Spine registration method, device, equipment, storage medium and program product

Abstract

The invention provides a spine registration method and device, equipment, a storage medium and a program product, and relates to the technical field of image registration. The method comprises the following steps: acquiring an MR image and a CT image of a spine to be registered, obtaining segmentation masks of vertebral bodies contained in the CT image based on the CT image, performing rigid registration on the vertebral bodies contained in the CT image based on the segmentation masks and the MR image to obtain rigid registration parameters corresponding to each vertebral body, and aiming at each vertebral body contained in the CT image, performing rigid registration on the vertebral bodies contained in the CT image to obtain rigid registration parameters corresponding to each vertebral body. Determining a first surface sampling point and a second surface sampling point corresponding to the vertebral body according to the segmentation mask and the rigid registration parameter corresponding to the vertebral body, obtaining a first surface sampling point set and a second surface sampling point set, and then obtaining a second surface sampling point set based on thin plate spline interpolation; according to the first surface sampling point set and the second surface sampling point set, elastic registration is carried out on the to-be-registered spine, a deformation field of surrounding soft tissue corresponding to the to-be-registered spine is generated, according to the deformation field, registration is carried out on the MR image and the CT image, and the accuracy of the registration result is improved.

Description

Technical Field

[0001] The present application relates to the technical field of image registration, and in particular to a spinal registration method, device, equipment, storage medium and program product. Background Art

[0002] Degenerative spinal diseases such as herniated disc, spinal stenosis and spondylolisthesis are usually caused by wear and aging of the spine. The disease may cause pain, nerve compression and other symptoms. Spinal surgery is usually a means of treating degenerative spinal diseases. Since the spine is buried deep under the skin surface, the visual environment is very limited. Three-dimensional imaging technologies such as computed tomography (CT) and magnetic resonance (MR) are usually used to observe spinal lesions. Since different three-dimensional imaging technologies focus on different information, such as CT usually displays information about bony structures and MR usually displays information about soft tissues, in order to fuse bony structure information and soft tissue information in spinal surgery, CT images and MR images need to be registered to provide comprehensive diagnostic information.

[0003] In the related art, feature points of the same anatomical structure in the CT image and the MR image are extracted, and the CT image and the MR image are registered by solving the transformation matrix corresponding to the feature points, and the registration result is further reconstructed by the ray casting method. However, the inventors have found that this method has the problem of low accuracy of the registration result. Summary of the invention

[0004] The present application provides a spine registration method, apparatus, device, storage medium and program product to solve the problem of low accuracy of spine registration results in related technologies.

[0005] In a first aspect, the present application provides a spinal alignment method, comprising: acquiring an MR image and a CT image of a spine to be aligned, and based on the CT image, obtaining a segmentation mask of the vertebrae contained in the CT image; based on the segmentation mask and the MR image, rigidly aligning the vertebrae contained in the CT image, respectively, to obtain rigid alignment parameters corresponding to each vertebra; for each vertebra contained in the CT image, determining a first surface sampling point and a second surface sampling point corresponding to the vertebra according to the segmentation mask and the rigid alignment parameters corresponding to the vertebra, to obtain a first surface sampling point set and a second surface sampling point set; based on thin plate spline interpolation, elastically aligning the spine to be aligned according to the first surface sampling point set and the second surface sampling point set, to generate a deformation field of the surrounding soft tissue corresponding to the spine to be aligned; and aligning the MR image with the CT image according to the deformation field.

[0006] In a possible implementation, based on the segmentation mask and the MR image, the vertebrae contained in the CT image are rigidly registered respectively to obtain the rigid registration parameters corresponding to each vertebra, including: for each vertebra to be registered contained in the CT image, the initial registration parameters of the vertebra to be registered are obtained; according to the initial registration parameters, the MR image is rotationally and translationally transformed to obtain the transformed MR image; according to the single vertebrae CT image and the segmentation mask corresponding to the vertebra to be registered, the vertebral area in the single vertebrae CT image is determined, and the single vertebrae CT image is obtained by performing single vertebrae segmentation on the CT image; the image similarity between the transformed MR image and the single vertebrae CT image in the vertebral area is calculated; the initial registration parameters are optimized according to the image similarity to obtain the rigid registration parameters.

[0007] In one possible implementation, the initial registration parameters of the vertebra to be registered are determined in the following manner: determine whether there is a vertebra that has completed rigid registration; if so, determine the rigid registration parameters of the vertebra that has completed rigid registration and is adjacent to the vertebra to be registered as the initial registration parameters; if not, determine the initial registration vertebra, and set the initial registration parameters corresponding to the initial registration vertebra to the preset values.

[0008] In a possible implementation, the initial registration vertebra is determined by: determining the center position of the CT image; calculating the distance between each vertebra in the CT image and the center position, and determining the vertebra corresponding to the minimum distance as the initial registration vertebra.

[0009] In a possible implementation, first surface sampling points and second surface sampling points corresponding to the vertebra are determined according to the segmentation mask and the rigid registration parameters, including: performing non-uniform sampling on the segmentation mask to obtain first surface sampling points corresponding to the vertebra; and performing rigid transformation on the first surface sampling points according to the rigid registration parameters to obtain second surface sampling points.

[0010] In one possible implementation, the segmentation mask is non-uniformly sampled to obtain first surface sampling points corresponding to the vertebra, including: extracting all first voxel points of the segmentation mask surface used to represent the vertebra, and forming a triangular mesh with all first voxel points; calculating the Gaussian curvature corresponding to each first voxel point in the triangular mesh; for each first voxel point, calculating the sampling probability according to the Gaussian curvature corresponding to the first voxel point; and obtaining the first surface sampling point according to the sampling probability.

