Ophthalmologic operation navigation positioning method, apparatus and device, and storage medium

Abstract

The invention discloses an ophthalmologic operation navigation positioning method, device and equipment and a storage medium, and the method can obtain detailed information of bone tissues and soft tissues at the same time after a CT image and an MR image are fused, can carry out the precise segmentation and planning of a registered image, and provides accurate navigation and guidance for an operation. After registration, under the guidance of navigation, a doctor can more accurately position a surgical site, damage to surrounding tissues is avoided, surgical wounds are reduced, surgical risks are reduced, and surgical efficiency is improved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of surgical assistance, in particular to an ophthalmic surgery navigation positioning method and device based on CT image and MR image fusion reconstruction, an ophthalmic surgery navigation positioning device, an ophthalmic surgery navigation positioning equipment and a storage medium. BACKGROUND

[0002] The ophthalmic surgery navigation positioning system is a medical device that uses computer, optical and mechanical technologies to assist ophthalmic surgery. The technical background of the system mainly includes the following aspects:

[0003] Optical technology: the ophthalmic surgery navigation positioning system uses optical technology to obtain image information of the surgical site, monitors the position and attitude of the surgical instrument in real time, and provides real-time data for surgery navigation.

[0004] Computer technology: through computer technology such as image processing and pattern recognition, the obtained image data is processed and analyzed to extract useful information and provide accurate navigation information for surgery navigation.

[0005] Mechanical technology: mechanical technology is the basis for precise operation of surgical instruments. The ophthalmic surgery navigation positioning system uses mechanical technology to achieve precise positioning and operation of surgical instruments.

[0006] The advantages of the ophthalmic surgery navigation positioning system include high precision, high reliability and low risk. The system can help doctors more accurately determine the location and range of lesions, improve the success rate of surgery and the treatment effect of patients. With the continuous development and improvement of technology, the ophthalmic surgery navigation positioning system will be applied in more ophthalmic surgeries to provide better treatment services for patients.

[0007] However, the traditional ophthalmic surgery navigation positioning system usually uses CT images or MR images as data basis to understand the patient's eye structure or lesion condition during the intraoperative planning stage. This method has the disadvantages that the obtained data is relatively single and cannot comprehensively understand the patient's eye structure and lesion condition.

[0008] At the same time, the registration method used in the intraoperative navigation stage is single and cannot help doctors more accurately determine the location and range of lesions. SUMMARY

[0009] In view of the above problems, the application provides an ophthalmic surgery navigation positioning method, device, equipment and storage medium for overcoming the above problems or at least partially solving the above problems. The method can obtain detailed information of bone tissue and soft tissue after fusing CT images and MR images, and can more accurately segment and plan in combination with other algorithms during planning.

[0010] The application provides the following solutions:

[0011] An ophthalmic surgery navigation positioning method, comprising:

[0012] obtaining CT slice sequence images and MR slice sequence images of a patient's surgery site;

[0013] aligning the CT slice sequence images and the MR slice sequence images by using an Elastix rigid deformation registration method to obtain registered images;

[0014] segmenting and planning the contour structure and lesion area of the patient's lesion site by using a target segmentation method on the registered images to obtain a target region and lesion condition;

[0015] planning a surgery path and target point according to the target region and lesion condition to obtain a displayable virtual medical image;

[0016] obtaining position coordinate information of a surgery instrument required for surgery after tip calibration and matching with the virtual medical image position; the surgery instrument is placed in the optical tracker field target range;

[0017] registering the virtual medical image and the patient's surgery site for intraoperative navigation by using a target registration method in combination with point cloud data of the patient's surgery site obtained by the surgery instrument and the position coordinate information.

[0018] Preferably: the CT slice sequence images and the MR slice sequence images are preprocessed to obtain preprocessed CT slice sequence images and preprocessed MR slice sequence images; the preprocessed CT slice sequence images and the preprocessed MR slice sequence images are registered by using the Elastix rigid deformation registration method.

[0019] Preferably: the preprocessing includes window width and window level adjustment and image cropping to improve the quality of the images and remove interference factors.

[0020] Preferably: the target segmentation method includes any one of 2D image segmentation, 3D segmentation, threshold segmentation, skin segmentation, and edge detection.

[0021] Preferably: the planning of the surgery path and target point includes the planning of roaming, path, and target point, and the planned targets are distinguished by different colors and names.

[0022] Preferably: the target registration method includes any one of T-type registration, C-type registration, or landmark point registration.

[0023] Preferably: the point cloud data is obtained by using a corresponding marking method with a marking needle in the patient's surgical area according to the determined target registration mode.

