CN116058965A - Bone registration method for joint replacement surgery and surgery navigation system - Google Patents
Bone registration method for joint replacement surgery and surgery navigation system Download PDFInfo
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Abstract
A bone registration method and system for joint replacement surgery, determining a transformation relationship between a preoperative coordinate system and an intraoperative coordinate system by initial registration and accurate registration based on a VSF curved surface under the preoperative coordinate system generated using preoperative bone image data and a source point set SPS under the intraoperative coordinate system generated using intraoperative point data acquired from an actual bone surface of a patient, comprising the first steps of: selecting a plurality of groups of corresponding point pairs from the SPS and VSF curved surfaces of the source point set, performing initial registration based on Singular Value Decomposition (SVD), and solving an initial transformation matrix; and a second step of: based on the initial transformation matrix, performing accurate registration based on an ICP method, and each ICP iteration adopts a least square optimization method for minimizing the sum of squares of distances between each point in the transformed source point set SPS and a corresponding projection point in the projection point set PSPS. When the error is smaller than the set value or the iteration number reaches the set value, the algorithm process is stopped.
Description
Technical Field
The invention relates to a bone registration method and a bone registration system for joint replacement surgery, in particular to a bone registration method and a surgery navigation system for knee joint replacement surgery and hip joint replacement surgery.
Background
Computer-aided surgery navigation systems are widely used in various surgeries in order to improve the accuracy and precision of the surgery. Navigation surgery exists in various fields of medicine, and this technology requires an optical tracking system to perform registration (also known as registration, registration) during surgery. Registration is an extremely important process that can assist in finding a transformation matrix between the preoperative planning coordinate system in the software and the actual coordinate system in the operating room, thereby visualizing the position and pose of the surgical instrument in a three-dimensional model in the vicinity of the surgical field.
Disclosure of Invention
The present invention has been made to solve the above problems, and an object of the present invention is to provide a bone registration method and system. The proposed algorithm allows registration between the coordinate system of the preoperative plan in the software and the coordinate system of the patient's real bone.
According to an aspect of the present invention, there is provided a bone registration method for joint replacement surgery, based on a VSF curved surface as a three-dimensional virtual curved surface in a preoperative coordinate system generated using preoperative bone image data and a source point set SPS in an intraoperative coordinate system generated using point data acquired intraoperatively from an actual bone surface of a patient, determining a transformation relationship between the preoperative coordinate system and the intraoperative coordinate system by initial registration and accurate registration between the source point set SPS and the VSF curved surface, characterized by comprising the steps of: a first step of: selecting 4 groups of corresponding point pairs from the SPS and the VSF curved surface, performing initial registration based on Singular Value Decomposition (SVD), and solving an initial transformation matrix; and a second step of: and performing accurate registration based on an ICP method based on the initial transformation matrix, wherein a corresponding transformed projection point set PSPS on the VSF curved surface is determined by projecting a plurality of points in the transformed source point set SPS onto the VSF curved surface, and a least squares optimization method for minimizing the square sum of distances between each point in the transformed source point set SPS and a corresponding projection point in the projection point set PSPS is performed as an evaluation criterion for judging whether the ICP method iteration is terminated, wherein the 4 sets of corresponding point pairs are suitable for knee joint replacement surgery, namely 4 points in the source point set SPS of femur and corresponding 4 points in the VSF curved surface of femur, or 4 points in the source point set SPS of tibia and corresponding 4 points in the VSF curved surface of tibia, and the plurality of points are more than 30 points.
Alternatively, the 4 sets of corresponding point pairs may be 3 sets of corresponding point pairs suitable for the hip replacement surgery, that is, 3 points in the source point set SPS of the acetabulum and 3 points corresponding to the VSF curved surface of the acetabulum, or 3 points in the source point set SPS of the femur and 3 points corresponding to the VSF curved surface of the femur.
Preferably, the plurality of points is 30 points.
Preferably, the 4 points in the source point set SPS of the femur refer to: femoral head center HC, femoral knee center FKC, lateral epicondyle LE and medial epicondyle ME. The 4 points in the source point set SPS of the tibia refer to: tibial knee center TKC, tibial tuberosity TT, lateral malleolus LM, medial malleolus MM.
