Accuracy of Muscle Moment Arms Estimated from MRI-Based Musculoskeletal Models of the Lower Extremity

Biomechanical Engineering Division, Mechanical Engineering Department, Stanford University, CA 94305-3030, USA.
Computer Aided Surgery (Impact Factor: 0.69). 01/2000; 5(2):108-19. DOI: 10.1002/1097-0150(2000)5:2<108::AID-IGS5>3.0.CO;2-2
Source: PubMed


Biomechanical models that compute the lengths and moment arms of soft tissues are broadly applicable to the treatment of movement abnormalities and the planning of orthopaedic surgical procedures. The goals of this study were to: (i) develop methods to construct subject-specific biomechanical models from magnetic resonance (MR) images, (ii) create models of three lower-extremity cadaveric specimens, and (iii) quantify the accuracy of muscle-tendon lengths and moment arms estimated using these models.
Models describing the paths of the medial hamstrings and psoas muscles for a wide range of body positions were developed from MR images in one joint configuration by defining kinematic models of the hip and knee, and by specifying "wrapping surfaces" that simulate interactions between the muscles and underlying structures. Our methods for constructing these models were evaluated by comparing hip and knee flexion moment arms estimated from models of three specimens to the moment arms determined experimentally on the same specimens. Because a muscle's moment arm determines its change in length with joint rotation, these comparisons also tested the accuracy with which the models could estimate muscle-tendon lengths over a range of hip and knee motions.
Errors in the moment arms calculated with the models, averaged over functional ranges of hip and knee flexion, were less than 4 mm (within 10% of experimental values).
The combination of MR imaging and graphics-based musculoskeletal modeling provides an accurate and efficient means of estimating muscle-tendon lengths and moment arms in vivo.

