Method and system for reconstructing lower limb force line based on intraoperative image of optical navigation

Abstract

The invention relates to a lower limb force line reconstruction method and system based on an intraoperative image of optical navigation, and belongs to the technical field of medical robot navigation. The method comprises the following steps of: acquiring intraoperative three-dimensional image data of an operative area of a patient by utilizing cone beam CT (Computed Tomography); acquiring position data of a tracker attached to the bone of the patient through an optical navigation system; based on the tracker data and the anatomical mark point, associating the tracker data with the image data through registration, and calculating the coordinate of the mark point in an image coordinate system; and reconstructing a lower limb force line. The system comprises CBCT scanning equipment, an optical navigation system and a processing unit. By means of various implementation schemes, for example, only scanning an operation area is combined with optical navigation or segmented scanning of spliced images, the problem that full-length lower limb force line reconstruction is difficult to achieve due to the fact that the CBCT view field is limited is effectively solved, and the method has the advantages of being low in radiation dose, capable of achieving real-time imaging and registration in an operation, high in precision, simple and convenient in process and the like.

Description

Technical Field

[0001] This invention relates to the field of medical robot navigation technology, specifically to a method and system for reconstructing lower limb force lines based on intraoperative images using optical navigation. Background Technology

[0002] In the field of joint replacement surgery, precise reconstruction of the lower limb alignment is a core factor affecting the success of the surgery and the patient's long-term functional recovery. Traditional manual surgery relies heavily on the surgeon's personal experience, making it difficult to guarantee the accuracy and consistency of alignment reconstruction. With the development of precision medicine and intelligent orthopedics, surgical robot-assisted systems are gradually becoming mainstream. These systems acquire three-dimensional imaging data of the patient before or during surgery, perform personalized surgical planning, and guide the robot to execute the procedure via navigation technology, thereby significantly improving surgical precision.

[0003] Currently, the foundation for achieving precise navigation lies in obtaining complete imaging data covering the relevant joints. For example, in total knee replacement surgery, to accurately reconstruct the lower limb mechanical axis from the femoral head center to the knee joint center and then to the ankle joint center, it is usually necessary to obtain images of three key areas: the hip, knee, and ankle. The standard clinical practice is to have patients undergo preoperative standing full-length lower limb X-rays combined with thin-slice computed tomography (CT) scans for preoperative modeling and planning. While this strategy can provide relatively reliable navigation data for robotic surgery, it has several limitations. Conventional CT scans involve relatively high radiation doses, increasing the health risks and psychological burden on patients; the examination process is complex, requiring patients to make a special trip to the radiology department before surgery to complete the scan, which not only prolongs the overall treatment time but may also delay the surgical timing due to multiple visits; furthermore, there may be discrepancies between the preoperative images and the patient's actual position during surgery, making true intraoperative real-time updates and registration impossible.

[0004] Cone-beam computed tomography (CBCT), as an emerging imaging technology, offers an ideal solution for real-time intraoperative imaging due to its significant advantages, including low radiation dose, portable equipment, and direct use within the operating room. However, CBCT technology faces a major technical bottleneck in its widespread application: its field of view in a single scan is limited, typically covering only a single joint region (e.g., only the knee joint), and cannot simultaneously acquire imaging information of the hip and ankle joints. This inherent limitation makes it difficult for traditional single CBCT scans to meet the needs of joint surgeries requiring full-length lower limb imaging for force line reconstruction, thus restricting its comprehensive application in related robot-assisted surgeries. Therefore, there is an urgent clinical need for a new imaging solution that can balance low radiation, high efficiency, and high precision, and enable real-time intraoperative reconstruction of the full-length lower limb force line to overcome the barriers of existing technologies.

[0005] Therefore, existing technologies still need further development. Summary of the Invention

[0006] The purpose of this invention is to overcome the above-mentioned technical deficiencies and provide a method and system for lower limb force line reconstruction based on intraoperative images of optical navigation, so as to solve the problems existing in the prior art.

[0007] To achieve the above-mentioned technical objectives, according to a first aspect of the present invention, the present invention provides a method for lower limb alignment reconstruction based on intraoperative images using optical navigation, comprising: S100. Acquire intraoperative three-dimensional image data of the patient's surgical area; S200: Obtain position data of a tracker attached to the patient's bones via an optical navigation system, the tracker being used to track the patient's anatomical structures; S300. Based on the position data of the tracker and the anatomical landmarks, the position data of the tracker is associated with the intraoperative three-dimensional image data through registration, so as to calculate the coordinates of the anatomical landmarks in the image coordinate system and reconstruct the force line of the patient's lower limb.

[0008] Specifically, the acquisition of intraoperative three-dimensional image data is achieved through CBCT scanning.

