Biomedical subjects

John Rasmussen

Publications and source records attributed to John Rasmussen.

5 recordsLinked to original sources

PubMed2006

Validation of a musculo-skeletal model of the mandible and its application to mandibular distraction osteogenesis.

Mandibular distraction osteogenesis will lead to a change in muscle coordination and load transfer to the temporomandibular joints (TMJ). The objective of this work is to present and validate a rigid-body musculo-skeletal model of the mandible based on inverse dynamics for calculation of the muscle activations, muscle forces and TMJ reaction forces for different types of clenching tasks and dynamic tasks. This approach is validated on a symmetric mandible model and an application will be presented where the TMJ reaction forces during unilateral clenching are estimated for a virtual distraction patient with a shortened left ramus. The mandible model consists of 2 rigid segments and has 4 degrees-of-freedom. The model was equipped with 24 hill-type musculotendon actuators. During the validation experiment one subject was asked to do several tasks while measuring EMG activity, bite force and kinematics. The bite force and kinematics were used as input for the simulations of the same tasks after which the estimated muscle activities were compared with the measured muscle activities. This resulted in an average correlation coefficient of 0.580 and an average of the Mean Absolute Error of 0.109. The virtual distraction model showed a large difference in the TMJ reaction forces between left and right compared with the symmetric model for the same loading case. The present work is a step in the direction of building patient-specific mandible models, which can assess the mechanical effects on the TMJ before mandibular distraction osteogenesis surgery.

Adult↗

PubMed2006

A generic detailed rigid-body lumbar spine model.

The objective of this work is to present a musculo-skeletal model of the lumbar spine, which can be shared and lends itself to investigation in many locations by different researchers. This has the potential for greater reproducibility and subsequent improvement of its quality from the combined effort of different research groups. The model is defined in a text-based, declarative, object-oriented language in the AnyBody Modelling System software. Text-based models will facilitate sharing of the models between different research groups. The necessary data for the model has been taken from the literature. The work resulted in a detailed lumbar spine model with seven rigid segments with 18 degrees-of-freedom and 154 muscles. The model is able to produce a maximum extension moment of 238 Nm around L5/S1. Moreover, a comparison was made with in vivo intradiscal pressure measurements of the L4-5 disc available from the literature. The model is based on inverse dynamics, where the redundancy problem is solved using optimization in order to compute the individual muscle forces and joint reactions. With the presented model it is possible to investigate a range of research questions, because the model is relatively easy to share and modify due to the use of a well-defined and self-contained scripting language. Validation is though still necessary for specific cases.

Compressive Strength↗

PubMed2006

Anatomy and biomechanics of the back muscles in the lumbar spine with reference to biomechanical modeling.

STUDY DESIGN: This article describes the development of a musculoskeletal model of the human lumbar spine with focus on back muscles. It includes data from literature in a structured form. OBJECTIVE: To review the anatomy and biomechanics of the back muscles related to the lumbar spine with relevance for biomechanical modeling. SUMMARY OF BACKGROUND DATA: To reduce complexity, muscle units have been incorporated in an abridged manner, reducing their actions more or less to a single force equivalent. In early models of the lumbar spine, this may have been a necessary step to reduce complexity and, thereby, calculation time. The muscles of the spine are well described in the literature, but mainly qualitatively. Most of the literature provides a description of the structures without precise data of fiber length, muscle length, cross-sectional areas, moment arms, forces, etc. The predicted output of musculoskeletal models is very much dependent on the input parameters. The information needed to improve models consists of better approximations of the attachments to the vertebrae, and more precise data. METHOD: Review of literature. RESULTS: The predicted output of musculoskeletal models is very much dependent on the input parameters. Moderate changes in the assumed muscle line-of-action (i.e., moment arm) could substantially alter the magnitudes of predicted muscle and spinal forces, while the choice of optimization formulation is less sensitive. CONCLUSIONS: Input parameters, moment arms, as well as physiologic cross-sectional areas have a profound effect on the predicted muscle forces. Therefore, it is important to choose the values for moment arm and physiologic cross-sectional area carefully because they are essential input parameters to biomechanical models.

Back↗

PubMed2006

Plane-wave fluorescence tomography with adaptive finite elements.

We present three-dimensional fluorescence yield tomography of a tissue phantom in a noncontact reflectance imaging setup. The method employs planar illumination with modulated light and frequency domain fluorescence measurements made on the illumination plane. An adaptive finite-element algorithm is used to handle the ill-posed and computationally demanding inverse image reconstruction problem. Tomographic images of fluorescent targets buried at 1-2 cm depths from the illumination surface demonstrate the feasibility of fluorescence tomography from reflectance tomography in clinically relevant tissue volumes.

Algorithms↗

PubMed2002

Computational method for muscle-path representation in musculoskeletal models.

This paper presents a new and efficient method to calculate the line-of-action of a muscle as it wraps over bones and other tissues on its way from origin to insertion. The muscle is assumed to be a one-dimensional, massless, taut string, and the surfaces of bones that the muscle may wrap around are approximated by cross-sectional boundaries obtained by slicing geometrical models of bones. Each cross-sectional boundary is approximated by a series of connected line segments. Thus, the muscle path to be calculated is piecewise linear with vertices being the contact points on the cross-sectional boundaries of the bones. Any level of geometric accuracy can be obtained by increasing the number of cross sections and the number of line segments in each cross section. The algorithm is computationally efficient even for large numbers of cross sections.

Algorithms↗