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1. Unified k-space theory of optical coherence tomography

   https://doi.org/10.1364/AOP.417102  

   Zhou, Kevin C. ; Qian, Ruobing ; Dhalla, Al-Hafeez ; Farsiu, Sina ; Izatt, Joseph A. ( June 2021 , Advances in Optics and Photonics)

   We present a general theory of optical coherence tomography (OCT), which synthesizes the fundamental concepts and implementations of OCT under a common 3D$k$-space framework. At the heart of this analysis is the Fourier diffraction theorem, which relates the coherent interaction between a sample and plane wave to the Ewald sphere in the 3D$k$-space representation of the sample. While only the axial dimension of OCT is typically analyzed in$k$-space, we show that embracing a fully 3D$k$-space formalism allows explanation of nearly every fundamental physical phenomenon or property of OCT, including contrast mechanism, resolution, dispersion, aberration, limited depth of focus, and speckle. The theory also unifies diffraction tomography, confocal microscopy, point-scanning OCT, line-field OCT, full-field OCT, Bessel beam OCT, transillumination OCT, interferometric synthetic aperture microscopy (ISAM), and optical coherence refraction tomography (OCRT), among others. Our unified theory not only enables clear understanding of existing techniques but also suggests new research directions to continue advancing the field of OCT.

    

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2. Investigation of multiple scattering in space and spatial-frequency domains: with application to the analysis of aberration-diverse optical coherence tomography

   https://doi.org/10.1364/BOE.439395  

   Wu, Meiqi ; Liu, Siyang ; Leartprapun, Nichaluk ; Adie, Steven ( November 2021 , Biomedical Optics Express)

   Optical microscopy suffers from multiple scattering (MS), which limits the optical imaging depth into scattering media. We previously demonstrated aberration-diverse optical coherence tomography (AD-OCT) for MS suppression, based on the principle that for datasets acquired with different aberration states of the imaging beam, MS backgrounds become decorrelated while single scattering (SS) signals remain correlated, so that a simple coherent average can be used to enhance the SS signal over the MS background. Here, we propose a space/spatial-frequency domain analysis framework for the investigation of MS in OCT, and apply the framework to compare AD-OCT (using astigmatic beams) to standard Gaussian-beam OCT via experiments in scattering tissue phantoms. Utilizing this framework, we found that increasing the astigmatic magnitude produced a large drop in both MS background and SS signal, but the decay experienced by the MS background was larger than the SS signal. Accounting for the decay in both SS signal and MS background, the overall signal-to-background ratio (SBR) of AD-OCT was similar to the Gaussian control after about 10 coherent averages, when deeper line foci was positioned at the plane-of-interest and the line foci spacing was smaller than or equal to 80 µm. For an even larger line foci spacing of 160 µm, AD-OCT resulted in a lower SBR than the Gaussian-beam control. This work provides an analysis framework to gain deeper levels of understanding and insights for the future study of MS and MS suppression in both the space and spatial-frequency domains.

    

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3. Aberration-diverse optical coherence tomography for suppression of multiple scattering and speckle

   https://doi.org/10.1364/BOE.9.004919  

   Liu, Siyang ; Lamont, Michael R. E. ; Mulligan, Jeffrey A. ; Adie, Steven G. ( September 2018 , Biomedical Optics Express)

   Multiple scattering is a major barrier that limits the optical imaging depth in scattering media. In order to alleviate this effect, we demonstrate aberration-diverse optical coherence tomography (AD-OCT), which exploits the phase correlation between the deterministic signals from single-scattered photons to suppress the random background caused by multiple scattering and speckle. AD-OCT illuminates the sample volume with diverse aberrated point spread functions, and computationally removes these intentionally applied aberrations. After accumulating 12 astigmatism-diverse OCT volumes, we show a 10 dB enhancement in signal-to-background ratio via a coherent average of reconstructed signals from a USAF target located 7.2 scattering mean free paths below a thick scattering layer, and a 3× speckle contrast reduction from an incoherent average of reconstructed signals inside the scattering layer. This AD-OCT method, when implemented using astigmatic illumination, is a promising approach for ultra-deep volumetric optical coherence microscopy.

    

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4. Computational 3D microscopy with optical coherence refraction tomography

   https://doi.org/10.1364/OPTICA.454860  

   Zhou, Kevin C. ; McNabb, Ryan P. ; Qian, Ruobing ; Degan, Simone ; Dhalla, Al-Hafeez ; Farsiu, Sina ; Izatt, Joseph A. ( June 2022 , Optica)

   Optical coherence tomography (OCT) has seen widespread success as anin vivoclinical diagnostic 3D imaging modality, impacting areas including ophthalmology, cardiology, and gastroenterology. Despite its many advantages, such as high sensitivity, speed, and depth penetration, OCT suffers from several shortcomings that ultimately limit its utility as a 3D microscopy tool, such as its pervasive coherent speckle noise and poor lateral resolution required to maintain millimeter-scale imaging depths. Here, we present 3D optical coherence refraction tomography (OCRT), a computational extension of OCT that synthesizes an incoherent contrast mechanism by combining multiple OCT volumes, acquired across two rotation axes, to form a resolution-enhanced, speckle-reduced, refraction-corrected 3D reconstruction. Our label-free computational 3D microscope features a novel optical design incorporating a parabolic mirror to enable the capture of 5D plenoptic datasets, consisting of millimetric 3D fields of view over up to$\pm <\#comment/>{75}^{\circ <\#comment/>}$without moving the sample. We demonstrate that 3D OCRT reveals 3D features unobserved by conventional OCT in fruit fly, zebrafish, and mouse samples.

    

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5. Neural network-based image reconstruction in swept-source optical coherence tomography using undersampled spectral data

   https://doi.org/10.1038/s41377-021-00594-7  

   Zhang, Yijie ; Liu, Tairan ; Singh, Manmohan ; Çetintaş, Ege ; Luo, Yilin ; Rivenson, Yair ; Larin, Kirill V. ; Ozcan, Aydogan ( July 2021 , Light: Science & Applications)

   Optical coherence tomography (OCT) is a widely used non-invasive biomedical imaging modality that can rapidly provide volumetric images of samples. Here, we present a deep learning-based image reconstruction framework that can generate swept-source OCT (SS-OCT) images using undersampled spectral data, without any spatial aliasing artifacts. This neural network-based image reconstruction does not require any hardware changes to the optical setup and can be easily integrated with existing swept-source or spectral-domain OCT systems to reduce the amount of raw spectral data to be acquired. To show the efficacy of this framework, we trained and blindly tested a deep neural network using mouse embryo samples imaged by an SS-OCT system. Using 2-fold undersampled spectral data (i.e., 640 spectral points per A-line), the trained neural network can blindly reconstruct 512 A-lines in 0.59 ms using multiple graphics-processing units (GPUs), removing spatial aliasing artifacts due to spectral undersampling, also presenting a very good match to the images of the same samples, reconstructed using the full spectral OCT data (i.e., 1280 spectral points per A-line). We also successfully demonstrate that this framework can be further extended to process 3× undersampled spectral data per A-line, with some performance degradation in the reconstructed image quality compared to 2× spectral undersampling. Furthermore, an A-line-optimized undersampling method is presented by jointly optimizing the spectral sampling locations and the corresponding image reconstruction network, which improved the overall imaging performance using less spectral data points per A-line compared to 2× or 3× spectral undersampling results. This deep learning-enabled image reconstruction approach can be broadly used in various forms of spectral-domain OCT systems, helping to increase their imaging speed without sacrificing image resolution and signal-to-noise ratio.

    

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