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1. High-performance, low-cost optical coherence tomography (OCT) digital Fourier Transform (dFT) spectrometer on a silicon photonic platform

   https://doi.org/10.1364/OCT.2024.CTu4E.2  

   Mojahed, Diana; Peters, Maarten; Hu, Juejun (January 2024, Optica Publishing Group)

   Optical Coherence Tomography (OCT) is limited by cost and size. We've developed a compact, chip-integrated OCT system with a spectrometer, delay line, and beam scanning. It features a 1310nm central wavelength, 100nm bandwidth, 0.0977nm resolution, 108.45dB sensitivity, and 8.8µm axial resolution. 

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2. Unsupervised OCT image interpolation using deformable registration and generative models

   Wei, Shizuo; Remedios, Samuel W; Bian, Zongyuan; Wang, Shuo; Chen, Jeya; Liu, Yihao; Jedynak, Bruno; Liu, Timothy Y; Saidha, Shiv; Calabresi, Peter A; et al (September 2025, Lecture notes in computer science)

   Optical coherence tomography (OCT) images are often acquired as highly anisotropic volumes, where the scanning step is dense along the fast axis but sparse along the slow axis. This affects image analysis, such as image registration for longitudinal alignment. To create more isotropic volumes, bicubic interpolation can be used along the slow axis, but it generally produces blurry features. Registration-based interpolation can reduce blurriness, but often fails to generate realistic OCT images. Deep generative models can sample realistic images, but lack the structural consistency constraints required for interpolation. In this paper, we propose an unsupervised image interpolation method that combines registration-based interpolation with a deep generative model to overcome their individual limitations and improve the structural accuracy and realism of interpolated OCT images. We compare the proposed method with both bicubic and registration-based interpolation on real OCT datasets, and show that it achieves the best interpolation performance. 

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3. 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. (January 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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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. (January 2022, Optica)

   Optical coherence tomography (OCT) has seen widespread success as an in vivo clinical 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 ± 75 ∘ 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. Globally optimal OCT surface segmentation using a constrained IPM optimization

   https://doi.org/10.1364/OE.444369  

   Xie, Hui; Pan, Zhe; Zhou, Leixin; Zaman, Fahim A.; Chen, Danny Z.; Jonas, Jost B.; Xu, Weiyu; Wang, Ya Xing; Wu, Xiaodong (January 2022, Optics Express)

   Segmentation of multiple surfaces in optical coherence tomography (OCT) images is a challenging problem, further complicated by the frequent presence of weak boundaries, varying layer thicknesses, and mutual influence between adjacent surfaces. The traditional graph-based optimal surface segmentation method has proven its effectiveness with its ability to capture various surface priors in a uniform graph model. However, its efficacy heavily relies on handcrafted features that are used to define the surface cost for the “goodness” of a surface. Recently, deep learning (DL) is emerging as a powerful tool for medical image segmentation thanks to its superior feature learning capability. Unfortunately, due to the scarcity of training data in medical imaging, it is nontrivial for DL networks to implicitly learn the global structure of the target surfaces, including surface interactions. This study proposes to parameterize the surface cost functions in the graph model and leverage DL to learn those parameters. The multiple optimal surfaces are then simultaneously detected by minimizing the total surface cost while explicitly enforcing the mutual surface interaction constraints. The optimization problem is solved by the primal-dual interior-point method (IPM), which can be implemented by a layer of neural networks, enabling efficient end-to-end training of the whole network. Experiments on spectral-domain optical coherence tomography (SD-OCT) retinal layer segmentation demonstrated promising segmentation results with sub-pixel accuracy. 

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