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1. 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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2. 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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3. In vivo imaging using surface enhanced spatially offset raman spectroscopy (SESORS): balancing sampling frequency to improve overall image acquisition

   https://doi.org/10.1038/s44303-024-00011-9  

   Nicolson, Fay ; Andreiuk, Bohdan ; Lee, Eunah ; O’Donnell, Bridget ; Whitley, Andrew ; Riepl, Nicole ; Burkhart, Deborah L. ; Cameron, Amy ; Protti, Andrea ; Rudder, Scott ; et al ( April 2024 , npj Imaging)

   In the field of optical imaging, the ability to image tumors at depth with high selectivity and specificity remains a challenge. Surface enhanced resonance Raman scattering (SERRS) nanoparticles (NPs) can be employed as image contrast agents to specifically target cells in vivo; however, this technique typically requires time-intensive point-by-point acquisition of Raman spectra. Here, we combine the use of “spatially offset Raman spectroscopy” (SORS) with that of SERRS in a technique known as “surface enhanced spatially offset resonance Raman spectroscopy” (SESORRS) to image deep-seated tumors in vivo. Additionally, by accounting for the laser spot size, we report an experimental approach for detecting both the bulk tumor, subsequent delineation of tumor margins at high speed, and the identification of a deeper secondary region of interest with fewer measurements than are typically applied. To enhance light collection efficiency, four modifications were made to a previously described custom-built SORS system. Specifically, the following parameters were increased: (i) the numerical aperture (NA) of the lens, from 0.2 to 0.34; (ii) the working distance of the probe, from 9 mm to 40 mm; (iii) the NA of the fiber, from 0.2 to 0.34; and (iv) the fiber diameter, from 100 µm to 400 µm. To calculate the sampling frequency, which refers to the number of data point spectra obtained for each image, we considered the laser spot size of the elliptical beam (6 × 4 mm). Using SERRS contrast agents, we performed in vivo SESORRS imaging on a GL261-Luc mouse model of glioblastoma at four distinct sampling frequencies: par-sampling frequency (12 data points collected), and over-frequency sampling by factors of 2 (35 data points collected), 5 (176 data points collected), and 10 (651 data points collected). In comparison to the previously reported SORS system, the modified SORS instrument showed a 300% improvement in signal-to-noise ratios (SNR). The results demonstrate the ability to acquire distinct Raman spectra from deep-seated glioblastomas in mice through the skull using a low power density (6.5 mW/mm²) and 30-times shorter integration times than a previous report (0.5 s versus 15 s). The ability to map the whole head of the mouse and determine a specific region of interest using as few as 12 spectra (6 s total acquisition time) is achieved. Subsequent use of a higher sampling frequency demonstrates it is possible to delineate the tumor margins in the region of interest with greater certainty. In addition, SESORRS images indicate the emergence of a secondary tumor region deeper within the brain in agreement with MRI and H&E staining. In comparison to traditional Raman imaging approaches, this approach enables improvements in the detection of deep-seated tumors in vivo through depths of several millimeters due to improvements in SNR, spectral resolution, and depth acquisition. This approach offers an opportunity to navigate larger areas of tissues in shorter time frames than previously reported, identify regions of interest, and then image the same area with greater resolution using a higher sampling frequency. Moreover, using a SESORRS approach, we demonstrate that it is possible to detect secondary, deeper-seated lesions through the intact skull.

    

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4. 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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5. Layer-based, depth-resolved computation of attenuation coefficients and backscattering fractions in tissue using optical coherence tomography

   https://doi.org/10.1364/BOE.427833  

   Cannon, Taylor M. ; Bouma, Brett E. ; Uribe-Patarroyo, Néstor ( July 2021 , Biomedical Optics Express)

   Structural optical coherence tomography (OCT) images of tissue stand to benefit from greater functionalization and quantitative interpretation. The OCT attenuation coefficientµ, an analogue of the imaged sample’s scattering coefficient, offers potential functional contrast based on the relationship ofµto sub-resolution physical properties of the sample. Attenuation coefficients are computed either by fitting a representativeµover several depth-wise pixels of a sample’s intensity decay, or by using previously-developed depth-resolved attenuation algorithms by Girardet al.[Invest. Ophthalmol. Vis. Sci.52,7738(2011).10.1167/iovs.10-6925] and Vermeeret al.[Biomed. Opt. Express5,322(2014).10.1364/BOE.5.000322]. However, the former method sacrifices axial information in the tomogram, while the latter relies on the stringent assumption that the sample’s backscattering fraction, another optical property, does not vary along depth. This assumption may be violated by layered tissues commonly observed in clinical imaging applications. Our approach preserves the full depth resolution of the attenuation map but removes its dependence on backscattering fraction by performing signal analysis inside individual discrete layers over which the scattering properties (e.g., attenuation and backscattering fraction) vary minimally. Although this approach necessitates the detection of these layers, it removes the constant-backscattering-fraction assumption that has constrained quantitative attenuation coefficient analysis in the past, and additionally yields a layer-resolved backscattering fraction, providing complementary scattering information to the attenuation coefficient. We validate our approach using automated layer detection in layered phantoms, for which the measured optical properties were in good agreement with theoretical values calculated with Mie theory, and show preliminary results in tissue alongside corresponding histological analysis. Together, accurate backscattering fraction and attenuation coefficient measurements enable the estimation of both particle density and size, which is not possible from attenuation measurements alone. We hope that this improvement to depth-resolved attenuation coefficient measurement, augmented by a layer-resolved backscattering fraction, will increase the diagnostic power of quantitative OCT imaging.

    

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