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A non invasive protocol to estimate muscle tendon lengths and moment arms through ultrasound images
No full textINTRODUCTION
One of the important aspects of the application of muscle-skeletal models in clinical diagnostic is the possibility to estimate muscle force contributions to joint moments, taking into account muscle co-contraction and physiological aspects of muscles. An accurate representation of the lower limb musculoskeletal system is required for the prediction of the muscle-tendon forces during human movement when using these models. The most recent muscle skeletal models are still sensitive to musculoskeletal geometry. Usually muscle- tendon lengths and moment arms are estimated from joint angles using published data obtained from cadaver specimens of different heights [1,2]. The determination of the length and of the line of action of the muscles personalized to subject morphology is one of the major steps in the development of reliable musculoskeletal models. In 2007, a non-invasive method for determining the line of action of lower limb muscles with means of manual pointing on the subject has been proposed [3]. The aim of this study is to further improve the accuracy of these estimations using ultrasound images for identifying muscle insertions, origins and via points of a specific subject.
CLINICAL SIGNIFICANCE
Functional evaluation of muscle co-contraction patterns can significantly improve the clinical decision process. The reliability of musculoskeletal models for the quantification of muscle co-contraction patterns is critically affected by the limited possibility to adapt muscle models to subject-specific characteristics. The possibility to quantify in-vivo subject-specific muscle parameters can significantly improve the clinical applicability of musculoskeletal models.
METHODS
One healthy young subject [25y, 1.72m, 61kg] participated in the study. He was asked to perform an initial step exercise and then to continue walking at self-selected speed while kinematic data (SmartE, BTS, Milan, Italy) were collected. A 3-segment model of the subject right lower limb (thigh, leg and foot) was obtained. Gastrocnemius, soleus and tibialis anterior were selected as representative muscles of the lower limb. Origins (O), insertions (I) and via points of these muscles were calibrated using manual pointing [3] and using ultrasound images [4] while the participant was standing in neutral position. Retinaculum was calibrated both in neutral position and in complete dorsi-flexion of the ankle. These points were then reconstructed during the walk exercise.
Muscle tendon lengths and moment arms were calculated through 5 different methods: i) using equations taken from literature [1,2]; ii) using IO points calibrated with manual pointing; iii) using IO points calibrated with ultrasound images.
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In methods ii) and iii) tibialis anterior length and moment arm were calculated first with retinaculum calibrated with the ankle in neutral position (ii.a and iii.a) and then in complete dorsiflexion (ii.b and iii.b).
RESULTS
(1.a) (1.b)
Figure 1: Tibialis anterior muscle-tendon length (1.a) and moment arms (1.b) obtained with different methods.
Muscle tendon length variations obtained with the various methods were similar during the exercise. Offset values were underestimated or overestimated with the equation approach, while were comparable if obtained with kinematic or ultrasound IO calibrations. Moment arm results varied up to 40% of the mean values with the different estimation methods. In figure 1 exemplificative results for tibialis anterior during initial step and stance phases are shown. Tibialis anterior length and moment arm varied calibrating the retinaculum at different ankle position.
DISCUSSION
Results of this study showed the importance of calibrating insertion, origin and via point of muscles in order to build reliable muscle skeletal models. Different methods showed similar trend of muscle tendon lengths during the exercise, while absolute value varied of about 5%. Moment arm results showed between method d..
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Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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