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1. Sparse decomposition light-field microscopy for high speed imaging of neuronal activity

   https://doi.org/10.1364/OPTICA.392805  

   Yoon, Young-Gyu ; Wang, Zeguan ; Pak, Nikita ; Park, Demian ; Dai, Peilun ; Kang, Jeong Seuk ; Suk, Ho-Jun ; Symvoulidis, Panagiotis ; Guner-Ataman, Burcu ; Wang, Kai ; et al ( October 2020 , Optica)

   One of the major challenges in large scale optical imaging of neuronal activity is to simultaneously achieve sufficient temporal and spatial resolution across a large volume. Here, we introduce sparse decomposition light-field microscopy (SDLFM), a computational imaging technique based on light-field microscopy (LFM) that takes algorithmic advantage of the high temporal resolution of LFM and the inherent temporal sparsity of spikes to improve effective spatial resolution and signal-to-noise ratios (SNRs). With increased effective spatial resolution and SNRs, neuronal activity at the single-cell level can be recovered over a large volume. We demonstrate the single-cell imaging capability of SDLFM within vivoimaging of neuronal activity of whole brains of larval zebrafish with estimated lateral and axial resolutions of$\sim <\#comment/>3.5\text{µ<\#comment/>}\mathrm{m}$and$\sim <\#comment/>7.4\text{µ<\#comment/>}\mathrm{m}$, respectively, acquired at volumetric imaging rates up to 50 Hz. We also show that SDLFM increases the quality of neural imaging in adult fruit flies.

    

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2. High-speed wide-field multi-parametric photoacoustic microscopy

   https://doi.org/10.1364/OL.391824  

   Zhong, Fenghe ; Bao, Youwei ; Chen, Ruimin ; Zhou, Qifa ; Hu, Song ( May 2020 , Optics Letters)

   Capable of imaging blood perfusion, oxygenation, and flow simultaneously at the microscopic level, multi-parametric photoacoustic microscopy (PAM) has quickly emerged as a powerful tool for studying hemodynamic and metabolic changes due to physiological stimulations or pathological processes. However, the low scanning speed poised by the correlation-based blood flow measurement impedes its application in studying rapid microvascular responses. To address this challenge, we have developed a new, to the best of our knowledge, multi-parametric PAM system. By extending the optical scanning range with a cylindrically focused ultrasonic transducer (focal zone,$76\text{µ<\#comment/>}\mathrm{m}\times <\#comment/>4.5\mathrm{m}\mathrm{m}$) for simultaneous acquisition of 500 B-scans, the new system is 112 times faster than our previous multi-parametric system that uses a spherically focused transducer (focal diameter, 40 µm) and enables high-resolution imaging of blood perfusion, oxygenation, and flow over an area of$4.5\times <\#comment/>1{\mathrm{m}\mathrm{m}}^2$at a frame rate of 1 Hz. We have demonstrated the feasibility of this system in the living mouse ear. Further development of this system into reflection mode will enable real-time cortex-wide imaging of hemodynamics and metabolism in the mouse brain.

    

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3. Needle-based deep-neural-network camera

   https://doi.org/10.1364/AO.415059  

   Guo, Ruipeng ; Nelson, Soren ; Menon, Rajesh ( March 2021 , Applied Optics)

   We experimentally demonstrate a camera whose primary optic is a cannula/needle ($\mathrm{d}\mathrm{i}\mathrm{a}\mathrm{m}\mathrm{e}\mathrm{t}\mathrm{e}\mathrm{r}=0.22\mathrm{m}\mathrm{m}$and$\mathrm{l}\mathrm{e}\mathrm{n}\mathrm{g}\mathrm{t}\mathrm{h}=12.5\mathrm{m}\mathrm{m}$) that acts as a light pipe transporting light intensity from an object plane (35 cm away) to its opposite end. Deep neural networks (DNNs) are used to reconstruct color and grayscale images with a field of view of 18° and angular resolution of$\sim <\#comment/>{0.4}^{\circ <\#comment/>}$. We showed a large effective demagnification of$127\times <\#comment/>$. Most interestingly, we showed that such a camera could achieve close to diffraction-limited performance with an effective numerical aperture of 0.045, depth of focus$\sim <\#comment/>16\text{µ<\#comment/>}\mathrm{m}$, and resolution close to the sensor pixel size (3.2 µm). When trained on images with depth information, the DNN can create depth maps. Finally, we show DNN-based classification of the EMNIST dataset before and after image reconstructions. The former could be useful for imaging with enhanced privacy.

    

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4. Super-resolution imaging with an achromatic multi-level diffractive microlens array

   https://doi.org/10.1364/OL.410013  

   Banerji, Sourangsu ; Meem, Monjurul ; Majumder, Apratim ; Sensale-Rodriguez, Berardi ; Menon, Rajesh ( November 2020 , Optics Letters)

   Compound eyes found in insects provide intriguing sources of biological inspiration for miniaturized imaging systems. Inspired by such insect eye structures, we demonstrate an ultrathin arrayed camera enabled by a flat multi-level diffractive microlens array for super-resolution visible imaging. We experimentally demonstrate that the microlens array can achieve a large fill factor (hexagonal close packing with$\mathrm{p}\mathrm{i}\mathrm{t}\mathrm{c}\mathrm{h}=120\text{µ<\#comment/>}\mathrm{m}$), thickness of 2.6 µm, and diffraction-limited ($\mathrm{S}\mathrm{t}\mathrm{r}\mathrm{e}\mathrm{h}\mathrm{l}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}=0.88$) achromatic performance in the visible band (450 to 650 nm). We also demonstrate super-resolution imaging with resolution improvement of$\sim <\#comment/>1.4$times by computationally merging 1600 images in the array.

    

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5. AI-assisted superresolution cosmological simulations

   https://doi.org/10.1073/pnas.2022038118  

   Li, Yin ; Ni, Yueying ; Croft, Rupert A. C. ; Di Matteo, Tiziana ; Bird, Simeon ; Feng, Yu ( May 2021 , Proceedings of the National Academy of Sciences)

   Cosmological simulations of galaxy formation are limited by finite computational resources. We draw from the ongoing rapid advances in artificial intelligence (AI; specifically deep learning) to address this problem. Neural networks have been developed to learn from high-resolution (HR) image data and then make accurate superresolution (SR) versions of different low-resolution (LR) images. We apply such techniques to LR cosmological N-body simulations, generating SR versions. Specifically, we are able to enhance the simulation resolution by generating 512 times more particles and predicting their displacements from the initial positions. Therefore, our results can be viewed as simulation realizations themselves, rather than projections, e.g., to their density fields. Furthermore, the generation process is stochastic, enabling us to sample the small-scale modes conditioning on the large-scale environment. Our model learns from only 16 pairs of small-volume LR-HR simulations and is then able to generate SR simulations that successfully reproduce the HR matter power spectrum to percent level up to$16\hspace{0.17em}{h}^{-1}\mathrm{\text{Mpc}}$and the HR halo mass function to within$10\%$down to$10^{11}\hspace{0.17em}{M}_{\odot }$. We successfully deploy the model in a box 1,000 times larger than the training simulation box, showing that high-resolution mock surveys can be generated rapidly. We conclude that AI assistance has the potential to revolutionize modeling of small-scale galaxy-formation physics in large cosmological volumes.

    

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