[0009] Specifically, the registration step includes using a high-density marker ball, which is visible in the intraoperative images and tracked by an optical navigation system, to establish a rigid transformation between the image coordinate system and the patient coordinate system.

[0010] Specifically, the high-density marker ball is placed on a scale, which is located near the patient and included in the imaging field of view.

[0011] Specifically, the CBCT scan is a segmented scan, which includes acquiring image data of the hip joint region, knee joint region and ankle joint region respectively, and stitching multiple local images into a complete lower limb image.

[0012] Specifically, the registration step includes transforming the coordinates of the hip joint center and the ankle joint center to the knee joint image coordinate system.

[0013] Specifically, the CBCT scan only covers the surgical area, which is the knee joint area, and the registration is achieved through a point cloud registration algorithm.

[0014] Specifically, the coordinates of the femoral head center and the ankle joint center required for calculating the coordinates of the anatomical landmarks in the image coordinate system are obtained by transforming the results of the point cloud registration algorithm.

[0015] Specifically, the CBCT scan only covers the surgical area, which is the knee joint region. The registration uses a high-density marker ball, which is visible in the intraoperative images and is tracked by an optical navigation system to establish a rigid transformation between the image coordinate system and the tracker coordinate system. The coordinates of the femoral head center and the ankle joint center are obtained for the hip joint region and the ankle joint region, respectively. The coordinates of the hip joint center and the ankle joint center in the patient coordinate system are transformed to the knee joint image coordinate system.

[0016] Specifically, the coordinates of the femoral head center are determined by holding the affected limb and moving it around the femoral head center as a fulcrum, and the position of the femoral tracker is collected in real time by the optical tracking system; the coordinates of the ankle joint center are determined by probes collecting the positions of the medial and lateral malleoli in the tibial tracker coordinate system.

[0017] According to a second aspect of the present invention, a system for lower limb alignment reconstruction based on intraoperative images using optical navigation is provided, comprising: CBCT scanning equipment is used to acquire intraoperative three-dimensional image data of the surgical area of ​​the patient; An optical navigation system includes an optical camera and a tracker, the tracker being attached to the patient's bones to track the patient's anatomical structures; The processing unit is configured to receive the intraoperative three-dimensional image data and the position data of the tracker; to calculate the coordinates of the anatomical landmarks in the image coordinate system based on the position data of the tracker and the anatomical landmarks; and to associate the position data of the tracker with the intraoperative three-dimensional image data through registration to reconstruct the force line of the patient's lower limb.

[0018] Beneficial effects: The method and system for lower limb alignment reconstruction based on optical navigation intraoperative imaging provided by this invention have several significant advantages over existing technologies. Firstly, by preferentially employing cone-beam computed tomography (CBCT) for intraoperative image acquisition, this invention fundamentally solves the problem of patients enduring high radiation doses and the hassle of travel associated with preoperative CT scans. It achieves a true "scan-and-operate" real-time diagnostic and treatment model, greatly shortening patients' waiting time and overall treatment cycle, and reducing their economic and health burden.

[0019] Secondly, this invention innovatively proposes multiple registration schemes, effectively overcoming the inherent limitations of CBCT's limited field of view. On one hand, by scanning only the core surgical area (such as the knee joint) and combining it with femoral and tibial tracker data acquired by the optical navigation system, point cloud registration technology can be used to obtain coordinate transformations, thereby reconstructing the complete lower limb force line in the image domain, minimizing radiation exposure while ensuring surgical accuracy. On the other hand, by introducing a high-density marker sphere as a registration reference and using the optical navigation system to track it, a high-precision rigid transformation relationship is established between the image coordinate system and the patient tracker coordinate system. This registration method is highly accurate and reliable, effectively reducing errors caused by factors such as intraoperative patient movement.

[0020] Furthermore, this invention also provides a solution for segmented scanning and image stitching. By acquiring CBCT images of key areas such as the hip, knee, and ankle separately, and using a tracker fixed to the bone as a spatial intermediary, multiple local images are precisely registered and stitched into a complete three-dimensional model of the full-length lower limb. This method not only fully leverages the advantages of CBCT—low dose and portability—but also successfully expands its functionality, enabling it to meet the comprehensive imaging needs for precise force line reconstruction.

[0021] Finally, this invention integrates the methods into a complete system, enabling the entire process to operate efficiently and smoothly in a standard operating room, improving the automation and repeatability of the surgery. In summary, this invention represents a breakthrough in reducing radiation damage, improving surgical timeliness, enhancing surgical navigation accuracy, and expanding the application of intraoperative imaging, and has significant clinical application value in promoting the development of orthopedic surgery towards low-dose, intelligent, and real-time methods. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating the method for lower limb alignment reconstruction based on intraoperative images using optical navigation, provided in a specific embodiment of the present invention. Figure 2 This is a schematic diagram of the system for reconstructing lower limb alignment based on intraoperative images using optical navigation, provided in a specific embodiment of the present invention. Detailed Implementation

[0023] To enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Based on the embodiments in this application, other similar embodiments obtained by those skilled in the art without creative effort should all fall within the scope of protection of this application. Furthermore, directional terms mentioned in the following embodiments, such as "up," "down," "left," and "right," are only for reference to the directions in the accompanying drawings; therefore, the directional terms used are for illustrative purposes and not for limiting the invention.

