Prediction of magnetic resonance imaging-derived trunk muscle geometry with application to spine biomechanical modeling
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- 作者：Jaejin Hwang hwang.285@osu.edu" class="auth_mail" title="E-mail the corresponding author ; Jonathan S. Dufour dufour.8@osu.edu" class="auth_mail" title="E-mail the corresponding author ; Gregory G. Knapik knapik.1@osu.edu" class="auth_mail" title="E-mail the corresponding author ; Thomas M. Best1 ; Tom.Best@osumc.edu" class="auth_mail" title="E-mail the corresponding author ; Safdar N. Khan2 ; Safdar.Khan@osumc.edu" class="auth_mail" title="E-mail the corresponding author ; Ehud Mendel3 ; Ehud.Mendel@osumc.edu" class="auth_mail" title="E-mail the corresponding author ; William S. Marras ; marras.1@osu.edu" class="auth_mail" title="E-mail the corresponding author
- 关键词：Muscle geometry ; Moment-arm ; Cross-sectional area ; Biomechanical model ; Regression model ; Spine
- 刊名：Clinical Biomechanics
- 出版年：2016
- 出版时间：August 2016
- 年：2016
- 卷：37
- 期：Complete
- 页码：60-64
- 全文大小：413 K
文摘
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A novel approach to predict the trunk muscle geometry was proposed.
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Gender, body mass, and height were found as the most important predictors.
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The regression model showed a good model fidelity compared to previous reports.
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This model will be useful to develop curved muscle paths in biomechanical models.