Preferably, the input is set to: source Point set SPS, VSF curved surface, four points PM selected from the VSF curved surface n Point data PB corresponding to four points selected from the actual femur n Maximum number of iterations m, error threshold μ e Let the target output of the algorithm be: second transformation T 2 ，
Wherein the first step further comprises a third step of: solving for representing the initial matrix T 1 Vector of (3)
Wherein alpha, beta, gamma are rotation angles, x, y, z are translation vectors,
the second step further comprises a fourth step of: rigid alignment based on ICP iteration for accurate registration is performed, expressed as: f (x, y, z) =r (α, β, γ) p+t (x, y, z), (formula 2), wherein (α, β, γ) is the rotation angle, T (x, y, z) is a translation vector, and P is the point to be transformed, wherein, by a predetermined iteration m times of the loop, first based on the vector saved in the third step
Projecting SPS points in the transformed SPS source point set onto the VSF curved surface to obtain corresponding PSPS points, obtaining registration errors E when the square sum of the distances between the transformed SPS points and the PSPS points is minimized through the least squares optimization method, and solving the optimal rigid by using a Gaussian-Newton method and a Jacobian matrixSex transformation T 2 Is>
Then comparing with the preset initial error, when the error is smaller than the preset error threshold mu e And ending the second step.
According to another aspect of the present invention, there is provided a computer-assisted surgery navigation system having an optical tracking system and a computer, by which the steps of the bone registration method described above are performed, with registration of an intra-operative coordinate system with a pre-operative coordinate system.
According to yet another aspect of the present invention, there is provided a bone registration system for intra-operative navigation for determining a transformation relationship between a pre-operative coordinate system and an intra-operative coordinate system, comprising: a VSF curved surface generation unit that establishes a 3D virtual model in a preoperative coordinate system using preoperative bone image data, and generates a VSF curved surface that is a surface of the 3D virtual model; a source point set acquisition unit that generates a source point set SPS under an intraoperative coordinate system using point data acquired intraoperatively from an actual bone surface of a patient; an initial registration unit, which selects a plurality of groups of corresponding point pairs from the source point set SPS and the VSF curved surface, performs initial registration between the source point set SPS and the VSF curved surface based on singular value decomposition SVD, and obtains an initial transformation matrix; an accurate registration unit that performs accurate registration between the source point set SPS and the VSF curved surface using an ICP method based on the initial transformation matrix, the accurate registration unit further comprising: a multi-point acquisition unit that acquires a plurality of points in the transformed source point set SPS; a projection registration unit that determines a transformed set of projection points PSPS corresponding to the transformed set of source points SPS from the VSF curved surface by projecting the plurality of points onto the VSF curved surface, as an evaluation criterion for judging whether or not the ICP method iteration is terminated, based on a least squares optimization method that minimizes a sum of squares of distances between each point in the transformed set of source points SPS and a corresponding projection point in the set of projection points PSPS.
According to the present invention there is also provided a computer readable storage medium storing a computer program for performing the above bone registration method.
There is also provided according to the present invention an electronic device including: a processor and a memory for storing processor-executable instructions, the processor being configured to read the executable instructions from the memory and execute the instructions to implement the bone registration method described above.
There is also provided according to the present invention a computer program product comprising a computer program for execution by a computer to implement the bone registration method described above.
According to the invention, it can be used, for example, in computer-assisted knee or hip joint replacement surgery.
Drawings
Fig. 1 schematically shows a flow chart for registration according to an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention are described in detail below with reference to the attached drawings. The exemplary embodiments described below and illustrated in the drawings are intended to teach the principles of the present invention to enable one skilled in the art to make and use the present invention in a number of different environments and for a number of different applications. The scope of the invention is therefore defined by the appended claims, and the exemplary embodiments are not intended, and should not be considered, as limiting the scope of the invention.
The present inventors have studied to propose a novel bone registration method for performing a registration procedure in, for example, knee joint replacement surgery and hip joint replacement surgery using a surgical navigation system.