[0024] The present invention will be further described below with reference to the accompanying drawings and preferred embodiments.

[0025] S100: Acquire intraoperative three-dimensional image data of the patient's surgical area; It should be further noted that in step S100, the intraoperative three-dimensional image data is preferably acquired by cone-beam computed tomography (CBCT) scanning due to its advantages such as low radiation dose, portable equipment, and suitability for the operating room environment. CBCT scanning parameter settings need to balance image quality and radiation safety. For example, the preferred scanning voltage is 80-120kV, the preferred current is 10-50mA, and the preferred scanning time is 10-30 seconds. These parameters are based on clinical validation and can ensure that the image resolution is sufficient for bone identification (e.g., spatial resolution ≥0.5mm), while controlling the effective dose below 2mSv, which is lower than the limit for conventional CT.

[0026] S200: Obtain position data of a tracker attached to the patient's bones through an optical navigation system, the tracker being used to track the patient's anatomical structures; It should be further explained that in step S200, the optical navigation system includes an optical camera and a tracker. The tracker is fixed on the femur and tibia, and is referred to as the femoral tracker and tibial tracker, respectively, for real-time tracking of the position of anatomical structures. The tracker coordinate system and the patient coordinate system are the same concept, both representing the skeletal spatial coordinates with the tracker as a reference.

[0027] S300. Based on the position data of the tracker and the anatomical landmarks, the position data of the tracker is associated with the intraoperative three-dimensional image data through registration, so as to calculate the coordinates of the anatomical landmarks in the image coordinate system and reconstruct the force line of the patient's lower limb.

[0028] It should be further explained that in step S300, the anatomical landmarks include the femoral head center, the knee joint center, and the ankle joint center. The lower limb force line is defined as the line connecting the femoral head center, the knee joint center, and the ankle joint center. Registration is achieved through rigid transformation, which maps the points in the tracker coordinate system to the image coordinate system. Specifically, it can be achieved through point cloud registration algorithm or high-density marker ball registration.

[0029] Understandably, this invention breaks through the limitations of traditional CT scanning, achieving real-time intraoperative imaging and registration, reducing patient radiation exposure and preoperative waiting time, and improving surgical accuracy and efficiency; at the same time, through a universal workflow design, it supports multiple implementation variations, enhancing the flexibility and applicability of the method.

[0030] Specifically, the acquisition of intraoperative three-dimensional image data is achieved through CBCT scanning.

[0031] It should be further noted that a mobile C-arm CBCT scanner (such as the Siemens Arcadis Orbic) is preferred for use directly in the operating room. During the scan, the patient is in a supine position with the affected limb fixed to the operating table to ensure imaging stability. The CBCT scanning parameters, as described above, need to be adjusted according to the specific surgical requirements; for example, for knee surgery, the field of view (FOV) diameter is preferably 200-300 mm, and the height is preferably 100-150 mm, to cover the entire knee joint structure. The rationale for choosing these parameters is that, based on human anatomical data, this range can completely image the knee joint area and meets the accuracy requirements of robot navigation (e.g., the error in identifying skeletal landmarks is <1 mm).

[0032] Understandably, CBCT scans reduce radiation dose by more than 50% compared to conventional CT scans, thus reducing patients' health risks and financial burden. At the same time, real-time intraoperative imaging avoids additional preoperative visits, simplifies the process, and enables "scan and operate immediately" in real time.

[0033] Specifically, the registration step includes using a high-density marker ball, which is visible in the intraoperative images and tracked by an optical navigation system, to establish a rigid transformation between the image coordinate system and the patient coordinate system.

[0034] It should be further noted that the high-density marker spheres are preferably titanium alloy spheres with a diameter of 3-5 mm, which have a higher density than bone and are easily identifiable in CBCT images. The number of marker spheres is preferably 4-6, fixed to a ruler placed near the patient's affected limb (approximately 10-20 cm away) and included in the imaging field of view. The registration steps specifically include: first, extracting the voxel coordinates of the marker spheres in the CBCT image through threshold segmentation, with a threshold preferably between 1000-2000 HU to ensure clear differentiation between the marker spheres and bone; then, converting the voxel coordinates to image physical coordinates. Simultaneously, the optical navigation system records the coordinates of the marker spheres in the patient's coordinate system. The rigid transformation matrix is ​​solved using the singular value decomposition (SVD) algorithm to minimize the distance error between the marker sphere points in the two coordinate systems, with the error controlled within 0.3 mm. The rationale for selecting these parameters is that the size and number of marker spheres, based on accuracy studies, ensure registration stability; and the threshold range, verified experimentally, effectively distinguishes the marker spheres from soft tissue.

