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Optical projection tomography (OPT) is a powerful tool for biomedical studies. It achieves 3D visualization of mesoscopic biological samples with high spatial resolution using conventional tomographic-reconstruction algorithms. However, various artifacts degrade the quality of the reconstructed images due to experimental imperfections in the OPT instruments. While many efforts have been made to characterize and correct for these artifacts, they focus on one specific type of artifacts, whereas a comprehensive catalog of all sorts of mechanical artifacts does not currently exist. In this work, we systematically document many mechanical artifacts. We rely on a 3D description of the imaging system that uses a set of angular and translational parameters. We provide a catalog of artifacts. It lists their cause, resulting effects, and existing correction methods. Then, we introduce an automatic calibration algorithm that is able to recover the unknown system parameters fed into the final 3D iterative reconstruction algorithm for a distortion-free volumetric image. Simulations with beads data and experimental results on a fluorescent textile fiber confirm that our algorithm successfully removes miscalibration artifacts in the reconstruction.(c) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Objective: Arterial restenosis is the pathological narrowing of arteries after endovascular procedures, and it is an adverse event that causes patients to experience recurrent occlusive symptoms. Following angioplasty, vascular smooth muscle cells (SMCs) change their phenotype, migrate, and proliferate, resulting in neointima formation, a hallmark of arterial restenosis. SIKs (salt-inducible kinases) are a subfamily of the AMP-activated protein kinase family that play a critical role in metabolic diseases including hepatic lipogenesis and glucose metabolism. Their role in vascular pathological remodeling, however, has not been explored. In this study, we aimed to understand the role and regulation of SIK3 in vascular SMC migration, proliferation, and neointima formation. Approach and Results: We observed that SIK3 expression was low in contractile aortic SMCs but high in proliferating SMCs. It was also highly induced by growth medium in vitro and in neointimal lesions in vivo. Inactivation of SIKs significantly attenuated vascular SMC proliferation and up-regulated p21 CIP1 and p27 KIP1 . SIK inhibition also suppressed SMC migration and modulated actin polymerization. Importantly, we found that inhibition of SIKs reduced neointima formation and vascular inflammation in a femoral artery wire injury model. In mechanistic studies, we demonstrated that inactivation of SIKs mainly suppressed SMC proliferation by down-regulating AKT (protein kinase B) and PKA (protein kinase A)-CREB (cAMP response element-binding protein) signaling. CRTC3 (CREB-regulated transcriptional coactivator 3) signaling likely contributed to SIK inactivation-mediated antiproliferative effects. Conclusions: These findings suggest that SIK3 may play a critical role in regulating SMC proliferation, migration, and arterial restenosis. This study provides insights into SIK inhibition as a potential therapeutic strategy for treating restenosis in patients with peripheral arterial disease.

Interferometric imaging is a widely used tool in physics, biology, and in clinical applications. Various imaging techniques exist, from interferometric scattering microscopy to phase contrast microscopy, spatial light interference microscopy, and off-axis holography.

Combining synthetic aperture approaches with reference-less setups, ptychography is a promising phase retrieval technique for label-free quantitative phase imaging. Within the phase retrieval community, spectral methods are known to accelerate gradient descent schemes, however their positive effect on experimental ptychographic datasets has not been proved. Inspired by the latest theories on optimal spectral estimation, we achieved 3 times faster ptychographic reconstructions than with a standard gradient descent algorithm, in both simulations and experiments. The algorithms and experimental parameters crucially impacting the convergence speed are discussed. We believe that spectral methods will help improve both theoretical understanding and experimental implementations of ptychography.

In our work, we show how the Cramér-Rao bound is calculated for any linear optical system and we demonstrate how this general framework can be applied for the design and optimization of classical phase microscopes.

Interferometric imaging is an emerging technique for particle tracking and mass photometry. Mass or position are estimated from weak signals, coherently scattered from nanoparticles or single molecules, and interfered with a co-propagating reference. In this work, we perform a statistical analysis and derive lower bounds on the measurement precision of the parameters of interest from shot-noise limited images. This is done by computing the classical Cramér–Rao bound (CRB) for localization and mass estimation, using a precise vectorial model of interferometric imaging techniques. We then derive fundamental bounds valid for any imaging system, based on the quantum Cramér–Rao formalism. This approach enables a rigorous and quantitative comparison of common techniques such as interferometric scattering microscopy (iSCAT), coherent brightfield microscopy, and dark-field microscopy. In particular, we demonstrate that the light collection geometry in iSCAT greatly increases the axial position sensitivity, and that the Quantum CRB for mass estimation yields a minimum relative estimation error of σ m / m = 1 / ( 2 N ) , where N is the number of collected scattered photons.

