Aberrations in an optical system cause a reduction in imaging resolution and poor image contrast, and limit the imaging depth when imaging biological samples. Computational adaptive optics (CAO) provides an inexpensive and simpler alternative to the traditionally used hardware-based adaptive optics (HAO) techniques. In this paper, we present an automated computational aberration correction method for broadband interferometric imaging techniques, e.g. optical coherence tomography (OCT) and optical coherence microscopy (OCM). In the proposed method, the process of aberration correction is modeled as a filtering operation on the aberrant image using a phase filter in the Fourier domain. The phase filter is expressed as a linear combination of Zernike polynomials with unknown coefficients, which are estimated through an iterative optimization scheme based on maximizing an image sharpness metric. The Resilient backpropagation (Rprop) algorithm, which was originally proposed as an alternative to the gradient-descent-based backpropagation algorithm for training the weights in a multilayer feedforward neural network, is employed to optimize the Zernike polynomial coefficients because of its simplicity and the robust performance to the choice of various parameters. Stochastic selection of the number and type of Zernike modes is introduced at each optimization step to explore different trajectories to enable search for multiple optima in the multivariate search space. The method was validated on various tissue samples and shows robust performance for samples with different scattering properties, e.g. a phantom with subresolution particles, an ex vivo rabbit adipose tissue, and an in vivo photoreceptor layer of the human retina.
Yuan-Zhi Liu, Paritosh Pande, Fredrick A. South, and Stephen A. Boppart, "Automated computational aberration correction method for OCT and OCM (Conference Presentation)," Proc. SPIE 10053, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXI, 100530V (Presented at SPIE BiOS: January 31, 2017; Published: 19 April 2017); https://doi.org/10.1117/12.2251514.5371723145001.
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