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. 2022 Feb 27:2022:2937056.
doi: 10.1155/2022/2937056. eCollection 2022.

Finite Element Analysis of Femoral-Acetabular Impingement (FAI) Based on Three-Dimensional Reconstruction

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Finite Element Analysis of Femoral-Acetabular Impingement (FAI) Based on Three-Dimensional Reconstruction

Xi Luo et al. J Healthc Eng. .

Retraction in

Abstract

In order to solve the problem that people often have pain in the hip joint, it is more meaningful to study femoral-acetabular impingement syndrome in the future. This article aims to study the finite element analysis of femoral-acetabular impingement based on three-dimensional reconstruction. This paper proposes a selective image matching strategy. In the feature matching stage, all images are not matched in pairs, but the corresponding camera distance between the images is calculated initially, which has little effect on the number of features and greatly reduces the time of feature matching, thereby reducing the time cost of 3D reconstruction. In this experiment, a double-blind experiment was used to check the range of motion of all hip joints. Two senior radiologists read the obtained hip joint orthographic films to screen out the hip joint orthographic films that meet the requirements. Experimental data shows that although the initial matching points of the algorithm in this paper are lower than those of the traditional algorithm, the final number of matching points is higher than that of the traditional algorithm. When the final number of patches is fixed to 10000, the initial patch required by the algorithm in this paper is more than that required by the SAD algorithm, nearly 13%, but the total storage requirement is 56.4% of the SAD algorithm, which is a big improvement.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Imaging examination of the hip joint.
Figure 2
Figure 2
Application of computer vision.
Figure 3
Figure 3
Schematic diagram of the small hole camera model.
Figure 4
Figure 4
Experimental flowchart.
Figure 5
Figure 5
Original image (a), rotated sideways image (b).
Figure 6
Figure 6
Traditional SAD algorithm.
Figure 7
Figure 7
The optimized algorithm.
Figure 8
Figure 8
Algorithm performance comparison.
Figure 9
Figure 9
3D reconstruction initial number of faces (a), 3D reconstruction final number of faces (b).
Figure 10
Figure 10
Comparison of mobility between simple cross sign and normal group (a), comparison of mobility between simple acetabular deep and normal group (b).
Figure 11
Figure 11
Comparison of mobility between the “cross sign + deep acetabulum” group and the normal group (a), comparison of mobility between the “cross sign + deep acetabulum” group and the normal group (b).

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