Inhomogeneities in the refractive index of a biological microscopy sample can introduce phase aberrations, severely impairing the quality of images. Adaptive optics can be employed to correct for phase aberrations and improve image quality. However, conventional adaptive optics can only correct a single phase aberration for the whole field of view (isoplanatic correction) while, due to the highly heterogeneous nature of biological tissues, the sample induced aberrations in microscopy often vary throughout the field of view (anisoplanatic aberration), limiting significantly the effectiveness of adaptive optics. This paper reports on a new approach for aberration correction in laser scanning confocal microscopy, in which a spatial light modulator is used to generate multiple excitation points in the sample to simultaneously scan different portions of the field of view with completely independent correction, achieving anisoplanatic compensation of sample induced aberrations, in a significantly shorter time compared to sequential isoplanatic correction of multiple image subregions. The method was tested in whole Drosophila brains and in larval Zebrafish, each showing a dramatic improvement in resolution and sharpness when compared to conventional isoplanatic adaptive optics.

We report on a universal sample-independent sensorless adaptive optics method, based on modal optimization of the second moment of the fluorescence emission from a point-like excitation. Our method employs a sample-independent precalibration, performed only once for the particular system, to establish the direct relation between the image quality and the aberration. The method is potentially applicable to any form of microscopy with epifluorescence detection, including the practically important case of incoherent fluorescence emission from a three dimensional object, through minor hardware modifications. We have applied the technique successfully to a widefield epifluorescence microscope and to a multiaperture confocal microscope.

In life sciences, interest in the microscopic imaging of increasingly complex three dimensional samples, such as cell spheroids, zebrafish embryos, and in vivo applications in small animals, is growing quickly. Due to the increasing complexity of samples, more and more life scientists are considering the implementation of adaptive optics in their experimental setups. While several approaches to adaptive optics in microscopy have been reported, it is often difficult and confusing for the microscopist to choose from the array of techniques and equipment. In this poster presentation we offer a small guide to adaptive optics providing general guidelines for successful adaptive optics implementation.

A methodology for retrieving the unknown object distribution and point-spread functions (PSFs) from a set of images acquired in the presence of temporal phase aberrations is presented in this paper. The method works by finding optimal complimentary linear filters for multi-frame deconvolution. The algorithm uses undemanding computational operations and few a priori, making it simple, fast and robust even at low signal-to-noise ratios. Results of numerical simulations and experimental tests are given as empirical proof, alongside comparisons with other algorithms found in the literature.

Light-sheet fluorescence microscopy techniques commonly use deconvolution to remove the effect of the illumination beam shape on the image formation, reducing the effects of side lobes and increasing the contrast in three-dimensional imaging. Deconvolution requires knowledge of the optical transfer function of the system and can only be estimated or measured imperfectly. Furthermore, in biological samples the optical transfer function is degraded by the presence of phase aberrations, this implies that any a priori deconvolution applied to the sample will be to some degree incorrect since it does not account for this unknown phase error in the optical transfer function. By combining adaptive optics and computational processing in this microscope, it is shown that by introducing perturbations to the optical transfer function it is possible to estimate the object distribution and remove aberrations.