Sebastián Ruiz-Lopera

arXiv2026

Polarization-Sensitive Module for Optical Coherence Tomography Instruments

Polarization-sensitive optical coherence tomography (PS-OCT) extends OCT by analyzing the polarization states of backscattered light to quantify tissue birefringence. However, conventional implementations require polarization-diverse detection and are therefore incompatible with most commercial OCT systems. As a result, PS-OCT has largely remained restricted to specialized research groups, limiting its broader scientific and clinical use. Here, we present a modular PS-OCT framework that integrates with a standard spectral-domain OCT platform through a detachable rotating achromatic half-wave plate in the sample arm. This waveplate modulates both incident and reflected polarization states. Three or more repeated measurements at distinct waveplate orientations enable reconstruction of the sample's round-trip Jones matrix and the corresponding polarization properties. To mitigate random phase variations between repeated measurements, we introduce a phase optimization strategy. We validate the framework with imaging of birefringent phantoms and the human retina in vivo, demonstrating reliable reconstruction of retardance and optic axis orientation. In this study, we implemented a polarization-sensitive module on the SPECTRALIS platform with minimal hardware modification. The framework could potentially be extended to other OCT systems with appropriate mechanical integration and access to acquisition data. By reducing the hardware complexity typically associated with conventional PS-OCT, this framework may facilitate broader adoption of PS-OCT imaging in both research and clinical settings.

physics.optics↗

arXiv2023

Probabilistic volumetric speckle suppression in OCT using deep learning

We present a deep learning framework for volumetric speckle reduction in optical coherence tomography (OCT) based on a conditional generative adversarial network (cGAN) that leverages the volumetric nature of OCT data. In order to utilize the volumetric nature of OCT data, our network takes partial OCT volumes as input, resulting in artifact-free despeckled volumes that exhibit excellent speckle reduction and resolution preservation in all three dimensions. Furthermore, we address the ongoing challenge of generating ground truth data for supervised speckle suppression deep learning frameworks by using volumetric non-local means despeckling-TNode to generate training data. We show that, while TNode processing is computationally demanding, it serves as a convenient, accessible gold-standard source for training data; our cGAN replicates efficient suppression of speckle while preserving tissue structures with dimensions approaching the system resolution of non-local means despeckling while being two orders of magnitude faster than TNode. We demonstrate fast, effective, and high-quality despeckling of the proposed network in different tissue types acquired with three different OCT systems compared to existing deep learning methods. The open-source nature of our work facilitates re-training and deployment in any OCT system with an all-software implementation, working around the challenge of generating high-quality, speckle-free training data.

eess.IV↗