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1. Spectroscopic optical coherence refraction tomography

   https://doi.org/10.1364/OL.389703  

   Zhou, Kevin_C; Qian, Ruobing; Farsiu, Sina; Izatt, Joseph_A (April 2020, Optics Letters)

   In optical coherence tomography (OCT), the axial resolution is often superior to the lateral resolution, which is sacrificed for long imaging depths. To address this anisotropy, we previously developed optical coherence refraction tomography (OCRT), which uses images from multiple angles to computationally reconstruct an image with isotropic resolution, given by the OCT axial resolution. On the other hand, spectroscopic OCT (SOCT), an extension of OCT, trades axial resolution for spectral resolution and hence often has superior lateral resolution. Here, we present spectroscopic OCRT (SOCRT), which uses SOCT images from multiple angles to reconstruct a spectroscopic image with isotropic spatial resolution limited by the OCTlateralresolution. We experimentally show that SOCRT can estimate bead size based on Mie theory at simultaneously high spectral and isotropic spatial resolution. We also applied SOCRT to a biological sample, achieving axial resolution enhancement limited by the lateral resolution. 

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2. 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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3. 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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4. Enhanced spectral-domain optical coherence tomography (SD-OCT) using in situ ultrasonic virtual tunable optical waveguides

   https://doi.org/10.1364/OE.462500  

   Karimi, Yasin; Yang, Hang; Liu, Junze; Park, B_hyle; Chamanzar, Maysamreza (September 2022, Optics Express)

   A conventional optical lens can enhance lateral resolution in optical coherence tomography (OCT) by focusing the input light onto the sample. However, the typical Gaussian beam profile of such a lens will impose a tradeoff between the depth of focus (DOF) and the lateral resolution. The lateral resolution is often compromised to achieve amm-scale DOF. We have experimentally shown that using a cascade system of an ultrasonic virtual tunable optical waveguide (UVTOW) and a short focal-length lens can provide a large DOF without severely compromising the lateral resolution compared to an external lens with the same effective focal length. In addition, leveraging the reconfigurability of UVTOW, we show that the focal length of the cascade system can be tuned without the need for mechanical translation of the optical lens. We compare the performance of the cascade system with a conventional optical lens to demonstrate enhanced DOF without compromising the lateral resolution as well as reconfigurability of UVTOW for OCT imaging. 

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5. Octascope: A Lightweight Pre-Trained Model for Optical Coherence Tomography

   https://doi.org/10.1109/ACCESS.2025.3595838  

   Cui, Haoyang; Wang, Chen; Calle, Paul; Liu, Yunlong; Zhang, Qinghao; Ly, Sinaro; Reynolds, Justin; Yan, Feng; Zhang, Ke; Liu, Ronghao; et al (August 2025, IEEE Access)

   Optical coherence tomography (OCT) imaging enables high resolution visualization of sub-surface tissue microstructures. However, OCT image analysis using deep learning is hampered by limited diverse training data to meet performance requirements and high inference latency for real-time applications. To address these challenges, we developed Octascope, a lightweight domain-specific convolutional neural network (CNN) - based model designed for OCT image analysis. Octascope was pre-trained using a curriculum learning approach, which involves sequential training, first on natural images (ImageNet), then on OCT images from retinal, abdominal, and renal tissues, to progressively acquire transferable knowledge. This multi-domain pre-training enables Octascope to generalize across varied tissue types. In two downstream tasks, Octascope demonstrated notable improvements in predictive accuracy compared to alternative approaches. In the epidural tissue detection task, our method surpassed single-task learning with fine-tuning by 9.13% and OCT-specific transfer learning by 5.95% in accuracy. Octascope outperformed VGG16 and ResNet50 by 5.36% and 6.66% in a retinal diagnosis task, respectively. In comparison to a Transformer-based OCT foundation model - RETFound, Octascope delivered 2 to 4.4 times faster inference speed with slightly better predictive accuracies in both downstream tasks. Octascope represented a significant advancement for OCT image analysis by providing an effective balance between computational efficiency and diagnostic accuracy for real-time clinical applications. 

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