Phase retrieval reconstruction is a central problem in digital holography, with various applications in microscopy, biomedical imaging, fluid mechanics. In an in-line configuration, the particular difficulty is the non-linear relation between the object phase and the recorded intensity of the holograms, leading to high indeterminations in the reconstructed phase. Thus, only efficient constraints and a priori information, combined with a finer model taking into account the non-linear behaviour of image formation, will allow to get a relevant and quantitative phase reconstruction. Inverse problems approaches are well suited to address these issues, only requiring a direct model of image formation and allowing the injection of priors and constraints on the objects to reconstruct, and hence offer good warranties on the optimality of the expected solution. In this context, following our previous works in digital in-line holography, we propose a regularized reconstruction method that includes several physicallygrounded constraints such as bounds on transmittance values, maximum/minimum phase, spatial smoothness or the absence of any object in parts of the field of view. To solve the non-convex and non-smooth optimization problem induced by our modeling, a variable splitting strategy is applied and the closed-form solution of the sub-problem (the so-called proximal operator) is derived. The resulting algorithm is efficient and is shown to lead to quantitative phase estimation of micrometric objects on reconstructions of in-line holograms simulated with advanced models using Mie theory. Then we discuss the quality of reconstructions from experimental inline holograms obtained from two different applications of in-line digital holography: tracking of an evaporating droplet (size~100μm) and microscopy of bacterias (size~1μm). The reconstruction algorithm and the results presented in this proceeding have been initially published in [Jolivet et al., 2018].<sup>1</sup>
Lensless color microscopy is a recent 3D quantitative imaging method allowing to retrieve physical parameters characterizing microscopic objects spread in a volume. The main advantages of this technique are related to its simplicity, compactness, low sensitivity of the setup to vibrations and the possibility to accurately characterize objects. The cost-effectiveness of the method can be further increased using low-end laser diodes as coherent sources and CMOS color sensor equipped with a Bayer filter array. However, the central wavelength delivered by this type of laser is generally known only with a limited precision and can evolve because of its dependence on temperature and power supply voltage. In addition, Bayer-type filters of conventional color sensors are not very selective, resulting in spectral mixing (crosstalk phenomenon) of signals from each color channel. Ignoring these phenomena leads to significant errors in holographic reconstructions. We have proposed a maximum likelihood estimation method to calibrate the setup (central wavelength of the laser sources and spectral mixing introduced by the Bayer filters) using spherical objects naturally present in the field of view or added (calibration objects). This calibration method provides accurate estimates of the wavelengths and of the crosstalk, with an uncertainty comparable to that of a high-resolution spectrometer. To perform the image reconstruction from color holograms following the self-calibration of the setup, we describe a regularized inversion method that includes a linear hologram formation model, sparsity constraints and an edge-preserving regularization. We show on holograms of calibrated objects that the self-calibration of the setup leads to an improvement of the reconstructions.
We propose a new imaging platform based on lens-free time-lapse microscopy for 3D cell culture and its dedicated algorithm lying on a fully 3D regularized inverse problem approach. First 3D+t results are presented
New microscopes are needed to help reaching the full potential of 3D organoid culture studies by gathering large quantitative and systematic data over extended periods of time while preserving the integrity of the living sample. In order to reconstruct large volumes while preserving the ability to catch every single cell, we propose new imaging platforms based on lens-free microscopy, a technic which is addressing these needs in the context of 2D cell culture, providing label-free and non-phototoxic acquisition of large datasets. We built lens-free diffractive tomography setups performing multi-angle acquisitions of 3D organoid cultures embedded in Matrigel and developed dedicated 3D holographic reconstruction algorithms based on the Fourier diffraction theorem. Nonetheless, holographic setups do not record the phase of the incident wave front and the biological samples in Petri dish strongly limit the angular coverage. These limitations introduce numerous artefacts in the sample reconstruction. We developed several methods to overcome them, such as multi-wavelength imaging or iterative phase retrieval. The most promising technic currently developed is based on a regularised inverse problem approach directly applied on the 3D volume to reconstruct. 3D reconstructions were performed on several complex samples such as 3D networks or spheroids embedded in capsules with large reconstructed volumes up to ~ 25 mm<sup>3</sup> while still being able to identify single cells. To our knowledge, this is the first time that such an inverse problem approach is implemented in the context of lens-free diffractive tomography enabling to reconstruct large fully 3D volumes of unstained biological samples.