< preoperative coordinate System and intraoperative coordinate System >
In intra-operative navigation, the surgical instrument tracked by a positioner, such as an optical tracking system, is displayed in real time on the preoperatively reconstructed three-dimensional anatomy. Thus, registration of the patient with the three-dimensional anatomy of the image space reconstruction is required to match each other. The transformation relation between the coordinate system (intraoperative coordinate system) of the positioning system and the coordinate system (preoperative coordinate system) of the preoperative three-dimensional medical image is determined through calculation, and the intraoperative actual body position of the patient and the preoperative three-dimensional anatomical structure are accurately registered, so that the three-dimensional model seen by a doctor on the display device can truly reflect the distance and the position relation of the surgical instrument relative to a target bone such as a focus.
The method and the device for establishing the coordinate system are not important to the invention, and are not described herein, and can be implemented by using the existing means.
The algorithm utilized for achieving registration between two sets of points is detailed below by way of example.
< first embodiment: bone registration for knee replacement >
Specifically, the registration method described in this embodiment further includes the following steps:
< step S01: construction of three-dimensional virtual Curve and acquisition of Source Point set
In one aspect, a three-dimensional medical image of a patient's bone is preoperatively input to construct a three-dimensional virtual surface.
Three-dimensional reconstruction of a bone model is performed using preoperative image data such as CT, MRI, X-ray, etc. as anatomical input data to obtain a three-dimensional virtual surface (VSF, 3D virtual surface).
On the other hand, point set information of the bone surface of the patient is output intraoperatively.
A three-dimensional scanner device such as a probe can be used to acquire data information of anatomical points on the surface of a bone of interest in an operation, so as to obtain a Source Point Set (SPS).
For this purpose, a total of points (in this embodiment, preferably 30 points or more, more preferably 30 points) may be extracted from the femoral surface and points (in this embodiment, preferably 30 points or more, more preferably 30 points) may be extracted from the tibial surface.
Preferably, SPS should be sparsely collected to cover as much of the bone area as possible.
In this embodiment, bone registration is advantageously achieved by performing an algorithm between the source point set SPS obtained from the patient's real bone surface and the three-dimensional virtual surface VSF of the bone.
< step S02: registration module or step)
Bone registration is divided into: the initial registration of the first step and the accurate registration of the second step. Wherein the initial registration is used for performing a first approximation between the two small point sets, and the accurate registration is used for searching an optimal alignment result between the source point set SPS and the curved surface VSF.
According to the present embodiment, the first step is to find the initial transformation matrix T by SVD 1 . More specifically, the doctor finds four groups of points corresponding to each other on the bone and the virtual three-dimensional model, and performs coarse registration of the four groups of corresponding points in combination with the SVD method, thereby obtaining an initial transformation matrix. The second step is to perform registration by combining multiple sets of corresponding points with an ICP (iterative closest point ) method based on an initial conversion matrix obtained by SVD, and complete registration by iterating the initial matrix to reduce errors.
< step S021: initial registration ]
For initial registration, a plurality of corresponding point pairs are selected from the source point set SPS and the curved surface VSF, and each corresponding point pair is selected from the same position of the corresponding bone surfaces of the source point set SPS and the curved surface VSF.
According to this embodiment, 4 anatomical points are preferably selected.
More specifically, the surgeon must locate on the patient's bone the four anatomical points he has previously selected in the curved VSF, a step that is very important because there is always an error in selecting the points in the bone, if the error is large, the algorithm may not converge to the optimal solution in the second step of accurate registration.
In this embodiment, the initial registration is based on 4 points in the femoral source point set SPS and their corresponding 4 points in the curved surface VSF, and 4 points in the tibial source point set SPS and their corresponding 4 points in the curved surface VSF. Such a selection of 4 point pairs is sufficient to support a rigid transformation in three dimensions.