[0024] An ophthalmic surgery navigation positioning device, comprising:

[0025] An image acquisition unit is configured to acquire CT slice sequence images and MR slice sequence images of a patient's surgical site.

[0026] An image registration and fusion unit is configured to register the CT slice sequence images and the MR slice sequence images by using an Elastix rigid deformation registration method, so as to align the images of two different modalities to obtain registered images.

[0027] A contour structure and lesion area segmentation unit is configured to segment and plan the contour structure and lesion area of the patient's lesion site on the registered images by using a target segmentation method to obtain a target area and a lesion condition.

[0028] A virtual medical image generation unit is configured to plan a surgical path and a target point according to the target area and the lesion condition to obtain a displayable virtual medical image.

[0029] A surgical instrument position matching unit is configured to acquire position coordinate information of a surgical instrument required for surgery after tip calibration and position matching with the virtual medical image; and the surgical instrument is placed in the target range of an optical tracker.

[0030] An intraoperative navigation registration unit is configured to register the virtual medical image and the patient's surgical site for intraoperative navigation by using a target registration mode in combination with point cloud data of the patient's surgical site acquired by the surgical instrument and the position coordinate information.

[0031] An ophthalmic surgery navigation positioning device, comprising a processor and a memory:

[0032] The memory is configured to store program code and transmit the program code to the processor.

[0033] The processor is configured to execute the ophthalmic surgery navigation positioning method according to the instructions in the program code.

[0034] A computer readable storage medium is configured to store program code for executing the ophthalmic surgery navigation positioning method.

[0035] According to the embodiments of the present application, the following technical effects are provided:

[0036] The method provided by the embodiment of the application can fuse a CT image and an MR image, can obtain detailed information of bone tissue and soft tissue at the same time, can perform accurate segmentation and planning on the registered image, and can provide accurate navigation and guidance for surgery. Under the guidance of navigation after registration, a doctor can more accurately position a surgery site, avoid damage to surrounding tissue, reduce surgery trauma, reduce surgery risk, and improve surgery efficiency.

[0037] Of course, implementing any product of the application does not necessarily need to achieve all the advantages described above at the same time. BRIEF DESCRIPTION OF DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments. Obviously, the drawings in the following description only constitute some embodiments of the present application, and other drawings can also be obtained by those of ordinary skill in the art without any creative effort based on these drawings.

[0039] Figure 1 is a flowchart of the ophthalmic surgery navigation positioning method provided by the embodiment of the present application;

[0040] Figure 2 is a mobile cube schematic diagram provided by the embodiment of the present application;

[0041] Figure 3 is a state configuration schematic diagram of a body element provided by the embodiment of the present application;

[0042] Figure 4 is a B-spline curve schematic diagram provided by the embodiment of the present application;

[0043] Figure 5 is a right-hand coordinate system schematic diagram provided by the embodiment of the present application;

[0044] Figure 6 is an application principle diagram of the Super4PCS algorithm provided by the embodiment of the present application;

[0045] Figure 7 is a schematic diagram of the ophthalmic surgery navigation positioning device provided by the embodiment of the present application;

[0046] Figure 8 is a schematic diagram of the ophthalmic surgery navigation positioning device provided by the embodiment of the present application. DETAILED DESCRIPTION

[0047] The technical solutions in the embodiments of the present application will be clearly and completely described with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art belong to the scope of protection of the present application.

[0048] Referring to Figure 1 An ophthalmic surgery navigation positioning method provided by the embodiments of the present application, as shown in the drawings, can include: Figure 1

[0049] S101: Obtain CT slice sequence images and MR slice sequence images of a patient's surgery site; in specific implementation, pre-process the CT slice sequence images and the MR slice sequence images to obtain pre-processed CT slice sequence images and pre-processed MR slice sequence images; and use an Elastix rigid deformation registration method to register the pre-processed CT slice sequence images and the pre-processed MR slice sequence images.

[0050] Further, the pre-processing includes window width and window level adjustment and image cropping, so as to improve the quality of the images and remove interference factors.

[0051] S102: Use the Elastix rigid deformation registration method to register the CT slice sequence images and the MR slice sequence images, so as to align the images of two different modalities to obtain registered images.

[0052] S103: On the registered images, use a target segmentation method to segment and plan the contour structure and lesion area of a patient's lesion site to obtain a target region and lesion condition; in specific implementation, the target segmentation method includes any one of 2D image segmentation, 3D segmentation, threshold segmentation, skin segmentation, and edge detection.

[0053] S104: According to the target region and the lesion condition, plan a surgery path and a target point to obtain a displayable virtual medical image; in specific implementation, the planning of the surgery path and the target point includes planning of roaming, path, and target point, and distinguishing the planned targets by different colors and names.

