Abstract
The imaging rate of structured illumination microscopy (SIM) reached 188 Hz recently. As the exposure time decreases, the camera detects fewer virtual photons, while the noise level remains the same. As a result, the signal-to-noise ratio (SNR) decreases sharply. Furthermore, the SNR decreases further because of photobleaching and phototoxicity. This decreased quality of SIM raw data may lead to surprising artifacts with various causes, which may confuse a new user of SIM microscopy. We summarize three significant possible sources of severe artifacts in reconstructed super-resolution (SR) images. Ultrafast motion of a biological sample or an uneven illumination pattern is the most difficult to be identified. The estimated parameter could also be incorrect, leading to artifact of regular patterns. Furthermore, reconstruction with the Wiener method generates stochastic artifacts due to the amplification of noise during the deconvolution process. To deal with these problems, we have established a protocol to reconstruct ultrafast SIM raw data obtained in low SNR conditions. First, we checked the quality of the raw data with the ImageJ plugin SIMcheck before reconstruction. Then, a modified parameter estimation method was used to improve the precision of the parameters. Finally, an iterative algorithm was used for SIM reconstruction under low signal-to-noise ratio conditions. This procedure effectively suppressed the artifacts in the super-resolution images reconstructed from raw data of low signal-to-noise ratio.
Highlights
Super-resolution (SR) fluorescence microscopy is a powerful tool to obtain higher spatial resolution in biological imaging
We find that parameter estimation and Wiener deconvolution, which are commonly used, cannot work well in low signal-to-noise ratio conditions (Gustafsson et al 2008; Lal et al 2016; Muller et al 2016)
Considering this condition, we summarized the parameter estimation algorithms and deconvolution methods proposed for low signal-to-noise ratio raw data in this study
Summary
Super-resolution (SR) fluorescence microscopy is a powerful tool to obtain higher spatial resolution in biological imaging. Many techniques, such as photoactivated localization microscopy (PALM) (Betzig et al 2006; Hess et al 2006), stochastic optical reconstruction microscopy (STORM) 2006), stimulated emission depletion (STED) (Klar and Hell 1999), super-resolution optical fluctuation imaging (SOFI) (Dertinger et al 2009), and structured illumination microscopy (SIM) (Gustafsson 2000; Gustafsson et al 2008), have been proposed.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
