Is laser repetition rate important for two-photon light sheet microscopy?

Gasparoli, Federico M.; Escobet-Montalbán, Adrià; Early, Jason J; Bruce, Graham D.; Dholakia, Kishan

doi:10.1364/osac.405369

Cited by 5 publications

(8 citation statements)

References 17 publications

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“…The setup and data acquisition methods are presented in detail in ref. 41 . Figure 7 illustrates the low-resolution Bessel images (LR) deconvolved with a DL network trained on a manually matched Bessel PSF.…”

Section: Resultsmentioning

confidence: 99%

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Experimentally unsupervised deconvolution for light-sheet microscopy with propagation-invariant beams

et al. 2022

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Deconvolution is a challenging inverse problem, particularly in techniques that employ complex engineered point-spread functions, such as microscopy with propagation-invariant beams. Here, we present a deep-learning method for deconvolution that, in lieu of end-to-end training with ground truths, is trained using known physics of the imaging system. Specifically, we train a generative adversarial network with images generated with the known point-spread function of the system, and combine this with unpaired experimental data that preserve perceptual content. Our method rapidly and robustly deconvolves and super-resolves microscopy images, demonstrating a two-fold improvement in image contrast to conventional deconvolution methods. In contrast to common end-to-end networks that often require 1000–10,000s paired images, our method is experimentally unsupervised and can be trained solely on a few hundred regions of interest. We demonstrate its performance on light-sheet microscopy with propagation-invariant Airy beams in oocytes, preimplantation embryos and excised brain tissue, as well as illustrate its utility for Bessel-beam LSM. This method aims to democratise learned methods for deconvolution, as it does not require data acquisition outwith the conventional imaging protocol.

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Section: Resultsmentioning

confidence: 99%

“…Figure 7 b–f illustrates DL deconvolution in images of a Zebrafish embryo, which is a common model organism for developmental and cardiovascular studies 41 . Figure 7 b–e shows the performance of DL in different sections of the Zebrafish corresponding to regions marked in Fig.…”

Section: Resultsmentioning

confidence: 99%

Experimentally unsupervised deconvolution for light-sheet microscopy with propagation-invariant beams

et al. 2022

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“…Towards this, we briefly demonstrate the application of DL to a multiphoton LSM system using a Bessel beam, generated using an axicon lens (11). The setup and data acquisition methods are presented in detail in (39). Figure 8 illustrates the low-resolution Bessel images (LR) de-convolved with a DL network trained on a manually matched Bessel PSF.…”

Section: Resultsmentioning

confidence: 99%

“…Figures 8(b–f) illustrate DL deconvolution in images of a Zebrafish embryo, which is a common model organism for developmental and cardiovascular studies (39). Figures 8(b– e) show the performance of DL in different sections of the Zebrafish corresponding to regions marked in Fig.…”

Section: Resultsmentioning

confidence: 99%

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Experimentally unsupervised deconvolution for light-sheet microscopy with propagation-invariant beams

Wijesinghe

Corsetti

Chow

et al. 2021

Preprint

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Structured propagation-invariant light fields, such as the Airy and Bessel beams, can encode high-resolution spatial information over an extended field of view. Their use in microscopy, however, has been limited due to the need for deconvolution, a challenging inverse problem. Here, we introduce a deep learning method that can deconvolve and super-resolve structured light-sheet images using such fields without the need for paired experimental data. We make use of the known physics of light propagation by constraining a generative adversarial network with estimated, simulated image data. We combine this with unpaired experimental data via a saliency constraint based on perceptual loss. The combined model results in an experimentally unsupervised network that is robust and lightweight, and that can be trained solely on a few regions of interest from one light-sheet volume. We demonstrate its performance on Airy light-sheet volumes of calibration beads, oocytes, preimplantation embryos, and excised brain tissue. This democratises the use of structured light fields and deconvolution, as it does not require data acquisition outwith the conventional imaging protocol.

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Structural and functional imaging of brains

Liu

Zhu²,

Zhang³

et al. 2022

Sci. China Chem.

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Analyzing the complex structures and functions of brain is the key issue to understanding the physiological and pathological processes. Although neuronal morphology and local distribution of neurons/blood vessels in the brain have been known, the subcellular structures of cells remain challenging, especially in the live brain. In addition, the complicated brain functions involve numerous functional molecules, but the concentrations, distributions and interactions of these molecules in the brain are still poorly understood. In this review, frontier techniques available for multiscale structure imaging from organelles to the whole brain are first overviewed, including magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), serial-section electron microscopy (ssEM), light microscopy (LM) and synchrotron-based X-ray microscopy (XRM). Specially, XRM for three-dimensional (3D) imaging of large-scale brain tissue with high resolution and fast imaging speed is highlighted. Additionally, the development of elegant methods for acquisition of brain functions from electrical/chemical signals in the brain is outlined. In particular, the new electrophysiology technologies for neural recordings at the single-neuron level and in the brain are also summarized. We also focus on the construction of electrochemical probes based on dual-recognition strategy and surface/interface chemistry for determination of chemical species in the brain with high selectivity and long-term stability, as well as electrochemophysiological microarray for simultaneously recording of electrochemical and electrophysiological signals in the brain. Moreover, the recent development of brain MRI probes with high contrast-to-noise ratio (CNR) and sensitivity based on hyperpolarized techniques and multi-nuclear chemistry is introduced. Furthermore, multiple optical probes and instruments, especially the optophysiological Raman probes and fiber Raman photometry, for imaging and biosensing in live brain are emphasized. Finally, a brief perspective on existing challenges and further research development is provided.

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Is laser repetition rate important for two-photon light sheet microscopy?

Cited by 5 publications

References 17 publications

Experimentally unsupervised deconvolution for light-sheet microscopy with propagation-invariant beams

Experimentally unsupervised deconvolution for light-sheet microscopy with propagation-invariant beams

Experimentally unsupervised deconvolution for light-sheet microscopy with propagation-invariant beams

Structural and functional imaging of brains

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