[0011] In a possible implementation, the MR image and the CT image are registered according to the deformation field, including: calculating the correspondence between the second voxel point in the deformed image to be generated and the third voxel point in the MR image according to the deformation field; for each second voxel point in the deformed image to be generated, determining, based on the correspondence, the three-dimensional position and voxel value of the third voxel point corresponding to the second voxel point in the MR image; and obtaining the deformed image for registering the MR image and the CT image according to the voxel value and the second voxel point.

[0012] In a second aspect, the present application provides a spinal alignment device, comprising:

[0013] An acquisition module is used to acquire the MR image and CT image of the spine to be registered, and based on the CT image, obtain the segmentation mask of the vertebrae contained in the CT image;

[0014] A rigid registration module is used to perform rigid registration on the vertebrae contained in the CT image based on the segmentation mask and the MR image, and obtain the rigid registration parameters corresponding to each vertebrae;

[0015] A determination module is used to determine, for each vertebra included in the CT image, the first surface sampling point and the second surface sampling point corresponding to the vertebra according to the segmentation mask and rigid registration parameters corresponding to the vertebra, to obtain a first surface sampling point set and a second surface sampling point set;

[0016] A generating module, configured to perform elastic registration of the spine to be registered based on the first surface sampling point set and the second surface sampling point set based on thin plate spline interpolation, and generate a deformation field of the surrounding soft tissue corresponding to the spine to be registered;

[0017] The registration module is used to register the MR image and the CT image according to the deformation field.

[0018] In one possible implementation, the rigid registration module is specifically used to: obtain initial registration parameters of the vertebra to be registered for each vertebra to be registered contained in the CT image; perform rotation and translation transformation processing on the MR image according to the initial registration parameters to obtain a transformed MR image; determine the vertebral area in the single vertebral CT image according to the single vertebral CT image and segmentation mask corresponding to the vertebra to be registered, and the single vertebral CT image is obtained by performing single vertebra segmentation on the CT image; calculate the image similarity between the transformed MR image and the single vertebral CT image in the vertebral area; optimize the initial registration parameters according to the image similarity to obtain the rigid registration parameters.

[0019] In one possible implementation, the initial registration parameters of the vertebra to be registered are determined in the following manner: determine whether there is a vertebra that has completed rigid registration; if so, determine the rigid registration parameters of the vertebra that has completed rigid registration and is adjacent to the vertebra to be registered as the initial registration parameters; if not, determine the initial registration vertebra, and set the initial registration parameters corresponding to the initial registration vertebra to the preset values.

[0020] In a possible implementation, the initial registration vertebra is determined by: determining the center position of the CT image; calculating the distance between each vertebra in the CT image and the center position, and determining the vertebra corresponding to the minimum distance as the initial registration vertebra.

[0021] In a possible implementation, the determination module is specifically used to: perform non-uniform sampling on the segmentation mask to obtain first surface sampling points corresponding to the vertebral body; and perform rigid transformation on the first surface sampling points according to the rigid registration parameters to obtain second surface sampling points.

[0022] In a possible implementation, the determination module is also used to: extract all first voxel points of the segmentation mask surface used to represent the vertebral body, and form all the first voxel points into a triangular mesh; calculate the Gaussian curvature corresponding to each first voxel point in the triangular mesh; for each first voxel point, calculate the sampling probability according to the Gaussian curvature corresponding to the first voxel point; and obtain the first surface sampling point according to the sampling probability.

[0023] In a possible implementation, the registration module is specifically used to: calculate the correspondence between the second voxel point in the deformed image to be generated and the third voxel point in the MR image based on the deformation field; for each second voxel point in the deformed image to be generated, determine the three-dimensional position and voxel value of the third voxel point corresponding to the second voxel point in the MR image based on the correspondence; and obtain the deformed image for registering the MR image and the CT image based on the voxel value and the second voxel point.

[0024] In a third aspect, the present application provides an electronic device, including: a memory, a processor;

[0025] The memory stores computer executable instructions;

[0026] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and / or various possible implementations of the first aspect.

[0027] In a fourth aspect, the present application provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are executed by a processor, they are used to implement the above first aspect and / or various possible implementations of the first aspect.

[0028] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above first aspect and / or various possible implementations of the first aspect.

[0029] The spine registration method, device, equipment, storage medium and program product provided by the present application obtain the MR image and CT image of the spine to be registered, and based on the CT image, obtain the segmentation mask of the vertebra contained in the CT image, further based on the segmentation mask and the MR image, respectively perform rigid registration on the vertebrae contained in the CT image, obtain the rigid registration parameters corresponding to each vertebra, for each vertebra contained in the CT image, according to the segmentation mask and rigid registration parameters corresponding to the vertebra, determine the first surface sampling point and the second surface sampling point corresponding to the vertebra, obtain the first surface sampling point set and the second surface sampling point set, then based on the thin plate spline interpolation, perform elastic registration on the spine to be registered according to the first surface sampling point set and the second surface sampling point set, generate the deformation field of the surrounding soft tissue corresponding to the spine to be registered, and register the MR image with the CT image according to the deformation field. The present application, while ensuring the rigid registration of the vertebrae corresponding to the spine to be registered, performs elastic registration on the surrounding soft tissue corresponding to the spine to be registered, thereby improving the accuracy of the registration result. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0031] Figure 1 The process of the spine registration method provided in the embodiment of the present application Figure 1 ;

[0032] Figure 2 The process of the spine registration method provided in the embodiment of the present application Figure 2 ;

[0033] Figure 3 A structural schematic diagram of a method for determining initial registration parameters provided in an embodiment of the present application;

[0034] Figure 4 The process of the spine registration method provided in the embodiment of the present application Figure 3 ;

[0035] Figure 5 A schematic diagram of the structure of a spinal alignment device provided in an embodiment of the present application;

[0036] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0037] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0038] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0039] The following first explains the technical terms involved in the embodiments of the present application.

