An orthopedic robot positioning method, system, device and medium

Abstract

The present invention belongs to the technical field of medical surgical supplies, and specifically discloses an orthopedic robot positioning method, system, device and medium. An orthopedic robot positioning method includes the following steps: fixing a tracer at a preset position and uploading a spinal CT image to the orthopedic robot host; the orthopedic robot host controls the structured light platform to move to the working position to be processed according to the spinal CT image; scanning the spine through the structured light platform to obtain a first surface point cloud and uploading it to the orthopedic robot host; the orthopedic robot host performs rough and fine point cloud registration on the first surface point cloud and the spinal CT image to obtain a 3D spine image; the orthopedic robot host performs positioning according to the 3D spine image. By performing rough and fine point cloud registration on the first surface point cloud and the spinal CT image, the present invention first determines approximately and then performs specific registration, improving the registration accuracy and streamlining the registration process.

Description

Technical Field

[0001] The present invention belongs to the technical field of medical surgical supplies, and particularly relates to an orthopedic robot positioning method, system, device and medium. Background Art

[0002] The spine is the most important bone tissue structure in the human body, undertaking the functions of supporting the trunk, protecting the internal organs and spinal cord, and driving body movement. There are many types of spinal diseases, mainly including spinal degenerative diseases, spinal traumas, spinal deformities, spinal tumors, and spinal infections, etc., bringing great pain and burden to patients. Surgical treatment is one of the most important treatment means for spinal diseases. The core surgical actions of spinal surgery include the establishment of bone channels for implants and internal fixation, spinal cord / nerve decompression, and osteotomy and other operations. Structures such as important blood vessels, nerves and spinal cord are adjacent to the spine (especially the cervical spine), all of which are dangerous areas. If there is a deviation in the placement of implants during the operation, it may cause secondary injuries to blood vessels and nerves and the failure of internal fixation, resulting in the failure of the operation; in addition, if there are mistakes during operations such as removing osteophytes, decompression or osteotomy, in the mild case, the patient's symptoms will not be relieved, and in the severe case, it may lead to paralysis or even endanger life.

[0003] Due to the high complexity of the clinical environment (especially the cervical spine), many problems have been exposed in the actual clinical application of orthopedic robots, and its main problems include insufficient clinical accuracy of orthopedic robots. During the intraoperative operation of orthopedic robots, due to the operating stress, relative displacement occurs between the vertebrae, resulting in image drift. The farther away from the patient tracer of the orthopedic robot, the greater the drift, which will affect the positioning accuracy of the orthopedic robot. Summary of the Invention

[0004] The purpose of the present invention is to improve the accuracy of orthopedic robot-assisted spinal surgery. To achieve the above purpose, the present invention adopts the following technical solutions:

[0005] In a first aspect, an orthopedic robot positioning device includes a tracer, a 3D C-arm, a structured light platform and an orthopedic robot host;

[0006] The tracer is used to provide the origin coordinates;

[0007] The 3D C-arm: is used to obtain a spinal CT image and upload it to the orthopedic robot host after the tracer is fixed at a preset position;

[0008] The structured light platform: is used to scan the spine to obtain the first surface point cloud and upload it to the orthopedic robot host;

[0009] The orthopedic robot host further includes a spinal 3D image generation module and a positioning module;

[0010] The spinal 3D image generation module: It is used to perform rough and fine registration of point clouds based on the first surface point cloud and the spinal CT image to obtain a spinal 3D image;

[0011] The positioning module: It is used to perform positioning based on the spinal 3D image.

[0012] A further improvement of the present invention lies in that: the structured light platform includes an industrial camera, a projector, a fill light, and a mobile platform. The projector and the industrial camera cooperate to perform structured light scanning. The fill light is used to supplement light during the structured light scanning process, and the mobile platform is used to adjust the position of the structured light platform.

[0013] In a second aspect, an orthopedic robot positioning method includes the following steps:

[0014] Fix the tracer at a preset position and obtain the spinal CT image and upload it to the orthopedic robot host;

[0015] The orthopedic robot host controls the structured light platform to move to the position to be worked according to the spinal CT image;

[0016] Scan the spine through the structured light platform to obtain the first surface point cloud and upload it to the orthopedic robot host;

[0017] The orthopedic robot host performs rough and fine registration of point clouds based on the first surface point cloud and the spinal CT image to obtain a spinal 3D image;

[0018] The orthopedic robot host performs positioning according to the spinal 3D image.

