Optics continuum最新文献

The proposed photoluminescence calibration standard comprises a solid-state phosphor film, neutral density gel filter(s), and a 3D-printed optomechanical cartridge. The proposed standard demonstrated exceptional photostability; photoluminescence did not deviate from the baseline more than 1.27% under 5 minutes of continuous illumination. Remarkably, there was no measurable degradation over a 3-year study. Precise photoluminescence intensity modulation was accomplished with neutral density gel filters (R² > 0.9982) and optical apertures (R² > 0.9970). A model for photoluminescence intensity as a function of neutral-density filter and optical aperture parameters yielded a mean percentage error (MPE) of 2.79%, indicating high precision. Inter-sample variability was low, with a mean coefficient of variation (CV) of 1.32%. Mean CV across 24 channels decreased from 11.88% to 1.51% following multi-point calibration of multichannel point-of-care (POC) fluorometers. Cost analysis revealed a per-unit cost between $0.49 to $1.80. This work suggests that the proposed calibration standards provide a cost-effective, highly stable solution for reliable fluorometer calibration in low-resource settings.

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 ΔF/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 ΔF/F time courses between ROIs in comparison with pWF, indicating the potential to remove background fluorescent transients from out-of-focus cells.

The normalized cross-correlation ( $NCC$ ) is widely used for image registration due to its simple geometrical interpretation and being feature-agnostic. Here, after reviewing $NCC$ definitions for images with an arbitrary number of dimensions and channels, we propose a generalization in which each pixel value of each channel can be individually weighted using real non-negative numbers. This generalized normalized weighted cross-correlation ( $NWCC$ ) and its zero-mean equivalent ( $ZNWCC$ ) can be used, for example, to prioritize pixels based on signal-to-noise ratio. Like a previously defined $NWCC$ with binary weights, the proposed generalizations enable the registration of uniformly, but not necessarily isotropically, sampled images with irregular boundaries and/or sparse sampling. All $NCC$ definitions discussed here are provided with discrete Fourier transform ( $DFT$ ) formulations for fast computation. Practical aspects of $NCC$ computational implementation are briefly discussed, and a convenient function to calculate the overlap of uniformly, but not necessarily isotropically, sampled images with irregular boundaries and/or sparse sampling is introduced, together with its $DFT$ formulation. Finally, examples illustrate the benefit of the proposed normalized cross-correlation functions.