[0035] Understandably, the high-density marker ball provides a stable reference point with high registration accuracy (sub-millimeter level), reducing errors caused by patient movement; this method is particularly suitable for dynamic areas such as the knee joint, enhancing the reliability of the surgery.

[0036] Specifically, the high-density marker ball is placed on a scale, which is located near the patient and included in the imaging field of view.

[0037] It should be further noted that the scale is preferably made of carbon fiber, with a length of 200-300mm and a linearity error of <0.1mm, to ensure the geometric stability of the marker ball arrangement. The marker balls are arranged in a straight line or grid, with a spacing of 20-30mm for easy automatic identification. The scale is fixed to the operating table track to remain stable during scanning and avoid displacement. The rationale for choosing these parameters is that carbon fiber is lightweight and highly rigid, reducing imaging artifacts; the spacing setting is based on best practices of the marker ball recognition algorithm, which improves registration efficiency.

[0038] Understandably, the scale improves the consistency of the marker ball layout, facilitates automatic identification and registration, reduces human intervention, and enhances the automation of the surgical procedure.

[0039] Specifically, the CBCT scan is a segmented scan, which includes acquiring image data of the hip joint region, knee joint region and ankle joint region respectively, and stitching multiple local images into a complete lower limb image.

[0040] It should be further noted that the preferred scanning order is hip joint first, then knee joint, and finally ankle joint, with each scan interval less than 5 minutes to avoid patient movement. Scanning parameters are adjusted according to the region: the preferred FOV diameter for the hip joint region is 250mm, and the preferred FOV height is 150mm; for the knee joint region, the preferred FOV diameter is 300mm, and the preferred FOV height is 100mm; for the ankle joint region, the preferred FOV diameter is 150mm, and the preferred FOV height is 120mm. The rationale for choosing these FOV ranges is to ensure coverage of key bony landmarks (such as the femoral head and ankle mortise) based on the anatomical dimensions of each joint. Stitching is achieved through a registration algorithm. First, each local image is registered to the patient coordinate system. Then, using the knee joint image as a reference, the hip and ankle joint images are stitched together using a transformation matrix.

[0041] Understandably, segmented scanning overcomes the limitation of CBCT's field of view, achieving full lower limb coverage while maintaining the advantage of low radiation (total dose is lower than conventional CT); the stitched model can be directly used for force line reconstruction, improving the comprehensiveness of surgical planning.

[0042] Specifically, the registration step includes transforming the coordinates of the hip joint center and the ankle joint center to the knee joint image coordinate system.

[0043] It should be further explained that the transformation is achieved through the mediation of the tracker coordinate system. Specifically, let the transformation matrix between the hip joint image and the femoral tracker be... The transformation matrix between the knee joint image and the femoral tracker is: The transformation matrix between the knee joint image and the tibial tracker is: The transformation matrix between the ankle joint image and the tibial tracker is: Then, using the knee joint image as the reference coordinate system: when transforming the hip joint image to the knee joint image coordinate system, the calculation... When transforming ankle joint images to the coordinate system of knee joint images, calculate... .

[0044] in: This represents the rigid transformation matrix from the femoral tracker coordinate system to the hip joint image coordinate system; This represents the rigid transformation matrix from the femoral tracker coordinate system to the knee joint image coordinate system; Represents the rigid transformation matrix from the tibial tracker coordinate system to the knee joint image coordinate system; This represents the rigid transformation matrix from the tibial tracker coordinate system to the ankle joint image coordinate system.

[0045] Understandably, this method uses a tracker as a common reference to achieve seamless stitching of multiple images with high accuracy (stitching error <1mm), and can be directly used for lower limb force line measurement, reducing cumulative error.

[0046] Specifically, the CBCT scan only covers the surgical area, which is the knee joint area, and the registration is achieved through a point cloud registration algorithm.

[0047] It should be further explained that the preferred point cloud registration algorithm is the Iterative Closest Point (ICP) algorithm. The specific steps include: in CBCT images, the bone surfaces of the distal femur and proximal tibia are segmented using a region growing algorithm to generate 3D point cloud data. The point cloud density is set to 5 points per square millimeter to ensure detail capture. Simultaneously, at the actual anatomical location of the patient, a probe from an optical navigation system is used to collect point clouds of the bone surface, preferably 35 points, evenly distributed. The ICP algorithm is iterated 100 times, with a convergence threshold set to 0.1 mm to balance accuracy and computational efficiency. The rationale for choosing these parameters is that the point cloud density and number of iterations, based on clinical trials, can guarantee a registration error of <1 mm.

