Faster and less phototoxic 3D fluorescence microscopy using a versatile compressed sensing scheme

Efficient compressed sensing reconstruction for 3D fluorescence microscopy using OptoMechanical Modulation Tomography (OMMT) with a 1+2D regularization

OptoMechanical Modulation Tomography (OMMT) exploits compressed sensing to reconstruct high resolution microscopy volumes from fewer measurement images compared to exhaustive section sampling in conventional light sheet microscopy. Nevertheless, the volumetric reconstruction process is computationally expensive, making it impractically slow to use on large-size images, and prone to generating visual artefacts. Here, we propose a reconstruction approach that uses a 1+2D Total Variation (TV1+2) regularization that does not generate such artefacts and is amenable to efficient implementation using parallel computing. We evaluate our method for accuracy and scaleability on simulated and experimental data. Using a high quality, but computationally expensive, Plug-and-Play (PnP) method that uses the BM4D denoiser as a benchmark, we observe that our approach offers an advantageous trade-off between speed and accuracy.

Light-sheets and smart microscopy, an exciting future is dawning

Light-sheet fluorescence microscopy has transformed our ability to visualize and quantitatively measure biological processes rapidly and over long time periods. In this review, we discuss current and future developments in light-sheet fluorescence microscopy that we expect to further expand its capabilities. This includes smart and adaptive imaging schemes to overcome traditional imaging trade-offs, i.e., spatiotemporal resolution, field of view and sample health. In smart microscopy, a microscope will autonomously decide where, when, what and how to image. We further assess how image restoration techniques provide avenues to overcome these tradeoffs and how "open top" light-sheet microscopes may enable multi-modal imaging with high throughput. As such, we predict that light-sheet microscopy will fulfill an important role in biomedical and clinical imaging in the future.

Single-shot optical projection tomography for high-speed volumetric imaging of dynamic biological samples

A single-shot adaptation of Optical Projection Tomography (OPT) for high-speed volumetric snapshot imaging of dynamic mesoscopic biological samples is presented. Conventional OPT has been applied to in vivo imaging of animal models such as D. rerio, but the sequential acquisition of projection images typically requires samples to be immobilized during the acquisition. A proof-of-principle system capable of single-shot tomography of a ~1 mm3 volume is presented, demonstrating camera-limited rates of up to 62.5 volumes/s, which has been applied to 3D imaging of a freely swimming zebrafish embryo. This is achieved by recording eight projection views simultaneously on four low-cost CMOS cameras. With no stage required to rotate the sample, this single-shot OPT system can be implemented with a component cost of under £5000. The system design can be adapted to different sized fields of view and may be applied to a broad range of dynamic samples, including high throughput flow cytometry applied to model organisms and fluid dynamics studies.

Optimizing sampling for surface localization in 3D-scanning microscopy

3D-scanning fluorescence imaging of living tissue is in demand for less phototoxic acquisition process. For the imaging of biological surfaces, adaptive and sparse scanning schemes have been proven to efficiently reduce the light dose by concentrating acquisitions around the surface. In this paper, we focus on optimizing the scanning scheme at a constant photon budget, when the problem is to estimate the position of a biological surface whose intensity profile is modeled as a Gaussian shape. We propose an approach based on the Cramér-Rao bound to optimize the positions and number of scanning points, assuming signal-dependant Gaussian noise. We show that, in the case of regular sampling, the optimization problem can be reduced to a few parameters, allowing us to define quasi-optimal acquisition strategies, first when no prior knowledge of the surface location is available and then when the user has a prior on this location.

Enlarged Field of View in Spatially Modulated Selective Volume Illumination Microscopy

Three-dimensional fluorescence microscopy is a key technology for inspecting biological samples, ranging from single cells to entire organisms. We recently proposed a novel approach called spatially modulated Selective Volume Illumination Microscopy (smSVIM) to suppress illumination artifacts and to reduce the required number of measurements using an LED source. Here, we discuss a new strategy based on smSVIM for imaging large transparent specimens or voluminous chemically cleared tissues. The strategy permits steady mounting of the sample, achieving uniform resolution over a large field of view thanks to the synchronized motion of the illumination lens and the camera rolling shutter. Aided by a tailored deconvolution method for image reconstruction, we demonstrate significant improvement of the resolution at different magnification using samples of varying sizes and spatial features.

