High-resolution Two-dimensional Images Research Articles

FUSION3D: Multimodal Data Fusion for 3D Shape Reconstruction - A Soft Sensing Approach

Traditional high-fidelity imaging techniques, such as X-ray computer tomography (CT), excel in capturing intricate shape details through high-resolution two-dimensional (2D) images. However, the extensive cost or impracticality of obtaining X-ray images poses a significant challenge in various applications (e.g. dense metal components in manufacturing). To overcome this limitation, we propose a soft sensing approach, named FUSION3D, aimed at reconstructing 3D shapes from 2D sliced images and heterogenous process inputs using a novel model based on multi-modal process knowledge. The developed FUSION3D method is built upon a deep learning framework that utilizes continuous normalizing flow to model the complex shape distribution of 3D objects. We propose a principled probabilistic framework to regularize 3D point clouds by modeling them as a distribution of distributions. Specifically, we learn a two-level hierarchy of distributions where the first level is the distribution of shapes, and the second level is the distribution of points given a shape. Our generative regularization model learns each level of the distribution with a continuous normalizing flow. The invertibility of normalizing flows enables the computation of the likelihood during training and allows us to train our model in the variational inference framework. Our methodology is validated through a case study in additive manufacturing, demonstrating its effectiveness as a soft sensing approach for accurate 3D shape reconstruction without relying on high-fidelity imaging techniques like CT during model deployment.

Three-dimensional hard X-ray ptychographic reflectometry imaging on extended mesoscopic surface structures

Many nanodevices and quantum devices, with their sizes often spanning from millimeters down to sub-nanometer, have intricate low-dimensional, non-uniform, or hierarchical structures on surfaces and interfaces. Since their functionalities are dependent on these structures, high-resolution surface-sensitive characterization becomes imperative to gain a comprehensive understanding of the function–structure relationship. We thus developed hard x-ray ptychographic reflectometry imaging, a new technique that merges the high-resolution two-dimensional imaging capabilities of hard x-ray ptychography for extended objects, with the high-resolution depth profiling capabilities of x-ray reflectivity for layered structures. The synergy of these two methods fully leverages both amplitude and phase information from ptychography reconstruction to not only reveal surface topography and localized structures, such as shapes and electron densities, but also yields statistical details, such as interfacial roughness that is not readily accessible through coherent imaging solely. The hard x-ray ptychographic reflectometry imaging is well-suited for three-dimensional imaging of mesoscopic samples, particularly those comprising planar or layered nanostructures on opaque supports, and could also offer a high-resolution surface metrology and defect analysis on semiconductor devices, such as integrated nanocircuits and lithographic photomasks for microchip fabrications.

High Spatial Resolution 2D Imaging of Current Density and Pressure for Graphene Devices under High Pressure Using Nitrogen-Vacancy Centers in Diamond.

Current density imaging is helpful for discovering interesting electronic phenomena and understanding carrier dynamics, and by combining pressure distributions, several pressure-induced novel physics may be comprehended. In this work, noninvasive, high-resolution two-dimensional images of the current density and pressure gradient for graphene ribbon and hBN-graphene-hBN devices are explored using nitrogen-vacancy (NV) centers in diamond under high pressure. The two-dimensional vector current density is reconstructed by the vector magnetic field mapped by the near-surface NV center layer in the diamond. The current density images accurately and clearly reproduce the complicated structure and current flow of graphene under high pressure. Additionally, the spatial distribution of the pressure is simultaneously mapped, rationalizing the nonuniformity of the current density under high pressure. The current method opens a significant new avenue to investigate electronic transport and conductance variations in two-dimensional materials and electrical devices under high pressure as well as for nondestructive evaluation of semiconductor circuits.

