Advanced Imaging and Simulation Tools for Personalized Corneal Disease Assessment and Surgery

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Simulations based on patient-specific inputs have the potential to drive major advances in personalized medicine. However, important gaps persist that must be addressed before simulation-based engineering can be fully leveraged in the treatment of corneal and refractive disorders. These include development of clinical tools for biomechanical characterization, integration of measurement and simulation systems, and model validation and verification. In this Bioengineering Research Grant, we will address each of these challenges by the following specific aims: Aim 1. Develop optical coherence elastography (OCE) for 3D characterization of heterogenous corneal biomechanical properties. In this developmental aim, we will develop and optimize a new system capable of 1) volumetric regional sampling; 2) true 3D displacement tracking and 3) simultaneous IOP and viscoelastic property measurement. The system will be used to establish more sensitive biomechanical biomarkers for keratoconus (KC) and to inform inverse FE models for 3D property estimation. Aim 2. Integrate 3D OCE and inverse FE modeling to characterize and compare 3D corneal biomechanical properties in normal, KC, and surgically altered states. Tomography and 3D OCE-derived measurements will be used to establish and validate patient-specific FE models. We will conduct human studies to test the hypothesis that OCE-derived biomarkers will better discriminate manifest KC and subclinical KC from normal eyes than available tomography and air puff-derived biomechanical metrics. We will also measure spatial biomechanical changes during a longitudinal study of KC progression and compare depth- dependent biomechanical changes in LASIK, small-incision lenticule extraction (SMILE), and corneal crosslinking (CXL). The latter comparison will directly test the widely promulgated hypothesis that SMILE causes less stromal weakening than LASIK. Aim 3. Develop and evaluate patient-specific computational models for predicting interventional outcomes, KC progression, and post-LASIK ectasia. We will test the hypothesis that models populated with subject-specific geometry and material property data are more accurate predictors of surgical outcomes metrics, KC progression rate, and post-LASIK ectasia risk than existing methods. Successful completion of the aims is expected to lead to the development and immediate translation of a personalized precision-medicine framework for leveraging such data for more effective diagnosis and personalized treatment planning in key clinical conditions.