Micrometer Resolution Research Articles

Non-destructive 3D reconstruction of an Hagia Sophia clone mosaic utilizing ultrasonography combined with an accurate motion planning

In the current work the endoscopy and retrieval of mortar covered mosaic patterns, such as the Hagia Sophia ones, is presented. In particular, an appropriate instrumentation is developed combining ultra-sonic tomography and an accurate motion planning. The acquisition of high-response tomographic images is performed utilizing transducers in a linear array through the efficient control of their phase characteristics both in transmit and receive modes. Moreover, an accurate mechanical adaptation is designed in order to move the ultrasonic probe with a con-stant velocity. Then, a sequence of tomographic images is recorded and the 3D endoscopic characteristics of the measured object are extracted via the effective reconstruction of the 3D volume. This device is used on a realistic Hagia Sophia mosaic replica that is covered by a thick mor-tar layer. The evaluated results indicate a complete 3D reconstruction of the hidden mosaic in micrometric resolution, while the inner details of individual tesserae are, also, recovered.

Printing of Conductive and Insulating Structures with Micrometer Resolution for Next-Generation Displays

Printing of Conductive and Insulating Structures with Micrometer Resolution for Next-Generation Displays

3D-printed microneedle-based potentiometric sensor for pH monitoring in skin interstitial fluid

Wearable electrochemical sensors are driven by the user-friendly capability of continuous monitoring of key biomarkers for diagnostic or therapeutic operations. Particularly, microneedle (MN)-based sensors can access the interstitial fluid (ISF) in the dermis layer of skin to carry out on-body transdermal detection of analytes. Interestingly, 3D-printing technology allows for rapid and versatile prototyping reaching micrometer resolution. Herein, for the first time, we explore 3D-printed hollow MN patches (1 mm height x 1 mm base with 0.3 mm hole) which are modified with conductive inks to develop a potentiometric sensor for pH monitoring. First, the piercing capability of 3D-printed MN patches is demonstrated by using the parafilm model and their insertion in porcine skin. Subsequently, the hollow MNs are filled with conductive inks to engineer a set of microelectrodes. Thereafter, the working and reference electrodes are properly modified with polyaniline and polyvinyl butyral, respectively, toward a highly stable potentiometric cell. A full in vitro characterization is performed within a broad range of pH (i.e. pH 4 to pH 9). Besides, the MN sensor is analytically assessed in phantom gel and pierced on porcine skin to evaluate the resilience of the MN sensor. Finally, the MN sensor is pierced on the forearm of a subject and tested for its on-body monitoring capability. Overall, 3D-printed MN-based potentiometric sensing brings a versatile and affordable technology to minimally-invasively monitor key physiological parameters in the body.

OCT-guided Robotic Subretinal Needle Injections: A Deep Learning-Based Registration Approach.

Subretinal injection (SI) is an ophthalmic surgical procedure that allows for the direct injection of therapeutic substances into the subretinal space to treat vitreoretinal disorders. Although this treatment has grown in popularity, various factors contribute to its difficulty. These include the retina's fragile, nonregenerative tissue, as well as hand tremor and poor visual depth perception. In this context, the usage of robotic devices may reduce hand tremors and facilitate gradual and controlled SI. For the robot to successfully move to the target area, it needs to understand the spatial relationship between the attached needle and the tissue. The development of optical coherence tomography (OCT) imaging has resulted in a substantial advancement in visualizing retinal structures at micron resolution. This paper introduces a novel foundation for an OCT-guided robotic steering framework that enables a surgeon to plan and select targets within the OCT volume. At the same time, the robot automatically executes the trajectories necessary to achieve the selected targets. Our contribution consists of a novel combination of existing methods, creating an intraoperative OCT-Robot registration pipeline. We combined straightforward affine transformation computations with robot kinematics and a deep neural network-determined tool-tip location in OCT. We evaluate our framework's capability in a cadaveric pig eye open-sky procedure and using an aluminum target board. Targeting the subretinal space of the pig eye produced encouraging results with a mean Euclidean error of 23.8μm.

