Skip to main content

Real-time automatic aberration correction for easy high-resolution imaging in complex specimens, by STED and other point-scanning microscopy techniques


Super-resolution methods have recently given new life to fluorescence microscopy; they promise molecular-scale resolution, while maintaining all the benefits of traditional diffraction limited techniques, such as robust labeling methods and three-dimensional imaging capability. However, the current super-resolution techniques only work reliably with thin, brightly labeled, low background samples. STimulated Emission Depletion (STED) super-resolution microscopy in principle is exceptionally well suited for deep imaging, because point-illumination makes it possible to use an optical pinhole that significantly reduces the out-of-focus background signal. However, current STED microscope implementations suffer from very low signal-to-noise ratio (SNR), and the STED depletion beam intensity distribution – that is used to reduce the size of the effective fluorescence volume at the focus – is extremely sensitive to optical aberrations. In AdaptiveSTED project both of these issues will be addressed. The main goal of the AdaptiveSTED project is to develop a real-time aberration correction scheme for STED (and other point-scanning microscopes) that will allow robust, high resolution imaging deep inside complex, aberrating samples. A novel Single Photon Avalanche diode (SPAD) array detector, will make it possible to combine real-time wavefront sensing with high-SNR fluorescence recording into a single detector. The aberration correction scheme will be compatible with any poin-scanning microscopy technique: it will be thoroughly tested with a variety of biological samples in an open-access setting (anyone can use), in STED, two-photon and confocal imaging modes. The aberration correction system will be realized in collaboration with Prof. Martin J. Booth’s group at University of Oxford.

Call for proposal

See other projects for this call


Via Morego 30
16163 Genova

See on map

Activity type
Research Organisations
EU contribution
€ 180 277,20