[0054] S105: Obtain position coordinate information of a surgery instrument required for surgery after a tip calibration operation and a position matching with the virtual medical image; and place the surgery instrument in a target range of an optical tracker.

[0055] ​S106: Register the virtual medical image with the patient's surgical site for intraoperative navigation by combining the point cloud data of the patient's surgical site acquired by the surgical instrument and the position coordinate information using a target registration method. The target registration method includes any one of T-type registration, C-type registration or landmark point registration. In specific implementation, the point cloud data is obtained by drawing points on the patient's surgical area using a corresponding drawing method by a drawing needle according to the determined target registration method.

[0056] The ophthalmic surgery navigation positioning method provided by the embodiments of the present application adopts a CT and MR image registration method in the intraoperative planning stage of the ophthalmic surgery navigation positioning system. Through this registration technology, multi-modal image data of the patient can be obtained, so that the eye structure and lesion condition can be more comprehensively understood. On this basis, the registered image can be accurately segmented and planned, and accurate navigation and guidance can be provided for the surgery.

[0057] In the intraoperative navigation stage of the ophthalmic surgery navigation positioning system, three registration methods of T-type registration, C-type registration and landmark point registration are provided. Doctors can match the virtual medical image with the surgical site through the registration technology, which can help doctors more accurately determine the position and range of the lesion and improve the accuracy and treatment effect of the surgery. After registration, doctors can more accurately position the surgical site under the guidance of navigation, avoid damage to the surrounding tissues, reduce surgical trauma, reduce surgical risk and improve surgical efficiency.

[0058] The ophthalmic surgery navigation positioning method provided by the embodiments of the present application will be described in detail below taking ophthalmic surgery as an example.

[0059] The technical problem to be solved by the present application is to provide a fusion reconstruction method based on CT and MR images. This method can fuse CT images and MR images, and can simultaneously obtain detailed information of bone tissue and soft tissue. In planning, other algorithms can be combined to more accurately segment and plan.

[0060] The intraoperative planning method includes the following steps:

[0061] Step one: Collect the 2D CT slice sequence and the MR slice sequence of the patient.

[0062] Step two: Preprocess the acquired CT and MR images as necessary, including window width and window level adjustment, image cropping, to improve the quality of the images and remove interference factors.

[0063] Step three: Accurately register the CT and MR images using Elastix rigid deformation registration. The purpose of registration is to align and fuse two images of different modalities, so as to facilitate subsequent image analysis and processing.

[0064] Step 4: On the registered image, 2D image segmentation, 3D segmentation, threshold segmentation, skin segmentation and edge detection methods are used to segment and plan the eye structure and lesion area.

[0065] Step 5: Based on the segmented target area and lesion condition, the surgical path and target are planned. The planning includes navigation, path, and target. The planned targets are also distinguished by different colors and names to ensure the accuracy and safety of the surgery.

[0066] The purpose of window width and window position adjustment is to map the image grayscale value from one range to another.

[0067] Clinical Function: Because the bit depth of radiological images such as CT scans is higher than what the human eye can perceive, CT values ​​must be mapped to a certain range for normal observation. Simultaneously, mapping CT values ​​by window width and window position can more clearly display local details in CT images, providing doctors with reliable diagnostic data.

[0068] Principle: Since the mapping of window width and window level is linear, it can be performed using a quadratic equation y = ax + b. Assuming that the CT value (window width w, window level l) is mapped to the range (0, 255), the values ​​of the equation coefficients a and b can be given by the following formula:

[0069]

[0070] Where "intercept" is the intercept, corresponding to the value of the DICOM file tag [0028,1052], and "slope" is the slope, corresponding to the value of the DICOM file tag [0028,1053]. By default, the slope value in the DICOM file is 1, and the intercept value is 0. The slope and intercept are used to control the dynamic adjustment range of the CT value.

[0071] Rigid deformation registration is a widely used medical image registration technology, which is used to align CT scan sequence slices and MR scan sequence slices through the Elastix rigid deformation registration algorithm.

[0072] Clinical function: Uses a rigid algorithm to align pre-acquired CT scan sequence slices with MR scan sequence slices to help doctors navigate and plan during surgery and locate important structures and targets.

[0073] principle:

[0074] 1. Load fixed images and moving images.

[0075] 2. Initialize the initial transformation parameters (e.g., translation, rotation, scaling).

[0076] 3. Loop the following steps until the maximum number of iterations is reached or the convergence condition is met:

[0077] a. Apply the moving image to the fixed image according to the current transformation parameters to get a deformed image.

[0078] b. Calculate the similarity measure between the deformed image and the fixed image, such as Mean Squared Differences (MSD).

[0079] c. Backpropagate the error and update the transformation parameters based on the similarity measure and image gradient information.