Modelling and protection of grotto temples based on iterative closest point and Poisson

As a non-renewable cultural heritage, grotto temples face many value threats and the crises of rapid extinction. Due to the unique environment, diverse volume and complex geometric structure of grotto temples, cultural relic protection faces huge challenges. Digitisation has become the most important and effective preventive protection method. This study proposes a comprehensive modeling method, which uses three-dimensional (3D) laser scanning technology to obtain the point cloud data of grotto ruins, close range photogrammetry to obtain multi-angle and high-resolution two-dimensional (2D) image data, and through the fusion modelling of the laser point cloud and image dense matching point cloud, a complete geometric model of grotto is obtained. The registration method of the improved algorithm is based on Euclidean distance residual. The performance is better, with residuals mainly distributed in the range of 0.02 to 0.03 mm. In terms of texture mapping, the accuracy of model mapping is adjusted by adjusting the constraints of control points. The gray values of 50 pixels around the control points show significant gradient changes in the X direction, while the changes in the Y direction are relatively small. These methods and technologies provide practical and effective means for the protection and preservation of cultural relics in grotto temples.

Vibration Measurement of High-Speed Railway Bridges Based on GB-MIMO Interferometric Radar

As a novel type of ground-based interferometric radar, ground-based multiple-input multiple-output (GB-MIMO) interferometric radar utilizes multiple transmitting and receiving antennas to compose a specific structure and acquire a large synthetic aperture. Using electronic beam scanning instead of common mechanical scanning, GB-MIMO interferometric radar can achieve two-dimensional high-resolution imaging with image acquisition frequency at tens of Hertz, and has the ability to perform bridge health monitoring through vibration measurement. This study introduces the basic principle of the MIMO technique and the first Chinese made GB-MIMO interferometric radar. Experiments taken on a corner reflector and a high-speed railway bridge are respectively utilized to verify the vibration measurement ability of this novel type of GB-MIMO interferometric radar

Imaging Biomarkers of Peripheral Nerves: Focus on Magnetic Resonance Neurography and Ultrasonography.

Peripheral neuropathy is a prevalent and debilitating condition affecting millions of individuals globally. Magnetic resonance neurography (MRN) and ultrasonography (US) are noninvasive methods offering comprehensive visualization of peripheral nerves, using anatomical and functional imaging biomarkers to ensure accurate evaluation. For optimized MRN, superior and high-resolution two-dimensional and three-dimensional imaging protocols are essential. The anatomical MRN and US imaging markers include quantitative measures of nerve and fascicular size and signal, and qualitative markers of course and morphology. Among them, quantitative markers of T2-signal intensity ratio are sensitive to nerve edema-like signal changes, and the T1-mapping technique reveals nerve and muscle tissue fatty and fibrous compositional alterations.The functional markers are derived from physiologic properties of nerves, such as diffusion characteristics or blood flow. They include apparent diffusion coefficient from diffusion-weighted imaging and fractional anisotropy and tractography from diffusion tensor imaging to delve into peripheral nerve microstructure and integrity. Peripheral nerve perfusion using dynamic contrast-enhanced magnetic resonance imaging estimates perfusion parameters, offering insights into nerve health and neuropathies involving edema, inflammation, demyelination, and microvascular alterations in conditions like type 2 diabetes, linking nerve conduction pathophysiology to vascular permeability alterations.Imaging biomarkers thus play a pivotal role in the diagnosis, prognosis, and monitoring of nerve pathologies, thereby ensuring comprehensive assessment and elevating patient care. These biomarkers provide valuable insights into nerve structure, function, and pathophysiology, contributing to the accurate diagnosis and management planning for peripheral neuropathy.

Spatial resolution enhancement with line-scan light-field imaging.

This Letter proposes a line-scan-based light-field imaging framework that records lines of a light-field image successively to improve its spatial resolution. In this new, to the best of our knowledge, light-field imaging method, a conventional square or hexagonal microlens array is replaced with a cylindrical one. As such, the spatial resolution along the cylindrical axis remains unaffected, but angular information is recorded in the direction perpendicular to the cylindrical axis. By sequentially capturing multiple rows of light-field images with the aid of a translation device, a high-resolution two-dimensional light-field image can then be constructed. As a proof of concept, a prototype line-scan light-field camera was built and tested with the 1951 USAF resolution chart and the high-precision calibration dot array. Good measurement accuracies in the x, y, and z directions are demonstrated and prove that line-scan light-field imaging can significantly improve spatial resolutions and could be an alternative for fast three-dimensional inspections in the production line.