[0040] Rigid registration refers to the process of translating and rotating one of two 3D data so that the translated and rotated 3D data coincides with the other 3D data or reaches the highest structural similarity.

[0041] In the related technology, on the one hand, the spine can be registered based on a deep learning model. However, in this method, when training the deep learning model, a large number of paired CT images, MR images, CT segmented images and MR segmented images need to be input, and there is a problem of difficulty in obtaining data. At the same time, the trained deep learning model uses the method of generating a deformation field and adding rigid constraints to the vertebral body to obtain the registered image, but even if the rigid constraints are used in this method, in the final registration result, the vertebral body still has a certain deformation, and it is not a completely rigid transformation, resulting in a low accuracy of its registration result. On the other hand, by extracting feature points of the same anatomical structure in the CT image and MR image, and by solving the transformation matrix corresponding to the feature points, the CT image and the MR image are registered, and the registration result is further reconstructed by the ray casting method. However, in this method, due to the existence of a certain degree of freedom between the vertebrae, there is a small amount of deformation in the entire spine during CT and MR imaging. The transformation of the feature points results in a completely rigid registration in which the small amount of deformation cannot be reflected, resulting in a low accuracy of the registration result. At the same time, in order to obtain accurate registration results, a certain number of feature points are usually required. The point selection operation is very complicated and time-consuming, and the registration efficiency is low.

[0042] Based on the problems existing in the related technology, the embodiment of the present application first rigidly aligns the vertebrae contained in the CT image of the spine to be registered, thereby realizing rigid constraints on the vertebrae. Then, based on thin plate spline interpolation, the spine to be registered is elastically registered, and a deformation field of the surrounding soft tissue corresponding to the spine to be registered is generated. Based on the deformation field, the MR image and CT image of the spine to be registered are registered. While ensuring the rigid registration of the vertebrae corresponding to the spine to be registered, the surrounding soft tissue corresponding to the spine to be registered is elastically registered, thereby improving the accuracy of the registration result.

[0043] It should be noted that the spinal alignment method provided in the embodiment of the present application can be applied to a spinal surgical robot. The spinal surgical robot is used as the executor below, and the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail in combination with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in combination with the accompanying drawings.

[0044] Figure 1 The process of the spine registration method provided in the embodiment of the present application Figure 1 .like Figure 1 As shown, the specific implementation of the spine registration method includes the following steps:

[0045] S101, acquiring an MR image and a CT image of the spine to be registered, and obtaining a segmentation mask of the vertebrae contained in the CT image based on the CT image.

[0046] Exemplarily, the spine to be registered may include a plurality of different vertebral bodies.

[0047] In a possible implementation, vertebral segmentation processing can be performed on the CT image based on a deep learning method to obtain a segmentation mask corresponding to each vertebra contained in the CT image.

[0048] Exemplarily, a deep learning model pre-trained based on CT image data is used to perform vertebral segmentation on the CT image to obtain a segmentation mask corresponding to each vertebra contained in the CT image.

[0049] Exemplarily, if the CT image of the spine to be registered contains N vertebrae, the number of segmentation masks obtained after vertebral segmentation is performed on the CT image is N.

[0050] Exemplarily, the segmentation mask of the vertebral body contained in the CT image can be expressed as:

[0051] Mask={M i |i=1,2,…,N}

[0052] Among them, Mi represents the segmentation mask of the i-th vertebra.

[0053] S102, based on the segmentation mask and the MR image, rigid registration is performed on the vertebrae contained in the CT image respectively to obtain rigid registration parameters corresponding to each vertebrae.

[0054] In a possible implementation, when rigid registration is performed on any vertebra included in a CT image, the MR image is used as a floating image, and the single vertebra CT image corresponding to any vertebra is used as a fixed image. Based on the segmentation mask corresponding to the vertebra, the vertebra is rigidly registered.

[0055] Exemplarily, the rigid registration parameters of all vertebrae contained in the CT image can be expressed as:

[0056] T={T i |i=1,2,…,N}

[0057] Among them, T i represents the rigid registration parameters of the i-th vertebra.

[0058] For example, T i It can be expressed as:

[0059] T i = {r xi ,r yi ,r zi ,t xi ,t yi ,t zi}

[0060] Among them, r xi ,r yi ,r zi The angle parameter used to represent the rotation, t xi ,t yi ,t zi The distance parameter used to represent the translation.

[0061] It can be understood that in this step, rigid registration is performed on all vertebrae included in the CT image.

[0062] S103, for each vertebra included in the CT image, determine the first surface sampling point and the second surface sampling point corresponding to the vertebra according to the segmentation mask and rigid registration parameters corresponding to the vertebra, and obtain the first surface sampling point set and the second surface sampling point set.

[0063] Exemplarily, the first surface sampling point can be expressed as:

[0064]

[0065] Among them, mi Indicates that the number of surface sampling points of the i-th vertebra is m i indivual.

[0066] For example, m i Not less than 200, that is, the number of surface sampling points of the i-th vertebra is not less than 200.

[0067] It is understandable that the number of first surface sampling points corresponding to any vertebra contained in the CT image can be multiple, and the number of first surface sampling points corresponding to different vertebrae can be the same or different. This application does not limit this and it can be determined according to actual application requirements.

[0068] Exemplarily, the first surface sampling point set may be a set consisting of first surface sampling points corresponding to each vertebral body included in the CT image.

