Polarization-resolved analysis of outer retinal bands: investigating ballistic and multiply scattered photons using full-field swept-source optical coherence tomography.

Precise interpretation of the anatomical origins of outer retinal optical coherence tomography (OCT) presents technical challenges owing to the delicate nature of the retina. To address this challenge, our study introduces a novel polarization-sensitive full-field swept-source OCT (FF-SS-OCT) that provides parallel-polarization and cross-polarization OCT measurements, predominantly capturing ballistically reflected photons and multiply scattered photons, respectively. Notably, parallel-polarization OCT unveils layer-like structures more effectively, including the inner plexiform layer (IPL) sub-layers, outer plexiform layer (OPL) sub-layers (in rod-dominant regions), and rod/cone outer segment (OS) tips, compared to cross-polarization OCT, where such sub-layers are not visible.

Adaptive vessel tracing and segmentation in OCT enables the robust detection of wall-to-lumen ratio abnormalities in 5xFAD mice.

The wall-to-lumen ratio (WLR) of retinal blood vessels promises a sensitive marker for the physiological assessment of eye conditions. However, measurement of vessel wall thickness and lumen diameter is still technically challenging, hindering the wide application of WLR in research and clinical settings. In this study, we demonstrate the feasibility of using optical coherence tomography (OCT) as one practical method for quantification of WLR in the retina.

Intrinsic signal optoretinography revealing AD-induced retinal photoreceptor hyperexcitability before a detectable morphological abnormality.

Neuronal hyperexcitability promises an early biomarker of Alzheimer's disease (AD). However, in vivo detection of neuronal hyperexcitability in the brain is technically challenging. The retina, one part of the central nervous system, presents a unique window for noninvasive monitoring of the brain function.

ADC-Net: An Open-Source Deep Learning Network for Automated Dispersion Compensation in Optical Coherence Tomography.

Chromatic dispersion is a common problem to degrade the system resolution in optical coherence tomography (OCT). This study is to develop a deep learning network for automated dispersion compensation (ADC-Net) in OCT. The ADC-Net is based on a modified UNet architecture which employs an encoder-decoder pipeline.