AI Article Synopsis
- Localization microscopy is advancing rapidly, providing detailed insights into cellular structures while facing challenges like additive noise and low emitter density for accurate imaging.
- The proposed technique enhances image quality through a two-step process: first, it employs a lock-in technique to distinguish the signal from background noise when imaging gold nanoparticles that label cellular targets.
- Secondly, the K-factor nonlinear image decomposition algorithm is applied, significantly increasing localization accuracy to as low as 5nm and improving the ability to locate closely spaced overlapping particles by 65% compared to traditional methods.
Article Abstract
Localization microscopy provides valuable insights into cellular structures and is a rapidly developing field. The precision is mainly limited by additive noise and the requirement for single molecule imaging that dictates a low density of activated emitters in the field of view. In this paper we present a technique aimed for noise reduction and improved localization accuracy. The method has two steps; the first is the imaging of gold nanoparticles that labels targets of interest inside biological cells using a lock-in technique that enables the separation of the signal from the wide spread spectral noise. The second step is the application of the K-factor nonlinear image decomposition algorithm on the obtained image, which improves the localization accuracy that can reach 5nm and enables the localization of overlapping particles at minimal distances that are closer by 65% than conventional methods.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4399665 | PMC |
| http://dx.doi.org/10.1364/BOE.6.001262 | DOI Listing |
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