High-speed structured illumination with self-supervised denoising

High-speed widefield fluorescence imaging of neural activity in vivo is fundamentally limited by fluctuations in recorded signal due to background contamination and stochastic noise. In this study, we show background and shot noise-reduced imaging of the ultrafast genetically encoded Ca²⁺ indicator GCaMP8f in CA1 pyramidal neurons using periodic structured illumination (SI) with computational image reconstruction. We implement what we believe to be a novel reconstruction method for data acquired using periodic structured illumination, termed pseudo-HiLo (pHiLo), that combines a pseudo-widefield (pWF) reconstruction with individual SI frames to perform a HiLo reconstruction. We compare this new technique to interleaved optical sectioning structured illumination microscopy (OS-SIM) and pWF reconstruction. We quantify the performance of each reconstruction by evaluating contrast, transient peak-to-noise ratio (PNR), pairwise correlation coefficients between ∆F/F time courses extracted from individual in-focus cells, and correlation coefficients between each cell with surrounding cell-free background pixels. We additionally incorporate a self-supervised deep learning method for real-time noise suppression (DeepCAD-RT) into our data preprocessing pipeline. At 500 Hz frame rates, we demonstrate a 75% increase in PNR using the denoised pHiLo reconstruction compared to pWF. Utilizing DeepCAD-RT, we show significant PNR improvements using both structured illumination (SI) reconstruction methods with OS-SIM showing a 59% increase in PNR after denoising. Both pHiLo and OS-SIM reconstructions result in a ≈65% decrease in the mean correlation coefficient of the ∆F/F time courses between ROIs in comparison with pWF, indicating the potential to remove background fluorescent transients from out-of-focus cells.