CN112826590A - Knee joint replacement spatial registration system based on multi-modal fusion and point cloud registration - Google Patents

Knee joint replacement spatial registration system based on multi-modal fusion and point cloud registration Download PDF

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CN112826590A
CN112826590A CN202110146163.0A CN202110146163A CN112826590A CN 112826590 A CN112826590 A CN 112826590A CN 202110146163 A CN202110146163 A CN 202110146163A CN 112826590 A CN112826590 A CN 112826590A
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史勇红
姚德民
刘颜静
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Fudan University
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
    • A61B34/10—Computer-aided planning, simulation or modelling of surgical operations
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
    • A61B34/20—Surgical navigation systems; Devices for tracking or guiding surgical instruments, e.g. for frameless stereotaxis
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B90/00—Instruments, implements or accessories specially adapted for surgery or diagnosis and not covered by any of the groups A61B1/00 - A61B50/00, e.g. for luxation treatment or for protecting wound edges
    • A61B90/36—Image-producing devices or illumination devices not otherwise provided for
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B90/00—Instruments, implements or accessories specially adapted for surgery or diagnosis and not covered by any of the groups A61B1/00 - A61B50/00, e.g. for luxation treatment or for protecting wound edges
    • A61B90/36—Image-producing devices or illumination devices not otherwise provided for
    • A61B90/37—Surgical systems with images on a monitor during operation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
    • A61B34/10—Computer-aided planning, simulation or modelling of surgical operations
    • A61B2034/101—Computer-aided simulation of surgical operations
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
    • A61B34/10—Computer-aided planning, simulation or modelling of surgical operations
    • A61B2034/101—Computer-aided simulation of surgical operations
    • A61B2034/105—Modelling of the patient, e.g. for ligaments or bones
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B90/00—Instruments, implements or accessories specially adapted for surgery or diagnosis and not covered by any of the groups A61B1/00 - A61B50/00, e.g. for luxation treatment or for protecting wound edges
    • A61B90/36—Image-producing devices or illumination devices not otherwise provided for
    • A61B90/37—Surgical systems with images on a monitor during operation
    • A61B2090/374—NMR or MRI
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B90/00—Instruments, implements or accessories specially adapted for surgery or diagnosis and not covered by any of the groups A61B1/00 - A61B50/00, e.g. for luxation treatment or for protecting wound edges
    • A61B90/36—Image-producing devices or illumination devices not otherwise provided for
    • A61B90/37—Surgical systems with images on a monitor during operation
    • A61B2090/376—Surgical systems with images on a monitor during operation using X-rays, e.g. fluoroscopy
    • A61B2090/3762—Surgical systems with images on a monitor during operation using X-rays, e.g. fluoroscopy using computed tomography systems [CT]

Abstract

The invention belongs to the technical field of medical equipment, and particularly relates to a knee joint replacement spatial registration system based on multi-mode fusion and point cloud registration. The system of the invention comprises three modules: a spatial coordinate construction module of knee joint cartilage surface point cloud on the CT image; a point cloud acquisition module for the cartilage surface of the knee joint of a patient in the space during operation; the knee joint cartilage surface point cloud layering registration module is used for realizing the spatial registration of the patient in the operation and the preoperative image; the registration error is around 2.5 mm. The invention can greatly reduce the intraoperative registration time, generally about 2 minutes, thereby greatly reducing the operation time; soft tissue information can be provided by using the CT and MR images fused before the operation, so that a doctor can perform better preoperative planning and intraoperative incision position judgment according to the soft tissue information; in addition, the degree of dependence on doctors can be greatly reduced, so that the doctors can concentrate on the operation per se.

Description

Knee joint replacement spatial registration system based on multi-modal fusion and point cloud registration
Technical Field
The invention belongs to the technical field of medical equipment, and particularly relates to a knee joint replacement space registration system.
Background
The existing knee joint replacement system is usually navigated based on a registration mode in a marking point operation, and during the navigation, the selection and confirmation of a plurality of marking points are involved, so that the registration time is long, and the problem of prolonged operation time is caused.
