A wrist joint mechanical testing method based on finite element analysis

Abstract

The present invention relates to the fields of image processing and biomechanics, and specifically is a wrist joint mechanical detection method based on finite element analysis, including: acquiring medical imaging data, image preprocessing, assigning material properties, setting mechanical loads and applying boundary conditions, finite element solution and result analysis. Through finite element analysis, the present invention can accurately simulate the mechanical behavior of the wrist joint under different loads, and comprehensively analyze its stress and strain distribution. The present invention can not only detect the stress conditions of the bones, but also conduct a detailed analysis of the mechanical response of the cartilage tissue, providing richer and more accurate information for clinical diagnosis, and can also perform separate segmentation and assignment processing for the lesions. The present invention fully considers the impact of the lesion tissue on the mechanical properties of the wrist joint, conducts personalized analysis for patients, and can formulate a more accurate treatment plan for each patient to improve the treatment effect.

Description

Technical Field

[0001] The present invention relates to the fields of image processing and biomechanics, and in particular to a wrist joint mechanics detection method based on finite element analysis. Background Art

[0002] As an important locomotor organ in the human body, the detection of the mechanical properties of the wrist joint is of great significance for clinical diagnosis, treatment plan formulation, and rehabilitation assessment. Traditional detection methods mainly include imaging examinations such as X-rays and MRI, as well as some biomechanical experiments. Although X-ray examinations can intuitively display the bone structure of the wrist joint, they cannot obtain detailed information about soft tissues such as ligaments and tendons, and it is difficult to accurately diagnose early cartilage injuries. Although MRI has advantages in soft tissue imaging, it is expensive and has limited ability to detect dynamic mechanical properties.

[0003] Biomechanical experiments typically require testing ex vivo specimens. While these methods can yield certain mechanical data, they cannot fully simulate the actual in vivo stress conditions, and the experimental conditions often struggle to fully replicate the human body's physiological environment. Furthermore, these traditional methods have limitations when examining the mechanical response of the wrist joint under varying loads, preventing them from comprehensively and accurately capturing the mechanical properties of the wrist joint under various complex stress conditions.

[0004] With the development of computer technology, finite element analysis (FEA), a powerful numerical simulation tool, has been widely used in the field of biomechanics. FEA discretizes complex geometric structures into a finite number of simple units, analyzing the mechanical behavior of these units to determine the mechanical response of the entire structure. In wrist joint mechanical testing, FEA can simulate the stress and strain distribution of the wrist joint under different loads, providing more detailed and accurate data support for clinical diagnosis and research. Summary of the Invention

[0005] The present invention aims to provide a wrist joint mechanical detection method based on finite element analysis, which can accurately and effectively simulate the mechanical behavior of the wrist joint under different loads, comprehensively analyze its stress and strain distribution, and provide more accurate information support for the treatment of patients and research on diseases.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] A wrist joint mechanics detection method based on finite element analysis comprises the following steps:

[0008] S1. Acquisition of medical imaging data

[0009] Obtaining a CT or MRI medical scan of the wrist joint;

[0010] S2. Image Preprocessing

[0011] Import the image into Mimics or 3-Matic software, pre-process the image, adjust the image clarity, segment the bone tissue and cartilage tissue in the image, and then perform three-dimensional reconstruction of the bone tissue and cartilage tissue respectively to generate a mesh model suitable for finite element analysis;

[0012] S3. Material property assignment

[0013] Import the processed mesh model into ANSYS software and assign mechanical parameters to the bone tissue and cartilage tissue respectively;

[0014] S4. Set mechanical loads and apply boundary conditions

[0015] Simulate the force action of the wrist joint and set the corresponding mechanical load and boundary conditions on the wrist joint;

[0016] S5. Finite element solution and result analysis

[0017] Set the convergence rules and number of iterations to perform finite element solution. After the solution is completed, analyze the results and generate corresponding stress diagrams and displacement vector diagrams to intuitively analyze the mechanical effects of different loads on the wrist joint.

[0018] As an improvement, the resolution of the medical scan image in step S1 is not less than 0.2 mm×0.2 mm×0.2 mm.

[0019] As an improvement, the image preprocessing method described in step S2 is: adjusting the contrast and brightness of the image to make the image clearer so as to clearly display the tissue structure; then setting the image recognition threshold according to the grayscale value and morphological characteristics of each tissue, and performing mask editing to segment the image and separate the bone structure and cartilage structure in the wrist joint.

