  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.