[0048] Understandably, point cloud registration requires no additional markers, has a simple process, and reduces intraoperative operation time; it is suitable for scenarios with mature registration algorithms and reduces hardware dependence.

[0049] Specifically, the coordinates of the femoral head center and the ankle joint center required for calculating the coordinates of the anatomical landmarks in the image coordinate system are obtained by transforming the results of the point cloud registration algorithm.

[0050] It should be further explained that after point cloud registration, the coordinates of the femoral head center are determined by fitting the femoral head center of gravity: the physician holds the affected limb and moves it around the femoral head center of gravity, while the optical system collects the position data of the femoral tracker, which is then calculated using a center of gravity fitting algorithm; the coordinates of the ankle joint center are determined by identifying the midpoints of the medial and lateral malleoli through the registered skeletal model. The center of gravity fitting uses the least squares method, with at least 50 sampling points, and the preferred movement speed is 5-10° / second to avoid soft tissue interference.

[0051] Specifically, the CBCT scan only covers the surgical area, which is the knee joint region. The registration uses a high-density marker ball, which is visible in the intraoperative images and is tracked by an optical navigation system to establish a rigid transformation between the image coordinate system and the tracker coordinate system. The coordinates of the femoral head center and the ankle joint center are obtained for the hip joint region and the ankle joint region, respectively. The coordinates of the hip joint center and the ankle joint center are then transformed into the knee joint image coordinate system.

[0052] It should be further explained that this method combines high-density marker ball registration and physical point acquisition. First, the coordinate system transformation between the image and the tracker is established through marker ball registration, with the specific steps being the same as the aforementioned marker ball scheme. Then, the coordinates of the femoral head center are determined by circling the affected limb: the movement radius is approximately 10-20 cm, the optical system collects data at a 100 Hz sampling rate, and the ball center fitting error is <0.5 mm; the coordinates of the ankle joint center are determined by acquiring the medial and lateral malleolus points using a probe with a diameter of 2 mm, collecting data 3-5 times and averaging the results. The coordinate transformation formula is as follows: in: This indicates the coordinates of the femoral head center in the imaging coordinate system; Represents the rigid transformation matrix from the femoral tracker coordinate system to the image coordinate system; This represents the coordinates of the femoral head center in the femoral tracker coordinate system; This indicates the coordinates of the ankle joint center in the image coordinate system; Represents the rigid transformation matrix from the tibial tracker coordinate system to the image coordinate system; This indicates the coordinates of the ankle joint center in the tibial tracker coordinate system.

[0053] Understandably, this method integrates the stability of marker ball registration with the intuitiveness of physical point acquisition, making it suitable for scenarios with high accuracy requirements, while reducing radiation by scanning only the surgical area.

[0054] In the method described, the coordinates of the femoral head center are determined by holding the affected limb and moving the affected limb around the femoral head center as a fulcrum, and the position of the femoral tracker is collected in real time by the optical tracking system; the coordinates of the ankle joint center are determined by the probe collecting the position of the medial and lateral malleoli in the tibial tracker coordinate system.

[0055] It should be further noted that when acquiring the femoral head center, the movement speed around the affected limb should be controlled at 5-10° / second, with at least 50 sampling points. The center of the sphere is fitted using the least squares method, with the formula being the minimum sum of squared errors. When acquiring the ankle joint center, the probe contacts the medial and lateral malleoli 3-5 times each, and the coordinates are recorded before calculating the midpoint. The coordinates are calculated as follows: ,in and These represent the coordinates of the medial and lateral malleoli in the tibial tracker coordinate system, respectively. The rationale for choosing these parameters is that the movement speed and sampling points are based on ergonomics to ensure data reliability; and the number of probe acquisitions has been statistically verified to reduce random errors.

[0056] Understandably, the physical point acquisition method is non-invasive, simple to operate, and highly accurate (sphere center fitting error <0.5mm), requiring no additional imaging and reducing process complexity.

[0057] For ease of understanding, the present invention will be further described below: It is understood that the present invention provides three specific embodiments, as follows: ① Example 1: The core feature of this embodiment is that only the surgical area of ​​the knee joint is scanned by CBCT, the patient space and the image space are mapped by point cloud registration algorithm, and the coordinates of key points of the hip and ankle are obtained by physical operation to reconstruct the force line of the lower limb. The specific implementation steps are as follows: First, the patient lies supine, and the femoral tracker and tibial tracker are securely fixed to the femur and tibia of the affected limb, respectively. Then, a single scan of the knee joint region is performed using a moving cone-beam CT scanner. The scan area should completely cover the distal femur and proximal tibia, with a preferred field of view (FOV) diameter of 280 mm and a height of 120 mm. Scanning parameters can be set to 110 kV voltage, 25 mA current, and a scan time of 15 seconds. This combination of parameters ensures sufficiently clear images for bone identification while minimizing radiation dose.