[0069] Exemplarily, the first surface sampling point set can be expressed as:

[0070] P = {ps i |i=1,2,…,N}

[0071] Exemplarily, the second surface sampling point is a rigidly deformed surface sampling point obtained by applying a rigid transformation of a corresponding vertebra to the first surface sampling point.

[0072] Exemplarily, the second surface sampling point can be expressed as:

[0073]

[0074] Exemplarily, the second surface sampling point set may be a set consisting of second surface sampling points corresponding to each vertebral body included in the CT image.

[0075] Exemplarily, the second surface sampling point set can be expressed as:

[0076] Q={qs i |i=1,2,…,N}

[0077] S104, elastically registering the spine to be registered according to the first surface sampling point set and the second surface sampling point set based on thin plate spline interpolation, and generating a deformation field of the surrounding soft tissue corresponding to the spine to be registered.

[0078] In a possible implementation manner, a deformation field of thin plate spline interpolation is constructed based on all surface sampling points in the first surface sampling point set and the second surface sampling point set.

[0079] Exemplarily, the deformation field may also be described as a deformation calculation formula of thin plate spline interpolation.

[0080] Exemplarily, the deformation field can be expressed by the following formula:

[0081]

[0082] Among them, U() represents the nonlinear distance weight calculation formula. For three-dimensional coordinates, the value can be the spatial distance between two sampling points; a 1x ,a 1y ,a 1z represents the movement parameter of the thin plate spline interpolation transformation; a xx , a xy , a xz , a yx , a yy , a yz , a zx , a zy , a zz represents the rotation parameter of the thin plate spline interpolation transformation; w kx , w ky , w kz represents a nonlinear parameter; k = 1, 2, ..., K, where K represents the number of all surface sampling points contained in the first surface sampling point set or the second surface sampling point set, that is, (x ′ k ,y ′ k , z ′ k ) represents the coordinates of the kth point in the second surface sampling point set.

[0083] It should be noted that, in the above formula for representing the deformation field, the movement parameters of the thin plate spline interpolation transformation, the rotation parameters of the thin plate spline interpolation transformation, and the nonlinear parameters are all unknown parameters. In order to solve the above unknown parameters, in a possible implementation method, all points in the first surface sampling point set and the second surface sampling point set are brought into the above calculation formula for representing the deformation field, and expressed in the form of a linear equation system. The form of the linear equation system is as follows:

[0084]

[0085] Among them, U ij It means (x i ′ ,y i ′ , z i ′ ) and (x j ′ ,y j ′ , z j ′), (x k ,y k , z k ) represents the coordinates of the kth point in the first surface sampling point set.

[0086] It can be understood that the above linear equations can be in the form of Ax=b. That is, when the number of all surface sampling points in the first surface sampling point set or the second surface sampling point set is K, the size of the corresponding matrix A can be (K+4)×(K+4).

[0087] The solution to the linear equation system Ax=b can be obtained by solving the inverse matrix of the matrix A, LU factorization of the matrix A, QR decomposition of the matrix A, or any other method.

[0088] S105, registering the MR image and the CT image according to the deformation field.

[0089] In a possible implementation, based on the deformation field, the three-dimensional position of any voxel point in the deformed image to be generated obtained by registering the MR image with the CT image in the original floating image, i.e., the MR image, is calculated, and the voxel value corresponding to the corresponding voxel point in the deformed image to be generated is further obtained based on the three-dimensional position. By traversing all voxel points in the deformed image to be generated, the voxel values ​​corresponding to all voxel points in the deformed image to be generated are obtained, and then the deformed image for registering the MR image with the CT image is generated.

[0090] In the embodiment of the present application, an MR image and a CT image of the spine to be registered are obtained, and based on the CT image, a segmentation mask of the vertebrae contained in the CT image is obtained. Further, based on the segmentation mask and the MR image, the vertebrae contained in the CT image are rigidly registered respectively to obtain the rigid registration parameters corresponding to each vertebra. For each vertebra contained in the CT image, the first surface sampling point and the second surface sampling point corresponding to the vertebra are determined according to the segmentation mask and the rigid registration parameters corresponding to the vertebra, and the first surface sampling point set and the second surface sampling point set are obtained. Then, based on thin plate spline interpolation, the spine to be registered is elastically registered according to the first surface sampling point set and the second surface sampling point set, and a deformation field of the surrounding soft tissue corresponding to the spine to be registered is generated. According to the deformation field, the MR image and the CT image are registered. In the embodiment of the present application, while ensuring the rigid registration of the vertebrae corresponding to the spine to be registered, the surrounding soft tissue corresponding to the spine to be registered is elastically registered, thereby improving the accuracy of the registration result.

[0091] Combine the following Figure 2The specific implementation method of performing rigid registration on the vertebrae contained in the CT image based on the segmentation mask and the MR image in step S102 to obtain the rigid registration parameters corresponding to each vertebrae is described in detail.

[0092] Figure 2 The process of the spine registration method provided in the embodiment of the present application Figure 2 .like Figure 2 As shown, based on the segmentation mask and the MR image, the vertebrae contained in the CT image are rigidly registered respectively, and the specific implementation method of obtaining the rigid registration parameters corresponding to each vertebra includes the following steps:

[0093] S201 : For each vertebra to be registered contained in the CT image, an initial registration parameter of the vertebra to be registered is obtained.

[0094] It is understandable that when rigid registration is performed on the spine to be registered, it is necessary to perform rigid registration on all vertebrae included in the CT image.

[0095] Optionally, the initial registration parameters of the vertebra to be registered are determined in the following manner: determine whether there is a vertebra that has completed rigid registration; if so, determine the rigid registration parameters of the vertebra that has completed rigid registration and is adjacent to the vertebra to be registered as the initial registration parameters; if not, determine the initial registration vertebra, and set the initial registration parameters corresponding to the initial registration vertebra to the preset values.