According to this embodiment, the anatomical points collected on the femur are as follows: femoral head Center (HC, hip Center), femoral knee Center (FKC, femur Knee Center), lateral epicondyle (LE, lateral Epicondyle), medial epicondyle (ME, medial Epicondyle); the anatomical points collected on the tibia were as follows: tibial knee center (TKC, tibia Knee Center), tibial tuberosity (TT, tibia Tubercle), lateral Malleolus (LM, lateral Malleolus), medial Malleolus (MM).
These anatomical points are easily precisely located by the surgeon, for example with a probe or the like, so that the accuracy and operability can be advantageously ensured.
After the first transformation (matrix T 1 ) Alignment is performed by using a Singular Value Decomposition (SVD) based method. The obtained first transformation matrix T 1 Will be used as an initial approximation for the second step of the exact registration step.
First transformation matrix T 1 Can be expressed as a vector:
where α, β, γ are rotation angles and x, y, z are translation vectors.
< step S022: accurate registration-
In order to further improve the overall registration accuracy and reduce the error, it is necessary to perform the second step of accurate registration, that is, accurate registration, based on the result of the initial registration.
Registration will be performed between multiple points (i.e., data information corresponding to multiple points, e.g., more than 30 points) in the SPS point set extracted by the surgeon from the patient's bone (femur or tibia) and the VSF surface of the three-dimensional virtual model. In the following examples, 30 points are taken as an example.
I.e. to solve the registration problem between several points and one grid surface.
For this purpose, two sub-steps are included as follows:
step S0221: a plurality of points (e.g., 30 points) in the SPS point set is acquired.
Step S0222: based on an initial transformation matrix T found with SVD 1 The matching position of the multipoint is further searched for by the ICP method (using the least squares optimization method described later as an evaluation criterion).
An initial transformation matrix T obtained by SVD 1 UsingThe ICP method iteratively registers the 30 points with the mesh surface of the VSF surface.
In this way, the ICP algorithm can merge point set data at different coordinates into the same coordinate system by finding the rotation parameter R and translation parameter T of one rigid transformation from the preoperative coordinate system to the intraoperative coordinate system. The point pairs of the corresponding relation are repeatedly selected in the algorithm, and the optimal rigidity transformation T is calculated 2 Until the convergence accuracy requirement of registration is met, the optimal matching between SPS and VSF data is met.
In the ICP algorithm, a least squares optimization method is used to align the SPS and VSF. The alignment process is a rigid alignment, which can be expressed as:
f (x, y, z) =r (α, β, γ) p+t (x, y, z), (formula 2),
wherein ,
wherein ,(Rx ，R y ，R z ) Is a rotation matrix with respect to each coordinate axis, (α, β, γ) is a rotation angle, T (x, y, z) is a translation vector, and P is a point to be transformed.
The least squares optimization method used is a method for calculating the minimum value of the sum of squares of the distances between corresponding pairs of points between SPS and VSF, as will be described later.
Specifically, passing the transformed SPS point
(see equation 1) onto the VSF surface and in this way define two sets of points, the transformed SPS point set and the PSPS point set (projected source point set, the projected points of the source point set on the VSF surface).
Once the SPS point set is projected onto the VSF surface to determine a transformed PSPS point set corresponding to the transformed SPS point set from the VSF surface, a least squares optimization method according to the present invention may be implemented that minimizes the sum of squares of distances between points in the transformed SPS point set (e.g., also referred to as SPS points) and corresponding projection points in the PSPS point set (e.g., also referred to as PSPS points).
Representing the variables used for the evaluation as vectors, one can obtain:
therefore, let θ= (α, β, γ) and t= (x, y, z), thereby obtaining ·:
the function to be minimized is due to the rotation matrix R (θ) in the function
Is a nonlinear function to solve this problem, and in this embodiment, the problem is redefined as follows using the gaussian-newton method:
wherein ,
is->
Is defined as:
the final solution is as follows:
the solution is combined with the principle of an ICP (iterative closest point) method, and each ICP iteration adopts a least squares optimization method. When the error is smaller than the set value or the iteration number reaches the set value, the algorithm process is stopped.
A registration flow chart according to the present embodiment is shown in fig. 1.