[0019] A further improvement of the present invention lies in that: the step of obtaining the first surface point cloud by scanning the spine through the structured light platform specifically includes the following steps:

[0020] Project a structured light pattern through the projector;

[0021] Collect the structured light pattern through the industrial camera and upload it to the orthopedic robot host;

[0022] The orthopedic robot host determines the three-dimensional coordinates according to the triangulation principle to obtain the first surface point cloud.

[0023] A further improvement of the present invention lies in that: the step of performing rough and fine registration of point clouds based on the first surface point cloud and the spinal CT image to obtain a spinal 3D image specifically includes the following steps:

[0024] Perform rough registration of point clouds on the first surface point cloud and the spinal CT image to obtain a roughly registered spinal CT image and a roughly registered first surface point cloud;

[0025] Perform fine registration of point clouds based on the roughly registered spinal CT image and the roughly registered first surface point cloud to obtain a rotation vector, a translation vector, and an optimal drift parameter;

[0026] Adjust the rotation vector, translation vector, and optimal drift parameter to adjust the coarsely registered spinal CT image and the coarsely registered first surface point cloud to obtain a complete CT point cloud.

[0027] A further improvement of the present invention lies in that: the coarse registration of the point cloud specifically includes the following steps:

[0028] In the orthopedic robot coordinate system, use the infrared positioning of the orthopedic robot with the tracer as the reference point to calculate the rotation vector and translation vector between the first surface point cloud and the spinal CT image;

[0029] Overlay the spinal CT image on the first surface point cloud so that both are in the same coordinate system to obtain a coarsely registered spinal CT image and a coarsely registered first surface point cloud.

[0030] A further improvement of the present invention lies in that: the fine registration of the point cloud specifically includes the following steps:

[0031] Based on the coarsely registered spinal CT image and the coarsely registered first surface point cloud, establish a spinal drift mathematical model;

[0032] Construct a K-D tree for the coarsely registered spinal CT image and the coarsely registered first surface point cloud;

[0033] Use the K-D tree for two-way distance search to calculate the Euclidean distance of each point pair;

[0034] According to the Euclidean distance and the drift mathematical model, adopt the weighted least squares method for joint optimization to obtain the optimal drift parameter and the rigid transformation matrix;

[0035] Judge whether the optimal drift parameter and the rigid transformation matrix converge. If they do not converge, iterate until convergence. If they converge, output the rotation vector, translation vector, optimal drift parameter, and optimal drift parameter at this time.

[0036] A further improvement of the present invention lies in that: when obtaining the spinal CT image, it is scanned uniformly by a 3D C-arm.

[0037] In a third aspect, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-mentioned orthopedic robot positioning method.

[0038] In a fourth aspect, a computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the above-mentioned orthopedic robot positioning method.

[0039] Compared with the prior art, the present invention at least includes the following beneficial effects:

[0040] 1. The present invention determines the approximate pose first and then performs specific registration by performing rough-to-fine slice registration on the first surface point cloud and the spinal CT image, which improves the registration accuracy and streamlines the registration process.

[0041] 2. The present invention performs rough registration through the infrared positioning of the orthopedic robot itself, which improves the accuracy and is more convenient compared to other rough registration methods.

[0042] 3. The present invention obtains accurate rotation vectors, translation vectors, and optimal drift parameters through point cloud fine registration to adjust the rough-registered spinal CT image and the rough-registered first surface point cloud, obtaining a complete CT point cloud with accurate positioning and small computational complexity. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings forming a part of this specification are used to provide a further understanding of the present invention. The schematic embodiments and descriptions thereof of the present invention are used to explain the present invention and do not constitute an improper limitation of the present invention.

[0044] In the drawings:

[0045] Figure 1 is a flowchart of a positioning method for an orthopedic robot according to the present invention;

[0046] Figure 2 is a flowchart of obtaining the first surface point cloud in a positioning method for an orthopedic robot according to the present invention;

[0047] Figure 3 is a flowchart of point cloud fine registration in a positioning method for an orthopedic robot according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments. It should be noted that, without conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0049] The following detailed descriptions are all exemplary descriptions, aiming to provide further detailed descriptions of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs. The terms used in the present invention are only for the purpose of describing specific embodiments and are not intended to limit the exemplary embodiments of the present invention.

