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

TL;DR

This paper introduces an iterative statistical reconstruction method for spectral-domain OCT that compensates for artifacts and noise, improving image quality using advanced resampling and regularization techniques.

Contribution

It develops a novel model-based iterative reconstruction approach incorporating non-uniform FFT and sparsity penalties, enhancing OCT image quality over existing methods.

Findings

Superior image quality demonstrated on micro-bead and cucumber samples.

Effective compensation for defocusing, intensity falloff, and noise artifacts.

Outperforms traditional reconstruction and ISAM methods in various regimes.

Abstract

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.