Image-guided computer-assisted knee replacement surgery can help physicians better complete the surgery [1 ]. The spatial registration technology is one of the most important key technologies for surgical navigation, is one of the main factors restricting the clinical end-to-end precision, and simultaneously influences the surgical navigation time [2 ].
The existing spatial registration technology in knee joint orthopedic navigation is mainly divided into three categories [1 ]: fiducial-based registration, anatomical-point-based registration, and surface-based registration.
Fiducial registration method (fiduciary markers based): before the patient undergoes a CT scan, fiducials are implanted as fiducial points that will be registered with intraoperative data points. The method can simply and intuitively determine the corresponding relation between the image space and the patient space, and has the highest precision rate [3 ]. However, this method requires additional pre-operative implantation of fiducials, sometimes requiring manual adjustment of the fiducials, and may also be associated with pain and infection risks. If the marking is performed on the surface of the skin, errors are introduced due to the relative movement between the skin and the bone, and the markers are easily detached, so that the method is rarely used in the clinical knee joint replacement.
Since fiducial points require additional preoperative manipulation, physicians have used anatomical points (Landmark-based) instead of fiducial points. A registration method based on the surface points of the knee joint in the probe picking operation is favored in clinical orthopedic navigation operations [4,5 ]. Before operation, medical tomographic images with small distance between acquisition layers of knee joints of patients and high resolution are segmented and reconstructed to obtain a high-precision three-dimensional medical tibiofemoral joint visualization model, and anatomical points are marked on the model. During the intraoperative registration, only a calibrated special probe is used for acquiring an anatomical point [6] corresponding to the tibiofemoral region of the patient during the operation, and the anatomical point and the tibiofemoral region are subjected to corresponding point registration. However, this method is susceptible to noise during the surgical procedure due to the small number of anatomical points, the registration is not robust, and the selection of anatomical points is time consuming, laborious and dependent on the physician's experience [7,8], and therefore costly.
A Surface-based registration method (Surface-based) 9 based on point cloud generally adopts a scanner to extract point cloud 10 on the Surface of a focus, and registration is realized based on the corresponding relation between the point cloud in operation and preoperative images.
The technology for spatial registration based on preoperative multi-modal image fusion information and the point cloud on the surface of the knee joint focus obtained by the intraoperative scanner replaces a repeated interactive point-taking mode that a navigation probe obtains an anatomical point coordinate through a physical contact joint surface (femur or tibia), so that not only is the registration error minimized (due to the fact that more point cloud data are obtained instantaneously), but also the registration robustness is improved, the registration time is remarkably reduced, and the precious operation time is saved.
According to the novel knee joint replacement surgery space registration system based on multi-modal fusion and point cloud registration, the system is used for registering the focus point cloud to the preoperative image in real time based on preoperative multi-modal image fusion information and the knee joint focus point cloud collected by the intraoperative scanner, so that rapid and accurate navigation of the preoperative image to the focus in the surgery is realized, and intraoperative space registration time is greatly shortened.
Reference to the literature
[1].Liu,Y.,Potential Risk of Intelligent Technologies in Clinical Orthopedics.2018.1093:p.281-288.
[2].Song,S.J.and D.K.Bae,Computer-Assisted Navigation in High Tibial Osteotomy.Clinics in Orthopedic Surgery,2016.8(4):p.349.
[3].Amiot,L.and F.O.Poulin,Computed Tomography-Based Navigation for Hip,Knee,and Spine Surgery.Clinical Orthopaedics and Related Research,2004.421:p.77-86.
[4].Bchler,R.,H.Bunke and L.Nolte,Restricted Surface Matching—Numerical Optimization and Technical Evaluation.Computer Aided Surgery,2001.6(3):p.143-152.
[5].Hamadeh,A.,et al.,Anatomy-based Registration for Computer-integrated Surgery.1995.
[6].Zheng,G.and L.P.Nolte,Computer-Assisted Orthopedic Surgery:Current State and Future Perspective.Frontiers in Surgery,2015.2:p.66.
