Unconventional Optical Imaging IV

I will explore various facets of photonic quantum systems and their application in photonic quantum technologies. Firstly, I will focus into quantum foundations and by discuss quantum interference, a key element in photonic quantum technologies. I will highlight how the distinguishability and mixedness of quantum states influence the interference of multiple single photons – and demonstrate novel schemes for generating multipartite entangled quantum states. I will then address photonic quantum computing, specifically focusing on the building blocks of photonic quantum computers. This includes the generation of resource states essential for photonic quantum computing. I will then shift to photonic quantum networks, covering both their hardware aspects and showcasing quantum-network applications that extend beyond bi-partite quantum communication. Lastly, I will outline how photonic integration facilitates the scalability of these systems and discuss the associated challenges.

Joining the rich photophysics of organic light-emitting materials with the exquisite sensitivity of optical resonances to geometry and refractive index enables a plethora of devices with unusual and exciting properties. Examples from my team include biointegrated microlasers for real time sensing of cellular activity and long-term cell tracking, as well as the development of photonic implants with extreme form factors and wireless power supply that support thousands of individually addressable organic LEDs and thus allow optogenetic targeting of neurons deep in the brain with unprecedented spatial control. Very recently, by driving the interaction between excited states in organic materials and resonances in thin optical cavities into the strong coupling regime, we unlocked new tuning parameters which may play a crucial role in the next generation of TVs and computer displays to achieve even more saturated colour while retaining angle-independent emission characteristics.

I will share how my experiences in Gabi Popescu's lab as a graduate student shaped me as a person and as a scientist. I will also discuss my research in his lab and how it has led me to my current work on using high resolution imaging technologies to study embryonic development.

Quantitative phase imaging (QPI) has recently emerged as a potentially valuable label-free approach which, due to its high resolution and sensitivity, has enabled a broad range of new applications. However, being a label-free technique, structures in a QPI image of a cell cannot always be readily identified because the image lacks specificity. To address this, machine learning methods have been deployed to map an acquired QPI image to a different image type, such as a simulated fluorescence image or an image representing a labeled segmentation mask. In this talk, I review several productive collaborations I had with Prof. Gabi Popescu on this topic. The applications surveyed include live-dead assay on unlabeled cells, cell stage classification and artificial confocal microscopy for deep label-free imaging.

Imaging with second harmonic generation (SHG) and third harmonic generation (SHG) are powerful methods biological samples but usually requires slow laser scanning. We demonstrate epi and transmission holographic SHG and THG imaging for aberration-free imaging by estimating and correcting phase distortions. Tomography and super resolution imaging with nonlinear wide field microscopy will also be discussed.

We utilize Spatial Light Interference Microscopy (SLIM) with enhanced computational analysis to quantify bioactive molecules, dynamic cellular topography, and tissue-level architectural tomography in various models of disease.

The histological optical imaging is a gold standard method to observe the biological tissues, which follows routine process such as dissection, embedding, sectioning, staining, visualization and interpretation of specimens. This technique has a long history of development, and is used ubiquitously in pathology, despite being highly time and labour-intensive. Staining-free optical imaging offers the unique advantage of bypassing the cumbersome staining process during specimen preparation and distinguishing the structure of tissue slides based solely on optical contrast. Here, we introduce the potential of quantitative phase imaging as a new digital histopathologic tool. Optical imaging based on phase contrast in histopathology could build fast feedback of anatomy of tissues or organs due to its simplicity, efficiency, robustness, and high-throughput capabilities. This presentation covers the latest work of large-scale and fast tissue imaging using quantitative phase imaging. Specifically, the talk will highlight comparison study over the conventional method in histopahology and its adaptation with artificial intelligence such as the virtual staining and resolution enhancement.

An emerging and developing paradigm in BioPhotonics concerns the Bio-lensing effect, that is, living organism as biological cells or their inner structures can be modeled as pure-phase objects with optical and photonics properties typical of micro-lenses. Nowadays micro-lenses are ubiquitous and a lot of effort is put into developing low-cost and low-impact fabrication processes and for searching smart and biocompatible materials. The lensing effect of different types of cells and sub-cellular compartments has been recently demonstrated generating a great impact. Indeed, the dimensions of bio-lenses allows to surpass all the issues related to the scaling down of the fabrication processes and, also, their natural origin solves the problem of material compatibility necessary for biomedical applications. Here, we will show the recent achievements based on the bio-lensing properties of different cells and organelles ranging from imaging, super-resolution, cell clustering and lithography.

In this work, we propose to perform an inverse problems based reconstruction for tomographic diffractive microscopy, that jointly estimate the 3D object map and an error map modeling the perturbations of light on the reference arm of the off-axis holographic acquisition setup. Our method can be assimilated to a self-calibration of the illumination of the reference arm in the reconstruction process, allowing to unmix these perturbations from the 3D map, and obtain a clean sample restitution. We validate the feasibility of our approach on reconstructions from simulated data under different experimental conditions.

Quantitative phase imaging (QPI) is essential in biomedicine and surface inspection for its 3D high-resolution imaging. Conventional QPI methods, hindered by large optical setups, face challenges in small spaces like in-vivo imaging of cancer tissues. To combat this, our study introduces a novel, ultra-thin, lensless microendoscope using multicore fiber, enabling effective QPI even in limited spaces. This device captures real-time holograms, reconstructing complex light fields with automatic digital refocusing, achieving up to 15 nm axial and 1-micron spatial resolution. Its compact 0.5 mm design significantly enhances access to restricted areas, revolutionizing surface inspection and in-vivo clinical diagnostic imaging.

One of the directions of development in quantitative phase imaging is to provide the capability to reconstruct the phase or preferably refractive index (RI) distribution within thick, highly scattering samples. This direction coincides with current trends in biology, where three-dimensional (3D) organoids are currently replacing standard 2D cultures as more physiological models for tissue growth and organ formation in a dish. The biological complexity of these 3D structures makes the imaging and RI reconstruction particularly challenging, and thus calibration as well as validation structures are important and sought-after tools in instrumentation development. For this reason, in this work, we present the full preparation and measurement procedure for organoid phantoms printed with two-photon polymerization along with the method to obtain the ground truth of the object structure independently of RI reconstruction errors and artifacts.

Fourier Ptychographic Microscopy (FPM) is a recent imaging technique that overcomes the limitations of conventional optics. The images it produces are particularly fine and super-resolved. They are also bimodal (intensity and phase) and therefore very rich. This makes FPM promising for various medical applications. In this work, the case of automatic diagnosis of malaria is considered. We study the benefits provided by phase images on stained blood smears cells in terms of detection sensitivity and specificity of parasites. We report with significant statistical tests that an appreciable improvement in detection performance is obtained when phase and intensity images are used jointly. To this end, a complex-valued convolution neural network (CV-CNN) is introduced for the classifier. We also show, from experimental measurements and simulations, that such bimodal exploitation can considerably relax the tolerances of the microscope conception (in terms of minimal microscope numerical aperture). This can be highly desirable for the realization of rapid diagnostic automate with an increased field of view.

Nowadays, fluorescence microscopy has made the leap from 2D to 3D or even 4D (xyz+t) imaging. On the other hand, Zernike phase contrast microscopy, which was awarded the Nobel Prize in Physics in 1953, has become the standard feature for modern biological microscopy, but is still limited to 2D imaging. Currently, life science research urgently needs a new "label-free 3D microscopy" mode that complements confocal/two-photon/super-resolution 3D fluorescence microscopy technology to meet the needs of rapid, high-resolution, long-term imaging of live cells in 3D. In this talk, we will present some of our research progress in "noninterferometic" intensity diffraction tomography, including: quantitative phase imaging and diffraction tomography based on transport of intensity and Fourier ptychography. Our results highlight a new era in which strict coherence and interferometry are no longer prerequisites for quantitative phase imaging and diffraction tomography, paving the way toward new generation label-free three-dimensional microscopy, with applications in all branches of biomedicine.

