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Session: Musculoskeletal System
Session starts: Thursday 24 January, 10:40
Presentation starts: 11:25
Room: Lecture room 558

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Vincenzo Carbone (University of Twente)
Marjolein van der Krogt (University of Twente)
Lara Vigneron (Materialise NV)
Jan Schepers (Materialise NV)
Sjoerd Kolk (Radboud University Medical Center)
Bart Koopman (University of Twente)
Nico Verdonschot (University of Twente)

Abstract:
Musculo-skeletal (MS) models represent a promising tool to predict the effects of surgery on individual patients. Unfortunately, MS geometry and musculo-tendon (MT) parameters, which greatly affect model force predictions, are difficult to measure directly; moreover, several parameters are known to vary with age, gender and activity. The aim of this study is to create subject-specific models of lower extremity, using medical imaging analysis techniques and functional scaling, in order to increase reliability of model force predictions. We use the Twente Lower Extremity Model (TLEM) [1] implemented in the AnyBody Modeling System (http://www.anybodytech.com/). TLEM consists of 12 body segments, 11 joints and 21 DOFs. Each leg contains 163 Hill-type MT element, representing 58 MT parts. •MS geometry is obtained using several medical imaging analysis techniques: bone contours and muscle volumes are semi-automatically segmented from MRI scans, muscle attachment sites and bony landmarks are estimated using an automatic morphing. •Functional scaling of MT parameters is based on dynamometer measurements during isometric and isokinetic maximal voluntary contractions (MVC): tendon slack length, optimal muscle fiber length and maximal isometric muscle force are optimized so that the model reproduces the measured subject-specific strength profiles. Image-based MS geometry and functionally scaled MT parameters have shown their potential in achieving more reliable model outcomes than simple anthropometric scaling, respectively, reducing hip reaction forces by 20% during deep knee bend [2] and muscle activity from 300% to 100% during maximal knee flexion torque [3]. Combining these two methods would permit to further reduce errors in muscle force predictions, improving the preciseness of subject-specific models and achieving the reliability necessary in surgical scenarios.