[0080] Output the final transformation parameters.

[0081] Initial transformation parameters: Represented using translation, rotation, and scaling, which can be represented as a rigid transformation matrix:

[0082] T_init = [s*cos(theta) -s*sin(theta) tx]

[0083] [s*sin(theta) s*cos(theta) ty] [0 0 1]

[0085] Where s represents the scaling factor, theta represents the rotation angle, and (tx, ty) represents the translation vector.

[0086] Apply transformation: Apply the moving image to the fixed image through linear interpolation, which can be represented using an Affine Transform.

[0087] X_new = T_init * X_old

[0088] Where X_new represents the point on the transformed moving image, and X_old represents the corresponding point on the fixed image.

[0089] Similarity measure: A common measure is Mean Squared Differences (MSD), which calculates the difference between the fixed image and the deformed image.

[0090] MSD = (1 / N) * Σ(I_fixed - I_deformed)^2

[0091] Where N represents the number of pixels, I_fixed represents the pixel value on the fixed image, and I_deformed represents the pixel value on the deformed image.

[0092] Backpropagation error and update parameters: according to the similarity measure and image gradient information, the gradient descent method is used to update the transformation parameters.

[0093]

[0094]

[0095]

[0096]

[0097] wherein a represents a learning rate, represents the gradient of the similarity measure with respect to the parameters.

[0098] The moving cube algorithm is used for isosurface extraction of medical images.

[0099] Clinical function: in order to reconstruct and display the patient tissue structure observed in the DICOM image in three dimensions, a mesh model needs to be established for the structure to render and display. In this process, a surface rendering algorithm can be used to complete the rendering process from DICOM image data to three-dimensional structure data. The moving cube (Marching Cubes) algorithm is a classic algorithm in surface rendering algorithms, which is a voxel-level reconstruction algorithm proposed by W. Lorensen et al. in 1987, also known as the "isosurface extraction" (Isosurface Extration) algorithm. Due to its high efficiency, accuracy and other characteristics, it has been widely used.

[0100] Principle: the main idea of the moving cube algorithm is to approximate the isosurface in a three-dimensional discrete data field by linear interpolation. In medical image segmentation and reconstruction, we define a threshold to determine the isosurface. As shown in Figure 2 , first, the concept of "cell" is determined, which is different from "voxel". A cell is a square composed of 8 pixels arranged in order, while each voxel (except the boundary) is shared by 8 cells.

[0101] Then there are three cases for the vertex value in the cell: higher than or equal to the isosurface indicates inside the surface, lower than the isosurface indicates outside the surface. In this way, one vertex of a cell has 2 possible states, so a cell (8 vertices) has a total of 256 states. As shown in Figure 3 , according to the rotation, mapping invariance and other characteristics, the state of the cell can be summarized as 15 configurations:

[0102] In other words, all 256 states of a voxel can be obtained through these 15 basic configurations through operations such as rotation and symmetry transformation. Each voxel state contains a number of three facets, and the specific positions of the triangle vertices in the voxel are calculated by linear interpolation based on the values ​​of the isosurface and the values ​​of the two vertices on the edge. A lookup table can be created for these 256 states to facilitate reconstruction. By traversing all voxels, finding the triangles within them, and combining them, the final triangular mesh surface data (Mesh) is formed.

[0103] To summarize, the steps of the marching cubes algorithm are:

[0104] Read 4 slices of the image into memory;

[0105] Scan 2 slices according to the voxel;

[0106] Calculate the index of the cube by comparing the 8 vertex values ​​of the voxel with the obtained isosurface value;

[0107] Use the index to find the edge list from the lookup table;

[0108] Using the grayscale value of each edge vertex, the exact position of the triangle vertex is calculated by linear interpolation;

[0109] Calculate the unit normal of each voxel vertex and interpolate the normal to each vertex of the triangle patch;

[0110] Outputs the vertices and normals of the triangle patch.

[0111] The purpose of path planning is to fit a smooth curve through discrete control points.

[0112] Clinical function: The system interactively selects points on the path and then fits a smooth surgical path using a B-spline curve.

[0113] principle:

[0114] B-spline curve is a linear combination of B-spline odd functions and a generalization of Bezier curve. Given n+1 control points P0, P1, ..., P n , and a node vector U={u0,u1,…,u n}, the p-degree B-spline curve is defined by these control points and the knot vector U, and its formula is:

[0115]

[0116] Among them, N i,p(u) is a p-th B-spline basis function. To define the basis function, the degree p of the basis function needs to be determined, the i-th p-th B-spline odd function is written as N i,p (u), which is recursively defined as follows:

[0117]

[0118]

[0119] As shown in Figure 4 , there are 8 control points in total, and they are connected in turn by line segments, and the B-spline curve is connected to form a series of 5 cubic Bezier curves. In general, the lower the degree (i.e., the smaller p), the easier it is for the B-spline curve to approximate its control polygon.

