Adaptive balanced detection spectral domain optical coherence tomography

Abstract

Balanced detection optical coherence tomography (BD-OCT) enables near-shot noise-limited imaging by suppressing wavelength-dependent relative intensity noise (RIN) originating from the light source. In spectral-domain BD-OCT (SD-BD-OCT), the level of RIN suppression relies on the co-registration accuracy of the spectra simultaneously captured by two independent spectrometers. However, existing matching methods require careful pre-calibration using a RIN-dominated dataset or subjective post-processing using a signal-dominated dataset. We developed an adaptive subpixel matching approach, referred to as adaptive balance, that can be applied to any SD-BD-OCT dataset regardless of RIN or signal level without the need for pre-calibration. We showed that adaptive balance performed comparable to or better than reported methods by imaging phantoms with varying spectrometer camera gain, exposure time, and supercontinuum laser repetition rate. We further demonstrated the benefits of adaptive balance in human retinal imaging.