Here we show the use of Fourier Ptychographic microscopy (FPM), in the framework of digital pathology. We image effectively large tissue slides portions, over a wide mm2 FOV, while attaining submicron resolution, in marker-free and quantitative mode. We show novel tissue analysis and classification techniques working on FPM. These are specifically tailored for exploiting the multi-scale imaging capabilities of FPM, and can be used to classify tissue portions working across the scales, i.e. analysing the phase-contrast signatures at the tissue organization level up to the single membrane level. We apply this novel approach to classify tissue slides from patients with breast cancer and fibroadenoma tissue.

In the context of multi-wavelength in-line holographic microscopy of micrometer-sized samples, we propose a 2-step methodology to estimate geometric and chromatic aberrations of the optical system and then use this calibration step to better reconstruct the complex transmittance at focus. The first step uses an aberration-wise Lorenz-Mie model to jointly estimate the parameters of calibration beads spread in the sample and 14 Zernike coefficients at each wavelength. Then, the reconstruction step is performed using a regularized inverse problems approach reconstruction of the whole multi-wavelength data set with a colocalization hypothesis. This general methodology is applied to the case of Gram-stained bacteria on blood smears. On these samples, in addition to providing a new information (phase), we show interesting improvements on the image quality, which promises better discrimination between bacteria types and enhanced repeatability.

In-line Digital holographic microscopy is easy to implement because it only requires a coherent illumination source. An out-of-focus image (hologram) of the sample is recorded and numerically reconstructed to retrieve phase shift information due to the sample. Effects of the optical setup, such as aberrations, can distort the hologram signal, and ignoring these effects results in biased reconstructions. In this context, we study the effects of misalignment and wrong cover-slip thickness by statistically analysing the reconstructions of beads distributed in the whole field of view. Our aberration-wise reconstruction approach produces debiased estimations and allows precise and realistic estimation of optical aberrations from a single hologram.

To unravel dynamic processes underpinning key functions in cell biology, it is essential to develop imaging technologies able to track the movement of individual bio-nano-objects under physiologically relevant conditions, hence at high speed (ms) and in 3D. We demonstrate interferometric gated off-axis reflectometry (iGOR) which detects the back-scattered light of the structure of interest using an external off-axis reference, enabling label-free high-speed tracking of nanoparticles and suspended membranes in 3D volumes. Employing coherence time-gating by femtosecond pulses, the axial extension of the detected volume is controlled. We show tracking of single nanoparticles down to 10nm size freely diffusing in volume, which allows us to determine their geometrical and hydrodynamic radius as well as non-sphericity. We also show the spatiotemporal dynamics of suspended lipid bilayers, and the influence of lipid phase transitions on these dynamics with sub-nm thickness precision.

We presented a computational Imaging technique that improves the Space-bandwidth product of an imaging system by expanding the FOV through innovative PSF engineering techniques. Our work uses the Multiple Point Impulse Response (MPIR) PSF, which captures object information within and beyond the sensor's active area in a scrambled form. By applying a sparse recovery algorithm to these scrambled images, we reconstructed the extended FOV object information.

We describe OpenSpyrit, an open source ecosystem for reproducible research in hyperspectral single-pixel imaging. We outline the three elements of OpenSpyrit, which include a Python software for acquiring data from a single pixel, a collection of single-pixel hyperspectral images (SPIHIM), and a Python toolkit for reconstructing single-pixel images (SPYRIT). In particular, we examine the unrolled algorithms and the plug-and-play methods available in SPYRIT that we evaluate on various datasets of the SPIHIM collection.

An extensive series of spatial modulation approaches has been tested using the single-pixel microscope in hyperspectral configuration to characterize their usability in low-intensity conditions and different wavelengths. Multiple samples have been used to demonstrate spatial and spectral resolution. To create a fair comparison, a fixed number of measurements has been used for all tests throughout the experiment. Along with that, three different resolutions have been used, creating different compressive ratios. The benefits and drawbacks of individual modulations have been discussed.

Co-design consists of optimizing the parameters of the lens and the processing together to obtain a gain in performance for the entire optical/processing chain, which requires new optimization tools, as traditional optical design ones can no longer be used easily. In the state of the art, joint optical/processing optimization methods based on statistical scene models and analytical performance models have been proposed, and recently, new approaches based on the joint optimization of a neural network and optical components with a large database have been developed. However, to the best of our knowledge, no comparison of co-design results using either a model-based or a data-based approach for the same task have been conducted, which is the scope of this paper. We consider here the optimization of phase masks to extend the camera depth of field. We compare the optimization results using a performance model based on restoration error using either a generalized Wiener filter, or a neural network. We investigate the optimization trend depending on the neural network complexity, the starting point of the optimization and the possible interaction between the two approaches.

Quantitative Phase Imaging is the method of choice to observe unlabelled biological cells, enabling the exploration of their intricate structures and dynamics. Differential Phase Contrast (DPC) is a fast and flexible approach but is limited to brightfield measurements, which limits the achievable resolution. In this study, we revisit DPC as a perturbative approach for high-resolution phase imaging with a minimal number of images. Our methodology combines both a perturbative phase retrieval algorithm and ring-shaped darkfield illumination patterns. The proposed approach leverages the advantages of DPC, such as its minimal image requirement, while addressing its inability to extend to darkfield imaging. By reducing the number of acquired images, our technique offers a rapid solution for examining dynamic systems.

Wavefront shaping allows focusing and imaging at depth in disordered media such as biological tissues, exploiting the ability to control multiply scattered light. Many so-called guide-star mechanisms have been investigated to deliver light and image non-invasively, among which incoherent processes such as non-linear fluorescence feedback. However, the most common microscopy contrast mechanism, linear fluorescence, remains extremely challenging. I will discuss some of our recent works, exploiting signal processing and machine learning frameworks, to recover images behind scattering layers exploiting linear fluorescence.

PISTIL interferometry (Piston and Tilt interferometry), dedicated to the measurement of regular segmented wave surfaces such as segmented mirrors or the near-field output of coherent combination lasers, has evolved into a new device called FULLPISTIL. More compact and more efficient, this new version of the device has been tested on a segmented mirror. The performance obtained is state of the art, and points the way towards a plug and play product adapted to the specific measurement needs of these surfaces. An initial laboratory prototype has been developed for users wishing to test the technique.

In optical imaging, light propagation is affected by the heterogeneities of the refractive index. At shallow depths, these fluctuations induce wave-front distortions that degrade the image resolution and contrast. Beyond a few scattering mean free paths, multiple scattering starts to predominate and gives rise to a random speckle image without any connection with the medium reflectivity. To overcome these detrimental phenomena, we develop a general matrix approach of optical imaging and demonstrate its benefit by means of numerical simulations. By stacking a set of random phase screens, we model forward multiple scattering and its short-range memory effect. A computational multi-conjugate adaptive optics strategy is then proposed to exploit these snake photons ad optimize the focusing process at any point inside the medium.

In this study we have developed a compact and versatile phase camera functioning as a wavefront sensor for macroscopy or microscopy applications. This device records two intensity images at different focal points and, with the integration of an ETL, operates in real time. Working with intensity images allows achieving high resolutions, near the actual CCD/CMOS sensor resolution. Here we show the application of the camera in two very different scenarios, a macroscopic application, where the camera was coupled with a simple lens relay to study the behaviour of a deformable mirror (DM); and characterise defocus and astigmatism in optical lenses. On the second example, the camera was attached directly to a microscope using a simple C-mount to follow human blood moving in real time.

