Showing papers by "Pietro Ferraro published in 2022"

Dual-plane coupled phase retrieval for non-prior holographic imaging

Abstract: Abstract Accurate depiction of waves in temporal and spatial is essential to the investigation of interactions between physical objects and waves. Digital holography (DH) can perform quantitative analysis of wave–matter interactions. Full detector-bandwidth reconstruction can be realized based on in-line DH. But the overlapping of twin images strongly prevents quantitative analysis. For off-axis DH, the object wave and the detector bandwidth need to satisfy certain conditions to perform reconstruction accurately. Here, we present a reliable approach involving a coupled configuration for combining two in-line holograms and one off-axis hologram, using a rapidly converging iterative procedure based on two-plane coupled phase retrieval (TwPCPR) method. It realizes a fast-convergence holographic calculation method. High-resolution and full-field reconstruction by exploiting the full bandwidth are demonstrated for complex-amplitude reconstruction. Off-axis optimization phase provides an effective initial guess to avoid stagnation and minimize the required measurements of multi-plane phase retrieval. The proposed strategy works well for more extended samples without any prior assumptions of the objects including support, non-negative, sparse constraints, etc. It helps to enhance and empower applications in wavefront sensing, computational microscopy and biological tissue analysis.

Speeding up reconstruction of 3D tomograms in holographic flow cytometry via deep learning.

Abstract: Tomographic flow cytometry by digital holography is an emerging imaging modality capable of collecting multiple views of moving and rotating cells with the aim of recovering their refractive index distribution in 3D. Although this modality allows us to access high-resolution imaging with high-throughput, the huge amount of time-lapse holographic images to be processed (hundreds of digital holograms per cell) constitutes the actual bottleneck. This prevents the system from being suitable for lab-on-a-chip platforms in real-world applications, where fast analysis of measured data is mandatory. Here we demonstrate a significant speeding-up reconstruction of phase-contrast tomograms by introducing in the processing pipeline a multi-scale fully-convolutional context aggregation network. Although it was originally developed in the context of semantic image analysis, we demonstrate for the first time that it can be successfully adapted to a holographic lab-on-chip platform for achieving 3D tomograms through a faster computational process. We trained the network with input-output image pairs to reproduce the end-to-end holographic reconstruction process, i.e. recovering quantitative phase maps (QPMs) of single cells from their digital holograms. Then, the sequence of QPMs of the same rotating cell is used to perform the tomographic reconstruction. The proposed approach significantly reduces the computational time for retrieving tomograms, thus making them available in a few seconds instead of tens of minutes, while essentially preserving the high-content information of tomographic data. Moreover, we have accomplished a compact deep convolutional neural network parameterization that can fit into on-chip SRAM and a small memory footprint, thus demonstrating its possible exploitation to provide onboard computations for lab-on-chip devices with low processing hardware resources.

Dual-plane coupled phase retrieval for non-prior holographic imaging

Abstract: Abstract Accurate depiction of waves in temporal and spatial is essential to the investigation of interactions between physical objects and waves. Digital holography (DH) can perform quantitative analysis of wave–matter interactions. Full detector-bandwidth reconstruction can be realized based on in-line DH. But the overlapping of twin images strongly prevents quantitative analysis. For off-axis DH, the object wave and the detector bandwidth need to satisfy certain conditions to perform reconstruction accurately. Here, we present a reliable approach involving a coupled configuration for combining two in-line holograms and one off-axis hologram, using a rapidly converging iterative procedure based on two-plane coupled phase retrieval (TwPCPR) method. It realizes a fast-convergence holographic calculation method. High-resolution and full-field reconstruction by exploiting the full bandwidth are demonstrated for complex-amplitude reconstruction. Off-axis optimization phase provides an effective initial guess to avoid stagnation and minimize the required measurements of multi-plane phase retrieval. The proposed strategy works well for more extended samples without any prior assumptions of the objects including support, non-negative, sparse constraints, etc. It helps to enhance and empower applications in wavefront sensing, computational microscopy and biological tissue analysis.

