Decoupled illumination detection in light sheet microscopy for 4D observation of spermatozoa at high-resolutions

¹ Centro de Investigación en Ingeniería y Ciencias Aplicadas, Universidad Autónoma del Estado de Morelos, Ave. Universidad 1001, Cuernavaca 62209, México
² ICFO–Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, Castelldefels 08860, Spain
³ Institut de Física d’Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Campus UAB, 08193 Bellaterra (Barcelona), Spain
⁴ Port d’Informació Científica (PIC), Campus UAB, C. Albareda s/n, 08193 Bellaterra (Barcelona), Spain
⁵ Centro de Investigación Científica y de Educación Superior de Ensenada, Carretera Ensenada-Tijuana, No. 3918, Zona Playitas, 22860 Ensenada B. C., México
⁶ Department of Physics, Universidad Nacional de Colombia, 111321, Bogotá, Colombia

^* Corresponding author: jacob.licea@uaem.mx, pablo.loza@icfo.eu

Published online: 31 October 2024

Abstract

We present the use of wavefront coding (WFC) combined with machine learning in a light sheet fluorescence microscopy (LSFM) system. We visualize the 3D dynamics of sperm flagellar motion at an imaging speed up to 80 volumes per second, which is faster than twice volumetric video rate. By using the WFC technique we achieve to extend the depth of field of the collection objective with high numerical aperture (NA=1) from 2.6 μm to 50 μm, i. e., more than one order of magnitude. To improve the quality of the final images, we applied a machine learning-based algorithm to the acquired sperm raw images and to the point spread function (PSF) of the generated cubic phase masks previous to the deconvolution process.