[0020] As an improvement, the three-dimensional reconstruction method in step S2 is to use Mimics software to process the segmented image, generate a three-dimensional model, and export it as an STL file;

[0021] Then, the mesh of the three-dimensional model in STL format was optimized and repaired in 3-Matic software. The mesh size and quality were controlled during mesh optimization, and the aspect ratio of the mesh did not exceed 2 to avoid excessive distortion and irregular units.

[0022] As an improvement, the mechanical parameters of the bone tissue and cartilage tissue in step S3 are:

[0023] The mechanical parameters of bone tissue are: Young's modulus in the longitudinal direction is 11-21 GPa, Young's modulus in the transverse direction is 5-13 GPa, and Poisson's ratio is 0.3;

[0024] The mechanical parameters of cartilage tissue are: Young's modulus 1-2MPa, Poisson's ratio 0.45.

[0025] As an improvement, the convergence rule in step S5 is that the residual force is less than 1e-5 and the maximum number of iterations is 100.

[0026] As an improvement, for the wrist joint of the patient, the lesion is manually selected when segmenting the image in step S2, and the lesion is individually segmented and three-dimensionally reconstructed.

[0027] As an improvement, the value assigned to the lesion is calculated separately, and the Young's modulus of the lesion bone tissue is calculated as follows:

[0028] E=2017.3ρ 2.46

[0029] Where E is the Young's modulus of the bone and ρ is the bone density at the lesion.

[0030] The advantages of the present invention are:

[0031] 1. Through finite element analysis, the present invention can accurately simulate the mechanical behavior of the wrist joint under different loads and comprehensively analyze its stress and strain distribution. The present invention can not only detect the stress conditions of the bones, but also conduct a detailed analysis of the mechanical response of the cartilage tissue, providing richer and more accurate information for clinical diagnosis. Compared with traditional detection methods, the present invention can avoid errors caused by experimental conditions and can simulate more complex stress scenarios, thereby more realistically reflecting the mechanical state of the wrist joint in real life.

[0032] 2. When the present invention detects the wrist joint of a patient, it can perform separate segmentation, three-dimensional reconstruction and special assignment of values ​​for the lesion, fully consider the impact of the lesion tissue on the mechanical properties of the wrist joint, and perform personalized analysis for the patient. The present invention can formulate a more accurate treatment plan for each patient and improve the treatment effect. At the same time, the present invention can clarify the mechanical changes of the lesion site through finite element analysis. Doctors can optimize surgical treatment plans, specify targeted rehabilitation training plans, etc. based on this information, so as to better meet the individualized medical needs of patients.

[0033] 3. This invention provides an efficient and precise method for studying wrist joint mechanics, facilitating in-depth investigations into the pathogenesis, progression, and post-treatment recovery of wrist joint diseases. Researchers can use finite element analysis to simulate the mechanical properties of the wrist joint under different pathological conditions, analyze the impact of lesions on wrist function, and provide a theoretical basis for the development of new treatments and rehabilitation equipment. Furthermore, this method can be used to evaluate the effectiveness of different treatment options, providing scientific support for clinical treatment decisions and promoting deeper and more precise medical research. DETAILED DESCRIPTION

[0034] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with 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. Generally, the components of the embodiments of the present invention described and illustrated herein can be arranged and designed in various different configurations. Example

[0035] This embodiment discloses a wrist joint mechanics detection method based on finite element analysis, comprising the following steps:

[0036] S1. Obtaining medical imaging data

[0037] A healthy volunteer underwent a CT scan of their wrist joint, acquiring a series of two-dimensional slice images. The scanning device had an accuracy of 0.1 mm and an image resolution of 0.2 mm × 0.2 mm × 0.2 mm.

[0038] S2. Image Preprocessing

[0039] Import the images into Mimics software for image preprocessing. Adjust the contrast and brightness of the images to make them clearer and show the tissue structure more clearly.

[0040] Because the grayscale of each tissue in the image is different, the image recognition threshold is set according to the grayscale value and morphological characteristics of each tissue, and mask editing is performed to segment the image and separate the bone structure and cartilage structure in the wrist joint; then the bone structure and cartilage structure are three-dimensionally reconstructed separately to form a three-dimensional model and exported as an STL file.