Holographic retinal imaging can be affected by optical distortions from the eye and lenses. A digital Shack-Hartmann algorithm corrects this by splitting the Fourier plane into sub-apertures to measure local wavefront gradients via cross-correlation of sub-images. We examine wavefront regularization by Zernike polynomials for better aberration correction, and introduce a new method for calculating retinal image shifts. Using the entire computed image as a reference, rather than just the central sub-image, minimizes bias. Furthermore, we use a direct wavefront reconstruction approach, using overlapping sub-apertures and a 2D gradient integration algorithm to estimate the wavefront without regularization. Our findings show that this direct wavefront estimation enhances image resolution and contrast for Doppler holography of the eye fundus compared to wavefront regularization by Zernike polynomials.

Imaging and measuring moving objects behind translucide layers is of high interest in numerous applications, in various fields. We developed in this study a method using digital holographic microscopy for that purpose, with a partial spatial coherence illumination. The presented study includes numeric simulations for the assessment of the measurement accuracy, as a function of both the object and the translucide layer parameters. Experiments are performed with the in vitro analysis of human red blood cells moving behind an edothelial cell layer. Such a configuration mimics the real blood vessel, which is of crucial importance for a better understanding of the behaviour of the blood cells in blood circulation.

High speed terahertz imaging based on optimized galvanometric illumination Yuzhe Zhang, Ran Ning, Jie Zhao, Shufeng Lin, Lu Rong, Dayong Wang The pursuit of high-resolution, high-fidelity, real-time imaging is receiving significant attention in terahertz community. In this study, a versatile illumination approach based on a dual-mirror galvanometer is proposed and optimized for terahertz full-field imaging and computed terahertz tomography. We analyzed the mechanism of galvanometric illumination and elucidated three main factors affecting its homogeneity properties. In this illumination module, the terahertz beam is deflected rapidly by the galvanometer which is driven by triangular voltage signals, and then focused by a self-designed aspherical f-θ lens to illuminate the object at an equal lateral scanning velocity. A homogeneous illumination field with a speckle contrast of 0.11 and isotropic imaging resolution is recorded by an array detector in the form of non-correlated accumulation in a single integration time. By virtue of leading illumination homogeneity and parallelism, a compact imaging system is built for 2D and 3D terahertz imaging with high imaging speed and fidelity.

Terahertz (THz) radiation has already been applied to many technologies and devices in various areas, including medical diagnostics, non-destructive testing, data transmission or imaging. However, one must remember that an extremely low ratio of wavelength to the aperture diameter in this spectral range poses unique challenges. This work investigates two types of spatial filtering methods – positive and negative phase contrast and dark field. The sets of phase objects and filters, have been manufactured by means of FDM 3D printing. The possibility of realizing the phase contrast and dark field imaging has been evaluated in the 4f optical setup, consisting of two HDPE lenses having 300-mm-diameter and 300-mm-focal length. The experimental data has proved without a doubt the usefulness of this method but also shown some unexpected effects connected with the very long wavelengths (in comparison to the dimensions of the setup and its elements).

Spatial and temporal control of thermally emitted terahertz radiation could pave the way for a new family of devices in imaging, spectroscopy and communication systems. We devise an all-optically controlled THz emissivity modulator and use it to generate structured THz illumination patterns. Using this unconventional THz source, we present a completely new approach to THz imaging based on computational (ghost) imaging with a single-pixel detector with potential to be competitive with commercial systems without the need for femtosecond lasers.

This study introduces a stainless steel-based complementary C-shaped single split ring resonator (CSRR) metasurface designed for Terahertz (THz) imaging applications. The CSRR metasurface was created from a 25 µm thick stainless-steel foil using laser ablation, serves as a zone plate, allowing precise manipulation of 100 GHz radiation. Investigations involved imaging beam shape with different geometrical parameters of the CSRR and examining the metasurface's functionality under mechanical bending which revealed a minimal reduction in beam intensity. Additionally, the metasurface demonstrated the ability to control polarization based on its rotational angle, enhancing THz imaging capabilities. Practical demonstrations, including imaging a plastic card with a key, showcase the metasurface's suitability for real-life scenarios, highlighting its value in THz imaging systems.

We present the application of THz monocycles to the Atom Probe Tomography (APT), an analytical microscope that allows the three-dimensional mapping of chemical heterogeneities in a material at the atomic scale. When a positive electric field of several volts per angstrom is applied to the surface of a material, the surface atoms evaporate as ions even at cryogenic temperatures, this is the work principle of APT. We prove that THz transient can induce the controlled evaporation of surface atoms due to the strong increase in the THz field in the near field of the sample. In addition, the use of THz pulses reduces the thermal effects reported when using laser pulses in the visible or near ultraviolet domain. We are also studying the effect of the THz pulses on the energy of the evaporated ions.

THz digital holography with camera sensor in off-axis configuration suffers from difficult recording conditions.In order to obtain a high resolution on the object, the latter must be close to the sensor, yielding high recording angles. It was already shown that iterative reconstruction methods can be used to limit the impact of spurious fringes obtained in such schemes. In this paper, we propose an inverse problem-based reconstruction technique that jointly reconstructs the object field and the amplitude of the reference field. Regularization in the wavelet domain promotes a sparse object solution. A single objective function combining the data-fidelity and regularization terms is optimized with a dedicated algorithm based on an ADMM framework. Each iteration alternates between two consecutive optimizations using projections operating on each solution and one soft thresholding operator applying to the object solution. The method is preceded by a windowing process to alleviate artifacts due to the mismatch between camera frame truncation and periodic boundary conditions assumed to implement convolution operators. Experiments demonstrate the effectiveness of the method.

Because of the unique properties of terahertz (THz) radiation, the development of THz imaging has attracted considerable attention. With the introduction of near-field technology, the resolution of THz imaging was significantly improved, which has greatly broadened the application fields of this technique. Here, a new approach was developed for THz near-field microscopy based on a cross-filament. The cross-filament was formed by two crossed air-plasmas, which opened a dynamic aperture to modulate the intensity of a THz beam on a sample surface. By using this technique, THz near-field information can be acquired without approaching the sample surface. To demonstrate the feasibility of this technique, sub-wavelength THz imaging of four different materials was achieved, including metallic, semiconductor, plastic, and greasy samples. The advantages of the technique are expected to accelerate the advancement of THz microscopy.

Optical cavities with nonlinear elements and delayed self-coupling are widely explored candidates for photonic reservoir computing (RC). For time series prediction applications that appear in many real-world problems, energy efficiency, robustness and performance are key indicators. With this contribution I want to clarify the role of internal dynamic coupling and timescales on the performance of a photonic RC system and discuss routes for optimization. By numerically comparing various delay-based RC systems e.g., quantum-dot lasers, spin-VCSEL (vertically emitting semiconductor lasers), and semiconductor amplifiers regarding their performance on different time series prediction tasks, to messages are emphasized: First, a concise understanding of the nonlinear dynamic response (bifurcation structure) of the chosen dynamical system is necessary in order to use its full potential for RC and prevent operation with unsuitable parameters. Second, the input scheme (optical injection, current modulation etc.) crucially changes the outcome as it changes the direction of the perturbation and therewith the nonlinearity. The input can be further utilized to externally add a memory timescale that is needed for the chosen task and thus offers an easy tunability of RC systems.

