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1. Development of Abel's inversion method to extract radially resolved optical emission maps from spectral data cubes collected via push-broom hyperspectral imaging with sub-pixel shifting sampling

   https://doi.org/10.1039/C9JA00239A  

   Shi, Songyue; Finch, Kevin; She, Yue; Gamez, Gerardo (January 2020, Journal of Analytical Atomic Spectrometry)

   null (Ed.)

   Optical emission spectroscopy (OES) imaging is often used for diagnostics for better understanding of the underlying mechanisms of plasmas. Typical spectral images, however, contain intensity maps that are integrated along the line-of-sight. A widespread method to extract the radial information is Abel's inversion, but most approaches result in accumulation of error toward the plasma axial position, which is often the region of most interest. Here, a Fourier-transform based Abel's inversion algorithm, which spreads the error evenly across the radial profile, is optimized for OES images collected on a push-broom hyperspectral imaging system (PbHSI). Furthermore, a sub-pixel shifting (SPS) sampling protocol is employed on the PbHSI in the direction of the radial reconstruction to allow improved fidelity from the increased number of data points. The accuracy and fidelity of the protocol are characterized and optimized with a software-based 3-dimensional hyperspectral model datacube. A systematic study of the effects of varying levels of representative added noise, different noise filters, number of data points and cosine expansions used in the inversion, as well as the spatial intensity distribution shapes of the radial profile are presented. A 3D median noise filter with 3-pixel radius, a minimum of 50 points and 8 cosine expansions is needed to keep the relative root mean squared error (rRMSE) <8%. The optimized protocol is implemented for the first time on OES images of a micro-capillary dielectric barrier discharge (μDBD) source obtained via SPS PbHSI system and the extracted radial emission of different plasma species (He, N 2 , N 2 + ) are shown. 

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2. Deep Learning-Based Point-Scanning Super-Resolution Imaging

   https://doi.org/10.1101/740548  

   Fang L, Monroe F (January 2019, PloS one)

   Point scanning imaging systems (e.g. scanning electron or laser scanning confocal microscopes) are perhaps the most widely used tools for high resolution cellular and tissue imaging. Like all other imaging modalities, the resolution, speed, sample preservation, and signal-to-noise ratio (SNR) of point scanning systems are difficult to optimize simultaneously. In particular, point scanning systems are uniquely constrained by an inverse relationship between imaging speed and pixel resolution. Here we show these limitations can be miti gated via the use of deep learning-based super-sampling of undersampled images acquired on a point-scanning system, which we termed point -scanning super-resolution (PSSR) imaging. Oversampled ground truth images acquired on scanning electron or Airyscan laser scanning confocal microscopes were used to generate semi-synthetictrain ing data for PSSR models that were then used to restore undersampled images. Remarkably, our EM PSSR model was able to restore undersampled images acquired with different optics, detectors, samples, or sample preparation methods in other labs . PSSR enabled previously unattainable xy resolution images with our serial block face scanning electron microscope system. For fluorescence, we show that undersampled confocal images combined with a multiframe PSSR model trained on Airyscan timelapses facilitates Airyscan-equivalent spati al resolution and SNR with ~100x lower laser dose and 16x higher frame rates than corresponding high-resolution acquisitions. In conclusion, PSSR facilitates point-scanning image acquisition with otherwise unattainable resolution, speed, and sensitivity. 

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3. Reconstruction, analysis, and segmentation of LA-ICP-MS imaging data using Python for the identification of sub-organ regions in tissues

   https://doi.org/10.1039/c9an02472g  

   Castellanos-García, Laura J.; Gokhan Elci, S.; Vachet, Richard W. (May 2020, The Analyst)

   Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) imaging has been extensively used to determine the distributions of metals in biological tissues for a wide variety of applications. To be useful for identifying metal biodistributions, the acquired raw data needs to be reconstructed into a two-dimensional image. Several approaches have been developed for LA-ICP-MS image reconstruction, but less focus has been placed on software for more in-depth statistical processing of the imaging data. Yet, improved image processing can allow the biological ramifications of metal distributions in tissues to be better understood. In this work, we describe software written in Python that automatically reconstructs, analyzes, and segments images from LA-ICP-MS imaging data. Image segmentation is achieved using LA-ICP-MS signals from the biological metals Fe and Zn together with k -means clustering to automatically identify sub-organ regions in different tissues. Spatial awareness also can be incorporated into the images through a neighboring pixel evaluation that allows regions of interest to be identified that are at the limit of the LA-ICP-MS imaging resolution. The value of the described algorithms is demonstrated for LA-ICP-MS images of nanomaterial biodistributions. The developed image reconstruction and processing approach reveals that nanomaterials distribute in different sub-organ regions based on their chemical and physical properties, opening new possibilities for understanding the impact of such nanomaterials in vivo . 

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4. Sensitivity analysis of a multibranched light guide for real time hyperspectral imaging systems

   https://doi.org/10.1117/12.2510506  

   Browning, Craig M.; Mayes, Sam; Deal, Joshua; Arshad, Arslan; Gunn Mayes, Samantha; Parker, Marina; Rich, Thomas; Leavesley, Silas J.; Azar, Fred S.; Intes, Xavier; et al (February 2019, Proc. SPIE 10871, Multimodal Biomedical Imaging XIV, 1087107)

   Hyperspectral imaging (HSI) is a spectroscopic technique which captures images at a high contrast over a wide range of wavelengths to show pixel specific composition. Traditional uses of HSI include: satellite imagery, food distribution quality control and digital archaeological reconstruction. Our lab has focused on developing applications of HSI fluorescence imaging systems to study molecule-specific detection for rapid cell signaling events or real-time endoscopic screening. Previously, we have developed a prototype spectral light source, using our modified imaging technique, excitationscanning hyperspectral imaging (HIFEX), coupled to a commercial colonoscope for feasibility testing. The 16 wavelength LED array was combined, using a multi-branched solid light guide, to couple to the scope’s optical input. The prototype acquired a spectral scan at near video-rate speeds (~8 fps). The prototype could operate at very rapid wavelength switch speeds, limited to the on/off rates of the LEDs (~10 μs), but imaging speed was limited due to optical transmission losses (~98%) through the solid light guide. Here we present a continuation of our previous work in performing an in-depth analysis of the solid light guide to optimize the optical intensity throughput. The parameters evaluated include: LED intensity input, geometry (branch curvature and combination) and light propagation using outer claddings. Simulations were conducted using a Monte Carlo ray tracing software (TracePro). Results show that transmission within the branched light guide may be optimized through LED focusing lenses, bend radii and smooth tangential branch merges. Future work will test a new fabricated light guide from the optimized model framework. 

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5. Super-resolution computational saturated absorption microscopy

   https://doi.org/10.1364/JOSAA.482203  

   Murray, Gabe; Stockton, Patrick_A; Field, Jeff; Pezeshki, Ali; Squier, Jeff; Bartels, Randy_A (June 2023, Journal of the Optical Society of America A)

   Imaging beyond the diffraction limit barrier has attracted wide attention due to the ability to resolve previously hidden image features. Of the various super-resolution microscopy techniques available, a particularly simple method called saturated excitation microscopy (SAX) requires only simple modification of a laser scanning microscope: The illumination beam power is sinusoidally modulated and driven into saturation. SAX images are extracted from the harmonics of the modulation frequency and exhibit improved spatial resolution. Unfortunately, this elegant strategy is hindered by the incursion of shot noise that prevents high-resolution imaging in many realistic scenarios. Here, we demonstrate a technique for super-resolution imaging that we call computational saturated absorption (CSA) in which a joint deconvolution is applied to a set of images with diversity in spatial frequency support among the point spread functions (PSFs) used in the image formation with saturated laser scanning fluorescence microscopy. CSA microscopy allows access to the high spatial frequency diversity in a set of saturated effective PSFs, while avoiding image degradation from shot noise. 

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