As inputs are: source Point Set (SPS), three-dimensional Virtual Surface (VSF), four points in model (PM) n ) Point data (PB) corresponding to four points on a bone n ) Maximum iteration number m, error threshold μ e Correspondingly, as output is the second transformation T 2 And its corresponding vector to be solved:
when the flow starts (step S03), an initial matrix T for the first-step initial registration is performed 1 Is stored, i.e. the initial vector is determined by SVD
(S04). And then is used for the firstICP iteration of two-step accurate registration (S05-S09). Through a loop of a predetermined iteration m times, first, the SPS points are projected onto the VSF curved surface based on the saved vector Φ, and the corresponding PSPS points are obtained (S06). Next, the registration error E is obtained by the least squares optimization method described above, in which the sum of squares of the distances between the transformed SPS points and the PSPS points is minimized (S07). Solving for optimal rigid transformation T using Gaussian-Newton method and Jacobian matrix 2 Is>
Then in step S09, the error is compared with a preset initial error, when the error is smaller than a preset error threshold mu e After that, the algorithm is ended (S10).
< second embodiment: bone registration for hip replacement
The steps of this embodiment are substantially the same as those of the first embodiment, and the description thereof will be omitted herein, and the description will be mainly directed to different individual details.
< step S01': construction of three-dimensional virtual Curve and acquisition of Source Point set
In total, a plurality of points (preferably 30 points or more, more preferably 30 points in this embodiment) can be extracted from the acetabular bone surface and a plurality of points (preferably 30 points or more, more preferably 30 points in this embodiment) can be extracted from the femoral bone surface.
< step S021': initial registration in registration Module or step S02
According to this embodiment, at initial registration, 3 points of the femur and their corresponding 3 points in the three-dimensional model will be taken, as well as 3 points of the acetabulum and their corresponding 3 points in the three-dimensional model.
Preferably, the 3 anatomical points collected on the femur are as follows: a point on the anterior side of the femoral neck region; a point on the anterior side of the greater trochanter distal femoral region; and a point outside the large rotor region.
Preferably, the 3 anatomical points collected on the acetabulum are as follows: a point in the posterior acetabular region; a point in the anterior acetabular region; and a point at the upper rim of the acetabular region.
It is important to match these registration points as closely as possible to the corresponding points in the virtual model, since these initial points are first aligned between the patient's bone and the patient virtual model.
Initial registration involves finding the true bone point set b= { B 1 ，b 2 ，b 3 Point set m= { M } and virtual model 1 ，m 2 ，m 3 Correspondence between, which is equivalent to finding a transition T (R, T) that minimizes the sum of squares of errors, where T is a translation vector, R is a rotation matrix, and i, j are natural numbers:
by centering the two sets of points in the origin, one can derive:
B′＝{b i -μ B }＝{b′ j (formula 15),
M′＝{m i -μ m }＝{m′ i (formula 16),
wherein ,μB and μM The problem of being an average for each set (set of points) and then minimizing E (R, t) is equivalent to minimizing:
this new problem, called the orthogonal Procrustes problem, can be solved by singular value decomposition SVD.
Calculating covariance matrix of the data W and obtaining SVD decomposition to obtain the following results:
SVD(W)＝UDV T (19),
where U, V is a 3 by 3 rotation matrix and D is a diagonal matrix.
If rank (W) =3, the parameter minimization E (R, t) is unique, given by:
R＝UV T (20),
t＝μ M -Rμ B (21),
this solution represents an initial transformation (T 1 ) And may be expressed as a vector:
where α, β, γ are rotation angles from R and x, y, z are translation vector components from t.
Such a transformation matrix (T 1 ) Will be used as an initial approximation for the second registration step, i.e. the exact registration.
< step S022': accurate registration >
Registration will be performed between the multiple points (i.e., data information corresponding to the multiple points, e.g., more than 30 points) of the SPS point set extracted by the surgeon from the femur or hip of the patient and the VSF surface of the three-dimensional virtual model. In the following examples, 30 points are taken as an example.
I.e. to solve the registration problem between several points and one grid surface.
Also for this purpose, two sub-steps are included as follows:
step S0221: a plurality of points (e.g., 30 points) in the SPS point set is acquired.
