Prediction of Breast Tissue and Plastic Reconstruction in Different Positions by Finite Element Method

DOI

10.2991/msota-16.2016.6How to use a DOI?

Keywords

augmentation mammoplasty; silicone prosthesis; finite element method; shape prediction

Abstract

Breast augmentation surgery is a way to improve the beauty of female body shapes. Based on magnetic resonance imaging (MRI) images of patient's breast providing real simulation data for breast augmentation surgery, this study investigated the effects of two different shapes of implants with the same size on the breast augmentation, and the impact on the breast deformation in lying and standing-up position. After the numerical simulation, the difference of the maximum total deformation between the anatomical prosthesis and the normal breast was 19.478mm in the standing-up position, which was 2.75% less than the total deformation of the round prosthesis. In the lying position, the difference between the round prosthesis and the normal breast was 20.591mm, which was 1.26% less than that of the anatomical prosthesis. The results indicated that the anatomical implanted breast in the standing position had the smaller total deformation caused by gravity, which is more conducive to the maintenance of the shape and more firm. Similarly, round prosthesis is superior to anatomical in lying position. The method proposed in this paper can reasonably predict the degree of deformation of the breast at different postures and reveal the potential capacity of visualization and prediction of the finite element(FE) simulation.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Dongwei Dong
AU  - Tingchun Shi
AU  - Jie Luan
AU  - Xiuyan Yue
PY  - 2016/12
DA  - 2016/12
TI  - Prediction of Breast Tissue and Plastic Reconstruction in Different Positions by Finite Element Method
BT  - Proceedings of 2016 International Conference on Modeling, Simulation and Optimization Technologies and Applications (MSOTA2016)
PB  - Atlantis Press
SP  - 22
EP  - 25
SN  - 2352-538X
UR  - https://doi.org/10.2991/msota-16.2016.6
DO  - 10.2991/msota-16.2016.6
ID  - Dong2016/12
ER  -