Ultrafast two-dimensional x-ray imager with temporal fiducial pulses for laser-produced plasmas

It is challenging to make an ultrafast diagnosis of the temporal evolution of small and short-lived plasma in two dimensions. To overcome this difficulty, we have developed a well-timed diagnostic utilizing an x-ray streak camera equipped with a row of multi-pinhole arrays. By processing multiple sets of one-dimensional streaked image data acquired from various pinholes, we are capable of reconstructing high-resolution two-dimensional images with a temporal resolution of 38 ps and a spatial resolution of 18 μm. The temporal fiducial pulses accessed from external sources can advance the precise timing and accurately determine the arrival time of the laser. Moreover, it can correct the nonlinear sweeping speed of the streak camera. The effectiveness of this diagnostic has been successfully verified at the Shenguang-II laser facility, providing an indispensable tool for observing complex physical phenomena, such as the implosion process of laser-fusion experiments.

The role of CenKR in the coordination of Rhodobacter sphaeroides cell elongation and division.

Cell elongation and division are essential aspects of the bacterial life cycle that must be coordinated for viability and replication. The impact of misregulation of these processes is not well understood as these systems are often not amenable to traditional genetic manipulation. Recently, we reported on the CenKR two-component system (TCS) in the Gram-negative bacterium Rhodobacter sphaeroides that is genetically tractable, widely conserved in α-proteobacteria, and directly regulates the expression of components crucial for cell elongation and division, including genes encoding subunit of the Tol-Pal complex. In this work, we show that overexpression of cenK results in cell filamentation and chaining. Using cryo-electron microscopy (cryo-EM) and cryo-electron tomography (cryo-ET), we generated high-resolution two-dimensional (2D) images and three-dimensional (3D) volumes of the cell envelope and division septum of wild-type cells and a cenK overexpression strain finding that these morphological changes stem from defects in outer membrane (OM) and peptidoglycan (PG) constriction. By monitoring the localization of Pal, PG biosynthesis, and the bacterial cytoskeletal proteins MreB and FtsZ, we developed a model for how increased CenKR activity leads to changes in cell elongation and division. This model predicts that increased CenKR activity decreases the mobility of Pal, delaying OM constriction, and ultimately disrupting the midcell positioning of MreB and FtsZ and interfering with the spatial regulation of PG synthesis and remodeling.IMPORTANCEBy coordinating cell elongation and division, bacteria maintain their shape, support critical envelope functions, and orchestrate division. Regulatory and assembly systems have been implicated in these processes in some well-studied Gram-negative bacteria. However, we lack information on these processes and their conservation across the bacterial phylogeny. In R. sphaeroides and other α-proteobacteria, CenKR is an essential two-component system (TCS) that regulates the expression of genes known or predicted to function in cell envelope biosynthesis, elongation, and/or division. Here, we leverage unique features of CenKR to understand how increasing its activity impacts cell elongation/division and use antibiotics to identify how modulating the activity of this TCS leads to changes in cell morphology. Our results provide new insight into how CenKR activity controls the structure and function of the bacterial envelope, the localization of cell elongation and division machinery, and cellular processes in organisms with importance in health, host-microbe interactions, and biotechnology.

Deep learning based image quality improvement of a light-field microscope integrated with an epi-fluorescence microscope

Light-field three-dimensional (3D) fluorescence microscopes can acquire 3D fluorescence images in a single shot, and followed numerical reconstruction can realize cross-sectional imaging at an arbitrary depth. The typical configuration that uses a lens array and a single image sensor has the trade-off between depth information acquisition and spatial resolution of each cross-sectional image. The spatial resolution of the reconstructed image degrades when depth information increases. In this paper, we use U-net as a deep learning model to improve the quality of reconstructed images. We constructed an optical system that integrates a light-field microscope and an epifluorescence microscope, which acquire the light-field data and high-resolution two-dimensional images, respectively. The high-resolution images from the epifluorescence microscope are used as ground-truth images for the training dataset for deep learning. The experimental results using fluorescent beads with a size of 10 µm and cultured tobacco cells showed significant improvement in the reconstructed images. Furthermore, time-lapse measurements were demonstrated in tobacco cells to observe the cell division process.