Step S0222: based on an initial transformation matrix T found with SVD 1 The matching position of the multipoint is further searched for by the ICP method (using the least squares optimization method described later as an evaluation criterion).
That is, a least squares optimization method is used to seek to reduce the square of the distance between SPS and VSF, and the transformed SPS point is passed through
(see equation 1) onto the VSF surface, and in this way define two sets of points, the transformed SPS point set and the PSPS point set (projected source point set,projection points of the source point set on the VSF surface). />
This also results in the solution shown in equation 13.
This solution is combined with the ICP (iterative closest point) method principle, each ICP iteration using the proposed least squares optimization solution. The algorithm process may terminate when the desired error is reached or when a preset number of iterations is reached.
Accordingly, in executing the flowchart shown in fig. 1, points as inputs are replaced with four points in the first embodiment, here three points (PM n ) Point data (PB) corresponding to three points on a bone n )。
< technical Effect >
According to the present invention, it is proposed to use SVD and ICP together, so that in the initial registration of an orthopedic operation, an initial matrix is obtained using several feature points and SVD methods, or using SVD methods against the orthogonal Procrustes problem. Then, according to the initial matrix, an ICP method (rotation and translation parameters in the matrix are continuously iterated) is used, and a least squares optimization method is used as a judgment standard, so that accurate registration is completed.
In the accurate registration, the conventional ICP generally uses the average distance between the corresponding points as a criterion, unlike the conventional ICP which uses a least squares optimization method of the distances from a plurality of points (preferably 30 or more) to the grid surface of the CT bone surface as a criterion, so that the interference of individual errors, which is large and is divided by a special point, is effectively removed, and the registration problem between a plurality of points (preferably 30 or more) and one grid surface is solved, thereby improving the accuracy of the determination.
For example, the corresponding PSPS points are obtained by transforming 30 points of the original SPS, that is, the points obtained by projecting the SPS onto the VSF, and each of the 30 points of the SPS is matched to the virtual grid plane, thereby obtaining the PSPS. By performing least squares optimization operation on the distance of the corresponding point between the PSPS after projection and the SPS before projection, and judging whether the iteration is ended or not.
According to an embodiment of the present invention, there is also provided a computer-assisted surgery navigation system having an optical tracking system and a computer, wherein the registration of the intra-operative coordinate system with the pre-operative coordinate system is performed by the computer performing the steps of the bone registration method described above.
According to an embodiment of the present invention, there is also provided a computer-readable storage medium storing a computer program for executing any one of the methods described above.
There is also provided according to the present invention an electronic device including: a processor and a memory for storing processor-executable instructions, the processor being configured to read the executable instructions from the memory and execute the instructions to implement any of the methods described above.
According to the present invention there is also provided a computer program product comprising a computer program which when executed by a computer performs the steps of the above method.
While the invention has been described with reference to various specific embodiments, it should be understood that numerous changes could be made within the spirit and scope of the inventive concepts described. Accordingly, it is intended that the invention not be limited to the described embodiments, but that it have the full scope defined by the language of the following claims.
Claims (10)
1. A bone registration method for joint replacement surgery, based on a VSF curved surface as a three-dimensional virtual curved surface in a preoperative coordinate system generated using preoperative bone image data, and a source point set SPS in an operative coordinate system generated using point data acquired in real time by a scanner from an actual bone surface of a patient, the transformation relationship between the preoperative coordinate system and the operative coordinate system being determined by initial registration and accurate registration between the source point set SPS and the VSF curved surface, characterized by comprising the steps of:
a first step of: selecting a plurality of groups of corresponding point pairs from the SPS and the VSF curved surface, performing initial registration based on Singular Value Decomposition (SVD), and solving an initial transformation matrix; and
and a second step of: performing accurate registration based on an ICP method based on the initial transformation matrix, wherein a corresponding transformed projection point set PSPS on the VSF surface is determined by projecting a plurality of points in the transformed source point set SPS to the VSF surface, a least squares optimization method that minimizes a sum of squares of distances between each point in the transformed source point set SPS and a corresponding projection point in the projection point set PSPS is performed as an evaluation criterion for judging whether or not the ICP method iteration is terminated, wherein the plurality of points is 30 or more points,
wherein, in the first step,
the plurality of groups of corresponding point pairs are 4 groups of corresponding point pairs, namely 4 points in a source point set SPS of the femur and 4 points corresponding to the VSF curved surface of the femur, or 4 points in the source point set SPS of the tibia and 4 points corresponding to the VSF curved surface of the tibia; or alternatively
The plurality of groups of corresponding point pairs are 3 groups of corresponding point pairs, namely 3 points in the source point set SPS of the acetabulum and 3 points corresponding to the VSF curved surface of the acetabulum, or 3 points in the source point set SPS of the femur and 3 points corresponding to the VSF curved surface of the femur.
