A new approach for the validation of skeletal muscle modelling using MRI data

Abstract

Active and passive experiments on skeletal muscles are in general arranged on isolated muscles or by consideration of the whole muscle packages, such as the arm or the leg. Both methods exhibit advantages and disadvantages. By applying experiments on isolated muscles it turns out that no information about the surrounding tissues are considered what leads to insufficient specifications of the isolated muscle. Especially, the muscle shape and the fibre directions of an embedded muscle are completely different to that of the same isolated muscle. An explicit advantage, in contrast, is the possibility to study the mechanical characteristics in an unique, isolated way. On the other hand, by applying experiments on muscle packages the aforementioned pros and cons reverse. In such situation, the whole surrounding tissue is considered in the mechanical characteristics of the muscle which are much more difficult to identify. However, an embedded muscle reflects a much more realistic situation as in isolated condition. Thus, in the proposed work to our knowledge, we, for the first time, suggest a technique that allows to study characteristics of single skeletal muscles inside a muscle package without any computation of the tissue around the muscle of interest. In doing so, we use magnetic resonance imaging data of an upper arm during contraction. By applying a three-dimensional continuum constitutive muscle model we are able to study the biceps brachii inside the upper arm and validate the modelling approach by optical experiments.

Generating fibre orientation maps in human heart models using Poisson interpolation

“…Even if we only measure a relatively sparse, yet spatially uniform, set of fiber directions, either histologically (Ennis et al 2008) or from diffusion tensor imaging (Toussaint et al 2010a), fiber diffusion can fill in the gaps and create a reasonable and smooth solution as shown in Figure 8. In addition, we can easily integrate experimental data at no-nodal points, e.g., from magnetic resonance imaging (Böl et al 2011), using constraint boundary conditions.…”

A Muscle’s Force Depends on the Recruitment Patterns of Its Fibers

“…This is particularly the case for human muscles. Recently, several studies have attempted to validate scaled length–tension relationships for whole muscles, 68 to simulate the three-dimensional geometry of muscles during contraction, 7 and to qualitatively confirm the actions of select muscles predicted by simulations. 27 However, the accuracy with which traditional, Hill-type models predict muscle forces during in vivo activities remains untested.…”

“…21 Some models have included different fiber-type properties; but these have been limited to the simulation of isometric contractions. 7,21 The recruitment of different muscle fiber-types is particularly relevant during dynamic movements. 32 Despite this, most existing models used to simulate such movements have assumed that the contractile function of a whole muscle can be scaled up from a single fiber with little, or no regard for the patterns of motor unit recruitment or the properties of the different motor units recruited.…”

A numerical validation approach of a finite element muscle model using optical data

“…Herein, I is the identity tensor and p depicts the hydrostatic pressure of the muscle material. For a detailed description of the numerical modelling approach we refer to Böl et al [2,4,5].…”

“…To the authors knowledge, to this date there exist only two investigations which involve the geometrical muscle shape during the contraction process. The technique of Tang et al [1] consider the two-dimensional silhouette of a frog gastrocnemius muscle whereas Böl et al [2] cover additionally the fibre alignment and the surrounding tissues. In the present approach, the whole muscle geometry during activation as well as the generated force characteristics are taken into account.…”