scholarly journals Simulation Case Study: Using Simscape for Human Knee Joint Models

2019 ◽  
Vol 29 (2) ◽  
pp. 101-104
Author(s):  
Ruth Leskovar ◽  
Andreas Körner ◽  
Felix Breitenecker
Author(s):  
Zhonglin Zhu ◽  
Guoan Li

Statistical shape model (SSM) has been established as a useful method for reconstruction of patient-specific 3D surface models, such as the hip or proximal femur using a single radiographic image of the joint [1, 2]. However, there are few studies that have reconstructed patient-specific 3D models of the entire knee joint. We propose to utilize the strong embedded spatial information in a 2D knee joint radiographic image to predict the 3D human knee joint shape model using the SSM method. We also present a preliminary study to test the accuracy of this method when applied to predict human knee joint shapes.


2013 ◽  
Vol 333-335 ◽  
pp. 934-937
Author(s):  
Yue Mei Han

Reconstruction of a 3D model for human knee joint is the basic step for its kinematics and dynamics analysis. To make further research on knee joint modeling, we present a new method to reconstruct 3D knee joint models based on magnetic resonance image (MRI). This method consists of steps as pretreatment of the images, the region growing for segmentation and the contour interpolation or the grey value interpolation and so on. The resulting 3D knee joint model are used for dynamics analysis of human knee joint after being imported into the finite-element platform which includes the tibia, the femur, the meniscus and the cartilages. The 3D model provides the possibility for the research on the movement roles and mechanics characteristics of the knee joint.


2018 ◽  
Vol 00 (1) ◽  
pp. 109-118
Author(s):  
Enas Y. Abdullah ◽  
◽  
Naktal Moid Edan ◽  
Athraa N. Kadhim ◽  
◽  
...  

1985 ◽  
Vol 18 (7) ◽  
pp. 541
Author(s):  
Ph. Edixhoven ◽  
R. Huiskes ◽  
Th.J.G. van Rens ◽  
T.J.J.H. Slooff

2014 ◽  
Vol 15 (5) ◽  
pp. 7250-7265 ◽  
Author(s):  
Congming Zhang ◽  
Xiaochun Wei ◽  
Chongwei Chen ◽  
Kun Cao ◽  
Yongping Li ◽  
...  

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