High-Quality 3D Visualization System for Light-Field Microscopy with Fine-Scale Shape Measurement through Accurate 3D Surface Data.

We propose a light-field microscopy display system that provides improved image quality and realistic three-dimensional (3D) measurement information. Our approach acquires both high-resolution two-dimensional (2D) and light-field images of the specimen sequentially. We put forward a matting Laplacian-based depth estimation algorithm to obtain nearly realistic 3D surface data, allowing the calculation of depth data, which is relatively close to the actual surface, and measurement information from the light-field images of specimens. High-reliability area data of the focus measure map and spatial affinity information of the matting Laplacian are used to estimate nearly realistic depths. This process represents a reference value for the light-field microscopy depth range that was not previously available. A 3D model is regenerated by combining the depth data and the high-resolution 2D image. The element image array is rendered through a simplified direction-reversal calculation method, which depends on user interaction from the 3D model and is displayed on the 3D display device. We confirm that the proposed system increases the accuracy of depth estimation and measurement and improves the quality of visualization and 3D display images.

Prostatic urinary tract visualization with super-resolution deep learning models.

In urethra-sparing radiation therapy, prostatic urinary tract visualization is important in decreasing the urinary side effect. A methodology has been developed to visualize the prostatic urinary tract using post-urination magnetic resonance imaging (PU-MRI) without a urethral catheter. This study investigated whether the combination of PU-MRI and super-resolution (SR) deep learning models improves the visibility of the prostatic urinary tract. We enrolled 30 patients who had previously undergone real-time-image-gated spot scanning proton therapy by insertion of fiducial markers. PU-MRI was performed using a non-contrast high-resolution two-dimensional T2-weighted turbo spin-echo imaging sequence. Four different SR deep learning models were used: the enhanced deep SR network (EDSR), widely activated SR network (WDSR), SR generative adversarial network (SRGAN), and residual dense network (RDN). The complex wavelet structural similarity index measure (CW-SSIM) was used to quantitatively assess the performance of the proposed SR images compared to PU-MRI. Two radiation oncologists used a 1-to-5 scale to subjectively evaluate the visibility of the prostatic urinary tract. Cohen's weighted kappa (k) was used as a measure of agreement of inter-operator reliability. The mean CW-SSIM in EDSR, WDSR, SRGAN, and RDN was 99.86%, 99.89%, 99.30%, and 99.67%, respectively. The mean prostatic urinary tract visibility scores of the radiation oncologists were 3.70 and 3.53 for PU-MRI (k = 0.93), 3.67 and 2.70 for EDSR (k = 0.89), 3.70 and 2.73 for WDSR (k = 0.88), 3.67 and 2.73 for SRGAN (k = 0.88), and 4.37 and 3.73 for RDN (k = 0.93), respectively. The results suggest that SR images using RDN are similar to the original images, and the SR deep learning models subjectively improve the visibility of the prostatic urinary tract.

Tunable Laser Absorption Imaging for 2D Gas Measurement With an Electronic Rolling Shutter Camera

High spatial resolution two-dimensional (2D) imaging of gas flow is essential for better understanding and characterization of a combustion field. We propose a method for 2D gas measurement with high spatial resolution by combining tunable laser absorption spectroscopy and an imager with an electronic rolling shutter through their common feature of time scanning. The method was demonstrated by oxygen measurement using a 764nm vertical cavity surface emitting laser and a low-cost CMOS camera. 2D imaging of the oxygen concentration distribution were obtained at a frame rate of 15fps with a horizontal spatial resolution of about <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$28\mu \text{m}$ </tex-math></inline-formula> . This method enjoys simple structure, low cost, high spatial resolution and shows potential in the 2D and 3D reconstruction of the combustion field.