[0120] As a key step of the surgical navigation system, inaccurate image registration will result in time loss, poor time or surgical failure, such as incorrect resection location or incomplete treatment. It is often necessary to select a coordinate system as the world coordinate system, and then match all coordinate systems in the system to the world coordinate system we selected.

[0121] In the intraoperative navigation stage of the system, we provide three registration methods of T-type registration, C-type registration and landmark registration, and instrument calibration. Doctors can match virtual medical images with surgical sites through registration technology, which can help doctors more accurately determine the location and range of lesions, improve surgical precision and treatment effect, and at the same time, when switching to different tools during surgery, different instruments can be fixed by clamps for tip calibration and used. After registration, doctors can more accurately locate the surgical site under the guidance of navigation, avoid damage to surrounding tissues, reduce surgical trauma, reduce surgical risk and improve surgical efficiency.

[0122] The intraoperative navigation method comprises the following steps:

[0123] Step one: after completing the intraoperative planning stage, switch to the surgical navigation module.

[0124] Step two: first, adjust the tool, place each instrument within the appropriate range of the optical tracker field of view, and perform tip calibration on the clamp instrument.

[0125] Step three: select the registration method: T-type registration, C-type registration or landmark registration, and perform registration.

[0126] Step four: after registration is complete, tool guidance or puncture guidance navigation work can be performed.

[0127] T-type registration: In intraoperative navigation, when T-type registration is used, first align the T-type template with the patient's head accurately. By cutting a certain area of the head, the T-type template can be registered. After registration is completed, the T-type point cloud can be drawn on the forehead of the head using the marking needle, and real-time registration can be performed to ensure the accuracy and safety of the operation.

[0128] C-type registration: In intraoperative navigation, when C-type registration is used, first convert the image of the patient's head into point cloud data (point cloud data represents the geometry of the head surface, and each point has its coordinates in three-dimensional space) by acquiring the image of the patient's head; use the marking needle to draw a C-type point cloud on the head, and real-time registration is performed between the point cloud drawn by the marking needle on the head and the image point cloud, and real-time registration is performed to ensure the accuracy and safety of the operation.

[0129] Landmark registration: In intraoperative navigation, landmark registration uses a point-based registration method. After selecting the landmark coordinates on the screen in the image space, the tracked marking needle is used to select points in the real part according to the order of the landmark, and the image landmark and the selected landmark are aligned through the conversion relationship between the head-mounted markers to ensure the accuracy and safety of the operation.

[0130] The above three registration techniques can help doctors more accurately locate the lesion or deformity site, thereby improving the surgical effect and the recovery rate of patients. In actual use, users can choose one of the above methods for registration operation according to their needs.

[0131] The algorithm used in the intraoperative navigation includes:

[0132] Iterative Closest Point (ICP) algorithm, which is used to align two point sets.

[0133] Clinical function: ICP (Iterative Closest Point) algorithm is an iterative calculation method, mainly used in computer vision for point cloud data registration and splicing, etc. In the system, we use the ICP algorithm to calculate the best transformation matrix between the CT and the patient, and complete the registration process between the CT and the patient.

[0134] Principle: Given two point sets (source point set P and target point set Q):

[0135]

[0136] We want to find the matching relationship T between the source point set and the target point set norm , so that the distance between the source point set after the transformation and the target point set is minimized. normThe rotation matrix R and the translation vector t. To find this transformation, one can solve for T by minimizing norm :

[0137]

[0138] The specific steps are:

[0139] (1) Subtract the centroid of each point set P, Q from itself

[0140] The centroid of the two point sets are:

[0141]

[0142] The coordinates of the two point sets after removing the centroid are:

[0143]

[0144] (2) Find the covariance matrix of the two point sets after removing the centroid:

[0145] Calculate the rotation matrix R

[0146] SVD decomposition of W in equation (3.4-5) has:

[0147] W = U∑V T

[0148] When W is full rank, there is a unique solution:

[0149] R = UV T

[0150] (3) Calculate the translation vector t

[0151] Through the above formula, the translation vector can be obtained:

[0152] t = μ q -Rμ p

[0153] (4) Iterative optimization

[0154] Calculate the value of the objective function E(R, t). If it is less than the threshold, stop iteration, otherwise continue to repeat steps (1)-(4).

[0155] The purpose of coordinate transformation is to realize the mapping of target data between different coordinate spaces.