Compensation of aberrations in holographic microscopes: main strategies and applications

Abstract: Abstract Digital holography is a technique that provides a non-invasive, label-free, quantitative, and high-resolution imaging employable in biological and science of matter fields, but not only. In the last decade, digital holography (DH) has undergone very significant signs of progress that made it one of the most powerful metrology tools. However, one of the most important issues to be afforded and solved for obtaining quantitative phase information about the analyzed specimen is related to phase aberrations. Sources of aberrations can be diverse, and several strategies have been developed and tested to make DH a reliable optical system with submicron resolution. This paper reviews the most effective and robust methods to remove or compensate phase aberrations in retrieved quantitative phase imaging by DH. Different strategies are presented and discussed in detail on how to remove or compensate for such disturbing aberrations. Among the various methods improvements in the optical setups are considered the numerical algorithms, the hybrid methods, and the very recent Artificial Intelligence (AI) approaches to compensate for all aberrations which affect the setups to improve the imaging quality and the accuracy of the reconstruction images’ procedures.

Digital holography as metrology tool at micro-nanoscale for soft matter

Abstract: The appearance of the first laser approximately 12 years after the invention of holography by Gabor (1948) revolutionized the field of optical metrology. In fact, the invention of holographic interferometry enabled the exploitation of interferometry on non-mirror surfaces and full-scale objects. The holography-based measurement methods has been implemented to several industrial systems or in support of R&D with the aim of improving new products in many fields (automotive, aerospace, electronics, etc.). To date, holography has been considered an important measurement tool for non-destructive inspection (NDI), strain-stress measurement, and vibration analysis at various engineering sites. Recently, the new paradigm of Industry4.0 has seen the introduction of new technologies and methods of processing materials as well as the development of manufacturing approaches for the realization of innovative products. For example, direct printing, additive, and bottom-up manufacturing processes are expected to involve new ways of making products in future, and most innovative fabrication processes will be based on the manipulation of soft matter (e.g., starting from the liquid phase) that will be shaped at the nanoscale. The inherent characteristics of digital holography (DH) make it a powerful and accurate tool for the visualization and testing of final products, as well as for in situ and real-time monitoring and quantitative characterization of the processes involved during the fabrication cycle. This review aims to report on the most useful applications of soft matter, where the capabilities offered by DH, such as three-dimensional (3D) imaging, extended focus, 3D tracking, full-field analysis, high sensitivity, and a wide range of measurements from nanometers to centimeters, permit completely non-invasive characterizations on a full-scale. Several holographic experimental results of typical samples are reported and discussed where DH plays a primary role as a tool gauge for soft matter. ACCEPTED ARTICLE PREVIEW

Stain-free identification of cell nuclei using tomographic phase microscopy in flow cytometry

Abstract: Quantitative Phase Imaging (QPI) has gained popularity in bioimaging because it can avoid the need for cell staining, which in some cases is difficult or impossible. However, as a result, QPI does not provide labelling of various specific intracellular structures. Here we show a novel computational segmentation method based on statistical inference that makes it possible for QPI techniques to identify the cell nucleus. We demonstrate the approach with refractive index tomograms of stain-free cells reconstructed through the tomographic phase microscopy in flow cytometry mode. In particular, by means of numerical simulations and two cancer cell lines, we demonstrate that the nucleus can be accurately distinguished within the stain-free tomograms. We show that our experimental results are consistent with confocal fluorescence microscopy (FM) data and microfluidic cytofluorimeter outputs. This is a significant step towards extracting specific three-dimensional intracellular structures directly from the phase-contrast data in a typical flow cytometry configuration.

Deep Learning-Based, Misalignment Resilient, Real-Time Fourier Ptychographic Microscopy Reconstruction of Biological Tissue Slides

Abstract: Fourier ptychographic microscopy probes label-free samples from multiple angles and achieves super resolution phase-contrast imaging according to a synthetic aperture principle. Thus, it is particularly suitable for high-resolution imaging of tissue slides over a wide field of view. Recently, in order to make the optical setup robust against misalignments-induced artefacts, numerical multi-look has been added to the conventional phase retrieval process, thus allowing the elimination of related phase errors but at the cost of a long computational time. Here we train a generative adversarial network to emulate the process of complex amplitude estimation. Once trained, the network can accurately reconstruct in real-time Fourier ptychographic images acquired using a severely misaligned setup. We benchmarked the network by reconstructing images of animal neural tissue slides. Above all, we show that important morphometric information, relevant for diagnosis on neural tissues, are retrieved using the network output. These are in very good agreement with the parameters calculated from the ground-truth, thus speeding up significantly the quantitative phase-contrast analysis of tissue samples.