[0050] Embodiment 1

[0051] A positioning method for an orthopedic robot, as Figure 1 shown, specifically includes the following steps:

[0052] S1. Fix the tracer at a preset position and upload the spinal CT image to the host of the orthopedic robot;

[0053] Perform origin positioning through a tracer.

[0054] Obtain spinal CT images through 3D C-arm scanning.

[0055] S2. The orthopedic robot host controls the structured light platform to move to the working position according to the spinal CT images;

[0056] The structured light platform includes an industrial camera, a projector, a fill light, and a moving platform. Subsequent structured light scanning is performed through the projector and the industrial camera. The fill light is used for filling light during the structured light scanning process, and the moving platform is used to adjust the structured light scanning position. The scanned structure is uploaded to the orthopedic robot host.

[0057] S3. Obtain the first surface point cloud by structured light scanning the spine and upload it to the orthopedic robot host;

[0058] As Figure 2 shown, when obtaining the first surface point cloud by structured light scanning the spine in S3, it specifically includes the following steps:

[0059] Project a structured light pattern through the projector;

[0060] Collect the structured light pattern through the camera and upload it to the orthopedic robot host;

[0061] The orthopedic robot host determines the three-dimensional coordinates according to the triangulation principle to obtain the first surface point cloud.

[0062] S4. The orthopedic robot host performs rough-to-fine registration of the point cloud based on the first surface point cloud and the spinal CT images to obtain a 3D spinal image;

[0063] When performing rough registration of the point cloud between the first surface point cloud and the spinal CT images in S4, it specifically includes the following steps:

[0064] In the orthopedic robot coordinate system, use the infrared positioning of the orthopedic robot, with the tracer as the reference point, to calculate the rotation vector and translation vector between the first surface point cloud and the spinal CT images;

[0065] Overlay the spinal CT images on the first surface point cloud so that both are in the same coordinate system, and perform rough registration on the two to obtain the roughly registered spinal CT images and the roughly registered first surface point cloud; at this time, due to stress causing intervertebral movement, the farther the spine is from the tracer, the greater its displacement, so the two groups of point clouds of the first surface point cloud and the spinal CT images do not completely overlap. However, at this time, the directions and postures of the two groups of point clouds are similar, which is conducive to performing the subsequent fine registration.

[0066] As Figure 3 shown, when performing fine registration of the point cloud in S4, it specifically includes the following steps:

[0067] Based on the roughly registered spinal CT image and the roughly registered first surface point cloud, establish a mathematical model of spinal drift:

[0068] Q j ′ = R j Q j + t j ;

[0069] Where j is the horizontal number from the reference spine, Q j is the coordinate of any point in the j-th spine point cloud, Q j ′ is the coordinate of the corresponding point after image drift during the operation, R j and t j are the rotation parameter and the translation parameter respectively;

[0070] Construct a K-D tree for the roughly registered spinal CT image and the roughly registered first surface point cloud;

[0071] Use the K-D tree for bidirectional distance search, and calculate the Euclidean distance ||P i - Q i ||, where: P i and Q i are a set of corresponding point pairs in the roughly registered spinal CT image and the roughly registered first surface point cloud;

[0072] Use the weighted least squares method to jointly optimize the unknown parameters in the spinal drift mathematical model and the unknown parameters of the rigid transformation, and calculate the optimal drift parameters and the rigid transformation matrix.

[0073] Expression of the optimal drift parameter:

[0074]

[0075] Rigid transformation matrix:

[0076]

[0077] Where n is the number of points in the roughly registered spinal CT image, m is the number of target spines, Q i is the nearest point of point P i in the roughly registered first surface point cloud, D j is the average value of the squared distances, W j is the weight corresponding to each spine, [R, T] is the rotation vector and the translation vector, R j and t j are the drift parameters;

[0078] Determine whether the optimal drift parameters and the rigid transformation matrix converge. If they do not converge, repeat the iteration of the optimal drift parameters and the rigid transformation matrix until convergence. When the optimal drift parameters and the rigid transformation matrix converge, register the two sets of point cloud data and output the final result to obtain the rotation vector, translation vector [R, T] between the two accurately registered point clouds and the optimal drift parameter R j and t j .