[0058] Further, after acquiring knee joint images, point cloud registration is performed. In the images, three-dimensional models of the bone surfaces of the distal femur and proximal tibia are extracted using threshold segmentation (e.g., setting a threshold of 200 HU) and a region growing algorithm, generating dense three-dimensional point clouds. Simultaneously, in the patient's actual anatomical space, the surgeon uses the tip probe of an optical navigation system to collect the coordinates of 50 spatial points on the bone surfaces of the distal femur and proximal tibia, respectively, forming a point cloud dataset in the patient's space. Then, the Iterative Closest Point (ICP) algorithm is used to register these two sets of point clouds. The ICP algorithm iteratively calculates and finds the optimal rigid transformation matrix (including the rotation matrix R and translation vector t) that minimizes the overall distance error between the two sets of point clouds. The algorithm can be set to iterate 100 times, with a convergence threshold of 0.1 mm to ensure registration accuracy. Through this process, a precise transformation relationship mapping points in the femoral / tibial tracker coordinate system to the knee joint image coordinate system can be obtained.

[0059] Furthermore, for the coordinates of the femoral head center and ankle joint center required for force line reconstruction, this embodiment uses a physical method to directly acquire them in the patient space. The coordinates of the femoral head center are determined by the "femoral rocking" method: the surgeon holds the patient's lower leg and slowly rotates the hip joint of the affected limb around the femoral head. The optical navigation system records the motion trajectory of the femoral tracker in space in real time at a high frequency (e.g., 100Hz). The multiple acquired position points are theoretically distributed on a sphere. The center of this sphere can be calculated using a sphere center fitting algorithm (e.g., least squares method), which is the coordinate of the femoral head center in the femoral tracker coordinate system. The coordinates of the ankle joint center are obtained through a "probe point-by-point" method: using the tip of the navigation probe, the most prominent bony landmarks at the medial and lateral malleoli are palpated several times each, and the coordinates of these points in the tibial tracker coordinate system are recorded. The average of the coordinates of the medial and lateral malleoli points is taken as the approximate coordinates of the ankle joint center in the tibial tracker coordinate system. .

[0060] Finally, using the transformation matrix obtained from the aforementioned point cloud registration, the physically acquired... and Transform to the knee joint image coordinate system to obtain and By combining the knee joint center coordinates directly segmented and identified from knee joint images, connecting these three points allows for the complete reconstruction of the lower limb biomechanical axis in image space. The advantage of this embodiment is that only a single low-dose scan of the surgical area is required, the registration process requires no additional instruments, and the procedure is relatively simple.

[0061] ② Example 2: The core feature of this embodiment is that only the surgical area of ​​the knee joint is scanned by CBCT, but registration is achieved by using a high-density marker ball (ruler). Similarly, the coordinates of key points of the hip and ankle are obtained through physical operations. The specific implementation steps are as follows: Patient preparation and tracker fixation are the same as in Example 1. Before scanning, a ruler with multiple high-density marker balls (usually 4-6 small titanium alloy balls) should be placed near the knee joint of the affected limb, ensuring it is included in the subsequent CBCT scan field of view. Subsequently, a single CBCT scan of the knee joint area is performed, with parameter settings the same as in Example 1.

[0062] Furthermore, the registration method is the main difference between this embodiment and Embodiment 1. After scanning, in the obtained knee joint CT images, high-density marker spheres will appear as significant bright signal points due to their material properties. Image processing algorithms (such as segmentation with a threshold of 1000-2000 HU) can automatically identify and extract the three-dimensional center coordinates of these marker spheres in the image coordinate system. Simultaneously, the optical navigation system can track these marker spheres fixed on the scale in real time, thereby obtaining their precise coordinates in patient space (i.e., the navigation system coordinate system). Since the scale is rigid, the relative positions of the marker spheres on it remain constant. Therefore, an optimal rigid transformation can be solved to achieve optimal alignment of the two sets of points (marker sphere coordinates in the image and marker sphere coordinates in patient space). This is typically achieved using the Singular Value Decomposition (SVD) algorithm, thereby obtaining the transformation matrix between the image coordinate system and the patient coordinate system. This "scale registration" method does not depend on the bone surface morphology and is highly accurate and stable.

[0063] Furthermore, in terms of force line acquisition, this embodiment is the same as Embodiment 1, that is, the coordinates of the femoral head center are obtained by "rocking the femur". The coordinates of the ankle joint center are obtained through "probe points". Finally, using the transformation matrix obtained from the scale registration, the coordinates of these key points acquired in patient space are transformed into the knee joint image coordinate system, thereby completing the force line reconstruction. The advantage of this embodiment is that the registration process relies on easily identifiable marker balls, which provides strong anti-interference capabilities and reliable accuracy, making it particularly suitable for cases with thick soft tissue coverage or indistinct bone surface features.