[0096] Exemplarily, when the vertebra that has completed rigid registration is vertebra i, the initial registration parameters corresponding to the vertebra i-1 to be registered and the vertebra i+1 to be registered are both the rigid registration parameters corresponding to the vertebra i.

[0097] Exemplarily, the initial registration parameters corresponding to the initial registration vertebra may be (0, 0, 0, 0, 0, 0).

[0098] Optionally, the initial registration vertebra may be determined by: determining the center position of the CT image; respectively calculating the distance between each vertebra in the CT image and the center position, and determining the vertebra corresponding to the minimum distance as the initial registration vertebra.

[0099] In one possible implementation, the center position of the CT image is determined according to the size of the CT image, the spatial distance between each vertebra and the center position is calculated according to the segmentation mask corresponding to each vertebra in the CT image, and the vertebra corresponding to the minimum distance is determined as the initial registration vertebra.

[0100] Exemplarily, the initially registered vertebra may be the vertebra closest to the center of the CT image.

[0101] Combine the following Figure 3 The determination of the initial registration parameters is described in detail.

[0102] Figure 3 This is a schematic diagram of the structure of the method for determining the initial registration parameters provided in the embodiment of the present application. Figure 3 As shown, when the vertebrae contained in the CT image are initially registered, the initial registration vertebrae are first determined. The specific determination method is similar to the above and will not be repeated here. After the initial registration vertebrae are determined, the initial registration parameters corresponding to the initial registration vertebrae are set to preset values ​​such as (0, 0, 0, 0, 0, 0), and the initial registration vertebrae are rigidly registered to obtain the rigid registration parameters of the initial registration vertebrae, that is, the rigid registration result.

[0103] like Figure 3 As shown in the figure, vertebra i is determined as the initial registration vertebra, and vertebra i is rigidly registered to obtain the rigid registration parameters of vertebra i, that is, the registration result is (r xi , r yi , r zi , t xi , t yi , t zi ), further align the vertebrae adjacent to the initial registered vertebrae, such as vertebrae i-1 and vertebrae i+1, and use the rigid registration parameters of vertebrae i as the initial registration parameters of vertebrae i-1 and vertebrae i+1, perform rigid registration on vertebrae i-1 and vertebrae i+1, and obtain the rigid registration parameters corresponding to vertebrae i-1 and vertebrae i+1, i.e., the registration result, and so on, to complete the rigid registration of all vertebrae contained in the CT image.

[0104] S202, performing rotation and translation transformation processing on the MR image according to the initial registration parameters to obtain a transformed MR image.

[0105] It can be understood that for each vertebra included in the CT image, a rotationally and translationally transformed MR image can be obtained.

[0106] S203, determining a vertebral region in the single vertebral CT image according to the single vertebral CT image corresponding to the vertebra to be registered and the segmentation mask, wherein the single vertebral CT image is obtained by performing single vertebra segmentation on the CT image.

[0107] In a possible implementation, for each vertebra included in the CT image, the vertebra is cut out from the CT image based on a segmentation mask corresponding to the vertebra, so as to obtain a single vertebra CT image corresponding to the vertebra.

[0108] Exemplarily, a single vertebral CT image obtained by cutting a CT image can be expressed as:

[0109] Fixed={F i |i=1,2,…,N}

[0110] Among them, F i Represents the single vertebral CT image of the i-th vertebra.

[0111] It is understandable that when performing vertebral segmentation on a CT image, the segmentation mask corresponding to each vertebra obtained can be divided into a vertebral region and a background according to the corresponding voxels, such as a voxel value of 1 represents a vertebral region, and a voxel value of 0 represents the background, which may be soft tissue, etc. Therefore, when performing rigid registration on a vertebra, it is necessary to determine the effective region of the vertebra in the single vertebral image corresponding to the vertebra, i.e., the vertebral region.

[0112] Exemplarily, the segmentation mask may be a binary classification mask.

[0113] S204, calculating the image similarity between the transformed MR image and the single vertebral CT image in the vertebral region.

[0114] It is understandable that the single vertebral CT image and MR image belong to image data of different modalities. In a possible implementation, mutual information can be used as a calculation function of image similarity.

[0115] Exemplarily, when calculating the histogram of mutual information, only voxels within the segmentation mask corresponding to a single vertebral CT image are used. This approach can effectively focus on the rigid transformation of the vertebral body, thereby ignoring the soft tissue around the vertebral body.

[0116] S205, optimizing the initial registration parameters according to the image similarity to obtain rigid registration parameters.

[0117] Exemplarily, a nonlinear optimization method is used to optimize the initial registration parameters.

[0118] Exemplarily, the nonlinear optimization method may be Powell method, covariance matrix adaptive evolution strategy, simplex search, etc.

[0119] In one possible implementation, based on a nonlinear optimization method, it is determined whether the change in image similarity satisfies a preset iteration stop condition, i.e., a convergence condition. If so, the current registration parameters are output and used as the rigid registration parameters of the vertebra. If not, the current registration parameters are updated, and the above steps S202 to S205 are repeated according to the updated registration parameters to obtain the rigid registration parameters of the vertebra.

[0120] In the embodiment of the present application, for each vertebra to be registered contained in the CT image, the initial registration parameters of the vertebra to be registered are obtained, and the MR image is subjected to rotation and translation transformation processing according to the initial registration parameters to obtain the transformed MR image, and the vertebral area in the single vertebral CT image is determined according to the single vertebral CT image and the segmentation mask corresponding to the vertebra to be registered, and the image similarity between the transformed MR image and the single vertebral CT image in the vertebral area is further calculated, and then the initial registration parameters are optimized according to the image similarity to obtain the rigid registration parameters. In the embodiment of the present application, the rigid constraint on each vertebra contained in the CT image is achieved by rigidly registering a single vertebra.

