Improved localization accuracy in stochastic super-resolution fluorescence microscopy by K-factor image deshadowing

Abstract

Localization of a single fluorescent particle with sub-diffractionlimit accuracy is a key merit in localization microscopy. Existing methods such as photoactivated localization microscopy (PALM) and stochastic optical reconstruction microscopy (STORM) achieve localization accuracies of single emitters that can reach an order of magnitude lower than the conventional resolving capabilities of optical microscopy. However, these techniques require a sparse distribution of simultaneously activated fluorophores in the field of view, resulting in larger time needed for the construction of the full image. In this paper we present the use of a nonlinear image decomposition algorithm termed K-factor, which reduces an image into a nonlinear set of contrast-ordered decompositions whose joint product reassembles the original image. The K-factor technique, when implemented on raw data prior to localization, can improve the localization accuracy of standard existing methods, and also enable the localization of overlapping particles, allowing the use of increased fluorophore activation density, and thereby increased data collection speed. Numerical simulations of fluorescence data with random probe positions, and especially at high densities of activated fluorophores, demonstrate an improvement of up to 85% in the localization precision compared to single fitting techniques. Implementing the proposed concept on experimental data of cellular structures yielded a 37% improvement in resolution for the same superresolution image acquisition time, and a decrease of 42% in the collection time of super-resolution data with the same resolution.

Superresolved labeling nanoscopy based on temporally flickering nanoparticles and the K-factor image deshadowing

“…The result was compared to a simple TA of the images for the same time period. The K-factor algorithm parameters were chosen according to previous work [24] to be k = 0.9, n = 48, h = 8. Figure 1(a) is the simulated sample with random diffraction limited spots originating from scattering of light from the GNPs with added background noise and shot noise so that the SNR was -20dB.…”

“…However, in a previous paper [27] we had shown that the first few factors of the decomposition, marked by f h , where h<M, contain most of the image data together with noise, while high order factors contains mostly noise together with some fine spatial information associated with low contrast levels. By multiplying the original image with the first few harmonies, the image data will be de-emphasized in the reconstruction and noise will be reduced.…”

“…The TSL technique increases the SNR, enables the extraction of signal even in poor photon count and improves the localization accuracy of a single particle. The reconstructed TSL image undergoes a second step of signal processing using the K-factor algorithm [24]. This algorithm reduces the overlap between closely spaced PSFs, therefore allows their detection and in addition improves the localization precision of each PSF, making this approach applicable for superresolution imaging.…”

Phase stretch transform for super-resolution localization microscopy

“…In a previous paper we suggested the use of the modified K-factor algorithm for the same purpose [35]. The PST has two major advantages in respect to the K-factor algorithm: The first is a larger narrowing of the PSF, by a factor of 2 with compared to the K-factor, resulting in the ability to detect even closer PSFs.…”

K-factor image deshadowing for three-dimensional fluorescence microscopy

“…The proposed algorithm is a computer post processing technique that can be applied to the raw data prior to emitter localization. In a previous paper, the algorithm was applied to 2D images 22 showing improvement in localization precision or data acquisition time of ~40%. Here we present a modified version, which is applied to 3D localization.…”