[0041] The mesh of the 3D model in STL format was optimized and repaired in 3-Matic software to ensure that the surface of the model was smooth and without holes. The model was then converted into a mesh model suitable for finite element analysis.

[0042] When optimizing the mesh, control the mesh size and quality, and keep the aspect ratio of the mesh no more than 2 to avoid excessive distortion and irregular elements.

[0043] S3. Material property assignment

[0044] The processed mesh model was imported into ANSYS software. The corresponding mechanical parameters were assigned to the bone and cartilage tissues. The mechanical parameters for the bone tissue were: a Young's modulus of 11-21 GPa in the longitudinal direction, a Young's modulus of 5-13 GPa in the transverse direction, and a Poisson's ratio of 0.3.

[0045] The mechanical parameters of cartilage tissue are: Young's modulus 1-2MPa, Poisson's ratio 0.45.

[0046] These parameters are assigned to the corresponding three-dimensional finite element model parts to ensure that the model can truly reflect the mechanical behavior of each tissue.

[0047] S4. Set mechanical loads and apply boundary conditions

[0048] Based on the wrist joint's applied forces, set corresponding mechanical loads and boundary conditions. These forces can include axial pressure, bending moment, shear force, and other factors to simulate the wrist joint's stresses during daily activities. Additionally, apply boundary conditions to constrain specific areas of the model to ensure they conform to actual conditions.

[0049] For example, to simulate the force condition of the wrist joint when a fist hits an object, you can first fix the distal ends of the ulna and radius to restrict their movement and rotation in space, and then apply an axial force of 500N to the end of the metacarpal bone.

[0050] S5. Finite element solution and result analysis

[0051] Start the solver of ANSYS software and set the convergence rule to residual force less than 1e -5 The maximum number of iterations is 100. Start solving and calculate the mechanical response of each part of the wrist joint according to the set load and boundary conditions.

[0052] After the solution is complete, the results are reviewed and a stress distribution cloud map and strain distribution map are generated. By analyzing these results, the stress concentration areas and deformation of various tissues in the wrist joint under axial pressure can be clearly seen, providing strong data support for clinical diagnosis and research. Example

[0053] This embodiment discloses a wrist joint mechanics detection method based on finite element analysis, comprising the following steps:

[0054] S1. Obtaining medical imaging data

[0055] A volunteer with a wrist disease was selected and an MRI scan was performed on their wrist to obtain medical imaging data of their wrist. The scanning equipment had an accuracy of 0.1 mm and an image resolution of 0.2 mm × 0.2 mm × 0.2 mm.

[0056] S2. Image Preprocessing

[0057] Import the images into Mimics software for image preprocessing. Adjust the contrast and brightness of the images to make them clearer and show the tissue structure more clearly.

[0058] Because each tissue has a different grayscale in the image, we set an image recognition threshold based on its grayscale value and morphological characteristics. Mask editing was then performed to segment the image, separating the bone and cartilage structures in the wrist joint. Three-dimensional reconstruction of the bone and cartilage structures was then performed separately to create 3D models, which were exported as STL files. During the segmentation process, special attention was paid to the separation of diseased tissue to accurately reflect the disease's impact on the wrist joint structure.

[0059] The STL-format 3D model was mesh optimized and repaired in 3-Matic software to ensure a smooth, flawless surface and a complete 3D restoration of the lesion. The model was then converted into a mesh suitable for finite element analysis. During mesh optimization, the mesh size and quality were controlled, with an aspect ratio of no more than 2 to avoid excessive distortion and irregular elements.

[0060] S3. Material property assignment

[0061] The processed mesh model was imported into ANSYS software. The corresponding mechanical parameters were assigned to the bone and cartilage tissues. The mechanical parameters for the bone tissue were: a Young's modulus of 11-21 GPa in the longitudinal direction, a Young's modulus of 5-13 GPa in the transverse direction, and a Poisson's ratio of 0.3.

[0062] The mechanical parameters of cartilage tissue are: Young's modulus 1-2MPa, Poisson's ratio 0.45.

[0063] The lesion location is assigned a special value based on the calculation. The Young's modulus of the lesion bone tissue is calculated as follows:

[0064] E=2017.3ρ 2.46

[0065] Where E is the Young's modulus of the bone and ρ is the bone density at the lesion.