Stochastic analysis of micro-cone penetration tests in snow

Abstract. Cone penetration tests have long been used to characterize snowpack stratigraphy. With the development of sophisticated digital penetrometers such as the SnowMicroPen, vertical profiles of snow hardness can now be measured at a spatial resolution of a few micrometers. By using small penetrometer tips at this high vertical resolution, further details of the penetration process are resolved, leading to many more stochastic signals. An accurate interpretation of these signals regarding snow characteristics requires advanced data analysis. Here, the failure of ice connections and the pushing aside of separated snow grains during cone penetration lead to a combination of (a) diffusive noise, as in Brownian motion, and (b) jumpy noise, as proposed by previous dedicated inversion methods. The determination of the Kramers–Moyal coefficients enables differentiating between diffusive and jumpy behaviors and determining the functional resistance dependencies of these stochastic contributions. We show how different snow types can be characterized by this combination of highly resolved measurements and data analysis methods. In particular, we show that denser snow structures exhibited a more collective diffusive behavior supposedly related to the pushing aside of separated snow grains. On less dense structures with larger pore space, the measured hardness profile appeared to be characterized by stronger jump noise, which we interpret as related to breaking of a single cohesive bond. The proposed methodology provides new insights into the characterization of the snowpack stratigraphy with micro-cone penetration tests.

Applications of Artificial Intelligence in Optical Coherence Tomography Angiography Imaging

Optical coherence tomography angiography (OCTA) and artificial intelligence (AI) are two emerging fields that complement each other. OCTA enables the noninvasive, in vivo, 3D visualization of retinal blood flow with a micrometer resolution, which has been impossible with other imaging modalities. As it does not need dye-based injections, it is also a safer procedure for patients. AI has excited great interest in many fields of daily life, by enabling automatic processing of huge amounts of data with a performance that greatly surpasses previous algorithms. It has been used in many breakthrough studies in recent years, such as the finding that AlphaGo can beat humans in the strategic board game of Go. This paper will give a short introduction into both fields and will then explore the manifold applications of AI in OCTA imaging that have been presented in the recent years. These range from signal generation over signal enhancement to interpretation tasks like segmentation and classification. In all these areas, AI-based algorithms have achieved state-of-the-art performance that has the potential to improve standard care in ophthalmology when integrated into the daily clinical routine.

A Novel Technique for Producing Three-Dimensional Data Using Serial Sectioning and Semi-Automatic Image Classification.

The three-dimensional characterization of internal features, via metrics such as orientation, porosity, and connectivity, is important to a wide variety of scientific questions. Many spatial and morphological metrics only can be measured accurately through direct in situ three-dimensional observations of large (i.e., big enough to be statistically representative) volumes. For samples that lack material contrast between phases, serial grinding and imaging—which relies solely on color and textural characteristics to differentiate features—is a viable option for extracting such information. Here, we present the Grinding, Imaging, Reconstruction Instrument (GIRI), which automatically serially grinds and photographs centimeter-scale samples at micron resolution. Although the technique is destructive, GIRI produces an archival digital image stack. This digital image stack is run through a supervised machine-learning-based image processing technique that quickly and accurately segments data into predefined classes. These classified data then can be loaded into three-dimensional visualization software for measurement. We share three case studies to illustrate how GIRI can address questions with a significant morphological component for which two-dimensional or small-volume three-dimensional measurements are inadequate. The analyzed metrics include: the morphologies of objects and pores in a granular material, the bulk mineralogy of polyminerallic solids, and measurements of the internal angles and symmetry of crystals.

NeuroConstruct: 3D Reconstruction and Visualization of Neurites in Optical Microscopy Brain Images.

We introduce NeuroConstruct, a novel end-to-end application for the segmentation, registration, and visualization of brain volumes imaged using wide-field microscopy. NeuroConstruct offers a Segmentation Toolbox with various annotation helper functions that aid experts to effectively and precisely annotate micrometer resolution neurites. It also offers an automatic neurites segmentation using convolutional neuronal networks (CNN) trained by the Toolbox annotations and somas segmentation using thresholding. To visualize neurites in a given volume, NeuroConstruct offers a hybrid rendering by combining iso-surface rendering of high-confidence classified neurites, along with real-time rendering of raw volume using a 2D transfer function for voxel classification score versus voxel intensity value. For a complete reconstruction of the 3D neurites, we introduce a Registration Toolbox that provides automatic coarse-to-fine alignment of serially sectioned samples. The quantitative and qualitative analysis show that NeuroConstruct outperforms the state-of-the-art in all design aspects. NeuroConstruct was developed as a collaboration between computer scientists and neuroscientists, with an application to the study of cholinergic neurons, which are severely affected in Alzheimer's disease.

Flexible and Transparent Ceramic Nanocomposite for Laboratory X-ray Imaging of Micrometer Resolution.