[0156] Clinical function: Coordinate transformation is the position description of spatial entities, which is the process of transforming from one coordinate system to another. It is achieved by establishing a one-to-one correspondence between the two coordinate systems. In the navigation system, the tracking device locates the spatial position of the instrument in real time and feeds it back to the software system. Through coordinate transformation, different spatial coordinate systems such as tracking system, patient, medical image, etc. are unified to complete the complex navigation positioning process, which is an essential step in various navigation and positioning systems.

[0157] Principle: In linear algebra, linear changes can be represented by matrices, and any linear transformation can be represented by a matrix in a consistent form for easy calculation, and multiple transformations can also be easily connected together by matrix multiplication. For coordinate transformation, the right-handed coordinate system is generally used, as shown in Figure 5 .

[0158] Assuming that the point coordinates in coordinate system o1 are (x, y, z), and the corresponding point coordinates in coordinate system o2 are (x', y', z'), the transformation relationship between the two points can be given by the following formula:

[0159]

[0160] where R is the rotation matrix, t is the translation vector, p T and s are the perspective parameters and scaling factor, respectively. In the navigation system, generally p T = [0, 0, 0] T and s = 1.

[0161] Using this formula, the positions of CT images, patients, and surgical instruments in the space registration and tracking navigation process can be calculated, thereby accurately positioning the relative positional relationship between them.

[0162] Point cloud registration algorithm uses Super4PCS (Super Four-Points Congruent Sets) algorithm, which is used for rough registration of point cloud data, especially for large and complex point cloud data sets

[0163] Clinical function: In the medical field, Super4PCS algorithm can be used to assist doctors in diagnosis and treatment planning, such as in surgical navigation, virtual simulation, and patient model reconstruction.

[0164] Principle: As shown in Figure 6 , the algorithm achieves rough registration by sampling and matching the sampling points between two point cloud data sets. Its core idea is to use a fixed number of four points to calculate the transformation matrix, maximizing the similarity of two point clouds. It is based on the quaternion method, which greatly improves the calculation efficiency while maintaining high registration accuracy.

[0165] Clinical Function: Super4PCS algorithm can be used to register patient's medical images and point cloud data of actual surgical site, assisting doctors in precise surgical navigation and operation. This helps to improve the accuracy and safety of surgery, and reduce postoperative complications.

[0166] Principle: This algorithm realizes coarse registration by sampling and matching the sampling points between two point cloud data sets. Its core idea is to use a fixed number of four points to calculate the transformation matrix, maximizing the similarity of two point clouds. It is based on the quaternion method, which greatly improves the computational efficiency while maintaining high registration accuracy.

[0167] Figure 6 There should be four points on each line on the right, and only the possible intersection points from one starting point are shown. (A line, the other point can also be the starting point.) Note that these line segments have direction, i.e., the starting point and the ending point are in order.

[0168] Find the coplanar four-point base in the target point cloud P that meets the requirements (the determination of the baseline has a great relationship with the overlap in the input parameters. The larger the overlap, the longer the baseline selection, and the long baseline can guarantee the robustness of matching and fewer matching quantities). Figure 6 Use B = {a, b, c, d} to represent, and then extract the topological information of the coplanar four-point base.

[0169] Calculate the two scale factors r1, r2 between the four-point base according to the following formula. The two scale factors have affine invariance in point cloud rotation and translation changes.

[0170]

[0171] Calculate the intersection point positions of q1, q2 ∈ Q according to the following formula, and then calculate the intersection point coordinates of all long baseline point pairs in Q. Compare the intersection point coordinates to determine the matching set, ei ≈ ej indicates that the corresponding congruent congruent four-point is found, i and j represent the i-th and j-th long baseline point pairs in Q.

[0172]

[0173] Figure 6 The congruent four-point pair of B = {a, b, c, d} is C = {q1, q3, q4, q5}. Find all coplanar four-point sets P in the point cloud and record them as E = {B1, B2, …, Bm}, m is the total number of four-point sets in P. Repeat the above steps to get the congruent four-point set D = {C1, C2, …, Cn}, n is the total number of congruent four-point sets.

[0174] Finally, in the set D = {C1, C2, …, Cn}, the optimal congruent four-point pair is searched, and the 4PCS uses the LCP strategy to search for the optimal congruent four-point matching, that is, the rotation and translation change parameters of the congruent four points are calculated, the four-point transformation is applied to the global point cloud transformation, and the matching of the global registration containing the largest consistent area is recorded as the optimal matching, and thus the local coarse registration of the 4PCS algorithm is completed.