Effects of fiddler crab bioturbation on the geochemical migration and bioavailability of heavy metals in coastal wetlands

Fiddler crabs, found in coastal wetlands worldwide, function as ecosystem engineers. Their burrowing activity can significantly alter biogeochemistry at the local scale, however, the mobility of heavy metals (HMs) in burrow sediments remains unclear. Here, we used diffusive gradients in thin-film probes to obtain bioavailable Fe and HMs (Cu, Zn, Ni, Cd, Pb, Co, and Mo) in crab burrows from coastal wetlands (mudflats, salt marshes, and mangroves). The depth-profile results showed that most HMs were enriched at shallow and deep depths but deficient at middle depths. We highlighted that bioturbation improved oxic conditions, enhanced HM concentrations, and favored dissolved HM retention in burrow sediments, which served as a sink for overlying water HMs via burrow flushing but a potential source of particle HMs via enhanced resuspension. In deep anoxic layers, Fe(III) reduction drove the remobilization of HMs, except Cu and Mo, leading to the co-release of HMs with Fe. This Fe-HM coupling/decoupling was verified using enhanced two-dimensional high-resolution imaging, which revealed highly spatial heterogeneity of multiple HMs. Moreover, the hydrological conditions regulating bioturbation effects on HM behavior varied across different coastal wetlands. With coastal environmental changes, the key role of ubiquitous bioturbation in HM migration and bioavailability should be reconsidered.

Optical coherence tomography in neurodegenerative disorders.

Structural imaging of the brain is the most widely used diagnostic tool for investigating neurodegenerative diseases. More advanced structural imaging techniques have been applied to early or prodromic phases, but they are expensive and not widely available. Therefore, it is highly desirable to search for noninvasive, easily accessible, low-cost clinical biomarkers suitable for large-scale population screening, in order to focus on making diagnoses at the earliest stages of the disease. In this scenario, imaging studies focusing on the structures of the retina have increasingly been used for evaluating neurodegenerative diseases. The retina shares embryological, histological, biochemical, microvascular and neurotransmitter similarities with the cerebral cortex, thus making it a uniquely promising biomarker for neurodegenerative diseases. Optical coherence tomography is a modern noninvasive imaging technique that provides high-resolution two-dimensional cross-sectional images and quantitative reproducible three-dimensional volumetric measurements of the optic nerve head and retina. This technology is widely used in ophthalmology practice for diagnosing and following up several eye diseases, such as glaucoma, diabetic retinopathy and age-related macular degeneration. Its clinical impact on neurodegenerative diseases has raised enormous interest over recent years, as several clinical studies have demonstrated that these diseases give rise to reduced thickness of the inner retinal nerve fiber layer, mainly composed of retinal ganglion cells and their axons. In this review, we aimed to address the clinical utility of optical coherence tomography for diagnosing and evaluating different neurodegenerative diseases, to show the potential of this noninvasive and easily accessible method.

Earthflow reactivation assessment by multichannel analysis of surface waves and electrical resistivity tomography: A case study

Abstract In this study, we investigated the stability and reactivation of preexisting Tonghua landslide deposits in China, including the adjacent stable slope. We used an integrated approach, combining a multichannel analysis of surface waves (MASW) and electrical resistivity tomography (ERT). We used ERT to determine groundwater seepage paths, weathering conditions, water content, and the depth to bedrock. High-resolution two-dimensional (2D) shear-wave velocity MASW images, on the other hand, played an essential role in detecting both horizontal and vertical compositions, disjointedness, and sliding surfaces related to lithological borders. Based on seismic models, we considered four geological layers encountered in the stable slope, including fractured (gravel) and weathered (phyllite) materials, as a sliding mass. We combined the 2D resistivity profiles obtained to create pseudo-three-dimensional ERT images to estimate water-saturated and unsaturated masses. From the tomography results, we identified different preexisting deposits, including buried arable clay deposits, old accumulated earthflow deposits, a water accumulation zone, and a fissure runoff. Based on the resistivity results, the bottom of the earthflow deposits is susceptible to water, and oversaturation can reactivate the earthflow.