[0121] Combine the following Figure 4 The specific implementation method of determining the first surface sampling points and the second surface sampling points corresponding to the vertebral body according to the segmentation mask and the rigid registration parameters in step S103 is described in detail.

[0122] Figure 4 The process of the spine registration method provided in the embodiment of the present application Figure 3 .like Figure 4 As shown, according to the segmentation mask and the rigid registration parameters, a specific implementation method of determining the first surface sampling points and the second surface sampling points corresponding to the vertebral body may include the following steps:

[0123] S401, performing non-uniform sampling on the segmentation mask to obtain first surface sampling points corresponding to the vertebral body.

[0124] Exemplarily, the surface of the segmentation mask is extracted to represent all first voxel points of the vertebral body, and a triangular mesh is formed, and the curvature of the triangular mesh is calculated. Among them, the area with higher curvature contains more details, and the surface of the area with lower curvature is smoother. Non-uniform sampling is performed according to the curvature. The first surface sampling points collected in the area with higher curvature are correspondingly more, and the first surface sampling points collected in the area with lower curvature are correspondingly less.

[0125] The description of the first surface sampling point is similar to the above and will not be repeated here.

[0126] In this step, in a possible implementation method, the segmentation mask surface is used to extract all the first voxel points representing the vertebral body, and all the first voxel points are formed into a triangular mesh; the Gaussian curvature corresponding to each first voxel point in the triangular mesh is calculated; for each first voxel point, the sampling probability is calculated according to the Gaussian curvature corresponding to the first voxel point; and the first surface sampling point is obtained according to the sampling probability.

[0127] Exemplarily, the sampling probability can be expressed by the following formula:

[0128]

[0129] Among them, probability represents the sampling probability, C represents the Gaussian curvature corresponding to the voxel point, and threshold represents the preset threshold.

[0130] For example, the preset threshold value can be dynamically set according to the number of sampling points on the first surface of the target. The present application does not limit the value of the preset threshold value, which can be determined according to actual application requirements.

[0131] According to the sampling probability, the first surface sampling point is obtained. In a possible implementation, a random number is generated between 0 and 1 to determine whether the sampling probability is greater than the random number. If the sampling probability is greater than the random number, the first voxel point corresponding to the sampling probability is retained as a first surface sampling point. If the sampling probability is less than or equal to the random number, the first voxel point corresponding to the sampling probability is discarded.

[0132] It can be understood that the higher the Gaussian curvature corresponding to the voxel point, the higher the calculated corresponding sampling probability, and the easier it is to retain the first voxel point during the sampling process, so that more sampling points can be achieved in areas with complex structures and large curvature, and fewer sampling points in areas with gentle structures and small curvature.

[0133] S402, performing a rigid transformation on the first surface sampling points according to the rigid registration parameters to obtain second surface sampling points.

[0134] In one possible implementation, the rigid registration parameter T is taken as i = {r x , r y , r z , t x , t y , t z}, the deformation matrix is ​​obtained through the rigid registration parameters, and then the product of the deformation matrix and the first surface sampling point is used as the second surface sampling point.

[0135] For example, the deformation matrix can be expressed as:

[0136]

[0137] Among them, Trans represents the deformation matrix.

[0138] The calculation formula of the second surface sampling point can be expressed by the following formula:

[0139]

[0140] In the embodiment of the present application, the first surface sampling points corresponding to the vertebral body are obtained by performing non-uniform sampling on the segmentation mask, and the first surface sampling points are rigidly transformed according to the rigid registration parameters to obtain the second surface sampling points. In the embodiment of the present application, the control point selection can be automatically completed by performing automatic non-uniform sampling on the vertebral body surface, thereby improving the registration efficiency.

[0141] Optionally, the specific implementation method of step S105 for registering the MR image with the CT image according to the deformation field may be: calculating the correspondence between the second voxel point in the deformed image to be generated and the third voxel point in the MR image according to the deformation field; for each first voxel point in the deformed image to be generated, determining the three-dimensional position and voxel value of the third voxel point corresponding to the second voxel point in the MR image based on the correspondence; and obtaining the deformed image for registering the MR image with the CT image according to the voxel value and the second voxel point.

[0142] Exemplarily, the three-dimensional position can be expressed by the following formula:

[0143]

[0144] Among them, (x ′ ,y ′ ,z ′ ) represents the second voxel point, and (x, y, z) represents the three-dimensional position.

[0145] It is understandable that the calculated three-dimensional position (x, y, z) is usually not an integer, and the voxel value corresponding to the third voxel point corresponding to the three-dimensional position cannot be directly obtained. In a possible implementation, eight points adjacent to the three-dimensional position are taken as And perform trilinear interpolation processing on these eight points to obtain the voxel value under the third voxel point.

[0146] in, It means rounding down, and the minimum value is 0. It means rounding up, and the maximum value is the dimension of the image in that direction.

[0147] Exemplarily, rounding down and rounding up can be expressed by the following formula:

[0148]

[0149] Exemplarily, by traversing the second voxel points and filling the voxels corresponding to the calculated second voxel points in the deformed image to be generated, a deformed image for registering the MR image and the CT image can be obtained.

[0150] In summary, the specific implementation of the spine registration method provided in the embodiment of the present application may include the following steps:

[0151] S1, obtaining the MR image and CT image of the spine to be registered.

[0152] S2, performing vertebral segmentation processing on the CT image to obtain a segmentation mask corresponding to each vertebra contained in the CT image.

[0153] S3, for each vertebra included in the CT image, the CT image is segmented according to the segmentation mask corresponding to the vertebra to obtain a single vertebra CT image corresponding to each vertebra.

