We present two neural networks: one capable of processing a raw spectrum into an A-scan with the second-order nonlinearity removed and another for processing a raw spectrum into an A-scan with the third-order nonlinearity removed. An algorithm is also proposed to enable to use these networks in a sequence for removal of both nonlinearities. The presented approaches allow for either independent switching off of each order or the simultaneous removal of all orders, offering a tool for analysing the effects of each nonlinearity order individually or simply for performing all-depth, blind OCT data linearisation.
We present a neural network able to fully linearise an OCT image without any a priori knowledge about the spectrometer characteristics or the extent of dispersion in the interferometer and the object. Unlike the earlier solutions, this blind line-arisation is not biased towards a specific object, nor its dispersion characteristics, and in the future can be made independent of the light source parameters.
In Intensity Correlation Optical Coherence Tomography (ICA-OCT), an OCT spectrum is processed into a two-dimensional signal incorporating elements which do not correspond to the structure of the imaged object. These elements, called artefacts, display a very well-defined behaviour in the presence of uncompensated chromatic dispersion. More importantly, their behaviour reflects only the dispersion of the layer which the artefacts uniquely correspond to. We show preliminary results indicating that a neural network can interpret this layer-specific behaviour and output corresponding Group Velocity Dispersion values, thus creating a depth-resolved dispersion profile of the object.
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