Quantum Optical Coherence Tomography (Q-OCT) is a non-classical equivalent of Optical Coherence Tomog- raphy (OCT) able to provide an increased axial resolution and immunity to even orders of dispersion. The main drawback of Q-OCT is artefacts which are additional elements that clutter an A-scan and lead to a complete loss of structural information for multilayered objects. To retrieve the structure of the object, we propose to use machine learning in Fourier domain Q-OCT. We present preliminary results for a model based on VGG16 architecture and compare it to analytical algorithms. We show on computer-generated data that machine learning outperforms previously proposed analytical algorithms. The trained neural network requires much less input data and achieves much better results than the analytical algorithms. Finally, due to the way the training of the neural network is performed and the increased axial resolution of Q-OCT, machine learning provides super-resolution - it is able to distinguish two peaks which are otherwise unresolved in traditional OCT.
Quantum Optical Coherence Tomography (Q-OCT) is a non-classical equivalent of Optical Coherence Tomog- raphy (OCT) able to provide an increased axial resolution and immunity to even orders of dispersion. The main drawback of Q-OCT is artefacts which are additional elements that clutter an A-scan and lead to a complete loss of structural information for multilayered objects. To retrieve the structure of the object, we propose to use machine learning in Fourier domain Q-OCT. We present preliminary results for a model based on VGG16 architecture and compare it to analytical algorithms. We show on computer-generated data that machine learning outperforms previously proposed analytical algorithms. The trained neural network requires much less input data and achieves much better results than the analytical algorithms. Finally, due to the way the training of the neural network is performed and the increased axial resolution of Q-OCT, machine learning provides super-resolution - it is able to distinguish two peaks which are otherwise unresolved in traditional OCT.
Quantum Optical Coherence Tomography (Q-OCT) is a non-classical equivalent of Optical Coherence Tomography and is able to provide a twofold axial resolution increase and immunity to resolution-degrading dispersion. The main drawback of Q-OCT are artefacts which are additional elements that clutter an A-scan and lead to a complete loss of structural information for multilayered objects. Whereas there are very practical and successful methods for artefact removal in Time-domain Q-OCT, no such scheme has been devised for Fourier-domain Q-OCT (Fd-Q-OCT), although the latter modality-through joint spectrum detection-outputs a lot of useful information on both the system and the imaged object. Here, we propose two algorithms which process a Fd-Q-OCT joint spectrum into an artefact-free A-scan. We present the theoretical background of these algorithms and show their performance on computer-generated data. The limitations of both algorithms with regards to the experimental system and the imaged object are discussed.
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 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: Fourier domain Q-OCT (Fd-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.
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.
Using spectrally correlated photon pairs instead of classical laser light and coincidence detection instead of light intensity detection, Quantum Optical Coherence Tomography (Q-OCT) outperforms classical OCT in several experimental terms. It provides twice better axial resolution with the same spectral bandwidth and it is immune to even-order chromatic dispersion, including Group Velocity Dispersion responsible for the bulk of axial resolution degradation in the OCT images. Q-OCT has been performed in the time domain configuration, where one line of the two-dimensional image is acquired by axially translating the mirror in the interferometer's reference arm and measuring the coincidence rate of photons arriving at two single-photon-sensitive detectors. Although successful at producing resolution-doubled and dispersion-cancelled images, it is still relatively slow and cannot compete with its classical counterpart. Here, we experimentally demonstrate Q-OCT in a much faster Fourier-domain configuration, theoretically proposed in 2020, where the reference mirror is fixed and the joint spectrum is acquired by inserting long fibre spools in front of the detectors. We propose two joint spectrum pre-processing algorithms, aimed at compensating resolution-degrading effects within the setup. While the first one targets fibre spool dispersion, an effect specific to this configuration, the other one removes the effects leading to the weakening of even-order dispersion cancellation, the latter impossible to be mitigated in the time-domain alternative. Being additionally contrasted with both the time-domain approach and the conventional OCT in terms of axial resolution, imaging range and multilayer-object imaging, Fourier-domain Q-OCT is shown to be a significant step forward towards a practical and competitive solution in the OCT arena.
Quantum sensing exploits quantum phenomena to enhance the detection and estimation of classical parameters of physical systems and biological entities, particularly so as to overcome the inefficiencies of its classical counterparts. A particularly promising approach within quantum sensing is Quantum Optical Coherence Tomography which relies on non-classical light sources to reconstruct the internal structure of multilayered materials. Compared to traditional classical probing, Quantum Optical Coherence Tomography provides enhanced-resolution images and is unaffected by even-order dispersion. One of the main limitations of this technique lies in the appearance of artifacts and echoes, i.e. fake structures that appear in the coincidence interferogram, which hinder the retrieval of information required for tomography scans. Here, by utilizing a full theoretical model, in combination with a fast genetic algorithm to post-process the data, we successfully extract the morphology of complex multilayered samples and thoroughly distinguish real interfaces, artifacts, and echoes. We test the effectiveness of the model and algorithm by comparing its predictions to experimentally-generated interferograms through the controlled variation of the pump wavelength. Our results could potentially lead to the development of practical high-resolution probing of complex structures and non-invasive scanning of photo-degradable materials for biomedical imaging/sensing, clinical applications, and materials science.
By using entangled photon pairs instead of a laser light, Quantum Optical Coherence Tomography (Q-OCT) outperforms conventional OCT, providing twice better axial resolution and immunity to image-degrading even-order chromatic dispersion. Despite these very sought-after features, its applicability is hindered by long acquisition times, image-scrambling artefacts and chromatic dispersion which reappears in configurations enabling the artefact suppression. Here, we experimentally demonstrate Q-OCT with a Fourier-domain acquisition scheme which allows both fast imaging and immunity to the re-appearance of chromatic dispersion. By delivering two-dimensional joint spectra, our Fourier-domain Q-OCT enables comfortable manipulation of spectral relationships between the photon pairs, and consequently the removal of the dispersion effects. We report attractive acquisition times, accompanied by a demonstration of two joint spectrum pre-processing algorithms, aimed at controlling dispersion effects within the setup. While the first algorithm reverses the effects specific to the joint spectral acquisition, the other one removes the effects leading to the weakening of even-order dispersion cancellation, impossible to be mitigated in other Q-OCT implementations. Contrasted with both the original, time-domain implementation and the conventional OCT, Fourier-domain Q-OCT is shown to be an important step forward towards becoming a practical and competitive solution.