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One native source of quality deterioration in medical imaging, and especiallyin our case optical coherence tomography (OCT), is the turbid biological mediain which photon does not take a predictable path and many scattering eventswould influence the effective path length and change the polarization ofpolarized light. This inherent problem would cause imaging errors even in thecase of high resolution of interferometric methods. To address this problem andconsidering the inherent random nature of this problem, in the last decadessome methods including Monte Carlo simulation for OCT was proposed. In thisapproach simulation would give us a one on one comparison of underlyingphysical structure and its OCT imaging counterpart. Although its goal was togive the practitioners a better understanding of underlying structure, it lacksin providing a comprehensive approach to increase the accuracy and imagingquality of OCT imaging and would only provide a set of examples on how imagingmethod might falter. To mitigate this problem and to demonstrate a new approachto improve the medical imaging without changing any hardware, we introduce anew pipeline consisting of Monte Carlo simulation followed by a deep neuralnetwork.

Neural network for estimation of optical characteristics of optically active and turbid scattering media