Method for improving occlusal scanning efficiency by splitting occlusal surface data

Abstract

The present invention relates to the field of medical technology, and discloses a method for improving the efficiency of occlusal scanning by splitting occlusal surface data, comprising the calculation of an occlusal optimization matrix and the splitting and reorganization of the occlusal surface: the calculation of the occlusal optimization matrix comprises the following steps: scanning the occlusal surface data and aligning it with the upper and lower jaw models; by using the method of splitting the occlusal surface, the number of times the occlusal surface is scanned is reduced, the efficiency of scanning the occlusion is improved, and the use experience of patients and users is improved; by using the method of splitting the occlusal surface, the local occlusal relationship of the upper and lower jaw models at multiple positions is obtained, and the occlusal relationship distribution of the upper and lower jaw models obtained after interpolation is more uniform; fewer times of scanning and occluding can minimize the problem of inconsistent patient occlusal states caused by multiple acquisitions of occlusal surface data, and avoid the failure to obtain the correct occlusal relationship during subsequent numerical optimization.

Description

Technical Field

[0001] The present invention relates to the field of medical technology, and in particular to a method for improving occlusal scanning efficiency by splitting occlusal surface data. Background Art

[0002] The traditional method of using impression technology to obtain occlusal relationships can obtain relatively accurate occlusal relationships, but this method cannot quantify the occlusal relationship, which means that the traditional impression technology cannot participate in digital design. Therefore, most current products use numerical calculations to obtain occlusal relationships.

[0003] When using an intraoral scanner to obtain the intraoral occlusal relationship, there are currently two main scanning methods. Taking the acquisition of the occlusal relationship of 14 units as an example, the first scanning method is to obtain the occlusal surface data only once, and match and optimize the acquired occlusal surface data with the upper and lower jaw models respectively, so as to obtain the occlusal relationship of the upper and lower jaws. The second occlusal method is to collect multiple times, obtain multiple occlusal surface data, and use the multiple occlusal surface data to match and optimize the upper and lower jaw models, so as to obtain the occlusal relationship of the upper and lower jaws.

[0004] The first scanning method only collects one occlusal surface data and optimizes the upper and lower jaw models, which is prone to local convergence. For example, if only the occlusal surface data on the left side of the model is scanned, the occlusal relationship on the right side of the model may be inaccurate after optimization.

[0005] In order to avoid the local convergence of the first scanning method, the second scanning method is currently used, that is, scanning multiple occlusal surface data to optimize with the upper and lower jaw models to obtain a more accurate occlusal relationship. Taking the occlusal relationship of scanning 14 units as an example, the user needs to scan the occlusal surface data of the left molar, left molar, right molar, and right molar to obtain the matching relationship between the corresponding occlusal surface data and the upper and lower jaw models, and optimize to obtain the occlusal relationship of the upper and lower jaw models. The occlusal relationship obtained by this method is more accurate than the first method, but the second method has the following two problems:

[0006] 1. The operation is cumbersome. Users need to obtain multiple occlusal surface data and perform occlusal scans multiple times, which increases the scanning time.

[0007] 2. Since the patient needs to cooperate with the user to scan multiple occlusal surface data, it is impossible to ensure that the patient's occlusal state is almost the same every time. The increase in the number of occlusal scans may cause large differences in the states of multiple occlusions, affecting the subsequent occlusal optimization and failing to obtain an accurate occlusal relationship. Summary of the invention

[0008] 1. Technical issues to be resolved

[0009] In view of the deficiencies of the prior art, the present invention provides a method for improving the efficiency of occlusal scanning by splitting occlusal surface data, so as to solve the problems raised in the above-mentioned background technology.

