  One native source of quality deterioration in medical imaging, and especially
in our case optical coherence tomography (OCT), is the turbid biological media
in which photon does not take a predictable path and many scattering events
would influence the effective path length and change the polarization of
polarized light. This inherent problem would cause imaging errors even in the
case of high resolution of interferometric methods. To address this problem and
considering the inherent random nature of this problem, in the last decades
some methods including Monte Carlo simulation for OCT was proposed. In this
approach simulation would give us a one on one comparison of underlying
physical structure and its OCT imaging counterpart. Although its goal was to
give the practitioners a better understanding of underlying structure, it lacks
in providing a comprehensive approach to increase the accuracy and imaging
quality of OCT imaging and would only provide a set of examples on how imaging
method might falter. To mitigate this problem and to demonstrate a new approach
to improve the medical imaging without changing any hardware, we introduce a
new pipeline consisting of Monte Carlo simulation followed by a deep neural
network.