[0154] S4, for each vertebra included in the CT image, rigid registration is performed on the vertebra according to the MR image and the single vertebra CT image and segmentation mask corresponding to the vertebra, so as to obtain rigid registration parameters corresponding to the vertebra.

[0155] In this step, rigid registration is performed on all vertebrae included in the CT image. The specific implementation method is similar to the above, and will not be repeated here.

[0156] S5, for all vertebrae included in the CT image, according to the segmentation mask corresponding to each vertebrae, obtain the first surface sampling point corresponding to each vertebrae, and then obtain the first surface sampling point set corresponding to all vertebrae.

[0157] In this step, first surface sampling points are collected for all vertebrae included in the CT image. The specific implementation method of obtaining the first surface sampling points corresponding to each vertebrae is similar to the above, and will not be repeated here.

[0158] S6, for all vertebrae included in the CT image, rigid transformation is performed on the first surface sampling points of each vertebrae to obtain the second surface sampling points corresponding to each vertebrae, and then a set of second surface sampling points corresponding to all vertebrae is obtained.

[0159] S7, constructing a thin plate spline interpolation deformation field through all points in the first surface sampling point set and the second surface sampling point set.

[0160] S8, calculating and obtaining a deformed image for registering the MR image and the CT image according to the deformation field.

[0161] It should be noted that in the spinal alignment method provided in the embodiment of the present application, when constructing the deformation field of thin plate spline interpolation based on the first surface sampling point set and the second surface sampling point set, due to the large number of control points, namely the first surface sampling points and the second surface sampling points, the matrix dimension of the calculated deformation field is large. By adopting a parallel computing architecture for parallel computing, the calculation time can be shortened, thereby improving the alignment efficiency.

[0162] The following is an embodiment of the device of the present application, which can be used to execute the embodiment of the method of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the method of the present application.

[0163] Figure 5 This is a schematic diagram of the structure of the spinal alignment device provided in the embodiment of the present application. Figure 5 As shown, the spinal column registration device 50 includes: an acquisition module 510 , a rigid registration module 520 , a determination module 530 , a generation module 540 and a registration module 550 .

[0164] The acquisition module 510 is used to acquire the MR image and the CT image of the spine to be registered, and obtain the segmentation mask of the vertebrae contained in the CT image based on the CT image;

[0165] A rigid registration module 520 is used to perform rigid registration on the vertebrae contained in the CT image based on the segmentation mask and the MR image, and obtain rigid registration parameters corresponding to each vertebrae;

[0166] A determination module 530 is used to determine, for each vertebra included in the CT image, the first surface sampling point and the second surface sampling point corresponding to the vertebra according to the segmentation mask and rigid registration parameters corresponding to the vertebra, to obtain a first surface sampling point set and a second surface sampling point set;

[0167] A generating module 540 is used to perform elastic registration of the spine to be registered based on the first surface sampling point set and the second surface sampling point set based on thin plate spline interpolation, and generate a deformation field of the surrounding soft tissue corresponding to the spine to be registered;

[0168] The registration module 550 is used to register the MR image and the CT image according to the deformation field.

[0169] In one possible implementation, the rigid registration module 520 is specifically used to: obtain initial registration parameters of the vertebra to be registered for each vertebra to be registered contained in the CT image; perform rotation and translation transformation processing on the MR image according to the initial registration parameters to obtain a transformed MR image; determine the vertebral area in the single vertebral CT image according to the single vertebral CT image and segmentation mask corresponding to the vertebra to be registered, and the single vertebral CT image is obtained by performing single vertebra segmentation on the CT image; calculate the image similarity between the transformed MR image and the single vertebral CT image in the vertebral area; optimize the initial registration parameters according to the image similarity to obtain rigid registration parameters.

[0170] In one possible implementation, the initial registration parameters of the vertebra to be registered are determined in the following manner: determine whether there is a vertebra that has completed rigid registration; if so, determine the rigid registration parameters of the vertebra that has completed rigid registration and is adjacent to the vertebra to be registered as the initial registration parameters; if not, determine the initial registration vertebra, and set the initial registration parameters corresponding to the initial registration vertebra to the preset values.

[0171] In a possible implementation, the initial registration vertebra is determined by: determining the center position of the CT image; calculating the distance between each vertebra in the CT image and the center position, and determining the vertebra corresponding to the minimum distance as the initial registration vertebra.

[0172] In a possible implementation, the determination module 530 is specifically configured to: perform non-uniform sampling on the segmentation mask to obtain first surface sampling points corresponding to the vertebral body; and perform rigid transformation on the first surface sampling points according to the rigid registration parameters to obtain second surface sampling points.

[0173] In a possible implementation, the determination module 530 is also used to: extract all first voxel points of the segmentation mask surface used to represent the vertebral body, and form all the first voxel points into a triangular mesh; calculate the Gaussian curvature corresponding to each first voxel point in the triangular mesh; for each first voxel point, calculate the sampling probability according to the Gaussian curvature corresponding to the first voxel point; and obtain the first surface sampling point according to the sampling probability.

[0174] In a possible implementation, the registration module 550 is specifically used to: calculate the correspondence between the second voxel point in the deformed image to be generated and the third voxel point in the MR image according to the deformation field; for each second voxel point in the deformed image to be generated, determine the three-dimensional position and voxel value of the third voxel point corresponding to the second voxel point in the MR image based on the correspondence; and obtain the deformed image for registering the MR image and the CT image according to the voxel value and the second voxel point.

[0175] The spinal alignment device provided in this embodiment can execute the method provided in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be described in detail here.

[0176] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 6 As shown, the electronic device 60 includes: at least one processor 601 and a memory 602 .

