0.6, 0.8];

[0080] In the coronal position, P femur Distance D from the posterior cortical contour line cor The intercondylar fossa width W satisfies: D cor =β·W, where β is an empirical coefficient, β∈[0.25, 0.35];

[0081] Tibial tunnel localization rule: The center point P of the virtual tibial tunnel is... tibia Located between the posterolateral and anteromedial bundles of the ACL footprint on the tibial plateau, its position satisfies:

[0082] P tibia Located in the posterior 42%–52% region of the anteroposterior diameter (AP) of the tibial plateau and the medial 45%–55% region of the medial diameter (ML);

[0083] Navigation registration module: Receives spatial pose information from the calibration device of the optical navigation system. This spatial pose information is derived from a group of optical markers M fixed to a reference frame attached to the skeleton and an instrument holder attached to the surgical instruments. bone and M tool Spatial coordinates (x, y, z, θ) x θ y θ z Define; the spatial transformation matrix T from the 3D model coordinate system to the surgical site skeleton coordinate system is calculated using the iterative nearest neighbor algorithm or singular value decomposition algorithm. reg This allows the virtual tunnel planning path to be registered to the real surgical space;

[0084] The display unit, communicatively connected to the processing unit, is used for real-time rendering and display of: the 3D model, the virtual tunnel planning path, and the registered and superimposed display of the actual pose P of the surgical instruments relative to the planning path. actual With ideal pose P ideal The deviation ΔP = |P actual -P ideal |

[0085] Example 2

[0086] Experimental methods:

[0087] Knee CT data from 24 patients who underwent arthroscopic ACL reconstruction were selected and randomly divided into two groups:

[0088] Experimental group (n=12): The intelligent planning and navigation system described in this invention was used for tunnel location planning and simulation.

[0089] Control group (n=12): The traditional method was used, with two senior arthroscopic surgeons planning the tunnel location based solely on two-dimensional CT images and clinical experience.

[0090] The following quantitative evaluation was performed on the two sets of planning results:

[0091] 1. Biomechanical performance assessment: In a unified knee joint dynamic model, flexion movements from 0° to 120° were simulated, and the length change (ΔL) of the transplanted ligament and the minimum distance (min(D)) to the top of the intercondylar fossa were calculated under two planning schemes. imp )).

[0092] 2. Anatomical conformity assessment: Measure the quadrant position of the femoral tunnel center point on the lateral radiograph (refer to the grid method of Bernard et al.), and calculate its Euclidean deviation from the ideal target area (deep zone: 24%-28%, high zone: 68%-72%).

[0093] 3. Execution Accuracy Simulation Evaluation: In the experimental group, the navigation registration and robot execution process was simulated, introducing a registration error of 0.5 mm and a robot repetitive positioning error of 0.1 mm, and the deviation between the final tunnel exit and the planned position was calculated. The control group, on the other hand, assumed a random error of 1.5 mm in the operator's operation.

[0094] Experimental results:

[0095] The calculation results of the two planning schemes are compared in the table below.

[0096] Experimental conclusion:

[0097] 1. Significantly optimized biomechanical performance: The tunnel planned by this invention exhibits a significantly smaller ligament length variation (ΔL) than traditional planning methods (1.8mm vs 3.5mm, p<0.001), better conforming to the principle of isochronous length and thus beneficial for maintaining joint stability. The minimum impact distance in all experimental groups was greater than the safety threshold (1.5mm), while three cases in the control group experienced impact (distance <0), indicating that this invention effectively avoids postoperative impact risks.

[0098] 2. Higher anatomical positioning accuracy: Whether it is the femoral tunnel or the tibial tunnel, the position after being planned and simulated by the present invention is significantly less than the deviation from the ideal anatomical target area than traditional empirical planning (0.7mm vs ~2.0mm, p<0.001), showing extremely high accuracy and repeatability.

