Model-based iterative reconstruction for spectral-domain optical coherence tomography

Spectral domain optical coherence tomography (OCT) offers high resolution multidimensional imaging, but generally suffers from defocussing, intensity falloff and shot noise, causing artifacts and image degradation along the imaging depth. In this work, we develop an iterative statistical reconstruct...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2021-08
Main Authors: Mason, Jonathan H, Reinwald, Yvonne, Yang, Ying, Waters, Sarah, Alicia El Haj, Bagnaninchi, Pierre O
Format: Article
Language:English
Subjects:
Algorithms
Domains
Image degradation
Image quality
Image reconstruction
Image resolution
Interpolation
Iterative methods
Nanoparticles
Optical Coherence Tomography
Penalty function
Regularization
Shot noise
Spectra
Statistical methods
Synthetic apertures
Titanium oxides
Tomography
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Spectral domain optical coherence tomography (OCT) offers high resolution multidimensional imaging, but generally suffers from defocussing, intensity falloff and shot noise, causing artifacts and image degradation along the imaging depth. In this work, we develop an iterative statistical reconstruction technique, based upon the interferometric synthetic aperture microscopy (ISAM) model with additive noise, to actively compensate for these effects. For the ISAM re-sampling, we use a non uniform FFT with Kaiser-Bessel interpolation, offering efficiency and high accuracy. We then employ an accelerated gradient descent based algorithm, to minimize the negative log-likelihood of the model, and include spatial or wavelet sparsity based penalty functions, to provide appropriate regularization for given image structures. We evaluate our approach with titanium oxide micro-bead and cucumber samples with a commercial spectral domain OCT system, under various subsampling regimes, and demonstrate superior image quality over traditional reconstruction and ISAM methods.
ISSN:2331-8422
DOI:10.48550/arxiv.2108.01438