Quantum-inspired optical coherence tomography using classical light in a single-photon counting regime.

Quantum optical coherence tomography (Q-OCT) presents many advantages over its classical counterpart, optical coherence tomography (OCT), provides an increased axial resolution, and is immune to even orders of dispersion. The core of Q-OCT is the quantum interference of negatively correlated entangled photon pairs which, in the Fourier domain, are observed by means of a joint spectrum measurement. In this work, we explore the use of a spectral approach in a novel configuration where classical light pulses are employed instead of entangled photons. The intensity of these light pulses is reduced to a single photon level. We report theoretical analysis along with its experimental validation to show that although such a classical light is much easier to launch into an experimental system, it offers limited benefits compared to Q-OCT based on the entangled light. We analyze the differences in the characteristics of the joint spectrum obtained with entangled photons and with classical optical pulses and point out to the differences' source: the lack of the advantage-bringing term in the signal.

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

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 sapphire as well as provide a qualitative GVD value distribution in a grape and cucumber. Compared to other GVD-retrieving methods, our solution does not require user input, automatically provides dispersion values for all the visualised layers and is scalable. We analyse the factors affecting the accuracy of determining GVD: noise in the experimental data as well as general physical limitations of the detection of GVD-induced changes, and suggest possible solutions.

Towards retrieving dispersion profiles using quantum-mimic optical coherence tomography and machine learning.

Artefacts in quantum-mimic optical coherence tomography are considered detrimental because they scramble the images even for the simplest objects. They are a side effect of autocorrelation, which is used in the quantum entanglement mimicking algorithm behind this method. Interestingly, the autocorrelation imprints certain characteristics onto an artefact - it makes its shape and characteristics depend on the amount of dispersion exhibited by the layer that artefact corresponds to. In our method, a neural network learns the unique relationship between the artefacts' shape and GVD, and consequently, it is able to provide a good qualitative representation of object's dispersion profile for never-seen-before data: computer-generated single dispersive layers and experimental pieces of glass. We show that the autocorrelation peaks - additional peaks in the A-scan appearing due to the interference of light reflected from the object - affect the GVD profiles. Through relevant calculations, simulations and experimental testing, the mechanism leading to the observed GVD changes is identified and explained. Finally, the network performance is tested in the presence of noise in the data and with the experimental data representing single layers of quartz, sapphire and BK7.

Broadband-excitation-based mechanical spectroscopy of highly viscous tissue-mimicking phantoms.

Standard rheometers assess mechanical properties of viscoelastic samples up to 100 Hz, which often hinders the assessment of the local-scale dynamics. We demonstrate that high-frequency analysis can be achieved by inducing broadband waves and monitoring their media-dependent propagation using optical coherence tomography. Here, we present a new broadband wave analysis based on two-dimensional Fourier transformation. We validated this method by comparing the mechanical parameters to monochromatic excitation and a standard oscillatory test data. Our method allows for high-frequency mechanical spectroscopy, which could be used to investigate the local-scale dynamics of different biological tissues and the influence of diseases on their microstructure.

Artefact-removal algorithms for Fourier domain quantum optical coherence tomography.

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.

Intensity correlation OCT is a classical mimic of quantum OCT providing up to twofold resolution improvement.

Quantum Optical Coherence Tomography (Q-OCT) uses quantum properties of light to provide several advantages over its classical counterpart, OCT: it achieves a twice better axial resolution with the same spectral bandwidth and it is immune to even orders of dispersion. Since these features are very sought-after in OCT imaging, many hardware and software techniques have been created to mimic the quantum behaviour of light and achieve these features using traditional OCT systems. The most recent, purely algorithmic scheme-an improved version of Intensity Correlation Spectral Domain OCT named ICA-SD-OCT-showed even-order dispersion cancellation and reduction of artefacts. The true capabilities of this method were unfortunately severely undermined, both in terms of its relation to Q-OCT and its main performance parameters. In this work, we provide experimental demonstrations as well as numerical and analytical arguments to show that ICA-SD-OCT is a true classical equivalent of Q-OCT, more specifically its Fourier domain version, and therefore it enables a true two-fold axial resolution improvement. We believe that clarification of all the misconceptions about this very promising algorithm will highlight the great value of this method for OCT and consequently lead to its practical applications for resolution- and quality-enhanced OCT imaging.

Fourier domain quantum optical coherence tomography.

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-inspired detection for spectral domain optical coherence tomography.

Intensity levels allowed by safety standards (ICNIRP or ANSI) limit the amount of light that can be used in a clinical setting to image highly scattering or absorptive tissues with optical coherence tomography (OCT). To achieve high-sensitivity imaging at low intensity levels, we adapt a detection scheme-which is used in quantum optics for providing information about spectral correlations of photons-into a standard spectral domain OCT system. This detection scheme is based on the concept of dispersive Fourier transformation, where a fiber introduces a wavelength-dependent time delay measured by a single-pixel detector, usually a high-speed photoreceiver. Here, we use a fast superconducting single-photon detector SSPD as a single-pixel detector and obtain images of a glass stack and a slice of onion at the intensity levels of the order of 10 pW. We also provide a formula for a depth-dependent sensitivity falloff in such a detection scheme, which can be treated as a temporal equivalent of diffraction-grating-based spectrometers.

Dispersion mapping as a simple postprocessing step for Fourier domain Optical Coherence Tomography data.

Optical Coherence Tomography (OCT) was originally conceived as a volumetric imaging method. Quickly, OCT images went beyond structural data and started to provide functional information about an object enabling for example visualization of blood flow or tissue elasticity. Minimal or no need for system alterations make functional OCT techniques useful in performing multimodal imaging, where differently contrasted images are produced in a single examination. We propose a method that further extends the current capabilities of OCT and requires no modifications to the system. Our algorithm provides information about the sample's Group Velocity Dispersion (GVD) and can be easily applied to any OCT dataset acquired with a Fourier domain system. GVD is calculated from the difference in material's optical thickness measured from two images obtained for different spectral ranges. Instead of using two separate light sources, we propose to apply a filter-based, numerical procedure that synthesizes two spectra from one broadband spectrum. We discuss the limitations of the method and present GVD values for BK7 and sapphire and ocular media: cornea and aqueous humour of a rat eye. Results corroborate previous measurements using two different light sources.

Blue-light Fourier-domain optical-coherence microscopy with linear k-sampling using second-harmonic generation.

We demonstrate Fourier-domain optical-coherence microscopy (OCM) method that uses blue light for high-resolution microscopic imaging. Spectrally broad bandwidth is obtained by means of second-harmonic generation of Ti:sapphire laser light on the nonlinear crystal. Angular scanning of the crystal performed by a resonant scanner results in second-harmonic generation for a broad range of frequencies producing blue light with central wavelength of 402 nm and bandwidth of 35 nm in one cycle. The axial resolution of the new setup is 3.5 µm in air, and the transverse resolution for Olympus 40× objective lens is 2.7 µm in X direction and 3.2 µm in Y direction. The developed technique enables registering spectral interferometric signal directly in k domain. Additionally, we present examples of imaging a biological specimen using the newly developed method.

Scanning-free imaging through a single fiber by random spatio-spectral encoding.

We present an approach for 2D imaging through a single fiber without the need for scanners. A random scattering medium placed next to the distal end of the fiber is used to encode the collected light from every imaged pixel with a different random spectral signature. We demonstrate imaging of externally illuminated 2D objects from a single measured spectrum at the fiber's proximal end. The technique is insensitive to fiber bending, an advantage for endoscopic applications.