[0099] 3. Inherent advantages of this system: This invention, through closed-loop feedback of three-dimensional anatomical reconstruction, parametric rule base, and biomechanical simulation optimization, transforms abstract clinical experience into a quantifiable computational model, fundamentally solving the pain points of traditional methods that rely on experience, are difficult to quantify, and are not reproducible. Multimodal navigation registration and high-precision robot execution ensure that the optimal planning scheme can be transferred to the surgical execution stage without loss, realizing precise control of the entire process from "planning" to "execution".

[0100] Example 3

[0101] As described in this embodiment, a tunnel location determination system and method for anterior / posterior cruciate ligament reconstruction includes the following steps in ACL reconstruction surgery based on the above system:

[0102] Data preparation and 3D reconstruction:

[0103] The patient's knee CT DICOM data was imported into the system. The medical image data processing module automatically segmented the femoral and tibial skeletal structures using a pre-trained U-Net model and generated a high-precision 3D model using the Marching Cubes algorithm. The system automatically identified key anatomical landmarks such as Blumensaat's line and the posterior cortical contour.

[0104] Automated planning and simulation optimization:

[0105] The tunnel planning module automatically calculates the initial femoral tunnel point P based on a preset rule base (α=0.65, β=0.30). femur and tibial tunnel point P tibia .

[0106] The biomechanical simulation submodule was activated. The simulation showed that the ligament length change ΔL in the initial design was 2.5 mm (slightly exceeding the threshold), and that at a flexion of 105°, D... imp It is 1.2mm (less than the safety margin δ=1.5mm).

[0107] The processing unit automatically employs gradient descent to fine-tune P. femur The position. After 3 iterations, the optimal solution is obtained: ΔL is reduced to 1.7mm, and the minimum value (D) is obtained throughout the process. imp The thickness reached 2.3mm. The system generated the final tunnel planning path. final It is displayed as a bright colored cylinder on the 3D model.

[0108] Intraoperative registration and navigation:

[0109] During the procedure, the optical reference frame was securely fixed to the patient's femur and tibia.

[0110] The physician uses a pointer with navigation tracking for point registration (collecting data from the medial and lateral epicondyles, the apex of the intercondylar fossa, etc.) and surface registration (sliding across the femoral condyle surface to collect hundreds of points). The navigation registration module calculates a high-precision spatial transformation matrix T_reg (registration error <0.5mm) using the ICP algorithm.

[0111] The doctor holds an electric drill equipped with an instrument rack. A navigation interface is displayed in real-time on the screen.

[0112] Main view: Displays the skeletal model, planned tunnel, and real-time drill model.

[0113] Axial view: This view is used by the doctor to align the center of the drill bit with the crosshairs that represent the center of the planned tunnel.

[0114] Probe view: Shows the distance and angle deviation between the drill bit tip and the planned tunnel entrance point.

[0115] Numerical display: Real-time display deviation ΔP = (0.3mm, 1.5°).

[0116] The doctor adjusts the position of the electric drill based on the navigation feedback. Once all deviation values ​​are within the green safety zone (e.g., <1mm & <2°), the tunnel can be drilled according to the navigation guidance.

[0117] Postoperative verification:

[0118] After drilling, C-arm X-rays were taken in anteroposterior and lateral views. The system compared the X-rays with preoperative images based on the Pathology protocol. final The generated DRR images are fused and compared to generate a report, confirming that the actual bone passage is highly consistent with the planned path.

[0119] In summary, this invention proposes a tunnel location determination system and method for anterior / posterior cruciate ligament reconstruction, comprising a medical image processing module, a tunnel planning module, a navigation registration module, and a display unit. It reconstructs a 3D model of the knee joint using a deep learning segmentation algorithm, calculates the virtual tunnel location based on a parametric anatomical rule base, and introduces a biomechanical simulation unit to optimize tunnel planning. During surgery, a high-precision spatial registration is achieved through an optical navigation system, providing real-time multi-view navigation guidance, and can be combined with a robotic execution platform to complete precise drilling. This invention solves the problems of traditional surgery relying on experience and the mismatch between static planning and dynamic function, achieving a leap from anatomical reconstruction to functional reconstruction, significantly improving surgical accuracy and postoperative outcomes.