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    • ". Hardware and procedure for measuring hip ab/adduction and rotation moment arms. The hardware consisted of a fixed table for aligning and securing the pelvis, an adjustable cart for moving the femur through a range of hip ab/adduction angles, and a set of concentric rings for rotating the femur about its mechanical axis, following Arnold et al. (2000) "
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    ABSTRACT: The human iliotibial band (ITB) is a poorly understood fascial structure that may contribute to energy savings during locomotion. This study evaluated the capacity of the ITB to store and release elastic energy during running, at speeds ranging from 2-5m/s, using a model that characterizes the three-dimensional musculoskeletal geometry of the human lower limb and the force-length properties of the ITB, tensor fascia lata (TFL), and gluteus maximus (GMax). The model was based on detailed analyses of muscle architecture, dissections of 3-D anatomy, and measurements of the muscles' moment arms about the hip and knee in five cadaveric specimens. The model was used, in combination with measured joint kinematics and published EMG recordings, to estimate the forces and corresponding strains in the ITB during running. We found that forces generated by TFL and GMax during running stretch the ITB substantially, resulting in energy storage. Anterior and posterior regions of the ITB muscle-tendon units (MTUs) show distinct length change patterns, in part due to different moment arms at the hip and knee. The posterior ITB MTU likely stores more energy than the anterior ITB MTU because it transmits larger muscle forces. We estimate that the ITB stores about 1J of energy per stride during slow running and 7J during fast running, which represents approximately 14% of the energy stored in the Achilles tendon at a comparable speed. This previously unrecognized mechanism for storing elastic energy may be an adaptation to increase human locomotor economy. Copyright © 2015 Elsevier Ltd. All rights reserved.
    Journal of Biomechanics 06/2015; 265(12). DOI:10.1016/j.jbiomech.2015.06.017 · 2.75 Impact Factor
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    • "Our results show no sex effect on any of the outcome measures (Table 1), and instead that l 0 , k and ε max varied with subject mass, height and shank length. The maximum Achilles tendon force achieved for the contractions varied with ankle angle, and this is to be expected due to the dependence of muscle force on length (Zajac, 1989), and the moment arm on joint angle (Arnold et al. 2000). The maximum force achieved for each subject significantly covaried with subject mass, but not with height or sex (Table 1), and when F max was included as a covariate, the variations in Achilles tendon stiffness were related to subject strength (F max ), and not the height, mass or sex. "
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    ABSTRACT: Tendons are elastic structures that connect muscle to the skeletal system and transmit force relative to the amount of stretch they experience. The mechanical properties of human tendons are difficult to measure non-invasively, so generic values are often assumed in musculoskeletal models to represent all subjects. We aimed to determine the in vivo mechanical properties of the human Achilles tendon by calculating tendon stiffness and resting length in 10 male and 10 female trained cyclists. B-mode ultrasound coupled with motion capture was used to track the tendon lengths for the medial and lateral gastrocnemii concurrently with ankle torque measurements during ramped isometric contractions. Achilles tendon stiffness was calculated as the slope of the linear portion of the force-length curve, and this was extrapolated to zero force to yield the tendon resting length. Average Achilles tendon stiffness was 201.8±5.9Nmm(-1). There was no difference in Achilles tendon stiffness or maximum isometric force between males and females, however tendon stiffness varied between individuals. The resting lengths of the MG and LG tendon were 0.209±0.002m and 0.222±0.002m respectively, and regression models determined that shank length was the best predictor of resting tendon length. Our results indicate that Achilles tendon stiffness varies with muscle strength and not sex. The variability in Achilles tendon stiffness between subjects support the need for experimentally measured subject-specific tendon properties as input parameters to improve the accuracy of musculoskeletal models. Copyright © 2015 Elsevier Ltd. All rights reserved.
    Journal of Biomechanics 06/2015; 48(12). DOI:10.1016/j.jbiomech.2015.06.009 · 2.75 Impact Factor
    • "or FORW and BACK the results were a bit more dispersed between subjects . Variations in parameters other than PCSA also influenced our model predictions . For the static force tasks that we analysed , the most important ones are muscle moment arms and optimal fibre lengths . Previously published methods for MRI - based estimations of moment arms ( Arnold et al . , 2000 ; Scheys et al . , 2011 ) did not work on most of our data , because ( 1 ) muscle attachment could not well be determined from muscles with short tendons ( for instance serratus anterior and pectoralis major attachments on the ribcage ) and ( 2 ) fibre directions are not visible on MRI scans ( and thus not the perpendicular distance fro"
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    ABSTRACT: Personalisation of model parameters is likely to improve biomechanical model predictions and could allow models to be used for subject- or patient-specific applications. This study evaluates the effect of personalising physiological cross-sectional areas (PCSA) in a large-scale musculoskeletal model of the upper extremity. Muscle volumes obtained from MRI were used to scale PCSAs of five subjects, for whom the maximum forces they could exert in six different directions on a handle held by the hand were also recorded. The effect of PCSA scaling was evaluated by calculating the lowest maximum muscle stress (σmax, a constant for human skeletal muscle) required by the model to reproduce these forces. When the original cadaver-based PCSA-values were used, strongly different between-subject σmax-values were found (σmax=106.1±39.9Ncm(-2)). A relatively simple, uniform scaling routine reduced this variation substantially (σmax=69.4±9.4Ncm(-2)) and led to similar results to when a more detailed, muscle-specific scaling routine was used (σmax=71.2±10.8Ncm(-2)). Using subject-specific PCSA values to simulate an shoulder abduction task changed muscle force predictions for the subscapularis and the pectoralis major on average by 33% and 21%, respectively, but was <10% for all other muscles. The glenohumeral (GH) joint contact force changed less than 1.5% as a result of scaling. We conclude that individualisation of the model's strength can most easily be done by scaling PCSA with a single factor that can be derived from muscle volume data or, alternatively, from maximum force measurements. However, since PCSA scaling only marginally changed muscle and joint contact force predictions for submaximal tasks, the need for PCSA scaling remains debatable. Copyright © 2015 Elsevier Ltd. All rights reserved.
    Journal of Biomechanics 05/2015; 48(10). DOI:10.1016/j.jbiomech.2015.05.005 · 2.75 Impact Factor
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