Phase-sensitive Fourier-domain optical coherence tomography (FD-OCT) enables label-free imaging of cellular movements in vivo with detection sensitivity down to the nanometer scale. Due to this high sensitivity, it is widely employed in various emerging functional imaging modalities such as optoretinography (ORG), optical coherence elastography (OCE), and optical coherence tomography angiography (OCTA). However, achieving shot-noise limited detection sensitivity remains a major challenge for in-vivo measurements where the sample is constantly affected by vascular pulsation, breathing, eye and head motion, and other involuntary movements. Here, we propose a phase-restoring subpixel motion correction (PRSMC) method for post-hoc image registration in FD-OCT. Based on a generalized FD-OCT model, this method enables translational shifts of OCT images by arbitrary displacements while accurately restoring physically meaningful phase components, both with subpixel precision. With the sample movements estimated from averaged Doppler shift or normalized cross-correlation, we reconstructed the OCT images by correcting the axial displacement in the spectrum (k) domain and the lateral displacement in the spatial frequency domain, respectively. We validated our method in simulations, phantom experiments, and in-vivo optoretinogram imaging, where the advantages over conventional approaches for both amplitude stability and phase accuracy were demonstrated. Our approach significantly reduces the motion-induced phase error (MIPE) when imaging moving samples, achieving systematic phase sensitivities close to the shot-noise regime.

Small animals, such as rodents, serve as an attractive option for investigating the intrinsic process of photoreceptor degeneration because of their wide availability and versatility in disease models and gene manipulations. However, there was a lack of an objective and quantitative approach to measure their outer retina dynamics while preserving spatial heterogeneity. Here, we demonstrate an automated, unbiased approach for functional retinal imaging in rodents based on unsupervised machine learning. Our method automatically searches for and classifies nanoscopic cellular dynamics from obscure speckle patterns captured by a low-cost, phase-sensitive optical coherence tomography. Using this approach, we revealed highly reproducible Type-I and Type-II signals in rodents related to different parts of the outer retina. The fast Type-I signal was not reported previously and we hypothesized that it originated from the movement of rod outer segments. We also characterized the light-induced response of the outer retina under scotopic and photopic conditions, and demonstrateden-facemapping of the outer retina function in an extended field of view (12°), analogous to multifocal electroretinograms, but only with a single shot and yielding much higher spatial resolution. Our approach can be widely applied to investigating tissue-specific retinal dynamics across animal models, as well as facilitating clinical translations for the early detection of neurodegenerative diseases.

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