[0010] (II) Technical solution

[0011] To achieve the above object, the present invention provides the following technical solution: a method for improving the efficiency of occlusal scanning by splitting occlusal surface data, including calculation of occlusal optimization matrix and splitting and reorganizing the occlusal surface:

[0012] The calculation of the occlusal optimization matrix includes the following steps:

[0013] Step (1), scanning the occlusal surface data and aligning it with the upper and lower jaw models;

[0014] Step (2), splitting the occlusal surface data according to a specified method to obtain multiple sets of new occlusal surface data;

[0015] Step (3), calculating the registration relationship between the new occlusal surface data and the upper and lower jaw models, and obtaining multiple upper and lower jaw transformation matrices;

[0016] Step (4), interpolating multiple upper and lower jaw matrices to obtain an interpolation transformation matrix of the upper and lower jaws;

[0017] Step (5): Based on the upper and lower jaw models and the new occlusal surface data, a numerical optimization model is established to solve the transformation matrix of the upper and lower jaws.

[0018] Preferably, the splitting and reassembling of the occlusal surface comprises the following steps:

[0019] Step (1), obtaining all voxels in the occlusal surface data, and calculating the maximum bounding box and center coordinates of all voxels;

[0020] Step (2), according to the voxel center coordinates, obtain the dimension with the largest variance as the subsequent classification dimension;

[0021] Step (3), classify all voxels along the specified dimension, and reorganize each class into a new occlusal surface data;

[0022] Step (4), obtaining the matching relationship between the new occlusal surface data and the upper and lower jaws.

[0023] Preferably, when the upper and lower jaws are aligned according to step (1), the occlusal surface needs to be successfully aligned with the upper and lower jaws.

[0024] Preferably, when scanning the occlusal surface data proposed in step (1), the number of scanned occlusal surfaces must meet the requirements.

[0025] Preferably, multiple sets of new occlusal surface data are aligned with the upper and lower jaw models, and the transformation matrices between the multiple upper and lower jaw models are obtained by matrix transformation. The multiple sets of upper and lower jaw transformation matrices are interpolated to obtain a transformation matrix between the upper and lower jaw models. Based on the multiple sets of occlusal surface data, the upper and lower jaw models, and the transformation matrix between the upper and lower jaw models obtained in the previous step, a numerical optimization model is established to obtain the occlusal optimization matrix between the upper and lower jaw models.

[0026] Preferably, for each voxel included in the occlusal surface data, the maximum bounding box and center coordinates of all voxels are calculated, and the voxel center coordinate information is counted to obtain the dimension with the largest variance.

[0027] Preferably, all voxels are divided in a specified dimensional direction to obtain multiple voxel groups, the voxels in each voxel group are recombined into a new occlusal surface data, and the registration relationship between the new occlusal surface data and the upper and lower jaw models is calculated based on the existing registration information.

[0028] (III) Beneficial effects

[0029] Compared with the prior art, the present invention provides a method for improving the efficiency of occlusal scanning by splitting occlusal surface data, which has the following beneficial effects:

[0030] 1. By segmenting the occlusal surface, the number of occlusal surface scans is reduced, the efficiency of occlusal scanning is improved, and the user experience of patients and users is improved;

[0031] 2. By segmenting the occlusal surface, the local occlusal relationship of the upper and lower jaw models at multiple positions is obtained. After interpolation, the occlusal relationship distribution of the upper and lower jaw models will be more uniform;

[0032] 3. Fewer occlusal scans can minimize the problem of inconsistent patient occlusal status caused by multiple acquisitions of occlusal surface data, thus avoiding the inability to obtain the correct occlusal relationship during subsequent numerical optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 is a calculation flow chart of the occlusal optimization matrix of the present invention;

[0034] Figure 2 The figure is a flow chart of the splitting and reassembling of the occlusal surface of the present invention;

[0035] Figure 3 This is the occlusal data diagram of the present invention. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] The present invention provides a technical solution to improve the efficiency of occlusal scanning by splitting occlusal surface data. Figure 1 As shown, including the calculation of the occlusal optimization matrix and the splitting and reorganization of the occlusal surface:

[0038] The calculation of the occlusal optimization matrix includes the following steps:

