Research Papers

Validation of a Numerical Model of Skeletal Muscle Compression With MR Tagging: A Contribution to Pressure Ulcer Research

[+] Author and Article Information
K. K. Ceelen

Department of Biomedical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlandsk.k.ceelen@tue.nl

A. Stekelenburg, J. L. J. Mulders, G. J. Strijkers, F. P. T. Baaijens, K. Nicolay, C. W. J. Oomens

Department of Biomedical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands

J Biomech Eng 130(6), 061015 (Oct 23, 2008) (8 pages) doi:10.1115/1.2987877 History: Received November 15, 2007; Revised July 21, 2008; Published October 23, 2008

Sustained tissue compression can lead to pressure ulcers, which can either start superficially or within deeper tissue layers. The latter type includes deep tissue injury, starting in skeletal muscle underneath an intact skin. Since the underlying damage mechanisms are poorly understood, prevention and early detection are difficult. Recent in vitro studies and in vivo animal studies have suggested that tissue deformation per se can lead to damage. In order to conclusively couple damage to deformation, experiments are required in which internal tissue deformation and damage are both known. Magnetic resonance (MR) tagging and T2-weighted MR imaging can be used to measure tissue deformation and damage, respectively, but they cannot be combined in a protocol for measuring damage after prolonged loading. Therefore, a dedicated finite element model was developed to calculate strains in damage experiments. In the present study, this model, which describes the compression of rat skeletal muscles, was validated with MR tagging. Displacements from both the tagging experiments and the model were interpolated on a grid and subsequently processed to obtain maximum shear strains. A correlation analysis revealed a linear correlation between experimental and numerical strains. It was further found that the accuracy of the numerical prediction decreased for increasing strains, but the positive predictive value remained reasonable. It was concluded that the model was suitable for calculating strains in skeletal muscle tissues in which damage is measured due to compression.

Copyright © 2008 by American Society of Mechanical Engineers
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Figure 1

Time schedule for tagging measurement based on force signal from strain gauges: A predetermined time after the pulse from the sensor (arrow 1), a signal was sent to the MR scanner to apply the tagging grid (arrow 2) and then the indenter was applied (arrow 3). The high-frequency fluctuations during the MR measurements are caused by the linear magnetic field gradients that are used for MR image encoding.

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Figure 2

MR images of (a) undeformed and (b) deformed hindlimb with contours used for the FE model superimposed: The thick white line represents the fixed outer boundary (plaster cast), and thin lines indicate the tibia and contact boundary for contact between the indenter and hindlimb in the undeformed situation. The indenter was identified in the images, and its final position was obtained by fitting its known geometry in the (b) deformed hindlimb. (c) Resulting mesh with indenter at its starting position outside the mesh. The scale bar in (a) is also applicable to the other images.

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Figure 3

(a) Tagging displacements in pixels (gray squares) were interpolated onto a grid point (cross) by using the surrounding pixel centers (black dots) as nodes of a linear quadrilateral element (black lines). (b) Model displacements were interpolated using the nodes (black dots) of the extended quadratic triangular element of the mesh (black lines) that contains the grid point (cross).

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Figure 4

C-SPAMM images with tagging grid perpendicular to the indentation direction (a) before (a) and (b) during indentation

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Figure 5

MR images of deformed hindlimbs of (a) 2.6 mm indentation and (b) 4.5 mm indentation, with numerically predicted deformed contours superimposed (white lines, black in swollen area for better visibility in (b))

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Figure 6

Example of total displacements calculated (a) from tag line displacements and (b) from model calculations, overlaid with grid (black dots). Maximum shear strains (c) from the tagging experiment and from (d) the model on the grid.

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Figure 7

Correlation between numerical and experimental maximum shear strains. The solid line is the regression line, and the dotted is line the unity line. Different markers indicate the different experiments (n=4).

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Figure 8

Number of true positive and false positive grid points for maximum shear strain threshold values up to 1 for all experiments taken together. The dots are corresponding positive predictive values, indicated on the right y-axis.

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Figure 9

Bland–Altman plots of differences between numerical and experimental values versus mean of numerical and experimental values for maximum shear strains. The solid line represents the mean difference, and the dashed denotes the mean ±2 standard deviations of the difference.




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