Search

In-line digital holography is a simple yet powerful tool to image absorbing and/or phase objects. However, the holograms of interest are corrupted by the background signal due to unwanted scattering elements located in the optical path. Using only two holograms of the same object, shifted to different locations, an inverse problems approach is applied to jointly estimate the complex transmittance of the sample and the contribution of the interferent background signal at the sensor plane. Experimental results with stained bacteria are presented and show improved reconstructions of the sample while also accounting for the background contribution.

International audience; This paper includes a tutorial on how to reconstruct in-line holograms using an inverse problems approach, starting with modeling the observations, selecting regularizations and constraints, and ending with the design of a reconstruction algorithm. A special focus is made on the connections between the numerous alternating projection strategies derived from Fienup's phase retrieval technique and the inverse problems framework. In particular, an interpretation of Fienup's algorithm as iterates of a proximal gradient descent for a particular cost function is given. Reconstructions from simulated and experimental holograms of micrometric beads illustrate the theoretical developments. The results show that the transition from alternating projection techniques to the inverse problems formulation is straightforward and advantageous.

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.

International audience; 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].

International audience; Reconstruction of phase objects is a central problem in digital holography, whose various applications include microscopy, biomedical imaging, and fluid mechanics. Starting from a single in-line hologram, there is no direct way to recover the phase of the diffracted wave in the hologram plane. The reconstruction of absorbing and phase objects therefore requires the inversion of the non-linear hologram formation model. We propose a regularized reconstruction method that includes several physically-grounded 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 on reconstructions of accurate simulations of in-line holograms based on the Mie theory. As our approach is adaptable to several in-line digital holography configurations, we present and discuss the promising results of reconstructions from experimental in-line holograms obtained in two different applications: the tracking of an evaporating droplet (size ∼ 100µm) and the microscopic imaging of bacteria (size ∼ 1µm).

International audience; 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.

International audience; We propose a three-dimensional (3D) imaging platform based on lens-free microscopy to perform multiangle acquisitions on 3D cell cultures embedded in extracellular matrices. Lens-free microscopy acquisitions present some inherent issues such as the lack of phase information on the sensor plane and a limited angular coverage. We developed and compared three different algorithms based on the Fourier diffraction theorem to obtain fully 3D reconstructions. These algorithms present an increasing complexity associated with a better reconstruction quality. Two of them are based on a regularized inverse problem approach. To compare the reconstruction methods in terms of artefact reduction, signal-to-noise ratio, and computation time, we tested them on two experimental datasets: an endothelial cell culture and a prostate cell culture grown in a 3D extracellular matrix with large reconstructed volumes up to ∼5 mm3∼5 mm3 with a resolution sufficient to resolve isolated single cells. The lens-free reconstructions compare well with standard microscopy.

In-line digital holography is a simple yet powerful tool to image absorbing and/or phase objects. Nevertheless, the loss of the phase of the complex wavefront on the sensor can be critical in the reconstruction process. The simplicity of the setup must thus be counterbalanced by dedicated reconstruction algorithms, such as inverse approaches, in order to retrieve the object from its hologram. In the case of simple objects for which the diffraction pattern produced in the hologram plane can be modeled using few parameters, a model fitting algorithm is very effective. However, such an approach fails to reconstruct objects with more complex shapes, and an image reconstruction technique is then needed. The improved flexibility of these methods comes at the cost of a possible loss of reconstruction accuracy. In this work, we combine the two approaches (model fitting and regularized reconstruction) to benefit from their respective advantages. The sample to be reconstructed is modeled as the sum of simple parameterized objects and a complex-valued pixelated transmittance plane. These two components jointly scatter the incident illumination, and the resulting interferences contribute to the intensity on the sensor. The proposed hologram reconstruction algorithm is based on alternating a model fitting step and a regularized inversion step. We apply this algorithm in the context of fluid mechanics, where holograms of evaporating droplets are analyzed. In these holograms, the high contrast fringes produced by each droplet tend to mask the diffraction pattern produced by the surrounding vapor wake. With our method, the droplet and the vapor wake can be jointly reconstructed.