Transparent nanocomposites have attracted considerable attention in many areas including X-ray imaging, wearable electronics, and volumetric display. However, both the transparency and the flexibility were largely jeopardized by the loading content of functional nanoparticles (NPs), posing a major challenge to material engineering. Herein, an ultra-high-loading-ceramic nanocomposite film was fabricated by a blade-coating technique. The film exhibited a high transparency over ∼89% in the whole visible region even with a fluoride-ceramic content up to ∼83 wt %. Based on a real-time investigation on the formation process of the film, the refractive-index difference between the nanoparticles and matrix was identified as the dominating factor to transparency. The transmittance spectra based on Rayleigh scattering theory were simulated to screen both nanoparticle radius and loading content, leading to the discovery of a transparency zone for film making. As a proof-of-concept experiment, the transparent film was used as an X-ray scintillation screen, which exhibited a comparable light yield to that of LYSO owing to the mitigated self-absorption effect. The homemade imager demonstrated a spatial resolution of 122 lp/mm, representing a record resolution of 4.1 μm for laboratory X-ray photography. Our work not only provided an experimental procedure to make high-loading functional films but also demonstrated a theoretical model to guide the search for gradients of transparent composites.

Hyperelastic characterization reveals proteoglycans drive the nanoscale strain-stiffening response in hyaline cartilage

Degenerative diseases such as osteoarthritis (OA) result in deterioration of cartilage extracellular matrix (ECM) components, significantly compromising tissue function. For measurement of mechanical properties at micron resolution, atomic force microscopy (AFM) is a leading technique in biomaterials research, including in the study of OA. It is common practice to determine material properties by applying classical Hertzian contact theory to AFM data. However, errors are consequential because the application of a linear elastic contact model to tissue ignores the fact that soft materials exhibit nonlinear properties even at small strains, influencing the biological conclusions of clinically-relevant studies. Additionally, nonlinear material properties are not well characterized, limiting physiological relevance of Young’s modulus. Here, we probe the ECM of hyaline cartilage with AFM and explore the application of Hertzian theory in comparison to five hyperelastic models: NeoHookean, Mooney-Rivlin, Arruda-Boyce, Fung, and Ogden. The Fung and Ogden models achieved the best fits of the data, but the Fung model demonstrated robust sensitivity during model validation, demonstrating its ideal application to cartilage ECM and potentially other connective tissues. To develop a biological understanding of the Fung nonlinear parameter, we selectively degraded ECM components to target collagens (purified collagenase), hyaluronan (bacterial hyaluronidase), and glycosaminoglycans (chondroitinase ABC). We found significant differences in both Fung parameters in response to enzymatic treatment, indicating that proteoglycans drive the nonlinear response of cartilage ECM, and validating biological relevance of these phenomenological parameters. Our findings add value to the biomechanics community of using two-parameter material models for microindentation of soft biomaterials.

Coral holobiont research needs spatial analyses at the microbial scale.

Coral holobiont research needs spatial analyses at the microbial scale.

Brief Communication: Elemental Models of Primate Nursing and Weaning Revisited.

Intra-tooth patterns of trace elements barium (Ba) and strontium (Sr) have been used to infer human and nonhuman primate nursing histories, including australopithecine and Neanderthal juveniles. Here we contrast the two elemental models in first molars (M1s) of four wild baboons and explore the assumptions that underlie each. Laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) was employed to create comprehensive calcium-normalized barium and strontium (Ba/Ca, Sr/Ca) maps of M1 enamel and dentine at 35 micron resolution. Postnatal Ba/Ca values were typically high, peaking ~0.5 years of age and then decreasing throughout M1 crown formation; all four individuals showed minimal Ba/Ca values between ~1.2-1.8 years, consistent with field reports of the cessation of suckling. Enamel Sr/Ca did not support patterns of previous LA-ICP-MS spot sampling as the enamel rarely showed discrete Sr/Ca secretory zonation. Increases in Sr/Ca appeared in coronal dentine beginning ~0.3 years, with varied peak value ages (~0.7-2.7 years) and no evidence of a predicted postweaning decline. Inferences of baboon weaning ages from initial Ba/Ca minima are more congruent with behavioral observations than Sr/Ca maxima; this is consistent with studies of captive macaques of known weaning ages. Elemental variation is more apparent in the coronal dentine than the enamel of these baboons, which may relate to its more rapid mineralization and protection from the oral environment. Inferences of nursing histories from enamel Sr/Ca patterns alone should be reconsidered, and elevated values of Ba/Ca and Sr/Ca in teeth formed after weaning require further study.

