Biology subjects

Corsetti, S.

Publications and source records attributed to Corsetti, S..

3 recordsLinked to original sources

bioRxiv2024

Sidelobe suppressed Bessel beams for one-photon light-sheet microscopy

Bessel beams (BB) have found widespread adoption in various forms of light-sheet microscopy. However, for one-photon fluorescence, the transverse profile of the beam poses challenges due to the detrimental effect of the sidelobes. Here, we mitigate this issue by using a computer generated phase element for generating a sidelobe suppressed Bessel beam (SSBB). We then progress to perform a comparison of biological imaging using SSBB to standard BB in a light-sheet geometry. The SSBB peak intensity is more than an order of magnitude higher than the first sidelobe. In contrast to a standard BB light-sheet, SSBB does not need deconvolution and propagates to depths exceeding 400m in phantom samples maintaining a transverse size of 5 m. Finally, we demonstrate the advantage of using a SSBB light-sheet for biological applications by imaging fixed early-stage zebrafish larvae. In comparison to the standard BB, we observe a two-fold increase in contrast-to-noise ratio (CNR) when imaging the labelled cellular eye structures and the notochords. Our results provide an effective approach to generating and using SSBB light-sheets to enhance contrast for one-photon light-sheet microscopy.

biophysics↗

bioRxiv2021

Learned deconvolution using physics priors for structured light-sheet microscopy

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 preserves perceptual content. Our method rapidly and robustly deconvolves and superresolves 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 1,000-10,000s 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, including in calibration beads, 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.

biophysics↗

bioRxiv2020

Widefield light sheet microscopy using an Airy beam combined with deep-learning super-resolution

Imaging across length scales and in depth has been an important pursuit of widefield optical imaging. This promises to reveal fine cellular detail within a widefield snapshot of a tissue sample. Current advances often sacrifice resolution through selective sub-sampling to provide a wide field of view in a reasonable time scale. We demonstrate a new avenue for recovering high-resolution images from sub-sampled data in light-sheet microscopy using deep-learning super-resolution. We combine this with the use of a widefield Airy beam to achieve high-resolution imaging over extended fields of view and depths. We characterise our method on fluorescent beads as test targets. We then demonstrate improvements in imaging amyloid plaques in a cleared brain from a mouse model of Alzheimers disease, and in excised healthy and cancerous colon and breast tissues. This development can be widely applied in all forms of light sheet microscopy to provide a two-fold increase in the dynamic range of the imaged length scale. It has the potential to provide further insight into neuroscience, developmental biology and histopathology.

biophysics↗