Programmable photonic circuits manipulate the flow of light on a chip by electrically controlling a set of tunable analog gates connected by optical waveguides. Light is distributed and spatially rerouted to implement various linear functions by interfering signals along different paths. A general-purpose photonic processor can be built by integrating this flexible hardware in a technology stack comprising an electronic monitoring and controlling layer and a software layer for resource control and programming. This processor can leverage the unique properties of photonics in terms of ultra-high bandwidth, high-speed operation, and low power consumption while operating in a complementary and synergistic way with electronic processors. This talk will review the recent advances in the field and it will also delve into the potential application fields for this technology including, communications, 6G systems, interconnections, switching for data centers and computing.

Tomographic diffraction microscopy is a 3D marker-less imaging technique allowing for quantitative characterization of the complex refractive index of the investigated sample. The method is a generalization of digital holography with a full control of the sample's illumination angle. Combining several holographic acquisitions through a 3D synthetic aperture process makes it possible to assess the optical field scattered by a 3D object. Recently we proposed to take benefits of the democratization of polarization array sensors to characterize birefringent samples. With this presentation, we demonstrate that using the same experimental configuration, with a modified demosaicking procedure, we can produce 3D Differential Interference Contrast microscope images of isotrope sample.

Digital holography is an imaging technique that enables a 3-dimensional reconstruction of both the amplitude and phase of an electromagnetic field after its interaction with an object. Second harmonic generation being a coherent process, it can also be used to generate interferences and holograms. Since collagen molecules exhibit a significant second order response, we apply harmonic holography to porcine cornea samples to obtain single-shot measurements of the 3D spatial distribution of the collagen fibers. In addition, we propose polarization multiplexing to study the polarization dependence of the sample response.

Structural characterization of self-assemblies of anisotropic nanoparticles remains challenging as most conventional imaging approaches are only able to assess the projected two-dimensional orientation. In this work, we use polarized luminescence to characterize the three-dimensional structure of assemblies of nanoparticles. We use a liquid crystal (LC) suspension of LaPO4:Eu nanorods exhibiting a variety of nematic domain structures upon confinement [1]. The orientation of the rods can be determined by spectroscopic analysis of their strongly polarized emission [2]. Using a confocal microscope, we are able to reconstruct the three-dimensional structure of the nematic assemblies with a high spatial resolution. [1] Kim, J. et al. Nat Commun 12, 1943 (2021). [2] Kim, J. et al. Adv. Funct. Mater. 22, 4949–4956 (2012).

This study introduces an innovative approach to volumetric polarimetry, introducing a method that combines Gabor in-line holography with conventional polarimetric setups to compute the complete Mueller Matrix of a sample. The study includes the validation of the technique through calibration targets and extends to complex volumetric samples, showcasing the potential of this method for characterizing intricate polarization properties in three-dimensional specimens.

This research presents the WUTScope, a novel interferometric microscope developed by the Quantitative Computational Imaging group at Warsaw University of Technology. This system, leveraging Quantitative Phase Microscopy and Optical Diffraction Tomography, provides insightful three-dimensional reconstructions of the refractive index distribution in semi-transparent objects. The WUTScope is distinguished by its compact design and capability to operate under partially coherent illumination, using polarization diffraction gratings for beam splitting and recombination. This approach allows for efficient phase shifting and reduces speckle noise, enhancing image signal-to-noise ratio. The system's achromatic nature, due to the identical optical paths of the diffraction orders, facilitates the use of less coherent light sources, a distinct advantage over traditional holographic methods. Its effectiveness is demonstrated through tomographic reconstruction of a 3D-printed brain sample and analysis of refractive index changes in HeLa cells' lipid droplets, revealing the impact of cholesterol accumulation.

Our ability to ask questions about living systems has been limited by our ability to measure their holistic, real-time structure, function and dynamics. When slow imaging speeds and challenging sample geometries necessitate complex preparations, perturbations and manipulations, or repeated induction of phenomena, we are not observing the system in its natural, evolving state. Recent developments in high-speed, multi-spectral, 3D single-objective light sheet microscopy have made it possible to image large regions, whole brains and even whole organisms in real-time, from 3D cell cultures to C. elegans worms, fruit flies and zebrafish larvae and the brains of living mice. Fluorescent reporters can provide real-time read-outs of cellular function, while tracking algorithms can simultaneously extract complex real-time behaviors. The next challenge is then exploring and interpreting the resulting TB-scale real-time data for scientific discovery. This talk will summarize recent high-speed 3D imaging advances combined with data-driven approaches to analyzing and interpreting physiological phenomena in a range of living systems.

We present a diffraction-phase and fluorescence 3D microscope as novel bimodal imaging technique, which provides simultaneous phase and multi-color epi-fluorescence acquisitions of living multicellular samples. The instrument consists of an LED-array to acquire intensity images at different illumination angles and an epifluorescence setup for fluorescence excitation. The 3D sample’s optical properties are reconstructed using the beam propagation method embedded inside a deep learning framework. To obtain the fluorescence reconstructions, we developed a novel incoherent model that takes into account the heterogeneous refractive indexes of the scattering sample. We validated the technique on long-term acquisitions of mouse embryos and 3D liver organoids under physiological conditions.

We present a computational approach for hyperspectral computational Selective Plane Illumination Microscopy (SPIM), offering fast 3D imaging with reduced photobleaching. Inspired by Hadamard spectroscopy, our method employs structured light sheets via a digital micromirror device. A data-driven reconstruction strategy, implemented through an end-to-end trained neural network, demonstrates robust performance under varying noise levels. Leveraging non-negative least squares minimization, we obtain component maps, exemplifying applications such as autofluorescence removal in transgenic zebrafish and discrimination of closely matched red proteins. Our findings showcase the potential of computational strategies to advance hyperspectral SPIM in photonic research.

We demonstrate a new approach to high-spectral-resolution 3D photoluminescence (PL) tomography using a 3D-structured excitation light and single-pixel camera technique. A suitable 3D speckle pattern was generated in the proximity of a lens focal plane by focusing a phase-modulated excitation laser beam. We calibrated the system so that the 3D pattern can be calculated with a high fidelity by using Fourier optics. We used a large set of unique 3D patterns to excite a testing sample and measure its total intensity. The set of total intensities enabled us, in turn, to retrieve the PL tomography. In our case, the PL datacube can be retrieved spectrally resolved with a 3 nm resolution. The presented method enables versatile microscopic 3D tomography of defects resolved in various domains, depending on the used detection mode.

Hyperspectral imaging has been explored for clinical applications in various medical disciplines. Based on our experiences, the potential of this versatile imaging method as well as pitfalls and limitations of current approaches are discussed. The use of reference samples and simple image modeling strategies are suggested to avoid misinterpretation and achieve a more conclusive image interpretation.

In the first part of the talk I will present recent advances in label-free metabolic imaging of living tissues by two-photon fluorescence lifetime microscopy (2P-FLIM) of endogenous biomarkers. We implement simultaneous two-photon excitation of NAD(P)H and FAD by wavelength mixing to acquire 2P-FLIM data of the two biomarkers at the same time and perform efficient multiparametric metabolic imaging in dynamic biological system. We demonstrate how 2P-FLIM of the two metabolic coenzymes reveals the richness and complexity of several metabolic processes in intact tissues with minimal phototoxicity and can be implemented non-invasively for longitudinal studies in vitro and in vivo such as stem cell differentiation, T cell activation and embryo development. In the second part of the talk I will present recent advances in polarization resolved second harmonic generation (pSHG) and modelling from protein molecular structure. We establish a general multi-scale numerical framework relating the micrometer-scale SHG measurements at the optical wavelength to the atomic-scale and molecular structure of the proteins under study and their supramolecular arrangement.