[7].M.,J.,et al.,The hands-on orthopaedic robot"acrobot":Early clinical trials of total knee replacement surgery.IEEE Transactions on Robotics and Automation,2003.19(5):p.902-911.
[8] Xu Yongsheng, etc., application research of total knee replacement under infrared computer navigation, Chinese medical equipment, 2012.27(12), page 138-.
[9].Lin,Q.,et al.,Real-time automatic registration in optical surgical navigation.Infrared Physics&Technology,2016.76:p.375-385.
[10].Joshi,S.V.and P.J.Rowe,A novel approach for intra-operative shape acquisition of the tibio-femoral joints using 3D laser scanning in computer assisted orthopaedic surgery.The International Journal of Medical Robotics and Computer Assisted Surgery,2018.14(1):p.e1855.
Disclosure of Invention
In order to solve the problem of overlong space registration time in a computer navigation knee joint replacement operation, the invention provides a knee joint replacement operation space registration system based on multi-mode information fusion and point cloud registration, which can keep higher registration precision and obviously reduce the registration time.
The invention provides a knee joint replacement surgery space registration system based on multi-modal information fusion and point cloud registration, which comprises three modules: (I) a spatial coordinate construction module of knee joint cartilage surface point cloud on the CT image; (II) a point cloud acquisition module of the cartilage surface of the spatial knee joint of the patient in the operation; (III) a knee joint cartilage surface point cloud hierarchical registration module for realizing spatial registration of the patient in the operation and the preoperative image; the overall architecture of the system is shown in fig. 1. Wherein:
the spatial coordinate construction module of the knee joint cartilage surface point cloud on the CT image comprises a knee joint CT image and an MR image which are collected before the operation of the same patient, and the two modal images are respectively subjected to tissue segmentation, namely, the hard bones of the tibia and the femur are obtained from the CT image, and the hard bones and the cartilages of the tibia and the femur are obtained from the MRI image; registering the MR image to the CT image to obtain the cartilage information of the tibia and the femur which are fused and enhanced on the CT; extracting surface point clouds of tibial cartilage and femoral cartilage on the CT through three-dimensional reconstruction;
the intraoperative patient space knee joint cartilage surface point cloud acquisition module is used for acquiring surface point clouds (comprising a large amount of cartilage surface point clouds) of a knee joint focus area of a patient during operation by using a scanner;
the knee joint cartilage surface Point cloud hierarchical registration module performs registration by using a cartilage surface Point cloud reconstructed in a preoperative CT image space and a cartilage Point cloud scanned in an operation, and comprises coarse registration based on Singular Value Decomposition (SVD) and fine registration based on ICP (inductive Conditional Point, ICP), so that registration of a preoperative virtual space (namely CT or MRI) and a real space (such as a patient) is finally realized.
The three modules are described in further detail below.
