Note: This is a virtual presentation. Here is the link for where the presentation will be taking place.
Title: Intraoperative Optical Coherence Tomography Guided Deep Anterior Lamellar Keratoplasty
Abstract: Deep anterior lamellar keratoplasty (DALK) is a highly challenging procedure requiring micron accuracy to guide a “big bubble” needle into the stroma of the cornea down to Descemet’s Membrane (DM). It has important advantages over Penetrating keratoplasty (PK) including lower rejection rate, less endothelial cell loss, and increased graft survival. Currently, this procedure relies heavily on the visualization through a surgical microscope, the surgeon’s own surgical experience, and tactile feel to determine the relative position of the needle and DM. Optical coherence tomography (OCT) is a well-established, non-invasive optical imaging technology that can provide high-speed, high-resolution, three-dimension images of biological samples. Since it was first demonstrated in 1991, OCT has emerged as a leading technology for ophthalmic visualization, especially for retinal structures, and has been widely applied in ophthalmic surgery and research. Common-path (CP) OCT systems use single A-scan image to deduce the tissue layer information and can be operated at a much higher speed. This synergizes well with handheld tools and automated surgical systems which require fast response time. CP-OCT has been integrated into a wide range of microsurgical tools for procedures such as epiretinal membrane peeling and subretinal injection.
In this proposal, the common-path swept-source OCT system (CP-SSOCT) is proposed to guide DALK procedures. The OCT distal sensor integrated needle and OCT guided micro-control ocular surgical system (AUTO-DALK) will be designed and evaluated. This device will allow for the autonomous insertion of a needle for pneumo-dissection based on the depth-sensing results from the OCT system. An earlier prototype of AUTO-DALK was tested on the ex-vivo porcine cornea including the comparison of expert manual needle insertion. The result showed the precision and consistency of the needle placement were increased, which could lead to better visual outcomes and fewer complications. Future work will include improving the overall design for in-vivo testing and clinical use, advanced convolutional neural network based tracking, and system validation on larger sample size.
Committee Members
Jin U. Kang (adviser), Department of Electrical and Computer Engineering
Israel Gannot, Department of Electrical and Computer Engineering
Xingde Li, Department of Biomedical Engineering