3D imaging lipidometry in single cell by in-flow holographic tomography

Abstract: The most recent discoveries in the biochemical field are highlighting the increasingly important role of lipid droplets (LDs) in several regulatory mechanisms in living cells. LDs are dynamic organelles and therefore their complete characterization in terms of number, size, spatial positioning and relative distribution in the cell volume can shed light on the roles played by LDs. Until now, fluorescence microscopy and transmission electron microscopy are assessed as the gold standard methods for identifying LDs due to their high sensitivity and specificity. However, such methods generally only provide 2D assays and partial measurements. Furthermore, both can be destructive and with low productivity, thus limiting analysis of large cell numbers in a sample. Here we demonstrate for the first time the capability of 3D visualization and the full LD characterization in high-throughput with a tomographic phase-contrast flow-cytometer, by using ovarian cancer cells and monocyte cell lines as models. A strategy for retrieving significant parameters on spatial correlations and LD 3D positioning inside each cell volume is reported. The information gathered by this new method could allow more in depth understanding and lead to new discoveries on how LDs are correlated to cellular functions. 3D D. Pirone, P. Memmolo, V. Bianco, L. Iom- marini and P. Ferraro analyzed and discussed the tomographic reconstruc-tions and data. I. Kurelac, S. Lemma and L. Iommarini performed FM ex- periments and data analysis; G. Pasquinelli and S. Valente performed the TEM; all the authors contributed to critical discussion of the results and contributed to write the manuscript. P. Ferraro supervised the research.

Identification of drug-resistant cancer cells in flow cytometry combining 3D holographic tomography with machine learning

Abstract: Identifying drug-resistant cancer cells is of fundamental importance to afford disease and find the most effective therapies for the patients. Recently, label-free imaging flow cytometry has been deeply investigated in cell recognition. In particular, the combination of flow cytometry and machine learning allows for achieving high accuracy in cell identification and high throughput. Despite the encouraging results, the potentialities of digital holography (DH) in flow-cytometry modality have not been exploited in full. Up to now, only 2D phase maps have been used in all previously reported research about the use of DH for analyzing flowing cells. Here we show that having access to the whole 3D information of each flowing cell can improve the cells identification. We used digital holographic flow cytometry to collect images of flowing cells and reconstructed their 3D tomographic phase. And for the first time, we extracted scores of meaningful morphometric features from the 3D and 2D phase maps through machine learning methods and finally compare their classification performance. The results show that 3D features can achieve higher classification accuracy with respect to sole 2D analysis demonstrating that 3D morphology information can yield advantages in recognizing drug-resistant endometrial cancer cells, thus allowing a significant step forward in performance of label-free cell classification.

Optical-force-controlled red-blood-cell microlenses for subwavelength trapping and imaging.

Abstract: We demonstrate that red blood cells (RBCs), with an adjustable focusing effect controlled by optical forces, can act as bio-microlenses for trapping and imaging subwavelength objects. By varying the laser power injected into a tapered fiber probe, the shape of a swelled RBC can be changed from spherical to ellipsoidal by the optical forces, thus adjusting the focal length of such bio-microlens in a range from 3.3 to 6.5 µm. An efficient optical trapping and a simultaneous fluorescence detecting of a 500-nm polystyrene particle have been realized using the RBC microlens. Assisted by the RBC microlens, a subwavelength imaging has also been achieved, with a magnification adjustable from 1.6× to 2×. The RBC bio-microlenses may offer new opportunities for the development of fully biocompatible light-driven devices in diagnosis of blood disease.

Kinematic analysis and visualization of Tetraselmis microalgae 3D motility by digital holography.

