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1. High-speed two-photon microscopy with adaptive line-excitation

   https://doi.org/10.1364/OPTICA.529930  

   Li, Yunyang; Guo, Shu; Mattison, Ben; Hu, Junjie; Man, Kwun_Nok_Mimi; Yang, Weijian (August 2024, Optica)

   We present a two-photon fluorescence microscope designed for high-speed imaging of neural activity at cellular resolution. Our microscope uses an adaptive sampling scheme with line illumination. Instead of building images pixel by pixel via scanning a diffraction-limited spot across the sample, our scheme only illuminates the regions of interest (i.e., neuronal cell bodies) and samples a large area of them in a single measurement. Such a scheme significantly increases the imaging speed and reduces the overall laser power on the brain tissue. Using this approach, we performed high-speed imaging of the neuronal activity in mouse cortexin vivo. Our method provides a sampling strategy in laser-scanning two-photon microscopy and will be powerful for high-throughput imaging of neural activity. 

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2. Adaptive line-illumination scheme for high-speed two-photon fluorescence microscopy

   Li, Y; Guo, S; Yang, W (March 2024, SPIE Neural Imaging and Sensing 2024)

   We present a two-photon fluorescence microscope designed for high-speed imaging of neural activity in cellular resolution. Our microscope uses line illumination with an adaptive sampling scheme. Instead of building images pixel by pixel via scanning a diffraction-limited spot across the tissue, our scheme only illuminates the regions of interest (i.e., neuronal cell bodies), and samples a large area of them in a single measurement. This significantly increases the imaging speed and reduces the overall laser power on the sample. We characterized the imaging resolution and verified the concept of adaptive sampling through phantom samples. Our approach holds great promise for high-throughput neural activity imaging. 

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3. 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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4. Deep compressed imaging via optimized pattern scanning

   https://doi.org/10.1364/PRJ.410556  

   Zhang, Kangning; Hu, Junjie; Yang, Weijian (March 2021, Photonics Research)

   The need for high-speed imaging in applications such as biomedicine, surveillance, and consumer electronics has called for new developments of imaging systems. While the industrial effort continuously pushes the advance of silicon focal plane array image sensors, imaging through a single-pixel detector has gained significant interest thanks to the development of computational algorithms. Here, we present a new imaging modality, deep compressed imaging via optimized-pattern scanning, which can significantly increase the acquisition speed for a single-detector-based imaging system. We project and scan an illumination pattern across the object and collect the sampling signal with a single-pixel detector. We develop an innovative end-to-end optimized auto-encoder, using a deep neural network and compressed sensing algorithm, to optimize the illumination pattern, which allows us to reconstruct faithfully the image from a small number of measurements, with a high frame rate. Compared with the conventional switching-mask-based single-pixel camera and point-scanning imaging systems, our method achieves a much higher imaging speed, while retaining a similar imaging quality. We experimentally validated this imaging modality in the settings of both continuous-wave illumination and pulsed light illumination and showed high-quality image reconstructions with a high compressed sampling rate. This new compressed sensing modality could be widely applied in different imaging systems, enabling new applications that require high imaging speeds. 

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5. Learnable-pattern scanning based deep compressed imaging

   https://doi.org/10.1117/12.2577998  

   Zhang, Kangning; Hu, Junjie; Yang, Weijian (March 2021, High-Speed Biomedical Imaging and Spectroscopy VI)

   Goda, Keisuke; Tsia, Kevin K. (Ed.)

   We present a new deep compressed imaging modality by scanning a learned illumination pattern on the sample and detecting the signal with a single-pixel detector. This new imaging modality allows a compressed sampling of the object, and thus a high imaging speed. The object is reconstructed through a deep neural network inspired by compressed sensing algorithm. We optimize the illumination pattern and the image reconstruction network by training an end-to-end auto-encoder framework. Comparing with the conventional single-pixel camera and point-scanning imaging system, we accomplish a high-speed imaging with a reduced light dosage, while preserving a high imaging quality. 

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