Purpose:
To determine IOP-related stress and strain within large-scale voxel-based finite element models of a monkey optic nerve head (ONH).
Methods:
We have developed novel parallel finite element code to handle large-scale, voxel-based models of 3-D reconstructions of the lamina cribrosa [IOVS, 2004; 45:4388]. Displacement boundary conditions corresponding to expansion of the scleral canal, as well as the modulus of the lamina, are obtained from macro-scale continuum finite element models of the entire posterior pole. Exploiting the small value of the neural tissue modulus relative to that of the lamina, independent computations are carried out for the two tissues with the laminar forces coming out of the computation for the neural tissue (with ~10 million voxels) serving as the imposed forces in the computation for the lamina (with ~3.3 million voxels).
Results:
The pressure in the neural tissue is compressive (<=IOP) and decays rapidly with depth into the lamina to become less than 10% of the IOP at a depth that is 40% of the laminar thickness. The support tissue pressure is predominantly tensile and averages more than an order of magnitude larger than the IOP. The angular variation of the laminar stress-strain invariants (pressure, von Mises stress, maximum principal strain (Fig. 1) & stress) plotted around the center of the ONH show positive central-to-peripheral correlation and negative anterior-to-posterior correlation.
Conclusions:
Rapid decay of pressure in the neural tissue with depth into the lamina does not agree with experimental measurements of the pressure within a dog lamina [IOVS 1995; 36:1163-72]. This suggests the need for refining the neural tissue pressurization approach for the laminar finite element model. Preliminary computations with direct input of a linear translaminar stress gradient on the laminar tissue (ignoring the neural tissues entirely) preserves the essential features in the angular variation of the stress-strain invariants and may be a better approach.
Keywords: lamina cribrosa • computational modeling • intraocular pressure