[0175] In summary, the ophthalmic surgery navigation positioning method provided in the application can fuse CT images and MR images, can obtain detailed information of bone tissue and soft tissue at the same time, can accurately segment and plan the registered images, and can provide accurate navigation and guidance for surgery. Under the guidance of navigation after registration, the surgeon can more accurately position the surgical site, avoid damage to the surrounding tissue, reduce surgical trauma, reduce surgical risk and improve surgical efficiency.

[0176] Referring to Figure 7 , the embodiments of the application can also provide an ophthalmic surgery navigation positioning device, as shown in Figure 7 , the device can include:

[0177] The image acquisition unit 701 is configured to acquire a CT slice sequence image and an MR slice sequence image of a surgical site of a patient.

[0178] The image registration and fusion unit 702 is configured to register the CT slice sequence image and the MR slice sequence image by using an Elastix rigid deformation registration method, so as to align the images of the two different modalities to obtain a registered image.

[0179] The contour structure and lesion area segmentation unit 703 is configured to segment and plan the contour structure and lesion area of the lesion site of the patient on the registered image by using a target segmentation method to obtain a target area and a lesion condition.

[0180] The virtual medical image generation unit 704 is configured to plan a surgical path and a target point according to the target area and the lesion condition to obtain a displayable virtual medical image.

[0181] The surgical instrument position matching unit 705 is configured to acquire position coordinate information of a surgical instrument required for surgery after tip calibration operation and position matching with the virtual medical image; and the surgical instrument is placed in the target range of the optical tracker.

[0182] The intraoperative navigation registration unit 706 is configured to use a target registration method to combine point cloud data of the surgical site of the patient acquired by the surgical instrument and the position coordinate information to register the virtual medical image with the surgical site of the patient for intraoperative navigation.

[0183] The embodiment of the present application can also provide an ophthalmic surgery navigation positioning device, which comprises a processor and a memory:

[0184] The memory is used for storing program codes and transmitting the program codes to the processor.

[0185] The processor is used for executing the steps of the ophthalmic surgery navigation positioning method according to the instructions in the program codes.

[0186] As shown in the figure, the ophthalmic surgery navigation positioning device provided by the embodiment of the present application can comprise a processor 10, a memory 11, a communication interface 12 and a communication bus 13. The processor 10, the memory 11 and the communication interface 12 can complete the communication among each other through the communication bus 13. Figure 8

[0187] In the embodiment of the present application, the processor 10 can be a central processing unit (CPU), a specific application integrated circuit, a digital signal processor, a field programmable gate array or other programmable logic devices, etc.

[0188] The processor 10 can call the program stored in the memory 11. Specifically, the processor 10 can execute the operations in the embodiment of the ophthalmic surgery navigation positioning method.

[0189] The memory 11 is used for storing one or more programs. The program can comprise program codes, and the program codes comprise computer operation instructions. In the embodiment of the present application, the memory 11 at least stores programs for realizing the following functions:

[0190] Obtaining CT slice sequence images and MR slice sequence images of a surgery site of a patient;

[0191] Using an Elastix rigid deformation registration method to register the CT slice sequence images and the MR slice sequence images, so as to align the images of two different modalities and obtain registered images;

[0192] Using a target segmentation method to segment and plan the contour structure and lesion area of a patient's lesion site on the registered images, so as to obtain a target area and lesion condition;

[0193] According to the target area and the lesion condition, planning a surgery path and a target point to obtain a displayable virtual medical image;

[0194] Obtaining position coordinate information of a surgery instrument required for surgery after a tip calibration operation and a position matching with the virtual medical image. The surgery instrument is placed in the target range of an optical tracker;

[0195] ​The target registration method is used to combine the point cloud data of the surgical site of the patient acquired by the surgical instrument and the position coordinate information to register the virtual medical image and the surgical site of the patient for intraoperative navigation.

[0196] In a possible implementation, the memory 11 can include a program storage area and a data storage area, where the program storage area can store an operating system and application programs required by at least one function (such as a file creation function, a data read-write function), and the like; and the data storage area can store data created during use, such as initialization data and the like.

[0197] In addition, the memory 11 can include a high-speed random access memory, and can also include a non-volatile memory, for example, at least one magnetic disk storage device or other volatile solid-state storage device.

[0198] The communication interface 12 can be an interface of a communication module, used to connect with other devices or systems.

[0199] Of course, it needs to be explained that, Figure 8 The structures shown do not constitute a limitation on the ophthalmic surgery navigation positioning device in the embodiments of the present application, and in actual applications, the ophthalmic surgery navigation positioning device can include more or fewer components than Figure 8 those shown, or combine certain components.

[0200] It needs to be explained that, in this document, relationship terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between the entities or operations. Moreover, the terms “include”, “contain” or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device. Without more limitations, the element defined by the statement “including a…” does not exclude the presence of another identical element in the process, method, article or device including the element.

