Porous material preparation quality prediction method based on CT image pore analysis

Abstract

The invention discloses a porous material preparation quality prediction method based on CT image pore analysis, and belongs to the technical field of porous material characterization. Comprising the following steps: acquiring a CT scanning original image data stack of a porous material; the method comprises the following steps of: preprocessing a CT scanning original image data stack, and keeping edge information of pores under the condition that the internal gray level of a smooth image fluctuates to obtain a preprocessed gray level image; segmenting the preprocessed grayscale image to generate a binary image in which pores and a matrix are distinguished; performing morphological processing on the binary image, optimizing boundaries of pores and a matrix, and removing noisy points to obtain an optimized binary image; calculating pore structure parameters; and inputting the calculated pore structure parameters into the performance prediction model, outputting a macroscopic performance prediction value, and controlling the preparation quality of the porous material. Compared with a traditional method for internal structure analysis and parameter acquisition of the regular porous material, the method has the advantages that the precision is improved by 90.2%, and the efficiency is improved by 67.6%.

Description

Technical Field

[0001] This invention relates to the field of porous material characterization technology, and in particular to a method for predicting the quality of porous material preparation based on CT image porosity analysis. Background Technology

[0002] Artificial vertebral body / interbody fusion devices achieve immediate mechanical support, permanent biological fusion, perfect morphological matching and reconstruction, and minimally invasive surgery. Their structural parameters (average pore size, average wire diameter, porosity, and connectivity) are the core key to their success as a "bone-integrated biological scaffold." Pore size and wire diameter together determine porosity and local structural strength, while porosity and connectivity determine bone ingrowth space, mechanical properties, weight, and permeability. Physicians and engineers will customize artificial vertebral body / interbody fusion devices with optimized pore size, wire diameter, porosity, and connectivity based on the patient's specific condition to improve the success rate and long-term stability of spinal fusion surgery.

[0003] Currently, structural parameter testing of artificial vertebral bodies / interbody fusion cages typically employs methods such as scanning electron microscopy (SEM), the Archimedes' drainage method, and computed tomography (CT). Among these, SEM and the Archimedes' drainage method are destructive testing methods, while CT is a non-destructive testing method that provides true three-dimensional data without statistical bias. However, as the structural complexity of artificial vertebral bodies / interbody fusion cages increases, their internal pores are gradually evolving from regular pores to irregular pores. Traditional CT cannot achieve rapid and accurate correlation analysis between irregular pore structures and the macroscopic properties of the material (such as mechanical properties and structural strength).

[0004] Therefore, there is a need for a method to predict the quality of porous material preparation based on CT image pore analysis, so as to quickly, non-destructively and accurately obtain the pore size distribution of porous materials with different morphologies from CT images, and use this data to predict or optimize a key property of the material. Summary of the Invention

[0005] The purpose of this invention is to propose a method for predicting the quality of porous material preparation based on CT image porosity analysis, comprising the following steps:

[0006] S1: Obtain the stack of raw CT scan image data of porous materials;

[0007] S2: The guided filtering algorithm is used to preprocess the original image data stack of CT scans, preserving the edge information of pores while smoothing the gray-level fluctuations inside the image, to obtain a preprocessed gray-level image.

[0008] S3: An adaptive threshold segmentation algorithm is used to segment the preprocessed grayscale image to generate a binary image in which pores and the matrix are distinguished.

[0009] S4: Perform morphological processing on the binary image to optimize the boundary between pores and the matrix, remove noise, and obtain the optimized binary image;

[0010] S5: Calculate the pore structure parameters based on the optimized binary image;

[0011] S6: Input the calculated pore structure parameters into the performance prediction model, output the macroscopic performance prediction value, and predict the quality of porous material preparation.

[0012] Further, step S1 specifically includes: acquiring three-dimensional structural voxel data of the porous material at CT scan resolution, segmenting the pore region using an automatic segmentation algorithm, and automatically extracting the image stack of the pore region.

[0013] Furthermore, the adaptive threshold segmentation algorithm in step S3 is the Bernsen algorithm based on a local window.

[0014] Furthermore, step S4 specifically includes: first performing a morphological opening operation on the binary image to eliminate small noise points and smooth the object boundary; then performing a morphological closing operation to fill the tiny holes inside the pores and connect the disconnected adjacent areas to prevent the micropores inside the material from affecting the accuracy of the structural porosity value calculation.

[0015] Furthermore, the pore structure parameters in step S5 include average pore diameter, average wire diameter, porosity, and connectivity.

