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

Adaptive Basis Scan by Wavelet Prediction for Single-Pixel Imaging

International audienceSingle pixel camera imaging is an emerging paradigm that allows high-quality images to be provided by a device only equipped with a single point detector. A single pixel camera is an experimental setup able to measure the inner product of the scene under view –the image– with any user-defined pattern. Post-processing a sequence of point measurements obtained with different patterns permits to recover spatial information, as it has been demonstrated by state-of-the art approaches belonging to the compressed sensing framework. In this paper, a new framework for the choice of the patterns is proposed together with a simple and efficient image recovery scheme. Our goal is to overcome the computationally demanding 1-minimization of compressed sensing. We propose to choose patterns among a wavelet basis in an adaptive fashion, which essentially relies onto the prediction of the significant wavelet coefficients' location. More precisely, we adopt a multiresolution strategy that exploits the set of measurements acquired at coarse scales to predict the set of measurements to be performed at a finer scale. Prediction is based on a fast cubic interpolation in the image domain. A general formalism is given so that any kind of wavelets can be used, which enables one to adjust the wavelet to the type of images related to the desired application. Both simulated and experimental results demonstrate the ability of our technique to reconstruct biomedical images with improved quality compared to CS-based recovery. Application to real-time fluorescence imaging of biological tissues could benefit from the proposed method

Large-scale single-photon imaging

Benefiting from its single-photon sensitivity, single-photon avalanche diode (SPAD) array has been widely applied in various fields such as fluorescence lifetime imaging and quantum computing. However, large-scale high-fidelity single-photon imaging remains a big challenge, due to the complex hardware manufacture craft and heavy noise disturbance of SPAD arrays. In this work, we introduce deep learning into SPAD, enabling super-resolution single-photon imaging over an order of magnitude, with significant enhancement of bit depth and imaging quality. We first studied the complex photon flow model of SPAD electronics to accurately characterize multiple physical noise sources, and collected a real SPAD image dataset (64 $\times$ 32 pixels, 90 scenes, 10 different bit depth, 3 different illumination flux, 2790 images in total) to calibrate noise model parameters. With this real-world physical noise model, we for the first time synthesized a large-scale realistic single-photon image dataset (image pairs of 5 different resolutions with maximum megapixels, 17250 scenes, 10 different bit depth, 3 different illumination flux, 2.6 million images in total) for subsequent network training. To tackle the severe super-resolution challenge of SPAD inputs with low bit depth, low resolution, and heavy noise, we further built a deep transformer network with a content-adaptive self-attention mechanism and gated fusion modules, which can dig global contextual features to remove multi-source noise and extract full-frequency details. We applied the technique on a series of experiments including macroscopic and microscopic imaging, microfluidic inspection, and Fourier ptychography. The experiments validate the technique's state-of-the-art super-resolution SPAD imaging performance, with more than 5 dB superiority on PSNR compared to the existing methods

Improved methods for functional neuronal imaging with genetically encoded voltage indicators

Voltage imaging has the potential to revolutionise neuronal physiology, enabling high temporal and spatial resolution monitoring of sub- and supra-threshold activity in genetically defined cell classes. Before this goal is reached a number of challenges must be overcome: novel optical, genetic, and experimental techniques must be combined to deal with voltage imaging’s unique difficulties. In this thesis three techniques are applied to genetically encoded voltage indicator (GEVI) imaging. First, I describe a multifocal two-photon microscope and present a novel source localisation control and reconstruction algorithm to increase scattering resistance in functional imaging. I apply this microscope to image population and single-cell voltage signals from voltage sensitive fluorescent proteins in the first demonstration of multifocal GEVI imaging. Second, I show that a recently described genetic technique that sparsely labels cortical pyramidal cells enables single-cell resolution imaging in a one-photon widefield imaging configuration. This genetic technique allows simple, high signal-to-noise optical access to the primary excitatory cells in the cerebral cortex. Third, I present the first application of lightfield microscopy to single cell resolution neuronal voltage imaging. This technique enables single-shot capture of dendritic arbours and resolves 3D localised somatic and dendritic voltage signals. These approaches are finally evaluated for their contribution to the improvement of voltage imaging for physiology.Open Acces

Nanoscale investigation of chromatin organization by structured illumination microscopy

This thesis work aims to present a novel approach to reconstruct Structured Illumination Microscopy, SIM, raw data and to analyze SIM reconstructed images. These new approaches will be demonstrated in the study of chromatin organization. The dissertation will be articulated as follows: Chapter 1 provides an introduction to chromatin nanoscale organization and optical fluorescence microscopy, which is one of the main tools involved in life sciences studies. Indeed, optical microscopy allowed investigating, with high specificity and sensitivity, living samples such as cells, and even tissues. The reader will be presented with a summary on the fluorescence optical microscopy and on the super-resolution, SR, techniques available today including SIM, which is the microscope used in this thesis work. In Chapter 2 the focus is on the introduction of a new reconstruction tool for specific SR-SIM microscopy powered by the Separation of Photons by Lifetime Tuning, SPLIT, method. The introduction of the concept, applied in other works to different SR techniques, will be followed by the practical implementation of the method on the SIM microscope. Then, the applicability of the technique, which we called SPLIT-SIM, will be demonstrated on several different samples. Indeed, it will be used on Simulated data, on test experimental beads, on biological samples both in one and two-color staining. In Chapter 3 the focus will move on the coupling of SIM reconstructed data to colocalization analysis. In particular, for the first time, SIM was coupled to Image Cross-Correlation Spectroscopy, ICCS, in the study of two-color images of a model sample. DNA origami-based structures were chosen as a model sample with precise distances allowing for evaluation of the analysis results. Moreover, all the images analyzed by the pixel-based technique, SIM-ICCS, were analyzed also with an object-based technique as a comparison to evaluate which could be the best choice in SIM acquisitions. Finally, Chapter 4 will be focused on the application of the analysis, performed in chapter 3, to two-color SIM images of nuclear structure. The analysis will be performed on \u2018positive control\u2019 in which the target structures will be colocalized and on a negative control in which the structured are spatially segregated within the nucleus. Both object-based and pixel-based analysis will be able to extract coherent results thus showing how SIM-ICCS can become an interesting and useful tool to analyze SIM multicolor acquisitions

Performance analysis of low-flux least-squares single-pixel imaging

A single-pixel camera is able to computationally form spatially resolved images using one photodetector and a spatial light modulator. The images it produces in low-light-level operation are imperfect, even when the number of measurements exceeds the number of pixels, because its photodetection measurements are corrupted by Poisson noise. Conventional performance analysis for single-pixel imaging generates estimates of mean-square error (MSE) from Monte Carlo simulations, which require long computational times. In this letter, we use random matrix theory to develop a closed-form approximation to the MSE of the widely used least-squares inversion method for Poisson noise-limited single-pixel imaging. We present numerical experiments that validate our approximation and a motivating example showing how our framework can be used to answer practical optical design questions for a single-pixel camera.This work was supported in part by the Samsung Scholarship and in part by the US National Science Foundation under Grant 1422034. (Samsung Scholarship; 1422034 - US National Science Foundation)Accepted manuscrip

Probing Cellular Uptake of Nanoparticles, One at a Time

Advanced fluorescence microscopy is the method of choice to study cellular uptake of nanoparticles with molecular specificity and nanoscale resolution; yet, direct visualization of nanoparticles entry into cells poses severe technical challenges. Here, we have combined super-resolution photoactivation localization microscopy (PALM) with single particle tracking (SPT) to visualize clathrin-mediated endocytosis (CME) of polystyrene nanoparticles at very high spatial and temporal resolution