Spatial Protein Analysis of the Tumor Microenvironment and Biomarkers in Hodgkin's Lymphoma

Introduction: The tumor microenvironment (TME) is the complex milieu of cells and molecules surrounding the tumor. Single cell methods have been used to great effect to identify subpopulations of cells that have pro- or anti-tumor properties, and selectively modulating these has great therapeutic benefits, especially in immunotherapy. However, there is limited information on the spatial organization of these subpopulations, which determines how they signal and their therapeutic potential. Single cell resolved spatial computational analysis is needed to describe the complex interactions of the TME and their effect on patient outcomes. Hodgkin's Lymphoma presents a unique spatial TME due to the sparse tumor distribution of Hodgkin's Reed-Sternberg tumor cells. Yet even though it is difficult to disentangle signaling in Hodgkin's tumor cells - which are immune derived - from the immune TME, Hodgkin's is highly receptive to checkpoint inihibitors among lymphomas and insights gleaned from the Hodgkin's TME could better inform immunotherapies across lymphomas. Materials and Methods: Here we apply Imaging Mass Cytometry (IMC), a technology to perform ~40-plex protein analysis with 1 micron resolution in tissue, to study a cohort of 260 matched samples at diagnosis and after relapse from 90 patients with relapsed/refractory Hodgkin's Lymphoma. We developed a computational pipeline to perform cell phenotyping, spatial analysis, and biostatistics, to describe tumor architecture and propose putative biomarkers of Hodgkin's clinical response and relapse. A novel feature of the pipeline is to quantify proteins and spatial analysis on the same numerical scale for each cell, to generate hybrid biomarkers. Results and Discussion: We analyzed over 7 million cells for their phenotype and spatial organization. We use IMC to describe spatial features of the tumor - cell subtypes and their positioning - that correlate to clinical outcomes. We identify proteins such as CXCR5 that correlate to survival in specific spatial contexts, and we describe spatial reorganization from diagnosis to the relapsed tumor as it relates to survival and relevant clinical factors such as MHC expression and EBV infection. A significant conceptual advance was to use spatial metrics to perform "digital biopsies", a selection of tumor regions comparable across patients. We also describe cell rosetting, a localized recruitment of immune cells to tumor that is characteristic of Hodgkin's. We validated multiple existing biomarkers in the literature using our data set and proposed novel biomarkers using IMC data. Conclusions: Spatial analysis of the HL microenvironment revealed composite features of the TME that predict clinical outcomes. These features cannot be described using single cell tools or low-plexed imaging, and represent a truer picture of HL biology. The pipeline developed here can be universally applied to other spatial protein data for biomarker discovery and analysis, and the biomarkers proposed here will be validated with IHC. Figure caption: IMC analysis of Hodgkin's Lymphoma. A. IMC generates a 40-plex protein image. B, C. The image is converted to single cells and analyzed for protein and spatial features. D. A UMAP of cell phenotypes and the spatial interactions of specific phenotypes. E. The spatial and protein values are used to generate predictive biomarkers of clinical response. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Enhancing detection of low-coherence interferometry signals acquired with a spatial heterodyne detector

The use of low-coherence interferometry for industrial applications is constantly growing due to its non-destructive nature and high resolution. Fourier domain systems are usually preferred as they provide a scanning-free method. Recently, a spatial heterodyne detector has proved to be useful in extending the measuring range without losing the micrometric resolution. This system Fourier transforms the interferometric signal, giving the information of interest as fringes in the output image. However, for low light intensities, the images might need to be processed to facilitate the visualization and detection of these fringes. In this work we analyze two main approaches to achieve this: one that uses a cross correlation method and the other one that uses a specially designed two-dimensional adaptive filtering technique. Both methods showed an improved performance when compared with conventional image processing techniques, enhancing the fringes contrast. Additionally, we showed that combining normalized cross correlation and the image gradient is an effective method to locate low intensity signals.

In situ volumetric imaging and analysis of FRESH 3D bioprinted constructs using optical coherence tomography

As 3D bioprinting has grown as a fabrication technology, so too has the need for improved analytical methods to characterize engineered constructs. This is especially challenging for engineered tissues composed of hydrogels and cells, as these materials readily deform when trying to assess print fidelity and other properties non-destructively. Establishing that the 3D architecture of the bioprinted construct matches its intended anatomic design is critical given the importance of structure-function relationships in most tissue types. Here we report development of a multimaterial bioprinting platform with integrated optical coherence tomography for in situ volumetric imaging, error detection, and 3D reconstruction. We also report improvements to the freeform reversible embedding of suspended hydrogels bioprinting process through new collagen bioink compositions, gelatin microparticle support bath optical clearing, and optimized machine pathing. This enables quantitative 3D volumetric imaging with micron resolution over centimeter length scales, the ability to detect a range of print defect types within a 3D volume, and real-time imaging of the printing process at each print layer. These advances provide a comprehensive methodology for print quality assessment, paving the way toward the production and process control required for achieving regulatory approval and ultimately clinical translation of engineered tissues.