Reservoir computing is a relatively recent computational paradigm that originates from a recurrent neural network and is known for its wide range of implementations using different physical technologies. Large reservoirs are very hard to obtain in conventional computers, as both the computation complexity and memory usage grow quadratically. We propose an optical scheme performing reservoir computing over very large networks potentially being able to host several millions of fully connected photonic nodes thanks to its intrinsic properties of parallelism and scalability. Our experimental studies confirm that, in contrast to conventional computers, the computation time of our optical scheme is only linearly dependent on the number of photonic nodes of the network, which is due to electronic overheads, while the optical part of computation remains fully parallel and independent of the reservoir size. To demonstrate the scalability of our optical scheme, we perform for the first time predictions on large spatiotemporal chaotic datasets obtained from the Kuramoto-Sivashinsky equation using optical reservoirs with up to 50 000 optical nodes. Our results are extremely challenging for conventional von Neumann machines, and they significantly advance the state of the art of unconventional reservoir computing approaches, in general., 9 pages, 7 figures, abstract is modified according to the published version

Accessing the point-spread function (PSF) of a complex optical system is important for a variety of imaging applications. However, placing an invasive point source is often impractical, and estimating it blindly with multiple frames is slow and requires a complex non-linear optimization. Here, we introduce a simple single-shot method to non-invasively recover the accurate PSF of an isoplanatic imaging system, in the context of multiple light scattering. Our approach is based on the reconstruction of any unknown sparse hidden object using the autocorrelation imaging technique, followed by a deconvolution with a blur kernel derived from the statistics of a speckle pattern. A deconvolution on the camera image then retrieves the accurate PSF of the system, enabling further imaging applications. We demonstrate numerically and experimentally the effectiveness of this approach compared to previous deconvolution techniques., 4 pages, 3 figures

There have been a number of rapid advances in the prediction of the dynamics of chaotic systems using a technique known as Reservoir Computing. These techniques are mostly not effective for large networks, as the complexity of the task increases quadratically both in time and memory. We report new advances in Optical Reservoir Computing using multiple light scattering to accelerate the recursive computation of the reservoir states. Different approaches to information encoding based on phase or amplitude spatial light modulations are compared. We demonstrate the scalability and the good prediction performance of our approach using the Kuramoto-Sivashinsky equation as an example of a spatiotemporally chaotic system.

Optical imaging deep inside scattering media remains a fundamental problem in bio-imaging. While wavefront shaping has been shown to allow focusing of coherent light at depth, achieving it non-invasively remains a challenge. Various feedback mechanisms, in particular acoustic or non-linear fluorescence-based, have been put forward for this purpose. Non-invasive focusing at depth on fluorescent objects with linear excitation is, however, still unresolved. Here we report a simple method for focusing inside a scattering medium in an epi-detection geometry with a linear signal: optimizing the spatial variance of low contrast speckle patterns emitted by a set of fluorescent sources. Experimentally, we demonstrate robust and efficient focusing of scattered light on a single source, and show that this variance optimization method is formally equivalent to previous optimization strategies based on two-photon fluorescence. Our technique should generalize to a large variety of incoherent contrast mechanisms and holds interesting prospects for deep bio-imaging.

Raman microscopy is a powerful method combining non-invasiveness with no special sample preparation. Because of this remarkable simplicity, it has been widely exploited in many fields, ranging from life and materials sciences to engineering. Notoriously, due to the required imaging speeds for bio-imaging, it has remained a challenge how to use this technique for dynamic and large-scale imaging. Recently, a supervised compressive Raman framework has been put forward, allowing for fast imaging, therefore alleviating the issue of speed. Yet, due to the need for strong a priori information of the species forming the hyperspectrum, it has remained elusive how to apply this supervised method for microspectroscopy of (dynamic) biological tissues. Combining an original spectral under-sampling measurement technique with a matrix completion framework for reconstruction, we demonstrate fast and inexpensive label-free molecular imaging of biological specimens (brain tissues and single cells). Using the matrix completion outcome with the supervised method allows for large compressions (64 × ) and bio-imaging speeds surpassing current technology in spontaneous Raman microspectroscopy. Therefore, our results open interesting perspectives for clinical and cell biology applications using the much faster compressive Raman framework.

International audience; Ptychography is a promising phase retrieval technique for label-free quantitative phase imaging. Recent advances in phase retrieval algorithms witnessed the development of spectral methods to accelerate gradient descent algorithms. Using spectral initializations on experimental data, for the first time, we report three times faster ptychographic reconstructions than with a standard gradient descent algorithm and improved resilience to noise. Coming at no additional computational cost compared to gradient-descent-based algorithms, spectral methods have the potential to be implemented in large-scale iterative ptychographic algorithms.

