ArticleBiomedical optics express2017
STED imaging performance estimation by means of Fourier transform analysis.
Article in Biomedical optics express, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 10 citations in OpenAlex.
- Volumetric imaging of fast cellular dynamics with deep learning enhanced bioluminescence microscopy.Communications biology · 2022Article
- Evaluation of sted super-resolution image quality by image correlation spectroscopy (QuICS).Scientific reports · 2021Article
- A simple empirical algorithm for optimising depletion power and resolution for dye and system specific STED imaging.Journal of microscopy · 2019Article
- Nanoscale structure of amyloid-β plaques in Alzheimer's disease.Scientific reports · 2019Article
- Synaptic phosphorylated α-synuclein in dementia with Lewy bodies.Brain : a journal of neurology · 2017Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Due to relatively high powers used in STED, biological samples may be affected by the illumination in the process of image acquisition. Similarly, the performance of the system may be limited by the sample itself. Optimization of the STED parameters taking into account the sample itself is therefore a complex task as there is no clear methodology that can determine the image improvement in an objective and quantitative manner. In this work, a method based on Fourier transform formalism is presented to analyze the performance of a STED system. The spatial frequency distribution of pairs of confocal and STED images are compared to obtain an objective parameter, the Azimuth Averaged Spectral Content Spread (AASCS), that is related to the performance of the system in which the sample is also considered. The method has been first tested on samples of beads, and then applied to cell samples labeled with multiple fluorescent dyes. The results show that a single parameter, the AASCS, can be used to determine the optimal settings for STED image acquisition in an objective way, only by using the information provided by the images from the sample themselves. The AASCS also helps minimize the depletion power, for better preservation of the samples.
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