[0120] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A tunnel location determination system for anterior / posterior cruciate ligament reconstruction, characterized in that, include: Medical image data processing module: Receives DICOM data from CT or MRI, extracts the bony contours of the distal femur and proximal tibia through image segmentation algorithms, and performs three-dimensional reconstruction based on Marching Cubes or surface rendering algorithms to generate a three-dimensional model of the target bone containing the geometry of the intercondylar fossa. Tunnel planning module: Calculates virtual tunnels on the 3D model based on a preset anatomical rule base; The anatomical rule base includes: Femoral tunnel positioning rules: The center point P of the virtual femoral tunnel is... femur The region located on the medial wall of the intercondylar fossa of the lateral femur, between the top of the femur and the center of the footprint area, is defined by a parametric proportional relationship: P femur =f(D sag ,D cor ,W); In the sagittal plane, P femur Distance D from Blumensaat's line sag With the total length L of Blumensaat's line blum Satisfy: D sag =α·L blum , where α is an empirical coefficient, α∈[0.6, 0.8]; In the coronal position, P femur Distance D from the posterior cortical contour line cor The intercondylar fossa width W satisfies: D cor =β·W, where β is an empirical coefficient, β∈[0.25, 0.35]; Tibial tunnel localization rule: The center point P of the virtual tibial tunnel is... tibia Located between the posterolateral and anteromedial bundles of the ACL footprint on the tibial plateau, its position satisfies: P tibia Located in the posterior 42% to 52% region of the anteroposterior diameter of the tibial plateau, and the medial 45% to 55% region of the medial and lateral diameters; Navigation registration module: Receives spatial pose information from the calibration device of the optical navigation system. This spatial pose information is derived from a group of optical markers M fixed to a reference frame attached to the skeleton and an instrument holder attached to the surgical instruments. bone and M tool Spatial coordinates (x, y, z, θ) x θ y θ z Define; the spatial transformation matrix T from the 3D model coordinate system to the surgical site skeleton coordinate system is calculated using the iterative nearest neighbor algorithm or singular value decomposition algorithm. reg This allows for the planning of virtual tunnel paths. final Registered to the actual surgical space; The display unit, communicatively connected to the processing unit, is used for real-time rendering and display of: the 3D model, the virtual tunnel planning path, and the registered and superimposed display of the actual pose P of the surgical instruments relative to the planning path. actual With ideal pose P ideal The deviation ΔP = |P actual -P ideal | 2. The tunnel location determination system for anterior / posterior cruciate ligament reconstruction as described in claim 1, characterized in that: The tunnel planning module also includes a biomechanical simulation unit: defining the knee flexion angle θ as a variable, simulating the movement of the knee joint from full extension to 120° flexion; at each flexion angle θ... i Below, calculate the change in length L(θ) of the transplanted ligament. i The minimum Euclidean distance D between the lateral condyle and the top of the intercondylar fossa. imp (θ) i ); Tunnel location optimization is based on the following judgment logic: The primary optimization objective is to minimize the change in ligament length ΔL = max(L(θ)). i )) - min(L(θ) i If ΔL is greater than the threshold L threshold If so, it is determined that the principle of equal length is not met, and the tunnel entrance point needs to be adjusted; Second optimization objective: Ensure D within the full buckling range imp (θ) i )>0, and a safety margin δ is reserved, i.e., min(D) imp (θ) i If any θ > δ; i D imp (θ) i If the value is less than or equal to δ, then a collision risk is identified, and the tunnel path needs to be adjusted. The processing unit is configured to iteratively adjust the position and orientation of the virtual tunnel based on the above judgment logic, using either gradient descent or a genetic algorithm, until a tunnel is found that simultaneously satisfies ΔL < L. threshold and min(D) imp (θ) i The optimal solution is δ.