[0079] According to the obtained final rotation vector, translation vector [R, T] and optimal drift parameter R j and t j , adjust the coarsely registered spinal CT image and the coarsely registered first surface point cloud to obtain the complete CT point cloud, complete the accurate overlap of the two sets of point clouds, and visually display the complete 3D spinal image in the structured light coordinate system.

[0080] S5. The orthopedic robot host locates according to the 3D spinal image.

[0081] Embodiment 2

[0082] An orthopedic robot positioning system, comprising:

[0083] Comprising a tracer, a 3D C-arm, a structured light platform and an orthopedic robot host;

[0084] The tracer is used to provide the origin coordinates;

[0085] 3D C-arm: After the tracer is fixed at a preset position, it is used to acquire the spinal CT image and upload it to the orthopedic robot host;

[0086] Structured light platform: Used to scan the spine to obtain the first surface point cloud and upload it to the orthopedic robot host;

[0087] The orthopedic robot host also includes a 3D spinal image generation module and a positioning module;

[0088] 3D spinal image generation module: Used to perform coarse-to-fine registration of point clouds based on the first surface point cloud and the spinal CT image to obtain a 3D spinal image;

[0089] Positioning module: Used to locate according to the 3D spinal image.

[0090] The structured light platform includes an industrial camera, a projector, a fill light and a mobile platform. Subsequent structured light scanning is performed through the projector and the industrial camera. The fill light is used to supplement light during the structured light scanning process, and the mobile platform is used to adjust the structured light scanning position. The scanned structure is uploaded to the orthopedic robot host.

[0091] Embodiment 3

[0092] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned orthopedic robot positioning method is implemented.

[0093] Embodiment 4

[0094] A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the above-mentioned orthopedic robot positioning method is implemented.

[0095] As is known by technical common sense, the present invention can be implemented by other embodiments that do not depart from its spirit or essential features. Therefore, the above-disclosed embodiments are illustrative in all aspects and not exclusive. All changes within the scope of the present invention or within the scope equivalent to the present invention are encompassed by the present invention.

[0096] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) that contain computer-usable program code.

[0097] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a system for implementing the functions specified in Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0098] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction system that implements the functions specified in Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0099] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions for implementing the steps of the process Figure 1 one process or a plurality of processes and / or blocks Figure 1 steps of the functions specified in one block or a plurality of blocks.

[0100] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent replacements can still be made to the specific embodiments of the present invention. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. An orthopedic robot positioning device, characterized in that, it includes a tracer, a 3D C-arm, a structured light platform and an orthopedic robot host; The tracer is used to provide the origin coordinates; The 3D C-arm is used to obtain a spinal CT image and upload it to the orthopedic robot host after the tracer is fixed at a preset position; The structured light platform is used to scan the spine to obtain the first surface point cloud and upload it to the orthopedic robot host; The orthopedic robot host also includes a spinal 3D image generation module and a positioning module; The spinal 3D image generation module is used to perform rough-to-fine registration of the point cloud based on the first surface point cloud and the spinal CT image to obtain a spinal 3D image; the rough-to-fine registration of the point cloud includes the following steps: In the orthopedic robot coordinate system, using the orthopedic robot infrared positioning with the tracer as the reference point, calculate the rotation vector and translation vector between the first surface point cloud and the spinal CT image; Overlay the spinal CT image on the first surface point cloud so that both are in the same coordinate system to obtain the roughly registered spinal CT image and the roughly registered first surface point cloud; Based on the roughly registered spinal CT image and the roughly registered first surface point cloud, establish a spinal drift mathematical model; Construct a K-D tree for the roughly registered spinal CT image and the roughly registered first surface point cloud; Use the K-D tree for bidirectional distance search to calculate the Euclidean distance of each point pair; According to the Euclidean distance and the drift mathematical model, adopt the weighted least squares method for joint optimization to obtain the optimal drift parameters and the rigid transformation matrix; Optimal drift parameter expression: D j = ||R j P i +t j -Q i || 2 ; Rigid transformation matrix: [R, T] = argmin ; Where n is the number of points in the coarsely registered spinal CT image, m is the number of target vertebrae, and Q i is the nearest point of point P i in the coarsely registered first surface point cloud, D j is the mean square distance, W j is the weight corresponding to each vertebra, [R, T] is the rotation vector and the translation vector, R j and t j are the drift parameters; Judge whether the optimal drift parameters and the rigid transformation matrix converge. If they do not converge, iterate until convergence. If they converge, output the rotation vector, translation vector and optimal drift parameters at this time; Adjust the roughly registered spinal CT image and the roughly registered first surface point cloud according to the rotation vector, translation vector and optimal drift parameters to obtain the complete CT point cloud; The positioning module is used to perform positioning according to the spinal 3D image.