Abstract: A study on locomotion in a 3D environment of Tetraselmis microalgae by digital holographic microscopy is reported. In particular, a fast and semiautomatic criterion is revealed for tracking and analyzing the swimming path of a microalga (i.e., Tetraselmis species) in a 3D volume. Digital holography (DH) in a microscope off-axis configuration is exploited as a useful method to enable fast autofocusing and recognition of objects in the field of view, thus coupling DH with appropriate numerical algorithms. Through the proposed method we measure, simultaneously, the tri-dimensional paths followed by the flagellate microorganism and the full set of the kinematic parameters that describe the swimming behavior of the analyzed microorganisms by means of a polynomial fitting and segmentation. Furthermore, the method is capable to furnish the accurate morphology of the microorganisms at any instant of time along its 3D trajectory. This work launches a promising trend having as the main objective the combined use of DH and motility microorganism analysis as a label-free and non-invasive environmental monitoring tool, employable also for in situ measurements. Finally, we show that the locomotion can be visualized intriguingly by different modalities to furnish marine biologists with a clear 3D representation of all the parameters of the kinematic set in order to better understand the behavior of the microorganism under investigation.

Response of glass/carbon hybrid composites subjected to repeated low velocity impacts

Abstract: This work investigated the effect of repeated low-velocity impacts at different energy levels on vinyl ester composite laminates. In particular, hybrid composite laminates made by carbon woven fabric and glass woven fabric impregnated by vinyl ester resin were subjected to 1, 5 and 10 impacts for three different energy levels (U=5 J, 10 J and 20 J). The multi-impact damage evolution was studied by combining several non-destructive techniques such as Pulsed Thermography, Electronic Speckle Pattern Interferometry and Ultrasonic C-scanning. Along with images of detected damaged areas, some impact parameters such as contact force, deflection and absorbed energy were provided.

Feedback control for distributed ledgers: An attack mitigation policy for DAG-based DLTs

Abstract: —In this paper we present a feedback approach to the design of an attack mitigation policy for DAG-based Distributed Ledgers. We develop a model to analyse the behaviour of the ledger under the so called Tips Inflation Attack and we design a control strategy to counteract this attack strategy. The efficacy of this approach is showcased through a theoretical analysis, in the form of two theorems about the stability properties of the ledger with and without the controller, and extensive Monte Carlo simulations of an agent-based model of the distributed ledger.

Finding intracellular lipid droplets from the single-cell biolens' signature in a holographic flow-cytometry assay.

Abstract: In recent years, intracellular LDs have been discovered to play an important role in several pathologies. Therefore, detection of LDs would provide an in-demand diagnostic tool if coupled with flow-cytometry to give significant statistical analysis and especially if the diagnosis is made in full non-invasive mode. Here we combine the experimental results of in-flow tomographic phase microscopy with a suited numerical simulation to demonstrate that intracellular LDs can be easily detected through a label-free approach based on the direct analysis of the 2D quantitative phase maps recorded by a holographic flow cytometer. In fact, we demonstrate that the presence of LDs affects the optical focusing lensing features of the embracing cell, which can be considered a biological lens. The research was conducted on white blood cells (i.e., lymphocytes and monocytes) and ovarian cancer cells. Results show that the biolens properties of cells can be a rapid biomarker that aids in boosting the diagnosis of LDs-related pathologies by means of the holographic flow-cytometry assay for fast, non-destructive, and high-throughput screening of statistically significant number of cells.

On monocytes and lymphocytes biolens clustering by in flow holographic microscopy

Abstract: Live cells act as biological lenses and can be employed as real‐world optical components in bio‐hybrid systems. Imaging at nanoscale, optical tweezers, lithography and also photonic waveguiding are some of the already proven functionalities, boosted by the advantage that cells are fully biocompatible for intra‐body applications. So far, various cell types have been studied for this purpose, such as red blood cells, bacterial cells, stem cells and yeast cells. White Blood Cells (WBCs) play a very important role in the regulation of the human body activities and are usually monitored for assessing its health. WBCs can be considered bio‐lenses but, to the best of our knowledge, characterization of their optical properties have not been investigated yet. Here, we report for the first time an accurate study of two model classes of WBCs (i.e., monocytes and lymphocytes) by means of a digital holographic microscope coupled with a microfluidic system, assuming WBCs bio‐lens characteristics. Thus, quantitative phase maps for many WBCs have been retrieved in flow‐cytometry (FC) by achieving a significant statistical analysis to prove the enhancement in differentiation among sphere‐like bio‐lenses according to their sizes (i.e., diameter d) exploiting intensity parameters of the modulated light in proximity of the cell optical axis. We show that the measure of the low intensity area (S: Iz

Instant in situ formation of a polymer film at the water–oil interface

Abstract: The water–oil interface is an environment that is often found in many contexts of the natural sciences and technological arenas. This interface has always been considered a special environment as it is rich in different phenomena, thus stimulating numerous studies aimed at understanding the abundance of physico-chemical problems that occur there. The intense research activity and the intriguing results that emerged from these investigations have inspired scientists to consider the water–oil interface even as a suitable setting for bottom-up nanofabrication processes, such as molecular self-assembly, or fabrication of nanofilms or nano-devices. On the other hand, biphasic liquid separation is a key enabling technology in many applications, including water treatment for environmental problems. Here we show for the first time an instant nanofabrication strategy of a thin film of biopolymer at the water–oil interface. The polymer film is fabricated in situ, simply by injecting a drop of polymer solution at the interface. Furthermore, we demonstrate that with an appropriate multiple drop delivery it is also possible to quickly produce a large area film (up to 150 cm2). The film inherently separates the two liquids, thus forming a separation layer between them and remains stable at the interface for a long time. Furthermore, we demonstrate the fabrication with different oils, thus suggesting potential exploitation in different fields (e.g. food, pollution, biotechnology). We believe that the new strategy fabrication could inspire different uses and promote applications among the many scenarios already explored or to be studied in the future at this special interface environment.

On-the-Fly Formation of Polymer Film at Water Surface

Abstract: The self-propulsion of bodies floating in water is of great interest for developing new robotic and intelligent systems at different scales, and whenever possible, Marangoni propulsion is an attractive candidate for the locomotion of untethered micro-robots. Significant cases have been shown using liquid and solid surfactants that allow an effective propulsion for bodies floating on water to be achieved. Here, we show for the first time a strategy for activating a twofold functionality where the self-propulsion of a floating body is combined with the formation of a polymer thin film at the water surface. In fact, we demonstrate that by using polymer droplets with an appropriate concentration of solvent and delivering such drops at specific locations onto freely floating objects, it is possible to form “on-the-fly” thin polymer films at the free water surface. By exploiting self-propulsion, a polymer thin film can be formed that could cover quite extensive areas with different shapes depending on the motion of the floating object. This intriguing twice-functionality activated though a single phenomenon, i.e., film formation and related locomotion, could be used in perspective to perform complex operations at water surfaces, such as dynamic liquid packaging, cleaning, and moving away floating particles, monolayer films, or macro-sized objects, as discussed in the text.

Oil Bodies Cream from Olive Paste: Extraction of a Functional Ingredient for Developing a Stable Food Emulsion

Abstract: Oil bodies (OBs) dispersed in an aqueous medium form a natural emulsion with high physical and microbiological stability. This work was focused on the development of a new protocol for extracting OBs from olive paste, through the extraction of an olive oil body cream (OOBC) with a yield of about 43% (wt/wt) in approximately 2 h. The proximate analysis revealed the presence of moisture, lipids and proteins as well as the contents of polyphenols and flavonoids, and the antioxidant powers were determined. The rheological and tribological performances of the OOBC were evaluated. Moreover, we measured a size distribution in the range of 0.7–1.7 m, by using a standard optical microscope. The results have demonstrated clearly that the OOBC extracted from the olive paste can be used as a functional and vegan ingredient in food emulsions.