[0064] ③ Example 3: The core feature of this embodiment is that it performs segmented CBCT scans of the hip, knee, and ankle joint regions, uses a high-density marker ball (ruler) for registration throughout the entire process, and directly obtains the force line on the full-length image through image stitching technology. The specific implementation steps are as follows: The patient's position and tracker fixation are the same as in Examples 1 and 2. In this example, three consecutive CBCT scans are required. First, a ruler with a marked ball is placed next to the affected limb to scan the hip joint area; then, keeping the ruler position relatively fixed, the knee and ankle joint areas are scanned sequentially. The parameters for each scan can be finely adjusted according to the location; for example, the field of view for the hip joint scan can be appropriately increased to include the entire femoral head.

[0065] Furthermore, after each part of the body is scanned, "ruler registration" is performed independently, and the process is exactly the same as the registration of the knee joint in Example 2. That is, the marker ball on the ruler is identified in the local images of the hip, knee, and ankle, respectively, and matched with the real-time coordinates tracked by the navigation system. The SVD algorithm is used to solve for three independent rigid transformation relationships between the hip joint image, knee joint image, ankle joint image and the patient (navigation) coordinate system.

[0066] The next crucial step is image stitching. This embodiment uses the knee joint image as a unified reference coordinate system. Since the images of the hip, knee, and ankle have already been registered with scales and transformed to the same patient coordinate system, the hip and ankle images can be easily "converted" to the knee joint image coordinate system. Specifically, utilizing the transitivity of the transformation matrix, the composite transformation matrix required to transform points in the hip joint image to the knee joint image coordinate system can be calculated. The ankle joint image stitching follows the same principle. In this way, the three local images are precisely stitched into a complete three-dimensional image model of the entire lower limb, from the femoral head to the ankle joint.

[0067] Furthermore, in the final stitched full-length lower limb image, the femoral head center, knee joint center, and ankle joint center can all be directly and automatically identified and calculated from the image data using image segmentation algorithms (such as threshold-based segmentation or model fitting), without any external physical manipulation. Connecting these three points yields the lower limb force line. The advantage of this embodiment is that it can obtain the most intuitive and complete anatomical information, and the force line measurement is entirely based on images, ensuring good objectivity. However, the scanning and computational workload is relatively large.

[0068] Please see Figure 2 The present invention provides another embodiment, which provides a system for lower limb alignment reconstruction based on intraoperative images using optical navigation. The system for lower limb alignment reconstruction based on intraoperative images using optical navigation includes: CBCT scanning device 100 is used to acquire intraoperative three-dimensional image data of the patient's surgical area; An optical navigation system 200 includes an optical camera and a tracker attached to the patient's bones for tracking the patient's anatomical structures. The processing unit 300 is configured to receive the intraoperative three-dimensional image data and the position data of the tracker; to calculate the coordinates of the anatomical landmarks in the image coordinate system based on the position data of the tracker and the anatomical landmarks; and to associate the position data of the tracker with the intraoperative three-dimensional image data through registration to reconstruct the force line of the patient's lower limb.

[0069] It should be further noted that the CBCT scanning device 100 can be equipped with a Siemens Cios Fusion mobile C-arm, the optical navigation system 200 can be equipped with an NDI Polaris Viera, and the processing unit 300 is an industrial computer (such as an Intel i7 processor with 16GB of memory) running customized software. The software algorithm includes coordinate transformation, registration, and stitching modules, and the user interface displays the reconstructed lower limb force lines.

[0070] Understandably, the system is highly integrated and can be deployed in a standard operating room to achieve one-stop preoperative and intraoperative navigation; the processing unit automatically performs calculations, reducing the workload of the surgeon and improving the repeatability and safety of the surgery.

[0071] In a preferred embodiment, this application also provides an electronic device, the electronic device comprising: The computer device includes a memory and a processor, wherein the memory stores computer-readable instructions that, when executed by the processor, implement the method for lower limb alignment reconstruction based on intraoperative images using optical navigation. The computer device can be broadly categorized as a server, terminal, or any other electronic device with the necessary computing and / or processing capabilities. In one embodiment, the computer device may include a processor, memory, network interface, communication interface, etc., connected via a system bus. The processor of the computer device can be used to provide the necessary computing, processing, and / or control capabilities. The memory of the computer device may include a non-volatile storage medium and internal memory. The non-volatile storage medium may store an operating system, computer programs, etc. The internal memory can provide an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface and communication interface of the computer device can be used to connect and communicate with external devices via a network. When the computer program is executed by the processor, it performs the steps of the method of the present invention.