2. An orthopedic robot positioning device according to claim 1, characterized in that, the structured light platform includes an industrial camera, a projector, a fill light and a moving platform. The projector and the industrial camera cooperate to perform structured light scanning. The fill light is used to supplement light during the structured light scanning process, and the moving platform is used to adjust the position of the structured light platform.

3. An orthopedic robot positioning method based on an orthopedic robot positioning device according to any one of claims 1-2, characterized in that, it includes the following steps: Fix the tracer at a preset position and obtain a spinal CT image and upload it to the orthopedic robot host; The orthopedic robot host controls the structured light platform to move to the working position according to the spinal CT image; Scan the spine through the structured light platform to obtain the first surface point cloud and upload it to the orthopedic robot host; The orthopedic robot host performs rough-to-fine registration of the point cloud based on the first surface point cloud and the spinal CT image to obtain a spinal 3D image; the rough-to-fine registration of the point cloud includes the following steps: In the orthopedic robot coordinate system, using the orthopedic robot infrared positioning with the tracer as the reference point, calculate the rotation vector and translation vector between the first surface point cloud and the spinal CT image; Superimpose the spinal CT image onto the first surface point cloud so that both are in the same coordinate system, obtaining a roughly registered spinal CT image and a roughly registered first surface point cloud; Establish a mathematical model of spinal drift based on the roughly registered spinal CT image and the roughly registered first surface point cloud; Construct a K-D tree for the roughly registered spinal CT image and the roughly registered first surface point cloud; Use the K-D tree to perform two-way distance search and calculate the Euclidean distance of each point pair; Adopt the weighted least squares method for joint optimization according to the Euclidean distance and the drift mathematical model to obtain the optimal drift parameters and the rigid transformation matrix; Expression of the optimal drift parameters: D j = ||R j P i +t j -Q i || 2 ; Rigid transformation matrix: [R, T] = argmin ; Where n is the number of points in the coarsely registered spinal CT image, m is the number of target vertebrae, and Q i is the nearest point of point P i in the coarsely registered first surface point cloud, D j is the average value of the squared distances, W j is the weight corresponding to each vertebra, [R, T] is the rotation vector and the translation vector, R j and t j are the drift parameters; Determine whether the optimal drift parameters and the rigid transformation matrix converge. If they do not converge, iterate until convergence. If they converge, output the rotation vector, translation vector, and optimal drift parameters at this time; Adjust the roughly registered spinal CT image and the roughly registered first surface point cloud according to the rotation vector, translation vector, and optimal drift parameters to obtain the complete CT point cloud; The mainframe of the orthopedic robot locates according to the 3D image of the spine.

4. An orthopedic robot positioning method according to claim 3, wherein, The step of obtaining the first surface point cloud by scanning the spine through the structured light platform specifically includes the following steps: Project a structured light pattern through a projector; Collect the structured light pattern through an industrial camera and upload it to the mainframe of the orthopedic robot; The mainframe of the orthopedic robot determines the three-dimensional coordinates according to the triangulation principle to obtain the first surface point cloud.

5. An orthopedic robot positioning method according to claim 3, wherein, When obtaining the spinal CT image, it is scanned uniformly by a 3D C-arm.

6. A computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein, When the processor executes the computer program, it implements an orthopedic robot positioning method according to claim 3.

7. A computer-readable storage medium storing a computer program, wherein, When the computer program is executed by the processor, it implements an orthopedic robot positioning method according to claim 3.

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