Compressed sensing in fluorescence microscopy.

Compressed sensing (CS) is a signal processing approach that solves ill-posed inverse problems, from under-sampled data with respect to the Nyquist criterium. CS exploits sparsity constraints based on the knowledge of prior information, relative to the structure of the object in the spatial or other domains. It is commonly used in image and video compression as well as in scientific and medical applications, including computed tomography and magnetic resonance imaging. In the field of fluorescence microscopy, it has been demonstrated to be valuable for fast and high-resolution imaging, from single-molecule localization, super-resolution to light-sheet microscopy. Furthermore, CS has found remarkable applications in the field of mesoscopic imaging, facilitating the study of small animals' organs and entire organisms. This review article illustrates the working principles of CS, its implementations in optical imaging and discusses several relevant uses of CS in the field of fluorescence imaging from super-resolution microscopy to mesoscopy

Compressive Fluorescence Microscopy for Biological and Hyperspectral Imaging

The mathematical theory of compressed sensing (CS) asserts that one can acquire signals from measurements whose rate is much lower than the total bandwidth. Whereas the CS theory is now well developed, challenges concerning hardware implementations of CS-based acquisition devices---especially in optics---have only started being addressed. This paper presents an implementation of compressive sensing in fluorescence microscopy and its applications to biomedical imaging. Our CS microscope combines a dynamic structured wide-field illumination and a fast and sensitive single-point fluorescence detection to enable reconstructions of images of fluorescent beads, cells and tissues with undersampling ratios (between the number of pixels and number of measurements) up to 32. We further demonstrate a hyperspectral mode and record images with 128 spectral channels and undersampling ratios up to 64, illustrating the potential benefits of CS acquisition for higher dimensional signals which typically exhibits extreme redundancy. Altogether, our results emphasize the interest of CS schemes for acquisition at a significantly reduced rate and point out to some remaining challenges for CS fluorescence microscopy.Comment: Submitted to Proceedings of the National Academy of Sciences of the United States of Americ

Single atom imaging with time-resolved electron microscopy

Developments in scanning transmission electron microscopy (STEM) have opened up new possibilities for time-resolved imaging at the atomic scale. However, rapid imaging of single atom dynamics brings with it a new set of challenges, particularly regarding noise and the interaction between the electron beam and the specimen. This thesis develops a set of analytical tools for capturing atomic motion and analyzing the dynamic behaviour of materials at the atomic scale. Machine learning is increasingly playing an important role in the analysis of electron microscopy data. In this light, new unsupervised learning tools are developed here for noise removal under low-dose imaging conditions and for identifying the motion of surface atoms. The scope for real-time processing and analysis is also explored, which is of rising importance as electron microscopy datasets grow in size and complexity. These advances in image processing and analysis are combined with computational modelling to uncover new chemical and physical insights into the motion of atoms adsorbed onto surfaces. Of particular interest are systems for heterogeneous catalysis, where the catalytic activity can depend intimately on the atomic environment. The study of Cu atoms on a graphene oxide support reveals that the atoms undergo anomalous diffusion as a result of spatial and energetic disorder present in the substrate. The investigation is extended to examine the structure and stability of small Cu clusters on graphene oxide, with atomistic modelling used to understand the significant role played by the substrate. Finally, the analytical methods are used to study the surface reconstruction of silicon alongside the electron beam-induced motion of adatoms on the surface. Taken together, these studies demonstrate the materials insights that can be obtained with time-resolved STEM imaging, and highlight the importance of combining state-ofthe- art imaging with computational analysis and atomistic modelling to quantitatively characterize the behaviour of materials with atomic resolution.The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007–2013)/ERC grant agreement 291522–3DIMAGE, as well as from the European Union Seventh Framework Programme under Grant Agreement 312483-ESTEEM2 (Integrated Infrastructure Initiative -I3)

On-chip Single Nanoparticle Detection and Sizing by Mode Splitting in an Ultra-high-Q Microresonator

The ability to detect and size individual nanoparticles with high resolution is crucial to understanding behaviours of single particles and effectively using their strong size-dependent properties to develop innovative products. We report real-time, in-situ detection and sizing of single nanoparticles, down to 30 nm in radius, using mode-splitting in a monolithic ultra-high-Q whispering-gallery-mode (WGM) microtoroid resonator. Particle binding splits a WGM into two spectrally shifted resonance modes, forming a self-referenced detection scheme. This technique provides superior noise suppression and enables extracting accurate size information in a single-shot measurement. Our method requires neither labelling of the particles nor apriori information on their presence in the medium, providing an effective platform to study nanoparticles at single particle resolution.Comment: 23 pages, 8 figure

Volumetric velocimetry for fluid flows

In recent years, several techniques have been introduced that are capable of extracting 3D three-component velocity fields in fluid flows. Fast-paced developments in both hardware and processing algorithms have generated a diverse set of methods, with a growing range of applications in flow diagnostics. This has been further enriched by the increasingly marked trend of hybridization, in which the differences between techniques are fading. In this review, we carry out a survey of the prominent methods, including optical techniques and approaches based on medical imaging. An overview of each is given with an example of an application from the literature, while focusing on their respective strengths and challenges. A framework for the evaluation of velocimetry performance in terms of dynamic spatial range is discussed, along with technological trends and emerging strategies to exploit 3D data. While critical challenges still exist, these observations highlight how volumetric techniques are transforming experimental fluid mechanics, and that the possibilities they offer have just begun to be explored.SD was partially supported under Grant No. DPI2016-79401-R funded by the Spanish State Research Agency (SRA) and the European Regional Development Fund (ERDF). FC was partially supported by the U.S. National Science Foundation (Chemical, Bioengineering, Environmental, and Transport Systems, Grant No. 1453538)