Orientation and depth dependent mechanical properties of the porcine cornea: Experiments and parameter identification.

Nambiar, Malavika H; Liechti, Layko; Müller, Fabian; Bernau, Werner; Studer, Harald; Roy, Abhijit S; Seiler, Theo G; Büchler, Philippe (2022). Orientation and depth dependent mechanical properties of the porcine cornea: Experiments and parameter identification. Experimental eye research, 224, p. 109266. Elsevier 10.1016/j.exer.2022.109266

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The porcine cornea is a standard animal model in ophthalmic research, making its biomechanical characterization and modeling important to develop novel treatments such as crosslinking and refractive surgeries. In this study, we present a numerical model of the porcine cornea based on experimental measurements that captures both the depth dependence and orientation dependence of the mechanical response. The mechanical parameters of the established anisotropic hyperelastic material models of Gasser, Holzapfel and Ogden (HGO) and Markert were determined using tensile tests. Corneas were cut with a femtosecond laser in the anterior (100 μm), central (350 μm), and posterior (600 μm) regions into nasal-temporal, superior-inferior, and diagonal strips of 150 μm thickness. These uniformly thick strips were tested at a low speed using a single-axis testing machine. The results showed that the corneal mechanical properties remained constant in the anterior half of the cornea regardless of orientation, but that the material softened in the posterior layer. These results are consistent with the circular orientation of collagen observed in porcine corneas using X-ray scattering. In addition, the parameters obtained for the HGO model were able to reproduce the published inflation tests, indicating that it is suitable for simulating the mechanical response of the entire cornea. Such a model constitutes the basis for in silico platforms to develop new ophthalmic treatments. In this way, researchers can match their experimental surrogate porcine model with a numerical counterpart and validate the prediction of their algorithms in a complete and accessible environment.

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