2. The bone registration method according to claim 1, wherein,
in the second step, the plurality of points is 30 points.
3. The bone registration method according to claim 1 or 2, wherein,
the 4 points in the source point set SPS of the femur refer to: femoral head center HC, femoral knee center FKC, lateral epicondyle LE and medial epicondyle ME; the 4 points in the source point set SPS of the tibia refer to: tibial knee center TKC, tibial tuberosity TT, lateral malleolus LM, medial malleolus MM.
4. The bone registration method according to claim 3, wherein,
the input is set as follows: source Point set SPS, VSF curved surface, four points PM selected from the VSF curved surface n Point data PB corresponding to four points selected from the actual femur n Maximum number of iterations m, error threshold μ e Let the target output of the algorithm be: second transformation T 2 ，
Wherein the first step further comprises a third step of: solving for a vector representing the initial matrix T1
Wherein alpha, beta, gamma are rotation angles, x, y, z are translation vectors,
the second step further comprises a fourth step of: rigid alignment based on ICP iteration for accurate registration is performed, expressed as: f (x, y, z) =r (α, β, γ) p+t (x, y, z), (formula 2),
where (α, β, γ) is the rotation angle, T (x, y, z) is a translation vector, P is the point to be transformed,
wherein, through a loop of m predetermined iterations, the vector saved in the third step is first based on
Projecting SPS points in the transformed SPS source point set onto the VSF curved surface to obtain corresponding PSPS points, obtaining registration errors E when the square sum of the distances between the transformed SPS points and the PSPS points is minimized through the least squares optimization method, and solving the optimal rigid transformation T by using a Gaussian-Newton method and a jacobian matrix 2 Is>
/>
wherein ,
is->
Is a matrix of jacobian type of the matrix,
then comparing with the preset initial error, when the error is smaller than the preset error threshold mu e And ending the second step.
5. The bone registration method according to claim 1 or 2, wherein,
the 3 points in the source point set SPS of the acetabulum refer to: a point in the posterior acetabular region; a point in the anterior acetabular region and a point in the upper rim of the acetabular region; the 3 points in the source point set SPS of the femur refer to: a point on the anterior side of the femoral neck region; a point on the anterior side of the greater trochanter distal femoral region; and a point outside the large rotor region.
6. The bone registration method according to claim 5, wherein,
the input is set as follows: source Point set SPS, VSF surface, 3 points PM selected from the VSF surface n Point data PB corresponding to 3 points selected from actual femur n Maximum number of iterations m, error threshold μ e Let the target output of the algorithm be: second transformation T 2 ，
Wherein the first step further comprises a fifth step of: based on the initial transformation T (R, T), a vector representing the initial matrix T1 is determined
Wherein t is a translation vector, R is a rotation matrix, α, β, γ are rotation angles, x, y, z are translation vectors, and
R＝UV T (20),
t＝μ M -Rμ B (21),
wherein U, V is a 3 by 3 rotation matrix, μ B and μM Is the average of the point sets.
