Multimodal ophthalmic imaging using spectrally encoded scanning laser ophthalmoscopy and optical coherence tomography

Ophthalmic surgery involves manipulation of delicate, layered tissue structures on milli- to micrometer scales. Traditional surgical microscopes provide an inherently two-dimensional view of the surgical field with limited depth perception which precludes accurate depth-resolved visualization of these tissue layers, and limits the development of novel surgical techniques. We demonstrate multimodal swept-source spectrally encoded scanning laser ophthalmoscopy and optical coherence tomography (SS-SESLO-OCT) to address current limitations of image-guided ophthalmic microsurgery. SS-SESLO-OCT provides inherently co-registered en face and cross-sectional field-of-views (FOVs) at a line rate of 400 kHz and >2 GPix/s throughput. We show in vivo imaging of the anterior segment and retinal fundus of a healthy volunteer, and preliminary results of multi-volumetric mosaicking for ultrawide-field retinal imaging with 90° FOV. Additionally, a scan-head was rapid-prototyped with a modular architecture which enabled integration of SS-SESLO-OCT with traditional surgical microscope and slit-lamp imaging optics. Ex vivo surgical maneuvers were simulated in cadaveric porcine eyes. The system throughput enabled volumetric acquisition at 10 volumes-per-second (vps) and allowed visualization of surgical dynamics in corneal sweeps, compressions, and dissections, and retinal sweeps, compressions, and elevations. SESLO en face images enabled simple real-time co-registration with the surgical microscope FOV, and OCT cross-sections provided depth-resolved visualization of instrument-tissue interactions. Finally, we demonstrate novel augmented-reality integration with the surgical view using segmentation overlays to aid surgical guidance. SS-SESLO-OCT may benefit clinical diagnostics by enabling aiming, registration, and mosaicking; and intraoperative imaging by allowing for real-time surgical feedback, instrument tracking, and overlays of computationally extracted biomarkers of disease.

Duke Scholars

Author David Li