scholarly journals Boosting the localization precision of dSTORM by biocompatible metal-dielectric coated glass coverslips

2017 ◽  
Author(s):  
Hannah S. Heil ◽  
Benjamin Schreiber ◽  
Monika Emmerling ◽  
Sven Hoefling ◽  
Martin Kamp ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Biological Imaging ◽  
Signal To Noise ◽  
Emission Properties ◽  
Coated Glass ◽  
Stochastic Optical Reconstruction Microscopy ◽  
Localization Precision ◽  
Noise Ratio ◽  
Optical Reconstruction

Super-resolution techniques such as direct Stochastic Optical Reconstruction Microscopy (dSTORM) have become versatile and well-established tools for biological imaging over the last century. Here, we theoretically and experimentally show that clever combination of different fluorescence modalities allows further improvements. We found that the interaction of fluorophores with plasmonic surfaces boost super-resolution performance in dSTORM approaches as it allows for tailoring the excitation and emission properties. The strength of the approach is that no further specialized microscope setup is required as the described enhancement solely rely on metal-dielectric coated glass coverslips that are straightforward to fabricate. Such biocompatible plasmonic nanolayers enhance the signal-to-noise ratio of dSTORM, and thus sharpens the localization precision by a factor of two.

scholarly journals Evaluation of sted super-resolution image quality by image correlation spectroscopy (QuICS)

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Elena Cerutti ◽  
Morgana D’Amico ◽  
Isotta Cainero ◽  
Gaetano Ivan Dellino ◽  
Mario Faretta ◽  
...  
Keyword(s):  
Autocorrelation Function ◽  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Image Contrast ◽  
Correlation Spectroscopy ◽  
Image Correlation ◽  
Signal To Noise ◽  
Image Correlation Spectroscopy ◽  
Noise Ratio

AbstractQuantifying the imaging performances in an unbiased way is of outmost importance in super-resolution microscopy. Here, we describe an algorithm based on image correlation spectroscopy (ICS) that can be used to assess the quality of super-resolution images. The algorithm is based on the calculation of an autocorrelation function and provides three different parameters: the width of the autocorrelation function, related to the spatial resolution; the brightness, related to the image contrast; the relative noise variance, related to the signal-to-noise ratio of the image. We use this algorithm to evaluate the quality of stimulated emission depletion (STED) images of DNA replication foci in U937 cells acquired under different imaging conditions. Increasing the STED depletion power improves the resolution but may reduce the image contrast. Increasing the number of line averages improves the signal-to-noise ratio but facilitates the onset of photobleaching and subsequent reduction of the image contrast. Finally, we evaluate the performances of two different separation of photons by lifetime tuning (SPLIT) approaches: the method of tunable STED depletion power and the commercially available Leica Tau-STED. We find that SPLIT provides an efficient way to improve the resolution and contrast in STED microscopy.

scholarly journals Super-Resolution Reconstruction of Cell Pseudo-Color Image Based on Raman Technology

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4076
Author(s):  
Yang ◽  
Zhu ◽  
Wang ◽  
Yang ◽  
Wu ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Raman Spectrum ◽  
Background Noise ◽  
Color Image ◽  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Reconstruction Algorithm ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Pseudo Color

Raman spectroscopy visualization is a challenging task due to the interference of complex background noise and the number of selected measurement points. In this paper, a super-resolution image reconstruction algorithm for Raman spectroscopy is studied to convert raw Raman data into pseudo-color super-resolution imaging. Firstly, the Raman spectrum data of a single measurement point is measured multiple times to calculate the mean value to remove the random background noise, and innovatively introduce the Retinex algorithm and the median filtering algorithm which improve the signal-to-noise ratio. The novel method of using deep neural network performs a super-resolution reconstruction operation on the gray image. An adaptive guided filter that automatically adjusts the filter radius and penalty factor is proposed to highlight the contour of the cell, and the super-resolution reconstruction of the pseudo-color image of the Raman spectrum is realized. The average signal-to-noise ratio of the reconstructed pseudo-color image sub-band reaches 14.29 db, and the average value of information entropy reaches 4.30 db. The results show that the Raman-based cell pseudo-color image super-resolution reconstruction algorithm is an effective tool to effectively remove noise and high-resolution visualization. The contrast experiments show that the pseudo-color image Kullback–Leiber (KL) entropy of the color image obtained by the method is small, the boundary is obvious, and the noise is small, which provide technical support for the development of sophisticated single-cell imaging Raman spectroscopy instruments.

Super-resolution methods in MRI: Can they improve the trade-off between resolution, signal-to-noise ratio, and acquisition time?

2012 ◽  
Vol 68 (6) ◽  
pp. 1983-1993 ◽  
Author(s):  
Esben Plenge ◽  
Dirk H. J. Poot ◽  
Monique Bernsen ◽  
Gyula Kotek ◽  
Gavin Houston ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Acquisition Time ◽  
Signal To Noise ◽  
Trade Off ◽  
Noise Ratio

scholarly journals Fast and High-Resolution Neonatal Brain MRI Through Super-Resolution Reconstruction From Acquisitions With Variable Slice Selection Direction

2021 ◽  
Vol 15 ◽  
Author(s):  
Yao Sui ◽  
Onur Afacan ◽  
Ali Gholipour ◽  
Simon K. Warfield
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Signal To Noise Ratio ◽  
Brain Mri ◽  
Super Resolution ◽  
Neonatal Brain ◽  
Signal To Noise ◽  
High Quality ◽  
Noise Ratio

The brain of neonates is small in comparison to adults. Imaging at typical resolutions such as one cubic mm incurs more partial voluming artifacts in a neonate than in an adult. The interpretation and analysis of MRI of the neonatal brain benefit from a reduction in partial volume averaging that can be achieved with high spatial resolution. Unfortunately, direct acquisition of high spatial resolution MRI is slow, which increases the potential for motion artifact, and suffers from reduced signal-to-noise ratio. The purpose of this study is thus that using super-resolution reconstruction in conjunction with fast imaging protocols to construct neonatal brain MRI images at a suitable signal-to-noise ratio and with higher spatial resolution than can be practically obtained by direct Fourier encoding. We achieved high quality brain MRI at a spatial resolution of isotropic 0.4 mm with 6 min of imaging time, using super-resolution reconstruction from three short duration scans with variable directions of slice selection. Motion compensation was achieved by aligning the three short duration scans together. We applied this technique to 20 newborns and assessed the quality of the images we reconstructed. Experiments show that our approach to super-resolution reconstruction achieved considerable improvement in spatial resolution and signal-to-noise ratio, while, in parallel, substantially reduced scan times, as compared to direct high-resolution acquisitions. The experimental results demonstrate that our approach allowed for fast and high-quality neonatal brain MRI for both scientific research and clinical studies.

Fast 2D super resolution ISAR imaging method under low signal‐to‐noise ratio

2017 ◽  
Vol 11 (10) ◽  
pp. 1495-1504 ◽  
Author(s):  
Shaodong Li ◽  
Wenfeng Chen ◽  
Weijian Liu ◽  
Jun Yang ◽  
Xiaoyan Ma
Keyword(s):  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Imaging Method ◽  
Signal To Noise ◽  
Isar Imaging ◽  
Noise Ratio
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