1,721,035 research outputs found
Multiplexing and demultiplexing of digital holograms recorded in microscopic configuration
No full textPhase-contrast tomography of plant cells’ nuclei enabled by reversible induced dehydration
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Speeding up reconstruction of 3D tomograms in holographic flow cytometry via deep learning
No full textTomographic 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
Recent advances in holographic 3D particle tracking
No full textParticle tracking is a fundamental technique for investigating a variety of biophysical processes, from intracellular dynamics to the characterization of cell motility and migration. However, observing three-dimensional (3D) trajectories of particles is in general a challenging task in classical microscopy owing to the limited imaging depth of field of commercial optical microscopes, which represents a serious drawback for the analysis of time-lapse microscopy image data. Therefore, numerous automated particle-tracking approaches have been developed by many research groups around the world. Recently, digital holography (DH) in microscopy has rapidly gained credit as one of the elective techniques for these applications, mainly due to the uniqueness of the DH to provide a posteriori quantitative multiple refocusing capability and phase-contrast imaging. Starting from this paradigm, a huge amount of 3D holographic tracking approaches have been conceived and investigated for applications in various branches of science, including optofluids, microfluidics, biomedical microscopy, cell mechano-trasduction, and cell migration. Since a wider community of readers could be interested in such a review, i.e., not only scientists working in the fields of optics and photonics but also users of particle-tracking tools, it should be very beneficial to provide a complete review of state-of-the-art holographic 3D particle-tracking methods and their applications in bio-microfluidics
SPADEDH: A sparsity-based denoising method of digital holograms without knowing the noise statistics
No full textNumerical multiplexing and demultiplexing of digital holographic information for remote reconstruction in amplitude and phase
No full textInvestigation of angular multiplexing and de-multiplexing of digital holograms recorded in microscope configuration
No full textNumerical multiplexing and de-multiplexing techniques for efficient storage and transmission of digital holographic information
No full textTyres shoulder section characterization by means of ESPI
No full textIn this work is exploited the possibility to use Electronic Speckle Pattern Interferometry (ESPI) for the characterization of different tyres with particular attention to the tyres shoulder section. Tyres characterization is of fundamental importance for vehicle dynamics modelling, since they are the main responsible of vehicles dynamical behaviour and thanks to their ability to deform, they allow to drive a vehicle generating the appropriate interaction forces at the interface with the road. Their behaviour is a consequence to their very complex structure. Two different racing tyres, one for car and other for motorcycle, have been considered. The investigation has been focused at the aim to evaluate and measure the section's components in order to get accurate information about the different layers along through the tyres shoulder section. Here we demonstrate that the different layers (rubber, nylon, steel) can be easily highlighted and identified by mean of the ESPI that, thanks to its high sensitivity, is capable to estimate the different out of plane displacement of the different layers that respond in a different way (i.e. with a different deformation) to a thermal stimulus highlighting the layers themselves. Moreover, we introduce a de-noising step in the reconstruction process: In particular we enhance the wrapped phase information by using a suitable algorithm called SPADEDH. It is important to note that the assessment about the different layers along the section is a very difficult task to obtain by visual inspection or classical microscopy. In fact, the condition of the cutted surface, or rather the strong inhomogeneity and the roughness make impossible to obtain good images especially in the shoulder area
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