The spatial coordinate construction module (I) of the knee joint cartilage surface point cloud on the CT image comprises the following working contents:
(1) performing linear registration on the CT image and the MR image of the same patient before the operation to obtain a transformation matrix Tmr→ct；
(2) Segmenting hard bone from CT image before operation and recording as HctSegmenting cartilage from the MR image before operation and recording as CmrBy transforming the matrix Tmr→ctTransforming the cartilage on the MR image to the CT image, enhancing the cartilage information on the CT image, and recording as
Figure BDA0002930364670000031
Constructing a soft and hard bone combined model on CT through three-dimensional reconstruction
Figure BDA0002930364670000032
This serves as model-guided intraoperative navigation of the preoperative image space;
(3) extracting cartilage surface point cloud from the reconstructed combined model, using the cartilage surface point cloud as point cloud navigation data of an image space, and recording the point cloud navigation data as Pimage。
(II)) The intraoperative patient space knee joint cartilage surface point cloud collection module (II) is different from the patient space point cloud coordinate in that the point cloud coordinate obtained by the direct scanning of the scanner belongs to the scanner space coordinate; in the operation, the knee joint focus cartilage space point cloud coordinate of a patient needs to be read in the optical positioning instrument Polaris reference space, therefore, firstly, the scanner needs to be calibrated, and a conversion matrix T from the adapter to the scanner is obtainedadapter→scan(ii) a And then calculating the spatial point cloud coordinates of the patient through a series of transformations. The specific process is as follows:
(1) scanner calibration
Pasting N marker points (for example, N is 8) on a desktop, and ensuring that the points are uniformly distributed and asymmetric as much as possible; picking up the center of a marker point by using a calibrated probe, and recording coordinates by Polaris; picking up and counting the variance of each point for multiple times, removing points with larger errors, and calculating the mean coordinate of each remaining point as the central coordinate of each marker point
Figure BDA0002930364670000041
To obtain
Figure BDA0002930364670000042
Meanwhile, an adapter is pasted on the (EinScan-Pro +) scanner, the self-contained software (namely EinScan-Pro.exe) of the scanner is opened, a fixed mode is selected, the marker point is scanned and stored in a corresponding format (with the extension name being p3), and then the central coordinate of the marker point can be obtained
Figure BDA0002930364670000043
Polaris simultaneously records transformation matrix T from Polaris space to adapter spacepolaris→adapterCalculating T by using the formula (1)adapter→scan，
Pscan＝Ppatient×Tpolaris→adapter×Tadapter→scan， (1)
Here, Ppatient×Tpolaris→adapter＝PadapterA 1 is to PadapterAs a floating set of points, PscanAs a reference point set, since PadapterAnd PscanIs a point set corresponding to one, so that the registration can be carried out by adopting an SVD algorithm, and the obtained transformation matrix is approximate to Tadapter→scanThe whole process is shown in FIG. 2.
(2) Scanning a spatial point cloud of a patient
The scanner scans the cartilage surface of the knee joint focus and manually removes the point cloud of the non-cartilage structure. Because the cartilage point clouds obtained by scanning are more, about 6000 point clouds can be obtained once, in order to improve the operation speed, the point clouds are downsampled to about 1000 points, and finally the point cloud coordinate of the scanner space is obtained
Figure BDA0002930364670000044
While Polaris records transformation matrix T from Polaris space to adapter spacepolaris→adapterThe point cloud coordinate P of the patient space is calculated by the formula (2)patient：
Ppatient＝Pscan×Tscan→adapter×Tadapter→polaris， (2)
Wherein, Tscan→adapterIs the adapter to scanner transformation matrix obtained by calibration.
The adapter is prepared by a plurality of unevenly distributed reflective balls fixed on a bracket. Specifically, as a component surrounded by a red ellipse in fig. 3, the adapter is composed of 3 light reflecting balls and a holder. Because the scanner cannot be directly tracked and positioned by the polar positioner and the reflective ball can be tracked and positioned by the polar positioner, the adapter is pasted on the scanner, the scanner can be indirectly tracked and positioned by the adapter, so that the space of the scanner can be converted into the space of the polar positioner, and the specific space conversion process is described in the second module.
Fig. 3 shows the entire transformation process for calculating the spatial coordinates of the patient. Wherein fig. 3(i) is a conversion of the scan point cloud to the adapter space, fig. 3(ii) is a conversion of the adapter space point cloud to the aligner space point cloud, and fig. 3(iii) shows that the aligner space point cloud is the patient space point cloud.
And (III) the knee joint cartilage surface point cloud layering registration module is used for performing cloud registration on the image space point cloud and the patient space point. Since the closest point Iteration (ICP) algorithm requires that two point clouds have good initial positions, the coarse registration method is often used to obtain the good initial positions in engineering. The method adopts a SVD-based method, and selects 5-7 corresponding points to perform coarse registration by using a graphical interface interaction mode. Followed by automatic ICP fine registration. The results show registration errors around 2.5 mm.