[0072] This invention can be implemented as a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, causes the steps of the methods of embodiments of the invention to be performed. In one embodiment, the computer program is distributed across multiple network-coupled computer devices or processors, such that the computer program is stored, accessed, and executed in a distributed manner by one or more computer devices or processors. A single method step / operation, or two or more method steps / operations, may be executed by a single computer device or processor or by two or more computer devices or processors. One or more method steps / operations may be executed by one or more computer devices or processors, and one or more other method steps / operations may be executed by one or more other computer devices or processors. One or more computer devices or processors may execute a single method step / operation, or execute two or more method steps / operations.

[0073] Those skilled in the art will understand that the method steps of this invention can be performed by a computer program instructing related hardware, such as a computer device or processor, to perform the steps of this invention when executed. Depending on the context, any references herein to memory, storage, databases, or other media may include non-volatile and / or volatile memory. Examples of non-volatile memory include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid-state drive, etc. Examples of volatile memory include random access memory (RAM), external cache memory, etc.

[0074] The technical features described above can be combined arbitrarily. Although not all possible combinations of these technical features are described, any combination of these technical features should be considered to be covered by this specification, provided that such combination does not contain contradictions.

[0075] The specific embodiments of the present invention described above do not constitute a limitation on the scope of protection of the present invention. Any other corresponding changes and modifications made in accordance with the technical concept of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A method for lower limb alignment reconstruction based on intraoperative images using optical navigation, characterized in that, Includes the following steps: S100. Acquire intraoperative three-dimensional image data of the patient's surgical area; S200: Obtain position data of a tracker attached to the patient's bones via an optical navigation system, the tracker being used to track the patient's anatomical structures; S300. Based on the position data of the tracker and the anatomical landmarks, the position data of the tracker is associated with the intraoperative three-dimensional image data through registration, so as to calculate the coordinates of the anatomical landmarks in the image coordinate system and reconstruct the force line of the patient's lower limb.

2. The method according to claim 1, characterized in that... The acquisition of intraoperative three-dimensional image data is achieved through CBCT scanning.

3. The method according to claim 2, characterized in that... The registration step includes using a high-density marker ball, which is visible in the intraoperative images and tracked by an optical navigation system, to establish a rigid transformation between the image coordinate system and the patient coordinate system.

4. The method according to claim 3, characterized in that, The high-density marker ball is placed on a scale, which is located near the patient and included in the imaging field of view.

5. The method according to claim 3 or 4, characterized in that, The CBCT scan is a segmented scan, which includes acquiring image data of the hip joint region, knee joint region and ankle joint region respectively, and stitching multiple local images into a complete lower limb image.

6. The method according to claim 5, characterized in that, The registration step includes transforming the center coordinates of the hip joint and the center coordinates of the ankle joint to the knee joint image coordinate system.

7. The method according to claim 2, characterized in that... The CBCT scan only covers the surgical area, which is the knee joint area, and the registration is achieved through a point cloud registration algorithm.

8. The method according to claim 7, characterized in that, The coordinates of the femoral head center and the ankle joint center required to calculate the coordinates of the anatomical landmarks in the image coordinate system are obtained by transforming the results of the point cloud registration algorithm.

9. The method according to claim 2, characterized in that... The CBCT scan only covers the surgical area, which is the knee joint region. The registration uses a high-density marker ball, which is visible in the intraoperative images and is tracked by an optical navigation system to establish a rigid transformation between the image coordinate system and the tracker coordinate system. The coordinates of the femoral head center and the ankle joint center are obtained for the hip joint region and the ankle joint region, respectively. The coordinates of the hip joint center and the ankle joint center in the patient coordinate system are transformed to the knee joint image coordinate system.

10. The method according to claim 7 or 9, characterized in that: The coordinates of the femoral head center are determined by holding the affected limb and moving it around the femoral head center as a fulcrum, with the position of the femoral tracker being collected in real time by an optical tracking system; the coordinates of the ankle joint center are determined by probes collecting the positions of the medial and lateral malleoli in the tibial tracker coordinate system.

11. A system for lower limb alignment reconstruction based on intraoperative images using optical navigation, characterized in that, include: CBCT scanning equipment is used to acquire intraoperative three-dimensional image data of the surgical area of ​​the patient; An optical navigation system includes an optical camera and a tracker, the tracker being attached to the patient's bones to track the patient's anatomical structures; The processing unit is used to receive the intraoperative three-dimensional image data and the position data of the tracker; Used to calculate the coordinates of the anatomical landmarks in the image coordinate system based on the position data of the tracker and the anatomical landmarks; used to associate the position data of the tracker with the intraoperative three-dimensional image data through registration to reconstruct the force line of the patient's lower limb.

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