Wherein the second step further comprises a sixth step of: rigid alignment based on ICP iteration for accurate registration is performed, expressed as: f (x, y, z) =r (α, β, γ) p+t (x, y, z), (formula 2),
where (α, β, γ) is the rotation angle, T (x, y, z) is a translation vector, P is the point to be transformed,
wherein, through a loop of a predetermined iteration m times, the vector saved in the fifth step is firstly based on
Projecting SPS points in the transformed SPS source point set onto the VSF curved surface to obtain corresponding PSPS points, obtaining registration errors E when the square sum of the distances between the transformed SPS points and the PSPS points is minimized through the least squares optimization method, and solving the optimal rigid transformation T by using a Gaussian-Newton method and a jacobian matrix 2 Is>
wherein ,
is->
Is a matrix of jacobian type of the matrix,
then comparing with the preset initial error, when the error is smaller than the preset error threshold mu e And ending the second step.
7. A computer-assisted surgery navigation system having an optical tracking system and a computer by which the steps of the bone registration method of any one of claims 1 to 6 are performed for registration of an intra-operative coordinate system with a pre-operative coordinate system.
8. A bone registration system for intra-operative navigation for determining a transformation relationship between a preoperative coordinate system and an intra-operative coordinate system, comprising:
a VSF curved surface generation unit which establishes a 3D virtual model under a preoperative coordinate system by utilizing preoperative bone image data and generates a VSF curved surface of the 3D virtual model;
a source point set acquisition unit that generates a source point set SPS under an intra-operative coordinate system using point data acquired in real time by a scanner from an actual bone surface of a patient during an operation;
an initial registration unit, which selects a plurality of groups of corresponding point pairs from the source point set SPS and the VSF curved surface, performs initial registration between the source point set SPS and the VSF curved surface based on singular value decomposition SVD, and obtains an initial transformation matrix;
an accurate registration unit that performs accurate registration between the source point set SPS and the VSF curved surface using an ICP method based on the initial transformation matrix,
the accurate registration unit further includes:
a multi-point acquisition unit that acquires a plurality of points in the transformed source point set SPS;
a projection registration unit that determines a transformed set of projection points PSPS corresponding to the transformed set of source points SPS from the VSF curved surface by projecting the plurality of points onto the VSF curved surface, as an evaluation criterion for judging whether or not the ICP method iteration is terminated, based on a least squares optimization method that minimizes a sum of squares of distances between each point in the transformed set of source points SPS and a corresponding projection point in the set of projection points PSPS.
9. A computer readable storage medium storing a computer program for performing the steps of the bone registration method according to any one of claims 1 to 6.
10. An electronic device, comprising: a processor and a memory for storing processor executable instructions for reading the executable instructions from the memory and executing the instructions to perform the steps of the bone registration method of any one of claims 1 to 6.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116439833A (en) * | 2023-06-13 | 2023-07-18 | 北京壹点灵动科技有限公司 | Pelvis registration processing method and device, storage medium and electronic equipment |
| CN116721137A (en) * | 2023-08-08 | 2023-09-08 | 北京爱康宜诚医疗器材有限公司 | Registration method and device, storage medium and electronic equipment |
| CN117132747A (en) * | 2023-10-25 | 2023-11-28 | 北京爱康宜诚医疗器材有限公司 | Bone resetting method and device based on bone model |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116439833A (en) * | 2023-06-13 | 2023-07-18 | 北京壹点灵动科技有限公司 | Pelvis registration processing method and device, storage medium and electronic equipment |
| CN116439833B (en) * | 2023-06-13 | 2023-09-12 | 北京壹点灵动科技有限公司 | Pelvis registration processing method and device, storage medium and electronic equipment |
| CN116721137A (en) * | 2023-08-08 | 2023-09-08 | 北京爱康宜诚医疗器材有限公司 | Registration method and device, storage medium and electronic equipment |
| CN116721137B (en) * | 2023-08-08 | 2023-10-27 | 北京爱康宜诚医疗器材有限公司 | Registration method and device, storage medium and electronic equipment |
| CN117132747A (en) * | 2023-10-25 | 2023-11-28 | 北京爱康宜诚医疗器材有限公司 | Bone resetting method and device based on bone model |
| CN117132747B (en) * | 2023-10-25 | 2024-03-19 | 北京爱康宜诚医疗器材有限公司 | Bone resetting method and device based on bone model |
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