Abstract
Purpose :
To estimate the biomechanical properties of the posterior eye structure directly from noisy optical coherence tomography measurements taken before and after deformation to help predict the progression of gaucoma, using a coupled optimization process.
Methods :
The problem being addressed in this study is formulated as a coupled optimization process that balances the accuracy of the reconstructed image based on the applicable imaging model with the accuracy of the assumed deformed shape based on the applicable mechanical model. The imaging model accounts for the interaction of light with matter throughout the entire volume of the sample, while the mechanical model includes constitutive parameters that may be partially known or entirely unknown. We evaluated the performance of our proposed method using layered structures that are commonly used to represent the posterior segment of the eye. The biomechanical properties of these tissues were assessed by measuring the deformation that occurs in response to an applied force, which was induced through uniaxial compression and inflation testing. During these tests, the measurements were captured using an optical coherence tomography system that was subject to different levels of additive noise. To optimize the results, we used an evolutionary process known as differential evolution, which is a population-based metaheuristic search that improves candidate solutions.
Results :
Our results show that the coupled regularization approach was able to accurately recover the unknown stiffness of layered structure, even in the presence of strong measurement noise, and that the accuracy of the method degraded gracefully as the noise level increased. When compared to an conventional uncoupled approach, this coupled approach also demonstrated improved accuracy.
Conclusions :
In this study, we demonstrated the accuracy and robustness of a coupled approach that combines elastic and hyperelastic constitutive models as the mechanical model with OCT as the imaging model. This coupled approach was able to accurately determine the stiffness and sample geometry of layered structures, which are potentially useful for glaucoma prediction.
This abstract was presented at the 2023 ARVO Annual Meeting, held in New Orleans, LA, April 23-27, 2023.