The invention has the advantages that:
(1) the intra-operative registration time is greatly reduced. The current surgical navigation system needs probes to pick paired points for intraoperative registration, 15-17 points are generally selected, and a large amount of intraoperative time is occupied by the probe picking anatomical points. The point cloud of the cartilage surface can be quickly scanned by the scanner-based intraoperative navigation, and is registered with the preoperative image space point cloud, the time is about 2 minutes generally, and the operation time is greatly reduced.
(2) The CT and MR images fused before the operation can provide soft tissue information, thereby being beneficial to doctors to carry out better preoperative planning and judgment of incision positions during the operation according to the soft tissue information.
(3) In the knee joint replacement surgery of computer-aided navigation, selection of the dissection points usually requires experienced doctors, and the more accurate the dissection point selection is, the more accurate the surgical registration is. And the point cloud-based registration mode can obtain registration with similar precision without selecting anatomical points, so that the degree of dependence on doctors is greatly reduced. This technical improvement greatly assists the surgeon, allowing him to concentrate more on the operation itself.
Drawings
FIG. 1 is a general framework diagram of the system of the present invention.
Fig. 2 is a diagram of a scanner calibration process in the present invention.
FIG. 3 is a schematic representation of the spatial transformation of the point cloud from scanner space to patient space. Wherein (i) the point cloud is converted from scanner space to adapter space, and (ii) from adapter space to locator space; (iii) the locator spatial coordinates are considered patient space.
Fig. 4 is a flow chart of the application of the system of the present invention.
Detailed Description
The invention is further described below with reference to examples and figures.
Taking femur as an example, the application process of the registration system of the present invention is shown in fig. 4.
And the module I is a process for enhancing the CT image before the operation and acquiring the spatial coordinates of the cartilage surface point cloud before the operation. Hard bone and cartilage are respectively segmented from preoperative CT and MR images, and registration transformation matrixes T of CT and MR are obtained simultaneouslymr→ct. The transformation matrix obtained by registration acts on the soft and hard bone labels obtained by segmentation, a soft and hard bone label fusion structure can be obtained, a model with cartilage is obtained after three-dimensional reconstruction, the model is used for intraoperative navigation, and a cartilage surface point cloud P is extracted from the cartilage surface point cloudimage。
And the module II is a process for collecting the point cloud of the cartilage surface of the focus of the patient in the space in the operation. Firstly, before operation, the scanner is calibrated to obtain Tscan→adapter. Then scanning the focus point cloud by a calibrated scanner in the operation, manually removing the non-cartilage surface point cloud by utilizing the self-contained software (namely EinScan-Pro +. exe) of the scanner to obtain the cartilage surface point cloud PscanThen obtaining the coordinate P of the patient space through transformation (see figure 3)patient。
Module III is preoperatively obtained PimageWith intraoperatively obtained PpatientAnd (5) carrying out a registration process. Firstly, coarse registration is carried out by utilizing an SVD algorithm to obtain a better initial position, but a large error exists, and then the positions of two pieces of point cloud are further adjusted by utilizing ICP fine registration.
Table 1 and table 2 show the results of the experiments on the hard bone model and the cartilage model, respectively. The scanners are respectively calibrated, and registration errors of the reference points are obtained by attaching marker points on the model and are used as gold standards. Fiducial-based registration errors are generally smaller than anatomical-point-based and scanner-based registration errors, and can therefore be used as a gold standard to determine whether experimental errors are within a feasible range. Due to the existence of system errors and calibration errors of the scanner in the experiment, errors obtained by point cloud registration are larger than those of the gold standard. But the registration of the scanner greatly reduces the operation time while ensuring the accuracy within the operation range. For example, the time for scanning the focus point cloud is about 10 seconds, and the time for manually removing the focus point cloud is about 30 seconds. The whole registration time is not more than 2 minutes, and the operation time is greatly reduced.