[0016] Furthermore, connectivity is calculated based on three-dimensional structural voxels, and average pore diameter, average wire diameter, and porosity are calculated based on the stack of three-dimensional structural voxels and pore region images.

[0017] The beneficial effects of this invention are as follows:

[0018] 1. The method of this invention enables non-destructive, rapid, and quantitative analysis of the internal structure of regular and irregular porous materials;

[0019] 2. The method of this invention establishes a direct correlation between microstructure and macroscopic properties, providing data support for material design and quality control;

[0020] 3. Compared with traditional methods for analyzing the internal structure and obtaining parameters of regular porous materials, the method of this invention improves the accuracy by 90.2% and the efficiency by 67.6%. Attached Figure Description

[0021] Figure 1 This is a flowchart of a method for predicting the quality of porous material preparation based on CT image porosity analysis;

[0022] Figure 2(a), (b), (c), and (d) are the original image, grayscale image, binary image, and pore-filling processed images of regular porous materials, respectively.

[0023] Figure 3 (a), (b), (c), and (d) are the original image, grayscale image, binary image, and processed image of pore filling for irregular porous materials, respectively.

[0024] Figure 4 Original slices generated for a 3D digital phantom;

[0025] Figure 5 Degraded slices generated for a 3D digital model. Detailed Implementation

[0026] This invention proposes a method for predicting the quality of porous material preparation based on CT image porosity analysis. The invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0027] Figure 1 This is a flowchart of a method for predicting the quality of porous material preparation based on CT image pore analysis. The specific steps are as follows: S1: Obtain the original CT scan image data stack of the porous material; S2: Preprocess the original CT scan image data stack using a guided filtering algorithm to preserve the edge information of the pores while smoothing internal grayscale fluctuations, resulting in a preprocessed grayscale image; S3: Segment the preprocessed grayscale image using an adaptive threshold segmentation algorithm to generate a binary image that distinguishes the pores from the matrix; S4: Perform morphological processing on the binary image to optimize the boundary between the pores and the matrix and remove noise, resulting in an optimized binary image; S5: Calculate the pore structure parameters based on the optimized binary image; S6: Input the calculated pore structure parameters into a performance prediction model to output macroscopic performance prediction values ​​and predict the quality of porous material preparation.

[0028] In S1, at CT scan resolution, the three-dimensional structure voxel data of porous materials are acquired, and an automatic segmentation algorithm is used to segment the pore region and automatically extract the image stack of the pore region.

[0029] The adaptive threshold segmentation algorithm in S3 is the Bernsen algorithm based on a local window.

[0030] S4 specifically includes: first, performing a morphological opening operation on the binary image to eliminate small noise points and smooth object boundaries; then performing a morphological closing operation to fill the tiny pores inside the pores and connect disconnected adjacent areas to prevent the micropores inside the material from affecting the accuracy of the structural porosity calculation.

[0031] Note that the extracted image stack should be a high-resolution image.

[0032] The grayscale processing and binary image processing are performed on the original image data stack by using the guided filter algorithm and automatic threshold algorithm built in the open-source image processing software ImageJ, so as to distinguish the pores and matrix in the original image stack.

[0033] Figure 2 (a), (b), (c), and (d) are respectively the original image, grayscale image, binary image, and processed image of hole filling of the regular porous material image. The calculated average wire diameter is 239.7132μm. The structural parameters of the regular porous material are manually measured by using the traditional measurement method. Five wire diameters are selected for measurement, and the measured values are 235.9352μm, 231.4685μm, 236.8631μm, 238.9473μm, and 237.4662μm respectively. After calculation, the average wire diameter is 236.1361μm. However, the distribution of each measured value under the traditional measurement method is relatively discrete, and the result stability is poor.

[0034] Figure 3 (a), (b), (c), and (d) are respectively the original image, grayscale image, binary image, and processed image of hole filling of the irregular porous material image. The noise reduction and boundary smoothing processing are respectively performed on the binary image, and then the tiny holes inside the pores are filled, and the disconnected adjacent regions are connected to prevent the internal micropores of the material from being added to the calculation results of the average pore diameter, average wire diameter, and connectivity.

[0035] Based on the optimized binary image, one or more pore structure parameters are calculated, including average pore diameter, average wire diameter, porosity, and connectivity. Different from the calculation of the average pore diameter, average wire diameter, and porosity, only the three-dimensional structure voxel of the pore region at the CT scan resolution is automatically extracted during the connectivity calculation. The average pore diameter, average wire diameter, and porosity are calculated according to the three-dimensional structure voxel and the pore region image stack.

