This Conference Presentation, High fidelity polarization-sensitive optical coherence tomography through deep learning, was recorded at SPIE Photonics West 2022 held in San Francisco, California, United States.”
Current processing techniques of polarization-sensitive optical coherence tomography (PS-OCT) can recover the tissue’s local, i.e. depth-resolved scalar retardance and optic axis orientation. However, system-induced polarization mode dispersion (PMD) and the presence of speckle in the measured tomograms complicate reconstruction and result in a detrimental trade-off with spatial resolution. We speculate that a machine learning approach should work well for generating an improved reconstruction. By training the model on simulated tomograms that encode the forward model and include system PMD and noise, and by testing the algorithm on experimentally acquired PS-OCT data, we aim to demonstrate a generalized PS-OCT reconstruction tool.
Significance: An advanced understanding of optical design is necessary to create optimal systems but this is rarely taught as part of general curriculum. Compounded by the fact that professional optical design software tools have a prohibitive learning curve, this means that neither knowledge nor tools are easily accessible.
Aim: In this tutorial, we introduce a raytracing module for Python, originally developed for teaching optics with ray matrices, to simplify the design and optimization of optical systems.
Approach: This module is developed for ray matrix calculations in Python. Many important concepts of optical design that are often poorly understood such as apertures, aperture stops, and field stops are illustrated.
Results: The module is explained with examples in real systems with collection efficiency, vignetting, and intensity profiles. Also, the optical invariant, an important benchmark property for optical systems, is used to characterize an optical system.
Conclusions: This raytracing Python module will help improve the reader’s understanding of optics and also help them design optimal systems.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.