[0066] These parameters are assigned to the corresponding three-dimensional finite element model parts to ensure that the model can truly reflect the mechanical behavior of each tissue.

[0067] S4. Set mechanical loads and apply boundary conditions

[0068] Based on the wrist joint's applied forces, set corresponding mechanical and boundary conditions. These can include axial pressure, bending moment, shear force, and other forces to simulate the wrist joint's stresses during daily activities. Additionally, apply boundary conditions to constrain specific areas of the model to ensure they conform to actual conditions.

[0069] For example, to simulate the force condition of the wrist joint when a fist hits an object, you can first fix the distal ends of the ulna and radius to restrict their movement and rotation in space, and then apply an axial force of 500N to the end of the metacarpal bone.

[0070] S5. Finite element solution and result analysis

[0071] Start the solver of ANSYS software and set the convergence rule to residual force less than 1e -5 The maximum number of iterations is 100. Start solving and calculate the mechanical response of each part of the wrist joint according to the set load and boundary conditions.

[0072] After the solution is complete, the results are reviewed and a stress distribution cloud map and strain distribution map are generated. By analyzing these results, the stress concentration areas and deformation of various tissues in the wrist joint under axial pressure can be clearly seen, providing strong data support for clinical diagnosis and research.

[0073] The above are only preferred specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solutions and inventive concepts of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A wrist joint mechanics detection method based on finite element analysis, characterized in that: The steps include: S1. Acquisition of medical imaging data Obtaining a CT or MRI medical scan of the wrist joint; S2. Image Preprocessing Import the image into Mimics or 3-Matic software, pre-process the image, adjust the image clarity, segment the bone tissue and cartilage tissue in the image, and then perform three-dimensional reconstruction of the bone tissue and cartilage tissue respectively to generate a mesh model suitable for finite element analysis; S3. Material property assignment Import the processed mesh model into ANSYS software and assign mechanical parameters to the bone tissue and cartilage tissue respectively; S4. Set mechanical loads and apply boundary conditions Simulate the force action of the wrist joint and set the corresponding mechanical load and boundary conditions on the wrist joint; S5. Finite element solution and result analysis Set the convergence rule and number of iterations to perform finite element solution. After the solution is completed, analyze the results and generate corresponding stress diagrams and displacement vector diagrams to intuitively analyze the mechanical effects of different loads on the wrist joint. The mechanical parameters of the bone tissue and cartilage tissue in step S3 are: The mechanical parameters of bone tissue are: Young's modulus 11-21GPa in the longitudinal direction, Young's modulus 5-13GPa in the transverse direction, Poisson's ratio 0.3; the mechanical parameters of cartilage tissue are: Young's modulus 1-2MPa, Poisson's ratio 0.45; For the patient's wrist joint, the lesion is manually selected during the image segmentation in step S2, and the lesion is individually segmented and 3D reconstructed; The values ​​assigned to the lesions are calculated separately, and the Young's modulus of the lesioned bone tissue is calculated as follows: E=2017.3ρ 2.46 Where E is the Young's modulus of the bone and ρ is the bone density at the lesion.

2. The wrist joint mechanics detection method based on finite element analysis according to claim 1, characterized in that: The resolution of the medical scan image in step S1 is not less than 0.2 mm×0.2 mm×0.2 mm.

3. The wrist joint mechanics detection method based on finite element analysis according to claim 1, characterized in that: The image preprocessing method described in step S2 is: adjusting the contrast and brightness of the image to make the image clearer so as to clearly display the tissue structure; then setting the image recognition threshold according to the grayscale value and morphological characteristics of each tissue, and performing mask editing to segment the image and separate the bone structure and cartilage structure in the wrist joint.

4. The wrist joint mechanics detection method based on finite element analysis according to claim 3, characterized in that: The three-dimensional reconstruction method in step S2 is to process the segmented image using Mimics software to generate a three-dimensional model and export it as an STL file; Then, the mesh of the three-dimensional model in STL format was optimized and repaired in 3-Matic software. The mesh size and quality were controlled during mesh optimization, and the aspect ratio of the mesh did not exceed 2 to avoid excessive distortion and irregular units.

5. The wrist joint mechanics detection method based on finite element analysis according to claim 1, characterized in that: The convergence rule in step S5 is that the residual force is less than 1e -5 , the maximum number of iterations is 100.