Extracting Group Velocity Dispersion values using quantum-mimic Optical Coherence Tomography and Machine Learning.

ABSTRACT: Quantum-mimic Optical Coherence Tomography (Qm-OCT) images are cluttered with artefacts - parasitic peaks which emerge as a by-product of the algorithm used in this method. However, the shape and behaviour of an artefact are uniquely related to Group Velocity Dispersion (GVD) of the layer this artefact corresponds to and consequently, the GVD values can be inferred by carefully analysing them. Since for multi-layered objects the number of artefacts is too high to enable layer-specific analysis, we employ a solution based on Machine Learning. We train a neural network with Qm-OCT data as an input and dispersion profiles, i.e. depth distribution of GVD within an A-scan, as an output. By accounting for noise during training, we process experimental data and estimate the GVD values of BK7 and

SUBMITTER: Maliszewski KA 

PROVIDER: S-EPMC10122646 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature