Carl G. Ebeling

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

Localization of a single fluorescent particle with sub-diffraction-limit 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 super-resolution image acquisition time, and a decrease of 42% in the collection time of super-resolution data with the same resolution.

K-factor image deshadowing for three-dimensional fluorescence microscopy.

The ability to track single fluorescent particles within a three dimensional (3D) cellular environment can provide valuable insights into cellular processes. In this paper, we present a modified nonlinear image decomposition technique called K-factor that reshapes the 3D point spread function (PSF) of an XYZ image stack into a narrow Gaussian profile. The method increases localization accuracy by ~60% with compare to regular Gaussian fitting, and improves minimal resolvable distance between overlapping PSFs by ~50%. The algorithm was tested both on simulated data and experimentally.

Two views on light sheets

A dual-view light-sheet microscope combines isotropic spatial resolution with high speed and minimal phototoxicity.

Interference based localization of single emitters

The ability to localize precisely a single optical emitter is important for particle tracking applications and super resolution microscopy. It is known that for a traditional microscope the ability to localize such an emitter is limited by the photon count. Here we analyze the ability to improve such localization by imposing interference fringes. We show here that a simple grating interferometer can introduce such improvement in certain circumstances and analyze what is required to increase the localization precision further.

Modified K-factor image decomposition for three-dimensional super resolution microscopy

The ability to track single fluorescent particles within a three dimensional (3D) cellular environment can provide valuable insights into cellular processes. In this paper, we present a modified nonlinear image decomposition technique called K-factor that reshapes the 3D point spread function (PSF) of an XYZ image stack into a narrow Gaussian profile. The method increases localization accuracy by ~60% with compare to regular Gaussian fitting, and improves minimal resolvable distance between overlapping PSFs by ~50%. The algorithm was tested both on simulated data and experimentally. This work presets a novel use of the nonlinear image decomposition technique called K-factor that reshapes the three dimensional (3D) point spread function (PSF) of an XYZ image stack into a narrow Gaussian profile. The experimentally obtained PSF of a Z-stack raw data that is acquired by a widefield microscope has a more elaborate shape that is given by the Gibson and Lanni model. This shape increases the computational complexity associated with the localization routine, when used in localization microscopy techniques. Furthermore, due to its nature, this PSF spreads over a larger volume, making the problem of overlapping emitters detection more pronounced. The ability to use Gaussian fitting with high accuracy on 3D data can facilitate the computational complexity, hence reduce the processing time required for the generation of the 3D superresolved image. In addition it allows the detection of overlapping PSFs and reduces the effects of the penetration of out of focus PSFs into in focused PSFs, therefore enables the increase in the activated fluorophore density by ~50%. The algorithm was tested both on simulated data and experimentally, where it yielded an increase in the localization accuracy by ~60% with compare to regular Gaussian fitting, and improved the minimal resolvable distance between overlapping PSFs by ~50%, making it extremely applicable to the field of 3D biomedical imaging,

Improvement in in-plane localization precision of nanoparticles using interference analysis

We present a method to improve the localization precision of nanoparticles over Gaussian fitting by imposing an interference pattern on the Point-Spread-Function. Localization precision of 0.1nm for a single emitter was obtained.

Sub-nanometer particle tracking by point-spread-function spatial modulation

We show that the localization precision of single nanoparticles can be improved by imposing an interference pattern on the Point-Spread-Function. Localization precision of 0.1nm for a single emitter was obtained.

New Directions in Super Resolved Imaging

Two new directions are presented. The first uses modified nonlinear image decomposition to reshapes the 3D point spread function of an XYZ Z-stack and improves the localization accuracy. The second improves the localization accuracy by labeling the specimens with GNPs and their subsequent flickering at different temporal frequencies.

Self-Interference of Coherent and Incoherent Signals for Sub-Nanometer Localization of Single Emitters

Localization precision of single emitters can be fundamentally improved by imposing interference fringes on the Point-Spread-Function. We show that this interference effect can be used for coherent and incoherent signals for sub nanometer localization.

Image processing for super-resolution localization in fluorescence microscopy

Localization of a single fluorescent particle with sub-diffraction limit accuracy is a key merit in fluorescence microscopy. Implementation of nonlinear filtering algorithms prior the localization process 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. In this paper we present the use of an 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 is implemented on the images, prior to the localization of the fluorescent probes. Numerical simulations of fluorescence data with random probe positions, and especially at high densities of activated fluorophores demonstrated an improvement in the localization precision with compare to single fitting techniques. Implanting the proposed concept also on experimental data of cellular structures yielded the theoretically predicted resolution enhancement.