[0039] Step (1), scanning the occlusal surface data and aligning it with the upper and lower jaw models;

[0040] Step (2), splitting the occlusal surface data according to a specified method to obtain multiple sets of new occlusal surface data;

[0041] Step (3), calculating the registration relationship between the new occlusal surface data and the upper and lower jaw models, and obtaining multiple upper and lower jaw transformation matrices;

[0042] Step (4), interpolating multiple upper and lower jaw matrices to obtain an interpolation transformation matrix of the upper and lower jaws;

[0043] Step (5): Based on the upper and lower jaw models and the new occlusal surface data, a numerical optimization model is established to solve the transformation matrix of the upper and lower jaws.

[0044] like Figure 2 As shown, the splitting and reassembling of the occlusal surface includes the following steps:

[0045] Step (1), obtaining all voxels in the occlusal surface data, and calculating the maximum bounding box and center coordinates of all voxels;

[0046] Step (2), according to the voxel center coordinates, obtain the dimension with the largest variance as the subsequent classification dimension;

[0047] Step (3), classify all voxels along the specified dimension, and reorganize each class into a new occlusal surface data;

[0048] Step (4), obtaining the matching relationship between the new occlusal surface data and the upper and lower jaws.

[0049] Among them, when aligning with the upper and lower jaws according to the magic of the upper and lower jaws proposed in step (1), the occlusal surface must be successfully aligned with the upper and lower jaws.

[0050] Wherein, when scanning the occlusal surface data proposed in step (1), the number of scanned occlusal surfaces must meet the requirements.

[0051] Among them, according to the registration relationship between multiple sets of new occlusal surface data and the upper and lower mandibular models, the transformation matrix between multiple upper and lower mandibular models is obtained by matrix transformation, and the transformation matrix between the upper and lower mandibular models is interpolated to obtain a transformation matrix between the upper and lower mandibular models. Based on the multiple sets of occlusal surface data, the upper and lower mandibular models and the transformation matrix between the upper and lower mandibular models obtained in the previous step, a numerical optimization model is established to obtain the occlusal optimization matrix between the upper and lower mandibular models.

[0052] For each voxel contained in the occlusal surface data, the maximum bounding box and center coordinates of all voxels are calculated, the voxel center coordinate information is counted, and the dimension with the largest variance is obtained.

[0053] Among them, all voxels are divided according to the specified dimensional direction to obtain multiple voxel groups, the voxels in each voxel group are recombined into a new occlusal surface data, and the registration relationship between the new occlusal surface data and the upper and lower jaw models is calculated based on the existing registration information.

[0054] The working principle of this device: Figure 3 As shown in the figure, a plurality of occlusal surface data are obtained by segmenting an occlusal surface, and each area of ​​different color depth in the figure represents a new occlusal surface data; during the scanning process of the occlusal surface, the occlusal surface data are divided into a plurality of voxels, and each voxel carries the relevant information of the current voxel and the neighboring voxels, and also carries the registration information of the current voxel and the upper and lower jaw models. In this algorithm, the voxels are clustered according to the relevant information of the known voxels, and the voxels in each class are fused to obtain new occlusal surface data, and the new occlusal surface data also carries the registration information with the current upper and lower jaw models;

[0055] According to the registration relationship between the multiple new occlusal data and the upper and lower jaw models, multiple sets of transformation matrices between the upper and lower jaws are obtained, and the multiple sets of transformation matrices are interpolated to obtain the registration matrix between the upper and lower jaw models;

[0056] Based on multiple sets of new occlusal data, maxillary and mandibular models, and the maxillary and mandibular registration matrices obtained by interpolation in the previous step, a numerical optimization model is established, and an iterative solution is performed to obtain a more accurate transformation matrix of the maxillary and mandibular models.

