Quality control in structured illumination-based super-resolution FRET imaging via machine learning.

Structured illumination-based super-resolution Förster resonance energy transfer microscopy (SISR-FRETM) has facilitated better observation of molecular behavior in living cells. However, SIM tends to produce artifacts in reconstruction, especially when the raw SIM inputs are of low signal-to-noise ratio (SNR) or out-of-focus, leading to erroneous signals in subsequent FRET. Current SIM quality evaluation metrics fail to utilize both SNR and out-of-focus features, making it challenging to classify unqualified raw data for FRET.