International audience; We introduce a generalized version of phase retrieval called multiplexed phase retrieval. We want to recover the phase of amplitude-only measurements from linear combinations of them. This corresponds to the case in which multiple incoherent sources are sampled jointly, and one would like to recover their individual contributions. We show that a recent spectral method developed for phase retrieval can be generalized to this setting, and that its performance follows a phase transition behavior. We apply this new technique to light focusing at depth in a complex medium. Experimentally, although we only have access to the sum of the intensities on multiple targets, we are able to separately focus on each one, thus opening potential applications in deep fluorescence imaging and light delivery.

Echo-State Networks and Reservoir Computing have been studied for more than a decade. They provide a simpler yet powerful alternative to Recurrent Neural Networks, every internal weight is fixed and only the last linear layer is trained. They involve many multiplications by dense random matrices. Very large networks are difficult to obtain, as the complexity scales quadratically both in time and memory. Here, we present a novel optical implementation of Echo-State Networks using light-scattering media and a Digital Micromirror Device. As a proof of concept, binary networks have been successfully trained to predict the chaotic Mackey-Glass time series. This new method is fast, power efficient and easily scalable to very large networks.

Surface roughness on a flat object causes natural speckle when imaged by an extreme ultraviolet (EUV) microscope under sufficient coherence. Using a phase-to-intensity transfer function theory, direct estimation of aberrations from the spectrum of the speckle intensity is demonstrated for various illumination angles.

Recently introduced angular-memory-effect based techniques enable non-invasive imaging of objects hidden behind thin scattering layers. However, both the speckle-correlation and the bispectrum analysis are based on the statistical average of large amounts of speckle grains, which determines that they can hardly access the important information of the point-spread-function (PSF) of a highly scattering imaging system. Here, inspired by notions used in astronomy, we present a phase-diversity speckle imaging scheme, based on recording a sequence of intensity speckle patterns at various imaging planes, and experimentally demonstrate that in addition to being able to retrieve diffraction-limited image of hidden objects, phase-diversity can also simultaneously estimate the pupil function and the PSF of a highly scattering imaging system without any guide-star nor reference., Comment: 4 pages, 5 figures

Reservoir Computing is a relatively recent computational framework based on a large Recurrent Neural Network with fixed weights. Many physical implementations of Reservoir Computing have been proposed to improve speed and energy efficiency. In this study, we report new advances in Optical Reservoir Computing using multiple light scattering to accelerate the recursive computation of the reservoir states. Two different spatial light modulation technologies, namely, phase or binary amplitude modulations, are compared. Phase modulation is a promising direction already employed in other photonic implementations of Reservoir Computing. Additionally, we report a Digital-Micromirror-based Reservoir Computing at up to 640 Hz, more than double the previously reported frequency using a remotely controlled optical device developed by LightOn, and present new binarization strategies to improve the performance of binarized Reservoir Computing.

Phase retrieval consists in the recovery of a complex-valued signal from intensity-only measurements. As it pervades a broad variety of applications, many researchers have striven to develop phase-retrieval algorithms. Classical approaches involve techniques as varied as generic gradient descent routines or specialized spectral methods, to name a few. However, the phase-recovery problem remains a challenge to this day. Recently, however, advances in machine learning have revitalized the study of phase retrieval in two ways: 1) significant theoretical advances have emerged from the analogy between phase retrieval and single-layer neural networks, and 2) practical breakthroughs have been obtained thanks to deep learning regularization. In this tutorial, we review phase retrieval under a unifying framework that encompasses classical and machine learning methods. We focus on three key elements: applications, an overview of recent reconstruction algorithms, and the latest theoretical results.

In biological microscopy, light scattering represents the main limitation to image at depth. Recently, a set of wavefront shaping techniques has been developed in order to manipulate coherent light in strongly disordered materials. The Transmission Matrix approach has shown its capability to inverse the effect of scattering and efficiently focus light. In practice, the matrix is usually measured using an invasive detector or low-resolution acoustic guide stars. Here, we introduce a non-invasive and all-optical strategy based on linear fluorescence to reconstruct the transmission matrices, to and from a fluorescent object placed inside a scattering medium. It consists in demixing the incoherent patterns emitted by the object using low-rank factorizations and phase retrieval algorithms. We experimentally demonstrate the efficiency of this method through robust and selective focusing. Additionally, from the same measurements, it is possible to exploit memory effect correlations to image and reconstruct extended objects. This approach opens up a new route towards imaging in scattering media with linear or non-linear contrast mechanisms., Light scattering represents the main limitation to image at depth in biological microscopy. The authors present a strategy to characterize light propagation in and out of a scattering medium based on linear fluorescence feedback and from the same measurements exploit memory effect correlations to image and reconstruct extended objects.