Mr. Sébastien Crombez Profile

Sébastien Crombez

at Institut Lumière Matière

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Proceedings Article | 30 May 2022 Presentation

Hyperspectral compressive microscopy based on structured light sheet and deep convolutionnal neural network

Sébastien Crombez, Chloé Exbrayat-Heritier, Florence Ruggiero, Cédric Ray, Nicolas Ducros

Proceedings Volume PC12136, PC121360E (2022) https://doi.org/10.1117/12.2616521

KEYWORDS: Structured light, Microscopy, Neural networks, Denoising, Spectral resolution, Reconstruction algorithms, Hyperspectral imaging, Spectroscopy, Spectroscopes, Optical imaging

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Optical imaging has become an invaluable tool in life science. Among the variety of available techniques, selective plane illumination microscopy (SPIM) allows for fast (x,y,z) imaging of fluorescent samples with reduced photobleaching. SPIM directly acquire the (x,y) slice corresponding to a thin light sheet that illuminates the sample, while the third spatial dimension is scanned. Promoted by the open source SPIM project, many designs variants are now available. This enables the study of various samples such as fly embryos, zebrafish embryos and others. The study of multi-labeled specimens implies to unmix the fluorophores, which usually relies on optical filters. As most of the light is rejected, this approach has a major drawback, as a large amount of information is lost (e.g., fluorophores with overlapping spectra cannot be unmixed). Therefore, there is a need for 3D imagers with hyperspectral capabilities, which can exploit the full-emission spectrum of a fluorescent sample. We will describe a computational hyperspectral light sheet microscope inspired from Hadamard spectroscopy. We generate structured light sheets using a digital micromirror device and focus the fluorescence signal onto the entrance slit of an imaging spectrometer. Then, we reconstruct the full hypercube from the raw data acquired for multiple structured light patterns. Our technique enjoys excellent spectral resolution and allows resolving overlapping fluorophores with up to nanometer resolution. Furthermore, the Hadamard patterns used for illumination allow maximizing the collected signal compared to previous hyperspectral SPIM setups. To reduce the acquisition time, we consider undersampled measurements for which we will present reconstruction results obtained using an algorithm based on a deep convolutional network.

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