[0057] The calculation flow chart of the occlusal optimization matrix is ​​shown in Figure 1As shown, taking the occlusal relationship of the scanned 14-unit model as an example, after respectively obtaining the occlusal surface data buccal_1 and buccal_2 on the left and right sides of the model and the registration relationship between the occlusal surface data and the upper and lower jaw models, buccal_1 is divided according to a specified method to obtain a plurality of occlusal surface data buccal_1n (n represents the number of decomposed occlusal surface data), and according to the registration relationship between buccal_1 and the upper and lower jaw models, the registration relationship between the decomposed occlusal surface data buccal_1n and the upper and lower jaw models is calculated, and similarly, the decomposed occlusal surface data buccal_2n of buccal_2 and the registration relationship between buccal_2n and the upper and lower jaw models are obtained;

[0058] According to the registration relationship between multiple sets of new occlusal surface data and upper and lower jaw models, a transformation matrix between multiple upper and lower jaw models is obtained through matrix transformation, and the transformation matrices of multiple sets of upper and lower jaws are interpolated to obtain a transformation matrix between the upper and lower jaw models;

[0059] Based on multiple sets of occlusal surface data, upper and lower jaw models, and the transformation matrix between the upper and lower jaw models obtained in the previous step, a numerical optimization model is established to solve the occlusal optimization matrix between the upper and lower jaw models.

[0060] See the flowchart of the splitting and reorganization of the occlusal surface for details. Figure 2 As shown, for each voxel contained in the occlusal surface data, the maximum bounding box and center coordinates of all voxels are calculated, the voxel center coordinate information is counted, the dimension with the largest variance is obtained, all voxels are divided according to the specified dimensional direction to obtain multiple voxel groups, the voxels in each voxel group are recombined into a new occlusal surface data, and the registration relationship between the new occlusal surface data and the upper and lower jaw models is calculated based on the existing registration information.

[0061] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0062] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and alterations may be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for calculating an occlusal optimization matrix, comprising the following steps: Step (1), scanning the occlusal surface data and aligning it with the upper and lower jaw models; Step (2), splitting the occlusal surface data to obtain multiple sets of new occlusal surface data; Step (3), obtaining a transformation matrix between the multiple sets of upper and lower jaw models according to the registration relationship between the multiple sets of new occlusal surface data and the upper and lower jaw models; Step (4), interpolating multiple sets of transformation matrices to obtain a registration matrix between the upper and lower jaw models; Step (5): Based on multiple sets of new occlusal surface data, the upper and lower jaw models, and the registration matrix between the upper and lower jaw models, a numerical optimization model is established, and an iterative solution is performed to obtain the occlusal optimization matrix between the upper and lower jaw models.

2. The method for calculating the occlusal optimization matrix according to claim 1, characterized in that: Splitting the occlusal surface data to obtain multiple sets of new occlusal surface data includes the following steps: Step (1), obtaining all voxels in the occlusal surface data, and calculating the maximum bounding box and center coordinates of all voxels; Step (2), according to the voxel center coordinates, obtain the dimension with the largest variance as the subsequent classification dimension; Step (3), classify all voxels along the specified dimension, and reorganize each class into a new occlusal surface data; Step (4), obtaining the registration relationship between the new occlusal surface data and the upper and lower jaw models.

3. The method for calculating an occlusal optimization matrix according to claim 1, characterized in that: When the occlusal surface data in step (1) is registered with the upper and lower jaw models, the occlusal surface must be compatible with the upper and lower jaw shapes.

4. The method for calculating an occlusal optimization matrix according to claim 1, characterized in that: For each voxel contained in the occlusal surface data, the maximum bounding box and center coordinates of all voxels are calculated, the voxel center coordinate information is counted, and the dimension with the largest variance is obtained.

5. The method for calculating an occlusal optimization matrix according to claim 1, characterized in that: All voxels are divided according to the specified dimensional direction to obtain multiple voxel groups. The voxels in each voxel group are recombined into a new occlusal surface data, and the registration relationship between the new occlusal surface data and the upper and lower jaw models is calculated based on the existing registration information.

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