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Subject-specific musculo-tendon parameters based on MRI and dynamometer tests
S4
ESMAC 2012 abstract / Gait & Posture 38 (2013) S1–S116
the TPAT but the midpoint between the maximum internal and external rotation range of the hip. FNA
TT
CT-scan
N subjects (N limbs) Mean (SD)
60 (120) 33 (14)
43 (86) 36 (12)
Difference PE-CT
Mean (SD) Range
−5 (13)* [−41, 30]
−16 (8) [−31, 3]
Linear regressions R2 Constant
12% 9.5
61% 15.4*
Slope
0.83*
1.03*
Correlation CT = f(PE)
Discussion & Conclusions: This study has shown that major discrepancies exist between the PE and CT-scan measures of lower limb bony torsion, especially for femoral neck anteversion. Given the clinical impact that these deformations have on the surgical decisions taken, further research is required to develop reliable measurements at the time of the gait analysis.
Fig. 1. Subject-specific musculo-skeletal model (center) based on segmented MRI scan (left) and dynamometer tests (right).
References [1] Ruwe PA, et al. Clinical determination of femoral anteversion. A comparison with established techniques. Journal of Bone and Joint Surgery 1992;74(6):820–30. [2] Davids JR, et al. Assessment of femoral anteversion in children with cerebral palsy: Accuracy of the trochanteric prominence angle test. Journal of Prosthetics and Orthotics 2002;22(2):173–8. [3] Milner CE, et al. A comparison of four in vivo methods of measuring tibial torsion. Journal of Anatomy 1998;193(1):139–44.
http://dx.doi.org/10.1016/j.gaitpost.2013.07.019 O07 Subject-specific musculo-tendon parameters based on MRI and dynamometer tests Vincenzo Carbone, Marjolein M. Van der Krogt, Bart F.J.M. Koopman, Nico Verdonschot University of Twente, Laboratory of Biomechanical Engineering, Enschede, The Netherlands Introduction: Subject-specific musculo-skeletal (MS) models are essential to reliably predict the effects of surgery on individual patients. Unfortunately, musculo-tendon (MT) parameters, which greatly affect model force predictions [1], are difficult to measure directly and are known to vary with age, gender and activity. The aim of this study was to estimate subject-specific MT parameters (tendon slack length, optimal muscle fiber length, and maximal isometric muscle force) of the lower extremity, by using functional scaling based on segmented MRI scan and dynamometer tests. Patients/materials and methods: We used the twente lower extremity model (TLEM) [2], implemented in the AnyBody Modeling System (version 5.1) (www.anybodytech.com). TLEM consists of 12 body segments, 11 joints and 21 DOFs, each leg containing 163 Hill-type MT elements. The model was scaled to one example subject. First, MT parameters were scaled according to the subject’s height and weight. Second, muscle volumes were estimated based on a segmented MRI scan of the subject, using Mimics software (www.materialise.com/mimics). Third, MT parameters were optimized so that the model reproduced subject-specific strength profiles measured during a complete set of isometric and isokinetic maximal voluntary contractions (MVC), under the assumption that during MVC predicted muscle activity should be equal to 100% (Fig. 1). Results: Model predictions became much more realistic after functional scaling. For instance, muscle activity necessary to reproduce measured knee flexion torque was too high after anthropometric (∼300%) and muscle volume (∼200%) scaling, but it
Fig. 2. Knee flexion torque (grey) and knee flexor muscle activity (min–max criterion) after anthropometric (dotted), muscle volume (dash-dot) and functional (solid) scaling.
improved drastically after functional scaling (∼100%) (Fig. 2). Similar results were found for other joints. Discussion & conclusions: The proposed functional scaling of MT parameters, based on MRI and dynamometer tests, was successful in achieving more reliable model outcomes, while simple anthropometric and muscle volume scaling were inadequate and caused unrealistic muscle activity prediction. Next, image-based muscle attachment sites will permit to further reduce errors in muscle force predictions [3] and hence improve the reliability of subject-specific model. References [1] Carbone, et al.Proceedings of XIII TGCS International Symposium, Leuven, Belgium. 2011. [2] Klein Horsman, et al. Clinical Biomechanics 2007;22:239–47. [3] Carbone, et al.Proceedings of XXIII ISB Congress, Brussels, Belgium. 2011. p. 18–37.
