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Article Dans Une Revue Journal of the Optical Society of America. A Optics, Image Science, and Vision Année : 2022

Optimizing sampling for surface localization in 3D-scanning microscopy

Marie-Anne Burcklen
Frédéric Galland
Loïc Le Goff
  • Fonction : Auteur

Résumé

3D-scanning fluorescence imaging of living tissue is in demand for less phototoxic acquisition process. For the imaging of biological surfaces, adaptive and sparse scanning schemes have been proven to efficiently reduce the light dose by concentrating acquisitions around the surface. In this paper, we focus on optimizing the scanning scheme at a constant photon budget, when the problem is to estimate the position of a biological surface whose intensity profile is modeled as a Gaussian shape. We propose an approach based on the Cramér–Rao bound to optimize the positions and number of scanning points, assuming signal-dependant Gaussian noise. We show that, in the case of regular sampling, the optimization problem can be reduced to a few parameters, allowing us to define quasi-optimal acquisition strategies, first when no prior knowledge of the surface location is available and then when the user has a prior on this location.
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Dates et versions

hal-03836803 , version 1 (02-11-2022)

Identifiants

Citer

Marie-Anne Burcklen, Frédéric Galland, Loïc Le Goff. Optimizing sampling for surface localization in 3D-scanning microscopy. Journal of the Optical Society of America. A Optics, Image Science, and Vision, 2022, 39 (8), pp.1479. ⟨10.1364/JOSAA.460077⟩. ⟨hal-03836803⟩
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