[0036] A model is established between the calculated pore parameters and the performance of the material. When the calculated average pore diameter and average wire diameter fall within the preset [X, Y]μm range, it is determined that the artificial vertebral body / intervertebral fusion device products of this batch are qualified, otherwise it is determined as unqualified. The calculated pore structure parameters are input into the performance prediction model, and the macroscopic performance prediction value is output. Among them, the preset range of the average pore diameter and average wire diameter is provided by the designer.

[0037] Figure 3 The final test results of the irregular porous material sample shown in

[0038] Table 1 Final test results and requirements of irregular porous material samples

[0039]

[0040] The accuracy and efficiency of the method of this invention are compared with those of traditional measurement methods (manual measurement of structural parameters in the original image stack). The calculation process is as follows:

[0041] Accuracy improvement calculation method:

[0042] Figure 4 Original slices generated for a 3D digital phantom; Figure 5 The degraded slice image generated for the 3D digital phantom. A 3D digital phantom with an average wire diameter of 240 μm was generated using a computer program to simulate the pore structure in porous materials. The generated image was then degraded to simulate the wear and tear during actual industrial CT imaging. Two-dimensional slices were then sampled from the final generated digital phantom, and the average wire diameter was analyzed using both the method of this invention and conventional methods. The final test values ​​and time results for both the conventional measurement method and the method of this invention are shown in Table 2.

[0043] Table 2 Statistical Analysis of Experimental Data

[0044]

[0045] The relative deviation of the method described in this invention from the true value is 0.16%, while the relative deviation of the traditional method is 1.64%, which improves the accuracy by 90.2%.

[0046] Formula for improving accuracy:

[0047]

[0048] The original time was 37 minutes, and the improved time is 12 minutes, which is an efficiency improvement of 67.6%, meaning the time was reduced by 67.6%.

[0049] Efficiency improvement calculation formula:

[0050]

[0051] In summary, this invention provides a method for predicting the quality of porous material preparation based on CT image pore analysis. Addressing the need for pore structure parameter analysis in porous materials using CT image pore analysis, it achieves non-destructive, rapid, and quantitative analysis of the internal structure of both regular and irregular porous materials. It establishes a direct correlation between microstructure and macroscopic properties, providing data support for material design and quality control. Compared to traditional methods for analyzing the internal structure and acquiring parameters of regular porous materials, it improves accuracy by 90.2% and efficiency by 67.6%.

Claims

1. A method for predicting the quality of porous material preparation based on CT image porosity analysis, characterized in that, Includes the following steps: S1: Obtain the stack of raw CT scan image data of porous materials; S2: The guided filtering algorithm is used to preprocess the original image data stack of CT scans, preserving the edge information of pores while smoothing the gray-level fluctuations inside the image, to obtain a preprocessed gray-level image. S3: An adaptive threshold segmentation algorithm is used to segment the preprocessed grayscale image to generate a binary image in which pores and the matrix are distinguished. S4: Perform morphological processing on the binary image to optimize the boundary between pores and the matrix, remove noise, and obtain the optimized binary image; S5: Calculate the pore structure parameters based on the optimized binary image; S6: Input the calculated pore structure parameters into the performance prediction model, output the macroscopic performance prediction value, and predict the quality of porous material preparation.

2. The method for predicting the quality of porous material preparation based on CT image porosity analysis as described in claim 1, characterized in that, Step S1 specifically includes: acquiring three-dimensional structural voxel data of porous materials at CT scan resolution, segmenting the pore region using an automatic segmentation algorithm, and automatically extracting the image stack of the pore region.

3. The method for predicting the quality of porous material preparation based on CT image porosity analysis as described in claim 1 or 2, characterized in that, The adaptive threshold segmentation algorithm in step S3 is the Bernsen algorithm based on local windows.

4. The method for predicting the quality of porous material preparation based on CT image porosity analysis as described in claim 3, characterized in that, Step S4 specifically includes: first, performing a morphological opening operation on the binary image to eliminate small noise points and smooth the object boundary; then, performing a morphological closing operation to fill the tiny holes inside the pores and connect the disconnected adjacent areas to prevent the micropores inside the material from affecting the accuracy of the structural porosity calculation.

5. The method for predicting the quality of porous material preparation based on CT image porosity analysis as described in claim 4, characterized in that, The pore structure parameters in step S5 include average pore diameter, average wire diameter, porosity, and connectivity.

6. The method for predicting the quality of porous material preparation based on CT image porosity analysis as described in claim 5, characterized in that, Connectivity is calculated based on 3D structural voxels, and average pore diameter, average wire diameter, and porosity are calculated based on the stack of 3D structural voxels and pore region images.

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