ALGORITHMS FOR OPTICAL COHERENCE TOMOGRAPHY AND ITS CLINICAL APPLICATIONS

Optical Coherence Tomography (OCT) is a clinically adapted, high resolution, non-invasive, non-contact imaging modality that gives a cross-sectional depth-resolved image of the biological sample. Several modifications in OCT instrumentation and development of new reconstruction algorithms have made OCT a potential diagnostic tool. This thesis discusses a free space experimental set-up and development of effective image reconstruction algorithm for Spectral Domain Optical Coherence Tomography (SD-OCT). This algorithm is initially validated on the in-house developed phantoms and later applied on the various biological samples. The same algorithm is implemented for oral tissue and stomach tissue analysis. Further, the OCT imaging was explored to map the spectroscopic property of sample on the depth-resolved intensity image of OCT. The MATLAB code for spectroscopic OCT is developed and applied on the various in-house developed phantoms and further applied on biological samples. Two metrics, spectral centroid and autocorrelation function are implemented for the spectroscopic OCT, and the image is displayed by HSV mapping of spectroscopic metric and a grey-scale intensity image of OCT. Different types of tissue showing the different spectroscopic property are shown using the spectral centroid mapping. This method can be effectively implemented for diagnosis of cancerous and normal tissues.