Compact prototype: The MAFIn module will be designed to include a deformable mirror, a wave-front sensor and any related optics to extend, by means of wavefront coding (WFC) techniques, the depth of field (DOF) of the imaging arm of an LSFM. The full module will be enclosed in a small breadboard to allow its transportation and, at the same time, an easy coupling to other LSFM microscopes.

Aberration compensation: The optical setup necessary for WFC is similar to that necessary for aberration corrections in any microscope. We will introduce to the cubic phase necessary for WFC, any necessary additional correction terms to eliminate some of the main aberrations such as tip, tilt and spherical. This will result in a more efficient extension of the depth of field during image acquisition, a higher signal to noise ratio and overall a more robust deconvolution process.

Real time deconvolution: The current method for observing the volumetric images involves a deconvolution step after the full acquisition of the experiment. This is not ideal as monitoring the sample is only performed before or after the fast-volumetric imaging has happened. To ensure a fast monitoring of the sample, a real-time deconvolution routine will be developed.

Easy-to-use user interface: We will construct a graphical user interface (GUI) that will guide any inexperienced user through all the necessary steps to obtain a proper image using our MAFIn module coupled to a LSFM. This will include the calibration routine, the calculation on the inverse matrix necessary to set the AO components, a routine for displaying and monitoring in real time (i.e. at 25 Hz) the imaged volume of the sample. Overall, this will consist of a unique GUI for running the actual experiment.

Market analysis and business plan: The technology currently is the subject of one patent family, which covers a light sheet microscope that allows for robust, fast 3D imaging without the need to move or perturb the specimen. An in-depth study of the needs of the light-sheet microscopy market, the product competitiveness and pricing will be performed.