Dynamic metabolic reprogramming among neurons and glial cells can be characterized by label-free two photon excited fluorescence intensity and lifetime (FLIM) imaging of engineered brain tissue models consisting of human neurons, astrocytes, and microglia tri-cultures. Lipofuscin is a significant contributor to the overall fluorescence detected. Its removal is important for accurate recovery of metabolic function metrics. Results reveal the important function of glial cells to reduce oxidative stress in neurons over fourteen weeks of co-culture. Distinct metabolic profiles and dynamic changes are also evident for neurons and glial cells. Our study highlights that engineered brain tissue models in combination with label-free two photon imaging offer an excellent platform for understanding cell-cell interactions and are well-suited to improve understanding of the role of metabolic and mitochondrial dysfunction in neurodegenerative diseases.

Optical imaging and beam delivery deep into biological tissue is a great challenge due to strong light scattering. It has been shown in the last years that wavefront shaping employing a digital spatial light modulator (SLM) has the power to exploit the scattered light to enhance the penetration depth. Typically, a guide star emitting a known wavefront is required to probe scattering events and determine the necessary mask to be displayed. The resulting Peak-to-Background Ratio (PBR) is commonly used as a quality measure for the Digital Optical Phase Conjugation (DOPC) procedure. Here, we introduce a new correction quality criterion to evaluate the measured phase mask. By placing the SLM into the object beam, it is possible to manipulate the phase measurement. We demonstrate a criterion in the angular spectrum of the hologram, which enables evaluation of the mask without considering any playback properties.

The combination of femtosecond pulses with microscopes resolves processes at ultrashort time scales with spatial resolution. However, an integration into three-dimensional imaging methods, allowing to retrieve ultrashort processes as a function of three-dimensional space and time in complex and crowded environments, is lacking. We have achieved the implementation of broadband optical pulses with more than 100 nm bandwidth into a holographic optical diffraction tomography (ODT) setup. Besides having overcome a critical step towards ultrafast three-dimensional imaging we realized spectrally resolved ODT, retrieving the specimen’s refractive index as a function of 3D space and wavelength over the entire bandwidth.

Minimally invasive endoscopic imaging is indispensable for several important biomedical imaging applications such as in neurology. However, traditional fiber-optic endoscopes require bulky lens systems that typically enable 2D imaging only. Unconventional imaging using a diffuser for encoding 3D objects into 2D speckle patterns is presented. A multicore fiber transmits the speckle intensity information of fluorescent light from biological tissue. The decoding of the speckle pattern is accomplished by neural networks with a combination of U-Net and single-layer perceptron. It turns out 3D image reconstruction at almost video rate. The diffuser fiber endoscope is promising for in vivo deep brain diagnostics with cellular resolution and keyhole access. Advances in physics-informed neural networks and quantum imaging with entangled photons are also outlined. In conclusion, unconventional lensless imaging using multicore fibers, deep learning and quantum imaging is highlighted as paradigm shift for biomedicine.

A neural network-based model to eliminate unwanted terms such as DC term, twin-image, and cross-term from synthetically generated digital holograms is proposed. Synthetically generated in-line holograms imitate images that are captured by low-cost microscopy systems. In general, methods used to reconstruct the original object from in-line holograms use more than one hologram, but the proposed method paves the way for calculating the object from just one hologram. We demonstrated that the proposed model can be assembled by utilizing fewer optical elements and can be an alternative to costly digital holographic microscopy systems.

In the tumor microenvironment, cell interactions play a crucial role in influencing the morphology and metastasis characteristics of non-cancerous and cancerous cells. Although machine learning techniques have been proven effective in classifying individually cultured cell lines, their accuracy may deteriorate in classifying a co-cultured mixture of cells. In the proposed work, the optical path difference images of human dermal fibroblast and melanoma A375 cells were recorded, individually and in a mixture, using the off-axis digital holographic microscopy setup. A dataset consisted of segmented and labeled images of both sample types. The dataset was used to train a convolutional neural network through transfer learning to extract the morphology relevant features. An XGBoost classifier trained on the extracted features is found to effectively recognize morphological changes in cells within a mixture and classify them accurately with a limited size dataset.

We present a learning-enabled lens-free microscope for quantitative analysis of cell cultures. Leveraging the advances of recent years in learning algorithms, we developed a suite of neural networks that detect, quantify and track the cells. The detection algorithm locates the cells. The quantification algorithm, measures different cell metrics directly from cell phase image patches centred on the cells detections. Measured features include among others: cell morphology (dry mass, thickness, aspect ratio, ...) and local neighbourhood (density, contact surface, …). Finally, the tracking algorithm predicts the position of a given cell at next time point, making it possible to monitor a cell across time. To train these models we designed a semi-automated pipeline able to generate a supervised training datasets of up to millions of cells. The measurements obtained from the proposed method open up for modelling the cell cultures and providing biological insights.

Circulating tumor cell clusters (CTCCs) are associated with high metastatic potential and poor patient prognosis. However, they are difficult to detect and isolate because of their extremely low numbers. Here, we report on the use of machine learning and deep learning based analysis to achieve accurate detection of CTCCs in flowing whole blood samples relying on the confocal detection of endogenous light scattering signals. Our custom flow cytometer utilizes laser excitation at 405, 488, and 633 nm and confocal detection of the corresponding light scattering signals as well as fluorescence in the 510-530nm range. Samples consist of whole blood isolated from rats spiked with varying concentrations of CTCCs, flowed through the channels of a microfluidic device. The CTCCs express GFP, so that we can detect the strong GFP signal with the 520 nm detector and use it as the ground truth for assessing the performance of algorithms relying on the endogenous signals of the same peaks detected by the other detectors. We achieve a low false alarm rate of 0.78 events/min, a detection purity of 72%, and a sensitivity of 35.3%.

Broadband CARS is commonly used to study tissues of biological samples utilizing the inherent vibrational contrast present due to molecular vibrations. This technique however is notably hindered by the ubiquitous nonresonant background (NRB) that plagues the analysis. Nevertheless, a promising avenue is polarization suppression which was previously reported for single frequency CARS and has shown efficacy in NRB removal. Here, we employ polarization suppression using two acquisitions for the NRB rejection. A spectral interferometric method using passive polarization optical elements was used which allows for real-time NRB suppression by means of spectral interferometry, thereby eliminating the unwanted NRB.

Photobleaching is a light-induced effect where a fluorophore loses its fluorescence property due to light-induced damage. Controlling photons of light inside the scattering media is a challenging task due to refractive index inhomogeneity, multiple scattering of light, and formation of speckle noise. In this work, we have digitally transferred the coherent light of 0.74 mW power and concentrated the photon intensity in a controlled manner using the principle of constructive interference at the target location inside a tissue media, which is stained with fluorescence for generating pathological signals. The localized fluorescence signal extraction from the deep chicken tissue has been demonstrated with the developed binary-phase modulation based wavefront shaping system. The low-intensity coherent light has been re-localized from a large field of view to a spatio-temporal focused point, and fluorescence light emission has been detected from the fluorophores embedded in chicken tissue even after being exposed to light for a few hours. The fluorescence emission photons are obtained from the tissue by the developed experimental setup in the reflection mode.

Ghost images are created by correlating the intensity of light from an object and a 2-D image output that contains spatial resolution but no information about the object. 2-D images that provide spatial resolution consist of multiple speckle patterns. This study implements Gold algorithm, an iterative deconvolution algorithm, to restore a ghost image. The image used in this study was acquired by correlating the speckle patterns from psuedothermal light and light intensity of the object obtained by simulation. The point-spread function necessary for the deconvolution was derived from multiple speckle patterns of the ghost imaging system. We show that it is possible to compensate for the intrinsic quality degradation of ghost images with a simple deconvolution algorithm.