[0177] Optionally, the electronic device 60 further includes a communication interface 603 . The processor 601 , the memory 602 and the communication interface 603 are connected via a system bus 604 .

[0178] In a specific implementation process, at least one processor 601 executes the computer execution instructions stored in the memory 602, so that at least one processor 601 executes the above method.

[0179] The specific implementation process of the processor 601 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.

[0180] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0181] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (Non-volatile Memory, NVM), such as at least one disk memory.

[0182] The system bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The system bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.

[0183] An embodiment of the present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0184] An embodiment of the present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.

[0185] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0186] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0187] The division of units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0188] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0189] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0190] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0191] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0192] Finally, it should be noted that those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention, which follow the general principles of the present invention and include common knowledge or customary technical means in the art not disclosed by the present invention, are not limited to the precise structure described above and shown in the drawings, and may be modified and changed in various ways without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A spine registration method, characterized in that: include: Acquire an MR image and a CT image of the spine to be registered, and based on the CT image, obtain a segmentation mask of the vertebrae contained in the CT image; Based on the segmentation mask and the MR image, rigid registration is performed on the vertebrae contained in the CT image to obtain rigid registration parameters corresponding to each vertebrae; For each vertebra included in the CT image, determining the first surface sampling point and the second surface sampling point corresponding to the vertebra according to the segmentation mask and the rigid registration parameter corresponding to the vertebra, and obtaining a first surface sampling point set and a second surface sampling point set; Based on thin plate spline interpolation, elastically align the spine to be registered according to the first surface sampling point set and the second surface sampling point set to generate a deformation field of surrounding soft tissue corresponding to the spine to be registered; The MR image and the CT image are registered according to the deformation field.

2. The spine registration method according to claim 1, characterized in that: The step of performing rigid registration on the vertebrae contained in the CT image based on the segmentation mask and the MR image to obtain rigid registration parameters corresponding to each vertebrae includes: For each vertebra to be registered contained in the CT image, obtaining initial registration parameters of the vertebra to be registered; According to the initial registration parameters, the MR image is subjected to rotation and translation transformation processing to obtain a transformed MR image; Determine a vertebral region in the single vertebral CT image according to a single vertebral CT image corresponding to the vertebra to be registered and a segmentation mask, wherein the single vertebral CT image is obtained by performing single vertebral segmentation on the CT image; Calculating the image similarity between the transformed MR image and the single vertebral CT image in the vertebral area; The initial registration parameters are optimized according to the image similarity to obtain the rigid registration parameters.

3. The spine registration method according to claim 2, characterized in that: The initial registration parameters of the vertebra to be registered are determined in the following manner: Determine whether there is a vertebral body that has completed rigid registration; If so, determining the rigid registration parameters of the rigidly registered vertebra adjacent to the vertebra to be registered as the initial registration parameters; If it does not exist, an initial registration vertebra is determined, and initial registration parameters corresponding to the initial registration vertebra are set to preset values.

4. The spine registration method according to claim 3, characterized in that: The initial registration vertebra is determined by: Determining the center position of the CT image; The distance between each vertebra in the CT image and the center position is calculated respectively, and the vertebra corresponding to the minimum distance is determined as the initial registration vertebra.

5. The spine registration method according to any one of claims 1 to 4, characterized in that: The step of determining the first surface sampling point and the second surface sampling point corresponding to the vertebral body according to the segmentation mask and the rigid registration parameter comprises: Performing non-uniform sampling on the segmentation mask to obtain first surface sampling points corresponding to the vertebral body; The first surface sampling points are rigidly transformed according to the rigid registration parameters to obtain the second surface sampling points.

6. The spine registration method according to claim 5, characterized in that: The non-uniform sampling of the segmentation mask to obtain the first surface sampling points corresponding to the vertebral body includes: Extracting all first voxel points of the segmentation mask surface for representing a vertebral body, and forming a triangular mesh with all the first voxel points; Calculating the Gaussian curvature corresponding to each first voxel point in the triangular mesh; For each first voxel point, calculating a sampling probability according to the Gaussian curvature corresponding to the first voxel point; The first surface sampling point is obtained according to the sampling probability.

7. The spine registration method according to any one of claims 1 to 4, characterized in that: The registering the MR image and the CT image according to the deformation field includes: Calculating, according to the deformation field, a correspondence between a second voxel point in the deformed image to be generated and a third voxel point in the MR image; For each second voxel point in the deformed image to be generated, based on the corresponding relationship, determining a three-dimensional position and a voxel value of a third voxel point corresponding to the second voxel point in the MR image; A deformed image for registering the MR image with the CT image is obtained according to the voxel value and the second voxel point.

8. A spinal alignment device, characterized in that: include: An acquisition module, used for acquiring an MR image and a CT image of the spine to be registered, and obtaining a segmentation mask of a vertebra contained in the CT image based on the CT image; A rigid registration module, configured to perform rigid registration on the vertebrae contained in the CT image based on the segmentation mask and the MR image, and obtain a rigid registration parameter corresponding to each vertebrae; a determination module, configured to determine, for each vertebra included in the CT image, a first surface sampling point and a second surface sampling point corresponding to the vertebra according to a segmentation mask and a rigid registration parameter corresponding to the vertebra, to obtain a first surface sampling point set and a second surface sampling point set; A generating module, configured to perform elastic registration of the spine to be registered based on the first surface sampling point set and the second surface sampling point set based on thin plate spline interpolation, and generate a deformation field of surrounding soft tissue corresponding to the spine to be registered; A registration module is used to register the MR image and the CT image according to the deformation field.

9. An electronic device, characterized in that: include: Memory, processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed.

11. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 7 when being executed.

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