Paper
21 February 2019 Model-based iterative reconstruction for spectral-domain optical coherence tomography
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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.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jonathan H. Mason, Yvonne Reinwald, Ying Yang, Sarah Waters, Alicia El Haj, and Pierre O. Bagnaninchi "Model-based iterative reconstruction for spectral-domain optical coherence tomography", Proc. SPIE 10883, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVI, 108830O (21 February 2019); https://doi.org/10.1117/12.2509424
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KEYWORDS
Optical coherence tomography

Model-based design

Fourier transforms

Reconstruction algorithms

Spectroscopy

Systems modeling

Tissues

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