High-accurate unwrapped phases are demanded in various research fields such as optical imaging optical holography, optical diffraction tomography, and magnetic resonance imaging. However, the ground-truth phase is not accessible due to 2π ambiguity which arises from phase jumps in the wrapped phase. In this study, we propose to improve the accuracy of unwrapping process by increasing the sampling frequency to reconstruct the unwrapped phase with high accuracy for the application of optical imaging. The simulation results show increasing the optical magnification from 4X to 8X enables improvement of the phase estimation accuracy by 51% for a highly refractive object. Experimental results validate the sensitivity of phase estimation on the sampling size. Our approach demonstrates significant achievement in obtaining ground-truth phases for highly refractive objects.

We describe the design of a diode-laser-based source for Fourier-transform acousto-optic imaging (FT-AOI) architecture. A parallel master oscillator power amplifier (MOPA) configuration is implemented with a CW-operated laser diode injecting two tapered semiconductor optical amplifiers operating in a quasi-continuous wave regime (100 µs – 100 Hz). The combined pulse power is actively stabilized by correction with a piezoelectric element. The combined peak power reaches 8.9 W with a combining efficiency > 83%. The stabilized peak power fluctuation during 1 hour of measurement is below 1.3 %. This laser source is implemented in a fully functional integrated FT-AOI architecture based on digital holographic detection. An increase in the laser power results in an improved penetration depth of the acousto-optic imaging.

Visualizing fiber morphology in detail contributes to a better understanding of human physiology and of diseases that affect fibrous tissues. Here, we present the recently developed imaging technique Computational Scattered Light Imaging (ComSLI). The technique provides information on the direction of individual, interwoven fiber pathways with micrometer resolution in a non-invasive and label-free manner, independent of the method of sample preparation. While originally been designed for retrieving nerve fiber orientations in brain tissue, our results show that ComSLI also works on other types of fibers, including collagen. By measuring the same tissue sections with other imaging techniques, e.g. polarimetric Second Harmonic Generation (pSHG) microscopy, we are able to validate our results.

Based on single-pixel imaging, a hyperspectral microscope has been developed. It allows to retrieve not only the spatial distribution of samples at micrometer scale, but also a continuous spectrum at a given position. The spectral information ranges from the visible to the near-infrared. The innovating experimental method will be presented as well as first reconstructions of inorganic and organic samples. Perspectives of this work will also be discussed.

A common-path off-axis DIC microscope is developed that generates only two orthogonally polarized beams of identical intensity which is essential to achieving high-contrast interference fringes. We have achieved gradient phase maps of human red blood cells (RBCs) and bacteria E.coli. The system's capability is demonstrated by displaying the dynamics of the U2OS cell. The digital holography technique is used to acquire quantitative phase gradients in orthogonal directions, and then the phase gradients are spiral integrated to provide a quantitative phase image. This approach is straightforward to apply on any ordinary microscope with minimal polarization optics requirements. Optical sectioning may be produced by increasing the illumination NA.

Deep co-design methods have been proposed to optimize simultaneously optical and neural network parameters for many separate tasks such as high dynamic range, extended depth of field (EDOF), depth from defocus (DfD), object detection or pose estimation. In contrast, we study the multi-task co-design of an imaging system for two antagonist tasks: EDOF and DfD. We model and optimize a chromatic Cooke triplet using differentiable ray tracing, and we compare the performances for DfD and EDOF tasks, in a single, parallel and collaborative optimization scheme. We show how one task can benefit from the result of the other task. We also explore the benefit of the local positional information to process images with spatially varying point spread functions related to optical field aberrations.

To assess demosaicing algorithms performance, the use of full-resolution reference images is usual. For Color Polarization Filter Array cameras, these images are composed of 12 full resolution channels, where each channel represents a specific spectral band (red, green or blue) and a specific polarization angle (0°, 45°, 90° or 135°). This work is about a study on the effect of inter-channel misregistration on the performance of demosaicing algorithms for Color Polarization Filter Array images.

X-ray propagation-based phase contrast imaging (XPCI) relies on the coherence of the X-ray beam to achieve contrast from phase shift by letting the beam propagate in free space, hence yielding a Fresnel or Fraunhofer diffraction pattern. The exploitation of such images requires a phase retrieval step, which has proven sensitive to noise in low spatial frequencies. It is thought that incoherent scattering in the sample might contribute to this noise. To this aim, we propose a new way to simulate phase contrast based on the Wigner Distribution Function (WDF). In this framework, the exit wave of the sample is calculated through ray-tracing, which would allow accounting for effects including refraction and reflection. As a first demonstration of the framework, we simulate the double-slit experiment, as well as a variant with a scatterer in one of the slits.

We developed a bimodal microscope adapted to perform 3D+time observation of around hundred micrometer wide biological samples. Refractive index (RI) or fluorescence yield (FY) maps are obtained through processing of raw acquisitions. Concerning FY, an acquisition set consists in a stack of around 100 fluorescence images sequentially acquired at various imaging depths (2 µm step). Mathematical developments were undergone to handle deconvolution problems with positivity constraints. We are now able to propose an algorithm that implements data-matching, positivity constraint (expected for FY) and regularization terms. We obtained quasi isotropic results from measurements on a mouse embryo at its blastocyst stage.

Spectral imaging is a technique that captures spectral information from a scene and maps it onto a 2D image, revealing hidden features and properties of objects that are invisible to the human eye, such as elemental and molecular compositions. Augmented reality (AR), on the other hand, is a technology that enhances the perception of reality by superimposing digital information on the physical world. While these technologies have different purposes, they can be considered one and the same in terms of providing an extension of reality. Spectral imaging provides the information that can reveal the underlying nature of objects, while AR provides the method of visualization that can display the information in an intuitive and interactive way. By combining spectral imaging and AR, we can create a novel system that can enrich the user’s experience and expand the user’s knowledge of the environment. In this work, we present a novel Unity toolkit that combines spectral imaging and a HoloLens 2 AR device to create an interactive and immersive experience for the user.

Imaging at depth in opaque materials has long been a challenge. Recently, wavefront shaping has enabled significant advance for deep imaging. Nevertheless, most non-invasive wavefront shaping methods require cameras, lack the sensitivity for deep imaging under weak optical signals, or can only focus on a single "guidestar". Here, we retrieve the transmission matrix (TM) noninvasively using two-photon fluorescence exploiting a general single-pixel detection framework, allowing to achieve single-target focus on multiple guidestars spread beyond the memory effect range. In addition, if we assume memory effect correlations exist in the transmission matrix, we are able to significantly reduce the number of measurements needed.

In this work, we present a method for simulating interferograms generated from white light and encoding them into an RGB image. The procedure simulates the operation of an optical interference microscope that utilizes a white light source, a Mirau-type interferometric objective, and records the interferogram using a camera equipped with an RGB sensor. The light source is characterized by its energy spectral density function, while in the interferometric system, the object beam is characterized by the object's reflectance. Additionally, for visual comparison, real interferogram recordings of iron and nickel samples were made using a Nikon Eclipse LV100 microscope and a Nikon RGB DSi camera.

An innovative method is presented for obtaining the three-dimensional profile of an object using single-phase holograms and the focus technique. The procedure involves precomputing a set of holograms that, through diffraction, generate point patterns on a specific plane at a predetermined distance along the axial axis. Subsequently, each pattern is sequentially projected onto the object's surface, and through a focus metric, the focused points are identified, thereby determining their axial position. This method offers significant advantages by allowing the arrangement of the hologram set on a spatial light modulator, eliminating the need for mechanical displacements inherent in conventional focus techniques. To illustrate the method's effectiveness, simulations were conducted to obtain the linear profile of an object. Computationally, we calculated a set of Fresnel holograms, projecting a row of points onto the object with each hologram. The simulation results corroborate the feasibility and effectiveness of the proposed focus.