[0201] Those skilled in the art can clearly understand the application by the description of the above embodiments. The technical solutions of the application can be implemented by means of software plus necessary universal hardware platforms. Based on such an understanding, the technical solutions of the application can be embodied in the form of a software product. The computer software product can be stored in a storage medium, such as a ROM / RAM, a magnetic disk, or an optical disk, and includes a plurality of instructions to cause a computer device (such as a personal computer, a server, or a network device) to execute the methods described in various embodiments or some parts of the embodiments.

[0202] The various embodiments in the specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment mainly describes the difference from other embodiments. In particular, the system or system embodiments are described more simply because they are basically similar to the method embodiments. The relevant parts can be referred to the part of the method embodiments. The above-described system and system embodiments are merely illustrative, and the units described as separate components can or can not be physically separated, and the components displayed as units can or can not be physical units, that is, they can be located in one place or distributed on multiple network units. According to actual needs, some or all of the modules can be selected to achieve the purpose of the embodiment. Those skilled in the art can understand and implement without creative labor.

[0203] The above only describes the preferred embodiments of the application and is not used to limit the protection scope of the application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the application is included in the protection scope of the application.

Claims

1. An ophthalmic surgical navigation positioning method, characterized by, The method comprises: obtaining CT slice sequence images and MR slice sequence images of a patient's surgical site; aligning the CT slice sequence images and the MR slice sequence images by using an Elastix rigid deformation registration method to obtain registered images; segmenting the contour structure and lesion area of the patient's lesion site by using a target segmentation method on the registered images to obtain a target region and lesion condition; planning a surgical path and target point according to the target region and lesion condition to obtain a displayable virtual medical image; obtaining position coordinate information of a surgical instrument required for surgery after a tip calibration operation and position matching with the virtual medical image; the surgical instrument is placed within the target range of an optical tracker; registering the virtual medical image and the patient's surgical site for intraoperative navigation by using a target registration method in combination with point cloud data of the patient's surgical site obtained by the surgical instrument and the position coordinate information.

2. The ophthalmic surgical navigation and positioning method of claim 1, wherein, The CT slice sequence images and the MR slice sequence images are preprocessed to obtain preprocessed CT slice sequence images and preprocessed MR slice sequence images; the preprocessed CT slice sequence images and the preprocessed MR slice sequence images are registered by using an Elastix rigid deformation registration method.

3. The ophthalmic surgical navigation and positioning method of claim 2, wherein, The preprocessing includes window width and window level adjustment and image cropping to improve the quality of the images and remove interference factors.

4. The ophthalmic surgical navigation and positioning method of claim 1, wherein, The target segmentation method includes any one of 2D image segmentation, 3D segmentation, threshold segmentation, skin segmentation, and edge detection.

5. The ophthalmic surgical navigation and positioning method of claim 1, wherein, The planning of the surgical path and target point includes the planning of roaming, path, and target point, and the planned target is distinguished by different colors and names.

6. The ophthalmic surgical navigation and positioning method of claim 1, wherein, The target registration method includes any one of T-type registration, C-type registration, or landmark point registration.

7. The ophthalmic surgical navigation and positioning method of claim 6, wherein, The point cloud data is obtained by using a corresponding marking method with a marking needle in the patient's surgical area according to the determined target registration method.

8. An ophthalmic surgical navigation positioning device, comprising: It comprises: an image acquisition unit configured to obtain CT slice sequence images and MR slice sequence images of a patient's surgical site; an image registration and fusion unit configured to register the CT slice sequence images and the MR slice sequence images by using an Elastix rigid deformation registration method to align the images of the two different modalities to obtain registered images; a contour structure and lesion area segmentation unit configured to segment the contour structure and lesion area of the patient's lesion site by using a target segmentation method on the registered images to obtain a target region and lesion condition; a virtual medical image generation unit configured to plan a surgical path and target point according to the target region and lesion condition to obtain a displayable virtual medical image; a surgical instrument position matching unit configured to obtain position coordinate information of a surgical instrument required for surgery after a tip calibration operation and position matching with the virtual medical image; the surgical instrument is placed within the target range of an optical tracker; An intraoperative navigation registration unit is configured to register the virtual medical image with the patient's surgical site using a target registration approach that combines point cloud data acquired from the patient's surgical site using the surgical instrument and the position coordinate information.

9. An ophthalmic surgical navigation positioning device, comprising: The device comprises a processor and a memory: The memory is configured to store program code and transmit the program code to the processor; The processor is configured to execute the ophthalmic surgery navigation positioning method according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium is configured to store program code, and the program code is configured to execute the ophthalmic surgery navigation positioning method according to any one of claims 1-7.

Images