Single-shot, depth-encoded multiplexed OCT for multi-spot tracking of induced transient corneal dynamics.

Fast, non-repeatable transient mechanical events in soft and scattering media are challenging to quantify because they demand high temporal bandwidth, high displacement sensitivity, and multi-point spatial coverage within a single realization. Current non-contact methods for assessing corneal biomechanics often rely on global metrics or single-meridian scanning, potentially missing the focal and asymmetric stiffness changes characteristic in corneal pathologies like keratoconus. In this work, we developed a simultaneous multi-spot air-puff optical coherence tomography (OCT) system as a generalizable parallel interferometric readout architecture to address the limitations of global metrics and single-meridian scanning in detecting focal corneal stiffness changes and to enable artifact-resistant measurement of rapid transients. By leveraging space-division multiplexing with depth encoding, our system tracks dynamic surface deformation at nine locations (one central, eight peripheral) simultaneously. This configuration eliminates sequential scanning artifacts and achieves an effective temporal resolution of 10 µs. We introduce the "Asymmetry Vector" to quantify the magnitude and direction of biomechanical imbalances. In experiments involving a keratoconus-mimicking phantom and human subjects (healthy and keratoconic), this vector correlated strongly with the specific location of corneal pathology. Furthermore, the system revealed a novel "dual-indentation" deformation profile resulting from a spatially widened air-puff stimulus. Beyond corneal elastography, the depth-encoded multiplexed OCT approach provides a scalable route to multi-point, high-speed characterization of transient dynamics where sequential scanning would otherwise induce spatiotemporal misregistration and waveform distortion. These findings establish the technical feasibility of simultaneous multi-spot OCT for biomechanical mapping without sequential-scanning-induced spatiotemporal misregistration and support its potential for automated clinical diagnosis.