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: 1.08). 01/2000; 5(2):108-19. DOI: 10.1002/1097-0150(2000)5:2<108::AID-IGS5>3.0.CO;2-2
Source: PubMed

ABSTRACT 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.

  • Source
    Paleobiology 01/2005; 31(4):676. DOI:10.1666/04044.1 · 2.46 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We determined muscle attachment points for the index, middle, ring and little fingers in an OpenSim upper-extremity model. Attachment points were selected to match both experimentally measured locations and mechanical function (moment arms). Although experimental measurements of finger muscle attachments have been made, models differ from specimens in many respects such as bone segment ratio, joint kinematics and coordinate system. Likewise, moment arms are not available for all intrinsic finger muscles. Therefore, it was necessary to scale and translate muscle attachments from one experimental or model environment to another while preserving mechanical function. We used a two-step process. First, we estimated muscle function by calculating moment arms for all intrinsic and extrinsic muscles using the partial velocity method. Second, optimization using Simulated Annealing and Hooke-Jeeves algorithms found muscle-tendon paths that minimized root mean square (RMS) differences between experimental and modeled moment arms. The partial velocity method resulted in variance accounted for (VAF) between measured and calculated moment arms of 75.5% on average (range from 48.5% to 99.5%) for intrinsic and extrinsic index finger muscles where measured data were available. RMS error between experimental and optimized values was within one standard deviation (S.D) of measured moment arm (mean RMS error = 1.5 mm < measured S.D = 2.5 mm). Validation of both steps of the technique allowed for estimation of muscle attachment points for muscles whose moment arms have not been measured. Differences between modeled and experimentally measured muscle attachments, averaged over all finger joints, were less than 4.9 mm (within 7.1% of the average length of the muscle-tendon paths). The resulting non-proprietary musculoskeletal model of the human fingers could be useful for many applications, including better understanding of complex multi-touch and gestural movements.
    PLoS ONE 04/2015; 10(4):e0121712. DOI:10.1371/journal.pone.0121712 · 3.53 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Muscle forces can be selected from a space of muscle recruitment strategies that produce stable motion and variable muscle and joint forces. However, current optimization methods provide only a single muscle recruitment strategy. We modelled the spectrum of muscle recruitment strategies while walking. The equilibrium equations at the joints, muscle constraints, static optimization solutions and 15-channel electromyography (EMG) recordings for seven walking cycles were taken from earlier studies. The spectrum of muscle forces was calculated using Bayesian statistics and Markov chain Monte Carlo (MCMC) methods, whereas EMG-driven muscle forces were calculated using EMG-driven modelling. We calculated the differences between the spectrum and EMG-driven muscle force for 1-15 input EMGs, and we identified the muscle strategy that best matched the recorded EMG pattern. The best-fit strategy, static optimization solution and EMG-driven force data were compared using correlation analysis. Possible and plausible muscle forces were defined as within physiological boundaries and within EMG boundaries. Possible muscle and joint forces were calculated by constraining the muscle forces between zero and the peak muscle force. Plausible muscle forces were constrained within six selected EMG boundaries. The spectrum to EMG-driven force difference increased from 40 to 108 N for 1-15 EMG inputs. The best-fit muscle strategy better described the EMG-driven pattern (R (2) = 0.94; RMSE = 19 N) than the static optimization solution (R (2) = 0.38; RMSE = 61 N). Possible forces for 27 of 34 muscles varied between zero and the peak muscle force, inducing a peak hip force of 11.3 body-weights. Plausible muscle forces closely matched the selected EMG patterns; no effect of the EMG constraint was observed on the remaining muscle force ranges. The model can be used to study alternative muscle recruitment strategies in both physiological and pathophysiological neuromotor conditions.
    Interface focus: a theme supplement of Journal of the Royal Society interface 04/2015; 5(2):20140094. DOI:10.1098/rsfs.2014.0094 · 3.12 Impact Factor

Full-text (2 Sources)

Available from
Jun 2, 2014