International audience; New microscopes are needed to help realize the full potential of 3D organoid culture studies bygathering large quantitative and systematic data over extended period of time while preserving the integrityof the living sample. On another hand, lensfree video microscopy is addressing these needs in the context of2D cell culture, providing label-free and non-phototoxic acquisition of large datasets. As scientistsroutinely adopt 3D culture techniques, the new challenging task is to extend lensfree microscopy techniquesto 3D structures.In order to image such large volume while preserving the ability to catch every single cell, we proposenew imaging platforms based on lensfree microscopy. We have built lensfree diffractive tomography setupsperforming multi-angle acquisitions of 3D organoid culture embedded in Matrigel® and developed adedicated 3D holographic reconstruction algorithm based on the Fourier diffraction theorem.With a first prototype we have been able to reconstruct a 3D volume as large as 21.5 mm^3 of a 3Dorganoid culture of prostatic RWPE1 cells in Matrigel® showing the ability of these cells to assemble in 3Dintricate cellular network at the mesoscopic scale. Comparisons with 2D images showed that it ispossible to detect single cells isolated from the main cellular structure.From results of the first prototype and numerical simulations, a second prototype wasdesigned to improve the reconstructions quality, especially on the vertical axis. First results on dust andbubbles lying at the bottom of a Petri® dish are promising.The adaptation of lensfree microscopy techniques to 3D organoid cultures imaging and thepresentation of the associated algorithms are the scopes of the present communication.

Lensfree imaging is an emerging microscopy technique based on in-line holography as invented by Gabor in 1948. Albeit the existence of the method since decades, the recent development of digital sensors, helped the realization of its full potential. Over the recent years, innovations and improvements in CMOS imaging technology design and fabrication have allowed to decrease the pixel pitch down to ∼1μm and the number of pixels has dramatically increased up to 250 million of pixels. As a result, the performance of lensfree microscopy, which features a bare CMOS sensor without any magnification optics, have tremendously increased while keeping the design simple, robust, and at a reasonable low cost. The detection ability improved from 10 μm (cell) in 2009, to 1 μm (bacteria) in 2010, down to 100 nm beads in 2012, paving the way to the detection of viruses in 2013.

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 2 ) 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 2 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 2 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 2 ) and in final point (10 cm 2 ) 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 2 ) with real time ability inside the incubator.

International audience; Data modelization in tomography is a key point for iterative reconstruction. The design of the projector, i.e. the numerical model of projection, is mostly influenced by the representation of the object of interest, decomposed on a discrete basis of functions. Standard projector models are voxel or ray driven; more advanced models such as distance driven, use simple staircase voxels, giving rise to modelization errors due to their anisotropic behaviour. Moreover approximations made at the projection step amplify these errors. Though a more accurate projection could reduce approximation errors, characteristic functions of staircase voxels constitute a too coarse basis for representing a continuous function. As a result, pure modelization errors still hold. Spherically symmetric volume elements (blobs) have already been studied to eradicate such errors, but at the cost of increased complexity, because they require some tuning parameters for adapting them to this use. We propose to use 3D B-splines, which are piecewise polynomials, as basis functions. When the degree of these polynomials is sufficiently high, they are very close from being with a spherical symmetry, i.e. blobs, avoiding projection inconsistencies, while keeping local influence and separability property. B-splines are considered, in sampling theory, as the almost optimal functions for the discretization of a continuous signal, not necessarily band-limited, potentially allowing to reduce the angular sampling of the data without any loss of quality. We show that the projection of B-splines can be approximated rather accurately by a separable function, independent from the angle of projection, easier to integrate on detector pixels. The higher the degree of the used B-splines, the better the quality of the approximation, but also the larger the number of required operations. Thanks to these approximations, a convenient tradeoff between the need of accuracy and a fast calculation can be obtained. This has resulted in the implementation of a more accurate numerical projector, which can deal with a reduced angular sampling without loss of performance. The additional computation cost is also efficiently reduced. We have studied the quality of enhancement involved by this projector on 2D iterative reconstructions of a Shepp-Logan phantom, from a small number of fan beam projections. Reconstructions have been performed by optimization methods, minimizing the squared data residuals with a regularization term, using an efficient Quasi-Newton optimization algorithm.