High-throughput widefield fluorescence imaging of 3D samples using deep learning for 2D projection image restoration

Fluorescence microscopy is a core method for visualizing and quantifying the spatial and temporal dynamics of complex biological processes. While many fluorescent microscopy techniques exist, due to its cost-effectiveness and accessibility, widefield fluorescent imaging remains one of the most widely used. To accomplish imaging of 3D samples, conventional widefield fluorescence imaging entails acquiring a sequence of 2D images spaced along the z-dimension, typically called a z-stack. Oftentimes, the first step in an analysis pipeline is to project that 3D volume into a single 2D image because 3D image data can be cumbersome to manage and challenging to analyze and interpret. Furthermore, z-stack acquisition is often time-consuming, which consequently may induce photodamage to the biological sample; these are major barriers for workflows that require high-throughput, such as drug screening. As an alternative to z-stacks, axial sweep acquisition schemes have been proposed to circumvent these drawbacks and offer potential of 100-fold faster image acquisition for 3D-samples compared to z-stack acquisition. Unfortunately, these acquisition techniques generate low-quality 2D z-projected images that require restoration with unwieldy, computationally heavy algorithms before the images can be interrogated. We propose a novel workflow to combine axial z-sweep acquisition with deep learning-based image restoration, ultimately enabling high-throughput and high-quality imaging of complex 3D-samples using 2D projection images. To demonstrate the capabilities of our proposed workflow, we apply it to live-cell imaging of large 3D tumor spheroid cultures and find we can produce high-fidelity images appropriate for quantitative analysis. Therefore, we conclude that combining axial z-sweep image acquisition with deep learning-based image restoration enables high-throughput and high-quality fluorescence imaging of complex 3D biological samples.

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.

Real-time multi-angle projection imaging of biological dynamics

We introduce a cost-effective and easily implementable scan unit that converts any camera-based microscope with optical sectioning capability into a multi-angle projection imaging system. Projection imaging reduces data overhead and accelerates imaging by a factor of >100, while also allowing users to readily view biological phenomena of interest from multiple perspectives on the fly. By rapidly interrogating the sample from just two perspectives, our method also enables real-time stereoscopic imaging and three-dimensional particle localization. We demonstrate projection imaging with spinning disk confocal, lattice light-sheet, multidirectional illumination light-sheet and oblique plane microscopes on specimens that range from organelles in single cells to the vasculature of a zebrafish embryo. Furthermore, we leverage our projection method to rapidly image cancer cell morphodynamics and calcium signaling in cultured neurons at rates up to 119 Hz as well as to simultaneously image orthogonal views of a beating embryonic zebrafish heart.

Minutes-timescale 3D isotropic imaging of entire organs at subcellular resolution by content-aware compressed-sensing light-sheet microscopy

Rapid 3D imaging of entire organs and organisms at cellular resolution is a recurring challenge in life science. Here we report on a computational light-sheet microscopy able to achieve minute-timescale high-resolution mapping of entire macro-scale organs. Through combining a dual-side confocally-scanned Bessel light-sheet illumination which provides thinner-and-wider optical sectioning of deep tissues, with a content-aware compressed sensing (CACS) computation pipeline which further improves the contrast and resolution based on a single acquisition, our approach yields 3D images with high, isotropic spatial resolution and rapid acquisition over two-order-of-magnitude faster than conventional 3D microscopy implementations. We demonstrate the imaging of whole brain (~400 mm3), entire gastrocnemius and tibialis muscles (~200 mm3) of mouse at ultra-high throughput of 5~10 min per sample and post-improved subcellular resolution of ~ 1.5 μm (0.5-μm iso-voxel size). Various system-level cellular analyses, such as mapping cell populations at different brain sub-regions, tracing long-distance projection neurons over the entire brain, and calculating neuromuscular junction occupancy across whole muscle, are also readily accomplished by our method.

Spatially modulated illumination allows for light sheet fluorescence microscopy with an incoherent source and compressive sensing

Light sheet fluorescence microscopy has become one of the most widely used techniques for three-dimensional imaging due to its high speed and low phototoxicity. Further improvements in 3D microscopy require limiting the light exposure of the sample and increasing the volumetric acquisition rate. We hereby present an imaging technique that allows volumetric reconstruction of the fluorescent sample using spatial modulation on a selective illumination volume. We demonstrate that this can be implemented using an incoherent LED source, avoiding shadowing artifacts, typical of light sheet microscopy. Furthermore, we show that spatial modulation allows the use of Compressive Sensing, reducing the number of modulation patterns to be acquired. We present results on zebrafish embryos which prove that selective spatial modulation can be used to reconstruct relatively large volumes without any mechanical movement. The technique yields an accurate reconstruction of the sample anatomy even at significant compression ratios, achieving higher volumetric acquisition rate and reducing photodamage biological samples.

Optimal compressive multiphoton imaging at depth using single-pixel detection

Compressive sensing can overcome the Nyquist criterion and record images with a fraction of the usual number of measurements required. However, conventional measurement bases are susceptible to diffraction and scattering, prevalent in high-resolution microscopy. In this Letter, we explore the random Morlet basis as an optimal set for compressive multiphoton imaging, based on its ability to minimize the space-frequency uncertainty. We implement this approach for wide-field multiphoton microscopy with single-pixel detection, which allows imaging through turbid media without correction. The Morlet basis promises a route for rapid acquisition with lower photodamage.