Enhanced super-resolution microscopy by extreme value based emitter recovery

Abstract

Super-resolution localization microscopy allows visualization of biological structure at nanoscale resolution. However, the presence of heterogeneous background can degrade the nanoscale resolution by tens of nanometers and introduce significant image artifacts. Here we develop a new approach, referred to as extreme value based emitter recovery (EVER), to accurately recover the distorted fluorescent emitters from heterogeneous background. Through numerical simulation and biological experiments, we demonstrate that EVER significantly improves the accuracy and fidelity of the reconstructed super-resolution image for a wide variety of imaging characteristics. EVER requires no manual adjustment of parameters and is implemented as an easy-to-use ImageJ plugin that can immediately enhance the quality of super-resolution images. Our method paves the way for accurate nanoscale imaging of samples with heterogeneous background fluorescence, such as thicker tissue and cells.

Super-resolution Surface-Enhanced Raman Scattering Imaging of Single Particles in Cells

“…We also explored the extreme value-based emitter recovery (EVER) , algorithm for background correction. In the EVER algorithm, , the emitters (or nanoparticles) and the background change at different frequencies are fit by separate Poisson distributions.…”

“…We also explored the extreme value-based emitter recovery (EVER) , algorithm for background correction. In the EVER algorithm, , the emitters (or nanoparticles) and the background change at different frequencies are fit by separate Poisson distributions. When the intensity fluctuations of the emitter are similar to the background, it can become difficult to differentiate them from the background.…”

WindSTORM: Robust online image processing for high-throughput nanoscopy

“…As illustrated in Fig. 1, we first perform background subtraction based on extreme value-based emitter recovery ( 17 ) to minimize artifacts in linear deconvolution, followed by non-iterative inverse filtering and frequency truncation to decompose the overlapping emitters (see Supplementary Materials). Note that all truncated spatial frequencies are to remove the noise that is much higher than the cutoff spatial frequency determined by the diffraction-limited resolution of the optical system, as indicated by fig.…”

“…We first benchmarked the performance of WindSTORM against three conventional approaches—a compressed sensing-based approach (implemented in FALCON) ( 12 ), a multi-emitter fitting algorithm [implemented in 3D–DAOSTORM ( 7 , 21 )], and a single-emitter fitting algorithm [implemented in ThunderSTORM ( 22 )] using simulated datasets with a wide range of emitter densities (0.1 to 6 emitters/μm 2 ) in the presence of uniform background. The point spread function (PSF) of each emitter is modeled with the classical Airy pattern derived from the diffraction theory ( 17 ), and the emitter intensity is modeled with a log-normal distribution ( 12 ). The PSF used in WindSTORM is approximated as a Gaussian function.…”

“…Second, the background estimation algorithm used in WindSTORM is developed on the basis of shot noise model (for sCMOS camera) for temporally slowly varying background. If the Electron Multiplying Charge Coupled Device (EMCCD) camera is used where the excess noise caused by electron multiplication cannot be ignored, then the background correction model needs to be adjusted to account for excess noise ( 17 ). However, for high-throughput nanoscopy, a sCMOS camera is highly preferable due to its large field of view and fast frame rate.…”