ENERGY BASED SELECTIVE AVERAGING APPROACH FOR MULTI-TRIAL OPTICAL IMAGING RECORDINGS

Functional optical imaging (OI) of intrinsic signals (like blood oxygenation coupled reflection changes) and of extrinsic properties of voltage sensitive probes (like voltage-sensitive dyes (VSD)) forms a group of invasive neuroimaging techniques, that possess up to date the highest temporal and spatial resolution on a meso- to macroscopic scale. The main challenge for OI-based functional studies is a very low signal to noise ratio (SNR). We propose a flow guided, selective averaging approach for a multi-trial experimental design. We use an optic flow based energy measure for a weighted averaging across the trials. Our approach is translation and illumination invariant and suited to remove periodic artefacts with less required samples for averaging. For benchmarking of OI denoising strategies, we present a novel, semi-synthetic dataset, that simulates a continuous recording of intrinsic signals over ten trials at one second each with alternating baseline and stimulus condition.