Physically-based in silico light sheet microscopy for visualizing fluorescent brain models

BMC Bioinformatics

Research

Henry Markram¹  Marwan Abdellah¹  Stefan Eilemann¹  Ahmet Bilgili¹  Felix Schürmann¹ 

[1] Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Biotech Campus, Chemin des Mines 9, 1202, Geneva, Switzerland;

关键词: Modelling and Simulation;    Light Sheet Fluorescence Microscopy;    Fluorescence Rendering and Visualization;    Neocortical Brain Models;    In Silico Neuroscience;   

DOI  :  10.1186/1471-2105-16-S11-S8

来源: Springer

【 摘 要 】

BackgroundWe present a physically-based computational model of the light sheet fluorescence microscope (LSFM). Based on Monte Carlo ray tracing and geometric optics, our method simulates the operational aspects and image formation process of the LSFM. This simulated, in silico LSFM creates synthetic images of digital fluorescent specimens that can resemble those generated by a real LSFM, as opposed to established visualization methods producing visually-plausible images. We also propose an accurate fluorescence rendering model which takes into account the intrinsic characteristics of fluorescent dyes to simulate the light interaction with fluorescent biological specimen.ResultsWe demonstrate first results of our visualization pipeline to a simplified brain tissue model reconstructed from the somatosensory cortex of a young rat. The modeling aspects of the LSFM units are qualitatively analysed, and the results of the fluorescence model were quantitatively validated against the fluorescence brightness equation and characteristic emission spectra of different fluorescent dyes.AMS subject classificationModelling and simulation

【 授权许可】

Unknown   
© Abdellah et al. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.