Substrate-Independent Maskless Writing of Functionalized Microstructures Using CHic Chemistry and Digital Light Processing.

Maskless photolithography based on digital light processing (DLP) is an attractive technique for the rapid, flexible, and cost-effective fabrication of complex structures with arbitrary surface profiles on the microscale. In this work, we introduce a new material system for structure formation by DLP that is based on photoreactive polymers for the local and light-induced C,H-insertion cross-linking (CHic). This approach allows a simple and versatile generation of microstructures with a broad spectrum of geometries and chemistries irrespective of the nature of the chosen substrates and thus allows direct writing of surface functionalization patterns with high spatial control. The CHicable prepolymer is first coated on a substrate to form a solvent-free (glassy) film, and then the DLP system patterns the light with arbitrary shape to induce local cross-linking of the prepolymer. Using this method, the desired structures with complex features with a lateral resolution of several microns and a topography of tens of nanometers could be fabricated within 30 s. Furthermore, the universal applicability of the CHic reaction enables the printing on a wide variety of substrates, which greatly broadens the using scenarios of this printing approach.

Stimulated-Raman-scattering amplification of attosecond XUV pulses with pulse-train pumps and application to local in-depth plasma-density measurement.

We present a scheme for amplifying an extreme-ultraviolet (XUV) seed isolated attosecond pulse via stimulated Raman scattering of a pulse-train pump. At sufficient seed and pump intensity, the amplification is nonlinear, and the amplitude of the seed pulse can reach that of the pump, one order of magnitude higher than the initial seed amplitude. In the linear amplification regime, we find that the spectral signature of the pump pulse train is imprinted on the spectrum of the amplified seed pulse. Since the spectral signature is imprinted with its frequency downshifted by the plasma frequency, it is possible to deduce the electron density in the region of interaction. This region can be of micrometer length scale longitudinally. By varying the delay between the seed and the pump, this scheme provides a local electron-density measurement inside solid-density plasmas that cannot be probed with optical frequencies, with micrometer resolution.

Determinants and therapeutic potential of calcium handling abnormalities in atrial fibrillation: what can we learn from computer models?

Determinants and therapeutic potential of calcium handling abnormalities in atrial fibrillation: what can we learn from computer models?

3D synchrotron imaging of muscle tissues at different atrophic stages in stroke and spinal cord injury: a proof-of-concept study

Synchrotron X-ray computed tomography (SXCT) allows 3D imaging of tissue with a very large field of view and an excellent micron resolution and enables the investigation of muscle fiber atrophy in 3D. The study aimed to explore the 3D micro-architecture of healthy skeletal muscle fibers and muscle fibers at different stages of atrophy (stroke sample = muscle atrophy; spinal cord injury (SCI) sample = severe muscle atrophy). Three muscle samples: a healthy control sample; a stroke sample (atrophic sample), and an SCI sample (severe atrophic sample) were imaged using SXCT, and muscle fiber populations were segmented and quantified for microarchitecture and morphology differences. The volume fraction of muscle fibers was 74.7%, 70.2%, and 35.3% in the healthy, stroke (atrophic), and SCI (severe atrophic) muscle fiber population samples respectively. In the SCI (severe atrophic sample), 3D image analysis revealed fiber splitting and fiber swelling. In the stroke sample (atrophic sample) muscle fiber buckling was observed but was only visible in the 3D analysis. 3D muscle fiber population analysis revealed new insights into the different stages of muscle fiber atrophy not to be observed nor quantified with a 2D histological analysis including fiber buckling, loss of fibers and fiber splitting.

Multi-Harmonic Electrothermal Spectroscopy (METS), a New Technique for Spatially Resolved Electrochemical Measurements

In this work, we introduce a new electrochemical technique, Multi-harmonic ElectroThermal Spectroscopy (METS) based on frequency dependent temperature sensing. We show that the thermal signatures generated at different harmonics when an Alternating Current (AC) is passed through an electrochemical cell can be used to extract thermodynamic (entropic), kinetic (charge transfer rate) and transport (charge and mass transport) properties associated with the electrochemical cell. From the frequency dependence of the thermal penetration depth, the spatial origin of the effect can be located within a resolution of a few microns, thereby enabling the attribution of the effect to a particular electrode. A four-point probe RTD sensor acting as a METS sensor is employed on the exterior of the current collector, which non-invasively provides spatially resolved information about the electrochemical processes in the cell. Through this work, we are able to, for the first time, determine the entropy change of a single charge transfer reaction in an electrode and spatially resolve the heat generation in a single Li-ion cell, with a length-scale resolution of a microns.