Digital holography in microplastic identification

Abstract: Plastic debris increase every day, invading the entire ecosystem, especially the marine environment. The damage caused by plastics, and even more by their fragmentation into micrometric samples, called microplastics (MPs), is becoming irrecoverable. Many techniques are used to analyse MPs, but a standardized procedure is still missing. Digital Holography (DH) has proved to be a powerful imaging tool for identifying MPs in water samples, highlighting highthroughput, label-free, high-coherent and non-invasive prowess. Besides, DH furnishes quantitative information, morphological parameters and numerical refocusing, suitable for microfluidic systems. Both physical and chemical information that characterize the optically denser object are completely enclosed in the phase contrast, measured by light waves transition. However, DH is not capable alone to be material specific and to gather polymers information. To overcome these constraints, artificial intelligence (AI) has been considered, demonstrating to be a powerful pawn for accurately identifying MPs samples. Here we identify, characterize, and classify MPs samples by means of DH, empowered by AI, providing an DH modality for a fast and high-throughput analysis of MPs at lab-on-chip scale, distinguishing them from marine diatoms. We use a machine learning (ML) approach on “holographic features” extracted from DH images for distinguishing MPs from diatoms with a well-established SVM classifier. Then, we couple DH microscopy and machine learning to a novel characterization of phase-contrast patterns based on the fractal geometry. Besides, we use a polarization-resolved DH flow cytometer to prove MPs birefringence, and to accurately discriminate between different types of MPs with fiber shape.

Real-time FPM reconstruction and misalignment correction by numerical Multi-Look and GAN

Abstract: Fourier Ptychographic Microscopy (FPM) is a powerful bioimaging tool. Here we show numerical Multi-Look FPM and GAN-based reconstruction of biological samples to get rid of system misalignments and hone the FPM use in clinical practice.

Developing a Reliable Holographic Flow Cyto-Tomography Apparatus by Optimizing the Experimental Layout and Computational Processing

Abstract: Digital Holographic Tomography (DHT) has recently been established as a means of retrieving the 3D refractive index mapping of single cells. To make DHT a viable system, it is necessary to develop a reliable and robust holographic apparatus in order that such technology can be utilized outside of specialized optics laboratories and operated in the in-flow modality. In this paper, we propose a quasi-common-path lateral-shearing holographic optical set-up to be used, for the first time, for DHT in a flow-cytometer modality. The proposed solution is able to withstand environmental vibrations that can severely affect the interference process. Furthermore, we have scaled down the system while ensuring that a full 360° rotation of the cells occurs in the field-of-view, in order to retrieve 3D phase-contrast tomograms of single cells flowing along a microfluidic channel. This was achieved by setting the camera sensor at 45° with respect to the microfluidic direction. Additional optimizations were made to the computational elements to ensure the reliable retrieval of 3D refractive index distributions by demonstrating an effective method of tomographic reconstruction, based on high-order total variation. The results were first demonstrated using realistic 3D numerical phantom cells to assess the performance of the proposed high-order total variation method in comparison with the gold-standard algorithm for tomographic reconstructions: namely, filtered back projection. Then, the proposed DHT system and the processing pipeline were experimentally validated for monocytes and mouse embryonic fibroblast NIH-3T3 cells lines. Moreover, the repeatability of these tomographic measurements was also investigated by recording the same cell multiple times and quantifying the ability to provide reliable and comparable tomographic reconstructions, as confirmed by a correlation coefficient greater than 95%. The reported results represent various steps forward in several key aspects of in-flow DHT, thus paving the way for its use in real-world applications.

On Unique Ergodicity Of Coupled AIMD Flows

Abstract: The AIMD algorithm, which underpins the Transmission Control Protocol (TCP) for transporting data packets in communication networks, is perhaps the most successful control algorithm ever deployed. Recently, its use has been extended beyond communication networks, and successful applications of the AIMD algorithm have been reported in transportation, energy, and mathematical biology. A very recent development in the use of AIMD is its application in solving large-scale optimization and distributed control problems without the need for inter-agent communication. In this context, an interesting problem arises when multiple AIMD networks that are coupled in some sense (usually through a nonlinearity). The purpose of this note is to prove that such systems in certain settings inherit the ergodic properties of individual AIMD networks. This result has important consequences for the convergence of the aforementioned optimization algorithms. The arguments in the paper also correct conceptual and technical errors in [1].

Deep learning for faster holographic reconstruction processing in microfluidics

Abstract: The huge amount of phase maps to be numerically retrieved from digital holograms is the actual bottleneck of the high-throughput holographic flow cytometry. An end-to-end neural network is discussed to speed up the holographic processing.