Assessing local levels of Cu pollutant doses in water samples is an important open issue. The effects of the Cu fraction that is bioavailable to marine species are not easy to assess. Diatoms can be considered as bioprobes due to their high sensitivity to heavy metals. We estimate the Cu dose in water samples based on its effects on a diatom species, namely Skeletonema pseudocostatum. We put diatom biosentinels in water samples containing Cu at different doses and expose them for different times. Then, we image them using a Fourier Ptychographic Microscope (FPM). We employ fractal geometry descriptors to analyse the high-resolution phase-contrast map. We show that the multi-scale lacunarity well describes the status of the sample containing bioprobes exposed to different Cu doses. Test experiments demonstrate the effectiveness of the proposed approach in determing seven Cu dose ranges diatoms are exposed to.

Alzheimer’s disease (AD) is a complex neurodegenerative disease that results in cognitive decline and memory loss. We demonstrate scale-dependent differences in wildtype and AD mouse model (5xFAD) brain sections. We combine label-free Spatial Light Interference microscopy (SLIM), Structured illumination microscopy (SIM), standard fluorescence microscopy, and CARITY techniques to quantify and map disease-specific altered cortical and hippocampal cell and disorder strength distribution patterns, perivascular and periventricular structural integrity changes, amyloid-beta (Aβ) plaque burden, the presence of neurofibrillary tangles, inflammation indicators, and myelin deterioration.

We utilize Color Spatial Light Interference Microscopy (cSLIM) to evaluate tissue microarray specimens, coupled with a novel deep learning scheme for automated cell/feature segmentation and analysis to create high-resolution breast (Ductal Carcinoma In situ (DCIS) and Invasive Ductal Cancer (IDC)) and bladder cancer microenvironment refractive index maps that reveal intrinsic cell and tissue microarchitectural alterations associated with cancer, invasion and thus, a potentially worsening prognosis.

Using fluorescence of dye molecules, we image whispering gallery modes in a cylindrical fiber. Scanning the wavelength of the exciting laser we observed interference fringes formed by whispering-gallery-modes counterpropagating along the axis of the cylinder.

In this talk we demonstrate numerically the feasibility of rapid steering of photonic nanojets (PNJs) involving no opto-mechanical intervention, and using lossless phase-only modulation of a Gaussian beam illuminating a simple homogeneous micro-element. Our phase modulation is a combination of linear and quadratic transverse phase factors and achieved using a single spatial light modulator (SLM) or a phase mask. Moreover, our numerical results indicate that the phase-only modulation of the Gaussian beam increases the field confinement relative to unmodulated Gaussian beam illumination of a circular dielectric micro-element.

To establish a new level of print quality and reduction in resource consumption in the 3D printing ceramics industry, there is a need for integrated, sub-surface and non-destructive inspection (NDI) during printing. Optical coherence tomography (OCT) is a laser-based technology that is fast, non-destructive and allows for imaging of very complex structures. This technology is well established in the biologic field for more than 30 years, but it is still less present within industrial manufacturing. In this presentation, we use mid-infrared (MIR) OCT at 4 μm centre wavelength, to demonstrate sub-surface NDI of 3D printed alumina parts to a depth of ∼0.7 mm, capable of resolving details of 7 μm in five high-definition images per second. The project was funded by Horizon Europe, Grant Agreements No. 101058054 (TURBO) and No. 101057404 (ZDZW), and by VILLUM Fonden (2021 Villum Investigator project no. 00037822: Table-Top Synchrotrons).

Stellar intensity interferometry (SII) is based on the correlation of the light intensity fluctuations of a star detected at two or more telescopes, with no need to combine the collected photons directly. A measurement of the correlation in full "photon-counting mode" was experimented with fast photon counters in Italy (2016-2020) and is currently being ported to the ASTRI Mini-Array. Performing image synthesis with "photon-counting" SII will eventually be pursued by steps, starting from the optimization of the available pipelines for the treatment of the time series acquired at extremely high count rates, continuing with the development of efficient and innovative algorithms for the cross-correlation of the arrival times in large time series, and finishing with the implementation of a dedicated pipeline for the synthesis of images starting from the interferometric data. Here we present the project and the present status of the activities.

In this study we analyze the effect of experimental errors on the optimization and calibration method of a Mueller matrix imaging polarimeter based on liquid crystal variable retarders. The study is carried out through numerical simulations, where an optimized Mueller matrix polarimeter is simulated considering misalignments of the polarimetric components, and variations in the induced retardance of the LCVRs. However, the final measurement error does not depend only on non-ideal elements, but also depends on the noise, in the irradiance measurements, and the accuracy of the calibration method. Thus, the eigenvalue calibration method is used in the simulations, including random variations in the irradiance matrix. The tolerances of the optimization and calibration method are analyzed, and the results presented.

3D laser nanoprinting based on multi-photon absorption (or multi-step absorption) has become an established commercially available and widespread technology. Here, we focus on recent progress concerning increasing print speed, improving the accessible spatial resolution beyond the diffraction limit, increasing the palette of available materials, and reducing instrument cost.

Biological discovery is a driving force of biomedical progress. With rapidly advancing technology to collect and analyze information from cells and tissues, we generate biomedical knowledge at rates never before attainable to science. Nevertheless, conversion of this knowledge to patient benefits remains a slow process. To accelerate the process of reaching solutions for healthcare, it would be important to complement this culture of discovery with a culture of problem-solving in healthcare. The talk focuses on recent progress with optical and optoacoustic technologies, as well as computational methods, which open new paths for solutions in biology and medicine. Particular attention is given on the use of these technologies for early detection and monitoring of disease evolution. The talk further shows new classes of imaging systems and sensors for assessing biochemical and pathophysiological parameters of systemic diseases, complement knowledge from –omic analytics and drive integrated solutions for improving healthcare.

Lensless digital holographic microscopy (LDHM), as one of key computational microscopy techniques, performs high throughput in silico imaging. Numerical propagation of digitally recorded in-line Gabor holograms allows for accessing both amplitude (absorption) and phase (refraction) contrast, devoid of microscope objective limitations, e.g., in depth of field and field of view. The in-line coherent holographic framework induces inherent twin image errors and various coherent artifacts, however. The signal-to-noise ratio of reconstructed holograms additionally deteriorates due to low photon budget environment, favorable in terms of time-lapse photostimulation-free bioimaging of live cells. In this contribution, we discuss several techniques for minimization of LDHM reconstruction errors, with the emphasis on simultaneous validation of phase measurement fidelity via calibration target testing. Crafted using two-photon polymerization, our targets enable large field of view phase imaging verification and assess the efficacy of the 3D printing method itself. We also present bio-applications of enhanced LDHM in dynamic (migrating neural cells) and static (brain tissue slices) scenarios.

We investigate an unconventional concept of near-eye display based on sparse aperture imaging principles. In this original approach, a pixel is projected onto the retina through the emission of emissive points randomly distributed on the surface of the glass. In this contribution, we evaluate the impact of the emissive point distribution characteristics on the imaging performances. We highlight the central role of the emissive point density on the contrast of the image projected onto the retinal plane. This result leads us to reconsider our initial display architecture.