TABLE 1 statistical results of hard bone model experiments
Figure BDA0002930364670000061
TABLE 2 statistics of cartilage model test results
Figure BDA0002930364670000062
Note: firstly, a gold standard error is an error obtained by attaching a marker point on a leg model, extracting an image space coordinate from a CT image with the marker point by using a probe to pick up an intraoperative point set coordinate and registering. Secondly, the point cloud registration error means that a transformation matrix obtained after point cloud registration acts on the reference point, and the root mean square error of the reference point is calculated. And thirdly, the point cloud post-processing time refers to the time for manually segmenting focus point cloud after scanning the point cloud (taking femoral cartilage as an example). And fourthly, the total time of spatial registration refers to the time taken by the whole process from point cloud acquisition to registration completion.

Claims (4)

1. A knee joint replacement surgery space registration system based on multi-modal fusion and point cloud registration is characterized by comprising three modules: (I) a spatial coordinate construction module of knee joint cartilage surface point cloud on the CT image; (II) a point cloud acquisition module of the cartilage surface of the spatial knee joint of the patient in the operation; (III) a knee joint cartilage surface point cloud hierarchical registration module for realizing spatial registration of the patient in the operation and the preoperative image; wherein:
the spatial coordinate construction module of the knee joint cartilage surface point cloud on the CT image comprises a knee joint CT image and an MR image which are collected before the operation of the same patient, and the two modal images are respectively subjected to tissue segmentation, namely, the hard bones of the tibia and the femur are obtained from the CT image, and the hard bones and the cartilages of the tibia and the femur are obtained from the MRI image; registering the MR image to the CT image to obtain the cartilage information of the tibia and the femur which are fused and enhanced on the CT; extracting surface point clouds of tibial cartilage and femoral cartilage on the CT through three-dimensional reconstruction;
the intraoperative patient space knee joint cartilage surface point cloud acquisition module is used for acquiring surface point cloud of a focus area of a knee joint of a patient in an operation by using a scanner;
the knee joint cartilage surface point cloud layering registration module performs registration by using a cartilage surface point cloud reconstructed in a preoperative CT image space and a cartilage point cloud scanned in an operation, and finally realizes registration of a preoperative virtual space, namely CT or MRI, and a real space, wherein the registration comprises coarse registration based on singular value decomposition and fine registration based on the fine registration.
2. The knee replacement spatial registration system of claim 1, wherein the spatial coordinates of the point cloud on the surface of the cartilage of the knee joint on the CT image are constructed by a module comprising:
(1) performing linear registration on the CT image and the MR image of the same patient before the operation to obtain a transformation matrix Tmr→ct；
(2) Segmenting hard bone from CT image before operation and recording as HctSegmenting cartilage from the MR image before operation and recording as CmrBy transforming the matrix Tmr→ctTransforming the cartilage on the MR image to the CT image, enhancing the cartilage information on the CT image, and recording as
Figure FDA0002930364660000012
Constructing a soft and hard bone combined model on CT through three-dimensional reconstruction
Figure FDA0002930364660000011
This serves as model-guided intraoperative navigation of the preoperative image space;
(3) extracting cartilage surface point cloud from the reconstructed combined model, using the cartilage surface point cloud as point cloud navigation data of an image space, and recording the point cloud navigation data as Pimage。