3. The tunnel location determination system for anterior / posterior cruciate ligament reconstruction as described in claim 1, characterized in that: The system also includes: A surgical robot execution platform, comprising a robotic arm and an end effector, the end effector being used to hold a drill bit or a guide needle; The processing unit further includes a robot control module, which receives the optimal virtual tunnel planning path calculated by the tunnel planning module, the path starting from the tunnel entrance point P. in Exit point P out and the central axis vector V tunnel definition; According to the spatial transformation matrix T reg , will P in P out V tunnel Transform to the robot's base coordinate system to obtain the target pose P. robot and direction vector V robot ; By solving the inverse kinematics equations of the robotic arm, the joint angles θ are calculated. joint =[θ1, θ2, ..., θ n ] T This makes the tool center point of the end effector align with P. robot Coincident, and the tool axis is V robot parallel; Drive the robotic arm to the calculated joint angle and control the end effector along V. robot The drilling operation is completed in the correct direction.

4. The tunnel location determination system for anterior / posterior cruciate ligament reconstruction as described in claim 1, characterized in that: The calibration device is an optical navigation system, which includes multiple infrared optical cameras for tracking passive reflective spheres or active infrared emitting markers on the reference frame and instrument frame; the calculation of the spatial pose information is based on the principle of binocular vision and rigid body transformation theory.

5. The tunnel location determination system for anterior / posterior cruciate ligament reconstruction as described in claim 1, characterized in that: The image segmentation algorithm is a semantic segmentation model based on deep learning. This model is trained using a medical image dataset that includes labeled femurs and tibias, and can automatically and accurately segment the target skeletal structure. The model is one of U-Net, V-Net, or Deeplab V3+ architectures.

6. The tunnel location determination system for anterior / posterior cruciate ligament reconstruction as described in claim 1, characterized in that: When planning the virtual tunnel, the processing unit determines the diameter d of the selected implant. graft Automatically calculates the required drill bit diameter d for the tunnel. drill Based on this, a solid with a radius r = d is generated on the 3D model. drill A 2 / 2 cylindrical tunnel model is used for more accurate collision detection and visualization of bone removal.

7. The tunnel location determination system for anterior / posterior cruciate ligament reconstruction as described in claim 1, characterized in that: The navigation registration module employs an iterative nearest-point algorithm, the objective function of which is to minimize the mean square error between the three-dimensional model surface point set X and the bone surface point set Y measured by the calibration device. Where R is the rotation matrix and T is the translation vector, the optimal R and T are obtained by SVD decomposition to construct the spatial transformation matrix T. reg .

8. The tunnel location determination system for anterior / posterior cruciate ligament reconstruction as described in claim 1, characterized in that: The processing unit is configured to perform virtual path verification before drilling operations: simulating surgical instruments along the planned path. final Throughout the drilling process, the minimum distance between the instrument and surrounding key anatomical structures is monitored. If this distance is less than a safety threshold, a visual or auditory alarm is issued, prompting a replanning.

9. A method for determining the location of a tunnel for anterior / posterior cruciate ligament reconstruction, characterized in that: The system described in any one of claims 1-8 is used, comprising the following steps: S1: Data Acquisition and 3D Reconstruction: Acquire medical imaging data of the patient's knee joint and reconstruct a 3D model containing the fine structure of the intercondylar fossa using image processing algorithms; S2: Virtual Tunnel Planning: Based on parametric anatomical rules, the femoral tunnel center point P is initially determined on a 3D model. femur and the center point P of the tibial tunnel tibia ; S3: Biomechanical Simulation and Optimization: Simulating knee flexion and extension movements, calculating ligament length change ΔL and minimum impact distance min (D). imp Based on the judgment logic ΔL < L threshold And min(D) imp If P > δ, an optimization algorithm is used to iteratively adjust P. femur and / or P tibia The spatial coordinates are determined until biomechanical standards are met, and the final tunnel planning path is output. final ; S4: Spatial Registration: Obtaining the spatial transformation relationship between the skeleton at the surgical site and the 3D model using a calibration device. reg , Path final Register to real space; S5: Execution and Navigation: Select one of the following methods to execute based on the system architecture: S5.1: Passive navigation mode: Displays surgical instruments and the planned path in real time on the display unit. final The deviation ΔP guides the doctor to manually operate the instrument along the planned path and drill; S5.2: Proactive Execution Mode: Path final The commands are converted into robot pose instructions, which control the surgical robot platform to automatically locate and perform drilling.

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