High-quality 3D display system for an integral imaging microscope using a simplified direction-inversed computation based on user interaction.

We propose and implement a high-quality three-dimensional (3D) display system for an integral imaging microscope using a simplified direction-inversed computation method based on user interaction. A model of the specimen is generated from the estimated depth information (via the convolutional neural network-based algorithm), the quality of the model is defined by the high-resolution two-dimensional image. The new elemental image arrays are generated from the models via a simplified direction-inversed computation method according to the user interaction and directly displayed on the display device. A high-quality 3D visualization of the specimen is reconstructed and displayed while the lens array is placed in front of the display device. The user interaction enables more viewpoints of the specimen to be reconstructed by the proposed system, within the basic viewing zone. Remarkable quality improvement is confirmed through quantitative evaluations of the experimental results.

High-resolution two-dimensional imaging using MIMO sonar with limited physical size

For an imaging sonar mounted onto a small sonar carrier (e.g., unmanned underwater vehicles), the angular resolution is strictly restricted by the limited physical size. To solve the problem, we propose a high-resolution imaging approach utilizing a multiple-input multiple-output (MIMO) sonar. The MIMO sonar is composed of two transmitting transducers and a receiving uniform linear array (ULA). The two transmitting transducers and the receiving ULA are located on two neighboring parallel straight lines, respectively. And the spacing between the two transmitting transducers is equal to the product of the hydrophone number and the inter-hydrophone spacing of the receiving ULA. Simultaneously, a pair of up- and down-chirp linear frequency modulation (LFM) pulses with the same frequency band (producing the same auto-correlation function for multibeam processing) and a large time-bandwidth product (ensuring low cross-correlation functions for range sidelobe suppression) are employed as transmitting waveforms. Based on the designed array layout and the transmitting waveforms, an improved hybrid beamformer combining the subarray processing, the phase shift and the shifted sideband beamformers together, is given, which ensures a fine imaging resolution and a low computation load. Via theoretical analysis, numerical simulations and a tank experiment, we show that the angular resolution of the proposed MIMO sonar imaging method doubles that of the traditional single-input multiple-output (SIMO) imaging method using the same array physical size and the same working frequency band.

High-Resolution Two-Dimensional Imaging of the 4H-SiC MOSFET Channel by Scanning Capacitance Microscopy

In this paper, a two-dimensional (2D) planar scanning capacitance microscopy (SCM) method is used to visualize with a high spatial resolution the channel region of large-area 4H-SiC power MOSFETs and estimate the homogeneity of the channel length over the whole device perimeter. The method enabled visualizing the fluctuations of the channel geometry occurring under different processing conditions. Moreover, the impact of the ion implantation parameters on the channel could be elucidated.

Deep learning-enhanced light-field imaging with continuous validation.

Visualizing dynamic processes over large, three-dimensional fields of view at high speed is essential for many applications in the life sciences. Light-field microscopy (LFM) has emerged as a tool for fast volumetric image acquisition, but its effective throughput and widespread use in biology has been hampered by a computationally demanding and artifact-prone image reconstruction process. Here, we present a framework for artificial intelligence-enhanced microscopy, integrating a hybrid light-field light-sheet microscope and deep learning-based volume reconstruction. In our approach, concomitantly acquired, high-resolution two-dimensional light-sheet images continuously serve as training data and validation for the convolutional neural network reconstructing the raw LFM data during extended volumetric time-lapse imaging experiments. Our network delivers high-quality three-dimensional reconstructions at video-rate throughput, which can be further refined based on the high-resolution light-sheet images. We demonstrate the capabilities of our approach by imaging medaka heart dynamics and zebrafish neural activity with volumetric imaging rates up to 100 Hz.