3. The knee replacement surgery spatial registration system according to claim 1, wherein the intraoperative patient spatial knee cartilage surface point cloud collection module comprises a point cloud coordinate obtained by direct scanning of a scanner, belonging to a scanner spatial coordinate, different from the patient spatial point cloud coordinate; in the operation, the knee joint focus cartilage space point cloud coordinate of a patient needs to be read in an optical positioning instrument Polaris reference space, therefore, firstly, a scanner is calibrated, and a conversion matrix T from the adapter to the scanner is obtainedadapter→scan(ii) a Then calculating the spatial point cloud coordinates of the patient through a series of transformations; the specific process is as follows:
(1) scanner calibration
Pasting N marker points on a desktop, and ensuring that the points are uniformly distributed and asymmetrical as much as possible; picking up the center of a marker point by using a calibrated probe, and recording coordinates by using an optical locator Polaris; picking up and counting the variance of each point for multiple times, removing points with larger errors, and calculating the mean coordinate of each remaining point as the central coordinate of each marker point
Figure FDA0002930364660000021
To obtain
Figure FDA0002930364660000022
Meanwhile, an adapter is pasted on the scanner, software carried by the scanner, namely EinScan-Pro.exe, is opened, a fixed mode is selected, the marker point is scanned and stored into a corresponding format, and the central coordinate of the marker point is obtained
Figure FDA0002930364660000023
Figure FDA0002930364660000024
Optical locator Polaris simultaneously records transformation matrix T from Polaris space to adapter spacepolaris→adapterCalculating T by using the formula (1)adapter→scan：
Pscan＝Ppatient×Tpolaris→adapter×Tadapter→scan， (1)
Here, Ppatient×Tpolaris→adapter＝PadapterA 1 is to PadapterAs a floating set of points, PscanAs a reference point set, since PadapterAnd PscanIs a point set corresponding to one, so that the SVD algorithm is adopted for registration, and the obtained transformation matrix is approximate to Tadapter→scan；
(2) Scanning a spatial point cloud of a patient
Scanning the cartilage surface of a knee joint focus by using a scanner, and manually removing point clouds of non-cartilage structures; down-sampling the point cloud to about 1000 points to obtain the point cloud coordinate of the scanner space
Figure FDA0002930364660000025
Figure FDA0002930364660000026
While the optical locator Polaris records the transformation matrix T from Polaris space to adapter spacepolaris→adapterThe point cloud coordinate P of the patient space is calculated by the formula (2)patient：
Ppatient＝Pscan×Tscan→adapter×Tadapter→polaris， (2)
Wherein, Tscan→adapterIs a conversion matrix from the adapter to the scanner obtained by calibration;
wherein the adapter is composed of a plurality of non-uniformly distributed reflective balls fixed on the bracket and used for being conveniently tracked and positioned by Polaris.
4. The knee replacement spatial registration system according to claim 1, wherein the knee cartilage surface point cloud hierarchical registration module is to perform cloud registration on an image spatial point cloud and a patient spatial point, wherein a graphical interface interaction mode is used to select 5-7 corresponding points for coarse registration by adopting an SVD-based method; followed by automatic ICP fine registration.
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* Cited by examiner, † Cited by third party
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CN113506334A (en) * 2021-06-07 2021-10-15 刘星宇 Multi-modal medical image fusion method and system based on deep learning
CN114974518A (en) * 2022-04-15 2022-08-30 浙江大学 Multi-mode data fusion lung nodule image recognition method and device
WO2022257344A1 (en) * 2021-06-07 2022-12-15 刘星宇 Image registration fusion method and apparatus, model training method, and electronic device
CN116245839A (en) * 2023-02-24 2023-06-09 北京纳通医用机器人科技有限公司 Knee joint cartilage segmentation method, device, equipment and medium

Citations (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103908345A (en) * 2012-12-31 2014-07-09 复旦大学 Volume data visualization method for surgical navigation based on PPC (Panel Personal Computer)
CN105078573A (en) * 2014-05-11 2015-11-25 复旦大学 Neuronavigation space aligning method based on handheld scanner