In order to extend the analysis of the datasets produced by lensfree video microscopy we have implemented a cell tracking algorithm to combine and correlate cell motility to the previously devised metrics to quantify e.g. cell adhesion and spreading, cell division, and cell death. In this paper we present the assessment of these new methodology on experiments involving three different cell lines, namely 3T3 fibroblast cells, primary HUVEC cells and macrophage THP1 cells. We demonstrate that the good spatial resolution and the fast frame rate obtained with of our lensfree video microscope allows standard cell tracking algorithm to be computed. The results is the possibility to analyze thousands of cells successfully tracked over tens of hours. The results is the possibility to compare different cell cultures in terms of e.g. cell motility and cell confinement ration. Ultimately we managed to measure the doubling time at single cell level over a large number of N=235 cells tracked over two days.
In this paper, we discuss a new methodology based on lens-free imaging to perform wound healing assay with unprecedented statistics. Our video lens-free microscopy setup is a simple optical system featuring only a CMOS sensor and a semi coherent illumination system. Yet it is a powerful means for the real-time monitoring of cultivated cells. It presents several key advantages, e.g., integration into standard incubator, compatibility with standard cell culture protocol, simplicity and ease of use. It can perform the follow-up in a large field of view (25 mm<sup>2</sup>) of several crucial parameters during the culture of cells i.e. their motility, their proliferation rate or their death. Consequently the setup can gather large statistics both in space and time. But in the case of tissue growth experiments, the field of view of 25 mm<sup>2</sup> remains not sufficient and results can be biased depending on the position of the device with respect to the recipient of the cell culture. Hence, to conduct exhaustive wound healing assay, here we propose to enlarge the field of view up to 10 cm<sup>2</sup> through two different approaches. The first method consists in performing a scan of the cell culture by moving the source/sensor couple and then stitch the stack of images. The second is to make an acquisition by scanning with a line scan camera. The two approaches are compared in term of resolution, complexity and acquisition time. Next we have performed acquisitions of wound healing assay (keratinocytes HaCaT) both in real-time (25 mm<sup>2</sup>) and in final point (10 cm<sup>2</sup>) to assess the combination of these two complementary modalities. In the future, we aim at combining directly super wide field of view acquisitions (>10 cm<sup>2</sup>) with real time ability inside the incubator.
Innovative imaging methods are continuously developed to investigate the function of biological systems at the microscopic scale. As an alternative to advanced cell microscopy techniques, we are developing lensfree video microscopy that opens new ranges of capabilities, in particular at the mesoscopic level. Lensfree video microscopy allows the observation of a cell culture in an incubator over a very large field of view (24 mm<sup>2</sup>) for extended periods of time. As a result, a large set of comprehensive data can be gathered with strong statistics, both in space and time. Video lensfree microscopy can capture images of cells cultured in various physical environments. We emphasize on two different case studies: the quantitative analysis of the spontaneous network formation of HUVEC endothelial cells, and by coupling lensfree microscopy with 3D cell culture in the study of epithelial tissue morphogenesis. In summary, we demonstrate that lensfree video microscopy is a powerful tool to conduct cell assays in 2D and 3D culture experiments. The applications are in the realms of fundamental biology, tissue regeneration, drug development and toxicology studies.
A challenge of adaptive optics (AO) on Extremely Large Telescopes (ELTs) is to overcome the difficulty of solving a huge
number of equations in real time, especially when atmospheric tomography is involved. This is particularly the case for
multi-conjugate or multi-objects AO systems. In addition, the quality of the wavefront estimation is crucial to optimize the
performances of the future systems in a situation where measurements are missing and noises are correlated.
The Fractal Iterative Method has been introduced as a fast iterative algorithm for minimum variance wavefront reconstruction
and control on ELTs. This method has been successfully tested on Classical Single Conjugate AO systems on
Octopus numerical simulator at ESO. But the minimum variance approach is expected to be mostly useful with atmospheric
We present the first results obtained with FrIM in the context of atmospheric tomography. We recall the principle of
the algorithm and we summarize the formalism used for modeling the measurements obtained from laser guide stars that
entail spot elongation and tip/tilt indetermination, mixed with low order measurements from natural guide stars. We show
the respective effects of tip/tilt indetermination, spot elongation, unseen modes on various configurations, as well as the
usefulness of priors and correct noise models in the reconstruction.
This analysis is essential for balancing the various errors that combine in a quite complex way and to optimize the
configuration of the future AO systems for specific science cases and instrument requirements.