Quantum Optical Coherence Tomography using two photon joint spectrum detection (JS-Q-OCT)

• URL: http://arxiv.org/abs/2005.13147v1
• Date: Wed, 27 May 2020 04:02:10 GMT
• Title: Quantum Optical Coherence Tomography using two photon joint spectrum detection (JS-Q-OCT)
• Authors: Sylwia M. Kolenderska, Frederique Vanholsbeeck, Piotr Kolenderski
• Abstract summary: Quantum Optical Coherence Tomography (Q- OCT) is a high-resolution 3D imaging technique based on white-light interferometry. Here, we present a theoretical analysis of a novel approach that is free of image-scrambling artefacts and slow acquisition times. We show that all the information about the internal structures of the object is encoded in the joint spectrum and can be easily retrieved through Fourier transformation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum Optical Coherence Tomography (Q-OCT) is the non-classical counterpart of Optical Coherence Tomography (OCT) - a high-resolution 3D imaging technique based on white-light interferometry. Because Q-OCT uses a source of frequency-entangled photon pairs, not only is the axial resolution not affected by dispersion mismatch in the interferometer, but is also inherently improved by a factor of square root of two. Unfortunately, practical applications of Q-OCT are hindered by image-scrambling artefacts and slow acquisition times. Here, we present a theoretical analysis of a novel approach that is free of these problems: Q-OCT with joint spectrum detection (JS-Q-OCT). Based on a photon pair coincidence detection as in the standard Q-OCT configuration, it also discerns, each photon pair by their wavelength. We show that all the information about the internal structures of the object is encoded in the joint spectrum and can be easily retrieved through Fourier transformation. No depth scanning is required, making our technique potentially faster than standard Q-OCT. Finally, we show that the data available in the joint spectrum enables artefact removal and discuss prospective algorithms for doing so.

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