CN106137395A (en) * 2016-07-22 2016-11-23 华南理工大学 It is applied to the full-automatic patient registry method of unmarked some optical operation navigation system
CN107330926A (en) * 2017-08-25 2017-11-07 上海嘉奥信息科技发展有限公司 Non-marked medical figure registration system and method in a kind of art in navigation system
CN107536643A (en) * 2017-08-18 2018-01-05 北京航空航天大学 A kind of augmented reality operation guiding system of Healing in Anterior Cruciate Ligament Reconstruction
CN109496143A (en) * 2016-05-27 2019-03-19 马科外科公司 It is preoperative to plan and be registrated in the related art to surgery systems
CN110169820A (en) * 2019-04-24 2019-08-27 艾瑞迈迪科技石家庄有限公司 A kind of joint replacement surgery pose scaling method and device
CN110353806A (en) * 2019-06-18 2019-10-22 北京航空航天大学 Augmented reality navigation methods and systems for the operation of minimally invasive total knee replacement
CN110443839A (en) * 2019-07-22 2019-11-12 艾瑞迈迪科技石家庄有限公司 A kind of skeleton model spatial registration method and device
CN110946659A (en) * 2019-12-25 2020-04-03 武汉中科医疗科技工业技术研究院有限公司 Registration method and system for image space and actual space
CN111493878A (en) * 2020-03-17 2020-08-07 北京天智航医疗科技股份有限公司 Optical three-dimensional scanning device for orthopedic surgery and method for measuring bone surface
CN111640143A (en) * 2020-04-12 2020-09-08 复旦大学 Nerve navigation rapid surface registration method and system based on PointNet
CN112155732A (en) * 2020-09-29 2021-01-01 苏州微创畅行机器人有限公司 Readable storage medium, bone modeling and registering system and bone surgery system

Patent Citations (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103908345A (en) * 2012-12-31 2014-07-09 复旦大学 Volume data visualization method for surgical navigation based on PPC (Panel Personal Computer)
CN105078573A (en) * 2014-05-11 2015-11-25 复旦大学 Neuronavigation space aligning method based on handheld scanner
CN109496143A (en) * 2016-05-27 2019-03-19 马科外科公司 It is preoperative to plan and be registrated in the related art to surgery systems
CN106137395A (en) * 2016-07-22 2016-11-23 华南理工大学 It is applied to the full-automatic patient registry method of unmarked some optical operation navigation system
CN107536643A (en) * 2017-08-18 2018-01-05 北京航空航天大学 A kind of augmented reality operation guiding system of Healing in Anterior Cruciate Ligament Reconstruction
CN107330926A (en) * 2017-08-25 2017-11-07 上海嘉奥信息科技发展有限公司 Non-marked medical figure registration system and method in a kind of art in navigation system
CN110169820A (en) * 2019-04-24 2019-08-27 艾瑞迈迪科技石家庄有限公司 A kind of joint replacement surgery pose scaling method and device
CN110353806A (en) * 2019-06-18 2019-10-22 北京航空航天大学 Augmented reality navigation methods and systems for the operation of minimally invasive total knee replacement
CN110443839A (en) * 2019-07-22 2019-11-12 艾瑞迈迪科技石家庄有限公司 A kind of skeleton model spatial registration method and device
CN110946659A (en) * 2019-12-25 2020-04-03 武汉中科医疗科技工业技术研究院有限公司 Registration method and system for image space and actual space
CN111493878A (en) * 2020-03-17 2020-08-07 北京天智航医疗科技股份有限公司 Optical three-dimensional scanning device for orthopedic surgery and method for measuring bone surface
CN111640143A (en) * 2020-04-12 2020-09-08 复旦大学 Nerve navigation rapid surface registration method and system based on PointNet
CN112155732A (en) * 2020-09-29 2021-01-01 苏州微创畅行机器人有限公司 Readable storage medium, bone modeling and registering system and bone surgery system

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113274130A (en) * 2021-05-14 2021-08-20 上海大学 Markless surgery registration method for optical surgery navigation system
CN113506334A (en) * 2021-06-07 2021-10-15 刘星宇 Multi-modal medical image fusion method and system based on deep learning
WO2022257344A1 (en) * 2021-06-07 2022-12-15 刘星宇 Image registration fusion method and apparatus, model training method, and electronic device
CN113506334B (en) * 2021-06-07 2023-12-15 刘星宇 Multi-mode medical image fusion method and system based on deep learning
CN114974518A (en) * 2022-04-15 2022-08-30 浙江大学 Multi-mode data fusion lung nodule image recognition method and device
CN116245839A (en) * 2023-02-24 2023-06-09 北京纳通医用机器人科技有限公司 Knee joint cartilage segmentation method, device, equipment and medium
CN116245839B (en) * 2023-02-24 2023-09-29 北京纳通医用机器人科技有限公司 Knee joint cartilage segmentation method, device, equipment and medium

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