http://dx.doi.org/10.1016/j.gaitpost.2013.07.020
ESMAC 2012 abstract / Gait & Posture 38 (2013) S1–S116
the TPAT but the midpoint between the maximum internal and external rotation range of the hip. FNA
TT
CT-scan
N subjects (N limbs) Mean (SD)
60 (120) 33 (14)
43 (86) 36 (12)
Difference PE-CT
Mean (SD) Range
−5 (13)* [−41, 30]
−16 (8) [−31, 3]
Linear regressions R2 Constant
12% 9.5
61% 15.4*
Slope
0.83*
1.03*
Correlation CT = f(PE)
Discussion & Conclusions: This study has shown that major discrepancies exist between the PE and CT-scan measures of lower limb bony torsion, especially for femoral neck anteversion. Given the clinical impact that these deformations have on the surgical decisions taken, further research is required to develop reliable measurements at the time of the gait analysis.
Fig. 1. Subject-specific musculo-skeletal model (center) based on segmented MRI scan (left) and dynamometer tests (right).
References [1] Ruwe PA, et al. Clinical determination of femoral anteversion. A comparison with established techniques. Journal of Bone and Joint Surgery 1992;74(6):820–30. [2] Davids JR, et al. Assessment of femoral anteversion in children with cerebral palsy: Accuracy of the trochanteric prominence angle test. Journal of Prosthetics and Orthotics 2002;22(2):173–8. [3] Milner CE, et al. A comparison of four in vivo methods of measuring tibial torsion. Journal of Anatomy 1998;193(1):139–44.
http://dx.doi.org/10.1016/j.gaitpost.2013.07.019 O07 Subject-specific musculo-tendon parameters based on MRI and dynamometer tests Vincenzo Carbone, Marjolein M. Van der Krogt, Bart F.J.M. Koopman, Nico Verdonschot University of Twente, Laboratory of Biomechanical Engineering, Enschede, The Netherlands Introduction: Subject-specific musculo-skeletal (MS) models are essential to reliably predict the effects of surgery on individual patients. Unfortunately, musculo-tendon (MT) parameters, which greatly affect model force predictions [1], are difficult to measure directly and are known to vary with age, gender and activity. The aim of this study was to estimate subject-specific MT parameters (tendon slack length, optimal muscle fiber length, and maximal isometric muscle force) of the lower extremity, by using functional scaling based on segmented MRI scan and dynamometer tests. Patients/materials and methods: We used the twente lower extremity model (TLEM) [2], implemented in the AnyBody Modeling System (version 5.1) (www.anybodytech.com). TLEM consists of 12 body segments, 11 joints and 21 DOFs, each leg containing 163 Hill-type MT elements. The model was scaled to one example subject. First, MT parameters were scaled according to the subject’s height and weight. Second, muscle volumes were estimated based on a segmented MRI scan of the subject, using Mimics software (www.materialise.com/mimics). Third, MT parameters were optimized so that the model reproduced subject-specific strength profiles measured during a complete set of isometric and isokinetic maximal voluntary contractions (MVC), under the assumption that during MVC predicted muscle activity should be equal to 100% (Fig. 1). Results: Model predictions became much more realistic after functional scaling. For instance, muscle activity necessary to reproduce measured knee flexion torque was too high after anthropometric (∼300%) and muscle volume (∼200%) scaling, but it
Fig. 2. Knee flexion torque (grey) and knee flexor muscle activity (min–max criterion) after anthropometric (dotted), muscle volume (dash-dot) and functional (solid) scaling.
improved drastically after functional scaling (∼100%) (Fig. 2). Similar results were found for other joints. Discussion & conclusions: The proposed functional scaling of MT parameters, based on MRI and dynamometer tests, was successful in achieving more reliable model outcomes, while simple anthropometric and muscle volume scaling were inadequate and caused unrealistic muscle activity prediction. Next, image-based muscle attachment sites will permit to further reduce errors in muscle force predictions [3] and hence improve the reliability of subject-specific model. References [1] Carbone, et al.Proceedings of XIII TGCS International Symposium, Leuven, Belgium. 2011. [2] Klein Horsman, et al. Clinical Biomechanics 2007;22:239–47. [3] Carbone, et al.Proceedings of XXIII ISB Congress, Brussels, Belgium. 2011. p. 18–37.
http://dx.doi.org/10.1016/j.gaitpost.2013.07.020