Optimizing sampling for surface localization in 3D-scanning microscopy.

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.

An adaptive microscope for the imaging of biological surfaces.

Scanning fluorescence microscopes are now able to image large biological samples at high spatial and temporal resolution. This comes at the expense of an increased light dose which is detrimental to fluorophore stability and cell physiology. To highly reduce the light dose, we designed an adaptive scanning fluorescence microscope with a scanning scheme optimized for the unsupervised imaging of cell sheets, which underly the shape of many embryos and organs. The surface of the tissue is first delineated from the acquisition of a very small subset (~0.1%) of sample space, using a robust estimation strategy. Two alternative scanning strategies are then proposed to image the tissue with an improved photon budget, without loss in resolution. The first strategy consists in scanning only a thin shell around the estimated surface of interest, allowing high reduction of light dose when the tissue is curved. The second strategy applies when structures of interest lie at the cell periphery (e.g. adherens junctions). An iterative approach is then used to propagate scanning along cell contours. We demonstrate the benefit of our approach imaging live epithelia from Drosophila melanogaster. On the examples shown, both approaches yield more than a 20-fold reduction in light dose -and up to more than 80-fold- compared to a full scan of the volume. These smart-scanning strategies can be easily implemented on most scanning fluorescent imaging modality. The dramatic reduction in light exposure of the sample should allow prolonged imaging of the live processes under investigation.

Joint digital-optical design of complex lenses using a surrogate image quality criterion adapted to commercial optical design software.

Like classical optical design, joint digital-optical design of complex lenses requires a skilled optical designer helped by powerful optical design software. Consequently, if optimization criteria have to be modified to take into account digital post-processing, the convenient optimization environment provided by commercial optical design software needs to be preserved. For that purpose, we define a joint-design criterion based on a merit function that contains terms classically implemented in optical design software but used in a non-standard way. After validation on a simple design problem, the proposed method is applied to the design of a very fast (f/0.75) complex lens. The obtained joint-designed lens is shown to be superior to a classically designed one in terms of weight and image quality in the field.