Cellular heterogeneity is the hallmark of many cancers, referring to the co-existence of different phenotypes with very distinct biological behaviours in single isolates. Automatically detecting single-cell heterogeneity is therefore critical, and can provide important information on cancer initiation. We present a clustering algorithm that allows identifying heterogeneity in cell culture from time-lapses of lensless microscopic images. A preliminary segmentation and tracking pipeline extract quantitative features (morphology, motility and reproduction cycle) for each cell. An unsupervised learning algorithm then clusters the time-series of the cell tracks measurements, in two steps. We validate our approach on co-cultures of mixed cells lines, and on murine fibroblasts isolated from genetically modified mice, where the modified genome promotes the establishment of cancers and heterogeneous cell morphologies and behaviours

Microsphere-assisted imaging is a label-free super-resolution imaging technique. Recently, dielectric microspheres coated with metallic patches on their surface was found to be able to significantly enhance the imaging contrast of microspheres. This method enables achieving high-quality super-resolution imaging, and opens a new way to develop advanced and reliable nano-imaging systems based on engineered microsphere lenses.

Straylight (SL) characterization using ultrafast time of flight imaging (ToF) has been demonstrated for the testing of refractive telescopes, using a streak tube with a femtosecond laser. It was shown that individual SL contributors such as different ghost reflections and scattering features can be measured individually and identified by temporal discrimination due to the specific optical path length of each of them. This allows to analyze them individually for a better understanding of straylight properties in instruments. Recently, we have used the ToF approach to characterize a testing facility that was then used in the frame of the calibration campaign for the Narrow Angle Camera (NAC) of the Earth Return Orbiter mission. The facility itself could generate its own SL that has to be retrieved from that coming from the instrument. Due to the large facility dimensions, optical path lengths can be discriminated by using a low temporal resolution that is enabled by picosecond lasers associated to a SPAD detector. At the end, the SL coming from the facility can be reverse engineered to find its origin and either removed by facility adaptation or by processing.

We increase single-photon LiDAR capabilities via a novel inpainting transformer model to intelligently downsample scanning area by up to 90% while maintaining image quality. We use sparse beam steering patterns such as Lissajous and Spiral to showcase our novel Physics Aware Transformer (PAT). This model reconstructs all non-observed information within the image plane, as it communicates with the beam steering hardware. Also, we apply this to 3D time of flight image (ToF) reconstruction, where objects obstruct each other. We use ToF histograms to distinguish the foreground and background, and their overlap will be treated as the dynamic mask for PAT to reconstruct. We believe our approach will be useful in applications for imaging and sensing dynamic targets with sparse single-photon data. In this paper, we explore the effects of unconventional beam steering, downsampling, and masking as they pertain to the quality of LiDAR scans. To our knowledge, this is the first deep learning application where communication between the reconstruction model and beam steering mechanics is essential.

This work presents a new burst mode CMOS image sensor in 0,35 µm SiGe BiCMOS technology that can achieve a pixel rate of 1 TS/s. The sensor uses a novel integrated Streak architecture that allows fill factor of 84% and sampling speed from 50µs to 200 ps, which is the highest recorded for a monolithic CMOS sensor to date. With such performances, this sensor is suitable for observation of sub nanosecond events for a variety of applications, such as fluorescence metrology, time-resolved spectroscopy, optical tomography or detonics.

Event-based Vision Sensor (EVS) characterization is a challenging task, and accurate interpretation of results requires a thorough understanding and analysis of pixel circuitry. We revisit the current standard characterization method of constructing step-response probability curves (S-curves) to determine nominal contrast threshold (NCT). We address common pitfalls in constructing accurate curves and demonstrate that a physics informed interpretation of these curves corrects an apparent anomaly that the contrast threshold varies with illumination suggested by previous reports. Additionally, we show that applying this improved interpretation allows other fundamental performance characteristics such as dark current and RMS noise power to be inferred from multiple S-curve measurements at varied illumination, resulting in more accurate parameterization for realistic EVS simulations.

By developing advanced single-photon LiDAR systems and tailored computational imaging algorithms, we show the capabilities of high-resolution 3D imaging over long ranges up to 200 km. We also present the recent progress to exploit single-photon LiDAR techniques for imaging through scattering media and non-line-of-sight (NLOS) imaging.

Combining the advantages of spatio-spectral multiplexing and computational imaging, we proposed a self-referenced single-shot spatiotemporal measurement technique called coherent modulation imaging for the spatio-spectrum (CMISS). Experimentally, CMISS acquired the multi-dimensional amplitude and phase information of an ultrashort pulse in a single frame, with femtosecond-scale temporal resolution, micron-scale spatial resolution, and 0.04 rad phase accuracy. Furthermore, this spatiotemporal measurement technique was applied to the single-shot ultrafast diagnosis of laser plasma evolution. An arbitrary time-wavelength-encoded biprism interferometer (TWEBI) was demonstrated with a temporal resolution of 200 fs, a spatial resolution of 4 μm, an effective frame rate of 5 trillion fps, and freely adjustable temporal range. By using plug-and-play biprisms, TWEBI experimentally realized the convenient switching between shadow-recording mode and phase-measurement mode for the dynamic characterization of femtosecond laser induced air filamentation process.

We propose a novel design to perform quantitative phase confocal imaging with super-resolution imaging capabilities. Our approach, derived from image scanning microscopy, allows to perform quantitative label-free images up to 24kHz for 2D imaging. We demonstrate the capability to quantitatively image living adherent cells and biological tissue slices.

Photonic nanojets (PNJs) are highly localized optical probes that promise label-free measurements beyond the classical diffraction limit. We here demonstrate numerically the feasibility of label-free, self-calibrating, super-resolution optical detection and imaging using far-field scatterometry in conjunction with rapid scanning photonic nanojet excitation achieved with no opto-mechanical intervention. We realize PNJ scanning by computed structured illumination of refractive dielectric micro-elements such as micro-spheres and micro-cubes. Our far-field measurement data are phaseless. In proof-of-concept computations, we use our steerable optical probe to extract information on nanoparticles, aggregates of nanoparticles, and thin-film structures beyond the classical lateral and vertical resolution limits, in the presence of supporting structures such as substrates.

A novel single-pixel fluorescence microscope with super-resolution based on structured illumination microscopy, has been developed. Spatially resolved patterns are used both to obtain 2D images with a bucket detector and to expand the information in the spatial frequency domain using appropriate algorithms.

We tested the ability of multiphoton microscopy (MPM) devices to produce label-free images of the virus Sars-Cov-2 responsible for the Covid global health crisis of the years 2020-2022. The virion cultures were inactivated to preserve the integrity of the virus without its contagious nature. We have determined a spectral range of excitation and emission, combined with adapted energy levels for the non-destructive excitation and efficient collection. We obtained a discriminating signature of these viral particles and produced an image of virion bundles concentrated around magnetic particles. We have confirmed the origin of the MPM images from the SARS-Cov-2 viral particles thanks to electron microscopy images. This superresolution imaging solution has allowed to reveal the shape of viral particles of SARS-Cov-2 in their entirety, combined in clusters around magnetic particles, the core of which is clearly identified by its diameter of 200 nm. We attributed the origin of the optical signature from the virions to the presence of DNA or RNA, made up of an assembly of nucleic acids with a known nonlinear optical property.

Time-Domain Optical Coherence Tomography (TD-OCT) utilizes low-coherence interferometry to capture high-resolution, cross-sectional images of biological tissue. This non-invasive technique offers real-time imaging within the gastrointestinal tract, reducing patient discomfort associated with conventional endoscopic procedures. Operating similarly to ultrasound, TD-OCT employs a probe, broadband light source, reference arm, sample arm, detector, and signal processing algorithm for image generation. The proposed TD-OCT system is simulated in Zemax Optic Studio for validation of ray tracing, lateral resolution, and depth of focus. Offering high-resolution, non-invasive imaging, TD-OCT shows promise for diverse applications in ophthalmology, pathology, surgery, and endoscopy.